## 2 Materials and Methods
### Water Stress Identification  
Thermal and RGB images were processed separately through two convolutional neural network-based DL models, MobileNetV2 and ResNet-50, to predict water-stressed and non-stressed crops. These models were selected for their successful application and extensive validation for various agricultural applications [12; 18; 23; 24]. MobileNetV2 is a fast and efficient framework for lightweight networks and suitable for deployment on mobile and embedded systems. One significant benefit of MobileNetV2 is separation of convolutions, where standard convolutions are divided into depth-wise and point-wise convolutions. This enables a much finer granularity of feature extraction from imagery inputs. Next, one-by-one point-wise convolutions linearly combine the outputs of depth-wise convolutions. This factorization significantly reduces model size and computational costs by almost 8-9 times compared to traditional convolution separation methods [25; 26]. ResNet-50 is a variant of ResNet architecture that has been pre-trained using a big dataset of over one million images from the ImageNet database. ResNet-50 comprises fifty layers and differentiates convolutions by depth. This design solves the vanishing gradient problem by creating a gradient flow shortcut. To alleviate concerns of overfitting with training data, ResNet-50 offers the ability to circumvent a CNN weight layer when it is determined to be redundant, using identity mapping. According to [PERSON] and [PERSON] [27], this strategic halting method makes the model more generalizable and stable by avoiding overfitting and making it adaptable to different datasets. Most ResNet-50 models use batch normalization and double- or triple-layer skips with non-linearity (ReLU). ResNet-50 uses softmax at the final layer and sequences of convolutional blocks with average pooling for classification. Among the five convolutional layers of ResNet-50, the first layer, with 64 filters and a \(7\times 7\) kernel size, is applied to the input image, followed by a max-pooling layer with a stride length of 2. Layers in ResNet-50 are paired to match residual network connection patterns in layer 2 [28]. This thoroughness in architecture and layer organization makes ResNet-50 an efficient and robust DL model to solve complex image characterization. The collected imagery datasets were divided into training (70%), validation (20%), and testing (10%) subsets. The DL model training involved identifying combinations of hyper-parameters from epochs (10, 20, 30, or 50) and batch sizes (8, 16, or 32) for a learning rate of 0.001, momentum of 0.9, and stochastic gradient descent with momentum (SGDM) solver. Additionally, the training and testing loss-accuracy graphs throughout the training process were monitored. By comparing these graphs, we ensured that the model's performance on the validation dataset remained consistent with its performance on the training dataset. Specifically, a significant divergence between the training and validation losses or a plateau in validation accuracy while training accuracy continued to improve was observed. Model performances were evaluated through accuracy and loss curves for training and validation. These curves highlight the need and directions for model hyperparameter tuning, thereby ensuring that the model neither overfits nor underfits and achieves a balance that can be generalizable to an independent dataset. In addition, confusion matrices and metrics of the accuracy, precision, sensitivity, and F1 score of the test datasets were also computed [24; 29]. These metrics are critical to agronomists and breeders and are calculated using true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs). A TP represents correctly identified instances of a particular target class (stressed), a TN indicates correctly identified negative instances