Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection based on Reconstructability of Colors

Ryoya Katafuchi, Terumasa Tokunaga

2021

Abstract

This paper proposes an unsupervised anomaly detection technique for image-based plant disease diagnosis. The construction of large and publicly available datasets containing labeled images of healthy and diseased crop plants led to growing interest in computer vision techniques for automatic plant disease diagnosis. Although supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data. The data mining technique of anomaly detection includes unsupervised approaches that do not require rare samples for training classifiers. We propose an unsupervised anomaly detection technique for image-based plant disease diagnosis that is based on the reconstructability of colors; a deep encoder-decoder network trained to reconstruct the colors of healthy plant images should fail to reconstruct colors of symptomatic regions. Our proposed method includes a new image-based framework for plant disease detection that utilizes a conditional adversarial network called pix2pix and a new anomaly score based on CIEDE2000 color difference. Experiments with PlantVillage dataset demonstrated the superiority of our proposed method compared to an existing anomaly detector at identifying diseased crop images in terms of accuracy, interpretability and computational efficiency.

Paper Citation

in Harvard Style

Katafuchi R. and Tokunaga T. (2021). Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection based on Reconstructability of Colors. In Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-511-1, pages 112-120. DOI: 10.5220/0010463201120120

in Bibtex Style

@conference{improve21,
author={Ryoya Katafuchi and Terumasa Tokunaga},
title={Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection based on Reconstructability of Colors},
booktitle={Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2021},
pages={112-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010463201120120},
isbn={978-989-758-511-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection based on Reconstructability of Colors
SN - 978-989-758-511-1
AU - Katafuchi R.
AU - Tokunaga T.
PY - 2021
SP - 112
EP - 120
DO - 10.5220/0010463201120120