Development of a Scalable Edge-Cloud Computing Based Variable Rate Irrigation Scheduling Framework

Currently, variable-rate precision irrigation (VRI) scheduling methods require large amounts of data and processing time to accurately determine crop water demands and spatially process those demands into an irrigation prescription. Unfortunately, irrigated crops continue to develop additional water stress when the previously collected data is being processed. Machine learning is a helpful tool, but handling and transmitting large datasets can be problematic; more rural areas may not have access to necessary wireless data transmission infrastructure to support cloud interaction. The introduction of “edge-cloud” processing to agricultural applications has shown to be effective at increasing data processing speed and reducing the amount of data transmission to remote processing computers or base stations. In irrigation in particular, edge-cloud computing has so far had limited implementation. Therefore, an initial logic flow concept has been developed to effectively implement this new processing technique for VRI. Utilizing edge-cloud computer nodes in the field, autonomous data collection devices such as center pivot-mounted infrared canopy thermometers, soil moisture sensors, local weather stations, and UAVs could transmit highly localized crop data to the edge-cloud computer for processing. The edge computer Following the implementation of an irrigation strategy created by the edge-cloud computer with a machine learning model, data would be transmitted to the cloud (requiring transmission of only minimal model parameters), resulting in a feedback loop for continual improvement of the global model on the cloud (federated learning). VRI prescription maps from the SETMI model were used as the training data for training the machine learning model

Assessing energy sources for powering “evakuula”

Technologies that are appropriate, affordable, and sustainable are needed to increase incomes and resilience among sub-Saharan African smallholder farmers. A combination of thermization and low-cost evaporative cooling, termed Evakuuling, was developed to enable rural smallholder dairy farmers to preserve their evening milk in the absence of grid-electricity. The “EvaKuula” was configured to be powered by biogas. Biogas is used for the thermization process of the system. The evaporative cooling component is powered by wind. Use of biogas from domestic biogas plants add circularity value to smallholder farms. However, domestic biogas plant set-ups are relatively high capital investments and as such, a financial barrier to co-adoption with the EvaKuula. To lower this barrier, other energy sources have been considered. The purpose of this study was to assess alternative energy sources to power the thermization component of the EvaKuula. The list of energy sources considered included biogas, butane, kerosene, charcoal, and firewood. These energy sources were assessed with respect to the sum of the social and market costs. The product of a unit of fuel cost and the units consumed represented the “market cost.” The product of the long-term social carbon cost and total carbon dioxide emission equivalence represented the “social cost.” Regular and improved stoves were included in the charcoal and firewood analysis. As expected, biogas ranked on top of the list, followed by butane and kerosene. However, butane and kerosene are not easily accessible in rural setting. Approximated 76% of farmers in rural sub-Saharan Africa rely on firewood to meet domestic needs like cooking. Butane and kerosene are the fuel sources predominantly used in urban and peri-urban areas, due to accessibility and affordability. Incomes are typically higher among urban dwellers. Therefore, with butane and kerosene not readily available to the target EvaKuula users, the next best option was firewood, provided it is combusted in improved efficient stoves such as Lorena type