Agricultural disease detection is increasingly benefiting from artificial intelligence, yet high predictive accuracy alone does not guarantee that an AI system can be trusted in real-world agricultural environments. This session presents PRISM, an explainable AIoT framework designed to make crop disease detection more transparent, reliable, and practically meaningful.

The talk explores how lightweight deep learning can be combined with explainability and reliability-aware components to support disease classification while providing greater insight into model decisions. The proposed framework is evaluated through a crop disease detection pipeline, with experiments demonstrating strong predictive performance alongside additional reliability-oriented evaluation.

The session will discuss the design of the PRISM framework, its feature-fusion strategy, explainability considerations, evaluation methodology, and the challenges of transferring AI models across different perennial crops. Particular attention will be given to the gap between “accurate AI” and “trustworthy AI” in agricultural applications.

The presentation is intended for researchers, students, AI practitioners, and anyone interested in Explainable AI, AIoT, computer vision, and intelligent agricultural systems. It will conclude with future directions toward robust, cross-crop, and deployment-oriented AI systems for climate-resilient agriculture.