Fusing explainable deep learning ensembles and LLM recommendations for real-time plant leaf disease diagnosis

Timely and accurate identification of plant leaf diseases plays a vital role in ensuring sustainable agriculture and universal food security. Accurate identification of plant leaf diseases ensures healthier plant cultivation, which is pivotal for sustainable agriculture operations. In this study, we present a plant leaf disease recognition mechanism that utilizes a stacking ensemble structure combined with a Large Language Model (LLM) and Explainable AI (XAI) mechanism to improve identification accuracy and comprehensibility. To capture high textural structure, we utilized the Gray Level Co-occurrence Matrix (GLCM), whereas the MobileNetV3 architecture was utilized to maintain low computational cost in feature extraction. GoogleNet was integrated to improve multi-scale feature extraction by employing inception blocks, which effectively obtain fine-grained details and universal spatial patterns. Our ensemble framework integrates improved versions of MobileNetV3, GoogleNet, and ConvNeXtSmall with CatBoost employed as a nonlinear meta-learner allowing the framework to effectively capture complex connections among the base models within the ensemble framework. Moreover, we utilized additional CNN models, including AlexNet and EfficientNetV2B0, to compare the result of our proposed stacking ensemble model and to evaluate its generalization ability over various architectural designs. In addition, we developed a real-time system integrating an LLM with the proposed ensemble model, ensuring automatic plant leaf disease recognition and delivering corresponding curing recommendations. Our findings contribute to plant-based agriculture by enabling early diagnosis of leaf diseases and providing real-time recommendations through DL and LLM technology. • GLCM captures rich textural features to differentiate specific leaf regions. • Developed an explainable staking ensemble for plant leaf disease identification. • Integrated enhanced MobileNetV3, GoogleNet, and ConvNeXtSmall as base methods. • Applied CatBoost as nonlinear meta-learner to improve feature fusion. • LeafCureX-LLM: A real-time system using LLM for leaf disease treatment.