Thermal-RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.)
| dc.contributor.author | Rajwade, Yogesh A. | en |
| dc.contributor.author | Chandel, Narendra S. | en |
| dc.contributor.author | Chandel, Abhilash K. | en |
| dc.contributor.author | Singh, Satish Kumar | en |
| dc.contributor.author | Dubey, Kumkum | en |
| dc.contributor.author | Subeesh, A. | en |
| dc.contributor.author | Chaudhary, V. P. | en |
| dc.contributor.author | Ramanna Rao, K. V. | en |
| dc.contributor.author | Manjhi, Monika | en |
| dc.date.accessioned | 2024-07-12T13:02:36Z | en |
| dc.date.available | 2024-07-12T13:02:36Z | en |
| dc.date.issued | 2024-06-27 | en |
| dc.date.updated | 2024-07-12T12:41:55Z | en |
| dc.description.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<sup>−1</sup> and crop water use efficiency of 1.16 kg m<sup>−3</sup> was recorded for flood irrigation, while 9876 kg ha<sup>−1</sup> and 1.24 kg m<sup>−3</sup> 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. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Rajwade, 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. | en |
| dc.identifier.doi | https://doi.org/10.3390/app14135623 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120662 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | okra crop | en |
| dc.subject | water stress | en |
| dc.subject | thermal–RGB imagery | en |
| dc.subject | deep learning | en |
| dc.subject | precision irrigation | en |
| dc.title | Thermal-RGB Imagery and Computer Vision for Water Stress Identification of Okra (<i>Abelmoschus esculentus</i> L.) | en |
| dc.title.serial | Applied Sciences | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |