Abstract
Early and accurate disease detection is important for increasing the agricultural output, decreasing the financial costs, and ensuring food security. Traditional diagnostic procedures take much time and effort, involve the necessity of having deep expertise, and are not always suitable for large scale farming disease detection. For this purpose, the current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images. The model utilizes several innovative architectural solutions such as depth-wise separable convolution, SE blocks, skip connections, and guided attention-based feature learning that allow enhancing the effectiveness of features extraction and decreasing computation load. Moreover, Grad-CAM is used to visualize affected areas on a map and thus increase the interpretability of the model. The suggested solution was implemented and tested on the PlantVillage dataset containing 54,305 images for 38 crop diseases out of 14 crops. The results show that the training, validation, and testing accuracies equal 97.6%, 88.3%, and 97.63%, correspondingly, along with the Macro-F1 measure of 0.867 and Micro-ROC-AUC equal to 0.99. A comparative study reveals that the presented model performs comparably well in terms of classification with lightweight structure and built-in interpretability capabilities to be applied in the mobile and edge-enabled agriculture environment. The results show that the presented approach is capable of being used as an effective and interpretable tool for diagnosing plant diseases in real-time.