Thermal–RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.)

Abstract

Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants’ biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal–RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning models (1) ResNet-50 and (2) MobileNetV2. On evaluation, the accuracy of water stress identification was higher with thermal imagery inputs (87.9% and 84.3%) compared to RGB imagery (78.6% and 74.1%) with ResNet-50 and MobileNetV2 models, respectively. In addition, irrigation treatment and levels had significant impact on yield and crop water use efficiency; the maximum yield of 10,666 kg ha−1 and crop water use efficiency of 1.16 kg m−3 was recorded for flood irrigation, while 9876 kg ha−1 and 1.24 kg m−3 were observed for sprinkler irrigation at 100% irrigation level. Developments and observations from this study not only suggest applications of thermal–RGB imagery with AI for water stress quantification but also developing and deploying automated irrigation systems for higher crop water use efficiency.