Understanding pesticide flow dynamics in the vadose and saturated zones in the soil profile is crucial for protecting the environment. Although predictive computational tools have effectively modeled pesticide flow within the soil, the flow through a biobed has not been reported. Hence Hydrus, a predictive modeling tool widely used in the simulation of water, heat, and solute in a variably saturated media, will be used to simulate pesticide flow in a Biobed. The Biobed, a system that consists of biomix (2:1:1v/v mixture of straw, soil, and peat) is an effective proven technology for the treatment of pesticide residues in rinsate water. This study evaluated the effectiveness of Hydrus 1D model in simulating the flow of pesticide rinsate through biobed located at the Ian Morrison Research Farm, Carman, Manitoba. A 1D domain depth profile was set up with five observation nodes located at 10 cm, 20 cm, 30 cm, 40 cm, and 50 cm depths. The measured volumetric water content of the biobed at these depths were compared to the simulated volumetric water content. The initial hydraulic parameters θs, α, n, l, Ks, and θr were estimated by fitting the van Genuchten equation from the biomix-water characteristics curve, textural values, and bulk density. The biomix hydraulic parameters were optimized using inverse modelling methods. The measured and predicted volumetric water contents were compared and assessed using statistical analysis. The result showed high efficiency in the model performance, this showed the Hydrus 1D model of the biobed represented the field conditions well.

Bioplastics represent promising alternatives to petroleum-based plastics, yet their degradation mechanisms remain insufficiently understood. Identifying bacteria and enzymes capable of degrading bioplastics could enhance end-of-life management practices. Proteobacteria, known for their remarkable metabolic versatility, present a promising avenue for exploration. Many proteobacteria produce diverse enzymes capable of breaking down various substrates for carbon utilization. This enzymatic diversity suggests a potential capability to degrade polymers such as bioplastics, even when they are not their natural substrates. We hypothesize that Burkholderia and Stenotrophomonas species will adapt to available nutrients, activating various metabolic processes, including the degradation of medium-chain-length polyhydroxyalkanoate (mcl-PHA). Through a screening method targeting extracellular mcl-PHA depolymerases, we identified several strains, including B. gladioli, B. multivorans, B. vietnamiensis, and S. maltophilia, capable of degrading extracellular mcl-PHA. Furthermore, these strains demonstrated the ability to degrade triglycerides under similar screening conditions, suggesting either substrate promiscuity or the production of multiple degradation enzymes. To elucidate the genetic basis of this activity, we performed transposon mutagenesis on B. vietnamiensis and identified several putative genes associated with extracellular mcl-PHA degradation. These genes include triacylglycerol lipase, lipase chaperone, type II secretion system components, HTH-type transcriptional activator, and polyhydroxyalkanoate synthesis repressor. To validate these genetic elements, we will generate insertional mutants using a CRISPR-associated transposase system (CAST). CAST system, employing Tn7-like transposase subunits and a V-K CRISPR effector (Cas12k), facilitates targeted DNA integration. We have successfully adapted this system for several Burkholderia species and intend to leverage its capabilities to elucidate the genetic determinants of PHA degradation further.

Massive plastic utilization has increased plastic pollution dramatically around the world. Biodegradable polymers are now displacing recalcitrant synthetic polymers to mitigate plastic pollution. However, those biodegradable plastics are not easily degraded or composted at ambient temperatures. For example, Polylactic acids (PLA) and other biodegradable and compostable bioplastics have different chemical and physical characteristics than other organic compounds (usually food wastes) in compost systems. Specifically, biodegradation of PLA requires a temperature of 58-60 ℃. As a result, they are mostly disposed of in landfills, where they do not fully degrade, resulting in production of microplastics which contaminate soil, water, and air, and create risks to human and animal health. Our study focuses on isolating PLA-degrading microorganisms from compost (industrial and backyard), optimizing the culture conditions, and exploiting them to degrade PLA completely under environmental conditions. This study will help reduce plastic pollution by enabling 100% biodegradation of PLA.

Polyethylene terephthalate (PET), a widely used synthetic polymer, is found in various products, from beverage bottles to textile fibers. Concerns about its long-term impact on humans have prompted research into mitigating its accumulation. After discovery of the efficient enzyme “PETase” from Ideonella sakaiensis in 2016, tremendous research was focused on application of the wild-type or engineered forms. Previously, we developed a hybrid construct fusing the signal peptide of a membrane-anchored Escherichia coli lipoprotein to express and deliver PETase to the outer membrane, aiming to streamline the soluble production and purification processes. However, traditional plasmid-based techniques utilized in this approach are susceptible to genetic instability and depends on the addition of inducers and antibiotics, which pose challenges for industrial scalability and application. To address these problems, we employed a “CRISPR-assisted transposition” approach to integrate a DNA payload containing the LPP-PETase gene cassette, under control of constitutive promoter, to develop a cell surface-displayed PETase capable of serving as a whole-cell biocatalyst. Stable expression and proper localization of the membrane anchored PETase were confirmed and evaluated via enzyme kinetic analysis and Western Blotting. Enzyme kinetic parameters were calculated utilizing a chromogenic substrate, and high-performance liquid chromatography quantification of Bis(2-Hydroxyethyl) terephthalate (BHET, PET derivative) hydrolysis. Our findings illustrate that the engineered whole-cell biocatalyst can present continuous functional PETase activity under controlled culture conditions. Genetically engineered "plastic-consuming" bacteria in fermenters, with plastic-degrading enzymes on their cell surfaces, offer a viable waste plastic disposal strategy. This closed, biosafe system converts plastic into valuable biomass sustainably.

LDPE is highly recalcitrant to natural biodegradation processes, and is among the most abundant plastic wastes in the environment. The high degree of crystallinity is thought to play a significant role in the resistance of LDPE to biodegradation. We previously identified three species of bacteria, Cupriavidus necator H16, Pseudomonas putida LS46, and Pseudomonas chlororaphis PA2361, with the ability to utilize untreated LDPE as a sole carbon source. Here we report changes in the molecular structure of LDPE after incubation with these bacteria. The changes in polymer structure were analyzed using Time-domain Nuclear Magnetic Resonance, High-Temperature Size-Exclusion Chromatography, Differential Scanning Calorimetry, X-Ray Diffraction, and Gas Chromatography. Overall, limited degradation of the LDPE powder was seen to occur in first 30 days of incubation with the bacteria. Residual LDPE from bacterial cultures showed a significant decrease in the percentage of amorphous regions (from > 47% to 40%), while the percentage of crystalline regions remained constant. The weight-average molecular numbers (Mw) and number-average molecular numbers (Mn) increased, while the polydispersity ratios decreased, indicating that branches of the LDPE with lower molecular weight were preferentially degraded. The limited degradation of LDPE was confirmed to occur in the low molecular weight branches, while the main branch remained untouched. LDPE hydrolysis products were detected in the supernatant with the majority being linear alkanes (heptane and undecane). The study is the first to report the connection between the structure of LDPE, and the degradability of the polymer, and explains the resistance of LDPE to complete biodegradation.

This study utilized vermicompost for the remediation of contaminated soils in Nigeria. Two soil washing methods adopted were batch and column processes. Characterization of the Vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of the washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental data. Artificial Neural Network (ANN) and Adaptive neuro fuzzy inference system (ANFIS) were used in modelling the removal efficiency of the process. The result showed removal efficiency of 97.8% for batch process remediation and 72.44% for column process. Optimization of the experimental factors gave optimal removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R^2) for ANN was (0.9974) and (0.9852) for batch and column process, respectively. The RSM coefficient of determination (R^2) for batch and column processes was (0.9712) and (0.9614), which also demonstrates agreement between observed and predicted. The coefficient of determination for the ANFIS model were (0.7115) and (0.9978) for the batch and column processes respectively. Machine learning models (ANN and ANFIS) accurately predict removal of crude oil from contaminated soil using vermicompost

Diverse activities, both natural and anthropogenic processes, notably the traffic of agricultural machinery, induce soil compaction in agricultural fields. This study quantifies the effects of vertical compaction on a sandy loam soil and its implications for the growth of canola. A John Deere 1023 E tractor equipped with ballasts traversed a field layout perpendicular to the direction of seeding. Three compaction levels were created by varying the number of tractor wheel passes. The effect on the soil properties and the performance of canola crops for each compaction level were determined in comparison to a control group (zero compaction). The results substantiated that vertical compaction exerted differential influences on the soil properties. Significant effects were observed between the average values of the control group and the other compaction levels in most measured parameters. Soil bulk density exhibited an average difference of 82.5 kgm-3, with a corresponding difference of 6 m-2 observed for crop count and a recorded difference of 0.046 kgm-2 for shoot biomass. However, no significant effect in terms of speed of emergence was recorded between the aforementioned compaction levels. The experimental findings serve as valuable resources for guiding further research into the interplay between soil compaction, soil dynamic properties, and canola growth.

Soil physical quality, more specifically soil compaction under cattle foot traffic caused by the intensive cattle grazing could cause long-term soil health problem for rangeland. Mature cattle can exert a static ground pressure of approximately 1.7 kg/cm2 of hoof-bearing area, which is equivalent or higher than the heavy-wheeled tractors. A little research has been conducted to understand the soil compaction under the cattle hoof. In addition, quantifying and visualizing the stress distribution in the top-soil layer is difficult to accomplish. To understand soil stress distribution, under the cattle hoof a discrete element model (DEM) will be developed. Model data will be calibrated and validated using laboratory experiments. A soil compaction test will be conducted using Loam soil for two different moisture contents. A predetermined soil mass for each soil moisture conditions was loosely filled into a square box (12”x12”'x15”) and the soil was compressed using a square plunger (150 mm) to the targeted soil bulk density levels of 1400 Mgm-3 on the bottom layer, 1550 Mg m-3 in the middle layer and 1250 Mg m-3 in the top layer. Soil resistance data will be measured using a cone penetrometer. A 3D printed life size cattle hoof will be used to apply compressive pressure on the compacted soil. Stress distribution under the cattle hoof for various compressive pressure, soil density, and soil moisture content will be determined using DEM. The results will be presented during the conference.

Obtaining information about spatially and temporally variable soil hydraulic properties is vital for many agricultural and environmental applications. However, challenges persist in acquiring such information due to the complexity, small-scale, and invasive nature of many of the current estimation methods. Our study investigated the potential of incorporating ground-penetrating radar (GPR) travel time data into a standardized infiltration procedure, like the Beerkan infiltration, to enhance estimates of soil hydraulic properties. The experiment was conducted on loamy sand-textured soil at Pasadena, Newfoundland, Canada, using a metal ring of 10 cm diameter and several doses of water of the same volume (200 mL). A surface GPR system with a center frequency of 500 MHz and 43 cm antenna offset was used to monitor wetting front movement during infiltration by collecting time-lapsed GPR traces every 5 s. Antenna separation of 43 cm was selected to clearly separate the direct ground wave from the air wave. The elapsed time required for complete infiltration of each volume of water was recorded. Estimated cumulative infiltration was obtained based on the GPR direct wave travel time relationship. The plot of the estimated cumulative infiltration with time was used to estimate soil-saturated hydraulic conductivity (Ks) and sorptivity (S) estimates according to the BEST-steady and BEST-intercept procedures. Preliminary results gave reasonable estimates of soil Ks and S based on the travel time of GPR direct waves and demonstrated the potential of obtaining soil hydraulic parameters from a convenient infiltration procedure.

Frequent and unpredictable occurrences of flood and drought events demand an efficient and sustainable water management system to cope with the moisture stress in field crops. A reliable design of combined irrigation and drainage system for the best- and worst-case scenario highly depends on soil water retention and permeability which depend on soil texture and structure. Soil structure is directly affected by management practices and crop root distribution. The objective of this study is to estimate soil hydraulic parameters in fine textured soil in Manitoba. HYDRUS inverse modelling coupled with PEST was used to estimate the van-Genuchten and Mualem soil hydraulic parameters. The observed soil water content determined at the site within 0-10, 10-30, 30-70 and 70-130 cm layers over the growing seasons from 2016 – 2019 under soybean-oat rotation was used for calibration and validation of the model. Results reflecting the changes in soil hydraulic parameters over the years will be presented along with its effect on water movement between the layers. The findings from this study will enhance the efficiency of subirrigation and drainage systems to produce a conducive environment for plant growth and performance.

Vertisolic soils (Vertisols) are pivotal for agricultural productivity in Manitoba due to their high fertility and superior moisture retention capabilities relative to other soil orders. However, managing Vertisols presents significant challenges due to their distinct swelling and shrinkage properties and low moisture buffer capacity. Consequently, these soils are prone to deformation, becoming excessively sticky and unworkable when wet, while they are rigid and develop cracks when dry, which severely restrict the window for field operations during the growing season. The behavior of Vertisols underscores the pressing need for innovative strategies to optimize agricultural practices and mitigate associated constraints. This requires a better understanding of their moisture dynamics as well as how they respond to extreme moisture conditions. The objective of this study was to investigate how Vertisols respond to extreme moisture relative to normal moisture conditions. Continuous soil moisture and weather data were collected from 17 study sites in the Red River Valley during the growing season (May to September) in 2018 – 2022. Soil water flux in the 0 - 5, 5 - 20, and 20 - 50-cm layers was determined. Results showing the soil water flux under different moisture conditions (extreme vs. normal) will be presented. Results from this study will contribute towards the development of effective soil water management strategies as well as adaption of agronomic management and field operations for sustainable crop production in Vertisols.

Excess soil moisture is typical of soils in Manitoba during the early growing season due to snowmelt and heavy rainfall. Managing soil moisture to attain optimal soil strength for trafficability is therefore crucial to ensure timely field operations in the region. Over the years, subsurface drainage has been the most popular approach for removal of excess soil water. However, this approach is not always feasible due to poor internal drainage and/or compromised soil structure. In this study, the potential of cover crops to improve soil strength for trafficability in Manitoba is explored. A multi-year study was initiated in the fall of 2020 near Cartwright, MB. Since fall 2022, rye has been grown at the site as a fall shoulder cover crop to assess its impact on soil moisture dynamics and its implications on soil strength for trafficability during spring field operations compared with no cover cropping. Continuous soil moisture measurements were taken in the 0-5, 5-15, 15-25, 25-35 and 45-55-cm layers. The critical soil moisture content for attaining soil strength sufficient to support trafficability was 90% of the lower plastic limit of the soil in the 0-25-cm layer. Preliminary results show that the fall rye cover crop significantly reduced soil moisture content in the 0-5-cm layer relative to no cover cropping. However, the fall shoulder cover crop fields did not meet the criterion for trafficability. Data on the impact of the 2023 fall rye cover crop on trafficability in spring 2024 will be analyzed and presented.

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.

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.