In this article, we focus on the critical tasks of plant protection in arable farms, addressing a modern challenge in agriculture: integrating ecological considerations into the operational strategy of precision weeding robots like \bbot. This article presents the recent advancements in weed management algorithms and the real-world performance of \bbot\ at the University of Bonn's Klein-Altendorf campus. We present a novel Rolling-view observation model for the BonnBot-Is weed monitoring section which leads to an average absolute weeding performance enhancement of $3.4\%$. Furthermore, for the first time, we show how precision weeding robots could consider bio-diversity-aware concerns in challenging weeding scenarios. We carried out comprehensive weeding experiments in sugar-beet fields, covering both weed-only and mixed crop-weed situations, and introduced a new dataset compatible with precision weeding. Our real-field experiments revealed that our weeding approach is capable of handling diverse weed distributions, with a minimal loss of only $11.66\%$ attributable to intervention planning and $14.7\%$ to vision system limitations highlighting required improvements of the vision system.

翻译：本文聚焦于耕地农场植物保护的关键任务，应对现代农业中的一项挑战：将生态考量整合到如\bbot等精准除草机器人的操作策略中。本文介绍了杂草管理算法的最新进展以及\bbot在波恩大学克莱因-阿尔滕多夫校区的实际性能表现。我们为BonnBot-I的杂草监测模块提出了一种新颖的滚动视角观测模型，该模型实现了平均绝对除草性能$3.4\%$的提升。此外，我们首次展示了精准除草机器人如何在具有挑战性的除草场景中纳入生物多样性感知考量。我们在甜菜田开展了全面的除草实验，涵盖纯杂草及作物-杂草混合情境，并发布了一个兼容精准除草任务的新数据集。实地实验表明，我们的除草方法能够处理多样化的杂草分布，其中仅$11.66\%$的性能损失归因于干预规划，$14.7\%$源于视觉系统局限，这凸显了视觉系统有待改进之处。