MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies


Jinhui Yi (University of Bonn), Yanan Luo (University of Bonn), Marion Deichmann (University of Bonn), Gabriel Schaaf (University of Bonn), Juergen Gall (University of Bonn)
The 35th British Machine Vision Conference
PDFPosterVideo (Right click to download)Supplementary

Abstract

An early, non-invasive, and on-site detection of nutrient deficiencies is critical to enable timely actions to prevent major losses of crops caused by lack of nutrients. While acquiring labeled data is very expensive, collecting images from multiple views of a crop is straightforward. Despite its relevance for practical applications, unsupervised domain adaptation where multiple views are available for the labeled source domain as well as the unlabeled target domain is an unexplored research area. In this work, we thus propose an approach that leverages multiple camera views in the source and target domain for unsupervised domain adaptation. We evaluate the proposed approach on two nutrient deficiency datasets. The proposed method achieves state-of-the-art results on both datasets compared to other unsupervised domain adaptation methods. The dataset and source code are available at https://github.com/jh-yi/MV-Match.

Citation

@inproceedings{Yi_2024_BMVC,
author    = {Jinhui Yi and Yanan Luo and Marion Deichmann and Gabriel Schaaf and Juergen Gall},
title     = {MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0998.pdf}
}


Copyright © 2024 The British Machine Vision Association and Society for Pattern Recognition
The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The Association is a Company limited by guarantee, No.2543446, and a non-profit-making body, registered in England and Wales as Charity No.1002307 (Registered Office: Dept. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK).

Imprint | Data Protection