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.
