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.

该论文介绍了一种半监督杂草检测的新方法，通过利用计算机视觉和深度学习进行自动化杂草检测，该方法在农业领域中具有较好的应用潜力和实际可行性。