BonnBot-I: A Precise Weed Management and Crop Monitoring Platform

Cultivation and weeding are two of the primary tasks performed by farmers today. A recent challenge for weeding is the desire to reduce herbicide and pesticide treatments while maintaining crop quality and quantity. In this paper we introduce BonnBot-I a precise weed management platform which can al...

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Veröffentlicht in:Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems S. 9202 - 9209
Hauptverfasser: Ahmadi, Alireza, Halstead, Michael, McCool, Chris
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2022
Schlagworte:
Actuators
Agricultural Automation
Crops
Field Robotics
Location awareness
Planning
Robotics and Automation in Agriculture and Forestry
Sensors
Spraying
Wheels
ISSN:2153-0866
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Zusammenfassung:Cultivation and weeding are two of the primary tasks performed by farmers today. A recent challenge for weeding is the desire to reduce herbicide and pesticide treatments while maintaining crop quality and quantity. In this paper we introduce BonnBot-I a precise weed management platform which can also performs field monitoring. Driven by crop monitoring approaches which can accurately locate and classify plants (weed and crop) we further improve their performance by fusing the platform available GNSS and wheel odometry. This improves tracking accuracy of our crop monitoring approach from a normalized average error of 8.3% to 3.5%, evaluated on a new publicly available corn dataset. We also present a novel arrangement of weeding tools mounted on linear actuators evaluated in simulated environments. We replicate weed distributions from a real field, using the results from our monitoring approach, and show the validity of our work-space division techniques which require significantly less movement (a 50% reduction) to achieve similar results. Overall, BonnBot-I is a significant step forward in precise weed management with a novel method of selectively spraying and controlling weeds in an arable field.
ISSN:2153-0866
DOI:10.1109/IROS47612.2022.9981304