Unraveling the Black Box: Interpreting CNNs for Leaf Disease Detection through Model Analysis and Feature Importance

Convolutional Neural Networks (CNNs) have been highly successful in computer vision tasks, including leaf disease detection. However, their lack of interpretability limits our understanding of their decision-making process and undermines trust in their predictions. In this study, we aim to unravel the black box of CNNs for leaf disease detection through a comprehensive model analysis and feature importance study. We review various techniques in explainable artificial intelligence and propose a methodology that combines model analysis and feature importance methods. We analyze the model’s internal representations and activations in order to understand how different layers process information. Additionally, we employ feature importance methods, like SHapley Additive exPlanations (SHAP), to quantify the influence of individual features on predictions. By assigning importance scores to each pixel or feature, we identify the most discriminative regions or characteristics used by the CNN for disease detection. These insights provide a deeper understanding of learned representations, the decision-making process, and the key visual cues used by CNNs. Our findings enhance interpretability, foster trust in automated leaf disease detection systems, and facilitate the development of more explainable and reliable models in precision agriculture.