Integrating Domain Knowledge for Enhanced Concept Model Explainability in Plant Disease Classification

Abstract

Deep learning-based plant disease detection has seen promising advancements, particularly in its remarkable ability to identify diseases through digital images. Nevertheless, these systems’ opacity and lack of transparency, which often offer no human-interpretable explanations for their predictions, raise concerns with respect to their robustness and reliability. While many methods have attempted post-hoc model explainability, few have specifically targeted the integration and impact of domain knowledge. In this study, we propose a novel framework that combines a tomato disease ontology with the concept explainability method Testing with Concept Activation Vectors (TCAV). Unlike the original TCAV method, which required users to gather diverse image concepts manually, our approach automates the creation of images based on relevant concepts used by domain experts in plant disease identification. This not only simplifies the concept collection and labelling process but also reduces the burden on users with limited domain knowledge, ultimately mitigating potential biases in concept selection. Besides automating the concept image generation for the TCAV method, our framework gives insights into the significance of disease-related concepts identified through the ontology in the deep learning model decision-making process. Consequently, our approach enhances the efficiency and interpretability of the model’s diagnostic capabilities, promising a more trustworthy and reliable disease detection model.