Alhammad Sarah M, Khafaga Doaa Sami, El-Hady Walaa M, Samy Farid M, Hosny Khalid M
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.
Front Artif Intell. 2025 Feb 3;7:1449329. doi: 10.3389/frai.2024.1449329. eCollection 2024.
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.
马铃薯叶部病害的准确分类对于确保作物的健康和产量起着关键作用。本研究提出了一种统一的方法来应对这一挑战，即在深度学习框架内利用可解释人工智能（XAI）和迁移学习的力量。在本研究中，我们提出了一种基于迁移学习的深度学习模型，该模型专门用于马铃薯叶部病害分类。迁移学习使模型能够受益于预训练的神经网络架构和权重，增强其从有限的标记数据中学习有意义表示的能力。此外，可解释人工智能技术被集成到模型中，以便对其决策过程提供可解释的见解，从而提高其透明度和可用性。我们使用一个公开可用的马铃薯叶部病害数据集来训练模型。得到的验证准确率为97%，测试准确率为98%。本研究应用梯度加权类激活映射（Grad-CAM）来增强模型的可解释性。这种可解释性对于提高预测性能、增强信任并确保无缝融入农业实践至关重要。
