Research Progress on Key Application Scenario of Intelligent Orchard

doi:10.13304/j.nykjdb.2024.0816

Journal of Agricultural Science and Technology ›› 2025, Vol. 27 ›› Issue (3): 12-23.DOI: 10.13304/j.nykjdb.2024.0816

• AGRICULTURAL INNOVATION FORUM • Previous Articles Next Articles

Research Progress on Key Application Scenario of Intelligent Orchard

Jianwei WU¹^,²^,³(), Lin ZHANG⁴, Wengang ZHENG³^,⁵, Xiangyang QIN³, Zhonggao WU⁶, Xihong FANG⁶, Yongquan WANG⁷, Tiangang LU⁷, Jian WANG⁸, Xiangshu PIAO⁹, Fang WANG¹⁰, Qiaoyun YUE¹¹, Haihe ZHANG¹², Jing JI¹³, Xiaoman CONG¹⁴(), Qifeng LI²^,³()

^1.Key Laboratory of System Integration for Internet of Things in Agriculture of Ministry of Agriculture and Rural Affairs，PAIDE Science and Technology Development Co. ，Ltd. ，Beijing 100097，China
^2.National Engineering Research Center for Information Technology in Agriculture，Beijing 100097，China
^3.Beijing Academy of Agriculture and Forestry Sciences，Beijing 100097，China
^4.Beijing Big Data Center，Beijing 100086，China
^5.National Engineering Research Center for Intelligent Equipment for Agriculture，Beijing 100097，China
^6.Beijing Municipal Forestry and Parks Industry Promotion Center，Beijing 100193，China
^7.Beijing Digital Agriculture Rural Promotion Center，Beijing 100021，China
^8.Beijing Association for Science and Technology Innovation Service Center，Beijing 100122，China
^9.Beijing Jingwa Agricultural Science and Technology Innovation Center，Beijing 101206，China
^10.Beijing CP Fruit Industry Co. ，Ltd. ，Beijing 101206，China
^11.Beijing LNXY Fruit Production and Marketing Professional Cooperative，Beijing 101206，China
^12.Beijing JGF Fruit Production and Marketing Professional Cooperative，Beijing 101206，China
^13.Beijing Tianhuiyuan Ecological Agriculture Development Co. ，Ltd. ，Beijing 102213，China
^14.Information Centre，Ministry of Agriculture and Rural Areas，Beijing 100125，China

Received:2024-10-08 Accepted:2024-12-26 Online:2025-03-15 Published:2025-03-14
Contact: Xiaoman CONG,Qifeng LI

智慧果园关键应用场景研究进展

吴建伟¹^,²^,³(), 张琳⁴, 郑文刚³^,⁵, 秦向阳³, 吴忠高⁶, 方锡红⁶, 王永泉⁷, 芦天罡⁷, 王健⁸, 朴香淑⁹, 王芳¹⁰, 岳巧云¹¹, 张海合¹², 季婧¹³, 丛小蔓¹⁴(), 李奇峰²^,³()

^1.北京派得伟业科技发展有限公司，农业农村部农业物联网系统集成重点实验室，北京 100097
^2.国家农业信息化工程技术研究中心，北京 100097
^3.北京市农林科学院，北京 100097
^4.北京市大数据中心，北京 100086
^5.国家农业智能装备工程技术研究中心，北京 100097
^6.北京市园林绿化产业促进中心，北京 100193
^7.北京市数字农业农村促进中心，北京 100021
^8.北京市科学技术协会创新服务中心，北京 100122
^9.北京京瓦农业科技创新中心，北京 101206
^10.北京正大果业有限公司，北京 101206
^11.北京绿农兴云果品产销专业合作社，北京 101206
^12.北京金果丰果品产销专业合作社，北京 101206
^13.北京天汇园生态农业发展有限公司，北京 102213
^14.农业农村部信息中心，北京 100125

通讯作者: 丛小蔓,李奇峰
作者简介:吴建伟 E-mail： wujw@nercita.org.cn
基金资助:
北京市智慧农业创新团队项目(BAIC10-2024-E11);北京市科技计划项目(Z231100003723002);北京市农林科学院课题(JJP2024-04)

Abstract

Abstract:

Intelligent orchards is a new production mode that deeply integrates modern information technology and intelligent equipment with all aspects of orchard production， achieving quantitative decision-making， precise input， digital and intelligent management. To enhance the digitalization level of the fruit industry， focusing on the latest research progress in key application scenarios of intelligent orchards，this paper analyzed the strategic position and layout significance of intelligent fruit industry in the process of agricultural modernization， and proposed several suggestions to promote the development of intelligent orchards based on the current research and application status in China. It also detaily elaborated the design and establishment of a intelligent orchard scenario in Beijing， which encompassing integrated environmental monitoring of the orchard’s “sky-ground” environment， integrated water and fertilizer input， machine-friendly intelligent equipment and a comprehensive management service platform. It aimed to create an efficient collaborative ecosystem from multiple dimensions， point the way for the future development of intelligent orchards， inject new vitality into the deep integration of rural revitalization and digital agriculture， and promote the improvement， efficiency and high-quality development of the fruit industry.

Key words: intelligent orchard, big data, model, digital empowerment

摘要：

智慧果园是将现代信息科技和智能化的装备与果园生产的各个环节深度融合，实现定量决策、精准投入和数字化、智能化管理的一种新型的生产方式。为提升果树产业数智化水平，聚焦智慧果园关键应用场景的最新研究进展，剖析了智慧果业在农业现代化进程中的战略地位与布局意义，并结合我国研究和应用现状提出了推动智慧果园发展的建议。详述了北京智慧果园场景设计与搭建，涵盖果园“天-空-地”一体化环境监测、水肥一体化投入、宜机化智能装备、综合管理服务平台等，从多维度打造高效协同生态，为智慧果园发展指明方向，为乡村振兴与数字农业的深度融合注入新的活力，推动果树产业提质增效和高质量发展。

关键词: 智慧果园, 大数据, 模型, 数智化

CLC Number:

S126

Jianwei WU, Lin ZHANG, Wengang ZHENG, Xiangyang QIN, Zhonggao WU, Xihong FANG, Yongquan WANG, Tiangang LU, Jian WANG, Xiangshu PIAO, Fang WANG, Qiaoyun YUE, Haihe ZHANG, Jing JI, Xiaoman CONG, Qifeng LI. Research Progress on Key Application Scenario of Intelligent Orchard[J]. Journal of Agricultural Science and Technology, 2025, 27(3): 12-23.

吴建伟, 张琳, 郑文刚, 秦向阳, 吴忠高, 方锡红, 王永泉, 芦天罡, 王健, 朴香淑, 王芳, 岳巧云, 张海合, 季婧, 丛小蔓, 李奇峰. 智慧果园关键应用场景研究进展[J]. 中国农业科技导报, 2025, 27(3): 12-23.

Figures/Tables 5

Fig. 1 Design of application scenarios for intelligent orchards

Fig. 2 Integrated data collection and monitoring of “air-space-ground” for intelligent orchard

Fig. 3 Integrated investment in orchard water and fertilizer

Fig. 4 Intelligent equipment system for orchard mechanization

Fig. 5 Service effect of intelligent orchard application system comprehensive management

References 51

1	朱彬彬,陶良如,孔德政.果树的观赏利用价值及其应用分析[J].中国果树,2020(2):117-121.
	ZHU B B, TAO L R, KONG D Z. Ornamental utilization value of fruit trees and its application analysis [J].China Fruits, 2020(2):117-121.
2	韩冷,何雄奎,王昌陵,等.智慧果园构建关键技术装备及展望[J].智慧农业(中英文),2022,4(3):1-11.
	HAN L, HE X K, WANG C L, et al.. Key technologies and equipment for smart orchard construction and prospects [J]. Smart Agric., 2022, 4(3):1-11.
3	兰玉彬,林泽山,王林琳,等.基于文献计量学的智慧果园研究进展与热点分析[J].农业工程学报,2022,38(21):127-136.
	LAN Y B, LIN Z S, WANG L L, et al.. Research progress and hotspots of smart orchard based on bibliometrics [J]. Trans. Chin. Soc. Agric. Eng., 2022, 38(21):127-136.
4	吴建伟.信息科技赋能智慧果园[J].北京农村经济,2022(5):35-37.
5	赵春江.智慧农业的发展现状与未来展望[J].华南农业大学学报,2021,42(6):1-7.
	ZHAO C J. Current situations and prospects of smart agriculture [J]. J. South China Agric. Univ., 2021, 42(6): 1-7.
6	孙九林,李灯华,许世卫,等.农业大数据与信息化基础设施发展战略研究[J].中国工程科学,2021,23(4):10-18.
	SUN J L, LI D H, XU S W, et al.. Development strategy of agricultural big data and information infrastructure [J]. Strateg. Study CAE, 2021, 23(4):10-18.
7	O’SHAUGHNESSY S A, KIM M, LEE S, et al.. Towards smart farming solutions in the U.S. and south Korea: a comparison of the current status [J]. Geogr. Sustain., 2021, 2(4):312-327.
8	USDA. USDA Science Blueprint: A roadmap for USDA science from 2020 to 2025 [EB/OL]. (2020-04-02) [2024-09-02]. .
9	SILVA F TDA, BAIERLE I C, DE FARIA CORREA R G, et al.. Open innovation in agribusiness:barriers and challenges in the transition to agriculture 4.0 [J/OL]. Sustainability, 2023, 15(11):8562. [2024-09-02]. .
10	赵春江.发展智慧农业建设数字乡村[J].山东农机化,2020(5):9-10.
11	刘旭,李文华,赵春江,等.面向2050年我国现代智慧生态农业发展战略研究[J].中国工程科学,2022,24(1):38-45.
	LIU X, LI W H, ZHAO C J, et al.. High-quality development of modern smart ecological agriculture [J]. Strateg. Study CAE,2022, 24(1): 38-45.
12	孙康泰,王小龙,蒋大伟,等.美国农业和食品领域2030科技突破计划及启示[J].全球科技经济瞭望,2020,35(11):25-32.
	SUN K T, WANG X L, JIANG D W, et al.. The 2030 plan of science and technology breakthrough on agricultural and food study in the United States and its enlightenment [J]. Glob. Sci. Technol. Econ. Outlook, 2020, 35(11):25-32.
13	THORNE J. Apple-picking robots gear up for U. S. debut in Washington state [EB/OL]. (2019-05-13) [2024-09-02]. .
14	翟长远,杨硕,王秀,等.农机装备智能测控技术研究现状与展望[J].农业机械学报,2022,53(4):1-20.
	ZHAI C Y, YANG S, WANG X, et al.. Status and prospect of intelligent measurement and control technology for agricultural equipment [J]. Trans. Chin. Soc. Agric. Mach., 2022, 53(4):1-20.
15	VERIS TECHNOLOGIES. Mobile sensor platform(MSP 3) [EB/OL]. [2024-09-02]. .
16	袁祥州,黄恩临.欧盟智慧农业发展经验及其借鉴[J].世界农业,2022(5):27-36.
	YUAN X Z, HUANG E L. Experience and reference of smart agriculture development in European union [J]. World Agric., 2022(5):27-36.
17	MOYSIADIS V, SARIGIANNIDIS P, VITSAS V, et al.. Smart farming in Europe [J/OL]. Comput. Sci. Rev., 2021, 39:100345[2024-09-02]. .
18	LI D P, NANSEKI T, CHOMEI Y, et al.. A review of smart agriculture and production practices in Japanese large-scale rice farming [J]. J. Sci. Food Agric., 2023,103(4):1609-1620.
19	MAFF. On smart agriculture [EB/OL]. [2024-09-02]. .
20	金建东,徐旭初.数字农业的实践逻辑、现实挑战与推进策略[J].农业现代化研究,2022,43(1):1-10.
	JIN J D, XU X C. The operational logic, realistic challenges, and promotion strategies of digital agriculture [J]. Res. Agric. Mod., 2022, 43(1):1-10.
21	周国民,丘耘,樊景超,等.数字果园研究进展与发展方向[J].中国农业信息,2018,30(1):10-16.
	ZHOU G M, QIU Y, FAN J C, et al.. Research progress and prospect of digital orchard techniques [J]. China Agric. Inform., 2018, 30(1):10-16.
22	安徽省人民政府办公厅.安徽省人民政府办公厅关于印发加快发展数字经济行动方案(2022—2024年)的通知(皖政办〔2022〕10号) [EB/OL]. (2022-11-18) [2023-11-02]. .
23	北京市园林绿化局.北京市果树产业助力乡村振兴三年行动计划实施方案(2023—2025年) [EB/OL]. (2023-05-09) [2024-09-02]. .
24	JUNAID M, SHAIKH A, HASSAN M U, et al.. Smart agriculture cloud using AI based techniques [J/OL]. Energies, 2021,14(16):5129 [2024-09-02]. .
25	饶晓燕,吴建伟,李春朋,等.智慧苹果园 “空-天-地” 一体化监控系统设计与研究[J].中国农业科技导报,2021,23(6):59-66.
	RAO X Y, WU J W, LI C P, et al.. Design and research on “space-air-ground” integrated monitoring system for intelligent orchard [J]. J. Agric. Sci. Technol., 2021,23(6):59-66.
26	杜青青. 基于LoRa的智慧果园墒情监测系统研究与设计[J]. 工业仪表与自动化装置,2022(3):13-17, 27.
	DU Q Q. Research and design of intelligent orchard moisture monitoring system based on LoRa [J]. Ind. Instrum. Autom., 2022(3):13-17, 27.
27	郭涛,李宗南,何泽涛,等.智慧果园移动管理服务平台设计与实现[J].智慧农业导刊,2023,3(11):5-8, 12.
	GUO T, LI Z N, HE Z T, et al.. Design and implementation of mobile management and service platform for smart orchard [J]. J. Smart Agric., 2023, 3(11):5-8, 12.
28	孙昌权,高菊玲.设施草莓智慧生产管控系统设计与实现[J].中国农机化学报,2021,42(12):80-86.
	SUN C Q, GAO J L. Design and implementation of intelligent control system for strawberry greenhouse [J]. J. Chin. Agric. Mech., 2021, 42(12):80-86.
29	SIMÓN-PORTILLO F J, ABELLÁN-LÓPEZ D, FABRA-RODRIGUEZ M, et al.. Detection of hollow heart disorder in watermelons using vibrational test and machine learning [J/OL]. J. Agric. Food Res., 2023,14:100779 [2024-09-02]. .
30	WANG D Z, WANG X C, CHEN Y Y, et al.. Strawberry ripeness classification method in facility environment based on red color ratio of fruit rind [J/OL]. Comput. Electron. Agric., 2023, 214:108313 [2024-09-02]. .
31	JIANG L G, JIANG H H, JING X D, et al.. UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning [J]. 2024(13):117-127.
32	王雯,张叶清,张静,等.基于采摘机器人触觉的猕猴桃成熟度感知方法[J].科技与创新,2021(18):59-60,64-65.
33	吴风扬,黄文倩,田喜,等.基于机器视觉的无序堆放西瓜识别与定位技术[J].新疆农业科学,2024,61(7):1805-1813.
	WU F Y, HUANG W Q, TIAN X, et al.. Research on recognition and localization of unordered stacked watermelons based on machine vision [J]. Xinjiang Agric. Sci., 2024, 61(7):1805-1813.
34	MASSAOUDI A, BERGUIGA A, HARCHAY A. Secure irrigation system for olive orchards using internet of things [J]. Comput. Mater. Continua, 2022, 72(3): 4663-4673.
35	TAN K, SUN S M, DU L Z, et al.. Design of an intelligent irrigation system for a jujube orchard based on IoT [J]. INMATEH Agric. Eng., 2021, 63(1):189-198.
36	MAHMUD M S, HE L, HEINEMANN P, et al.. Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications [J/OL]. Smart Agric. Technol., 2023, 4:100153 [2024-09-02]. .
37	REIM S, PFLANZ M, MAß V, et al.. Development of a digital monitoring system for pear rust and fire blight in fruit orchards [J]. Acta Hortic., 2023(1360):291-298.
38	翟长远,张燕妮,窦汉杰,等.果园风送喷雾机出风口风场CFD建模与试验[J].智慧农业(中英文), 2021,3(3):70-81.
	ZHAI C Y, ZHANG Y N, DOU H J, et al.. CFD modeling and experiment of airflow at the air outlet of orchard air-assisted sprayer [J]. Smart Agric., 2021, 3(3):70-81.
39	吴建伟,黄杰,熊晓菲,等.基于AI的桃树病害智能识别方法研究与应用[J].中国农业科技导报,2022,24(5):111-118.
	WU J W, HUANG J, XIONG X F, et al.. Research and application of intelligent recognition method of peach tree diseases based on AI [J]. J. Agric. Sci. Technol., 2022, 24(5):111-118.
40	PAN S, AHAMED T. Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithms- IoT and AI in Agriculture [M]. Singapore: Springer Singapore, 2023:219-252.
41	吕沐华,丁珠玉.基于机器视觉的果园喷药除草机器人视觉系统设计[J].中国农机化学报,2021,42(5):42-48.
	LYU M H, DING Z Y. Design of visual systems for orchard spraying weeding robot based on machine vision [J]. J. Chin. Agric. Mech., 2021, 42(5):42-48.
42	任栋宇,李晓娟,林涛,等.基于Kinect v2传感器的果树枝干三维重建方法[J].农业机械学报,2022,53():197-203.
	REN D Y, LI X J, LIN T, et al.. 3D reconstruction method for fruit tree branches based on Kinect v2 sensor [J]. Trans. Chin. Soc. Agric. Mach., 2022, 53(S2):197-203.
43	张凝,杨贵军,赵春江,等.作物病虫害高光谱遥感进展与展望[J].遥感学报,2021,25(1):403-422.
	ZHANG N, YANG G J, ZHAO C J, et al.. Progress and prospects of hyperspectral remote sensing technology for crop diseases and pests [J]. Natl. Remote. Sens. Bull., 2021,25(1):403-422.
44	MA S X, LIU Y C, TIAN H W, et al.. Investigation of resonance excitation of trace elements using resonant laser-induced breakdown spectroscopy (RLIBS) [J]. J. Anal. At. Spectrom., 2023, 38(2):342-346.
45	冯青春,赵春江,李涛,等.苹果四臂采摘机器人系统设计与试验[J].农业工程学报,2023,39(13):25-33.
	FENG Q C, ZHAO C J, LI T, et al.. Design and test of a four-arm apple harvesting robot [J]. Trans. Chin. Soc. Agric. Eng., 2023, 39(13):25-33.
46	HUANG W Q, LI J B, WANG Q Y, et al.. Development of a multispectral imaging system for online detection of bruises on apples [J]. J. Food Eng., 2015, 146:62-71.
47	吴文斌,史云,段玉林,等.天空地遥感大数据赋能果园生产精准管理[J].中国农业信息,2019,31(4):1-9.
	WU W B, SHI Y, DUAN Y L, et al.. The precise management of orchard production driven by the remote sensing big data with the SAGI [J]. China Agric. Inform., 2019, 31(4):1-9.
48	KUMAR R, KUMAR P, TRIPATHI R, et al.. SP2F:a secured privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles [J/OL]. Comput. Netw., 2021,187:107819[2024-09-02]. .
49	BALDUCCI F, IMPEDOVO D, PIRLO G. Machine learning applications on agricultural datasets for smart farm enhancement [J/OL]. Machines, 2018, 6(3):38 [2024-09-02]. .
50	DURAI S K S, SHAMILI M D. Smart farming using machine learning and deep learning techniques [J/OL]. Decis. Anal. J., 2022, 3:100041 [2024-09-02]. .
51	张凝,杨贵军,赵春江,等.作物病虫害高光谱遥感进展与展望[J].遥感学报,2021,25(1):403-422.
	ZHANG N, YANG G J, ZHAO C J, et al.. Progress and prospects of hyperspectral remote sensing technology for crop diseases and pests [J]. Natl. Remote. Sens. Bull., 2021,25(1):403-422.

[1]	Danyi SHI, Yu QIU, Chengzhen HUANG, Juan WANG. Effect of Acid Modified Biochar on Infiltration Characteristics of Coastal Saline Soil [J]. Journal of Agricultural Science and Technology, 2024, 26(9): 183-192.
[2]	Ziqin LI, Jiaqiang WANG, Zhen LI, Deqiu ZOU, Xiaogong ZHANG, Xiaoyu LUO, Weiyang LIU. Estimation of Chlorophyll Density of Cotton Leaves Based on Spectral Index [J]. Journal of Agricultural Science and Technology, 2024, 26(8): 103-111.
[3]	Lijun FU, Xiaoyu LIN, Jianhua LIN, Huinan SHEN, Yongzhen WU. Research on Processing Technology and Shelf Life of Red Matsutake Beef Sauce [J]. Journal of Agricultural Science and Technology, 2024, 26(6): 148-158.
[4]	Yue HUANG, Yanfen XIE, Xuanquan ZHU, Meng JIA, Ge WANG, Yuxiang BAI, Yu DU, Peng ZHOU, Yuting ZHAO, Hongqiong ZHU, Fan YANG, Zhiwen XIAO, Wenbo WANG, Zhipeng FANG, Jiabao HAN, Na WANG. Risk Assessment and Influencing Factors Analysis of Chlorine Content in Tobacco Leaves in Tobacco Planting Areas [J]. Journal of Agricultural Science and Technology, 2024, 26(6): 206-213.
[5]	Xiaoshuang CHEN, Xingqian XU, Xi ZHAO, Xin QU, Haijun WANG, Guangcan PENG. Study on Resistivity Characteristics and Evaluation Model of Cadmium Contaminated Laterite [J]. Journal of Agricultural Science and Technology, 2024, 26(4): 164-173.
[6]	Yue PAN, Baoqing WANG, Jijiao WANG, Yong MA, Yalan LI. CO₂ Response Model Fitting and Evaluation of Vitis amurensis [J]. Journal of Agricultural Science and Technology, 2024, 26(4): 58-66.
[7]	Jiaqiang ZHENG, Huichun ZHANG, Youlin XU, Hongping ZHOU. Research Review on Modeling and Simulation for Pesticide Spraying System [J]. Journal of Agricultural Science and Technology, 2024, 26(3): 76-90.
[8]	Wei WANG, Hongyu FU, Jianning LU, Yunkai YUE, Ruifang YANG, Guoxian CUI, Wei SHE. Research on Interpretation of Ramie Lodging Information Based on Unmanned Aerial Vehicles [J]. Journal of Agricultural Science and Technology, 2024, 26(3): 91-97.
[9]	Wencheng CONG, Limin YUAN, Zhongju MENG, Yu YANG. Effects of Corncob on Capillary Water Transport and Evaporation Characteristics of Sandy Soil [J]. Journal of Agricultural Science and Technology, 2024, 26(2): 198-207.
[10]	Yunzhu LIU, Zhian ZHOU, Jiewei WANG. Research on Provincial Digital Rural Architecture Under the Strategy of “Big Data + Rural Revitalization” [J]. Journal of Agricultural Science and Technology, 2024, 26(11): 91-96.
[11]	Meixia LIANG, Xinqiang ZHANG, Bingqing LIN. Simulation of Sediment Reduction Effect Under Different Scenarios in Red Soil Erosion Watershed in South China [J]. Journal of Agricultural Science and Technology, 2024, 26(10): 177-185.
[12]	Yao XIAO, Mingliang WU. Application Status and Development Trend of Computational Fluid Dynamics in Agricultural Engineering [J]. Journal of Agricultural Science and Technology, 2023, 25(8): 1-9.
[13]	Fengxia BIAN, Kaige LIU, Xinmin RONG. Development and Verification of Prediction Model for Grape Downy Mildew Based on Machine Learning [J]. Journal of Agricultural Science and Technology, 2023, 25(8): 126-137.
[14]	Zhuang YU, Zhongke FENG, Biao ZHANG, Tiantian MA. Research and Construction of Pinus massoniana Lamb. Volume Model in Chongqing Area by Non-destructive Method [J]. Journal of Agricultural Science and Technology, 2023, 25(7): 97-104.
[15]	Haochun KE, Kun LI, Ruifeng CHENG. Simulation and Optimization on Ultraviolet LED Nutrient Solution Sterilization Module Based on Response Surface Method [J]. Journal of Agricultural Science and Technology, 2023, 25(4): 132-146.

Research Progress on Key Application Scenario of Intelligent Orchard

智慧果园关键应用场景研究进展

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 51

Related Articles 15

Recommended Articles

Metrics