Rajwade, Yogesh A.Chandel, Narendra S.Chandel, Abhilash K.Singh, Satish KumarDubey, KumkumSubeesh, A.Chaudhary, V. P.Ramanna Rao, K. V.Manjhi, Monika2024-07-122024-07-122024-06-27Rajwade, Y.A.; Chandel, N.S.; Chandel, A.K.; Singh, S.K.; Dubey, K.; Subeesh, A.; Chaudhary, V.P.; Ramanna Rao, K.V.; Manjhi, M. Thermal-RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.). Appl. Sci. 2024, 14, 5623.https://hdl.handle.net/10919/120662Crop 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.application/pdfenCreative Commons Attribution 4.0 Internationalokra cropwater stressthermal–RGB imagerydeep learningprecision irrigationArticle - Refereed2024-07-12https://doi.org/10.3390/app14135623