Deep learning and explainable AI for classification of potato leaf diseases.

Abstract

The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In this research, we propose a transfer learning-based deep learning model that is tailored for potato leaf disease classification. Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. Additionally, Explainable AI techniques are integrated into the model to provide interpretable insights into its decision-making process, contributing to its transparency and usability. We used a publicly available potato leaf disease dataset to train the model. The results obtained are 97% for validation accuracy and 98% for testing accuracy. This study applies gradient-weighted class activation mapping (Grad-CAM) to enhance model interpretability. This interpretability is vital for improving predictive performance, fostering trust, and ensuring seamless integration into agricultural practices.

Plant phenotyping relevance

ジャガイモ葉の病害状態を画像から分類する深層学習・Grad-CAM手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper's potato leaf disease classification uses the publicly available PlantVillage dataset from Kaggle, cited with an explicit URL in the references. No author analysis code or trained model is deposited.