BonnBot-I: A Precise Weed Management and Crop Monitoring Platform
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929610799710208 |
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| author | Ahmadi, Alireza Halstead, Michael McCool, Chris |
| author_facet | Ahmadi, Alireza Halstead, Michael McCool, Chris |
| contents | 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 that 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 the 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_12588 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BonnBot-I: A Precise Weed Management and Crop Monitoring Platform Ahmadi, Alireza Halstead, Michael McCool, Chris Robotics Hardware Architecture Multiagent Systems Systems and Control 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 that 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 the 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. |
| title | BonnBot-I: A Precise Weed Management and Crop Monitoring Platform |
| topic | Robotics Hardware Architecture Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2307.12588 |