An Interpretable Real-Time Plant Disease Detection and Severity Assessment System Based on Optimized CNN Activation function

Plant diseases have a significant impact on agricultural productivity and food security worldwide, making timely disease detection techniques essential. Convolutional neural networks (CNNs) have shown promise in plant disease classification, but existing methods primarily use supervised data, are difficult to interpret, and cannot forecast disease severity or offer timely predictions. This paper presents a novel method for real-time plant disease detection, offering interpretability and severity estimation using a lightweight convolutional neural network (CNN) combined with an op- timization algorithm. The proposed activation function improves convergence speed and mitigates the dead neuron problem associated with ReLU activation. GradCAM, an AI-based interpretable analysis tool, is used to detect disease foci, and subsequent clustering determines disease severity. Furthermore, the model, designed to support rational decision-making regarding agricultural crops, incorporates fertilization and treatment strategies based on the severity of the problems. Ex- perimental evaluations demonstrate greater accuracy, faster learning speed, and better results than existing methods based on convolutional neural networks.