用自动概念识别提升植物病害分类模型的可解释性
Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
- 用ACE方法自动提取图像中的视觉概念，揭示模型决策依据
- 发现病害相关特征及背景光照等偶然偏差，影响模型鲁棒性
- 适合关注农业AI透明度与可靠性研究的读者
深度学习已显著推动基于图像的植物病害自动检测，但提升模型可解释性对可靠诊断仍至关重要。本研究采用自动化概念解释（ACE）方法，针对广泛使用的InceptionV3模型和PlantVillage数据集进行植物病害分类分析。ACE能自动识别图像数据中的视觉概念，并揭示影响模型预测的关键特征。实验表明，该方法不仅识别出有效的病害相关模式，还发现了由背景或光照引起的偶然偏差，这些偏差可能削弱模型的鲁棒性。通过系统性实验，ACE帮助我们定位关键特征并明确模型优化方向。结果表明，ACE在提升深度学习植物病害分类的可解释性方面具有潜力，对于构建农业中透明可靠的病害管理工具至关重要。
原文摘要 · Abstract (English)
While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable disease detection. In this study, we apply the Automated Concept-based Explanation (ACE) method to plant disease classification using the widely adopted InceptionV3 model and the PlantVillage dataset. ACE automatically identifies the visual concepts found in the image data and provides insights about the critical features influencing the model predictions. This approach reveals both effective disease-related patterns and incidental biases, such as those from background or lighting that can compromise model robustness. Through systematic experiments, ACE helped us to identify relevant features and pinpoint areas for targeted model improvement. Our findings demonstrate the potential of ACE to improve the explainability of plant disease classification based on deep learning, which is essential for producing transparent tools for plant disease management in agriculture.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。