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

TL;DR

This paper introduces BonnBot-I Plus, a precision weed management robot that incorporates biodiversity considerations, improves weed monitoring with a novel observation model, and demonstrates effective real-world performance in diverse agricultural scenarios.

Contribution

It presents a biodiversity-aware approach to precision weeding, a novel rolling-view observation model, and real-world validation in sugar-beet fields with a new dataset.

Findings

Weed monitoring performance improved by 3.4% with the new observation model.

The system effectively handles diverse weed distributions in real fields.

Vision system limitations caused up to 14.7% performance loss.

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.