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From laboratory to field : unsupervised domain adaptation for plant disease recognition in the wild
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Wu, Xinlu, Fan, Xijian, Luo, Peng, Choudhury, Sruti Das, Tjahjadi, Tardi and Hu, Chunhua (2023) From laboratory to field : unsupervised domain adaptation for plant disease recognition in the wild. Plant Phenomics, 5 . 0038. doi:10.34133/plantphenomics.0038 ISSN 2643-6515.
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Official URL: https://doi.org/10.34133/plantphenomics.0038
Abstract
Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g., laboratory vs. field environment, machine learning-based recognition models generated within a specific dataset (source domain) tend to lose their validity when generalized to a novel dataset (target domain). To this end, domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains. In this paper, we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization, namely, Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training. Specifically, MSUN comprises multirepresentation, subdomain adaptation modules and auxiliary uncertainty regularization. The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain. This effectively alleviates the problem of large interdomain discrepancy. Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation. Finally, the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer. MSUN was experimentally validated to achieve optimal results on the PlantDoc, Plant-Pathology, Corn-Leaf-Diseases, and Tomato-Leaf-Diseases datasets, with accuracies of 56.06%, 72.31%, 96.78%, and 50.58%, respectively, surpassing other state-of-the-art domain adaptation techniques considerably.
Item Type: | Journal Article | ||||||||||||
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Subjects: | S Agriculture > SB Plant culture | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||
SWORD Depositor: | Library Publications Router | ||||||||||||
Library of Congress Subject Headings (LCSH): | Plant diseases, Phytopathogenic microorganisms -- Detection, Agriculture -- Data processing, Artificial intelligence -- Agricultural applications | ||||||||||||
Journal or Publication Title: | Plant Phenomics | ||||||||||||
Publisher: | American Association for the Advancement of Science (AAAS) | ||||||||||||
ISSN: | 2643-6515 | ||||||||||||
Official Date: | 28 March 2023 | ||||||||||||
Dates: |
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Volume: | 5 | ||||||||||||
Article Number: | 0038 | ||||||||||||
DOI: | 10.34133/plantphenomics.0038 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 16 May 2023 | ||||||||||||
Date of first compliant Open Access: | 17 May 2023 | ||||||||||||
RIOXX Funder/Project Grant: |
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