ArticleFrontiers in artificial intelligence2024
Deep learning and explainable AI for classification of potato leaf diseases.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis.Scientific reports · 2026Article
- Explainable CNN framework for accurate crop disease detection using plant leaf images.Scientific reports · 2026Article
- A novel approach for disease and pests detection in potato production system based on deep learning.Scientific reports · 2026Article
- A comprehensive evaluation of lightweight deep learning models for tomato disease classification on edge computing environments.Scientific reports · 2026Article
- Comparative deep learning approaches for bean leaf disease recognition.Frontiers in plant science · 2026Article
- PotatoLeafNet: two-stage convolutional neural networks for effective Potato Leaf disease identification and classification.Frontiers in artificial intelligence · 2025Article
- Enhancing multiclass plant disease classification using GAN-boosted vision transformer with XAI insights.Frontiers in plant science · 2025Article
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Authors and funding
5 authors.
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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.
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