Conference Agenda

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.