Conference Agenda

In Canadian prairie cropping systems, most farmers apply urea during the fall before freezing. This practice is inspired by lower fertilizer prices in the fall and the need to minimize tillage and other farm operational tasks in the spring. Farmers are advised to apply fertilizers at a temperature of 5°C or lower to reduce nitrogen (N) losses. However, N losses such as nitrous oxide (N2O) emissions occur during thawing even when fertilizers are treated with inhibitors. This is due to the coupled effects of soil moisture and temperature in driving these emissions. However, there is a lack of information on the extent of these effects in Manitoba. The objective of this study was to evaluate the effects of soil freezing and varying thawing temperatures on N2O emissions from urea treated with double inhibitors (SuperU) under varying moisture contents. Treatment effects were tested using a sandy loam soil collected from Carman, Manitoba. The soil was homogenized and packed into Nalgene containers at a bulk density of 1.2 g/cm-3, followed by band-application of SuperU. The units were watered with deionized water to attain moisture levels of 25%, 50%, and 100% saturation. The soils were then frozen at -20 °C, followed by thawing at 4, 8, 12, and 16°C. Gas samples were taken at 0, 1, 3, 6, 10, 20, 30, 40, 50, and 60 h using the static chamber technique. Results on the effects of temperature and moisture levels on N2O emission will be reported. Findings will help design nutrient management practices.

Soybean is the third most important crop in Manitoba based on seeded area and farm income. However, drought and excess moisture affects potential crop yield. Tile drainage is becoming popular as a method to remove excess moisture. To overcome drought conditions, subirrigation through tile drains was investigated as a viable alternative to overhead irrigation. This study investigated the effects of subirrigation through the drainage systems on soybean (Glycine max (L.) Merr.) grain yield and yield components, actual crop evapotranspiration, yield response factor (Ky), and harvest index (HI). Field experiments were conducted in 2023 at Prairies East Sustainable Agriculture Initiative (PESAI), Arborg, Manitoba, Canada. Two subsurface irrigation treatments (on tile and midway between tiles) and rainfed conditions were evaluated with soybean seeded at 444,789 plants per ha (180,000 plants/ac) in three replicated plots. Soybean yield, nodule counts, above and below ground biomass, height, number of pods, number grains, and grain weight were significantly higher (p < 0.05) in the subirrigated through tile drainage treatment compared to the rainfed control treatment. Grain yield on the tile treatment was significantly higher at 2820.9 kg ha-1 compared to 2216.9 kg ka-1 in the rainfed treatment. Nodule counts ranged from 67 for the rainfed to 614 for on tile treatment. Seasonal ETa was 189.6 mm for the rainfed compared to 299.7 mm in the midway between tiles. The average irrigation water requirement was 182.8 mm. This study showed that subirrigation through tile drains is important for better soybean yield in an area subjected to drought.

The study of watershed management has become essential in an era of increasing environmental challenges and developed agricultural activity. Increasing attention is being given to the complex relationships that exist between the water quality of freshwater and agricultural activities. The role of non-point source (NPS) pollution is crucial to understand since it is an important issue for preserving the environmental quality of water bodies. The relationship between agricultural practices and the quality of water is being examined more closely, considering NPS pollution. The spatio-temporal patterns of precipitation events that significantly contribute to sediment and phosphorus loads are the focus of the comparative study. The frequency as well as the magnitude of threshold events, both annually and seasonally, are examined for different watersheds in Southern Ontario (Canada). The primary emphasis of this study is on threshold precipitation events, which create significant hydrological responses, including higher levels of sediment and phosphorus loads and surface runoff. These events are critical to understand the NPS pollution and their correlation with land use changes. By focusing on threshold events, the study elucidates the direct relationship between precipitation events and the subsequent hydrological processes, such as surface runoff, sediments, and phosphorus loads. This work contributes to our understanding of the diverse patterns exhibited by watersheds in response to NPS pollution during these threshold events. The ultimate objective of this research is to contribute towards the assessment of effective of Best Management Practices.

Subsurface drainage is a water management strategy that uses tile drains to remove excess water from poorly drained soils and maintain soil conditions near field capacity following heavy rainfall. Due to spring snowmelt, Manitoba experiences excess soil water during the early growing season. High-intensity storm events during the growing season also led to fluctuating water table depths. This research aimed to evaluate soybean yield under different water table depths under subsurface drainage and no drainage conditions in heavy clay soil in the Interlake region of Manitoba. Tile drains were installed at a depth of 0.9 to 1.1 m at 4.5, 9, 13.7 m (14, 30, 45 ft) spacing. The soybean yield was obtained over the tile, midway between tiles, and in no tile plots. Water table depths were continuously monitored. During the dry years of 2020 and 2021, the yield was lower in all the plots. During the wet year of 2022, the over the tile yield was significantly higher than the control because the watertable was maintained lower during vegetative stage.

Soil quality plays a crucial role in sustainable agricultural practices and the mitigation of climate change. A major indicator of soil quality and fertility is soil organic matter (SOM) which is derived from decomposed organic material. High levels of SOM have been shown to resist erosion, preventing nutrient loss and retain water. However, conventional practices like soil tilling degrade arable lands by disrupting soil structure and depleting organic matter. Current research on agricultural applications of nanoparticles (NPs) is gaining attention from farmers and agrochemical industries, providing successful alternatives to chemical-based fertilizers. NPs improve nutrient release timing, duration, and rate, enhancing plant absorption. Their use reduces soluble salt volumes in fields, leading to increased crop growth, biomass, and nutritional content. The work presented here has focused on the development and application of iron oxide nanoparticles (IONPs) as soil amendments with the intention of enhancing soil organic matter and retaining nutrients. The interaction of IONPs with various nutrients (phosphate and nitrate) along with organic matter was assessed using breakthrough experiments and soil columns under various conditions including NP condemnation, water flow rate, and mixing style. Additionally, the impact of NPs on the microbial community will be discussed.

Globally, climate change poses substantial challenges to agricultural sustainability. The Upper Humber River Watershed (UHRW), a vital agricultural producing region in western Newfoundland, similarly faces significant challenges due to climate change. This study investigates the potential impacts of climate change on agricultural sustainability in UHRW using the SWAT model and downscaled CMIP5 climate data from General Circulation models. Based on emission scenarios RCP8.5 and RCP4.5, by the 2080s, the annual average temperature is projected to increase by 4.8°C and 2.6°C, respectively and the annual average precipitation to increase by 146 mm and 65 mm, respectively. Spring temperature and winter precipitation show the most significant increases with 0.04°C/10a (0.03°C/10a) and with 0.59 mm /10a (0.35 mm /10a) under scenario RCP8.5 (RCP4.5). Simulations indicated an increase in mean values of flow across RCP scenarios by the 2030s, 2050s, and 2080s, where mean streamflow projections increased from the baseline by 11.1% (RCP8.5) and 3.7% (RCP4.5) by the 2080s. Due to this, it is expected that the monthly average total nitrogen and phosphorous loads will increase from May to November and decrease from December to April by the 2080s. Additionally, anticipated climate fluctuations are likely to result in substantial alterations to crop yields within UHRW, with resultant outcomes influenced by the inherent uncertainties associated with predictive modeling. Increased winter precipitation and temperature will lead to earlier melting of spring snowpack and increasing trends in non-point source pollution. Consequently, the paper emphasizes the imperative of implementing adaptation measures to ensure agricultural sustainability in the watershed.

As the competing demand for water increases, effective water management is necessary to optimize water use in agricultural fields. Knowledge of soil water content and crop water uptake pattern with different drain spacing will help farmers optimize water use and maintain a stable ground water table to maximize production by protecting the crops from water stress. On the other hand, the challenge posed by poor drainage in heavy clay soils affect crop performance. This research aims to analyze soil moisture patterns within the wheat root zone at different growth stages using multi-layer remote sensing, comparing different drain spacings (13.7 m (45ft) and 9.1 m (30ft) and control). METER soil moisture sensors were installed at depths of 20, 60, and 90 cm to measure soil moisture and soil temperature. Continuous measurement of water table elevations was carried out using Solinst dataloggers. The field data will be simulated using DRAINMOD. Weather parameters were collected throughout the season. The data collected will be used in the simulation model, predicting the impact of drain tile spacing and soil moisture on wheat crop performance, estimating maximum yield, and comparing it with actual yields. This research has the potential to optimize water management and contribute valuable insights into global agricultural sustainability.

Amending soils with hydrogels is known to improve soil fertility. Use of acrylamide-based synthetic hydrogels (SH) in soil could be problematic due to concerns about their toxicity and slower biodegradability. Therefore, the development of natural hydrogels as soil amendments is gaining popularity as a greener alternative. A two-year field study using pots, filled with sandy soil, was conducted to compare the effects of SH and wastepaper-based natural (NH) hydrogels on green pepper plant growth and yield under water stress levels. Both hydrogels were applied at the rate of 0.5% (w/w), and the pots were arranged in a randomized complete block design with three treatments (SH, NH, and non-amended control) and four blocks (rows). Both hydrogels increased green pepper yield by improving water-use and nutrient-use efficiencies. No significant differences (p<0.05) were observed in the performance of SH and NH hydrogels in terms of plant yield and nutrient leaching at 100% irrigation level. Therefore, the wastepaper-based natural hydrogel can serve as an environment-friendly alternative to synthetic acrylamide-based commercial hydrogels as it eliminates the potential threat of acrylamide monomer to the environment.

PAMI is performing research aimed at exploring the variables that impact agricultural productivity and the overall economic advantages and disadvantages of wetland drainage scenarios. The availability of yield data and field path data obtained over multiple years provides the opportunity for an observational study of the yield response in distinct field zones including the upland acres, the buffer zone around a wetland, and the wetland itself.

A financial model was created based on yield response in each zone to drive scenarios of no wetland drainage, partial drainage, and full drainage. Crop values and agronomic assumptions were applied based the Saskatchewan Crop Planning Guide. Consistently, the models showed an economic incentive to the producer for draining wetlands of $18 to $33 per cultivated acre.

The findings reveal that drained and farmed wetlands produce slightly lower yields then field average with substantial variability due to crop type, soil zone and precipitation. Furthermore, it is found that the effects of wetlands on crop yields extend well beyond the wetland where noticeably higher yields are found in the surrounding 50 m buffer zones of drained versus undrained wetlands. The results demonstrate the importance of considering wetland and buffer zone yield effects in wetland drainage decisions and improve our understanding of the costs and potentially required incentives for wetland conservation. Ongoing work explores the relationships of percent wetland area to overlap of farming inputs and to nuisance cost and the validation of the buffer yield effect using pre- and post-drainage yield data.

Food loss and waste (FLW) is a major sustainability challenge at all levels from local to global. FLW in industrial, commercial, and institutional (IC&I) sectors is large in quantity, wide in scope, and complex in source. Information regarding the scale, patterns, and environmental impacts of food waste in IC&I sectors is particularly fragmented. The goal of this research, therefore, is to better characterize the quantity, distribution, and impact of FLW in the IC&I sectors and recommend better strategies for managing FLW. Using Montreal as a case study, a geospatial model will be developed of patterns and the carbon emissions of food waste from the IC&I sectors. First, fragmented data from various IC&I sector stakeholders will be gathered within a common framework to build a systematized database of FLW across different sources and actors. Second, spatial analysis and systems modeling will be used to show the spatial distribution and identify spatial factors of FLW on a city scale. Third, life cycle assessment (LCA) will be used to estimate the carbon emissions related to FLW management. Finally, strategies will be investigated to reduce carbon emissions from FLW flow paths. This study will improve data, decision-making tools, and policy strategies for FLW management at all levels of government.

The management of municipal solid waste (MSW) poses a significant global environmental challenge. Landfills are important sources of greenhouse gas (GHG) emissions, particularly methane. However, accurate estimation of methane emissions from landfills in Canada remains challenging due to the lack of primary data and accurate models. The goal in this research is to improve estimates of methane emissions from Canadian landfills by using the one at Complexe Environ Connexions, Terrebonne, Quebec, as a case study. Several techniques are being used to measure methane fluxes, including eddy covariance, automated flux chambers, and smart survey flux chambers. Together, these techniques capture high-quality methane emission data, including quantification of any diurnal and seasonal variations. These data are being used together with published information to calibrate and validate computational models such as LandGEM. This research will inform the development of sustainable engineering solutions for the mitigation of methane emissions from Canadian landfills. The research is aligned with Canada's climate objectives and global agreements, thereby reinforcing the nation's commitment to reducing GHG emissions and promoting sustainable waste management practices.

The depletion of nutrients in the soil and the accumulation of nutrients in the water bodies are two major contradicting environmental issues. Hence, it is imperative to recover these nutrients from the water bodies and introduce them into the soil with slow-release properties to prevent further accumulation in the water bodies. Therefore, in this study, a nanocellulose based nanocomposite was fabricated to recover and release the nutrients. The synthesized nanocomposite was characterized through different techniques such as X-ray Diffraction Spectroscopy, Fourier Transform Infrared spectroscopy, Energy Dispersive X-ray Fluorescence spectroscopy, and Differential Scanning Calorimetry to decipher its structural properties, chemical composition, and thermal properties. The specific surface area and the pore distribution were determined through Brunauer-Emmett-Teller analysis. The surface charge and the stability of the nanocomposite was evaluated using Zeta sizer and the morphology was evaluated through Scanning Electron Microscopy. Further, the adsorption of the nutrients by the nanocomposites from wastewater were examined. Subsequently, the effect of different parameters influencing the adsorption including the pH, temperature, and concentration of the adsorbents and adsorbate were evaluated. The influence of the commonly present ions in the water and the organic matter were also evaluated. In addition, the reusability of the adsorbent was examined. Finally, the nutrient release studies were performed to understand its potential application of the nanocellulose nanocomposite as a slow-release organic fertilizer.

In response to the challenge of reducing GHG emissions and achieving carbon neutrality in livestock production, we propose a holistic systems approach tailored to integrated swine and corn/soybean systems. Our strategy encompasses integrated changes to cropping systems, feed production, and manure management, focusing on producing high-moisture corn and integrating anaerobic co-digestion systems to mitigate emissions.

Fundamental changes include harvesting high-moisture corn to eliminate grain drying and extend the growing season for cover crops, thus reducing carbon emissions and enhancing cover crop production opportunities. Our proposed approach incorporates co-digestion of swine manure, corn stover silage, and winter cover crops, alongside techniques to mitigate CH4, NH3, and N2O emissions from animal housing, manure management, and crop production.

Implementation of these changes requires adjustments throughout the production system. Row crop producers must transition to earlier corn harvests and cover crop planting, while farmers and feed processors must adapt storage and handling practices for high-moisture grain. Additionally, the feed production industry must effectively process high-moisture corn into swine feed, and the pork industry must adopt feeds derived from high-moisture grains. Furthermore, developing co-digestion systems supports these changes and facilitates biomass processing. Successful implementation hinges on the cooperation and adaptation of all stakeholders within this circular system.

This paper presents a comprehensive system plan detailing proposed technology changes and addressing obstacles to implementation. We aim to establish a more sustainable and environmentally friendly approach to swine and corn/soybean production through these integrated solutions while significantly reducing GHG emissions.

There has been a surge of interest in sustainable diets due to the growing climate crisis across the globe. In response, several attempts to estimate sustainable dietary intake scenarios have emerged for many countries including Canada. Yet, the influence of age, gender, and spatial distribution details have not been adequately incorporated into the existing proposed dietary scenarios. These factors vary the type, quantity, and nutrient quality requirements of dietary intake and in turn may impact the associated environmental implications. Therefore, this study examines the variations induced by age, gender, and spatial distribution in the environmental, nutritional, and economic implications of dietary intake among Canadians. Dietary intake data from the Canadian Community Health Survey, CCHS 2015, are examined with the SPSS Statistical software as well as Microsoft Excel models. Environmental impact data from dataFIELD and Our World in Data were sourced for environmental calculations. Also, food price data from the Food Price Hub of Statistics Canada were employed to compute the dietary cost. The expected impacts associated with the dietary intake across the life stages, gender, and provincial locations are highlighted as well as spatial hotspots. The application of these models is useful for outlining easy-to-adopt targeted sustainable dietary patterns for Canadians in the global bid to build sustainable and resilient food systems. Moreover, these findings will also form the basis for further work focused on designing sustainable dietary transition scenarios for all consumers in Canada.

This study presents the results of preparing a charcoal briquette derived from a combination of pine wood sawdust and pine cone biomass without any binders. The investigation focuses on the effects of hydrothermal heat pretreatments (HT) at temperatures of 280, 320, and 360 °C, alongside ozonation pretreatment (OZ) at durations of 60, 75, and 90 minutes. For each experiment, the pretreated samples were densified to prepare biomass briquette and then carbonized at 430 °C to prepare charcoal briquette without any binders. All experiments maintained consistent conditions regarding compressive force, initial humidity, particle size, and biomass material composition. Thermal and physical properties of the charcoal briquettes were measured for each experiment, with results subjected to statistical analysis. The findings showed that charcoal briquettes subjected to HT pretreatment and ozonation exhibit higher combustion character index and lower ash yields than those without pretreatment.

Furthermore, the pretreated samples demonstrated superior physical properties, including mass density and compressive strength, compared to untreated samples. Based on the research outcomes, optimal conditions for charcoal briquette production from pine wood waste and sawdust pretreated by HT method at 280 °C, and an OZ method duration of 75 minutes. It was found that the charcoal briquette prepared by pine wood sawdust and pine cone biomass pretreated by an OZ duration of 75 minutes and an HT pretreatment at 280 °C showed the best combustion and physical characteristics.

Traditional farming methods for fruits and vegetables often involve labor-intensive tasks such as planting, harvesting, and sorting, which can be time-consuming and expensive. While automation technologies have made significant advancements in other sectors of agriculture, adapting these technologies to the unique requirements of fruits and vegetable production poses challenges. Recent advancements in robotics, computer vision, and machine learning are helping to overcome these challenges. However, there remains a notable gap in the adoption of automation technologies, specifically lack of technical skills among the agricultural workforces. Our research focused on the adaptive modular automation for adaptive transformation. Modular components would easily mount on a self-propelled unit (tractor, ATV, or others) as an auxiliary component or attach to fully autonomous equipment (UGV) to perform the intended operation. In this study, will present the performance intelligent cutting mechanism for autonomous lettuce harvester as a modular automation system. It is essential to maintain the consistent cutting height for supermarket quality products. An electrohydraulic control has been designed for autonomous height adjustment for consistent cut. Performance will be evaluated for control system accuracy and lettuce harvesting accuracy at the lab setting. Lettuce cutting will be evaluated for three different blade types, two different blade speed and two different forward speed. Results will be presented during the conference.

This study focuses on the development of an ISOBUS (i.e. ISO 11783) compatible Electronic Control Unit (ECU) for seamless integration between a boom sprayer and machine vision to control individual nozzles for targeted pest spraying. The ECU, powered by a Raspberry Pi 4B and a CAN transceiver, utilizes the AgIsoStack++ (formerly known as ISOBUS++), an open-source library for ISOBUS communication. Through this library, we register our ECU to the ISOBUS network with a manufacturer ID (0x1C) and all parameters of ISOBUS NAME from a registered and inactive manufacturer under the Agricultural Industry Electronics Foundation (AEF). The developed algorithm is specifically ISOBUS Section Control Function (SC) which manages the individual sections of implements, such as nozzle sections on a sprayer controlled by individual cameras for precise spraying operations. The interactive Virtual Terminal (VT) screen design utilizes another freely available software, ISO-Designer. After successful handshaking messages holding 0x1C as the source or destination ID, the designed VT screen becomes visible. The ECU can adjust and control parameters such as camera and nozzle ratios through the ISOBUS compatible VT. Deployment across three different ISOBUS systems resulted in zero error messages, ensuring seamless network registration. The ECU evaluation reveals a 7.1% busload increase on the ISOBUS. The maximum observed busload over those three systems reached 21.3%, remaining within the acceptable limit of 25%. Despite the increase in busload, the ECU increases robustness by integrating the machine vision to VT and simplifies operation by reducing the need for additional displays and interfaces, thereby enhancing farm efficiency.

Wild blueberries (Vaccinium angustifolium Ait.) are a perennial crop in northeastern North America. Conventional management relies on costly broadcast applications of herbicides to manage weeds. There is an opportunity to reduce agrochemical usage by implementing targeted spray applications with the help of deep learning. A machine vision system for targeting weeds in wild blueberry fields was developed as a retrofit package for commercial boom sprayers. The YOLOv5n convolutional neural network (CNN) was trained to detect hair fescue (Festuca filiformis Pourr.) in images of wild blueberry fields. Cameras with an integrated deep learning processor were used to capture images and detect target weeds in real-time. An ISOBUS individual nozzle control system consisting of addressable nozzle bodies and a rate controller was connected to the cameras using a local area network and a custom-built machine vision node. This machine vision spraying system was installed on a 12-nozzle prototype research sprayer built on a John Deere Gator XUV 825i. Testing occurred in Nova Scotia wild blueberry fields in November and December 2023, then April and May 2024. Future work will involve testing the machine vision system on a 27-nozzle commercial wild blueberry sprayer and testing CNNs for targeting different species of weeds in wild blueberry fields. The machine system is adaptable to other crops by retraining the CNN with new images of the desired target weeds. A machine vision smart sprayer will allow growers to achieve substantial cost savings by selectively applying herbicides based on real-time visual data.

Since 1972, wild blueberry harvesters have relied on 0.86 m wide picking heads to optimize field efficiency while ensuring maximum yields. Recently, Doug Bragg Enterprises introduced a 1.47 m picking head, and the purpose of this study was to compare its field efficiency to the conventional 0.86 m header and hand raking. The experiment used a randomized complete block design with four blocks in each field. Within each block, a 75 m strip was harvested with the 0.86 m and 1.47 m heads and the total harvested berry weights were compared to the harvested area. For hand raking, 10, 1 m by 1 m, randomly positioned quadrats were harvested per 75 m strip. In terms of harvested yield by area, there were no significant differences between the 0.86 m head, the 1.47 m head, or hand raking (p = 0.676). This result is encouraging, considering the 1.47 m picking head can harvest 0.166 ha h-1 while the 0.86 m head can only harvest 0.097 ha h-1 at a typical harvesting speed of 0.31 m s-1. Finally, the tested fields had maximum slopes of 9.4 degrees, making them reasonably flat among Nova Scotian wild blueberry fields, which often range up to 30 degrees of slope. Future research should consider header performance in fields with more uneven topography.

Remote supervision of autonomous agricultural machines involves the interaction of the human supervisor and these autonomous machines through an interface. These interfaces need to provide humans with accessible and effective information to perform their supervisory roles, which include setting tasks, allocating resources, monitoring the execution of tasks, and intervening in cases of emergencies. Various warning methods, specifically visual, auditory and tactile cues have been used to inform remote supervisors about any abnormalities in the autonomous machines. Failure to adequately understand the warning information may lead to system breakdown or crop damage. The present study assessed which warning method (visual, auditory or vibrotactile) is most effective in conveying comprehension of machine malfunctions to remote supervisors of autonomous agricultural machines. Twenty-five (25) participants were recruited and asked to interact with a simulation of an autonomous sprayer. Three (3) unimodal warning methods (visual, auditory and tactile sensory cues) were evaluated. The effectiveness of the warning methods was assessed based on the modality’s ability to convey comprehension (which is a measure of level two situation awareness) as a function of accuracy of the participants’ responses. At this time data analysis is being conducted; we plan to share preliminary results at the conference. This study will inform designs that can enhance performance and minimize hazards experienced by remote supervisors during field operation with the autonomous agricultural machines.

With the rise of semi-autonomous tractors in agriculture, there might be a chance to rethink traditional tractor cab designs. This research involves transforming these cabs into ergonomic office spaces, acknowledging that the operator's focus has shifted away from driving. In the initial phase of the project, the emphasis was on conducting an ergonomic assessment of the existing tractor cab design, specifically concentrating on the factors contributing to discomfort. This examination involved analyzing the duration spent on rearward observation, the sequence and frequency of control usage, and ergonomic triggers leading to discomfort. The investigation into tractor ergonomics encompassed interviews with farmers and design engineers, as well as practical experiences of operating tractors through ride-along. Subsequent research delved into an anthropometric analysis, examining the proportions of the human body, and identifying areas prone to discomfort, with a particular emphasis on hand positioning. The results of the ergonomic analysis have pinpointed several issues, including neck and back pain resulting from frequent backward glances, prolonged exposure to vibrations, restricted legroom, and discomfort arising from complex button layouts. Simultaneously, the findings of the anthropometric analysis are being consolidated to generate recommendations tailored for engineers. These recommendations offer insights into key considerations for enhancing human comfort during tractor operation. The ultimate objective of the project's concluding phase is to integrate the insights gained from both ergonomic evaluations and anthropometric analysis into a conceptual design of a comfortable environment for operators of semi-autonomous tractors, effectively transforming the tractor cab into a workspace resembling of an office setting.

Conventional protein extraction techniques such as alkaline extraction and isoelectric precipitation encounter limitations, including negatively affecting protein properties and significant environmental impact. In this study, the potential of pulsed-ultrasound extraction techniques was investigated as an alternative protein extraction technique with improved physico-chemical, functional and structural properties.

A conventional method involving alkaline extraction followed by isoelectric precipitation was employed as a control. A slurry (1:8 w/v) of navy bean flour was made with distilled water and after pH adjustment (pH 9), stirring, and centrifugation, the resulting supernatant was adjusted to isoelectric pH (4.5), further centrifuged, and the protein. Pulsed lyophilized to produce the navy bean protein concentrates (NBPC). Pulsed-ultrasound extraction was conducted at varying time (5–20 min) with 5 sec pulses on and 3 sec off time, followed by centrifugation and lyophilization. The impact of ultrasound treatment on protein recovery, solubility, emulsifying activity, foaming capacity, in vitro-protein digestibility, amino acid profile, and protein microstructure, were analyzed and compared.

Conventional method poses the highest protein recovery while for pulsed-ultrasound technique, protein functionality, amino acid profile and physico-chemical properties improved significantly. due to high energy and cavitation effect of ultrasonication, the extraction efficiency was improved with time. Protein microstructure changed significantly, creating holes in ultrasound treated samples. Hence, pulsed ultrasound-based extraction could be a sustainable and efficient technology for obtaining proteins from navy beans for various food processing applications.

Papaya, a perishable yet nutritious fruit, presents a challenge in maintaining nutritional integrity during the off-season’s prolong storage. Food scientists are seeking effective drying techniques for year-round availability of nutritious papaya, recognizing its importance in a balanced diet. This study was carried out under Biotechnology Industry Research Assistance Council (BIRAC) supported "Secondary Agriculture Entrepreneurial Network (SAEN) Punjab" to provide a reliable protocol for a consistent supply of nutritious papaya in dried form. Therefore, the present study proposes a comparative analysis of papaya fruit using the refractance window and convective tray drying technique. The crop after preliminary examination was blanched (3, 4 and 5 min), pureed and dried using two different techniques i.e. convective tray (50, 60 and 70 ˚C) and refractance window (designed and fabricated in Punjab Agricultural University) maintaining water temperature: 60, 70 and 80 ˚C for drying. Refractive window drying (RWD) was found to be better drying method than convective drying (CD) with reduced drying time. Reduction in value of moisture ratio was observed with drying time regardless of drying method. Among the five selected thin layer models for analyzing drying behavior, the Logarithmic model and the Wang and Singh model for CD and RWD, respectively described the drying kinetics very well. Therefore, the outcomes of present study suggested that refractance window dried product had better quality, color and functional component retention in a shorter time at a minimum expense, as compared to conventional drying method.

Selecting optimal processing conditions during food processing is necessary for preventing structural collapse. The glass transition theory enables the selection of optimal drying temperature, tempering period, and processing time and it explains fissure formation in processed crop grain kernels. For example, during freeze drying, the structural stability of a frozen food can be explained using the glass transition theory. Glass transition temperatures are used to construct state diagrams. A state diagram provides information about the different states of food. It is constructed as a function of water content or solid content and temperature. It identifies state/ phase transition temperature and water content or solid content, freezing curve, solubility curve, glass transition curve, melting curve, eutectic temperature, glass transition temperature (GTT), and maximal freeze concentration. Although various techniques like dynamic mechanical thermal analysis (DMTA), thermo-mechanical analysis (TMA), nuclear magnetic resonance (NMR), and others are used to determine the GTT and to construct state diagrams, the common technique used for food is differential scanning calorimetry (DSC). This review is focused on understanding glass transition, state diagrams, and its applications for low-temperature operations. The application, advantages, and limitations of DSC are also reviewed.

Analyzing the volatile profile of food, especially dairy products, plays a major role in the flavor and acceptability of foods. Variations in human sensing qualities, in addition to being costly, laborious and time consuming can be replaced with E-nose (Electronic nose) for getting uniform analysis of volatile components in the dairy products, especially milk. This paper reviews the working of E-nose system for milk based on three main broad categories: data collection by sensors, data transmission and finally data analysis that leads to smell recognition as output. The gas sensors used in E-nose are usually made of conducting polymers, metal oxide sensors, metal oxide semiconductor field effective sensors, and quartz. The sensor data along with the machine learning techniques namely principal component analysis, support vector machine, discriminant function analysis, and partial least square regression have been frequently used in milk quality determination. More than 90% accuracy was yielded in classification of contaminated and uncontaminated milk samples, and even 100% accuracy for bacterial strain classification in milk. If used in industrial scale, the information analyzed by E-nose would need to be secured as it could reveal the details of the food processing industry if tampered. Thus, cloud computing along with block chain implementation would serve as a promising tool in real-time analysis of volatilome analysis of milk.

Paper-based microfluidics have emerged as a promising platform due to their low cost, ease of use, and environmentally friendly attributes. Wax printing, inkjet printing, and flexographic printing are commonly used for the fabrication of these devices. However, these methods require specific fabrication facilities and involve high cost. This study introduces a stamp-based manufacturing method to address these challenges. Fluid flow simulation was conducted to identify a suitable paper substrate (Whatman filters 1 to 5) for the safety analysis of liquid milk samples. Whatman filter 4 was found to be a superior fabricating material. Design of experiment was conducted using StatEase Design-Expert software to optimize the influence of process parameters, namely molten wax temperature (100-160°C), pressure (0.1-6 g/mm2), stamp width (0.5-2 mm), and holding time (1-10 sec), on the printing quality and wax spreading. An SLA 3D printer was used to print the stamps with varying widths according to the experimental design. The pattern of the stamped microfluidic channels on paper was evaluated using ImageJ software. Results indicated that the optimal conditions for achieving higher printing quality and lower wax spreading were observed at 145°C, 4.525 g/mm2, 0.875 mm, and 3.25 sec for temperature, applied pressure on the stamp, stamp width, and holding time, respectively. The obtained optimum process parameters were utilized to develop a pH indicator device capable of detecting pH levels in the range of 2 to 10 in various liquid food samples within 5 minutes. This study provides a simple and cost-effective fabrication method of paper-based microfluidic devices.

An existing solar dryer and drying pavement were used to compare the drying of scotch Bonnet pepper and Roma tomatoes. A known weight of 33grams (divided into groups of 3.0 g) was placed in the solar dryer and another outside. Temperature readings was automatically taken by a data logger and the weight recorded at thirty minutes’ interval. The control sample was not subject sun or solar drying. After drying, the nutritional quality (crude protein, crude fat, crude fiber, ash content, carotenoid and vitamin c) of the sun and solar dried pepper and tomatoes where compared with that of the fresh undried sample. Results gotten showed that the samples dried using the solar dryer dried faster than that dried with open sunlight. The nutritional content of the control sample was also higher than that of both sun and solar dryers, with nutrient loss in open sun dried samples significantly higher than observed in the solar dried sample.

Panama, a country located in Central America, has made significant efforts in terms of transitioning from fossil fuels towards renewable sources of energy. However, the country still faces the challenge of having a diversified and balanced energy mix. This study assesses the availability and shows the spatial distribution of biomass derived from agricultural residues throughout the country at the municipal level. To assess the amounts of residues available for bioenergy purposes, statistical data on crop production, residues-to-product ratios and correction factors were used. The correction factors refer to the fractions of residues that must be disregarded due to soil conservation and sustainability purposes, animal feeding, and handling losses. Geographical Information System (GIS) based maps were also developed to show the distribution of residues available throughout the country at the municipal level. It was identified that the main producers of residues are sugarcane, corn, rice, red kidney bean and vigna bean, generating a total of 400 k dry t of residues annually. Chiriquí, Veraguas, Coclé, Los Santos and Herrera, are the provinces of Panama with the greatest potential, accounting for 93% of the total available residues of the country. Based on the findings of this study, agricultural residues represent an important opportunity for Panama to increase the accessibility and diversification of renewable sources of energy in the country. The developed framework can be used for assessment of residues in different jurisdictions globally.

Pulse starch side streams have become abundantly available due to the increased demand for pulse proteins in the context of sustainable food production and sustainable diets. These pulse starches are of low value. Generating high-value bioproducts from them will create new market opportunities while addressing a circular economy and sustainable model for the total utilization of agri-food feedstocks in western Canada. Therefore, this project aimed to use side-stream pea starch to produce biobutanol. Upon enzymatic saccharification of the pea starch to release glucose, anaerobic bacterial strains, Clostridium acetobutylicum and C. saccharoperbutylacetonicum, were used to produce butanol. Initially, percentage ranges of 10, 20, 30, 40 and 50% (w/v) pea starch were tested to determine the optimal conditions for saccharification and maximum release of glucose. A 50% (w/v) saccharified pea starch released sufficient glucose (158.0 ± 0.7 g/L) to grow the bacterial strains in ten fermentative processes over 24 hours for biobutanol production. From 500 mL working volumes in anaerobic bottles, we tested a 5 L working volume of the diluted saccharified starch in a 10 L fermenter. Compared to the 500 mL, higher levels of butanol were produced in the 10 L fermenter. The Clostridial fermentative process also generates acetone, ethanol, and butanol through the well-established Acetone-Butanol-Ethanol (ABE) pathway. The gas chromatography/mass spectrometry (GC/MS) results showed butanol concentrations ranged from 4.4 – 5.67 g/L. These preliminary results will be improved with further optimization and removal of toxicity effects of accumulated butanol.

This research presents the findings of an experimental study to assess H2 production from Canadian agricultural and forest biomass residues. The investigation utilized a 2 kg hr-1 lab-scale unit that includes an intermediate pyrolysis and thermo-reforming reactor, known as TCR. The aim is to evaluate the process performance by conducting feedstock and parametric experiments. While there has been extensive research on biomass gasification for H2 production, pyrolysis remains relatively unexplored. The study focused on understanding the impact of various pelletized feedstocks and operating conditions on the yield of H2-rich syngas. The tests were conducted across various temperatures, spanning from 400 to 550 °C for the reactor and from 500 to 700 °C for the reformer. The results indicate that the synthesis gas output fluctuates between approximately 45% and 70%. Among the diverse experimental conditions investigated, the H2 yield potential was found to be most responsive to changes in the temperature of the reactor-reformer, exhibiting variations from 24% to 35%. Furthermore, the method could generate economically valuable high-quality bio-oil (HHV: 30.92 to 37.28 MJ kg-1) and biochar (HHV: 33.73 to 30.50 MJ kg-1). TCR bio-oil, designed for higher quality (O/C: 0.07 to 0.16) compared to fast pyrolysis bio-oil, demands less pretreatment in subsequent processing within conventional refineries. This study's outcomes provide valuable insights for future research, particularly in the economic assessment and implementation of this technology on industrial and pilot plant scales. This underscores the significance of pyrolysis, filling a research gap compared to the well-studied area of biomass gasification.

In response to the global imperative for sustainable energy solutions, biofuels have emerged as renewable and potentially carbon-neutral alternatives to fossil fuels. Microbial lipids from oleaginous yeasts offer promising pathways for sustainable biofuel production. One such oleaginous yeast, Rhodosporidium toruloides, an oleaginous yeast, distinguished by its lipid accumulation capacity and ability to metabolize diverse substrates and tolerate toxic compounds. However, R. toruloides implementation is limited due to its lower C5 consumption ability of xylose, which is the second most abundant sugar in lignocellulosic biomass-based hydrolysates. This study focused on enhancing the xylose uptake efficiency of R. toruloides-1588 through adaptive laboratory evolution (ALE) to improve its bioconversion capabilities. R. toruloides was evolved in minimal media with 10 g/L xylose over 13 generations. The evolved strain showed 80% increase in the xylose consumption rate, with complete xylose assimilation and about 30% increase in biomass within 16 h compared to the native strain. This advancement not only demonstrated the potential of ALE in optimizing microbial strains for biofuel production but also set a precedent for the efficient use of lignocellulosic biomass, contributing to the development of more sustainable and cost-effective biofuel production processes. Further research into the genetic modifications in Rhodosporidium toruloides using genome sequencing and proteomics will help in understanding how these changes improve xylose utilization This will offer strategic targets for future bioengineering endeavours in the biofuel industry.

Industrial biotechnology can change how we manufacture many products (i.e., biofuels, biopolymers and other chemical building blocks) toward a renewable and more sustainable future. These processes usually rely on heterotrophic metabolism, in which an organic carbon molecule (glucose, glycerol, plant-based oils) is converted to other products through the pathways of specific microbes. In the current economic climate, these bio-based alternatives are expected to compete with low-priced petroleum, and sources of organic carbon account for 40-50% of the process costs, yet as much as 50% of the carbon is lost as CO2 under aerobic conditions. Further, this practice links the ecological footprint of supposedly ‘green’ products to that of the agriculture sector while also competing for productive acres that could otherwise be used to feed an increasing world population. It is therefore important to look to waste materials to produce high-volume, lower-value chemical commodities. Carbon dioxide is arguably the largest and most problematic source of anthropogenic waste, that can be captured by certain species of chemolithoautotrophic bacteria called hydrogen oxidizing bacteria (HOB). This talk will explore the potential, challenges, and limitations of HOBs to be used as a carbon capture and valorization to biofuels, polymers, and other products of interest. This is pursued with the motivation of developing CO2-based biorefinery that could reduce pressure on agricultural lands for production of high-volume chemical commodities This vision can contribute to several Sustainable Development Goals pertaining to climate, clean energy, hunger, as well as responsible production.

Butyric acid is a valuable platform chemical with a market value of upto 2500 USD/t and a broad range of applications in the pharmaceutical, food, polymer, and perfume industry. It’s production has been previously studied using various substrates and specific bacterial species. However, the fermentation of food waste offers a more sustainable and economical alternative. This study compares the acidogenic fermentation for volatile fatty acid (VFA) production at psychrophilic temperature against mesophilic temperature as it requires lesser energy input, especially in colder countries. The experiment, conducted at 37°C, 27°C, and 17°C, revealed distinct pH trend and VFA profiles. While the pH decreases rapidly to more acidic levels under mesophilic conditions, it is slower at 17°C, indicating delayed acidogenesis at lower temperatures. This maintenance of pH around 6 at 17°C, however, supported specific microbial activity, influencing VFA composition. Propionic acid concentration decreased at 17°C, which is in contrast with digestion studies performed at psychrophilic temperature. Notably, butyric acid became undetectible at 37°C, while sustaining longer at 17°C with 7-8 fold concentration of 500 mg/L, likely due to favourable pH conditions and lesser competition from competing microbial species. This prolonged presence of butyric acid at lower temperatures offers opportunities for enhanced production and prevention of its conversion to other compounds. The findings suggest potential strategies for optimizing VFA production at psychrophilic temperatures, including pretreatment, pH control, and extended retention times to promote butyric acid accumulation. Further exploration of microbial diversity could highlight metabolic pathways contributing to sustained butyric acid at low temperatures.

The evolution of sustainability has heightened the importance of circular business models, particularly in the food industry. Thus, stakeholders in the food system are increasingly focused on finding ways to valorize co/byproducts throughout the value chain, aiming to reduce global waste, enhance resource efficiency, and bolster sustainability. However, challenges persist in the form of a lack of a comprehensive framework guiding agro-industrial practices and difficulties in assessing the sustainability implications of circular pathways before implementation. Stakeholder engagement, particularly on the consumer side, is identified as a significant gap, introducing uncertainties about public interest and the commercial success of circular interventions. To address these challenges and foster a sustainable circular bioeconomy, a holistic accounting framework is proposed. This framework integrates stakeholder engagement, value chain analysis, sustainability assessment, and multicriteria decision analysis. It intends to provide an adaptable guideline to enable the co-creation of optimal, sustainable, and high-value upcycling solutions for a given system, especially while the concept gradually peaks and transitions to the commercial niche. The framework utilizes life cycle assessment and costing for environmental and economic analysis, introducing a novel employment index as a social metric. The sustainability metrics are modeled into a multivariable index, the circular bioeconomy index (CBI), to facilitate efficient communication of circular bioeconomy performance to non-technical stakeholders. Additionally, a multicriteria decision analysis approach, BWM-CoCoSo, is deployed as a robust approach for multiobjective trade-off analysis to facilitate policy and business actions toward identifying optimal circular bioeconomy decisions that align with predefined sustainability decision contexts.

Preserving food products from oxidation is crucial in enhancing their quality, safety and shelf-life. The present study aims to determine the impact of storage on the stability and functionality of tea-based preparations following spray-drying or lyophilization and stored for 60days at 39±1°C. The main active components (L-theanine and caffeine) and antioxidant activities were analyzed in tea preparations every 20days using high-performance liquid chromatography (HPLC) and DPPH radical scavenging assay, respectively. Both drying methods were efficient in restoring the antioxidant activity of the tea products. A comparison of the active components in freeze-dried and spray-dried samples showed that in the freeze-dried samples, both L-theanine and caffeine levels remained relatively stable after 60days. However, in the spray-dried samples, there was a small decrease (~10%) in L-theanine after 60days. In undried tea extract (control group) L-theanine and caffeine contents were reduced by ~32% and ~22% respectively after 60days of storage. The decrease in active components was associated with a substantial decrease (~80%) in the antioxidant activity of undried extract. The moisture content of freeze-dried samples increased by ~10% from day 0 to 60. However, the moisture contents of the spray-dried samples remained unchanged during the storage. Freeze-drying and spray-drying yielded functional tea products with stable antioxidant activity. Freeze-drying was more efficient in preserving L-theanine for 60days. However, spray-drying kept a more stable moisture level during this time. Both drying techniques are suitable and reliable for preserving natural tea preparations that can be used as functional antioxidant ingredients for food products prone to oxidation.

Paddy field fertilization operations require the design of furrow openers to achieve high performance with accuracy and precision. The bionic design method has been identified as the optimized approach to attain the desired results. This study utilized the bionic design approach in designing a bionic furrow opener for paddy field. The bionic furrow opener was designed based on the physical characteristics of the North American badger claws (curvature radius (R)), with the fitted curve of the badger claw enlarged eight times. A paddy field soil-opener interaction model was constructed to analyze the performance of the designed furrow opener. The effects of different combinations of opener dimensions: curvature radius (R) and width (Wo) on soil resistance force, soil disturbance characteristics, and soil collapse time were monitored. Force measurement experiments in field and high-speed camera experiments in soil tank were conducted to validate the interaction models. The designed bionic furrow opener achieved the best performance with a R and Wo of 25 mm and 30 mm, respectively. The performance validation results showed relative errors of 17.3% and 13.9% for soil average resistance force in the horizontal and vertical directions respectively, 4.62% for soil disturbance width, and 7.81% for soil collapse time between the experiment and simulation. These findings provide a theoretical basis to enhance the efficiency and optimization of furrow openers in fertilizer application in paddy fields, thereby contributing to improved agricultural productivity in rice cultivation.

Texture profile of meat-analog (MA) based fried food products is a complex structure, and rarely studied subject. MA-based fried products mechanical-texture-profile were considered as “fractal geometry” to characterize their textural properties by employing complex statistical approach namely multifractal analysis (MFA). Wheat and rice flour-based batter systems were used to coat the MA, and were fried (at 180°C) for 2, 4, and 6 minutes in canola oil. Instrumental puncture test was employed to get mechanical-texture-profile of MA-based fried products and obtained profile was evaluated by MFA. Results revealed that batter-formulation and frying time (FT) impacts the evolution of textural attributes (hardness, brittleness, crispiness), moisture-fat profile and microstructural properties of MA-based coated fried product. The MFA outcomes (Singularity spectra & Rényi spectra) depicts that breakage structure (force distribution) of studied MA-based fried products are non-homogeneous and possesses multifractal scaling behavior. Higher heterogeneity of force distribution is observed in lower concentration of force at outer-crust region compared to inner-core region of coated MA. Heterogeneity of force distribution are positively correlated with FT. Batter-formulation showed substantial impact on texture-profile of MA-based coated fried products and consequently influenced the obtained multifractal parameters. Principal component analysis (PCA) reveals varying extent of correlation between moisture-fat, textural attribute, microporosity and selected multifractal parameters (Δα, Δfα, R-L, ΔD) of the fried meat-analogs.

Algae are classified into green algae, brown algae, and red algae depending on their composition, pigment, and where they live and also considered as an excellent source of antioxidant. In this study, we compared and investigated the changes in antioxidant of algae according to the following extraction technologies: conventional extraction (CE; hot water and ethanol), green extraction (GE; MAE; microwave assisted extraction and SFE; supercritical fluid extraction), and combined green extraction (CGE; MAE+UAE (ultrasound assisted extraction), MAE+HPP (high pressure processing), SFE+UAE, and SFE+HPP). DPPH radical scavenging activity (%) and Total phenolic content (TPC, mg/g) of algae extract with CE were ranged from 5.15 to 66.43% and 7.93 to 88.25 mg/g, respectively. DPPH and TPC of algae extract with GE (optimum MAE and SFE extractions condition through response surface methodology) were ranged from 19.25 to 55.01% and 19.84 to 50.50% and 31.80 to 127.99 mg GAE/g and 17.76 to 82.93 mg GAE/g, respectively. These with CGE (MAE+UAE, MAE+HPP, SFE+UAE and SFE+HPP) were found to be higher than these with CE and GE. Therefore, green extraction technology can be used as an eco-friendly extraction technology that can complement and replace existing extraction methods.

Accurate prediction of streamflow is crucial for effective water resource management. This study explores various modeling approaches for flow prediction in the Upper Humber River Watershed (UHRW), in western Newfoundland, ranging from the commonly used methods like the Soil and Water Assessment Tool (SWAT) model to modern artificial intelligence (AI) techniques. Five AI models, including support vector regression, multiple linear regression, k-nearest neighbor regression, random forest regression, and the long short-term memory (LSTM) deep learning model, were assessed. Daily meteorological and streamflow data spanning from 1982 to 2022 within the UHR watershed were utilized. The models were calibrated using 70% of available data and tested with the remaining 30%. Evaluation metrics such as R2 (coefficient of determination) and Nash–Sutcliffe Efficiency (NSE) were employed. Results indicate that all models performed adequately in predicting streamflow in the UHRW, with the LSTM model demonstrating strong performance during both the training and testing phases. While the SWAT model offers a holistic representation of hydrological dynamics, AI models demonstrate superior performance in forecasting specific variables such as streamflow. This study emphasizes the value of AI techniques, especially the LSTM, for streamflow forecasting. These findings provide insights to better understand and manage water resources, in a boreal climate and similar hydrological systems, that are essential for sustainable agriculture.

Canola is the third most abundant lignocellulosic bast fiber plant around the world. Preliminary studies have determined that textile-grade fibre can be extracted from canola stems harvested by hand, but no research has been completed on the effect of mechanical processes associated with the harvesting of canola seeds on the fibre properties of canola stems. Hence, canola stems collected at three stages (i.e., pre-combine, post-combine, and post-baler) were investigated to determine the effect of typical harvesting machines on textile fibre properties. In this study, the canola stalks were cut and placed in a windrow using a swather. The canola seeds were threshed from the stalks using a conventional combine (with the straw chopper disengaged) once the stalks and seeds had dried sufficiently. Finally, the residue was baled using a round baler. The post-combine, and post-baler stems had various forms of mechanical damage, while the pre-combine stems did not exhibit any defects or mechanical damage. The mechanical damage caused by the stems passing through the threshing chamber of the combine (i.e., narrow space between the rasp bars and the concave) can be described as splits, peel cuts, sharp cuts, bends and partial sharp cuts, and splitting into multiple parts, while the damage observed post-baler was best described as compression, compression and splitting, fibrillation cuts, and splintering. Following visual characterization of stems, water retting was employed to extract the fibre for subsequent analysis. The testing and analysis of fibre properties is ongoing and will be presented at the conference.

Brominated flame retardants (BFRs) are additive compounds to prevent or delay ignition of combustible substances. Among them, twelve BFRs (TBX, PBT, PBEB, DPTE, HBBz, PBBA, EHTBB, HCDBCO, BTBPE, BEHTBP, OBIND and DBDPE) are used widely to make plastics and construction materials. Some BFRs cause chronic toxicity and cancer in animals. Therefore, the detection of BFRs in foods is needed for food safety. However, the simultaneous analytical method of twelve BFRs has not been developed. This study aimed to develop a simultaneous analytical method of BFRs in liquid type foods by GC-MS/MS. Soybean oil, milk, eggs, and coffee were selected as samples because they were highly consumed. Samples were extracted by Lipase, liquid-liquid extraction with hexane and dichloromethane and then purified by a florisil-based multilayer silica column. The developed GC-MS/MS conditions using DB-5HT column (15 m x 0.25 mm, 0.1 µm) were splitless injection mode, Injection volume of 2 µL at ionization of 70 eV. The temperature of inlet, transfer line and ion source were 280 ℃, 320 ℃ and 280 ℃. Oven temperature was increased from 50 ℃ to 360 ℃for 17.75 min. LODs of twelves BFRs in soybean oil, milk, egg, and coffee were 0.013-15.158, 0.031-9.979, 0.016-10.490, and 0.009-6.907 ng/g, and those LOQs were 0.034-39.006, 0.079-25.679, 0.041-26.995, and 0.023-17.773 ng/g. The developed method was accordance with Codex method validation guideline. Twenty-five foods were analyzed for BFRs monitoring. The developed method for BFRs were will be useful to keep food safety from BFRs risk.

Detecting the moisture content of grain accurately and rapidly has important signifi-cance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low de-tection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capaci-tor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the mois-ture content. Finally, the support vector machine (SVM) regressions between the ca-pacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dy-namic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.

The escalating threat of climate change, coupled with the projected growth in global populations, necessitates an alternative protein source that will alleviate pressures on conventional agricultural systems while also meeting human nutritional demands. This literature review investigates the potential of hydrogen-oxidizing bacteria (HOB) as a source of single cell protein (SCP) using the abundant greenhouse gas, carbon dioxide (CO2), as a substrate for aerobic fermentation. Six isolated strains of HOB were studied and compared against Cupriavidus necator, a well-studied HOB known for its successful growth and high nutritional value. Evaluation of volumetric productivity and titre revealed significantly lower values in the six strains. Direct comparison of the produced protein content was not feasible due to the variations in the analytical methods used for quantification; however, analysis of its amino acid composition revealed to be comparable to C. necator, with all essential amino acids present in sufficient quantities except tryptophan. Improvements in the low productivity, food safety, and the upcycling of waste streams would be instrumental toward a circular economy that mitigates the ecological impacts on the environment amidst climate change, while also addressing the growing demands of the world’s population.

Replacing fossil-based plastic with degradable bioplastic is an effective technique for managing plastic waste and reducing harmful plastic build-up in the environment. Medium chain length polyhydroxyalkanoates (mcl-PHAs) are a class of intracellularly stored biopolymers synthesized primarily by microbial species of the Pseudomonas genera that are biodegradable and renewable. Mcl-PHAs can be synthesized with high yield during the metabolism of oleaginous substrates. Fatty acids derived from flax oil have a high degree of unsaturation that can be incorporated in the mcl-PHA monomers, providing reactive sites within the polymer that may support further chemical modification and tailoring toward specific applications. A chemical hydrolysis process was developed to cleave the flax oil into fatty acids, which were then fed to Pseudomonas putida LS46 and found to support biomass production similar to that of previous work using free fatty acids from biodiesel waste. The kinetics of growth and PHA synthesis of this process will be further characterized (biomass production, PHA content of the biomass and monomer distribution of the PHA, and analysis of residual nutrient levels in the culture supernatant). These data will be modelled to automate a high cell density fed-batch process in bench scale bioreactors for optimized productivity. The resultant cell mass and/or isolated mcl-PHA biopolymers will be utilized as a hydrophobic barrier in a fully compostable coffee cup in the next phase of the project. This application demonstrates a tangible example of how mcl-PHAs can replace unsustainable alternatives and have an immediate impact on products used today.

Bioconversion of agri-food resources to valuable bio-products is an attractive approach for providing economic and environmental sustainability solutions to the agri-food sector. The present work investigated the bioproduction of rhamnolipids, a class of bio-based surfactants, using side streams from purposely grown biomasses, miscanthus and switchgrass. Rhamnolipids are high-valued products with a global market of US$ 4.27 billion (2021) which is projected to reach US$ 6.07 billion in 2030. Their bioprocessing from low-value biomass side streams provides a means to tap into this burgeoning market. In this work, the nutrient rich side streams and process waters from these biomasses were used as fermentation feedstock to produce rhamnolipids by non-pathogenic bacterium, Burkholderia thailendensis. Shake flask experiments were performed at 30°C temperature and agitation rate of 150 rpm using biomass side streams without any pre-treatment to investigate the potential of rhamnolipids production from these waste streams. In the next step, the influence of varying pH of biomass streams on microbial synthesis of rhamnolipids was investigated at pH 5, 7, and 9 which provided insights into optimizing their yield and functionality. The extraction of rhamnolipids were performed using a solvent extraction method which resulted in approximate yield of 1 g/L under optimal conditions of pH 7 at shake flask level. Characterisation by Fourier Transform Infrared Spectroscopy (FTIR) revealed the characteristic peaks of rhamnolipids with Rha-Rha-C12-C14 congeners which are proven to exhibit high emulsification ability. Formulation of rhamnolipids into nanoparticles for nutrient absorption and improved soil health to close the nutrient loop is being investigated.

Advancements in the textiles industry, specifically within the near-infrared (NIR) range (750nm – 2500nm), have occurred in recent years. NIR-absorbing dyes interact favorably with wavelengths in this region, which is useful for many applications. By using these dyes, garments that can be undetectable against their background, desirable for military uniforms, could be created. Along with the dyes, the type of fabric used could affect detectability. Having differing cover factor values could impact the results. This research aims to characterize NIR-dyed cotton fabrics using short-wave infrared (SWIR) hyperspectral imaging to discern spectral changes cased by variations in fabric cover factors and dye concentrations. The three fabrics used had different cover factors (22.5, 21.59, 27.45) and were dyed at two concentrations (2.5% and 10%). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify the data based on the experimental design. PCA models provided excellent results in grouping the data based on the cover factor and dye concentration. PLS-DA had a 100% classification accuracy for all three fabric weights in calibration and cross-validation models. The results highlight that different weights of fabrics can be discriminated well in the SWIR region and dye concentrations within those fabric weights. This underscores the importance of selecting proper fabric weights and dye concentrations to match the background spectra. The PLS-DA excluded external validation since this procedure used a smaller sample size. Increasing the sample size to include validation ensuring accurate results are acquired is essential in any future research within this field.

Around 48 million dry tonnes of agri-food residues are generated per year in Canada, with the majority ending up as waste. These resources are enriched in nutrients and can support the growth of microorganisms when used as substrates in fermentation processes. In this context, we developed an efficient bioprocess for value-addition of nutrient rich agri-food biomass to renewable biomaterials from fungi. Fungi have a multitude of enzymes that can metabolize these complex nutrients to high-value biomaterials and products such as leather and plastic packaging alternatives.

In this work, we investigated the impact of several growth media differing in carbohydrate and protein composition on the growth, composition and mechano-physical properties of fungal mycelium material. Our study involved production of mycelium biomaterial by fungal species such as Pleurotus ostreatus, Ganoderma lucidum and Hericium erinaceus on various growth media via submerged fermentation. Fourier Transform Infrared Spectroscopy (FTIR) revealed that the material grown on nitrogen and carbohydrate rich substrate stimulated the production of protein, polysaccharides and lipids whereas the substrate with more carbohydrate (glucose) and protein content resulted in chitin enriched biomaterial. This distinction in constituents is due to different feeding substrates. Thermogravimetric Analysis (TGA) and Differential Scanning Spectroscopy (DSC) demonstrated the thermal degradation profiles with major degradation in the range 225-330°C indicating a high thermal stability which makes them amenable to industrial processing for various material applications. The above results hold great importance for valorization of agri-food resources into high-value bioproducts such as vegan leather, and packaging material.

The reduction of food waste is crucial for sustainable food production and resource conservation especially amid growing global concerns over food insecurity. The valorization of agri-food waste through extraction and application of value-added products from diverse waste streams has been an emerging strategy for supporting agricultural sustainability and development of a circular economy. The work presented here developed a model for green synthesis of inorganic nanoparticles employing co-precipitation with small molecules including gallic acid as reducing agents. Emphasis was placed on achieving control over particle size, distribution, and functionalization. Comprehensive characterization of the nanoparticles was conducted, assessing size distributions, shape uniformity, and chemical compositions through various advanced techniques, including Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), Energy-Dispersive X-ray Fluorescence (EDXRF), and Fourier Transform Infrared Spectroscopy (FT-IR). The efficacy of the model was validated utilizing various agri-food waste extracts such as sour cherry and pineapple peels. Nanoparticles were applied in combination with biodegradable packaging materials of sodium alginate-calcium chloride assessing their antimicrobial properties and ability to prevent food loss though spoilage of fresh fruits.

Globally, livestock manure is one of the largest sources of water pollution and climate change from greenhouse gas emissions. Thus, making advancements in sustainable manure management essential for reducing the overall impact of the agricultural sector. Biodigesters facilitating anaerobic digestion are a promising solution as they process a wide variety of wastes, capture greenhouse gasses typically emitted in traditional manure management, while producing biogas (a clean energy source) and digestate (a nutrient dense effluent).

This study explores how the environmental impacts of land-applied digestate vary spatially across Ontario’s agricultural regions. Using the DeNitrification-DeComposition (DNDC) model, a carbon and nitrogen process-based model, the nitrogen leaching and greenhouse gas emissions from the land-application of digestate, untreated manure, and inorganic fertilizer were assessed for a 10-year corn-soybean rotation across 277 regions in Ontario. Model parameters were calibrated using observed corn and soybean yields.

The findings from this research show the influence that climate, soil, and field conditions have on the environmental implications which stem from digestate use in place of commercial fertilizer or untreated manure. It provides insights to the locations or conditions in which digestate can be applied with minimal impacts – an essential step for the advancement of biodigesters as a sustainable, circular manure management solution.

The ultra-processed nature of plant-based meat patties (PBP) and rapid food digitalization underscores the need for optimizing storage practices through non-destructive techniques. This study aims to map the surface quality variations within retail-packed patties (WRP) using portable hyperspectral imaging (HSI) through polyvinyl chloride (PVC) packaging film, while also exploring the use of WRP spectral signals to model degradation kinetics and estimate shelf life through multivariate accelerated shelf-life testing (MASLT). Model-PBP formulation was developed and stored at -20, 5, and 10 °C, with HSI and instrumental quality measurements (IQM) recorded on days 1, 4, 6, 8, 11, and 15. Quality deterioration index (QDI) was assigned based on Discriminant factor analysis (DFA) on IQMs. Principal component analysis (PCA) and Partial least square regression (PLSR) were used to explore spectral features and prediction modeling. PCA indicated correlated optimized warping (COW) to correct spectral contributions from PVC. PLSR built on spectra from WRP and without package predicted the IQMs with comparable accuracies. With these validations, WRP spectra were subjected to shelf-life prediction modeling. Time-related first principal component (PC1) and first latent variable (LV1) scores versus days yielded kinetic charts showing exponential decay at 10 °C, and mixed-order reactions at 5°C and 10 °C, evaluated by R2 values. WRP spectra from unacceptable samples set cut-off values for shelf-life estimations at three temperatures. PLSR models showed high accuracies in predicting unacceptable QDI, shelf-life, and mapping surface quality variations. In conclusion, this non-contact on-package spectral sensing enables real-time shelf-life prediction, assisting decisions on grading, labeling, and storage.

Pesticide target application rate errors may occur due to pressure changes which are observed when individual nozzle valves are actuated simultaneously in agricultural boom sprayers. To reduce application rate errors, flow control valves, that is, CDA .5 and CDA .75 referred to as V1 and V2 respectively, were installed between the electric nozzle valve and the spray nozzle. Flow stability experiments were conducted on a crossover utility vehicle on which a hydraulic system was mounted. The system consisted of a gas engine-powered centrifugal pump, pressure gauge, electric nozzle valves, and spray nozzles. Flow rates were estimated from three types of nozzles, that is, AIC 110° 04, 3DN 90° 06, and 3DN 90° 08 referred to as N1, N2, and N3 respectively. All nozzles were tested at 275, 345, 415, and 482 kPa system pressure. V2 stabilised the flow rate to within 10.5% and 12.1% for N2 and N3 respectively between 345-482 kPa. Similarly, V1 maintained the flow rate within 10% between 345-482 kPa. Flow rate percentage difference between 275-482 kPa was more than 30% for all flow control valve and nozzle combinations. To ensure that a 10% ASABE recommended application error is achieved, system pressure in the boom should be limited between 345-482 kPa if V1/N1 and V2/N2 flow control valve to nozzle combinations are to be used in spot applications. At a travel speed of 4 m/s, V1/N1 and V2/N2 flow control valve to nozzle combinations meet the target application rate for herbicides and insecticides respectively.

Agriculture is a predominant contributor to global ammonia emissions, with 81% originating from agricultural activities, particularly livestock production and fertilization. These emissions have adverse effects on both human health and the environment. Nevertheless, a critical knowledge gap exists regarding the accuracy of measuring methods at low concentrations as on-field production. Establishing easy and cost-effective methods to measure ammonia emissions during fertilization in multi-plot field trials is crucial for comparing different fertilization treatments and finding strategies to mitigate this emission. This study introduces the development of a passive flux sampler specifically designed for measuring ammonia emissions during agricultural fertilization processes. The innovation incorporates a comprehensive analysis of both aerodynamic behavior and adsorption characteristics using various sorbents impregnated with an acid coating. To identify the optimal sorbent, three distinct materials, specifically glass microfiber filters, zeolite, and biochar, undergo thorough testing in a wind tunnel to assess their performance. The evaluation process encompasses crucial parameters such as adsorbent characterization, aerodynamic efficiency, and adsorption efficacy. The primary objective of this study is to pinpoint the sorbent that strikes a balance between effectiveness, and cost-efficiency considerations. This selection will facilitate accurate and practical measurements of ammonia emissions during fertilization activities, thereby contributing to the development of sustainable agricultural practices.

Food packaging waste accounts for almost the 50% of the plastics derived from fossil fuels and a major portion of them ends up in the landfills. Hence, in this study, one of the widely overlooked sources of cellulose, the waste parchment and wrapping papers were valorised to fabricate nanocellulose-based biodegradable packages for applications in the food industries. The nanocellulose extracted from the waste paper was utilized to synthesize the nanocellulose-starch based nanocomposites. The structural and chemical composition of the nanocomposites were deciphered through X-Ray Diffraction spectroscopy, Fourier Transform Infrared Spectroscopy and Energy Dispersive X-ray Fluorescence spectroscopy. The thermal properties were evaluated through Differential Scanning Calorimetry and the morphology through Scanning Electron Microscopy. Further, the physical and mechanical properties of the nanocomposite films were examined including the tensile strength, color, thickness, water vapour transmission rate, moisture content, surface hydrophobicity, and light transmittance. The antimicrobial and biodegradable properties of the nanocomposite were also evaluated. Finally, the shelf-life of fresh foods on employing the nanocomposite via two methods namely the spray coating and covering films were evaluated.

With a change in lifestyle and becoming more health conscious, consumers are concerned with the excessive use of preservatives in processed foods. Innovative packaging materials are being explored to curb the use of preservatives during processing. Antimicrobial and antioxidant packaging with natural ingredients is one of the promising forms of active packaging that reduce, inhibit, and retarding the growth of microbes. The active packaging was fabricated with the help of a monolayer blow film extruder, where nanocellulose crystals (2-6%) were combined with polypropylene granules. Compatibilizers (5-15%) were added to the mixture to act as a binding agent, and essential oils (1-3%) were added to enhance the antimicrobial and antioxidant properties in the films. The developed nanocomposite films were tested for their antimicrobial and antioxidative properties. Barrier properties like gas and water permeability were improved in the nanofilms. The mechanical properties of the movie, like tensile strength, elongation, and thickness, were on par with the commercially available packaging films. Food products in the movie (chicken breast and sliced carrots) remained suitable for consumption for up to 12 and 24 days at refrigerated conditions with a high sensory acceptability of 8.5 out of 9. With the help of nanotechnology, we can ensure the safe and slow delivery of active compounds like essential oils in food to extend the shelf life of perishable food products.

Wild blueberries (Vaccinium angustifolium Ait.) are a perennial species native to northeastern North America. Commercial management of this crop includes applications of fungicides to prevent diseases such as Monilinia blight (Monilinia vaccinii-corymbosi) and Botrytis blight (Botrytis cinerea). Wild blueberries are susceptible to these diseases starting in their F2 stage of growth when the flower buds crown. The timing of fungicide application is critical for successful prevention of these diseases. In Nova Scotia, Canada, extension specialists travel to fields across the province to examine, quantify, and report the percentage of flower buds in the F2 stage or later in each region. This is a time-consuming process that provides general guidance for each area. A smartphone-based tool that identifies bud stages would allow growers to quickly gather site-specific information about their fields to help with management decisions. Convolutional neural networks are image-processing algorithms that can automatically detect and classify features in pictures. Images of wild blueberry branches with varying amounts of F2 buds were captured against single-colour backgrounds in May and June 2020 and 2022. The flower buds in the images were labelled according to their growth stage as F0, F1, F2, or F3. The images were used to train the YOLOv5 CNN to automatically detect and classify the flower buds. The trained CNN will be deployed in a downloadable smartphone application and an online web-based application to facilitate streamlined bud stage identification and management information delivery to wild blueberry growers.

Near-surface geophysical techniques, including ground-penetrating radar (GPR) and electromagnetic induction (EMI), have become alternative methods to estimate soil properties and state variables (soil information) in agricultural landscapes to support precision agriculture. Understanding the soil profile is crucial for interpreting geophysical data to accurately estimate soil information. Traditionally, soil coring and open pit excavation provide valuable insight into soil profiles, although they are tedious and destructive. This study aims to evaluate the effectiveness of an integrated GPR-EMI technique for understanding soil stratification and subsurface distributions within a podzolic soil site in Newfoundland. Soil samples were collected for basic soil properties (texture, gravel and organic matter percentages, bulk density) from five locations within a 12 x 18 m² area. Geophysical data were collected using a multi-coil EMI sensor and a 500 MHz frequency GPR system. EMI depth inversions, GPR depth slices, and 2D profiles were prepared to observe distinct soil stratification patterns in the soil profile. Analysis of soil sampling (0-0.60 m depth) and geophysical data (0-1.8 m depth) exhibited consistent patterns reflecting the changing soil profiles with depth. Basic properties assessed for shallow soil layers (0-0.30 m) showed a significant (p<0.05) difference from deep layers (0.30-0.60 m). Similarly, a distinct layer was found from GPR and EMI, around 0.3-1.4 m depth. The integrated technique confirmed each method's findings and revealed insights into the spatial variability of soil density, electrical conductivity, and structural changes in the soil profile. This non-destructive approach can advance precision agriculture by facilitating more accurate soil management strategies.

Understanding the intricate dynamics of soil-plant interactions is vital for advancing both biosystems and civil engineering practices. This review study delves into the application of the Discrete Element Method (DEM) in simulating and analyzing these interactions, with a focus on seeds, roots, root vegetables, and residues. The comprehensive analysis synthesizes current research findings, methodologies, and the versatility of DEM in various contexts, ranging from seed germination to crop residue management. The study highlights the significant role of DEM in simulating the physical interactions between soil and different plant materials, emphasizing the method's ability to quantify these interactions from a microscopic perspective. It assesses how DEM has been employed to explore soil's influence on seed sowing and emergence, root system development, and the growth and harvest of root vegetables, all critical components in agricultural engineering. Additionally, the paper examines the utilization of DEM in understanding residue-soil interactions, which are essential for sustainable agricultural practices and soil health management. Challenges in modelling such complex systems are outlined, including the need for accurate calibration methods and the integration of environmental factors like moisture and temperature. The review proposes future research directions, highlighting emerging opportunities in applying machine learning techniques and multi-software integrations to enhance DEM simulations. These advancements promise to offer more detailed and accurate insights into soil-plant interactions, driving innovations in agricultural machinery design, crop cultivation strategies, and civil engineering solutions.

Water footprint and industrial water pollution, caused by the textile industry, accounting for approximately 20% of total global pollution along with greenhouse gas (GHG) emissions, are major issues facing humankind today. While cotton production requires thousands of litres of water to produce a single garment, the production of biomass fibres, such as flax and hemp, also requires 3783 and 2720 litres of water per kg of fibre production, respectively. Furthermore, GHG emissions from the production of flax and hemp fibres were measured at 340 and 366 kg CO2e per ton of fibre production, respectively.

To mitigate water and GHG emissions, waste biomass fibre extracted from canola stems is being investigated for industrial and apparel applications. It has been agreed that the production of canola fibres requires zero water and emits zero GHG in farming, as it is obtained from the waste stream of canola stalks. The only water requirement to obtain canola fibre is for the retting process, which consumes 392 litre per kg, and emits 201 kg CO2e per tons of fibre production using the 2020 Manitoba Hydro’s emission factor. Canola fibre extraction was carried out using the retting machine fabricated by the Department of Biosystems Engineering using the published patent by university researchers.

The successful transition of the extracted canola fibre into industrial and apparel applications will save billions of litres of water and significantly reduce GHG emission.

Due to climate change, Churchill Manitoba is on a fast-track to replace their current mixed municipal waste burial (in permafrost) into separated streams that will approach their zero waste target. Household organics will likely be composted in an in-vessel composter using a tumbling tube composter called a Biovator. During tumbling, moisture is moderated with a dry bulking agent such as wood chips, but due Churchill’s location above the tree line, none are available. In this experiment, corrugated cardboard, which is abundant in Churchill’s waste stream, was used as a replacement for wood chips. This experiment took place at the SiAF site at the University of Manitoba using collected residential waste (300 kg total) and strips of normal cardboard (80 kg total). The compost reached thermophilic temperatures within one week and cardboard equalized moisture, but was ineffective to keep the compost aerobic, as olfactory signals indicated. A greater quantity of finely shredded cardboard may have improved the composting conditions.

Tribo-electrostatic separation (TES) as a dry fractionation technique for the separation of protein was performed in this work. The experiments were carried out using a custom-built solid particle dispenser, coupled with a triboelectrostatic separator, with the latter comprising of a cubic chamber with two electrodes on which the separated particles were deposited. Faba bean flour of known particle sizes were tested with the electrostatic separator via the particle dispenser with air as the dispersing medium. Using a tribocharging tube, Faraday cup, and an electrometer, the type of charge (positive or negative) acquired by the components of the flour sample and their chargeability in coulombs per unit mass were established prior to separation. A high voltage generator was utilized to supply the electric field required to ensure proper separation within the separator. The effects of electric field strength (100 – 200 kV/m) and air flow rate, varied between 5 – 15 l/min on the yield, protein enrichment and separation efficiency were explored. Higher separation efficiency was confirmed based on the scanning electron. The positive results from these experiments prove that the TES technique is a viable means for the commercial fractionation of both cereal and pulse flours without the drawbacks associated with existing wet fractionation techniques. Further experiments comparing the functional properties of the post-TES with the pre-TES materials are currently underway to explore the effect of this technique on these and other feed materials.

The process of extracting fiber from flax requires a crucial step known as retting. Traditionally, this method involves the use of microbial or enzymatic degradation of the plant material to separate the fiber from the woody core. However, the introduction of retting machines presents an opportunity to improve efficiency and consistency in fiber extraction. This study explores the feasibility of utilizing retting machines for the extraction of flax fiber. The results indicate promising prospects for the adoption of retting machines, with considerations for optimizing operational parameters to maximize fiber yield and quality. By utilizing modern technologies for fiber extraction, this study contributes to the advancement of sustainable and efficient practices in the flax industry.

Canola (Brassica napus) is a versatile oilseed crop known for its edible oil and protein-rich meal. Beyond its traditional uses, canola fibers offer significant potential as a sustainable raw material for textile and composite applications. Derived from stems of canola plants, these fibers possess inherent strength, biodegradability, and minimal environmental impact. However, their application in textiles and composites necessitates individualization—a process that enhances their properties and unlocks novel applications. Our research aims to investigate methods for individualizing canola fibers, specifically targeting textile and bio-composite materials. By tailoring treatment approaches, we seek to optimize fiber characteristics for diverse applications. Canola fibers were extracted using water retting at a controlled temperature of 40°C. Three treatment approaches were investigated: (a) hot water washing followed by ultrasonic treatment for varying durations of 1, 2, and 3 hours, (b) a combination of hot water treatment followed by oil treatment, and subsequent ultrasonic treatment, and (c) integration of hot water, vegetable oil, lysozyme enzyme, and ultrasonic treatment.

This research is ongoing, and results regarding individualization, wetting behavior, morphological characteristics, and hand properties will be presented

The utilization of green synthesis approaches to produce nanoparticles has gained significant interest due to their eco-friendly nature and improved biocompatibility, opening new potential application areas, such as the food and beverage sector. In this study, focus was placed on the SeNPs production for the application in antimicrobial films, which can further be employed as active food packaging. Selenium nanoparticles were synthesized with the use of an aqueous plant extract as a reducing and stabilizing agent. The reduction of selenium ions to nanoparticles was achieved under room temperature reaction conditions, avoiding the use of harsh chemicals or high-energy inputs. Characterization techniques such as Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), Energy-Dispersive X-ray Fluorescence (EDXRF) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to analyze particle composition, size distribution, shape, uniformity and stability. The synthesized SeNPs were then incorporated into biopolymer-based films to evaluate their antimicrobial efficacy against common food spoilage microorganisms. Overall, this study highlights the promising role of plant extract-mediated synthesis of SeNPs in developing sustainable and effective antimicrobial food packaging solutions, contributing to food safety and quality preservation in the food industry.

The normalized Difference Vegetation Index (NDVI) is a crucial indicator of crop health and stress levels. Without NDVI data, crop monitoring becomes challenging, pest and disease detection is delayed, yield loss risks increase and decision-making becomes more difficult. Therefore, NDVI data is essential for effective farming. There are two primary ways to obtain NDVI data: manually using an NDVI meter, which is very tedious, and using a drone equipped with a camera for efficient aerial imaging. While the latter method is remarkable, it has drawbacks. Multispectral cameras are expensive, complex to operate, and not always compatible with existing systems.

Our team devised a unique and straightforward solution: using an RGB camera instead. We aimed to capture RGB images and train an AI model to perform the domain conversion. RGB cameras are affordable, lightweight, and offer high pixel resolution.

We collected data from five different potato fields in PEI and processed it using a Generative Adversarial Network (GAN) model. Initially, there were some mismatches, but as the GAN iterated, the final images closely resembled the real images. The final images generated by the GAN were compared with real images obtained from a multispectral camera, demonstrating impressive accuracy.

Our study revealed that RGB images could be transformed into five distinct domains. In conclusion, our approach is sustainable, allowing us to gather more information with available resources. This benefits farmers by providing timely crop health diagnostics through an information platform, ultimately helping to increase crop productivity and income from the same land.

Sustainable agriculture focuses on eco-friendly, health-conscious, and economically viable farming practices. Precision agriculture uses technology to optimize crop management. These methods are vital for meeting global food demand sustainably. Automation in agriculture boosts efficiency, reduces labor needs, and lowers environmental impact.We introduce AgriScout, an autonomous, multifunctional robot for precision agriculture. AgriScout is an electric robot with a modular design, adapting to various crops.In 2023, we used AgriScout to detect PVY disease in potato crops. Equipped with high-resolution cameras, the robot captures detailed images of the plants. These images are processed using deep learning and AI algorithms to identify diseased plants. The robot geo-locates the diseased plants, generating an infestation map for growers, replacing labor-intensive manual crop inspection, enhancing accuracy and efficiency, and reducing insecticide use by targeting only diseased plants.In 2024, we advanced AgriScout by developing an automatic soil compaction attachment. This tool automates soil compaction data collection by probing a rod with a cone into the soil, recording pressure readings at every inch up to 18 inches deep. Traditionally performed manually, this task often results in inconsistent force application and inaccurate readings due to operator fatigue. Our system ensures consistent force application, precisely measuring compacted soil layers. The robot autonomously navigates to pre-mapped locations, taking multiple readings to provide comprehensive soil compaction data, aiding in variable rate tillage.AgriScout merges sustainable and precision agriculture by automating tasks, reducing manual labor, and improving data accuracy. Future advancements will add tools to further automate farming processes, enhancing sustainability and precision.

This study performed a field evaluation of a novel precision spot applicator for granular agrochemicals. Specifically, the design was assessed for its ability to selectively apply Casoron® G4 to patches of hair fescue in wild blueberry fields. Sticky traps were placed in both target and non-target locations within the field, and predeveloped prescriptions were followed. The system demonstrated promising performance, with accuracy, precision, sensitivity, and specificity rates of 95%, 91%, 99%, and 91%, respectively. The system's potential to spot apply Casoron® G4 while maintaining comparable hair fescue management to conventional broadcast application was also evaluated. There were no significant differences in hair fescue vegetative tuft counts between spot and broadcast applications (p < 0.05 at all sites). Overall, the system's performance was highly encouraging, marking the first successful development of a precision spot applicator for any cropping system. For wild blueberry cultivation, this system has the potential to significantly reduce the cost of granular agrochemical applications while offering a cost-effective solution for managing hair fescue.

This research outlined a comprehensive workflow to assess the viability of AI-generated imagery in training machine learning models for improving the detection of ripe wild blueberries (Vaccinium angustifolium Ait.). A dataset comprising of 200 high-resolution (26 MP) ground truth images of ripe wild blueberries were collected and augmented with AI-generated variations using DALLE 2 to increase overall dataset size. Models were then trained on three datasets: ground truth, generated, and a combination (40% of the dataset contribution being generated images). Evaluation metrics included precision, recall, mAP50, and mAP50-95, each analyzed using ANOVA multiple mean comparisons and Tukey’s HSD test through a completely randomized design. The results revealed that the ground truth models and the combination models had no significant difference across most performance metrics (p < 0.001) (mAP50, precision, and recall). The ground truth model achieved a mAP50 of 0.806, precision of 0.819, and recall of 0.723. The combination model achieved the highest mean performance across all metrics (mAP50: 0.834, precision: 0.854, recall: 0.755), with significantly higher performance on the mAP50-95 metric (0.478). This demonstrated the potential of AI-generated images to enhance training datasets. However, models trained solely on generated images showed significantly lower performance (mAP50: 0.642, mAP50-95: 0.308, precision: 0.743, recall: 0.566) when validated on ground truth images, indicating AI-generated images can augment datasets and improve generalization, but cannot fully replace ground truth data and maintain model performance. These findings highlighted the importance of a balanced approach to optimizing data collection protocols for wild blueberry ripeness detection.

Considering temperature conditions plays a crucial role in maximizing the fruit quality during wild blueberry (Vaccinium angustifolium Ait.) harvesting. This study explores the optimal temperature ranges for harvesting wild blueberries using three harvesting methods: Hand rake (HR), Walk-behind (WH), and Mechanical harvester (MH). The study was conducted in commercial wild blueberry fields in central Nova Scotia. The daytime temperature was categorized into four ranges TH-1 (10-15°C), TH-2 (16-21°C), TH-3 (22-27°C), and TH-4 (28-33°C) for harvesting wild blueberries using HR, WH, and MH. The harvested berries were categorized into four categories including undamaged berries, bruised berries, cut or split berries, and debris, and the impact of temperature on berries quality for each harvesting method was investigated. One-way ANOVA analysis identified that the temperature range TH-1 and TH-2 was most conducive to achieving high-quality berries across all harvesting methods. Based on the results, the maximum (80%) yield of good quality berries was achieved within the temperature range of 15-25°C, especially during early morning and early afternoon. This study proved that early morning and early afternoon harvesting is best for optimizing fruit quality and farm profitability. To enhance results will play a pivotal role for blueberry growers, offering sustainable practices for enhancing fruit quality. This research bridges the knowledge gap related to the implications of temperature during harvest on the blueberry quality.

Abstract:

Efficient bioresource management of agricultural fields can alter soil biochemistry and physical properties, reducing greenhouse gas (GHG) emissions. This study aims to evaluate the role of organic amendment, including sugar kelp (SK) and its combination with inorganic fertilizer (IF), in reducing GHG emissions and increasing crop productivity. Four soil amendments, including SK (Sugar Kelp), IF (Inorganic Fertilizer), SK + IF (Sugar Kelp + Inorganic Fertilizer), and control (no amendment), were replicated four times under a randomized block design during the potato growing season of in Prince Edward Island (PEI), Canada. A LI-COR trace gas analyzer and LI-COR portable photosynthesis systems were used to monitor emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from treatment plots and to monitor GHG exchange between plant leaves and the environment, respectively to check the effect of sugar kelp under mentioned four conditions. Analysis of variance (ANOVA) results was able to depict which treatment plots have higher soil moisture-holding capacities measured by LI-COR trace gas analyzer. Soil moisture impacts soil temperature and rainfall events so a HOBO weather station was installed at the sampling site for the local and accurate measurement of the average temperature of the GHGs and harvesting events. ANOVA results were used to understand the effect of each treatment and environmental factors on resulting GHG emissions and potato crop productivity.

Dill (Anethum Greveolens L.), a biennial or annual herb is globally known for its utilization in various food industries, including refrigeration and food concentrates, to impart its distinctive flavor and seasoning to a range of dishes. Dill seed also offers nutritional benefits as it contains various essential nutrients such as vitamin A, calcium, magnesium, sodium, potassium, fiber, protein and niacin. Dill seed is usually adulterated with fennel, cumin seeds, Indian dill (Anethum sowa) and other resembling dill cultivars or varieties for economic gain. In this study, shortwave infrared (SWIR) hyperspectral imaging was deployed to differentiate three dill varieties based on reflectance features. Three hundred hyperspectral images captured in the wavelength range of 900 to 2500 nm were processed, segmented and subjected to machine learning algorithms. Multiclass classifier was used to classify the dill varieties based on reflectance spectra. All the three dill varieties were classified correctly with a classification accuracy of 100%.

In recent years, frequent reports of food adulteration have increased the concerns of consumers, food industries and regulatory agencies. Food adulteration is termed as a deliberate offensive act to delude people for monitory benefits. Chickpea is broadly consumed pulse in the world. It is rich in protein and also a good source of dietary fiber, carbohydrates, vitamins, minerals, and several bioactive components. It is often adulterated with low cost and low protein resembling materials like corn flour. In the present study, chickpea flour was adulterated with different percentage levels (1, 3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 97, 99, and 100%) of corn flour. Images of mixed flour samples were acquired with 25 replications using shortwave infrared (SWIR) hyperspectral imaging (HSI) in the wavelength range between 900 and 2500 nm. The captured images were processed, segmented and subjected to machine learning algorithms. The classification of different levels of corn flour adulteration was achieved with 98% accuracy using multiclass classifier. Variations in protein and starch contents of adulterated samples can be attributed to the higher classification accuracy.

The significant shift in customer eating habits towards high-protein, low- carb, gluten-free alternatives has forced the food processing industry to come up with more innovative solutions to meet rising demands. Specifically, the pasta industry is currently developing techniques to incorporate more protein per serving while ensuring the palatability of the product. Potato flour (PF) is a gluten- free, versatile flour that can be incorporated into baked goods such as pasta and gnocchi, while chickpea flour (CF), known for its low glycemic index, high protein and essential vitamins which is ideal for fortification. In this research, potato and chickpea flour blends of various concentrations were prepared and analyzed for their physiochemical properties; specifically starch and protein content, which could help pasta- making. The NIR hyperspectral imaging system was used in the range of 900-2500 nm to scan 17 different flour blends in reflectance mode. Spectral features were extracted from the acquired images which were further processed and analyzed using machine learning approaches. The regression analysis predicted protein and starch content with a correlation coefficient of 0.999 and 0.996 respectively.

Potatoes are the widest grown vegetable in Canada, in addition to being the fifth largest primary agricultural crop. Due to their unique end-uses and value addition to the market as processed potatoes, it becomes vital to ensure potato quality from harvest to the consumption. This study intended to understand the effect of storage on dry matter (DM) and accumulated sugars. Hence, the tubers were stored at two different temperatures 2℃ and 15℃, in boxes that admitted little to no light. For the DM study, 277 Russet Burbank (RB) potatoes were scanned in the short-wave infrared (SWIR) of 900-2500 nm range using a hyperspectral imaging system. The relationship between tuber specific gravity (SG) vs DM for individual potatoes was also explored by using a contraption attached to the texture analyzer, that was developed in-house. Similarly, a separate batch containing 130 RB potatoes (stored at 2℃ and 15℃) was scanned for their sugar content; followed by wet chemical techniques to estimate total glucose and sucrose. A 10-fold cross-validation test conducted on the dataset revealed a 90.2% and 82.8% classification accuracy, with a root mean square error of 0.105 and 0.2403, respectively, for observed DM and sugar values, imaged on various days.

Glyphosate, a widely used organophosphate herbicide, poses significant concerns globally due to its persistent presence in pulse crops, leading to food safety challenges. This research investigates the potential of Near-Infrared (NIR) hyperspectral imaging, operating between 900 to 2500 nm wavelengths, for identifying glyphosate residues in red lentil flour. Red lentil flour samples were tested at five different glyphosate concentrations (0 ppm, 5 ppm, 10 ppm, 15 ppm, and 20 ppm) to assess residue levels. Spectral data preprocessing involved smoothing and first derivate techniques. Partial Least Square Regression (PLSR) models were developed incorporating chemical reference measurements and spectral data, refined using a variable importance in projection (VIP)-based method to identify crucial wavelengths for glyphosate detection. Results demonstrated robust predictive capabilities, with a correlation coefficient of 0.930 and a root mean square error of cross-validation (RMSEP) of 0.871 across the concentration range. These outcomes underscore the potential of NIR hyperspectral imaging in accurately quantifying glyphosate residue levels in red lentil flour, thereby mitigating food safety risks associated with agricultural products. This study highlights the efficacy of this non-destructive technique, providing swift and precise detection methods. Future research could explore integrating this approach into routine monitoring practices in food processing facilities to ensure strict adherence to food safety standards.

Rapid, non-destructive and precise prediction of yellow pea flour adulteration in chickpea flour is crucial for food inspection agencies. In present study, short wave infra-red (SWIR) hyperspectral imaging (HSI) in the wavelength range between 900 nm and 2500 nm was deployed to predict the protein and starch in the chickpea flour adulterated with yellow pea. Adulteration was formulated at different levels of yellow pea, i.e. 1, 3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 97, 99, and 100%. Gaussian process regression (GPR), support vector machine regression (SVMR) and neural network models based on different spectral preprocessing techniques were developed by correlating the hyperspectral data with measured reference protein and starch values of the adulterated samples. Competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV) algorithms were used to select the effective wavelengths with full spectrum. The optimum prediction model for protein was obtained using GPR yielding correlation coefficient of prediction (R2p) and root mean square error of prediction (RMSEP) values of 0.99 and 0.089, respectively. Similarly, the same GPR model predicted the starch content with R2p and RMSEP values of 0.86 and 0.549, respectively.

Natural polymers, including proteins and polysaccharides, have been receiving increasing attentions recently as sustainable materials for the development in biomedical applications. Natural polymers extracted from plants or animal products provide great biocompatibility and biodegradability, which are highly desirable in the biomedical field. Hydrogels are highly hydrated, porous, and soft materials with a touch similar to that of the human skin and have therefore attracted extensive interest in the development of wearable biosensors. Egg white is a major nutrient source that has been utilized for extraction of functional proteins. However, the costly and complex process of isolation and purification limits the utilization of egg white and its derivatives. Recently, there has been immense interests in using raw egg white with advanced applications such as supercapacitors and semiconductors, although they still involve complex fabrication methods. Here, we prepared a physically crosslinked egg white (EW) hydrogel with extraordinary stretchability using a simple method. The prepared EW hydrogel showed shear-thinning and self-healing properties that enabled its 3D-printing to fabricate the complex architectures of the biosensor. Incorporation with carbon nanotubes, the versatile EW hydrogel-based biosensors can capture a wide range of human motion including finger bending, wrist pulse, and respiration rate. The biosensor also effectively discerned the cardiovascular system's radial augmentation and stiffness indexes. A highly sensitive humidity-responsive reversible actuator was fabricated through a design of gradient crosslinking density structure. This low-cost natural material provides an easy and effective way for the tailored fabrication of multifunctional biosensors and actuators.

Microwave ovens are popular for their convenience, but uneven heating can affect the quality and safety of cooked food. A modified microwave oven with a fibre optic system was developed to investigate the effect of packaging on temperature distribution. The oven had a frequency of 2450 MHz and a rated output power of 1000 W. To evaluate the package geometry effect, baby potatoes (3-5 cm diameter; about 450 g) were arranged in layers within airtight polypropylene containers equipped with a steam vent hole and cooked at maximal power for 6 min. Potatoes were stacked in 3, 2, and 1 layer in the cylindrical (5.2 cm radius, 14.7 cm depth), cuboidal (14.5 L x 14.5 W x 8.5 H cm), and rectangular (19.5 L x 12.5 W x 4 H cm) containers, respectively. The results showed that package geometry and potato arrangement did not significantly affect the temperature profile in headspace and potatoes positioned at the container periphery in all the packages tested. Steam vent hole of 4 mm diameter had no significant effect on the time taken for the potatoes and headspace to reach the maximal temperature (100oC). However, the time taken to reach 100oC for the potatoes located at the centre was considerably longer for the cylindrical container (6 min) than the rectangular container (2 min). This study showed that minimizing the potato stack layer within a package would improve product temperature uniformity during microwave cooking.

Potato (Solanum tuberosum L.) is an important food crop and ranks fourth in the world after wheat, rice, and maize. Potato is usually consumed after processing with or without its peel where most of the phenolic compounds and glycoalkaloids are concentrated. Glycoalkaloids, a group of nitrogen-containing compounds are toxic to humans if consumed in high concentrations. Plant glycoalkaloids are toxic steroidal glycosides and the common types found in potatoes are α-solanine and α-chaconine. Canadian consumers are rarely exposed to toxic levels of glycoalkaloids that cause serious health effects. However, there are occasional reports of short-term adverse symptoms, usually from eating potatoes that contain elevated concentrations of glycoalkaloids like burning sensation in the mouth, nausea, vomiting, stomach and abdominal cramps, and diarrhea. Therefore, Health Canada has set the level of total glycoalkaloids (TGA ) concentrations to not exceed 200 mg/kg (2,00,000 ppb) fresh weight, in any of the potato variety before marketing. Thus, the current study seeks to estimate the level of TGA using analytical high-performance liquid chromatography (HPLC) technique in various potato varieties. Samples were placed under LED light, continuously for a period of twenty days after which the tubers were assessed for their TGA content at 0, 3-, 5-, 7- and 20-day intervals of light exposure and assessed for TGA content. The levels of TGA found in the analyzed potato samples were below maximum residue level, rendering them safe for human consumption.

Unripe plantain slices were pretreated, deep-fried, then their quality and shelf life were evaluated in this work. Pretreatment techniques were; an ultrasound probe; 20KHz frequency at 600W maximum power, dipping in honey, and soaking in sugar solution. Plantain slices, with average weight, thickness, and diameter of 3g, 3mm, and 30mm respectively were fried using sunflower oil at a temperature of 170 o C for 2, 4, 6, 8, and 10 minutes. The Moisture content (MC), oil uptake (OU), moisture diffusivity (MD), and carotenoid content (CC) of fried plantain chips were evaluated. The honey and sugar pretreatments caused the samples to have lower MC before frying compared with ultrasound and control (samples with no treatment). The lowest OU was seen with ultrasound and sugar samples. Moisture transfer rate correlation coefficient (R2) ranged from 0.90 to 0.99 demonstrating a good fit for the experimental data with the sugar sample having the highest MD. Statistically, the honey sample with the lowest k-value and highest CC retention was the best in taste, aroma, crispiness, greasiness, and overall acceptability when fried for 10 minutes using 30 semi-panelists. The pretreated sample fried for 2 minutes was best in color with a mean and S.D value of 9.18±0.94^e at P<0.05. Microbial analysis of samples pretreated with ultrasound had minimal coliform and other pathogenic bacteria as compared with other samples after 28 days in aluminum foil stored in 27o C condition. However, the various pretreatments were found to have some advantages in improving the quality of plantain chips.

The optimization of drying techniques for preserving the structural integrity and quality of dehydrated food products is critical in the food industry. This study examines the microstructural changes in potato samples subjected to freeze drying (FD), infrared drying (ID), and hot air drying (HD). The 3D X-ray micro-computed tomography was used to quantify morphological alterations across various drying intervals 4, 8, 12, and 16 hours. An Artificial Neural Network (ANN) was used to predict the quality changes across drying time. The study revealed that FD samples consistently demonstrated minimal shrinkage, with a notable increase in total porosity, predominantly open porosity, as drying time increased. Conversely, ID and HD samples exhibited considerable shrinkage and density changes, although ID samples experienced a significant porosity increase over time. The findings from this study indicated FD’s superiority in preserving microstructural integrity, enhancing porosity, minimizing density changes, and optimizing color retention, thereby underscoring its potential to improve product quality and shelf life but at the cost of increased energy consumption. ID and HD presented a more favorable energy consumption profile but compromised the sample structure. This study provided valuable insights for the food processing industry, guiding the selection of optimal drying techniques that balance energy efficiency to achieve desired product quality.

This research focused on the structural and allergenic characteristics of cod proteins, which pose challenges for seafood product development. The objective was to explore the impact of high-intensity ultrasound (HIU) on cod, particularly regarding protein structural changes and allergenicity reduction. Ultrasound was applied for various periods (0-60 minutes) to assess its effect on protein attributes. Significant findings include the decrease in total soluble protein and the transition of protein secondary structures from α-helices to β-sheets and disordered formations, as validated by FTIR and CD spectroscopy (p < 0.05). UV spectroscopy and scanning electron microscopy corroborated these alterations, showing protein denaturation and potential Maillard reactions. Analysis through SDS-PAGE revealed protein degradation and aggregation, and ELISA tests indicated a reduction in allergenic potential by up to 31.82%, especially after prolonged exposure (60 minutes). These changes demonstrate that ultrasound treatment can effectively modify protein configurations and diminish allergenicity. The findings suggest that HIU could be a valuable method for enhancing the safety and quality of seafood, paving the way for innovation within the food sector.

This research investigated the influence of a pulsing microwave pretreatment, on the osmotic dehydration of waxy skin highbush blueberries. Initially, fresh blueberries were subjected to 20% microwave power for one and a half minutes before undergoing osmotic dehydration for 8 hours in a 60 °Brix sucrose solution. The study evaluated the mass transfer and the reduction of total soluble solid content during osmotic dehydration, alongside assessments of texture and color, across four temperature levels (room temperature, 60 °C, 65 °C, and 70 °C). Results revealed that the most substantial decrease in total soluble solid content within the osmotic solution occurred during the initial phase of the process (0-4 hours), with a negligible and gradual decline thereafter (4-8 hours). Microwave pretreatment showed no impact on the chromatic characteristics of blueberries color, particularly in parameters a, b, and L parameters. Although microwave pretreatment did not significantly alter texture compared to untreated blueberry samples, it notably boosted the efficiency of osmotic dehydration at higher temperatures. Optimizing microwave pretreatment parameters hold promise for reducing both processing time and temperature requirements in osmotic dehydration processes, especially beneficial for large-scale processing of waxy skinned berries.

The aim of this study was to develop a synbiotic legume-based beverage powder using spray drying with maximum probiotic viability, enhanced shelf life, and consumer acceptability via response surface methodology. The synbiotic beverage was made using green mung beans (Vigna radiata L.) and red kidney beans (Phaseolus vulgaris L.) as prebiotic sources and Lacticaseibacillus casei as the probiotic. The optimization was done using Box-Behnken design with independent variables viz., inlet temperature of spray dryer (130-150 ℃), feed flow rate (20-30 mL/min), and gum acacia (GA) concentration (1-3%). The responses were total viable probiotic count (log CFU/mL), powder yield (%), and moisture content (%). The results showed that the optimized spray-dried power could be obtained at 130 ℃ inlet temperature, 20 mL/min feed rate and 2.32% GA concentration with maximum probiotic count (8.80 ± 0.06 log CFU/mL), yield (50.92 % ± 0.12) and desirable moisture content (50.92 % ± 0.12). The obtained synbiotic powder at optimized conditions also showed good powder characteristics (bulk, tapped & particle density, porosity, flowability, cohesiveness, dissolution, color, and encapsulation efficiency), desirable probiotic growth (>7 log CFU/mL) under simulated gut conditions (acidic and bile juices), and higher shelf life (> 55 days). The particle morphology and thermal properties were also desirable in comparison to the commercial control sample. Thus, the developed synbiotic legume-based beverage powder can be used as a novel dairy alternative and vegan product in the functional food industry by serving as a ready-to-reconstitute instant mix and a dairy alternative with added health benefits.

Maximizing yield in controlled environments is vital for sustainable indoor farming, necessitating an understanding of environmental impacts on plant growth. This study investigates the impact of varying carbon dioxide (CO2) concentrations (400, 800 and 1200 ppm) and light treatments 1) white LED with 28 % red and 25 % blue, 2) white and far-red LED with 23 % red and 29 % blue, 3) amber and blue LED with 24 % red and 35 %blue, all under the same DLI of 12.96 mol m-²day-1 on romaine lettuce (Teton). Lettuce seeds were germinated for 21 days and transplanted into three growth rooms with distinct CO2 levels, relative humidity of 65 % and temperature of 21°C. After 28 days post-transplantation, plant growth parameters such as biomass (fresh and dry mass), height, and leaf characteristics (area, count, chlorophyll, and anthocyanin content) were measured. Findings revealed that using amber and blue light at 1200 ppm CO2 yielded the highest average shoot fresh mass (FM) of 298 g in plants, marking a 30 % increase compared to the same light at 400 ppm CO2. Additionally, an increase of CO2 from 400 ppm to 1200 ppm resulted in a yield boost of 29 % and 18 % for plants under white and far-red, and white light, respectively. These findings suggest a synergistic effect of elevated CO2 and specific light wavelengths on lettuce growth, potentially enhancing photosynthesis. The study provides important insights for horticulture and suggests applications in controlled agriculture, optimizing resources for enhanced crop production.

Greenhouse robots are usually pre-programmed to navigate a predetermined path or move on rails. Once the greenhouse geometry changes, the robots must be re-programmed, or the rails rearranged. To solve this problem, we developed Artificial Intelligence (AI) models capable of identifying the geometry and orientation of the seedbed beneath growing strawberry crops in greenhouses, notwithstanding the age of the crop, presence or absence of mulching sheets, the color of the mulching sheet and background color of the greenhouse floor. Movies of strawberry crops growing in three greenhouses (Greenhouses FWM and TWM with white mulching sheet and Greenhouse TBM with black mulching sheet.) were taken almost daily for about five months after transplanting. These movies were converted to about 2000 images for each of the greenhouses and were separately trained, and evaluated, and a model each (TWM, TBM, and FWM) were developed using the MVTec Halcon 20.11 software. A combination of all the pictures was also used to develop another model (FWMTWMTBM). The four models were tested with about 360 images (120 images from each of the three greenhouses evenly spread over the growing period) randomly taken from seedbeds different from those used for the training. FWMTWMTBM has the highest average Intersection over Union (IoU(0.5)) of 0.935 followed by FWM, TWM, and TBM with average IoUs(0.5) of 0.836, 0.786, and 0.525, respectively. Statistical analyses showed that the precision of FWMTWMTBM was not significantly affected by any of the factors investigated which implies that FWMTWMTBM made accurate predictions notwithstanding the above-mentioned variations.

The optimization of root system architecture (RSA) in crop breeding significantly influences plant vitality, crop yield, and quality. Traditionally, RSA evaluation methods have required root extraction from the soil, a destructive approach that does not preserve the architectural integrity of roots in situ. Alternatives to direct RSA assessment through soil, such as computed tomography (CT) and magnetic resonance imaging (MRI), are prohibitively expensive or lack detail, as is the case with microprobe or rhizobox techniques. A cost-effective strategy involves integrating three-dimensional (3D) computer vision with hydroponic cultivation systems to reconstruct RSA. However, this technique faces considerable challenges due to distortions during the data collection phase, primarily caused by the refraction of the container and nutrient solution. These distortions prevent the reconstruction of high-precision 3D point clouds using traditional Structure from Motion (SfM) algorithms. Fortunately, recent advances in the 3D reconstruction, such as Neural Radiance Fields (NeRF) and 3D Gaussian splatting, have shown promise in rendering high-fidelity, distortion-resistant 3D models. Our methodology is structured as follows. First, we acquire multi-view stereo RGB images of roots within a hydroponic system. Second, we employ multiple latest 3D reconstruction pipelines, including Colmap, NeRF, and 3D Gaussian Splatting, to obtain 3D reconstruction models of roots in the hydroponic system. Third, we perform cross-validation with open-source 3D reconstruction pipelines to ascertain the superiority of a particular method. This study facilitates the identification of the most effective 3D root reconstruction method for use in hydroponic settings, thereby enabling detailed subsequent analyses of various root traits.

A novel remote beehive monitoring technology employing an interdigital capacitor array is presented. This capacitor array consists of 12 interdigitated capacitors on a standard beehive frame, with the goal of monitoring beehive contents and activity through changes in electrical permittivity. Six of these sensor frames are used in the beehive, split between a single honey super and a single brood box in a Langstroth hive. The sensor boards of each box interface with a microcontroller for data capture. Temperature and humidity sensors, and light sensors were included in the system, and periodically a weight sensor was used, as they are well established in the smart-hive literature. Data was collected over three years, during which events such as colony collapse, nectar flow, and swarming events took place. Compared to the common method of monitoring hive contents, a weight sensor, this capacitive system is shown to monitor hive contents and their distribution throughout the hive—at a greatly reduced cost. From continuous monitoring at 30 minute intervals, the time series data can be decomposed to a trend and periodic components. The trend component readily captures the beginning of nectar flow, colony collapse, and the early detection and aftermath of a swarm. The periodic component shows the bees leaving and returning each day, which allows for qualitative estimation of forager numbers, and detection of localised clustering.

Ultraviolet (UV) C radiation finds extensive application as a non-chemical disinfection method in the medical field, food processing and water treatment; however, its germicidal effectiveness depends on factors such as radiation wavelength, radiant exposure, microbial physiology, biological matrices, and surface characteristics. This research aimed to develop and evaluate the control and radiation parameters of an automated intelligent UV disinfection system operating in both pulsed and continuous modes, that respects threshold limit values of radiant exposure in human-occupied spaces. For this purpose, a user-friendly interface was constructed to include a 222 nm KrCl excimer lamp and 280 nm UV-LEDs. Other design parameters included programmable switching between continuous and pulsed modalities, UV sensors to monitor and record UV irradiance, a CCD camera to observe and track notable alterations throughout the experiment, a distance sensor to measure the absorbed UV dose by experimental media, motion detection, temperature, relative humidity, pressure drop and energy consumption of system in a real-time manner. Data sampling and process control were conducted using MyDAQ through LabVIEW® software integrated with Arduino software. UV light mapping was performed at different distances from the radiation sources and assessed for optimal surface decontamination within an 8-hour period. Other potential solutions to enhance germicidal effectiveness were explored, including overpowering the radiation sources to increase irradiance. Future experimentation will comprise testing on contaminated surfaces and popular greenhouse crops to determine microbial log reduction. Anticipated outcomes include a safe and user-friendly pathogen control tool with potential applications in room surface disinfection and controlled environment agriculture.

Controlled Environment Agriculture has emerged as a fundamental pillar of the primary-level food production industry. Getting desirable results in controlled environments depends on efficiently manipulating and managing the factors that directly or indirectly interact with the plants. Thus, this study aims to determine the effect of different LED wavelengths on Oats and Barley while also determining the performance of a novel hydroponic substrate, permeable concrete in supporting plant growth in greenhouse conditions. This research investigated the plant-light response of 3 LED light treatments; Narrow Amber+ 430nm, 5000k + Wide Amber and Narrow Amber +430nm +485nm and additional treatment under natural sunlight Plant-light response and a comparison between the hydroponic substrates was measured based on data acquired on plant growth rate over 45 days in the hydroponic setup. In addition, data on fresh and dry biomass production was investigated at the time of harvest. The preliminary results showed 5000k + Wide Amber showed 25% better results than both single and double narrow amber standing close to each other, quantitatively followed by the natural light treatments in terms of increased plant growth rate. The future implications of this research will allow to enumerate the potential edible yield under the same conditions that will have commercial significance in selecting the optimum artificial light sources under controlled environments.

Global warming has escalated energy consumption in cooling, emphasizing the pivotal role of Heating, Ventilating, and Air-Conditioning (HVAC) systems in providing comfort and indoor air quality. However, conventional HVAC systems struggle with excess moisture during high-humidity periods. This study introduces a novel dehumidification approach inspired by natural fog formation. Employing a vortex cooling gun and cold plate, this method can extract moisture from saturated vapor, transforming it into fog or ice particles. By manipulating the temperature, relative humidity, and pressure, we achieved a remarkable temperature reduction, concurrently reducing indoor relative humidity from 100% to 19%. This innovation offers energy-efficient humidity control for HVAC systems, addressing climate change challenges.

Farms and agro-industrial processes generate wastewater that may exceed discharge quality criteria bringing risks to the environment. In maple syrup production, wastewater from equipment washing is strongly alkaline or acidic due to the characteristics of the soaps. To limit the impacts associated to wastewater management, it is necessary to assess treatment strategies that are simple, efficient, cost-effective, and adaptable to the sector. This study aimed to validate the manual neutralization as a strategy for treating wastewater from the reverse osmosis machine washing processes in maple industry. A wastewater characterization was conducted in 2022-2023 covering 56 alkaline washes and 14 acidic washes. Parameters such as pH, conductivity, phosphorus, suspended solids, biochemical oxygen demand, and total nitrogen were monitored at 5-minute intervals up to 20 minutes or the end of each wash. Three neutralizing agents were proposed for neutralization citric acid, sodium hydroxide and sodium bicarbonate. A calculation tool was developed to determine the required neutralizing agent quantity based on soap type, amount, and solution volume used in the washes. For alkaline washes, the critical volume accumulation time for neutralization occurs within the first 15 minutes, while for acidic washes, it extends beyond 25 minutes. A subset of characterization samples was used to validate the tool's accuracy, achieving a pH range of 6 to 9.5 (regulation limits) after neutralization process. The validation of the tool will continue throughout 2024 in 4 maples facilities, with results to be presented subsequently.

Bacterial resistance in human and animal waste streams has been identified as a major concern for the environment and human health. While it is commonly understood that the presence of antibiotics (above minimum selective concentrations) drive selection of Antibiotic Resistant Bacteria (ARBs) and Antibiotic Resistance Genes (ARGs), increasing evidence suggests that environmental conditions may also significantly effect the selection and proliferation of resistance genes. In the present study shotgun metagenomic Next Generation Sequencing (NGS) and open source bioinformatic tools, in tandem with High Performance Computing (HPC), were used to investigate the water chemistry effects of three different biological treatments (macrophyte engineered floating wetland, duckweed, and algae) on corresponding microbial diversity and resistance in wastewater. Mesocosms (in triplicate per treatment) were studied over a 100 day growth period. TP, pH, DO, COD, and heavy metals were measured throughout the study. The results showed significant ( p < 0.05) treatment effect on water chemistry and nutrients (TP, pH, DO, COD, and heavy metals), as well as, microbial taxonomy, ARGs, Mobile Genetic Elements (MGEs), and Metal Resistant Genes (MRGs). Strong correlation (r > 0.7, p < 0.05) was found to exist between key water parameters and microbial diversity (and resistance). The results suggest that water chemistry parameters play a critical role in microbial diversity and genetic selection. The results support further investigation into the implementation of engineered biological treatment systems to alter microbial water column microbial resistance.

In Canada, the extremely high concentrations of dissolved organic and inorganic material in surface waters make potable water production very challenging. Unfortunately, nanofiltration (NF) membranes, widely utilized to purify these sources, experience serious fouling, increasing the associated cost. A comprehensive investigation of NF fouling is necessary to prevent or mitigate this phenomenon and increase access to NF technology, especially for small and remote communities. We conducted analytical, spectroscopic, and chromatographic experiments on water and fouled NF membrane samples from a water treatment pilot plant supplied by a challenging water source (Assiniboine River) at Brandon, MB. NF hydrogel-like foulant was ~97% organic. The mechanically removed foulant (MRF) comprised similar fractions of low molecular weight & building blocks (<1 kDa; 28.3%), humic substances (1–20 kDa; 38.7%), and biopolymers (>20 kDa; 33.1%). However, part of the foulant, mostly hydrophobic and 95.4% low molecular weight & building blocks, remained strongly attached to the NF membrane surface and pores, implying this would be difficult to clean. Also, calcium and magnesium (water hardness) bridge the organic substances, promoting NF fouling. Likely, the higher biopolymer fraction in the MRF caused most of the fouling. Researchers have associated some of those biopolymers with a microbiological origin. Thus, the microbes found in water and NF foulant may be contributing significantly to the fouling development by producing or shedding the polymers. We are currently performing deep-amplicon sequencing and Nanopore metagenomics on microbial DNA to identify the microbial community composition and its possible influence on the NF foulant formation.