Explainable ai techniques for deep convolutional neural network based plant disease identification

Abstract

Deep learning-based computer vision has shown improved performance in image classification tasks. Due to the complexities of these models, they have been referred as opaque models. As a result, users need justifications for predictions to enhance trust. Thus, Explainable Artificial Intelligence (XAI) provides various techniques to explain predictions. Explanations play a vital role in practical application, to apply the exact treatment for a plant disease. However, application of XAI techniques in plant disease identification is not popular. This paper discusses the key concerns and taxonomies available in XAI and summarizes the recent developments. Also, it develops a tomato disease classification model and uses different XAI techniques to validate model predictions. It includes a comparative analysis of XAI techniques and discusses the limitations and usefulness of the techniques in plant disease symptom localization.