Edge compute algorithm enabled localized crop physiology sensing system for apple (Malus domestica Borkh.) crop water stress monitoring

• Published in: Computers and electronics in agriculture Vol. 224; p. 109137
• Main Authors: Amogi, Basavaraj R., Pukrongta, Nisit, Khot, Lav R., Sallato, Bernardita V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: agriculture; air temperature; algorithms; Apple orchards; apples; cooling; Crop Water Stress Index; Cyber Physical System; Edge compute; electronics; evapotranspiration; fruits; Heat stress; irrigation management; Malus domestica; microirrigation; physiology; Precision irrigation; solar radiation; stress management; sunburn; Thermal-RGB imaging; trees; water potential; water stress; Apple orchards; Thermal-RGB imaging; Edge compute; Precision irrigation; Crop Water Stress Index; Heat stress; Cyber Physical System
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.109137

•Crop physiology sensing system was modified with an edge-compute algorithm.•Developed was an algorithm for real-time apple crop water stress monitoring.•Crop water stress index (CWSI) was estimated using segmented shaded canopy.•CWSI was highly correlated with mid-day stem water potential (r = -0.80)•Apple heat mitigation techniques substantially affected algorithms performance.•Estimations were robust under fogging (r = -0.76) compared to other techniques. Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.