Self-supervised cross-iterative clustering for unlabeled plant disease images
Article
Article Title | Self-supervised cross-iterative clustering for unlabeled plant disease images |
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ERA Journal ID | 18092 |
Article Category | Article |
Authors | Fang, Uno, Li, J., Lu, X., Gao, Longxiang, Ali, Mumtaz and Xiang, Yong |
Journal Title | Neurocomputing |
Journal Citation | 456, pp. 36-48 |
Number of Pages | 13 |
Year | 07 Oct 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0925-2312 |
1872-8286 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.neucom.2021.05.066 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0925231221008195 |
Abstract | Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive. In this paper, we propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means. The main idea is to establish a cross iterative under-clustering algorithm based on Kernel K-means to produce the pseudo-labeled training set and a chaotic cluster to be further classified by a deep learning module. In order to verify the effectiveness of our proposed framework, we conduct extensive experiments on three different plant disease datatsets with five plants and 17 plant diseases. The experimental results show the high superiority of our method to do image-based plant disease classification over balanced and unbalanced datasets by comparing with five state-of-the-art existing works in terms of different metrics. |
Keywords | Pattern Recognition; Clustering; Dimensional analysis and similarity; Self-supervised learning; Automated annotation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Deakin University |
https://research.usq.edu.au/item/w2z27/self-supervised-cross-iterative-clustering-for-unlabeled-plant-disease-images
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