## 3 Results and Discussion
### Water Stress Identification
The architecture of this system is favored above others owing to its straightforward design and effective use of memory. The computational and storage requirements of this architecture are lower than those of ResNet-50 due to its smaller number of parameters. This could be beneficial in creating a mobile application that utilizes the MobileNetV2 model for automatically detecting and classifying plant stress. This would enable users or farmers to identify stress promptly, even with minimal knowledge of the subject. Similarly, MobileNetV2 could classify 54 non-stressed images and 38 stressed images out of 73 and 51 RGB images, respectively. Moreover, with thermal imagery, 60 images were correctly classified as non-stressed and 47 as stressed out of a total of 71 and 56 images in each class (Figure 7). ResNet-50's utilization of residual structures employ skip connections to mitigate the vanishing gradient, unlike MobileNetV2 or other deeper network-based models, which is an advantage [36]. By employing this strategy, the network transforms the identity map into a residual fit, thereby bypassing the necessity for the layers to learn the underlying mapping themselves. Moreover, ResNet-50 efficiently addresses the degradation problem by fragmenting large convolution filters into smaller ones and reducing training parameters without sacrificing performance. In addition, ResNet-50 achieves greater depth due to inclusion of a global pooling layer rather than fully connected layers [37]. Conversely, MobileNetV2 has a much simpler architecture, especially designed to be implemented on micro- or mobile computers. MobileNetV2 has been effectively used for classification of fruit images [38], seeds [39], maize seed varieties [40], etc. Comparable performance in biotic stress prediction on coffee leaves by ResNet-50 (97%) and MobileNetV2 (96%) has been reported by [41]. CNNs are being increasingly used in agricultural applications, particularly for crop phenotyping. Their ability to simulate complex agricultural processes is due to their capacity to identify and extract data patterns [42]. Both the ResNet-50 and MobileNetV2 models accurately predicted the stress levels of crops using both thermal and RGB images. Water stress lowers the levels of chlorophyll and carotenoids, causing changes in leaf coloring. This shift in color may be detected using RGB images, which have been shown to provide reliable results [43]. Thermal imaging, as compared to RGB imagery, provides a more thorough estimate of crop water stress by assessing canopy temperature, which is influenced by emissivity patterns in a proportionate manner [44; 45]. This may lead to relatively lower accuracy of water stress detection using RGB images (74-79%) over thermal imagery (84-88%). In a study by [44], a higher (89%) accuracy of wheat ear counting with thermal imagery over RGB imagery (82%) was found using DCNN models. For identifying water stress in wheat, ResNet-50 had the highest accuracy among the deep learning models [12]. Convolutional neural network (CNN) models were utilized to assess the thermal images and classify water stress in maize. The study classified maize plants into well-irrigated, moderately irrigated, and water-stressed treatments, achieving an impressive total accuracy of 89% [19]. This study contributes to the advancement of water stress identification using multidimensional data inputs that would enhance robustness across various agro-climatic conditions.