激光除草机器人关键技术与展望

doi:10.12133/j.smartag.SA202410031

Smart Agriculture ›› 2025, Vol. 7 ›› Issue (2): 132-145.doi: 10.12133/j.smartag.SA202410031

激光除草机器人关键技术与展望

余忠义¹, 王洪宇²(), 何雄奎¹(), 赵磊², 王媛媛², 孙海²()

^1. 中国农业大学理学院，北京 100193，中国
^2. 北京市植物保护站，北京 100029，中国

收稿日期:2024-09-29 出版日期:2025-03-30
基金项目:
国家现代农业产业技术体系资助项目(CARS-28); 中国农业大学2115人才培育发展支持计划项目(2115-89052); 国家重点研发计划项目(2017YFD0700903); 国家自然科学基金(31761133019)
作者简介:
余忠义，博士研究生，研究方向为智能装备与智慧植保施药技术，E-mail： yuzhongyi6@cau.edu.cn；
王洪宇，农艺师，研究方向为植保机械与施药技术，E-mail： wanghy_1994@163.com
（余忠义、王洪宇并列第一作者）
通信作者:
1. 何雄奎，博士，教授，研究方向为植保机械与施药技术、智慧农业与无人机系统，Email： xiongkui@cau.edu.cn；2
孙海，正高级农艺师，研究方向为病虫害绿色防控技术、智能植保装备与应用技术，E-mail： 552987343@qq.com

Key Technologies and Prospects of Laser Weeding Robots

YU Zhongyi¹, WANG Hongyu²(), HE Xiongkui¹(), ZHAO Lei², WANG Yuanyuan², SUN Hai²()

^1. College of Science, China Agricultural University, Beijing 100193, China
^2. Beijing Plant Protection Station, Beijing 100029, China

Received:2024-09-29 Online:2025-03-30
Foundation items:The Earmarked Fund for China Agriculture Research System(CARS-28); The 2115 Talent Development Program of China Agricultural University(2115-89052); The National Key Research and Development Program of China(2017YFD0700903); The National Natural Science Foundation of China(31761133019)
Corresponding author:
HE Xiongkui, E-mail: xiongkui@cau.edu.cn;
SUN Hai, E-mail: 552987343@qq.com

摘要/Abstract

摘要：

【目的/意义】 农田草害制约着作物种植生产的质量和产量，激光除草技术作为一种生态环保防控田间杂草革新方法，具有环保、高效、灵活和自动化特点，可以很好地减少人力需求，降低化学药剂用量和污染，极大地缓解农田劳动力短缺、作物减产压力，在生态环境保护方面具有重要意义。 【进展】 首先介绍了激光除草技术的研究背景，概述了激光除草技术体系和作业系统，围绕激光除草机器人关键技术展开论述和讨论，涵盖杂草自动识别定位技术、机器人导航与路径规划、除草执行机构控制技术，以及整机研制等进展。最后结合国内外激光除草机器人的发展现状，综述了激光除草机器人发展目前存在的问题及未来趋势。 【结论/展望】 激光除草属于精密的智能化除草方式，是目前国内外学者研究和开发智慧农业关键技术和装备的研究热点，并取得了一系列成果，促进了除草机器人田间实际应用和推广示范。结合不同地区田间草害，未来还应开展大量的激光除草室内外杂草实验研究，以进一步验证激光田间除草的技术可行性并获取准确的激光能耗、效率和效益等数据，为激光除草的装备技术研发与应用提供支持。

关键词: 激光除草, 技术原理, 智能系统, 识别定位, 执行控制, 激光除草机器人

Abstract:

[Significance] Grass damage in farmland seriously restricts the quality and yield of crop planting and production, and promotes the occurrence of pests and diseases. Weed control is a necessary measure for high yield and high quality of crops. Currently, there are five main weed control methods: Manual, biological, thermal, mechanical, and chemical weed control. Traditional chemical weed control methods are gradually limited due to soil pollution and ecological balance disruption. Intelligent laser weeding technology, with the characteristics of environmental protection, high efficiency, flexibility, and automation, as an emerging and promising ecological and environmental protection new object control method for field weeds, has become the core direction to replace chemical weeding in recent years. The laser weeding robot is the carrier of laser weeding technology, an important manifestation of the development of modern agriculture towards intelligence and precision, and has great application and promotion value. [Progress] Laser weeding is currently a research hotspot to develop and study key technologies and equipment for smart agriculture, and has achieved a series of significant results, greatly promoting the promotion and application of intelligent laser weeding robots in the field. Laser weed control technology achieves precise weed control through thermal, photochemical, and photodynamic effects. In this article, the research background of laser weeding was introduced, its key technologies, operation system and equipment were discussed in details, covering aspects such as operating principles, system architecture, seedling, weed recognition and localization, robot navigation and path planning, as well as actuator control technologies. Then, based on the current research status of laser weeding robots, the existing problems and development trends of intelligent laser weeding robots were prospected. [Conclusion and Prospect] Based on the different field grass conditions in different regions, a large number of indoor and outdoor experiments on laser weed control should be carried out in the future to further verify the technical effectiveness and feasibility of laser field weed control, providing support for the research and application of laser weed control equipment technology. Despite facing challenges such as high costs and poor environmental adaptability, with the integration of technologies such as artificial intelligence and the Internet of Things, as well as policy support, laser weeding is expected to become an important support for sustainable agricultural development.

Key words: laser weeding, technical principles, intelligent system, identify and locate, execution control, laser weeding robot

中图分类号:

余忠义, 王洪宇, 何雄奎, 赵磊, 王媛媛, 孙海. 激光除草机器人关键技术与展望[J]. 智慧农业(中英文), 2025, 7(2): 132-145.

YU Zhongyi, WANG Hongyu, HE Xiongkui, ZHAO Lei, WANG Yuanyuan, SUN Hai. Key Technologies and Prospects of Laser Weeding Robots[J]. Smart Agriculture, 2025, 7(2): 132-145.

图/表 15

图1

图2

图3

图4

图5

图6

表1

表2

田间杂草定位技术分类及优缺点

杂草定位技术	原理	优点	缺点
静态定位	依赖外部基准或预先部署的基础设施（如卫星信号、固定标志物或物理轨道）实现杂草位置的精准确定，其核心是利用预先建立的坐标系统或基础设施完成绝对或相对定位	定位精度较高（厘米级），无需复杂环境建模，系统成本较低，抗干扰能力较强等	依赖卫星信号或固定基础，环境适应性差，动态障碍物处理能力弱，路径灵活性受限等
自主定位	通过多模态传感器实时感知环境，结合SLAM（Simultaneous Localization and Mapping）算法和惯性导航，在无需外部基准的情况下动态构建地图并估计自身位置	无需外部基础设施，适应复杂动态环境，路径规划灵活，具备障碍物规避能力等	计算资源需求高，存在累积误差（需定期校准），复杂场景下精度下降（如杂草遮挡），系统成本较高等
双目视觉定位	基于计算机的模仿人类双眼的视觉系统，通过使用两个摄像头从不同角度获取同一物体的图像，来计算物体在三维空间中的位置和姿态，包括图像获取、特征提取、立体匹配和深度计算等	非接触式测量，定位精度较高，获取三维结构、视觉信息全面，实时性较好，尤其在机器人实时避障、动态目标跟踪等方面	对环境要求较高，定位精度易受影响，视场范围受限，计算量大，系统标定复杂等
结构光定位	一种常用的三维视觉测量技术，通过向物体表面投射特定的结构光图案，然后分析反射光来获取物体的三维信息，包括结构光投射、图像采集、分析及解码	测量精度高、速度快，可同时获取区域三维模型信息，减少测量误差，对环境光干扰抵抗力较好	对物体表面材质要求高，测量范围有限，系统复杂且成本高，不适用于动态测量

表2

图7

图8

图9

图10

图11

图12

图13

参考文献 51

1	朱家健. 激光技术在农业中的应用及其展望[J]. 农机化研究, 2009, 31( 4): 222- 225.
	ZHU J J. The prospect and applation of laser technology in agriculture[J]. Journal of agricultural mechanization research, 2009, 31( 4): 222- 225.
2	ANDREASEN C, VLASSI E, SALEHAN N, et al. Laser weed seed control: Challenges and opportunities[J]. Frontiers in agronomy, 2024, 6: ID 1342372.
3	PANNACCI E, LATTANZI B, TEI F. Non-chemical weed management strategies in minor crops: A review[J]. Crop protection, 2017, 96: 44- 58.
4	孙君亮, 闫银发, 李法德, 等. 智能除草机器人的研究进展与分析[J]. 中国农机化学报, 2019, 40( 11): 73- 80.
	SUN J L, YAN Y F, LI F D, et al. Research progress and analysis of intelligent weeding robot[J]. Journal of Chinese agricultural mechanization, 2019, 40( 11): 73- 80.
5	梁雪梅, 贾鹏, 秦莉, 等. 激光在土地保护、农作物生长及害虫防治领域应用研究进展[J]. 吉林农业大学学报, 2021, 43( 2): 130- 137.
	LIANG X M, JIA P, QIN L, et al. Research progress of laser application in land protection, crop growth and pest control[J]. Journal of Jilin agricultural university, 2021, 43( 2): 130- 137.
6	周强, 郑永军, 杜文亮, 等. 探索物理植保新领域提供绿色农业新技术[J]. 农业工程, 2012, 2( S1): 16- 19.
	ZHOU Q, ZHENG Y J, DU W L, et al. Exploration to new technology of physical plant protection for green agricultural production[J]. Agricultural engineering, 2012, 2( S1): 16- 19.
7	马德彪, 唐登勇, 廖攀, 等. 一种基于单片机的激光除草装置[J]. 中国科技信息, 2023( 22): 81- 85.
	MA D B, TANG D Y, LIAO P, et al. Laser weeding device based on single chip microcomputer[J]. China science and technology information, 2023( 22): 81- 85.
8	WIELICZKA D M, WENG S, QUERRY M R. Wedge shaped cell for highly absorbent liquids: Infrared optical constants of water[J]. Applied optics, 1989, 28( 9): 1714- 1719.
9	ANDREASEN C, VLASSI E, SALEHAN N. Laser weeding: Opportunities and challenges for couch grass ( Elymus repens (L.) Gould) control[J]. Scientific reports, 2024, 14: ID 11173.
10	BAUER M V, MARX C, BAUER F V, et al. Thermal weed control technologies for conservation agriculture: A review[J]. Weed research, 2020, 60( 4): 241- 250.
11	何义川, 汤智辉, 李光新, 等. 葡萄园除草技术研究现状与发展趋势[J]. 中国农机化学报, 2018, 39( 9): 34- 37.
	HE Y C, TANG Z H, LI G X, et al. Research on current status and developing tendency of the vineyard weeding[J]. Journal of Chinese agricultural mechanization, 2018, 39( 9): 34- 37.
12	VIJAYAKUMAR V, AMPATZIDIS Y, SCHUELLER J K, et al. Smart spraying technologies for precision weed management: A review[J]. Smart agricultural technology, 2023, 6: ID 100337.
13	陈树人, 栗移新, 潘雷. 热除草技术现状和展望[J]. 安徽农业科学, 2007, 35( 33): 10695- 10697.
	CHEN S R, LI Y X, PAN L. Review and prospect of thermal weed control technologies[J]. Journal of Anhui agricultural sciences, 2007, 35( 33): 10695- 10697.
14	ROBERTS J, FLORENTINE S. Advancements and developments in the detection and control of invasive weeds: A global review of the current challenges and future opportunities[J]. Weed science, 2024, 72( 3): 205- 215.
15	陈浩, 杨亚莉. 保护性耕作模式下非化学除草技术的研究[J]. 农机化研究, 2011, 33( 9): 241- 244.
	CHEN H, YANG Y L. Study on non-chemical weeding technology under conservation tillage system[J]. Journal of agricultural mechanization research, 2011, 33( 9): 241- 244.
16	MATHIASSEN S K, BAK T, CHRISTENSEN S, et al. The effect of laser treatment as a weed control method[J]. Biosystems engineering, 2006, 95( 4): 497- 505.
17	HEISEL T, SCHOU J, ANDREASEN C, et al. Using laser to measure stem thickness and cut weed stems[J]. Weed research, 2002, 42( 3): 242- 248.
18	HEISEL T, SCHOU J, CHRISTENSEN S, et al. Cutting weeds with a CO₂ laser[J]. Weed research, 2001, 41( 1): 19- 29.
19	TRAN D, SCHOUTETEN J J, DEGIETER M, et al. European stakeholders' perspectives on implementation potential of precision weed control: The case of autonomous vehicles with laser treatment[J]. Precision agriculture, 2023, 24( 6): 2200- 2222.
20	WÖLTJEN C, HAFERKAMP H, RATH T, et al. Plant growth depression by selective irradiation of the meristem with CO₂ and diode lasers[J]. Biosystems engineering, 2008, 101( 3): 316- 324.
21	NADIMI E S, ANDERSSON K J, JØRGENSEN R N, et al. Designing, modeling and controlling a novel autonomous laser weeding system[C]// 7th World Congress on Computers in Agriculture Conference Proceedings. St. Joseph, Michigan: American Society of Agricultural and Biological Engineers, 2009: 22- 24.
22	COLEMAN G, BETTERS C, SQUIRES C, et al. Low energy laser treatments control annual ryegrass ( Lolium rigidum)[J]. Frontiers in agronomy, 2021, 2: ID 601542.
23	MARX C, BARCIKOWSKI S, HUSTEDT M, et al. Design and application of a weed damage model for laser-based weed control[J]. Biosystems engineering, 2012, 113( 2): 148- 157.
24	YU Z Y, HE X K, QI P, et al. A static laser weeding device and system based on fiber laser: Development, experimentation, and evaluation[J]. Agronomy, 2024, 14( 7): ID 1426.
25	ANDREASEN C, VLASSI E, SALEHAN N. Laser weeding of common weed species[J]. Frontiers in plant science, 2024, 15: ID 1375164.
26	GAO W-T, SU W-H. Weed management methods for herbaceous field crops: A review[J]. Agronomy, 2024, 14( 3): ID 486.
27	SRIVASTAVA R. Recent advances in weed management[J]. Current science, 2016, 110( 1): 99- 100.
28	邢钦淞, 丁素明, 薛新宇, 等. 智能田间除草机器人发展现状研究[J]. 中国农机化学报, 2022, 43( 8): 173- 181.
	XING Q S, DING S M, XUE X Y, et al. Research on the development status of intelligent field weeding robot[J]. Journal of Chinese agricultural mechanization, 2022, 43( 8): 173- 181.
29	于合龙, 周雷进雨, 徐兴梅, 等. 激光农业研究进展和展望[J]. 智能化农业装备学报(中英文), 2024( 3): 1- 13.
	YU H L, ZHOU L J Y, XU X M, et al. Advancements and prospects in laser agriculture[J]. Journal of intelligent agricultural mechanization, 2024( 3): 1- 13.
30	何东健, 乔永亮, 李攀, 等. 基于SVM-DS多特征融合的杂草识别[J]. 农业机械学报, 2013, 44( 2): 182- 187.
	HE D J, QIAO Y L, LI P, et al. Weed recognition based on SVM-DS multi-feature fusion[J]. Transactions of the Chinese society for agricultural machinery, 2013, 44( 2): 182- 187.
31	华嘉伟, 李霞, 段方涛, 等. 基于深度学习的激光除草机器人杂草识别定位方法研究[J/OL]. 天津理工大学学报, 2024: 1- 9. ( 2024-05-15).
	HUA J W, LI X, DUAN F T, et al. Weed identification and localization method of laser weeding robot based on deep learning[J/OL]. China industrial economics, 2024: 1- 9. ( 2024-05-15).
32	LYU Z X, LU A J, MA Y L. Improved YOLOv8-seg based on multiscale feature fusion and deformable convolution for weed precision segmentation[J]. Applied sciences, 2024, 14( 12): ID 5002.
33	ZHU H B, ZHANG Y Y, MU D L, et al. YOLOX-based blue laser weeding robot in corn field[J]. Frontiers in plant science, 2022, 13: ID 1017803.
34	牟丹磊. 激光除草机器人执行机构和识别算法的研究[D]. 昆明: 昆明理工大学, 2022.
	MU D L. Research on the actuator and recognitionalgorithm of laser weeding robot[D]. Kunming: Kunming University of Science and Technology, 2022.
35	吴旺旺. 激光除草机器人执行机构研究[D]. 昆明: 昆明理工大学, 2014.
	WU W W. Research on actuator for laser weeding robot[D]. Kunming: Kunming University of Science and Technology, 2014.
36	FATIMA H S, HASSAN IUL, HASAN S, et al. Formation of a lightweight, deep learning-based weed detection system for a commercial autonomous laser weeding robot[J]. Applied sciences, 2023, 13( 6): ID 3997.
37	XIONG Y, GE Y Y, LIANG Y L, et al. Development of a prototype robot and fast path-planning algorithm for static laser weeding[J]. Computers and electronics in agriculture, 2017, 142: 494- 503.
38	QIN L M, XU Z, WANG W H, et al. YOLOv7-based intelligent weed detection and laser weeding system research: targeting veronica didyma in winter rapeseed fields[J]. Agriculture, 2024, 14( 6): ID 910.
39	GABRYŚ B M, BRONDER J, KRUPANEK J. Social life cycle assessment of laser weed control system: A case study[J]. Sustainability, 2024, 16( 6): ID 2590.
40	GRIEPENTROG H W, NØRREMARK M J, SORIANO J F. Close-to-crop thermal weed control using a CO₂ laser[C]// CIGR World Congress Agricultural Engineering for a Better World. Bonn, Germany: EurAgEng, CIGR, 2006.
41	潘雷, 陈树人, 栗移新, 等. CO₂激光除草应用初步研究[J]. 农机化研究, 2008, 30( 6): 171- 173.
	PAN L, CHEN S R, LI Y X, et al. Weeding control application primary study by CO₂ laser[J]. Journal of agricultural mechanization research, 2008, 30( 6): 171- 173.
42	ZHANG H B, CAO D, ZHOU W J, et al. Laser and optical radiation weed control: A critical review[J]. Precision agriculture, 2024, 25( 4): 2033- 2057.
43	CORDOVA-CARDENAS R, EMMI L, GONZALEZ-DE-SANTOS P. Enabling autonomous navigation on the farm: A mission planner for agricultural tasks[J]. Agriculture, 2023, 13( 12): ID 2181.
44	傅雷扬, 李绍稳, 张乐, 等. 田间除草机器人研究进展综述[J]. 机器人, 2021, 43( 6): 751- 768.
	FU L Y, LI S W, ZHANG L, et al. Research progress on field weeding robots: A review[J]. Robot, 2021, 43( 6): 751- 768.
45	WANG M F, LEAL-NARANJO J A, CECCARELLI M, et al. A novel two-degree-of-freedom gimbal for dynamic laser weeding: Design, analysis, and experimentation[J]. IEEE/ASME transactions on mechatronics, 2022, 27( 6): 5016- 5026.
46	HUSSAIN A, FATIMA H S, ZIA S M, et al. Development of cost-effective and easily replicable robust weeding machine: Premiering precision agriculture in Pakistan[J]. Machines, 2023, 11( 2): ID 287.
47	王毅平, 王应宽. Carbon Robotics推出可自主根除杂草的新型激光除草机[J]. 农业工程技术, 2022, 42( 12): 117- 118.
	WANG Y P, WANG Y K. Carbon robotics launches new laser weeding machine capable of autonomously eradicating weeds[J]. Agricultural engineering technology, 2022, 42( 12): 117- 118.
48	WANG X L, HUANG J, ZHAO D J, et al. Kinematics and statics analysis of a novel 4-dof parallel mechanism for laser weeding robot[J]. INMATEH - agricultural engineering, 2016, 50( 3): 29- 38.
49	张良安, 唐锴, 赵永杰, 等. 四足激光除草机器人腿部结构参数优化[J]. 农业工程学报, 2020, 36( 2): 7- 15.
	ZHANG L A, TANG K, ZHAO Y J, et al. Optimization of leg structure parameter of quadruped laser weeding robot[J]. Transactions of thE CHINESE SOCIETY OF AGRICULTURAL ENGIneering, 2020, 36( 2): 7- 15.
50	CANICIUS M, C. R G, ERIC P. Evaluation of diode laser treatments to manage weeds in row crops[J]. Agronomy, 2022, 12( 11): ID 2681.
51	MWITTA C, RAINS G C, PROSTKO E P. Autonomous diode laser weeding mobile robot in cotton field using deep learning, visual servoing and finite state machine[J]. Frontiers in agronomy, 2024, 6: ID 1388452.

杂草识别技术	原理	优点	缺点
机器视觉技术	通过实时采集、处理、分析图像完成杂草和作物的识别和定位，可结合人工智能、杂草生物学、形态学特征进行识别和管理	可进行实时识别杂草图像，作业精度高、灵活性强、无接触识别且成本低，符合激光除草要求	采集的信息易受环境、机具影响，数据采集样本多，对激光除草设备稳定性要求高
光谱分析技术	一定光谱波段内利用农田作物、杂草、土壤等背景产生的电磁辐射反射率不同进行识别	识别迅速，不会对农田环境造成污染、破坏	光谱在农田作业时易受光照影响，波段识别精度低、易受干扰，激光除草精度不高
遥感识别技术	运用遥感图像进行杂草识别的技术，通过向靶标发射电磁波、红外线、可见光等并根据反射的作物、杂草位置信息进行判断	测量范围广，能够快速识别田间杂草	遥感图像分辨率不高，只能识别个体较大的杂草，激光除草识别率低，无法准确识别定位杂草
激光传感技术	通过X-射线、激光对射传感器等	成本低、操作方便、系统简单	精度低、受作物杂草的大小密度影响，难以做到激光精准除草

激光除草机器人关键技术与展望

Key Technologies and Prospects of Laser Weeding Robots

在线阅读

知网下载

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 51

相关文章 0

编辑推荐

Metrics

本文评价