Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiency

• URL: http://arxiv.org/abs/2405.07399v1
• Date: Sun, 12 May 2024 23:34:06 GMT
• Title: Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiency
• Authors: Alzayat Saleh, Alex Olsen, Jake Wood, Bronson Philippa, Mostafa Rahimi Azghadi,
• Abstract summary: This paper introduces a novel method for semi-supervised weed detection, comprising two main components. Firstly, a multi-scale feature representation technique is employed to capture distinctive weed features across different scales. Secondly, we propose an adaptive pseudo-label assignment strategy, leveraging a small set of labelled images during training.
• Score: 2.8444649426160304
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Weeds present a significant challenge in agriculture, causing yield loss and requiring expensive control measures. Automatic weed detection using computer vision and deep learning offers a promising solution. However, conventional deep learning methods often require large amounts of labelled training data, which can be costly and time-consuming to acquire. This paper introduces a novel method for semi-supervised weed detection, comprising two main components. Firstly, a multi-scale feature representation technique is employed to capture distinctive weed features across different scales. Secondly, we propose an adaptive pseudo-label assignment strategy, leveraging a small set of labelled images during training. This strategy dynamically assigns confidence scores to pseudo-labels generated from unlabeled data. Additionally, our approach integrates epoch-corresponding and mixed pseudo-labels to further enhance the learning process. Experimental results on the COCO dataset and five prominent weed datasets -- CottonWeedDet12, CropAndWeed, Palmer amaranth, RadishWheat, and RoboWeedMap -- illustrate that our method achieves state-of-the-art performance in weed detection, even with significantly less labelled data compared to existing techniques. This approach holds the potential to alleviate the labelling burden and enhance the feasibility and deployment speed of deep learning for weed detection in real-world agricultural scenarios.

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