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。

植物病害分类的概念解释性

Concept explainability for plant diseases classification

Biotic stress consists of damage to plants through other living organisms.
Efficient control of biotic agents such as pests and pathogens (viruses, fungi,
bacteria, etc.) is closely related to the concept of agricultural
sustainability. Agricultural sustainability promotes the development of new
technologies that allow the reduction of environmental impacts, greater
accessibility to farmers and, consequently, increase on productivity. The use
of computer vision with deep learning methods allows the early and correct
identification of the stress-causing agent. So, corrective measures can be
applied as soon as possible to mitigate the problem. The objective of this work
is to design an effective and practical system capable of identifying and
estimating the stress severity caused by biotic agents on coffee leaves. The
proposed approach consists of a multi-task system based on convolutional neural
networks. In addition, we have explored the use of data augmentation techniques
to make the system more robust and accurate. The experimental results obtained
for classification as well as for severity estimation indicate that the
proposed system might be a suitable tool to assist both experts and farmers in
the identification and quantification of biotic stresses in coffee plantations.

本研究设计了一个基于卷积神经网络的实用系统，通过计算机视觉和深度学习方法对咖啡树叶上生物胁迫病原体进行早期和准确的识别和评估，实验结果表明该系统可能是一个适合专家和农民识别和量化咖啡种植区生物胁迫的工具。