Authors:
Jihen Amara
1
;
Birgitta König-Ries
1
;
2
and
Sheeba Samuel
1
;
2
Affiliations:
1
Heinz Nixdorf Chair for Distributed Information Systems, Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
;
2
Michael-Stifel-Center for Data-Driven and Simulation Science, Jena, Germany
Keyword(s):
Plant Disease Classification, Explainable Artificial Intelligence, Convolutional Neural Networks, Testing with Concept Activation Vectors (TCAV).
Abstract:
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.
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