• 百种中国杰出学术期刊
  • 中国精品科技期刊
  • 中国高校百佳科技期刊
  • 中国高校精品科技期刊
  • 中国国际影响力优秀学术期刊
  • 中国科技核心期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

人工智能在农药精准施药应用中的研究进展

周长建 宋佳 向文胜

周长建, 宋佳, 向文胜. 人工智能在农药精准施药应用中的研究进展[J]. 农药学学报, 2022, 24(5): 1099-1107. doi: 10.16801/j.issn.1008-7303.2022.0069
引用本文: 周长建, 宋佳, 向文胜. 人工智能在农药精准施药应用中的研究进展[J]. 农药学学报, 2022, 24(5): 1099-1107. doi: 10.16801/j.issn.1008-7303.2022.0069
ZHOU Changjian, SONG Jia, XIANG Wensheng. Advance research on artificial intelligence based precision pesticide application[J]. Chinese Journal of Pesticide Science, 2022, 24(5): 1099-1107. doi: 10.16801/j.issn.1008-7303.2022.0069
Citation: ZHOU Changjian, SONG Jia, XIANG Wensheng. Advance research on artificial intelligence based precision pesticide application[J]. Chinese Journal of Pesticide Science, 2022, 24(5): 1099-1107. doi: 10.16801/j.issn.1008-7303.2022.0069

人工智能在农药精准施药应用中的研究进展

doi: 10.16801/j.issn.1008-7303.2022.0069
基金项目: 国家自然科学基金 (32030090)
详细信息
    作者简介:

    周长建,zhouchangjian@neau.edu.cn

    通讯作者:

    向文胜,xiangwensheng@neau.edu.cn

  • 中图分类号: S252.3

Advance research on artificial intelligence based precision pesticide application

Funds: the National Natural Science Foundation of China (32030090).
  • 摘要: 传统农药施药方式大多依靠人工经验识别单位种植面积内作物的主要病虫草害并针对该症状均匀连续喷洒农药。该方法难以根据作物的不同病虫草害种类和严重程度及时调整农药种类及用量,可能会导致不足或过量用药,喷洒在非症状区域的农药还会对生态环境造成污染。精准施药技术在平衡使用农药与保护生态安全之间给出了一种有效的解决方案,值得大力推广。近年来,人工智能技术的发展推动了精准施药相关研究。为进一步总结人工智能在农药精准施药关键技术中的应用进展,探索人工智能在农药精准施药未来发展方向,本文分析了人工智能在农药精准施药关键技术领域的应用现状,并展望了人工智能在农药精准施药应用中的发展趋势。
  • 图  1  精准施药关键技术

    Figure  1.  Key techniques in precision pesticide application

  • [1] 何雄奎. 中国植保机械与施药技术研究进展[J]. 农药学学报, 2019, 21(Z1): 921-930. doi: 10.16801/j.issn.1008-7303.2019.0089

    HE X K. Research and development of crop protection machinery and chemical application technology in China[J]. Chin J Pestic Sci, 2019, 21(Z1): 921-930. doi: 10.16801/j.issn.1008-7303.2019.0089
    [2] 傅泽田, 祁力钧, 王俊红. 精准施药技术研究进展与对策[J]. 农业机械学报, 2007, 38(1): 189-192. doi: 10.3969/j.issn.1000-1298.2007.01.048

    FU Z T, QI L J, WANG J H. Developmental tendency and strategies of precision pesticide application techniques[J]. Trans Chin Soc Agric Mach, 2007, 38(1): 189-192. doi: 10.3969/j.issn.1000-1298.2007.01.048
    [3] LIGEZA A. Artificial intelligence: a modern approach[J]. Appl Mech Mater, 2009, 263(2): 2829-2833.
    [4] 邓巍, 陈立平, 张瑞瑞, 等. 无人机精准施药关键技术综述[J]. 农业工程, 2020, 10(4): 1-10. doi: 10.3969/j.issn.2095-1795.2020.04.002

    DENG W, CHEN L P, ZHANG R R, et al. Review on key technologies for UAV precision agro-chemical application[J]. Agric Eng, 2020, 10(4): 1-10. doi: 10.3969/j.issn.2095-1795.2020.04.002
    [5] 闫晓静, 杨代斌, 薛新宇, 等. 中国农药应用工艺学20年的理论研究与技术概述[J]. 农药学学报, 2019, 21(Z1): 908-920. doi: 10.16801/j.issn.1008-7303.2019.0114

    YAN X J, YANG D B, XUE X Y, et al. Overview in theories and technologies for pesticide application in China during the last two decades[J]. Chin J Pestic Sci, 2019, 21(Z1): 908-920. doi: 10.16801/j.issn.1008-7303.2019.0114
    [6] 何雄奎. 高效植保机械与精准施药技术进展[J]. 植物保护学报, 2022, 49(1): 389-397. doi: 10.13802/j.cnki.zwbhxb.2022.2022827

    HE X K. Research and development of efficient plant protection equipment and precision spraying technology in China: a review[J]. J Plant Prot, 2022, 49(1): 389-397. doi: 10.13802/j.cnki.zwbhxb.2022.2022827
    [7] 谭文豪, 桑永英, 胡敏英, 等. 基于机器视觉的高地隙喷雾机自动导航系统设计[J]. 农机化研究, 2022, 44(1): 130-136. doi: 10.3969/j.issn.1003-188X.2022.01.022

    TAN W H, SANG Y Y, HU M Y, et al. Design of automatic navigation system for highland gap sprayer based on machine vision[J]. J Agric Mech Res, 2022, 44(1): 130-136. doi: 10.3969/j.issn.1003-188X.2022.01.022
    [8] 陈魁. 丘陵山地果园自动喷雾机的研制[D]. 重庆: 西南大学, 2017.

    CHEN K. The development of an automatic orchard spraying machine in hilly and mountainous region[D]. Chongqing: Southwest University, 2017.
    [9] 李进海, 王昱潭, 杨术明, 等. 温室自走式自动喷雾机的研制[J]. 河南农业科学, 2016, 45(7): 137-142. doi: 10.15933/j.cnki.1004-3268.2016.07.028

    LI J H, WANG Y T, YANG S M, et al. Design of self-contained automatic spray machine for greenhouse[J]. J Henan Agric Sci, 2016, 45(7): 137-142. doi: 10.15933/j.cnki.1004-3268.2016.07.028
    [10] 王宇, 王文浩, 徐凡, 等. 基于改进蚁群算法的植保无人机路径规划方法[J]. 农业机械学报, 2020, 51(11): 103-112,92. doi: 10.6041/j.issn.1000-1298.2020.11.011

    WANG Y, WANG W H, XU F, et al. Path planning approach based on improved ant colony optimization for sprayer UAV[J]. Trans Chin Soc Agric Mach, 2020, 51(11): 103-112,92. doi: 10.6041/j.issn.1000-1298.2020.11.011
    [11] 闵洁, 姜明富. 喷施作业植保无人机的轨迹优化与控制方法[J]. 农机化研究, 2022, 44(2): 28-33,38. doi: 10.3969/j.issn.1003-188X.2022.02.005

    MIN J, JIANG M F. Method of trajectory optimization and control for plant protection UAV in spraying operation[J]. J Agric Mech Res, 2022, 44(2): 28-33,38. doi: 10.3969/j.issn.1003-188X.2022.02.005
    [12] CHAI X Z, ZHENG Z S, XIAO J M, et al. Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment[J]. Aerosp Sci Technol, 2022, 121: 107287. doi: 10.1016/j.ast.2021.107287
    [13] VASANTHAN C, NGUYEN D T. Combining supervised learning and digital twin for autonomous path-planning[J]. IFAC-PapersOnLine, 2021, 54(16): 7-15. doi: 10.1016/j.ifacol.2021.10.066
    [14] LIU Y F, NOGUCHI N, LIANG L G. Development of a positioning system using UAV-based computer vision for an airboat navigation in paddy field[J]. Comput Electron Agric, 2019, 162: 126-133. doi: 10.1016/j.compag.2019.04.009
    [15] 刘立臣. 基于毫米波雷达和视觉的旋翼植保无人机自主避障研究[D]. 哈尔滨: 东北林业大学, 2020.

    LIU L C. Research on automous obstacle avoidance of rotor plant protection UAV based on millimeter-wave radar and vision[D]. Harbin: Northeast Forestry University, 2020.
    [16] LIU Y L, XU Z L, LI N, et al. A path planning algorithm for plant protection UAV for avoiding multiple obstruction areas[J]. IFAC-PapersOnLine, 2018, 51(17): 483-488. doi: 10.1016/j.ifacol.2018.08.163
    [17] RADOGLOU-GRAMMATIKIS P, SARIGIANNIDIS P, LAGKAS T, et al. A compilation of UAV applications for precision agriculture[J]. Comput Netw, 2020, 172: 107148. doi: 10.1016/j.comnet.2020.107148
    [18] 赵春江. 植物表型组学大数据及其研究进展[J]. 农业大数据学报, 2019, 1(2): 5-18. doi: 10.19788/j.issn.2096-6369.190201

    ZHAO C J. Big data of plant phenomics and its research progress[J]. J Agric Big Data, 2019, 1(2): 5-18. doi: 10.19788/j.issn.2096-6369.190201
    [19] 周长建, 宋佳, 向文胜. 基于人工智能的作物病害识别研究进展[J]. 植物保护学报, 2022, 49(1): 316-324. doi: 10.13802/j.cnki.zwbhxb.2022.2022803

    ZHOU C J, SONG J, XIANG W S. Research progresses in artificial intelligence-based crop disease identification[J]. J Plant Prot, 2022, 49(1): 316-324. doi: 10.13802/j.cnki.zwbhxb.2022.2022803
    [20] XIAO M H, DENG Z A, MA Y, et al. Ratings of rice leaf blast disease based on image processing and stepwise regression[J]. Appl Eng Agric, 2019, 35(6): 1037-1043. doi: 10.13031/aea.13131
    [21] NEELAKANTAN P. Analyzing the best machine learning algorithm for plant disease classification[J/OL]. Materials Today: Proceedings.[2022-01-10]. https://www.sciencedirect.com/science/article/pii/S2214785321052172. 2021.
    [22] TOO E C, LI Y J, NJUKI S, et al. A comparative study of fine-tuning deep learning models for plant disease identification[J]. Comput Electron Agric, 2019, 161: 272-279. doi: 10.1016/j.compag.2018.03.032
    [23] JIANG Z C, DONG Z X, JIANG W P, et al. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning[J]. Comput Electron Agric, 2021, 186: 106184. doi: 10.1016/j.compag.2021.106184
    [24] SETHY P K, BARPANDA N K, RATH A K, et al. Deep feature based rice leaf disease identification using support vector machine[J]. Comput Electron Agric, 2020, 175: 105527. doi: 10.1016/j.compag.2020.105527
    [25] 卢伟, 邵昱宁, 王玲, 等. 利用径向生长修复算法检测玉米根系表型[J]. 农业工程学报, 2021, 37(18): 195-202. doi: 10.11975/j.issn.1002-6819.2021.18.023

    LU W, SHAO Y N, WANG L, et al. Radial growth repair algorithm for maize root phenotype detection[J]. Trans Chin Soc Agric Eng, 2021, 37(18): 195-202. doi: 10.11975/j.issn.1002-6819.2021.18.023
    [26] 申志超. 基于YOLO的颗粒状农作物检测算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.

    SHEN Z C. Research on detection algorithm of granular crops based on YOLO[D]. Harbin: Harbin Institute of Technology, 2021.
    [27] 张卫正. 基于视觉与图像的植物信息采集与处理技术研究[D]. 杭州: 浙江大学, 2016.

    ZHANG W Z. Study of the plant information acquisition and processing technology based on vision and image[D]. Hangzhou: Zhejiang University, 2016.
    [28] 杨洲, 李洋, 段洁利, 等. 基于毫米波雷达的果园单木冠层信息提取[J]. 农业工程学报, 2021, 37(21): 173-182. doi: 10.11975/j.issn.1002-6819.2021.21.020

    YANG Z, LI Y, DUAN J L, et al. Extraction of the crown information of single tree in orchard based on millimeter wave radar[J]. Trans Chin Soc Agric Eng, 2021, 37(21): 173-182. doi: 10.11975/j.issn.1002-6819.2021.21.020
    [29] 李鹏. 基于激光传感器的果树冠层信息探测及风送式对靶喷雾机研发[D]. 重庆: 西南大学, 2021.

    LI P. Tree canopy information detection and air-assisted target spraying machine development based on laser sensor[D]. Chongqing: Southwest University, 2021.
    [30] 孙娜. 农田玉米高通量表型信息采集系统研究[D]. 保定: 河北农业大学, 2019.

    SUN N. Research on high-throughput phenotyping system for open field maize crops[D]. Baoding: Hebei Agricultural University, 2019.
    [31] 姜红花, 刘理民, 柳平增, 等. 面向精准喷雾的果树冠层体积在线计算方法[J]. 农业机械学报, 2019, 50(7): 120-129. doi: 10.6041/j.issn.1000-1298.2019.07.012

    JIANG H H, LIU L M, LIU P Z, et al. Online calculation method of fruit trees canopy volume for precision spray[J]. Trans Chin Soc Agric Mach, 2019, 50(7): 120-129. doi: 10.6041/j.issn.1000-1298.2019.07.012
    [32] 白雪冰, 余建树, 傅泽田, 等. 光谱成像技术在作物病害检测中的应用进展与趋势[J]. 光谱学与光谱分析, 2020, 40(2): 350-355.

    BAI X B, YU J S, FU Z T, et al. Application of spectral imaging technology for detecting crop disease information: a review[J]. Spectrosc Spectr Anal, 2020, 40(2): 350-355.
    [33] 王磊. 基于高光谱的草地冠层物种丰度估算与叶面积指数反演[D]. 南京: 南京大学, 2019.

    WANG L. Estimation of species abundance and leaf area index retrieval of grassland canopy based on hyperspectral data[D]. Nanjing: Nanjing University, 2019.
    [34] 吴伟康. 多旋翼无人机风场分布及其对作物冠层光谱信息采集影响的研究[D]. 杭州: 浙江大学, 2019.

    WU W K. Research on wind field distribution of multi-rotor UAV and its influence on spectral information acquisition of crop canopy[D]. Hangzhou: Zhejiang University, 2019.
    [35] NUTTER F W, TYLKA G L, GUAN J, et al. Use of remote sensing to detect soybean cyst nematode-induced plant stress[J]. J Nematol, 2002, 34(3): 222-231.
    [36] 张向君. 基于无人机影像的杨梅树目标识别方法研究[D]. 赣州: 江西理工大学, 2021.

    ZHANG X J. Object detection algorithm of bayberry tree based on UAV image[D]. Ganzhou: Jiangxi University of Science and Technology, 2021.
    [37] 刘佳, 王利民, 杨福刚, 等. 基于高光谱微分指数监测春玉米大斑病的研究[J]. 中国农学通报, 2019, 35(6): 143-150. doi: 10.11924/j.issn.1000-6850.casb18090024

    LIU J, WANG L M, YANG F G, et al. Spring corn leaf blight monitoring based on hyperspectral derivative index[J]. Chin Agric Sci Bull, 2019, 35(6): 143-150. doi: 10.11924/j.issn.1000-6850.casb18090024
    [38] 刘洋. 手机端植物病害识别与严重程度估计[D]. 兰州: 甘肃农业大学, 2021.

    LIU Y. Plant disease recognition and severity assessment in mobile phone[D]. Lanzhou: Gansu Agricultural University, 2021.
    [39] 韩新立. 基于网络的植物病害严重性预估系统开发[D]. 杨凌: 西北农林科技大学, 2020.

    HAN X L. Development of a web based system to practice the estimation of plant disease severity[D]. Yangling: Northwest A&F University, 2020.
    [40] 黄双萍, 齐龙, 马旭, 等. 基于高光谱成像的水稻穗瘟病害程度分级方法[J]. 农业工程学报, 2015, 31(1): 212-219. doi: 10.3969/j.issn.1002-6819.2015.01.029

    HUANG S P, QI L, MA X, et al. Grading method of rice panicle blast severity based on hyperspectral image[J]. Trans Chin Soc Agric Eng, 2015, 31(1): 212-219. doi: 10.3969/j.issn.1002-6819.2015.01.029
    [41] WSPANIALY P, MOUSSA M. A detection and severity estimation system for generic diseases of tomato greenhouse plants[J]. Comput Electron Agric, 2020, 178: 105701. doi: 10.1016/j.compag.2020.105701
    [42] AHMAD F, QIU B J, DONG X Y, et al. Effect of operational parameters of UAV sprayer on spray deposition pattern in target and off-target zones during outer field weed control application[J]. Comput Electron Agric, 2020, 172: 105350. doi: 10.1016/j.compag.2020.105350
    [43] 刘雪美, 李扬, 李明, 等. 喷杆喷雾机精确对靶施药系统设计与试验[J]. 农业机械学报, 2016, 47(3): 37-44. doi: 10.6041/j.issn.1000-1298.2016.03.006

    LIU X M, LI Y, LI M, et al. Design and test of smart-targeting spraying system on boom sprayer[J]. Trans Chin Soc Agric Mach, 2016, 47(3): 37-44. doi: 10.6041/j.issn.1000-1298.2016.03.006
    [44] 李井祝, 朱凤武. 基于PLC自动对靶喷雾控制系统的设计与试验[J]. 中国农机化学报, 2017, 38(8): 55-58. doi: 10.13733/j.jcam.issn.2095-5553.2017.08.011

    LI J Z, ZHU F W. Design and experiment of automatic targeting spraying control system based on PLC[J]. J Chin Agric Mech, 2017, 38(8): 55-58. doi: 10.13733/j.jcam.issn.2095-5553.2017.08.011
    [45] 丁宁, 欧佳顺, 董国朝. 可调节式对靶农药喷雾装置设计与试验[J]. 农机化研究, 2021, 43(5): 178-182. doi: 10.3969/j.issn.1003-188X.2021.05.030

    DING N, OU J S, DONG G C. Design and experiment of adjustable target pesticide spray device[J]. J Agric Mech Res, 2021, 43(5): 178-182. doi: 10.3969/j.issn.1003-188X.2021.05.030
    [46] 赵栋杰, 张宾, 王学雷, 等. 基于图像矩的室内喷雾机器人自动对靶研究[J]. 农业机械学报, 2016, 47(12): 22-29. doi: 10.6041/j.issn.1000-1298.2016.12.004

    ZHAO D J, ZHANG B, WANG X L, et al. Automatic target of indoor spray robot based on image moments[J]. Trans Chin Soc Agric Mach, 2016, 47(12): 22-29. doi: 10.6041/j.issn.1000-1298.2016.12.004
    [47] 王林惠, 甘海明, 岳学军, 等. 基于图像识别的无人机精准喷雾控制系统的研究[J]. 华南农业大学学报, 2016, 37(6): 23-30. doi: 10.7671/j.issn.1001-411X.2016.06.004

    WANG L H, GAN H M, YUE X J, et al. Design of a precision spraying control system with unmanned aerial vehicle based on image recognition[J]. J South China Agric Univ, 2016, 37(6): 23-30. doi: 10.7671/j.issn.1001-411X.2016.06.004
    [48] 张泉勇. 基于神经网络的植保无人机变量喷雾系统设计[D]. 广州: 华南农业大学, 2019.

    ZHANG Q Y. Design of plant protection UAV variable spray system based on neural network[D]. Guangzhou: South China Agricultural University, 2019.
    [49] 马秀博, 孙熊伟, 张德青, 等. 基于机器视觉的对靶喷雾系统时延估计方法研究[J]. 农机化研究, 2017, 39(6): 50-54. doi: 10.3969/j.issn.1003-188X.2017.06.010

    MA X B, SUN X W, ZHANG D Q, et al. Time delay estimation for automatic target spraying system based on machine vision[J]. J Agric Mech Res, 2017, 39(6): 50-54. doi: 10.3969/j.issn.1003-188X.2017.06.010
    [50] 郭爱静. 植保机运行速度与喷药量最佳匹配优化方法研究[D]. 沈阳: 沈阳工业大学, 2020.

    GUO A J. Research on best matching optimization method of plant protection machinery running speed and flow[D]. Shenyang: Shenyang University of Technology, 2020.
    [51] 戢冰. 基于ARM的变量喷药控制系统设计[D]. 北京: 中国农业机械化科学研究院, 2018.

    JI B. Design of variable rate spraying control system based on ARM[D]. Beijing: Chinese Academy of Agricultural Mechanization Science, 2018.
    [52] 张菡. 植保无人机变量喷药系统研制[D]. 泰安: 山东农业大学, 2017.

    ZHANG H. Development of variable spraying system for plant protection unmanned aerial vehicle[D]. Taian: Shandong Agricultural University, 2017.
    [53] 包佳林. 变量喷药控制系统的研究与实现[D]. 长春: 吉林农业大学, 2017.

    BAO J L. Research and implementation of the control system of variable spraying[D]. Changchun: Jilin Agricultural University, 2017.
    [54] 李宏泽. 基于CFD理论的植保无人机喷洒系统优化设计与雾滴运动特性研究[D]. 长春: 吉林大学, 2021.

    LI H Z. Optimization design of spraying system of plant protection UAV based on CFD theory and research of droplet motion[D]. Changchun: Jilin University, 2021.
    [55] 朱晓文. 基于多传感器融合的果园风送式喷雾机控制系统设计[D]. 杨凌: 西北农林科技大学, 2021.

    ZHU X W. Design of orchard air-assisted sprayer control system based on multi sensor fusion[D]. Yangling: Northwest A & F University, 2021.
    [56] 贾卫东, 李信, 周慧涛, 等. 风幕式喷杆喷雾雾滴漂移距离计算方法研究[J]. 农机化研究, 2018, 40(8): 10-15,20. doi: 10.3969/j.issn.1003-188X.2018.08.002

    JIA W D, LI X, ZHOU H T, et al. Research on the calculation method of droplet drift in air curtain boom sprayer[J]. J Agric Mech Res, 2018, 40(8): 10-15,20. doi: 10.3969/j.issn.1003-188X.2018.08.002
    [57] ZHU H, LI H Z, ZHANG C, et al. Performance characterization of the UAV chemical application based on CFD simulation[J]. Agronomy, 2019, 9(6): 308. doi: 10.3390/agronomy9060308
    [58] MENG Y H, LAN Y B, MEI G Y, et al. Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle for wheat aphids control[J]. Int J Agric Biol Eng, 2018, 11(5): 46-53.
    [59] 刘思瑶. 基于机器视觉的喷雾质量检测与评价研究[D]. 沈阳: 沈阳农业大学, 2018.

    LIU S Y. Research on spray quality evaluation indicators detection based on machine vision[D]. Shenyang: Shenyang Agricultural University, 2018.
    [60] BRANDOLI B, SPADON G, ESAU T, et al. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage[J]. Comput Electron Agric, 2021, 180: 105906. doi: 10.1016/j.compag.2020.105906
  • 加载中
图(1)
计量
  • 文章访问数:  90
  • HTML全文浏览量:  20
  • PDF下载量:  38
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-01-21
  • 录用日期:  2022-06-29
  • 网络出版日期:  2022-07-21
  • 刊出日期:  2022-10-10

目录

    /

    返回文章
    返回