Plant Leaf Disease Detection Using Ensemble Learning and Explainable AI

Plants are integral to the agriculture industry, profoundly impacting a nation’s economy and environmental stability, with a significant portion of certain countries’ economies reliant on crop production. Much like human health, plants face susceptibility to diseases induced by viruses and bacteria, necessitating careful attention to plant care and disease identification. This study introduces an AI (Artificial Intelligence) model that detects and explains plant diseases through image analysis. The proposed system, distinct from existing detectors, identifies numerous diseases in vegetables and fruits by employing our proposed ensemble learning classifier involving four deep learning models: VGG16, VGG19, ResNet101 V2, and Inception V3, achieving an accuracy exceeding 90%. The reason for using ensemble learning is to obtain accurate predictions. Furthermore, the system sets itself apart by providing explanations for predictions using LIME (Local Interpretable Model-Agnostic Explanations), applied to interpret the predictions of deep learning models. The visualizations generated from multiple methods point to specific pixels’ influence on accurate and incorrect predictions, clearly illustrating the model’s decision-making process. This technique shows areas of the image that contributed positively to the model’s decision, like key regions where the object of interest was most prominent, and areas that added negative values, where irrelevant or misleading features were present. By exploring these features, we gained insights into how the model interprets and prioritizes different aspects of the image during prediction. The study aims to address existing limitations in plant disease detection, offering a comprehensive solution to enhance agricultural practices, foster economic growth, and contribute to environmental sustainability. © 2013 IEEE.