BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform

• URL: http://arxiv.org/abs/2405.09118v2
• Date: Thu, 4 Jul 2024 20:49:51 GMT
• Title: BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform
• Authors: Alireza Ahmadi, Michael Halstead, Claus Smitt, Chris McCool,
• Abstract summary: 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 $3.4%$. For the first time, we show how precision weeding robots could consider bio-diversity-aware concerns in challenging weeding scenarios.
• Score: 2.3961612657966946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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