Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.