无人智慧农场关键技术与构建模式——以“吨半粮”无人农场为例

doi:10.12133/j.smartag.SA202410033

Smart Agriculture ›› 2025, Vol. 7 ›› Issue (1): 70-84.doi: 10.12133/j.smartag.SA202410033

• 专题--农业知识智能服务和智慧无人农场（下） • 上一篇下一篇

无人智慧农场关键技术与构建模式——以“吨半粮”无人农场为例

刘力宁²^,³^,⁴, 张洪奇¹^,³^,⁴, 章子文¹^,³^,⁴, 张正辉²^,³^,⁴, 王甲玉¹^,³^,⁴, 李宣宣¹^,³^,⁴, 朱珂¹^,³^,⁴, 柳平增¹^,³^,⁴()

^1. 山东农业大学信息科学与工程学院，山东泰安 271018，中国
^2. 山东农业大学机械电子与工程学院，山东泰安 271018，中国
^3. 山东农业大学农业大数据研究中心，山东泰安 271018，中国
^4. 农业农村部黄淮海智慧农业技术重点实验室，山东泰安 271018，中国

收稿日期:2024-10-20 出版日期:2025-01-30
基金项目:
中央引导地方科技发展专项资金(YDZX2022073); 山东省重点研发技术(2022CXGC010609); 山东省重点研发计划(2022TZXD0030)
作者简介:
刘力宁，博士研究生，研究方向为农业工程。E-mail： nk18754882706@163.com
通信作者:
柳平增，博士，教授，研究方向为农业物联网、农业大数据分析和农产品质量安全溯源。E-mail： pzliu@sdau.edu.cn

Key Technologies and Construction model for Unmanned Smart Farms: Taking the "1.5-Ton Grain per Mu" Unmanned Farm as An Example

LIU lining²^,³^,⁴, ZHANG Hongqi¹^,³^,⁴, ZHANG Ziwen¹^,³^,⁴, ZHANG Zhenghui²^,³^,⁴, WANG Jiayu¹^,³^,⁴, LI Xuanxuan¹^,³^,⁴, ZHU Ke¹^,³^,⁴, LIU Pingzeng¹^,³^,⁴()

^1. College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China
^2. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China
^3. Agricultural Big Data Research Center, Shandong Agricultural University, Tai'an 271018, China
^4. Key Laboratory of Huang-Huai-Hai Agriculture Technology, Ministry of Agriculture and Rural Affairs, P. R. China, Tai'an 271018, China

Received:2024-10-20 Online:2025-01-30
Foundation items:Special Funds for the Central Government to Guide Local Scientific and Technological Development(YDZX2022073); Shandong Provincial Key Research and Development Technology Project(2022CXGC010609); Shandong Provincial Key Research and Development Plan(2022TZXD0030)
About author:
LIU lining, E-mail: nk18754882706@163.com
Corresponding author:
LIU Pingzeng, E-mail: pzliu@sdau.edu.cn

摘要/Abstract

摘要：

【目的/意义】 无人智慧农场是智慧农业的重要实践模式。本研究以山东德州“吨半粮”无人智慧农场为实验场所，攻克大田智慧农场建设中的核心技术难题，探索其建设模式与服务机制。 【方法】 运用物联网技术，研发了智慧农场的立体感知网络，能够高效采集并汇聚传输环境、作物长势和设备状态等关键数据。借助数据分析挖掘技术，精准提取了小麦的物候期、麦穗特征等关键表型信息。进一步结合智能农机与智能决策技术，研发了集云管控平台、智能化设备及智能农机于一体的智能控制系统。此外，依托多源数据融合、分布式计算和地理信息系统（Geographic Information System, GIS）等技术，构建了农业生产全过程智能管控平台。 【结果和讨论】 “吨半粮”无人智慧农场感知系统不仅提高了数据传输质量，同时可以完成麦穗、物候期等表型特征的本地分析；智能控制系统可帮助农机提升自主作业精度和灌溉、施药效率、质量，通过农业设备的改造升级实现了农场耕作、种植、管理、收获的全链条智能化管控；大数据智慧服务平台为农户提供了气象预测、灾害预警、最佳播期等农事管理服务，极大地提高了农场管理的数字化、智能化水平。实验结果表明，自组网络数据准确率保持在85%以上，无人机施药可节药55%，灌溉模型可节水20%，“济南17”和“济麦44”分别增产10.18%和7%。 【结论】 研究结果可为智慧农场建设提供参考和借鉴。

关键词: 无人智慧农场, 物联网, 信息采集, 智能控制, 大数据分析

Abstract:

[Objective] As a key model of smart agriculture, the unmanned smart farm aims to develop a highly intelligent and automated system for high grain yields. This research uses the "1.5-Ton grain per Mu" farm in Dezhou city, Shandong province, as the experimental site, targeting core challenges in large-scale smart agriculture and exploring construction and service models for such farms. [Methods] The "1.5-Ton grain per Mu" unmanned smart farm comprehensively utilized information technologies such as the internet of things (IoT) and big data to achieve full-chain integration and services for information perception, transmission, mining, and application. The overall construction architecture consisted of the perception layer, transmission layer, processing layer, and application layer. This architecture enabled precise perception, secure transmission, analysis and processing, and application services for farm data. A perception system for the unmanned smart farm of wheat was developed, which included a digital perception network and crop phenotypic analysis. The former achieved precise perception, efficient transmission, and precise measurement and control of data information within the farm through perception nodes, self-organizing networks, and edge computing core processing nodes. Phenotypic analysis utilized methods such as deep learning to extract phenotypic characteristics at different growth stages, such as the phenological classification of wheat and wheat ear length. An intelligent controlled system had been developed. The system consisted of an intelligent agricultural machinery system, a field irrigation system, and an aerial pesticided application system. The intelligent agricultural machinery system was composed of three parts: the basic layer, decision-making layer, and application service layer. They were responsible for obtaining real-time status information of agricultural machinery, formulating management decisions for agricultural machinery, and executing operational commands, respectively. Additionally, appropriate agricultural machinery models and configuration references were provided. A refined irrigation scheme was designed based on the water requirements and soil conditions at different developmental stages of wheat. And, an irrigation control algorithm based on fuzzy PID was proposed. Finally, relying on technologies such as multi-source data fusion, distributed computing, and geographic information system (GIS), an intelligent management and control platform for the entire agricultural production process was established. [Results and Discussions] The digital perception network enabled precise sensing and networked transmission of environmental information within the farm. The data communication quality of the sensor network remained above 85%, effectively ensuring data transmission quality. The average relative error in extracting wheat spike length information based on deep learning algorithms was 1.24%. Through the coordinated operation of intelligent control system, the farm achieved lean and unmanned production management, enabling intelligent control throughout the entire production chain, which significantly reduced labor costs and improved the precision and efficiency of farm management. The irrigation model not only saved 20% of irrigation water but also increased the yield of "Jinan 17" and "Jimai 44" by 10.18% and 7%, respectively. Pesticide application through spraying drones reduced pesticide usage by 55%. The big data platform provided users with production guidance services such as meteorological disaster prediction, optimal sowing time, environmental prediction, and water and fertilizer management through intelligent scientific decision support, intelligent agricultural machinery operation, and producted quality and safety traceability modules, helping farmers manage their farms scientifically. [Conclusions] The study achieved comprehensive collection of environmental information within the farm, precise phenotypic analysis, and intelligent control of agricultural machinery, irrigation equipment, and other equipment. Additionally, it realized digital services for agricultural management through a big data platform. The development path of the "1.5-Ton grain per Mu" unmanned smart farm can provid references for the construction of smart agriculture.

Key words: unmanned smart farm, IoT, information collection, intelligent control, big data analysis

中图分类号:

刘力宁, 张洪奇, 章子文, 张正辉, 王甲玉, 李宣宣, 朱珂, 柳平增. 无人智慧农场关键技术与构建模式——以“吨半粮”无人农场为例[J]. 智慧农业(中英文), 2025, 7(1): 70-84.

LIU lining, ZHANG Hongqi, ZHANG Ziwen, ZHANG Zhenghui, WANG Jiayu, LI Xuanxuan, ZHU Ke, LIU Pingzeng. Key Technologies and Construction model for Unmanned Smart Farms: Taking the "1.5-Ton Grain per Mu" Unmanned Farm as An Example[J]. Smart Agriculture, 2025, 7(1): 70-84.

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物候期	精确率	召回率	F ₁值
Class 0	0.93	0.85	0.89
Class 1	0.91	0.82	0.86

序号	实际长度/cm	测试长度/cm	误差/%	序号	实际长度/cm	测试长度/cm	误差/%
1	7.50	7.88	5.07	6	6.20	6.18	0.32
2	6.80	6.74	0.88	7	7.10	7.05	0.70
3	7.50	7.28	2.93	8	7.00	6.85	0.72
4	7.50	7.64	1.87	9	8.20	8.12	0.98
5	7.20	7.18	0.28

名称	型号	产品参数
名称	型号	整机主要参数	智能驾驶主要参数
智能轮式拖拉机	M2004-5RP	潍柴200马力国四发动机，智能液晶仪表，四轮驱动，380 L油箱，24+24换挡，电控湿式动力输出，强压提升器	±2.5 cm精度自动驾驶，支持远程操控及多卫星信号，作业功能全面，IP67防护等级，系统可远程升级监控
自走式谷物联合收割机	4LZ-10M7（G4）	潍柴200 ps发动机，3 m割台，损失破碎含杂率低，雷沃变速箱，静液压前驱，400 L油箱，2.8 m³粮箱	智能驾驶±2.5 cm精度，支持远程作业遥控监视，手机/PC操控，多卫星信号，IP67防护，远程升级打点控制
自走式玉米收获机	4YZ-4EP	发动机162 kw，4行作业，速度1.83~9 km/h，轮胎15R24/300/80R15.3，整机7 600 kg，轴距3 m，油箱400 L	远程自主作业，APP/PC遥控，系统可升级，支持四大卫星信号，防护等级IP67。
复式精量条播机	2BGXF-20C	动力耙+条播复式作业，147~183 kw动力，20行，行距15 cm，排种无级调，链传动，橡胶轮覆土镇压，驱动靶整地	—
气吸式免耕精量播种机	2BMQE-6E	气力牵引式6行播种机，147~192.5 kw动力，种肥箱大，双圆盘开沟，离心风机，独立镇压，多级调节	—

工况	比例系数（ K_p ）	积分系数（ K_i ）	微分系数（ K_d ）	响应时间/s	超调量/%
平坦地形	0.8	0.2	0.1	1.2	5
复杂地形	1.2	0.1	0.3	1.5	8
播种作业	1.0	0.1	0.2	1.3	6

发育期	适宜范围/%	持续日期（月/日）	阶段天数/天
播种期	50—80	10/20—11/12	24
出苗期	60—80	11/13—11/29	17
越冬期	60—80	11/30—2/29	92
返青期	70—80	3/1—3/15	15
起身期	70—80	3/16—4/4	20
拔节期	70—80	4/5—4/19	15
孕穗期	70—80	4/20—4/22	3
抽穗期	70—80	4/23—4/27	5
开花期	70—80	4/28—4/30	3
灌浆期	70—80	5/1—5/21	21
成熟期	70—80	5/22—6/12	22

无人智慧农场关键技术与构建模式——以“吨半粮”无人农场为例

Key Technologies and Construction model for Unmanned Smart Farms: Taking the "1.5-Ton Grain per Mu" Unmanned Farm as An Example

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Metrics

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K_p/K_i/K_d		E
K_p/K_i/K_d		NB	NM	NS	Z	PS	PM	PB
Ec	NB	PB/NB/PS	PB/NB/PS	PM/NB/Z	PM/NM/Z	PS/NM/Z	PS/Z/PB	Z/Z/PS
	NM	PB/NB/NS	PB/NB/NS	PM/NM/NS	PM/NM/NS	PS/NS/Z	Z/Z/PS	Z/Z/PM
	NS	PM/NM/NB	PM/NM/NB	PM/NS/NM	PS/NS/NS	Z/Z/Z	NS/PS/PS	NM/PS/PM
	Z	PM/NM/NB	PS/NS/NM	PS/NS/NM	Z/Z/NS	NS/PS/Z	NM/PS/PS	NM/PM/PM
	PS	PS/NS/NB	PS/NS/NM	Z/Z/NS	NS/PS/NS	NS/PS/Z	NM/PM/PS	NM/PM/PS
	PM	Z/Z/NM	Z/Z/NS	NS/PS/NS	NM/PM/Z	NM/PM/Z	NM/PB/PS	NB/PB/PS
	PB	Z/Z/PS	NS/Z/Z	NS/PS/Z	NM/PM/Z	NM/PB/Z	NB/PB/PB	NB/PB/PB

实验品种	灌溉方式	产量/（kg/hm ²）
济南17	传统灌溉	7 963.59
济南17	模糊PID灌溉	8 774.16
济麦44	传统灌溉	8 641.20
济麦44	模糊PID灌溉	9 246.54

机型	代表产品	优点
油动单旋翼	汉和CD-15、天鹰TY999B	负载大；续航时间长，适合大面积作业
电动单旋翼	天鹰TY-777、高新S40、高科新农HY-B-15L	适用于高精度施药，降低药剂浪费；下压风场大，药物能有效穿透和覆盖农作物根部
油动多旋翼	Y620、天马-1	具备较长的续航时间；多种喷洒模式，降低农药使用量
电动多旋翼	大疆T30、Parrot Bluegrass、DJI Agras T40、极飞P150	高智能、高精度；易操作，安全；配备多种传感器，适合不同的农业监测

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施药方式	平均作业效率 /（hm ²/h）	最高作业效率/（hm ²/h）	平均农药使用量/（hm ²/h）
植保无人机	2.13	2.33	1 380
人工	0.17	0.22	3 066