Digital Twins, advanced tools that virtually mirror physical assets, have found applications in various industries, but their adoption in agriculture, particularly grain storage systems, remains limited. This paper aims to conceptualize a Digital Twin for grain bins by focusing on the mathematical models integral to their efficient functioning.

The proposed Digital Twin will incorporate Janssen-type equations for accurate pressure distribution within grain bins, and empirical models to account for packing effects, moisture content, and grain type. This approach holds significant potential for optimizing aeration and drying airflow in grain storage systems, thereby enhancing efficiency and ensuring the preservation of stored grain quality.

By developing a mathematical model-based Digital Twin for grain bins, we aim to revolutionize on-farm grain storage management, enhancing efficiency, supporting informed decision-making, and contributing to the optimization of agricultural practices at the farm level.

Exposure to bioaerosols, noxious gases, and high dust concentrations in confined livestock facilities have been linked to human and animal health hazards. In this study, an electro-nanospray system was developed for pig barn decontamination. This technology generates engineered water nanostructures (EWNS) responsible for bacterial inactivation through electrospraying and ionization of water. Laboratory-scale experiments on bioaerosol deactivation were conducted to optimize various design and operating parameters using an acrylic chamber and Escherichia coli as test pathogen. Using the optimized values, the nanospray system was tested in two pilot-scale rooms (Control vs. Treatment) at the Prairie Swine Centre barn, with four grower/finisher pigs used per room. Over the 8-week trial, air samples were collected to evaluate the impact of the electro-nanospray system on levels of airborne dust and bacteria, gases (i.e., ammonia, hydrogen sulphide, carbon dioxide) and microbial population on various types of surfaces including plastic, metal, wood, and concrete. Three replicate trials were conducted. In the completed in-barn experiments with two electro-nanospray chambers installed in the Treatment room, 41% and 59% reduction on bioaerosol concentration and dust concentration were observed, respectively. Over the duration of the trial, lower rate of increase in ammonia, carbon dioxide concentrations, and microbial population (relative to initial levels at the start of the trial) in the Treatment room was observed compared to the Control room. No significant difference in pig performance was observed between the two experimental rooms. Additional data from the completed trials will be presented.

The use of modelling tools to predict the daily soil water balance has become widespread in the development of irrigation management strategies. These predictions provide insights at the field scale and can also be used for regional water resource management and planning. This study is focused on the water needs of irrigators in Lanoraie, Quebec. In this region, there are competing water demands between the major irrigated high value crops and an ecologically-sensitive wetland complex. The growing season is expected to be increasingly drier and more variable due to climate change. Thus, to balance the agricultural and ecological water needs of the region, it is necessary to optimize irrigation requirements. This study estimates the net irrigation requirements of the major irrigated crops in Lanoraie for the 1-in-25 dry and average years using the AquaCrop model. AquaCrop is also used to predict the effect of climate change on irrigation requirements. Field data from potato, squash, strawberry, and cranberry fields over the 2022 growing season and a sensitivity analysis are used to refine the simulations according to soil water content of the rootzone. Three irrigation treatments are investigated, comprising full irrigation, deficit irrigation, and no irrigation, for the crops and soil types of the region. The soil water content, net irrigation requirement, water productivity, and crop yield are simulated for historical and future climate scenarios. The resulting water needs of each crop are mapped over the respective field areas and combined into water supply units. Potential water supply scenarios are also discussed.

Jasmine flowers are prized for their exotic scent, and their essential oils have a wide range of uses in industries such as perfume, food, pharmaceutical, and medicine. However, these flowers have a short shelf life. They are vulnerable to light, temperature, and humidity, which can cause photo-oxidative stress and lead to the breakdown of pigments and dehydration, resulting in petal browning and reduced visual appeal and overall value. To determine the proper shelf life for packing and storage of jasmine flowers, it is necessary to measure the temporal color variation and quantify the color degradation post-harvest. Manual methods of color measurement and variation over time are cumbersome and not feasible, therefore digital image capture, image processing, and analysis techniques were applied using ImageJ software. Jasmine flowers were captured digitally on a black background at fixed intervals using a smartphone camera under ambient conditions. The ImageJ plugin processes the image and analyzes the flower color variation and petals opening over time and models the kinetics. Image processing operations such as, image cropping, artifacts removal, thresholding for background extraction, petal color measurement, shape parameters evaluation, color and shape kinetics, visualization, and model fitting were followed. The results of this analysis provide insights into the color degradation of flower petals and flowering kinetics, which can influence the overall flower quality and customer acceptance. Further research may be needed to investigate the effect of storage environment conditions, especially temperature, and relative humidity, and determine the optimal conditions for extending shelf life.