Zhang Wen, Miao Zhonghua, Li Nan, He Chuangxin, Sun Teng
Intelligent Equipment and Robotics Lab, Department of Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shangda Street No. 99, Baoshan District, Shanghai, China.
Curr Robot Rep. 2022;3(3):139-151. doi: 10.1007/s43154-022-00086-5. Epub 2022 Jul 22.
The goal of this review is to provide an overview of current robotic approaches to precision weed management. This includes an investigation into applications within this field during the past 5 years, identifying which major technical areas currently preclude more widespread use, and which key topics will drive future development and utilisation.
Studies combining computer vision with traditional machine learning and deep learning are driving progress in weed detection and robotic approaches to mechanical weeding. Integrating key technologies for perception, decision-making, and control, autonomous weeding robots are emerging quickly. These effectively save effort while reducing environmental pollution caused by pesticide use.
This review assesses different weed detection methods and weeder robots used in precision weed management and summarises the trends in this area in recent years. The limitations of current systems are discussed, and ideas for future research directions are proposed.
本综述旨在概述当前用于精准杂草管理的机器人技术方法。这包括对过去5年该领域应用的调查，确定目前哪些主要技术领域阻碍了更广泛的应用，以及哪些关键主题将推动未来的发展和应用。
将计算机视觉与传统机器学习和深度学习相结合的研究正在推动杂草检测及机械除草机器人技术的进步。通过整合感知、决策和控制等关键技术，自主除草机器人正在迅速涌现。这些机器人有效地节省了人力，同时减少了农药使用造成的环境污染。
本综述评估了精准杂草管理中使用的不同杂草检测方法和除草机器人，并总结了近年来该领域的发展趋势。讨论了当前系统的局限性，并提出了未来研究方向的思路。
