## 3 Results and Discussion
### Water Stress Identification  
Among various combinations of epoch (10, 20, 30, 50) and batch sizes (8, 16, 32), both DL models, (ResNet-50 and MobileNetV2), demonstrated superior performance at 30 epochs and 16 batch size combinations with both RGB and thermal imagery inputs (Table 3). In the case of ResNet-50, as the number of epochs increased from 10 to 30, the model accuracy improved. However, beyond 30 epochs, overfitting and a decline in accuracy was observed with both RGB and thermal imagery inputs. Similarly, an increase in batch size from 8 (72.4%) to 16 (78.6%) enhanced testing accuracy, but a further increment (32 batch size) resulted in reduced (76.2%) accuracy with RGB imagery. The performance of ResNet-50 surpassed that of MobileNetV2, as it achieved an accuracy of 87.9% for thermal imagery and 78.6% for RGB imagery on testing dataset. A similar trend was noted with thermal imagery inputs, though the prediction accuracy was higher compared to that of RGB imagery inputs. The training and validation plots of ResNet-50 and MobileNetV2 models for both RGB and thermal imagery inputs are shown in Figure 6 while pertaining \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Irrigation Type/Level** & **100\% ET** & **75\% ET** & **50\% ET** & **25\% ET** & **Mean** \\ \hline Sprinkler irrigation & 9876 & 7765 & 6605 & 6086 & 7583 \\ Flood irrigation & 10,666 & 9751 & 9158 & 7586 & 9290 \\ \hline Mean & 10,271 & 8758 & 7882 & 6836 & \\ \hline  & **Type** & **Level** & **Type \(\times\) Level** & & \\ \hline Standard error of mean & 110.8 & 81.0 & 135.3 & & \\ Least significant & 457.3 & 222.5 & 497.0 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of least significant difference tests between okra yield and water use efficiency observed under different irrigation treatments. Figure 5: Okra yield ((**a**), kg ha\({}^{-1}\)) and water use efficiency ((**b**), kg m\({}^{-3}\)) under flood (F) and sprinkler (S) irrigation methods with different levels irrigation (100, 75, 50, and 25% crop evapotranspiration). From the confusion matrices pertaining to the testing dataset, the optimized ResNet-50 model (batch size 16, epoch 30) was able to successfully classify 45 images as non-stressed out of 57 images and misclassified 12 images from the thermal imagery dataset. In addition, the model successfully identified 57 water-stressed images out of 67 and misclassified 10 images from the RGB imagery dataset. Similarly, in the testing dataset of thermal images, ResNet-50 correctly classified 61 non-stressed images out of 67 and accurately identified 48 out of 57 stress images (Figure 7). The accuracy, precision, sensitivity, and recall metrics obtained for the RGB imagery inputs using ResNet-50 model were 82.3%, 78.9%, 81.8%, and 80.3%, respectively, whereas the same for thermal imagery inputs were 87.9%, 91%, 87.1%, and 89.0%, respectively. Similarly, for MobileNetV2 model, maximum accuracy was recorded for epoch 30 and batch size 16 for RGB as well as thermal imagery inputs. The testing accuracy, precision, recall, and F1 score were 74.1%, 73.9%, 80.6%, and 77.1%, respectively, for RGB images and 84.3%, 84.5%, 86.9%, and 85.7%, respectively, for thermal imagery (Table 3). However, MobileNetV2 exhibited a notable difference in training and validation loss, indicating overfitting (Figure 6). MobileNetV2 utilizes an inverse residual structure and linear bottleneck structure to effectively decrease the computational load of convolution. The input for this module is a low-dimensional compressed representation, which is then enlarged to high dimensions and filtered using a  
Figure 7: Confusion matrix of ResNet-50 (**a,b**) and MobileNetV2 (**c,d**) for identification of water stress using RGB and thermal imagery inputs. lightweight depth-wise convolution to achieve the desired effect. After that, the features are projected back to a low-dimensional representation using a linear convolution for further analysis [35].