Toward Objective Variety Testing Score Based on Computer Vision and Unsupervised Machine Learning: Application to Apple Shape

Mouad Zine-El-Abidine, Helin Dutagaci, Pejman Rasti, Pejman Rasti, Maria Aranzana, Christian Dujak, David Rousseau

2024

Abstract

While precision agriculture or plant phenotyping are very actively moving toward numerical protocols for objective and fast automated measurements, plant variety testing is still very largely guided by manual practices based on visual scoring. Indeed, variety testing is regulated by definite protocols based on visual observation of sketches provided in official catalogs. In this article, we investigated the possibility to shortcut the human visual inspection of these sketches and base the scoring of plant varieties on computer vision similarity of the official sketches with the plants to be inspected. A generic protocol for such a computer vision based approach is proposed and illustrated on apple shape classification. The proposed unsupervised algorithm is demonstrated to be of high value by comparison with classical supervised and self supervised machine and deep learning if some rescaling of the sketches is performed.

Paper Citation

in Harvard Style

Zine-El-Abidine M., Dutagaci H., Rasti P., Aranzana M., Dujak C. and Rousseau D. (2024). Toward Objective Variety Testing Score Based on Computer Vision and Unsupervised Machine Learning: Application to Apple Shape. In Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE; ISBN 978-989-758-693-4, SciTePress, pages 15-22. DOI: 10.5220/0012549700003720

in Bibtex Style

@conference{improve24,
author={Mouad Zine-El-Abidine and Helin Dutagaci and Pejman Rasti and Maria Aranzana and Christian Dujak and David Rousseau},
title={Toward Objective Variety Testing Score Based on Computer Vision and Unsupervised Machine Learning: Application to Apple Shape},
booktitle={Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE},
year={2024},
pages={15-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012549700003720},
isbn={978-989-758-693-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE
TI - Toward Objective Variety Testing Score Based on Computer Vision and Unsupervised Machine Learning: Application to Apple Shape
SN - 978-989-758-693-4
AU - Zine-El-Abidine M.
AU - Dutagaci H.
AU - Rasti P.
AU - Aranzana M.
AU - Dujak C.
AU - Rousseau D.
PY - 2024
SP - 15
EP - 22
DO - 10.5220/0012549700003720
PB - SciTePress