大田无人农场关键技术研究现状与展望

doi:10.12133/j.smartag.SA202212005

摘要/Abstract

摘要：

无人农场是智慧农业的一种表现形式，也是建设农业强国和实现农业现代化的重要探索。无人农场以数据、知识和智能装备为核心要素，将现代信息技术与农业深度融合，实现农业全过程生产所需的信息感知、定量决策、智能控制、精准投入及个性化服务一体化。本文系统地阐述了大田无人农场的概念与总体技术架构，讨论了信息感知与智能决策、精准作业系统与装备、自动驾驶、无人化农机装备以及无人农场管控平台等五项大田无人农场的关键技术与装备，深入分析了发展中国大田无人农场亟待解决的关键科学与技术问题。以吉林省公主岭市玉米无人农场为例介绍了物联网、大数据、云计算以及人工智能等技术在玉米全程无人化生产中的具体应用及效果。最后，展望了无人农场在解决全球农业生产面临的“无人种田”等共性问题中发挥的重要作用，分析了中国发展无人农场存在的机遇和挑战，提出了中国发展无人农场的战略目标与思路。

关键词: 无人农场, 自动驾驶, 智能感知, 智能决策, 智能装备, 管控平台

Abstract:

As one of the important way for constructing smart agriculture, unmanned farms are the most attractive in nowadays, and have been explored in many countries. Generally, data, knowledge and intelligent equipment are the core elements of unmanned farms. It deeply integrates modern information technologies such as the Internet of Things, big data, cloud computing, edge computing, and artificial intelligence with agriculture to realize agricultural production information perception, quantitative decision-making, intelligent control, precise input and personalized services. In the paper, the overall technical architecture of unmanned farms is introduced, and five kinds of key technologies of unmanned farms are proposed, which include information perception and intelligent decision-making technology, precision control technology and key equipment for agriculture, automatic driving technology in agriculture, unmanned operation agricultural equipment, management and remote controlling system for unmanned farms. Furthermore, the latest research progress of the above technologies both worldwide are analyzed. Based on which, critical scientific and technological issues to be solved for developing unmanned farms in China are proposed, include unstructured environment perception of farmland, automatic drive for agriculture machinery in complex and changeable farmland environment, autonomous task assignment and path planning of unmanned agricultural machinery, autonomous cooperative operation control of unmanned agricultural machinery group. Those technologies are challenging and absolutely, and would be the most competitive commanding height in the future. The maize unmanned farm constructed in the city of Gongzhuling, Jilin province, China, was also introduced in detail. The unmanned farms is mainly composed of information perception system, unmanned agricultural equipment, management and controlling system. The perception system obtains and provides the farmland information, maize growth, pest and disease information of the farm. The unmanned agricultural machineries could complete the whole process of the maize mechanization under unattended conditions. The management and controlling system includes the basic GIS, remote controlling subsystem, precision operation management subsystem and working display system for unmanned agricultural machineries. The application of the maize unmanned farm has improved maize production efficiency (the harvesting efficiency has been increased by 3－4 times) and reduced labors. Finally, the paper summarizes the important role of the unmanned farm technology were summarized in solving the problems such as reduction of labors, analyzes the opportunities and challenges of developing unmanned farms in China, and put forward the strategic goals and ideas of developing unmanned farm in China.

Key words: unmanned farm, automatic driving, intelligent perception, intelligent decision-making, intelligent equipment, management and control platform

中图分类号:

F324.1

尹彦鑫, 孟志军, 赵春江, 王昊, 温昌凯, 陈竞平, 李立伟, 杜经纬, 王培, 安晓飞, 尚业华, 张安琪, 颜丙新, 武广伟. 大田无人农场关键技术研究现状与展望[J]. 智慧农业(中英文), 2022, 4(4): 1-25.

YIN Yanxin, MENG Zhijun, ZHAO Chunjiang, WANG Hao, WEN Changkai, CHEN Jingping, LI Liwei, DU Jingwei, WANG Pei, AN Xiaofei, SHANG Yehua, ZHANG Anqi, YAN Bingxin, WU Guangwei. State-of-the-art and Prospect of Research on Key Technical for Unmanned Farms of Field Corp[J]. Smart Agriculture, 2022, 4(4): 1-25.

参考文献

1	李道亮, 李震. 无人农场系统分析与发展展望[J]. 农业机械学报, 2020, 51(7): 1-12.
1	LI D, LI Z. System analysis and development prospect of unmanned farming[J]. Transactions of the CSAM, 2020, 51(7): 1-12.
2	HFF.Hands Free Farm successfully completes first drilling operation[EB/OL].[2022-12-01]..
3	罗锡文, 廖娟, 胡炼, 等. 我国智能农机的研究进展与无人农场的实践[J]. 华南农业大学学报, 2021, 42(6): 8-17.
3	LUO X, LIAO J, HU L, et al. Research progress of intelligent agricultural machinery and practice of unmanned farm in China[J]. Journal of South China Agricultural University, 2021, 42(6): 8-17.
4	和贤桃. 处方图式变量播种控制系统研究与试验[D]. 北京: 中国农业大学, 2018.
4	HE X. Research and experiment on map-based control system of variable rate seeding[D]. Beijing: China Agricultural University, 2018.
5	张东兴, 刘江, 杨丽, 等. 基于VIS-NIR的播种沟内土壤水分测量传感器研究[J]. 农业机械学报, 2021, 52(2): 218-226.
5	ZHANG D, LIU J, YANG L, et al. Soil moisture measurement sensor research in seeding ditch based on VIS-NIR[J]. Transactions of the CSAM, 2021, 52(2): 218-226.
6	朱文静, 冯展康, 吴抒航, 等. 机载非接触式近红外土壤墒情检测系统研制[J]. 农业工程学报, 2022, 38(9): 73-80.
6	ZHU W, FENG Z, WU S, et al. Development of an airborne non-contact near-infrared soil moisture detection system[J]. Transactions of the CSAE, 2022, 38(9): 73-80.
7	WEATHERLY E, BOWERS J. Automatic depth control of a seed planter based on soil drying front sensing[J]. Transactions of the ASAE, 1997, 40(2): 295-305.
8	PRICE R, GAULTNEY L. Soil moisture sensor for predicting seed planting depth[J]. Transactions of the ASAE, 1993, 36(6). 1703-1711.
9	MOUAZEN AM, JOSSE D, HERMAN R. Towards development of on-line soil moisture content sensor using a fibre-type NIR spectrophotometer[J]. Soil and Tillage Research, 2005, 80(1-2): 171-183.
10	杨玮, 韩雨, 李民赞, 等. 基于数字示波器的车载式土壤电导率检测系统研究[J]. 农业机械学报, 2020, 51(S2): 395-401.
10	YANG W, HAN Y, LI M, et al. Vehicle mounted soil conductivity detection system based on digital oscilloscope[J]. Transactions of the CSAM, 2020, 51(S2): 395-401.
11	杨文奇. 大田土壤电导率快速检测系统研究[D]. 乌鲁木齐: 新疆农业大学, 2021.
11	YANG W. Research on the rapid detection system of field soil electrical conductivity[D]. Urumqi: Xinjiang Agricultural University.
12	Geonics Limited: EM 38-MK2. [EB/OL]. [2022-12-02]..
13	Veris Technologies: Veris 3150[EB/OL]. [2022-12-03]. .
14	ESS DR. Implementing site-specific management: Map-versus sensor-based variable rate application[EB/OL].[2022-12-10]. .
15	Precision Planting: Precision Planting SmartFirmer[EB/OL]. [2022-11-09]. .
16	唐海涛, 孟祥添, 苏循新, 等. 基于CARS算法的不同类型土壤有机质高光谱预测[J]. 农业工程学报, 2021, 37(2): 105-113.
16	TANG H, HENG X, SU X, et al. Hyperspectral prediction on soil organic matter of different types using CARS algorithm[J]. Transactions of the CSAE, 2021, 37(2): 105-113.
17	XIE S, LI Y, WANG X, et al. Research on estimation models of the spectral characteristics of soil organic matter based on the soil particle size[J]. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, 2021, 260(2): ID 119963.
18	崔玉露, 杨玮, 王炜超, 等. 基于光谱学原理的便携式土壤有机质检测仪设计与实验[J]. 农业机械学报, 2021, 52(S1): 323-328.
18	CUI Y, YANG W, WANG W, et al. Design and experiment of portable soil organic matter detector based on spectroscopy principle[J]. Transactions of the CSAM, 2021, 52(S1): 323-328.
19	陈建国. 小麦精量播种与精准控制智能决策系统研究与设计[D]. 上海: 上海交通大学, 2019.
19	CHEN J. Research and design of wheat precision seeding and precision control and intelligent designed system[D]. Shangghai: Shanghai Jiao Tong University, 2019.
20	杨丽, 杜兆辉, 张东兴, 等. 一种基于动态处方图的变量播种控制系统及方法: CN114568077A[P]. 2022.
21	杨贵军, 李长春, 于海洋, 等. 农用无人机多传感器遥感辅助小麦育种信息获取[J]. 农业工程学报, 2015, 31(21): 184-190.
21	YANG G, LI C, YU H, et al. UAV based multi-load remote sensing technologies for wheat breeding information acquirement[J]. Transactions of the CSAE, 2015, 31(21): 184-190.
22	孙红, 邢子正, 张智勇, 等. 基于RED-NIR的主动光源叶绿素含量检测装置设计与试验[J]. 农业机械学报, 2019, 50(S1): 175-181.
22	SUN H, XING Z, ZHANG Z. Design and experiment of chlorophyll content detection device for active light source based on RED-NIR[J]. Transactions of the CSAM, 2019, 50(S1): 175-181.
23	林维潘, 李怀民, 倪军, 等. 基于便携式三波段作物生长监测仪的水稻长势监测[J]. 农业工程学报, 2020, 36(20): 203-208.
23	LIN W, LI H, NI J. Monitoring rice growth based on a portable three-band instrument for crop growth monitoring and diagnosis[J]. Transactions of the CSAE, 2020, 36(20): 203-208.
24	矫雷子, 董大明, 赵贤德, 等. 基于调制近红外反射光谱的土壤养分近场遥测方法研究[J]. 智慧农业(中英文), 2020, 2(2): 59-66.
24	JIAO L, DONG D, ZHAO X, et al. Near-field telemetry detection of soil nutrient based on modulated near-infrared reflectance spectrum[J]. Smart Agriculture, 2020, 2(2): 59-66.
25	周鹏, 李民赞, 杨玮, 等. 基于近红外漫反射测量的车载式原位土壤参数检测仪开发[J]. 光谱学与光谱分析, 2020, 40(9): 2856-2861.
25	ZHOU P, LI M, YANG W, et al. Development of vehicle-mounted in-situ soil parameters detector based on NIR diffuse reflection[J]. Spectroscopy and Spectral Analysis, 2020, 40(9): 2856-2861.
26	LUKINA E, FREEMAN K, WYNN K, et al. Nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake[J]. Journal of Plant Nutrition, 2001, 24(6): 885-898.
27	WALSH OS, KLATT AR, SOLIE JB, et al. Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.)[J]. Precision Agriculture, 2013, 14(3): 343-356.
28	SHI Y, MAN C, WANG X. Efficiency analysis and evaluation of centrifugal variable-rate fertilizer spreading based on real-time spectral information on rice[J]. Computers and Electronics in Agriculture, 2023, 204:ID 107505.
29	CAO Q, MIAO Y, WANG H, et al. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor[J]. Field Crops Research, 2013, 154: 133-144.
30	何雄奎. 中国精准施药技术和装备研究现状及发展建议[J]. 智慧农业(中英文), 2020, 2(1): 133-146.
30	HE X. Research progress and developmental recommendations on precision spraying technology and equipment in China[J]. Smart Agriculture, 2020, 2(1): 133-146.
31	潘冉冉, 骆一凡, 王昌, 等. 高光谱成像的油菜和杂草分类方法[J]. 光谱学与光谱分析, 2017, 37(11): 3567-3572.
31	PAN R, LUO Y, WANG C, et al. Classifications of oilseed rape and weeds based on hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2017, 37(11): 3567-3572.
32	周巧黎, 马丽, 曹丽英, 等. 基于改进轻量级卷积神经网络MobileNetV3的番茄叶片病害识别[J]. 智慧农业(中英文), 2022, 4(1): 47-56.
32	ZHOU Q, LI M, CAO L, et al. Identification of tomato leaf diseases based on improved lightweight convolutional neural networks MobileNetV3[J]. Smart Agriculture, 2022, 4(1): 47-56.
33	REDDY T V, REKHA K S. Deep leaf disease prediction framework (DLDPF) with transfer learning for automatic leaf disease detection[C]// 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). Piscataway, New York, USA: IEEE, 2021.
34	BRAVO C, MOSHOU D, WEST J, et al. Early disease detection in wheat fields using spectral reflectance[J]. Biosystems Engineering, 2003, 84(2): 137-145.
35	LIU Z, WU H, HUANG J. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[J]. Computers and Electronics in Agriculture, 2010, 72(2): 99-106.
36	STOORVOGEL J, KOOISTRA L, BOUMA J. Managing soil variability at different spatial scales as a basis for precision agriculture[M]. Boca Raton, Florida: CRC Press, 2015.
37	LI Y, YAN B, YU Y, et al. Global overview of research progress and development of precision maize planters[J]. International Journal of Agricultural and Biological Engineering, 2016, 9(1): 9-26.
38	PLANTINGPRECISION.Monitoring & Measurement[EB/OL].[2022-11-30]. .
39	大马力农机. 能播"拐子苗"的格兰智能电驱播种机,开始在黑龙江垦区应用[EB/OL]. [2022-11-30]. .
40	HE X, CUI T, ZHANG D, et al. Development of an electric-driven control system for a precision planter based on a closed-loop PID algorithm[J]. Computers and Electronics in Agriculture, 2017, 136: 184-192.
41	HE X, DING Y, ZHANG D, et al. Development of a variable-rate seeding control system for corn planters Part I: Design and laboratory experiment[J]. Computers and Electronics in Agriculture, 2019, 162: 318-327.
42	HE X, ZHANG D, YANG L, et al. Design and experiment of a GPS-based turn compensation system for improving the seeding uniformity of maize planter[J]. Computers and Electronics in Agriculture, 2021, 187:ID 106250.
43	廖庆喜, 邓在京, 黄海东. 高速摄影在精密排种器性能检测中的应用[J]. 华中农业大学学报, 2004(5): 570-573.
43	LIAO Q, DENG Z, HUANG H. Application of the high speed photography checking the precision metering performances[J]. Journal of Huazhong Agricultural, 2004(5): 570-573.
44	史智兴, 高焕文. 玉米精播机排种监测报警装置[J]. 中国农业大学学报, 2003(2): 18-20.
44	SHI Z, GAO H. A seeding monitoring & alarming device for corn precision seeder[J]. Journal of China Agricultural University, 2003(2): 18-20.
45	周利明, 王书茂, 张小超, 等. 基于电容信号的玉米播种机排种性能监测系统[J]. 农业工程学报, 2012, 28(13): 16-21.
45	ZHOU L, WANG S, ZHANG X, et al. Seed monitoring system for corn planter based on capacitance signal[J]. Transactions of the CSAE, 2012, 28(13): 16-21.
46	赵博, 樊学谦, 周利明, 等. 气流输送播种机压电式流量传感器设计与试验[J]. 农业机械学报, 2020, 51(8): 55-61.
46	ZHAO B, FAN X, ZHOU L, et al. Design and test of piezoelectric flow sensor for pneumatic seeder[J]. Transactions of the CSAM, 2020, 51(8): 55-61.
47	付卫强, 董建军, 梅鹤波, 等. 玉米播种单体下压力控制系统设计与试验[J]. 农业机械学报, 2018, 49(6): 68-77.
47	FU W, DONG J, MEI H, et al. Design and test of maize seeding unit downforce control system[J]. Transactions of the CSAM, 2018, 49(6): 68-77.
48	高原源, 王秀, 杨硕, 等. 播种机气动式下压力控制系统设计与试验[J]. 农业机械学报, 2019, 50(7): 19-29.
48	GAO Y, WANG X, YANG S, et al. Design and test of pneumatic downforce control system for planting[J]. Transactions of the CSAM, 2019, 50(7): 19-29.
49	LIU Q, CUI T, ZHANG D, et al. Design and experimental study of seed precise delivery mechanism for high-speed maize planter[J]. International Journal of Agricultural and Biological Engineering, 2018, 11(4): 81-87.
50	AMAZONE. 140 years of Amazone and 140 years of innovation[EB/OL]. [2022-12-07]. .
51	雷小龙, 廖宜涛, 张闻宇, 等. 油麦兼用气送式集排器输种管道气固两相流仿真与试验[J]. 农业机械学报, 2017, 48(3): 57-68.
51	LEI X, LIAO Y, ZHANG W, et al. Simulation and experiment of gas-solid flow in seed conveying tube for rapeseed and wheat[J]. Transactions of the CSAM, 2017, 48(3): 57-68.
52	张晓辉, 王永振, 仉利, 等. 小麦气力集排器排种分配系统设计与试验[J]. 农业机械学报, 2018, 49(3): 59-67.
52	ZHANG X, WANG Y, ZHANG L. Design and experiment of wheat pneumatic centralized seeding distributing system[J]. Transactions of the CSAM, 2018, 49(3): 59-67.
53	袁文胜, 冯玉岗, 金诚谦, 等. 播种机播种量、施肥量自动标定装置及方法: CN202011541466.4[P]. 2021-04-20.
54	孟志军, 赵春江, 刘卉, 等. 基于处方图的变量施肥作业系统设计与实现[J]. 江苏大学学报(自然科学版), 2009, 30(4): 338-342.
54	MENG Z, ZHAO C, LIU H, et al. Development and performance assessment of map-based variable rate granule application system[J]. Journal of Jiangsu University(Natural Science Edition), 2009, 30(4): 338-342.
55	付卫强, 孟志军, 黄文倩, 等. 基于CAN总线的变量施肥控制系统[J]. 农业工程学报, 2008, 24(S2): 127-132.
55	FU W, MENG Z, HUANG W, et al. Variable rate fertilizer control system based on CAN bus[J]. Transactions of the CSAE, 2008, 24(S2): 127-132.
56	张季琴, 刘刚, 胡号, 等. 排肥单体独立控制的双变量施肥控制系统研制[J]. 农业工程学报, 2021, 37(10): 38-45.
56	ZHANG J, LIU G, HU H, et al. Development of bivariate fertilizer control system via independent control of fertilizing unit[J]. Transactions of the CSAE, 2021, 37(10): 38-45.
57	刘刚, 胡号, 黄家运, 等. 变量施肥滞后时间检测与位置修正方法研究[J]. 农业机械学报, 2021, 52(S1): 74-80.
57	LIU G, HU H, HUANG J, et al. Lag Time detection and position correction method of variable rate fertilization[J]. Transactions of the CSAM, 2021, 52(S1): 74-80.
58	ZHANG J, LIU G, HUANG J, et al. A study on the time lag and compensation of a variable-rate fertilizer applicator[J]. Applied Engineering in Agriculture, 2021, 37(1): 43-52.
59	王大可, 衣淑娟, 赵雪, 等. 气吸精播机施肥量无线计量监测系统的研究[J]. 农机化研究, 2017, 39(3): 84-88.
59	WANG D, YI S, ZHAO X, et al. Research on precise seeder wireless measurement monitoring system[J]. Journal of Agricultural Mechanization Research, 2017, 39(3): 84-88.
60	杨柳, 杨莎, 杨璎珞. 一种电动玉米精量播种机施肥监测装置设计[J]. 南方农机, 2022, 53(15): 9-11.
61	陈金成, 张惠, 汤智辉, 等. 分层施肥机作业监测系统的设计与田间试验[J]. 江苏农业科学, 2021, 49(20): 205-210.
61	CHEN J, ZHANG H, TANG Z, et al. Design and field experiment of operation monitoring system of layered fertilization device[J]. Jiangsu Agricultural Sciences, 2021, 49(20): 205-210.
62	孙文斌, 王荣, 高荣华, 等. 基于可见光谱和改进注意力的农作物病害识别[J]. 光谱学与光谱分析, 2022, 42(5): 1572-1580.
62	SUN W, WANG R, GAO R, et al. Crop disease recognition based on visible spectrum and improved attention module[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1572-1580.
63	TRONTHMANNA W, RUCKELSHAUSENA A, HERTZBERG J, et al. Plant classification with In-Field-Labeling for crop/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system[J]. Computers and Electronics in Agriculture, 2017, (134): 79-93.
64	CAO Y, YUAN P, XU H, et al. Detecting asymptomatic infections of rice bacterial leaf blight using hyperspectral imaging and 3-dimensional convolutional neural network with spectral dilated convolution[J]. Frontiers in Plant Science, 2022, 13: ID 963170.
65	SPEEDTUBE.The SprayKit: Two worksteps in one[EB/OL]. [2022-11-19]. .
66	MEGA米格. 悬挂式喷雾机[EB/OL]. 2022-11-28..
67	CHEN Y, ZHANG S, MAO E, et al. Height stability control of a large sprayer body based on air suspension using the sliding mode approach[J]. Information Processing in Agriculture, 2020, 7(1): 20-29.
68	张春凤, 翟长远, 赵学观, 等. 对靶喷药系统压力波动特性的试验研究[J]. 农业工程学报, 2022, 38(18): 31-39.
68	ZHANG C, ZHAI C, ZHAO X, et al. Experimental study on the pressure fluctuation characteristics of target spray system[J]. Transactions of the CSAE, 2022, 38(18): 31-39.
69	JEON H, ZHU H, DERKSEN R, et al. Evaluation of ultrasonic sensor for variable-rate spray applications[J]. Computers and Electronics in Agriculture, 2011, 75(1): 213-221.
70	WEI D, HE X, DING W. Droplet size and spray pattern characteristics of PWM-based continuously variable spray[J]. International Journal of Agricultural and Biological Engineering, 2009, 2(1): 8-18.
71	WEN S, HAN J, NING Z, et al. Numerical analysis and validation of spray distributions disturbed by quad-rotor drone wake at different flight speeds[J]. Computers and Electronics in Agriculture, 2019, 166: ID 105036.
72	朱昱, 潘耀忠, 张杜娟. 基于深度卷积网络和分水岭分割的耕地地块识别方法[J]. 地球信息科学学报, 2022, 24(12): 2389-2403.
72	ZHU Y, PAN Y, ZHANG D. An agriculture parcel identification method based on convolutional neural network and watershed segmentation[J]. Journal of Geo-information Science, 2022, 24(12): 2389-2403.
73	郑明雪, 罗治情, 陈娉婷, 等. 基于改进Mean Shift的无人机农业应用遥感影像地块边界提取[J]. 湖北农业科学, 2021, 60(23): 153-156.
73	ZHENG M, LUO Z, CHEN P, et al. Farmland boundary extraction from UAV remote sensing images for agricultural applications based on improved Mean Shift[J]. Hubei Agricultural Sciences, 2021, 60(23): 153-156.
74	吴晗, 林晓龙, 李曦嵘, 等. 面向农业应用的无人机遥感影像地块边界提取[J]. 计算机应用, 2019, 39(1): 298-304.
74	WU H, LIN X, LI X, et al. Land parcel boundary extraction of UAV remote sensing image in agricultural application[J]. Journal of Computer Applications, 2019, 39(1): 298-304.
75	宋建涛, 李大军, 郭丙轩. 基于遥感影像的地块边界半自动提取[J]. 北京测绘, 2019, 33(10): 1171-1175.
75	SONG J, LI D, GUO B. Semi-automatic boundary extraction based on gaussian model[J]. Beijing Surveying and Mapping, 2019, 33(10): 1171-1175.
76	刘东, 欧阳安, 陈聪, 等. 基于归一化植被指数的农田边界识别方法[J]. 江苏农业科学, 2022, 50(11): 196-201.
76	LIU D, OUYANG A, CHEN C, et al. Farmland boundary recognition method based on NDVI[J]. Jiangsu Agricultural Sciences, 2022, 50(11): 196-201.
77	ZHANG M, LI L, WANG A, et al. A novel farmland boundaries extraction and obstacle detection method based on unmanned aerial vehicle[C]// 2019 ASABE Annual International Meeting. St. Joseph, Michigan, USA: the American Society of Agricultural and Biological Engineers, 2019.
78	王侨, 刘卉, 杨鹏树, 等. 基于机器视觉的农田地头边界线检测方法[J]. 农业机械学报, 2020, 51(5): 18-27.
78	WANG Q, LIU H, YANG P, et al. Detection method of headland boundary line based on machine vision[J]. Transactions of the CSAM, 2020, 51(5): 18-27.
79	乔榆杰, 杨鹏树, 孟志军, 等. 面向自动驾驶农机的农田地头边界线检测系统[J]. 农机化研究, 2022, 44(11): 24-30.
79	QIAO Y, YANG P, MENG Z, et al. Detection system of headland boundary line based on machine vision[J]. Journal of Agricultural Mechanization Research, 2022, 44(11): 24-30.
80	阚韬, 辜彬. 农田边界检测方法及装置: CN108596937A[P]. 2018.
81	JIANG G, WANG X, WANG Z, et al. Wheat rows detection at the early growth stage based on Hough transform and vanishing point[J]. Computers and Electronics in Agriculture, 2016, 123: 211-223.
82	姜国权, 杨小亚, 王志衡, 等. 基于图像特征点粒子群聚类算法的麦田作物行检测[J]. 农业工程学报, 2017, 33(11): 165-170.
82	JIANG G, YANG X, WANG Z, et al. Crop rows detection based on image characteristic point and particle swarm optimization-clustering algorithm[J]. Transactions of the CSAE, 2017, 33(11): 165-170.
83	孟庆宽, 刘刚, 张漫, 等. 基于线性相关系数约束的作物行中心线检测方法[J]. 农业机械学报, 2013, 44(S1): 216-223.
83	MENG Q, LIU G, ZHANG M, et al. Crop rows detection based on constraint of liner correlation coefficient[J]. Transactions of the CSAM, 2013, 44(S1): 216-223.
84	王侨, 孟志军, 付卫强, 等. 基于机器视觉的玉米苗期多条作物行线检测算法[J]. 农业机械学报, 2021, 52(4): 208-220.
84	WANG Q, MENG Z, FU W. Detection algorithm of multiple crop row lines based on machine vision in maize seedling stage[J]. Transactions of the CSAM, 2021, 52(4): 208-220.
85	CHOI K, HAN S, HAN S, et al. Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields[J]. Computers and Electronics in Agriculture, 2015, 113: 266-274.
86	HU W, JIANG P, XIAO F, et al. Identifying rice seedling bands based on slope virtualization clustering[J]. Computers and Electronics in Agriculture, 2020, 175: ID 105470.
87	ZHAI Z, ZHU Z, DU Y, et al. Multi-crop-row detection algorithm based on binocular vision[J]. Biosystems Engineering, 2016, 150: 89-103.
88	GASPARINO M, HIGUTI V, VELASQUEZ A, et al. Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(11): 1-10.
89	YANG L, NOGUCHI N. Human detection for a robot tractor using omni-directional stereo vision[J]. Computers and Electronics in Agriculture, 2012, 89: 116-125.
90	LI Y, LIDA M, SUYAMA T, et al. Implementation of deep-learning algorithm for obstacle detection and collision avoidance for robotic harvester[J]. Computers and Electronics in Agriculture, 2020 174: ID 105499.
91	尚业华, 张光强, 孟志军, 等. 基于欧氏聚类的三维激光点云田间障碍物检测方法[J]. 农业机械学报, 2022, 53(1): 23-32.
91	SHANG Y, ZHANG G, MENG Z, et al. Field obstacle detection method of 3D Lidar point cloud based on euclidean clustering[J]. Transactions of the CSAM, 2022, 53(1): 23-32.
92	郎朗, 冯晓蓉, 刘浪. 基于LiDAR的农田地形环境三维重建方法设计与研究[J]. 中国农机化学报, 2020, 41(1): 155-160.
92	LANG L, FENG X, LIU L. Design and research of three-dimensional reconstruction method of farmland terrain environment based on LiDAR[J]. Journal of Chinese Agricultural Mechanization, 2020, 41(1): 155-160.
93	ZENG L, FENG J, HE L. Semantic segmentation of sparse 3D point cloud based on geometrical features for trellis-structured apple orchard[J]. Biosystems Engineering, 2020, 196, 46-55.
94	ROVIRA-MAS F, REID J, HAN S. Obstacle detection using stereo vision to enhance safety of autonomous machines[J]. Transactions of the ASAE, 2005, 48(6): 2389-2397.
95	YIN X, NOGUCHI N, ISHII K. Development of an obstacle avoidance system for a field robot using a 3D camera[J]. Engineering in Agriculture, Environment and Food, 2013, 6(2): 41-47.
96	ADAM K. Precision guidance system for agricultural vehicles: EP3534684B1[P]. 2021.
97	张健. 农机自动驾驶路径规划及控制方法[D]. 哈尔滨: 哈尔滨理工大学, 2021.
97	ZHANG J. Path planning and control method for automatic driving of agricultural machinery[D]. Harbin: Harbin University of Science and Technolog, 2021.
98	NILSSON R S, ZHOU K. Method and bench-marking framework for coverage path planning in arable farming[J]. Biosystems Engineering, 2020, 198: 248-265.
99	林乙蘅. 基于多源信息融合的智能农机路径规划和路径跟踪研究[D]. 南京: 东南大学, 2018.
99	LIN Y. Path planning and path tracking of intelligent agricultural vehicle based on multi-source information fusion[D]. Nanjing: Southeast University, 2018.
100	唐小涛. 智能水稻穴直播机导航控制系统的研究[D]. 上海: 上海交通大学, 2018.
100	TANG X. Research on navigation control system of rice planter[D]. Shanghai: Shanghai JiaoTong University, 2018.
101	BOCHTIS D, SORENSEN C, BUSATO P, et al. Advances in agricultural machinery management: A review[J]. Biosystems Engineering, 2014, 126: 69-81.
102	罗承铭, 熊陈文, 黄小毛, 等. 四边形田块下油菜联合收获机全覆盖作业路径规划算法[J]. 农业工程学报, 2021, 37(9): 140-148.
102	LUO C, XIONG C, HUANG X. Coverage operation path planning algorithms for the rape combine harvester in quadrilateral fields[J]. Transactions of the CSAE, 2021, 37(9): 140-148.
103	PLESSEN M G, BEMPORAD A. Shortest path computations under trajectory constraints for ground vehicles within agricultural fields[C]// IEEE International Conference on Intelligent Transportation Systems. Piscataway, New York, USA: IEEE, 2016: 1733-1738.
104	KHAN A, NOREEN I, RYU H, et al. Online complete coverage path planning using two-way proximity search[J]. Intelligent Service Robotics, 2017, (3): 229-240.
105	LIU H, MA K, HUANG W, et al. Sensor-based complete coverage path planning in dynamic environment for cleaning robot[J]. CAAI Transactions on Intelligence Technology, 2018, (1): 65-72.
106	袁加红. 水稻插秧机最优覆盖路径规划研究[D]. 合肥: 安徽农业大学, 2016.
106	YUAN J. Research on optimal complete coverage path planning for rice transplanter[D]. Hefei: Anhui Agricultural University, 2016.
107	WANG H, NOGUCHI N. Adaptive turning control for an agricultural robot tractor[J]. International Journal of Agricultural and Biological Engineering, 2018, 11(6): 113-119.
108	翟卫欣, 王东旭, 陈智博, 等. 无人驾驶农机自主作业路径规划方法[J]. 农业工程学报, 2021, 37(16): 1-7.
108	ZHAI W, WANG D, CHEN Z, et al. Autonomous operation path planning method for unmanned agricultural machinery[J]. Transactions of the CSAE, 2021, 37(16): 1-7.
109	刘一帆, 施光林, 陈耀峰, 等. 基于纯跟踪模型的模糊路径跟踪控制方法[J]. 机械设计与研究, 2022, 38(3): 136-140.
109	LIU Y, SHI G, CHEN Y, et al. Fuzzy path following control method based on pure pursuit model[J]. Machine Design ＆ Research, 2022, 38(3): 136-140.
110	NAGASAKA Y, SAITO H, TAMAKIK, et al. An autonomous rice transplanter guided by global positioning system and inertial measurement unit[J]. Journal of Field Robotics, 2009, (No.6-7): 537-548.
111	NETTO M, BLOSSEVILLE J, LUSETTI B, et al. A new robust control system with optimized use of the lane detection data for vehicle full lateral control under strong curvatures[C]// 2006 IEEE Intelligent Transportation Systems Conference (ITSC 2006). Piscataway, New York, USA: IEEE, 2006.
112	AL-MAYYAHI A, WANG W, BIRCH P, et al. Design of fractional-order controller for trajectory tracking control of a non-holonomic autonomous ground vehicle[J]. Journal of Control, Automation and Electrical Systems, 2016(1): 29-42.
113	GARCíA C E, PRETT D M, Morari M. Model predictive control: Theory and practice—A survey[J]. Automatica, 1989, 25(3): 335-348.
114	WANG H, NOGUCHI N. Real-time states estimation of a farm tractor using dynamic mode decomposition[J]. GPS Solutions, 2021, 25(1): 1-12.
115	张万枝, 白文静, 吕钊钦, 等. 线性时变模型预测控制器提高农业车辆导航路径自动跟踪精度[J]. 农业工程学报, 2017, 33(13): 104-111.
115	ZHANG W, BAI W, LYU Z, et al. Linear time-varying model predictive controller improving precision of navigation path automatic tracking for agricultural vehicle[J]. Transactions of the CSAE, 2017, 33(13): 104-111.
116	WANG H, NIU W, FU W, et al. A low-cost tractor navigation system with changing speed adaptability[C]//2021 33rd Chinese Control and Decision Conference (CCDC). Piscataway, New York, USA: IEEE, 2021.
117	WU G, TAN Y, ZHENG Y, et al. Walking goal line detection based on DM6437 on harvesting robot[C]// International Conference on Computer and Computing Technologies in Agriculture. Berlin, German: Springer, 2011.
118	ZHAO T, NING N, YANG L, et al. Development of uncut crop edge detection system based on laser rangefinder for combine harvesters[J]. International Journal of Agricultural and Biological Engineering, 2016, (2): 21-28.
119	ZHANG Z, CAO R, PENG C, et al. Cut-edge detection method for rice harvesting based on machine vision[J]. Agronomy, 2020, 10(4): ID 590.
120	JIANG W, WANG P, CAO Q. Navigation path curve extraction method based on depth image for combine harvester[C]// 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). Piscataway, New York, USA: IEEE, 2020.
121	SNIDER JM. Automatic steering methods for autonomous automobile path tracking[EB/OL].[2022-11-08]..
122	COEN T, VANRENTERGHEM A, SAEYS W, et al. Autopilot for a combine harvester[J]. Computers and Electronics in Agriculture, 2008, 63(1): 57-64.
123	赵腾. 基于激光扫描的联合收割机自动导航方法研究[D]. 杨凌: 西北农林科技大学, 2017.
123	ZHAO T. Development of automatic navigation method for combine harvester based on laser scanner[D]. Yangling: Northwest A&F University, 2017.
124	王立辉, 石佳晨, 王乐刚, 等. 智能收获机定位和自适应路径追踪方法[J]. 导航定位学报, 2020, 8(6): 29-36.
124	WANG L, SHI J, WANG L. A location and adaptive path tracking methods for intelligent harvesters[J]. Journal of Navigation and Positioning, 2020, 8(6): 29-36.
125	LIDA M, KUDOU M, ONO K, et al. Automatic following control for agricultural vehicle[C]// 6th International Workshop on Advanced Motion Control. Piscataway, New York, USA: IEEE, 2000.
126	NOGUCHI N, WILL J, REID J, et al. Development of a master-slave robot system for farm operations[J]. Computers and Electronics in Agriculture, 2004(1): 1-19.
127	LI S, ZHANG M, WANG N, et al. Intelligent scheduling method for multi-machine cooperative operation based on NSGA-III and improved ant colony algorithm[J]. Computers and Electronics in Agriculture, 2023, 204: ID 107532.
128	宫金良, 王伟, 张彦斐, 等. 基于农田环境的农业机器人群协同作业策略[J]. 农业工程学报, 2021, 37(2): 11-19.
128	GONG J, WANG W, ZHANG Y, et al. Cooperative working strategy for agricultural robot groups based on farmland environment[J]. Transactions of the CSAE, 2021, 37(2): 11-19.
129	姚竟发, 滕桂法, 霍利民, 等. 联合收割机多机协同作业路径优化[J]. 农业工程学报, 2019, 35(17): 12-18.
129	YAO J, TENG J, HUO L, et al. Optimization of cooperative operation path for multiple combine harvesters without conflict[J]. Transactions of the CSAE, 2019, 35(17): 12-18.
130	QIN J, WANG W, MAO W, et al. Research on a map-based cooperative navigation system for spraying–dosing robot group[J]. Agronomy, 2022, 12(12): ID 314.
131	ZHANG P, QIAO J, ZHANG H, et al. Path planning and tracking for agricultural master-slave robot system[C]// International Conference on Computer and Communication Technologies in Agriculture Engineering. Piscataway, New York, USA: IEEE, 2010.
132	SHOJAEI K, ABDOLMALEKI M. Saturated observer‐based adaptive neural network leader‐following control of N tractors with n‐trailers with a guaranteed performance[J]. International Journal of Adaptive Control and Signal Processing, 2021, 35(1): 15-37.
133	MOOREHEAD S, WELLINGTON C, GILMORE B, et al. Automating orchards: A system of autonomous tractors for orchard maintenance[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop on Agricultural Robots. Piscataway, New York, USA: IEEE, 2012.
134	ZHANG X, GEIMER M, NOACK P, et al. Development of an intelligent master-slave system between agricultural vehicles[C]// IEEE Intelligent Vehicles Symposium. Piscataway, New York, USA: IEEE, 2010.
135	ZHANG C, NOGUCHI N, YANG L, et al. Leader–follower system using two robot tractors to improve work efficiency[J]. Computers and Electronics in Agriculture, 2016, 121: 269-281.
136	LUO C, MOHSENIMANESH A, LAGU? C, et al. Synchronous tracking control for agricultural wide-span implement carrier (WSIC)[J]. Transactions of the ASABE, 2018(3): 873-883.
137	LI S, XU H, JI Y, et al. Development of a following agricultural machinery automatic navigation system[J]. Computers and Electronics in Agriculture, 2019, 158: 335-344.
138	MAO W, LIU H, HAO W, et al. Development of a combined orchard harvesting robot navigation system[J]. Remote Sensing, 2022, 3: ID 675.
139	白晓平, 胡静涛, 王卓. 基于视觉伺服的联合收割机群协同导航从机定位方法[J]. 农业工程学报, 2016, 32(24): 59-68.
139	BAI X, HU J, WANG Z. Slave positioning method for cooperative navigation of combine harvester group based on visual servo[J]. Transactions of the CSAE, 2016, 32(24): 59-68.
140	白晓平, 王卓, 胡静涛, 等. 基于领航-跟随结构的联合收获机群协同导航控制方法[J]. 农业机械学报, 2017, 48(7): 14-21.
140	BAI X, WANG Z, HU J, et al. Harvester group corporative navigation method based on leader-follower structure[J]. Transactions of the CSAM, 2017, 48(7): 14-21.
141	GARKUHA S, SKAZHENNIK M, CHIZHIKOV V, et al. Analysis of the use of a combine harvester equipped with a precision farming system in rice growing conditions[J]. IOP Conference Series: Materials Science and Engineering, 2020: ID 012027.
142	SENTS N. Case ih unveils afs connect magnum tractor series[J]. Successful Farming, 2019, 117(6): 24.
143	THOMASSON J, BAILLIE C, ANTILLE D, et al. A review of the state of the art in agricultural automation. Part II: On-farm agricultural communications and connectivity[C]// 2018 ASABE Annual International Meeting. St. Joseph, Michigan, USA: the American Society of Agricultural and Biological Engineers, 2018.
144	CHAUDHARY R, PANDEY J, PANDEY P, et al. Case study of Internet of Things in area of agriculture, 'AGCO's Fuse Technology's' 'Connected Farm Services'[C]// 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). Piscataway, New York, USA: IEEE, 2015.
145	MUGGE P, GUDERGAN G, MUGGEP, et al. The gap between the practice and theory of digital transformation[C]// The 50th Hawaiian International Conference of System Science. Piscataway, New York, USA: IEEE, 2017.
146	王培, 孟志军, 安晓飞, 等. 拖拉机功率与深松作业效率关系研究[J]. 农业机械学报, 2019, 50(S1): 87-90.
146	WANG P, MENG Z, AN X. Relationship between agricultural machinery power and agricultural machinery subsoiling operation[J]. Transactions of the CSAM, 2019, 50(S1): 87-90.
147	孟志军, 武广伟, 魏学礼, 等. 农机作业监管信息化技术应用与展望[J]. 农机科技推广, 2019(5): 9-11.
148	冀福华. 农机田间作业大数据处理关键技术研究及平台构建[D]. 北京: 中国农业机械化科学研究院, 2020.
148	JI F. Research on key technology and platform construction of agricultural machinery field operation big data processing[D]. Beijing: Chinese Academy of Agricultural Mechanization Sciences, 2020.
149	崔征泽. 农机监测管理系统的设计与实现[D]. 哈尔滨: 哈尔滨工业大学, 2019.
149	CUI Z. Design and implementation of agricultural machinery monitoring management system[D]. Harbin: Harbin Institute of Technology, 2019.
150	房黎明. 雷沃阿波斯发布农机行业首个智慧农业解决方案iFarming[J]. 农业机械, 2016, (11): 62-63.

[1]	刘又夫, 肖德琴, 周家鑫, 卞智逸, 招胜秋, 黄一桂, 王文策. 水禽智能化养殖研究现状及发展趋势[J]. 智慧农业(中英文), 2023, 5(1): 99-110.
[2]	李莉, 李民赞, 刘刚, 张漫, 汪懋华. 中国大田作物智慧种植目标、关键技术与区域模式[J]. 智慧农业(中英文), 2022, 4(4): 26-34.
[3]	郭志明, 王郡艺, 宋烨, 邹小波, 蔡健荣. 果蔬品质劣变传感检测与监测技术研究进展[J]. 智慧农业(中英文), 2021, 3(4): 14-28.
[4]	陈学庚, 温浩军, 张伟荣, 潘佛雏, 赵岩. 农业机械与信息技术融合发展现状与方向[J]. 智慧农业（中英文）, 2020, 2(4): 1-16.