BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform (2405.09118v2)
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.
- M. L. Zingsheim and T. F. Döring, “What weeding robots need to know about ecology,” Agriculture, Ecosystems & Environment, vol. 364, p. 108861, 2024.
- I. Heap, “Global perspective of herbicide-resistant weeds,” Pest management science, vol. 70, no. 9, pp. 1306–1315, 2014.
- B. L. Steward, L. F. Tian, and L. Tang, “Distance–based control system for machine vision–based selective spraying,” Transactions of the ASAE, vol. 45, no. 5, p. 1255, 2002.
- O. Bawden, J. Kulk, R. Russell, C. McCool, A. English, F. Dayoub, C. Lehnert, and T. Perez, “Robot for weed species plant‐specific management,” Journal of Field Robotics, vol. 34, pp. 1179–1199, 2017.
- C.-L. Chang, B.-X. Xie, and S.-C. Chung, “Mechanical control with a deep learning method for precise weeding on a farm,” Agriculture, vol. 11, no. 11, p. 1049, 2021.
- X. Wu, S. Aravecchia, P. Lottes, C. Stachniss, and C. Pradalier, “Robotic weed control using automated weed and crop classification,” Journal of Field Robotics, vol. 37, no. 2, pp. 322–340, 2020.
- J. Ascard, P. Hatcher, B. Melander, M. Upadhyaya, and R. Blackshaw, “10 thermal weed control,” Non-chemical weed management: principles, concepts and technology, pp. 155–175, 2007.
- Y. Xiong, Y. Ge, Y. Liang, and S. Blackmore, “Development of a prototype robot and fast path-planning algorithm for static laser weeding,” Computers and Electronics in Agriculture, vol. 142, pp. 494–503, 2017.
- A. Ahmadi, M. Halstead, and C. McCool, “Bonnbot-i: A precise weed management and crop monitoring platform,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 9202–9209.
- E.-C. Oerke, “Crop losses to pests,” The Journal of Agricultural Science, vol. 144, no. 1, pp. 31–43, 2006.
- R. L. Zimdahl, “Weed-crop competition: a review,” 2007.
- D. Slaughter, D. Giles, and D. Downey, “Autonomous robotic weed control systems: A review,” Computers and electronics in agriculture, vol. 61, no. 1, pp. 63–78, 2008.
- A. Guglielmini, A. Verdú, and E. Satorre, “Competitive ability of five common weed species in competition with soybean,” International journal of pest management, vol. 63, no. 1, pp. 30–36, 2017.
- P. Dentika, H. Ozier-Lafontaine, and L. Penet, “Weeds as pathogen hosts and disease risk for crops in the wake of a reduced use of herbicides: Evidence from yam (dioscorea alata) fields and colletotrichum pathogens in the tropics,” Journal of Fungi, vol. 7, no. 4, p. 283, 2021.
- H. Zhu, Y. Zhang, D. Mu, L. Bai, X. Wu, H. Zhuang, and H. Li, “Research on improved yolox weed detection based on lightweight attention module,” Crop Protection, vol. 177, p. 106563, 2024.
- M. Halstead, A. Ahmadi, C. Smitt, O. Schmittmann, and C. Mccool, “Crop agnostic monitoring driven by deep learning,” Frontiers in Plant Science, p. 2937, 2021.
- K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961–2969.
- M. Vahdanjoo, R. Gislum, and C. A. G. Sørensen, “Operational, economic, and environmental assessment of an agricultural robot in seeding and weeding operations,” AgriEngineering, vol. 5, no. 1, pp. 299–324, 2023.
- M. Zhou, H. Jiang, Z. Bing, H. Su, and A. Knoll, “Design and evaluation of the target spray platform,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421996146, 2021.
- C.-L. Chang, B.-X. Xie, and S.-C. Chung, “Mechanical control with a deep learning method for precise weeding on a farm,” Agriculture, vol. 11, no. 11, 2021. [Online]. Available: https://www.mdpi.com/2077-0472/11/11/1049
- M. Halstead, C. McCool, S. Denman, T. Perez, and C. Fookes, “Fruit quantity and ripeness estimation using a robotic vision system,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 2995–3002, 2018.
- A. Ahmadi, M. Halstead, and C. McCool, “Virtual temporal samples for recurrent neural networks: applied to semantic segmentation in agriculture,” in German conference on pattern recognition, 2021.
- M. Halstead, P. Zimmer, and C. McCool, “A cross-domain challenge with panoptic segmentation in agriculture,” The International Journal of Robotics Research, vol. 0, no. 0, p. 0, 2024.