Review of Current Robotic Approaches for Precision Weed Management.

Current robotics reports Pub Date : 2022-01-01 Epub Date: 2022-07-22 DOI:10.1007/s43154-022-00086-5

Wen Zhang, Zhonghua Miao, Nan Li, Chuangxin He, Teng Sun

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Abstract

Purpose of review: 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.

Recent findings: 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.

Summary: 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.

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当前杂草精准管理机器人方法回顾。

综述的目的：本综述的目的是概述当前杂草精准管理的机器人方法。这包括对过去 5 年中这一领域的应用情况进行调查，确定哪些主要技术领域目前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利用：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和机械除草机器人方法的进步。集成了感知、决策和控制等关键技术的自主除草机器人正在迅速崛起。摘要：本综述评估了用于精准杂草管理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。文中讨论了当前系统的局限性，并提出了未来研究方向的设想。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Current robotics reports

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