Concept Explainability for Plant Diseases Classification

Jihen Amara, Birgitta König-Ries, Birgitta König-Ries, Sheeba Samuel, Sheeba Samuel

2023

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.

Paper Citation

in Harvard Style

Amara J., König-Ries B. and Samuel S. (2023). Concept Explainability for Plant Diseases Classification. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 246-253. DOI: 10.5220/0011667900003417

in Bibtex Style

@conference{visapp23,
author={Jihen Amara and Birgitta König-Ries and Sheeba Samuel},
title={Concept Explainability for Plant Diseases Classification},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={246-253},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011667900003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Concept Explainability for Plant Diseases Classification
SN - 978-989-758-634-7
AU - Amara J.
AU - König-Ries B.
AU - Samuel S.
PY - 2023
SP - 246
EP - 253
DO - 10.5220/0011667900003417
PB - SciTePress