Plant diseases remain a considerable threat to food security and agricultural
sustainability. Rapid and early identification of these diseases has become a
significant concern motivating several studies to rely on the increasing global
digitalization and the recent advances in computer vision based on deep
learning. In fact, plant disease classification based on deep convolutional
neural networks has shown impressive performance. However, these methods have
yet to be adopted globally due to concerns regarding their robustness,
transparency, and the lack of explainability compared with their human experts
counterparts. Methods such as saliency-based approaches associating the network
output to perturbations of the input pixels have been proposed to give insights
into these algorithms. Still, they are not easily comprehensible and not
intuitive for human users and are threatened by bias. In this work, we deploy a
method called Testing with Concept Activation Vectors (TCAV) that shifts the
focus from pixels to user-defined concepts. To the best of our knowledge, our
paper is the first to employ this method in the field of plant disease
classification. Important concepts such as color, texture and disease related
concepts were analyzed. The results suggest that concept-based explanation
methods can significantly benefit automated plant disease identification.

利用深度卷积神经网络进行植物疾病分类在快速和早期识别植物疾病方面显示出卓越性能，然而由于它们相对于人类专家的鲁棒性、透明性和可解释性的问题，这些方法尚未全球范围内得到采用。本研究首次使用了 Testing with Concept Activation Vectors (TCAV) 方法，将重点从像素转移到用户定义的概念，结果表明基于概念的解释方法对自动化植物疾病识别有显著好处。