MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies
This addresses the challenge of expensive labeled data collection for crop monitoring, offering a practical solution for agricultural applications, though it is incremental in adapting existing domain adaptation techniques to multi-view scenarios.
The paper tackles the problem of early detection of plant nutrient deficiencies by proposing an unsupervised domain adaptation method that leverages multiple camera views, achieving state-of-the-art results on two datasets.
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.