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。

半监督杂草检测用于快速部署和提高效率

Semi-Supervised Weed Detection for Rapid Deployment and Enhanced  Efficiency

We present a specialized procedural model for generating synthetic
agricultural scenes, focusing on soybean crops, along with various weeds. This
model is capable of simulating distinct growth stages of these plants, diverse
soil conditions, and randomized field arrangements under varying lighting
conditions. The integration of real-world textures and environmental factors
into the procedural generation process enhances the photorealism and
applicability of the synthetic data. Our dataset includes 12,000 images with
semantic labels, offering a comprehensive resource for computer vision tasks in
precision agriculture, such as semantic segmentation for autonomous weed
control. We validate our model's effectiveness by comparing the synthetic data
against real agricultural images, demonstrating its potential to significantly
augment training data for machine learning models in agriculture. This approach
not only provides a cost-effective solution for generating high-quality,
diverse data but also addresses specific needs in agricultural vision tasks
that are not fully covered by general-purpose models.

为精准农业中的计算机视觉任务提供包含语义标签的 12,000 张图像数据集，通过在合适的光照条件下模拟大豆作物和杂草的成长阶段、不同的土壤条件和随机的田地布局，将真实世界纹理和环境因素融入到程序生成过程中，提高合成数据的逼真度和适用性，从而有效地增加了农业机器学习模型的训练数据。