# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 3 Results and Discussion  
### Configuration of CNN Models  
The CNN framework for drought stress detection in sunflower images, proposed in this paper, consists of six layers. The first layer is the input layer, which takes images as inputs. The second layer is a pre-trained model on ImageNet images, and it is the base model in our framework. The next layer is the global average layer, which reduces the dimensionality and complexity of the computation. After that, a fully connected dense layer with ReLU activation was added to introduce non-linearity. The next layer is the dropout layer with a rate of 0.5, which was added to reduce overfitting by randomly omitting a subset of features [35]. The last layer is a dense layer, consisting of a single neuron with a sigmoidal activation function, and it produces a probability output, indicating the probability that the sunflower plant was under drought stress. The model is compiled with the Adam optimizer and binary cross-entropy loss. Finally, it was evaluated against standard metrics commonly used in binary classification with an imbalanced dataset to evaluate its effectiveness [36; 37].  
All five CNN models were trained multiple times with different manually adjusted parameters to find optimal parameters and improve performance. This was performed for both the original and grayscale datasets to determine the optimal learning rates, batch sizes, and number of epochs. In the preprocessing step, images were resized to 224 \(\times\) 224 and rescaled by dividing pixel values by 255 to adjust them from a range of 0-255 to 0-1. This step simplifies the data, speeding up the learning and convergence of neural networks. We tested batch sizes of 16, 32, and 64 and discovered that 32 produced the best initial results for all models. This size provides good computing efficiency without exhausting the system memory. A batch size of 64 may result in memory limitations and maybe less consistent gradient estimations, whereas a batch size of 16 was less effective and slowed down the training process. Moreover, we determined that VGG16 required a lower learning rate of 0.00001 and a longer training period of 50 epochs. In contrast, VGG19, DenseNet, Inception V3, and MobileNet were successfully trained over 30 epochs at a higher learning rate of 0.0001 (Table 1). We used grayscale images to determine whether color information is necessary for accurate drought stress detection in sunflowers since in agriculture simplified imaging methods, like grayscale, can be effectively utilized for detecting and categorizing plant stress [38; 39].  
The goal was to determine which CNN architecture and dataset, original or grayscale images, give the best results in terms of generalization and prediction accuracy. Once the optimal underlying CNN architecture was chosen, the capacity of the models to use the enriched dataset was evaluated. Enhancement of the dataset involved standard data augmentation techniques, which included horizontal flipping, rotation, width shift, height shift, zoom, and brightness adjustments. Additionally, we employed targeted data augmentation, which incorporated new images based on well-known image features related to drought stress characteristics in sunflowers.