中国大田作物智慧种植目标、关键技术与区域模式

doi:10.12133/j.smartag.SA202207003

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (4): 26-34.doi: 10.12133/j.smartag.SA202207003

• 专题--大田作物智慧种植 • 上一篇下一篇

中国大田作物智慧种植目标、关键技术与区域模式

李莉¹(), 李民赞²(), 刘刚¹, 张漫², 汪懋华^1,²

^1.中国农业大学农业农村部农业信息获取技术重点实验室，北京 100083
^2.中国农业大学智慧农业系统集成研究教育部重点实验室，北京 100083

收稿日期:2022-07-07 出版日期:2022-12-30
基金资助:
中国工程院重大咨询研究项目(2019-ZD-5);中国农业大学中央高校基本科研业务费(2022TC053)
作者简介:李莉（1978－），女，博士，副教授，研究方向为智慧农业信息获取与系统集成。E-mail：lily@cau.edu.cn
通信作者: 李民赞（1963－），男，博士，教授，研究方向为智慧农业系统集成。E-mail：limz@cau.edu.cn

Goals, Key Technologies, and Regional Models of Smart Farming for Field Crops in China

LI Li¹(), LI Minzan²(), LIU Gang¹, ZHANG Man², WANG Maohua^1,²

^1.Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China
^2.Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China

Received:2022-07-07 Online:2022-12-30
corresponding author: LI Minzan，E-mail：limz@cau.edu.cn
About author:LI Li, E-mail：lily@cau.edu.c
Supported by:
Chinese Academy of Engineering Major Consultation and Research Project (2019-ZD-5); China Agricultural University Central University Basic Research Business Fee (2022TC053)

摘要/Abstract

摘要：

大田作物智慧种植业是智慧农业的重要内容。本文通过分析智慧农业发展历程，明确了大田作物智慧种植业发展战略总体目标和重点任务，凝练出关键技术，有针对性地提出适宜中国区域特征的发展模式。大田作物智慧种植的关键技术面临的主要挑战有：缺乏原位精准测量技术与农业专用传感器，作物模拟模型与实际生产有较大差别，信息传输技术的实时性、可靠性、通用性和稳定性有待改进，智能农业装备还需要进一步解决好农机/农艺相结合问题。在以上分析基础上，提出了大田作物智慧种植关键技术的5个一级技术以及相应的18个二级技术。5个一级技术包括环境与生物信息感知技术、信息移动互联与农业物联网技术、云计算与云服务技术、大数据分析与决策技术、智能农机装备与农业机器人技术。根据中国种植业区域特色提出了相应的6种智慧农业发展区，即东北与内蒙古规模化智慧生产发展区，京津冀鲁智慧都市农业与节水农业发展区，西北旱区棉花规模化智慧种植和旱作智慧农业绿色发展综合试验区，东南沿海循环型水稻智慧种植业综合发展试验区，长江中下游平原智慧粮油优化发展区，以及西南山区智慧特色农业发展区。最后从基础设施建设、技术、人才和政策角度给出了发展建议。

关键词: 智慧种植, 感知技术, 物联网, 智能农机装备, 无人农场, 大数据, 移动互联, 智慧农业

Abstract:

Smart farming for field crops is a significant part of the smart agriculture. It aims at crop production, integrating modern sensing technology, new generation mobile communication technology, computer and network technology, Internet of Things(IoT), big data, cloud computing, blockchain and expert wisdom and knowledge. Deeply integrated application of biotechnology, engineering technology, information technology and management technology, it realizes accurate perception, quantitative decision-making, intelligent operation and intelligent service in the process of crop production, to significantly improve land output, resource utilization and labor productivity, comprehensively improves the quality, and promotes efficiency of agricultural products. In order to promote the sustainable development of the smart farming, through the analysis of the development process of smart agriculture, the overall objectives and key tasks of the development strategy were clarified, the key technologies in smart farming were condensed. Analysis and breakthrough of smart farming key technologies were crucial to the industrial development strategy. The main problems of the smart farming for field crops include: the lack of in-situ accurate measurement technology and special agricultural sensors, the large difference between crop model and actual production, the instantaneity, reliability, universality, and stability of the information transmission technologies, and the combination of intelligent agricultural equipment with agronomy. Based on the above analysis, five primary technologies and eighteen corresponding secondary technologies of smart farming for field crops were proposed, including: sensing technologies of environmental and biological information in field, agricultural IoT technologies and mobile internet, cloud computing and cloud service technologies in agriculture, big data analysis and decision-making technology in agriculture, and intelligent agricultural machinery and agricultural robots in fireld production. According to the characteristics of China's cropping region, the corresponding smart farming development strategies were proposed: large-scale smart production development zone in the Northeast region and Inner Mongolia region, smart urban agriculture and water-saving agriculture development zone in the region of Beijing, Tianjin, Hebei and Shandong, large-scale smart farming of cotton and smart dry farming green development comprehensive test zone in the Northwest arid region, smart farming of rice comprehensive development test zone in the Southeast coast region, and characteristic smart farming development zone in the Southwest mountain region. Finally, the suggestions were given from the perspective of infrastructure, key technology, talent and policy.

Key words: smart farming, sensing technologies, Internet of Things, intelligent agricultural machinery, unmanned farm, big data, mobile internet, smart agriculture

中图分类号:

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.

LI Li, LI Minzan, LIU Gang, ZHANG Man, WANG Maohua. Goals, Key Technologies, and Regional Models of Smart Farming for Field Crops in China[J]. Smart Agriculture, 2022, 4(4): 26-34.

参考文献

1	新华网. 2021年全国粮食总产量达13657亿斤同比增长2.0%[EB/OL]. (2021-12-06)[2022-06-22]..
2	汪懋华院士: 关于智慧农业释义与创新驱动发展的思考[J]. 农业工程技术, 2018, 38(21): 24-28.
3	齐美娟. 精细农业的实践助推智慧农业创新发展——访中国工程院院士、国际欧亚科学院院士汪懋华[J]. 中国国情国力, 2018(7): 6-7, 5.
4	IDOJE G, DAGIUKLAS T, IQBAL M. Survey for smart farming technologies: Challenges and issues[J]. Computers & Electrical Engineering, 2021, 92: ID 107104.
5	The World Bank Group. Climate-Smart Agriculture [EB/OL]. (2021-04-05)[2022-06-22]. .
6	胡瑞法, 刘万嘉文. 科技革命、颠覆性技术与智慧农业[J/OL]. [2022-07-24].智慧农业(中英文.
6	HU R, LIU W. Technological revolution, disruptive technology and smart agriculture[J/OL]. [2022-07-24].. .
7	王亚华, 臧良震, 苏毅清. 2035年中国农业现代化前景展望[J]. 农业现代化研究, 2020, 41(1): 16-23.
7	WANG Y, ZANG L, SU Y. Prospects for China's agricultural modernization in 2035 [J]. Research of Agricultural Modernization, 2020, 41 (1): 16-23.
8	马龙. 现代农业发展中物联网应用的困境分析[J]. 中国管理信息化, 2021, 24(24): 106-107.
9	李道亮, 杨昊. 农业物联网技术研究进展与发展趋势分析[J]. 农业机械学报, 2018, 49(1): 1-20.
9	LI D, YANG H. State-of-the-art review for Internet of Things in agriculture[J]. Transactions of the CSAM, 2018, 49 (1): 1-20.
10	黄魏, 许伟, 汪善勤, 等. 基于不确定性模型的土壤——环境关系知识获取方法的研究[J]. 土壤学报, 2018, 55(1): 54-63.
10	HUANG W, XU W, WANG S, et al. Extraction of knowledge about soil-environment relationship based on an uncertainty model[J]. Acta Pedologica Sinica, 2018, 55(1):54-63.
11	赵福年, 王润元. 基于模式识别的半干旱区雨养春小麦干旱发生状况判别[J]. 农业工程学报, 2014, 30(24): 124-132.
11	ZHAO F, WANG R. Discrimination of drought occurrence for rainfed spring wheat in semi-arid area based on pattern recognition[J]. Transactions of the CSAE, 2014, 30(24): 124-132.
12	WHITE E L, THOMASSON J A, AUVERMANN B, et al. Report from the conference, 'identifying obstacles to applying big data in agriculture'[J]. Precision Agriculture, 2021, 22: 306-315.
13	KENDALL H, CLARK B, LI W . et al. Precision agriculture technology adoption: A qualitative study of small-scale commercial "family farms" located in the North China Plain[J]. Precision Agriculture, 2022, 23: 319-351.
14	陈学庚, 温浩军, 张伟荣, 等. 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.
14	CHEN X, WEN H, ZHANG W, et al. Advnces and progress of agricultural machinery and sensing technology fusion[J]. Smart Agriculture, 2020, 2 (4): 1-16.
15	吴嘉敏, 贺玉成, 徐征, 等. 用于土壤水分测量的磁共振射频线圈宽频匹配方法[J]. 波谱学杂志, 2021, 38(3): 414-423.
15	WU J, HE Y, XU Z, et al. A wide-band matching method for radio frequency coils used in soil moisture measurement[J]. Chinese Journal of Magnetic Resonance, 2021, 38 (3): 414-423.
16	吕中秀. 土壤环境监测过程中有机质测定方法对比分析[J]. 山东化工, 2021, 50(18): 291-292.
16	LYU Z. Comparative analysis of organic matter determination methods in the process of soil environment monitoring[J]. Shandong Chemical Industry, 2021, 50 (18): 291-292.
17	张飞扬, 胡月明, 谢英凯, 等. 天空地一体耕地质量监测移动实验室集成设计[J]. 农业资源与环境学报, 2021, 38(6): 1029-1038.
17	ZHANG F, HU Y, XIE Y, et al. Design of integrated space-air-ground farmland quality monitoring mobile laboratory[J]. Journal of Agricultural Resources and Environment, 2021, 38(6): 1029-1038.
18	路逍, 潘林沛, 李雁华, 等. 基于ISE的土壤硝态氮原位检测模型比较[J]. 农业机械学报, 2021, 52(S1): 297-303.
18	LU X, PAN L, LI Y, et al. Comparison of detection models for soil nitrate concentration based on ISE[J]. Transactions of the CSAM, 2021, 52 (S1): 297-303.
19	蒋焕煜, 应义斌, 谢丽娟. 光谱分析技术在作物生长信息检测中的应用研究进展[J]. 光谱学与光谱分析, 2008(6): 1300-1304.
19	JIANG H, YING Y, XIE L. Research progress on application of spectral analysis technology in detection of crop growth information[J]. Spectroscopy and Spectral Analysis, 2008(6): 1300-1304.
20	张晓东, 毛罕平, 倪军, 等. 作物生长多传感信息检测系统设计与应用[J]. 农业机械学报, 2009, 40(9): 164-170.
20	ZHANG X, MAO H, NI J, et al. Intelligent detection system of multi-sensor information for growing crops[J]. Transactions of the CSAM, 2009, 40(9): 164-170.
21	邓若玲, 潘威杰, 王志琪, 等. 农作物表型技术及其智能装备研究进展与展望[J]. 现代农业装备, 2021, 42(1): 2-9.
21	DENG R, PAN W, WANG Z, et al. Research progress and prospect of crop phenotyping technology and its intelligent equipment[J]. Modern Agricultural Equipment, 2021, 42(1): 2-9.
22	程曼, 袁洪波, 蔡振江, 等. 田间作物高通量表型信息获取与分析技术研究进展[J]. 农业机械学报, 2020, 51(S1): 314-324.
22	CHENG M, YUAN H, CAI Z, et al. Research progress on high-throughput phenotypic information acquisition and analysis technology of field crops[J]. Transactions of the CSAM, 2020, 51(S1): 314-324.
23	卢少志, 杨蒙, 杨万能,等. 田间作物表型检测平台设计与试验[J]. 华中农业大学学报, 2021, 40(4): 209-218.
23	LU S, YANG M, YANG W, et al. Design and experiment of a platform for detecting phenotype of field crop[J]. Journal of Huazhong Agricultural University, 2021, 40 (4): 209-218.
24	朱荣胜, 李帅, 孙永哲,等. 作物三维重构技术研究现状及前景展望[J]. 智慧农业(中英文), 2021, 3(3): 94-115.
24	ZHU R, LI S, SUN Y, et al. Research advances and prospects of crop 3D reconstruction technology[J]. Smart Agriculture, 2021, 3(3): 94-115.
25	王翔宇, 温皓杰, 李鑫星, 等. 农业主要病害检测与预警技术研究进展分析[J]. 农业机械学报, 2016, 47(9): 266-277.
25	WANG X, WEN H, LI X, et al. Research progress analysis of mainly agricultural diseases detection and early warning technologies[J]. Transactions of the CSAM, 2016, 47(9): 266-277.
26	杨国峰, 何勇, 冯旭萍, 等. 无人机遥感监测作物病虫害胁迫方法与最新研究进展[J]. 智慧农业(中英文), 2022, 4(1): 1-16.
26	YANG G, HE Y, FENG X, et al. Methods and new research progress of remote sensing monitoring crop diseases and pests stress using unmanned aerial vehicle[J]. Smart Agriculture, 2022, 4(1): 1-16.
27	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.
27	DENG W, CHEN L, ZHANG R, et al. Review on key technologies for UAV precision agro-chemical application[J]. Agricultural Engineering, 2020, 10 (4): 1-10.
28	邬贺铨: 移动互联网已进入"大智移云"时代[EB/OL]. (2015-01-23) [2022-06-22]. .
29	SOLDANI D, MANZALINI A. Horizon 2020 and beyond: On the 5G operating system for a true digital society[J]. IEEE Vehicular Technology Magazine, 2015, 10(1): 32-42.
30	黄韬, 刘江, 霍如, 等. 未来网络体系架构研究综述[J]. 通信学报, 2014, 35(8): 184-197.
30	HUANG T, LIU J, HUO R, et al. Survey of research on future network architectures[J]. Journal on Communications, 2014, 35(8): 184-197.
31	杨普, 赵远洋, 李一鸣, 等. 基于多源信息融合的农业空地一体化研究综述[J].农业机械学报, 2021, 52(S1): 185-196.
31	YANG P, ZHAO Y, LI Y, et al. Review of research on integration of agricultural air-ground integration based on multi-source information fusion[J]. Transactions of the CSAM, 2021, 52(S1): 185-196.
32	陈天恩, 刘军萍, 王登位,等. 农业云服务可适性技术研究进展[J]. 中国农业信息, 2018, 30(1): 67-78.
32	CHEN T, LIU J, WANG D, et al. Advances in adaptable technologies of agricultural cloud services[J]. China Agricultural Information, 2018, 30(1): 67-78.
33	郑勇, 王光华, 杜鹏飞. 智慧农业云平台建设探索与实践[J]. 农业工程技术, 2018, 38(21): 31-35.
34	许世卫. 农业高质量发展与农业大数据建设探讨[J]. 农学学报, 2019, 9(4): 13-17.
34	XU S. Discussion on agricultural high-quality development and agricultural big data construction [J]. Journal of Agriculture, 2019, 9 (4): 13-17.
35	姜侯, 杨雅萍, 孙九林. 农业大数据研究与应用[J]. 农业大数据学报, 2019, 1(1): 5-15.
35	JIANG H, YANG Y, SUN J. Research and application of big data in agriculture[J]. Journal of Agricultural Big Data, 2019, 1(1): 5-15.
36	张立辉. 农业大数据的应用及发展建议[J]. 南方农机, 2020, 51(14): 47-48.
36	ZHANG L. Application and development suggestions of big data in agriculture[J]. Southern Agricultural Machinery, 2020, 51(14): 47-48.
37	宋长青, 温孚江, 李俊清, 等. 农业大数据研究应用进展与展望[J]. 农业与技术, 2018, 38(22): 153-156.
38	YOU Y. Data mining of regional economic analysis based on mobile sensor network technology[J]. Journal of Sensors, 2022, 2022: 1-13.
39	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.
39	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.
40	杨印生, 薛春序, 许莹, 等.智慧农业的社会经济特征、发展逻辑与系统阐释[J].吉林农业大学学报, 2021, 43(2): 146-152.
40	YANG Y, XUE C, XU Y, et al. Social and economic characteristics,development logic and systematic interpretation of smart agriculture[J]. Journal of Jilin Agricultural University, 2021, 43(2): 146-152.

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中国大田作物智慧种植目标、关键技术与区域模式

Goals, Key Technologies, and Regional Models of Smart Farming for Field Crops in China

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