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.

本文主要研究内容是关于农田植物保护的关键任务，集中讨论一个农业的现代挑战：如何将生态考虑融入像 BonBot 这样的精准除草机器人的操作策略。通过在波恩大学 Klein-Altendorf 校区的实际表现，本文介绍了除草管理算法的最新进展以及 BonBot 在实际场景中的表现。同时，本文首次展示了精准除草机器人如何在具有生物多样性关注的除草情境中运作。以甜菜田为实验场地，本文进行了全面的除草实验，涵盖了仅有杂草和混合作物 - 杂草的情况，并引入了与精准除草兼容的新数据集。实地实验结果显示，我们的除草方法能够处理各种杂草分布，并且仅有 11.66% 的损失归因于干预规划，14.7% 的损失归因于视觉系统限制，强调了视觉系统需要改进的地方。