Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability. However, most previous works require fast and constant-time weed detection systems to achieve real-time treatment, which forecloses the implementation of more capable but time-consuming algorithms, e.g. learning-based methods. In this paper, a non-overlapping multi-camera system is applied to provide flexibility for the weed control system in dealing with the indeterminate classification delays. The design, implementation, and testing of our proposed modular weed control unit with mechanical and chemical weeding tools are presented. A framework that performs naive Bayes filtering, 3D direct intraand inter-camera visual tracking, and predictive control, while integrating state-of-the-art crop/weed detection algorithms, is developed to guide the tools to achieve high-precision weed removal. The experimental results show that our proposed fully operational weed control system is capable of performing selective mechanical as well as chemical in-row weeding with indeterminate detection delays in different terrain conditions and crop growth stages.

中文翻译：

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使用自动杂草和作物分类的机器人杂草控制

精准农业中的自主机器人除草系统已证明其完全有潜力减轻目前对除草剂和杀虫剂等农用化学品的依赖，从而减少环境污染并提高可持续性。然而，大多数以前的工作需要快速和恒定时间的杂草检测系统来实现实时处理，这阻碍了更强大但耗时的算法的实现，例如基于学习的方法。在本文中，应用非重叠多相机系统为杂草控制系统处理不确定分类延迟提供灵活性。介绍了我们提出的带有机械和化学除草工具的模块化杂草控制单元的设计、实施和测试。一个执行朴素贝叶斯过滤的框架，3D 直接相机内和相机间视觉跟踪和预测控制，同时集成了最先进的作物/杂草检测算法，用于指导工具实现高精度杂草去除。实验结果表明，我们提出的完全可操作的杂草控制系统能够在不同的地形条件和作物生长阶段以不确定的检测延迟执行选择性机械和化学行内除草。