Energy efficiency of a grain drying operation is a crucial parameter to promote precision drying approaches. Studies emphasizing on specific energy provides the actual energy consumption that cannot be considered for process optimization due to variability in weather and grain conditions. This outlines the importance of determining theoretical energy that identifies the base energy requirement for a unit amount of water to be taken out during the drying process that should be a temperature dependent.

This study is conducted to compare two theoretical energy determination approaches namely sorption-based and empirical model to quantify their credibility. Sorption-based model was determined using moisture sorption experiments (at 16, 18, 20 and 22% wb; 5, 15, 25, 35, 45 and 55℃) followed by integrating heat of sorption over initial and final moisture contents.

Modified Chung Pfost’s equilibrium relative humidity (ERH) model predicted the sorption behavior for wheat more accurately than Modified Henderson, Modified Halsey, Modified Oswin and Chen-Clayton’s model. Dynamic vapor pressure analysis showed that sorption-based model’s theoretical data were significantly higher than the empirical based model. Furthermore, sensitivity analysis surfaced the influence of change in vapor pressure at higher and lower temperatures on heat required for a unit mass of water to evaporate from the grain surface. Thus, it could be concluded that instead of modifying the credible range for empirical models, sorption models could be automated into drying controls to determine the theoretical energy for the efficiency determination process.

This study aims to detect adult Colorado potato beetles (CPB) using in-field images of potato plant leaves taken from a moving vehicle. We employed the transfer learning method with deep learning object detection architecture "You Only Look Once v7" (YOLOv7). First, YOLO v7 model was trained on images of CPB acquired from a handheld mobile device placed 1 m above the plant at the McCain's fFarms of the fFuture in New Brunswick. Second, in-field images were acquired on-the-go by mounting an imaging system on a boom of a sprayer moving at 4 m/s. Furthermore, theseThe images were cropped to a resolution of 640 x 640 to increase CPB magnification from 0.02% of the whole to 0.15% and to obtain a 1:1 aspect ratio. FinallyNext, a second YOLO v7 model was trained on the in-field images acquired on-the-go using the initial YOLO v7 model as a pre-trained weight, allowing for transferred learning.

We demonstrated the feasibility of our method to detect CPB in real-lifefield conditions by testing on 42 in-field images with complex background. By using the model trained on mobile images as a pre-trained weight, the detection accuracy increased from 49% to 64% compared to using ImageNet as a pre-trained weight. This study shows how transfer learning on similar objects may significantly improve performance, especially in the case of small object detection.

Light quality has a highly complicated impact on plant physiology and light emitting diodes (LEDs) can provide a specific light spectrum, rendering them ideal for studying the effect of light quality on plant responses. Stomatal behavior is critical in balancing a plant's need for water conservation while allowing for photosynthetic and respiratory gas exchange. Many researchers have investigated the stomatal response to red and blue light, yet little data on green light plant stomatal responses exists, particularly with post-harvest storage. This research aimed to examine lettuce (Lactuca sativa) and basil (Ocimum basilicum) plants' stomatal opening response to varying light quality (430, 630, 500, 530, 560 nm) and quantity (50, 75, 100, 400 µmol m-2s-1) and determine wavelengths that result in reduced stomatal opening. Data demonstrate that the stomatal aperture increased when treated with blue and red light, yet decreased with green light (530–560 nm) at a lower intensity. These findings imply that the postharvest quality of leafy greens can be preserved and shelf life extended using wavelengths in the green spectrum with lower intensity LEDs, likely by minimizing respiration rate and water loss postharvest via stomata closure.

There has been an increase in the number of farms that cultivate cannabis ever since its been legalised in Canada. Studies have shown that indoor cannabis growing is water and energy intensive. Outdoor cannabis growing is associated with water and soil resource degradation. These environmental impacts associated with the industry are also impacted by where the production sites are located. Indeed, major differences exist in the technologies that are used to generate electricity across Canada.

Measuring the environmental impact of the Canadian cannabis company needs to account for these region-specific energy source. Life-cycle assessment (LCA) are ISO-certified methodologies capable of measuring the environmental footprint of cannabis cultivation in any Canadian province. Using province-specific electrical grid information from the EcoInvent database, it was possible to quantify how much exterior versus interior cannabis cultivation impacts the industry environmental impact.

Taken together, these results will help policy maker have a spatially informed opinion when allocating subsidies or deciding on province-wide funding plans. Furthermore, cannabis cultivators will be able to more accurately predict how their growing strategies might impact the overall sustainability of the industry.