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.
@article{arxiv.2405.09118,
title = {BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform},
author = {Alireza Ahmadi and Michael Halstead and Claus Smitt and Chris McCool},
journal= {arXiv preprint arXiv:2405.09118},
year = {2024}
}