Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI - Tariq, Maria; Ali, Usman; Abbas, Sagheer; Hassan, Shahzad; Naqvi, Rizwan Ali; Khan, Muhammad Adnan; Jeong, Daesik (Frontiers in Plant Science, 2024)
An intelligent agricultural system is vital to food and the economy. Diseases harm crops and reduce yields. Early detection and classification can minimize further damage. The model's ability to explain its decisions can ensure trust and credibility in machine learning models. However, Deep Learning(DL) models pose a significant challenge regarding explainability. Layer-wise Relevance Propagation (LRP) is a well-established explainability technique aimed at deep models in computer vision. It generates heat maps of input images that are intuitive and human-readable. This paper uses the Visual Geometry Group 16(VGG16) model to classify corn leaf diseases.The dataset contains four classes: blight, gray spot, common rust, and healthy. LRP is used to interpret the decision-making process of the VGG16 model and enhance its interpretability. The simulation results demonstrate that the proposed approach is more accurate as compared to previously published approaches.