Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification

• URL: http://arxiv.org/abs/2510.10573v1
• Date: Sun, 12 Oct 2025 12:45:53 GMT
• Title: Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification
• Authors: Farouq Benchallal, Adel Hafiane, Nicolas Ragot, Raphael Canals,
• Abstract summary: The identification of weeds is a challenging problem due to their shared similarities with crop plants and the variability related to the differences in terms of their types.<n>To fully benefit from deep learning-based methods, large fully annotated datasets are needed.<n>We propose a deep semi-supervised approach, that combines consistency regularization with similarity learning.
• Score: 3.9023554886892438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Weed species classification represents an important step for the development of automated targeting systems that allow the adoption of precision agriculture practices. To reduce costs and yield losses caused by their presence. The identification of weeds is a challenging problem due to their shared similarities with crop plants and the variability related to the differences in terms of their types. Along with the variations in relation to changes in field conditions. Moreover, to fully benefit from deep learning-based methods, large fully annotated datasets are needed. This requires time intensive and laborious process for data labeling, which represents a limitation in agricultural applications. Hence, for the aim of improving the utilization of the unlabeled data, regarding conditions of scarcity in terms of the labeled data available during the learning phase and provide robust and high classification performance. We propose a deep semi-supervised approach, that combines consistency regularization with similarity learning. Through our developed deep auto-encoder architecture, experiments realized on the DeepWeeds dataset and inference in noisy conditions demonstrated the effectiveness and robustness of our method in comparison to state-of-the-art fully supervised deep learning models. Furthermore, we carried out ablation studies for an extended analysis of our proposed joint learning strategy.

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