Key Technologies and Equipment for Smart Orchard Construction and Prospects

doi:10.12133/j.smartag.SA200201014

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 1-11.doi: 10.12133/j.smartag.SA200201014

• Special Issue--Key Technologies and Equipment for Smart Orchard • Next Articles

Key Technologies and Equipment for Smart Orchard Construction and Prospects

HAN Leng^1,²(), HE Xiongkui^1,^2,³(), WANG Changling^1,^2,³, LIU Yajia^1,^2,³, SONG Jianli¹, QI Peng¹, LIU Limin¹, LI Tian¹, ZHENG Yi⁴, LIN Guihai⁴, ZHOU Zhan⁴, HUANG Kang⁵, WANG Zhong⁶, ZHA Hainie⁷, ZHANG Guoshan⁸, ZHOU Guotao⁹, MA Yong¹⁰, FU Hao¹¹, NIE Hongyuan¹², ZENG Aijun^1,^2,³, ZHANG Wei¹³

^1.College of Agricultural Unmanned System, China Agricultural University, Beijing 100193, China
^2.College of Science, China Agricultural University, Beijing 100193, China
^3.Center of Chemicals Application Technology, China Agricultural University, Beijing 100193, China
^4.Yantai Research Institute, China Agricultural University, Yantai 264670, China
^5.Huawei Technologies Ltd, Beijing 100010, China
^6.Beijing Pinggu District Government Village Committee of Xiying Village, Beijing 101200, China
^7.Anhui Yigang Information Technology Ltd, Anqing 246100, China
^8.Zhejiang Liangshan Technology Ltd, Huzhou 313302, China
^9.Henan Yunfei Technology Ltd, Zhengzhou 450003, China
^10.China Topsoaring Technology Ltd, BeiJing 100085, China
^11.Beijing Sanyi Zhinong Data Technology Ltd, Beijing 102199, China
^12.DJ-Innovations Ltd, Shenzhen 518057, China
^13.Anhui Zhongke Intelligent Sense Technology Ltd, Wuhu 241000, China

Received:2021-12-29 Online:2022-09-30
corresponding author: HE Xiongkui, Email：xiongkui@cau.edu.cn
About author:HAN Leng, Email：cauleng@163.com
Supported by:
Special Fund Project for the Construction of Modern Agriculture Industry Technology System(CARS-28);National Natural Science Foundation of China(31761133019);The Deutsche Forschungsgemeinschaft, DFG, German Research

Abstract

Abstract:

Traditional orchard production is facing problems of labor shortage due to the aging, difficulties in the management of agricultural equipment and production materials, and low production efficiency which can be expected to be solved by building a smart orchard that integrates technologies of Internet of Things(IoT), big data, equipment intelligence, et al. In this study, based on the objectives of full mechanization and intelligent management, a smart orchard was built in Pinggu district, an important peaches and pears fruit producing area of Beijing. The orchard covers an aera of more than 30 hm² in Xiying village, Yukou town. In the orchard, more than 10 kinds of information acquisition sensors for pests, diseases, water, fertilizers and medicines are applied, 28 kinds of agricultural machineries with intelligent technical support are equipped. The key technologies used include: intelligent information acquisition system, integrated water and fertilizer management system and intelligent pest management system. The intelligent operation equipment system includes: unmanned lawn mower, intelligent anti-freeze machine, trenching and fertilizer machine, automatic driving crawler, intelligent profiling variable sprayer, six-rotor branch-to-target drone, multi-functional picking platform and finishing and pruning machine, etc. At the same time, an intelligent management platform has been built in the smart orchard. The comparison results showed that, smart orchard production can reduce labor costs by more than 50%, save pesticide dosage by 30% ~ 40%, fertilizer dosage by 25% ~ 35%, irrigation water consumption by 60% ~ 70%, and comprehensive economic benefits increased by 32.5%. The popularization and application of smart orchards will further promote China's fruit production level and facilitate the development of smart agriculture in China.

Key words: smart orchard, Internet of Things, intelligent agricultural equipment system, driverless machine, smart orchard management platform, information acquisition system, intelligent profiling sprayer

CLC Number:

S126

HAN Leng, HE Xiongkui, WANG Changling, LIU Yajia, SONG Jianli, QI Peng, LIU Limin, LI Tian, ZHENG Yi, LIN Guihai, ZHOU Zhan, HUANG Kang, WANG Zhong, ZHA Hainie, ZHANG Guoshan, ZHOU Guotao, MA Yong, FU Hao, NIE Hongyuan, ZENG Aijun, ZHANG Wei. Key Technologies and Equipment for Smart Orchard Construction and Prospects[J]. Smart Agriculture, 2022, 4(3): 1-11.

References

1	赵春江. 智慧农业发展现状及战略目标研究[J]. 智慧农业, 2019, 1(1): 1-7.
1	ZHAO C. State-of-the-art and recommended developmental strategic objectives of smart agriculture[J]. Smart Agriculture, 2019, 1(1): 1-7.
2	罗朝阳. 基于区块链技术的农产品溯源系统研究[D]. 成都: 成都大学, 2021.
2	LUO C. Research on the traceability system of agricultural products based on blockchain technology[D]. Chengdu: Chengdu University, 2021.
3	潘明, 张丽慧, 黄晓财, 等. 空天地一体化智慧果园平台设计与应用[J]. 现代农业装备, 2021, 42(4): 43-47.
3	PAN M, ZHANG L, HUANG X, et al. Design and application of the air-space-ground integrated intelligent orchard platform[J]. Modern Agricultural Equipment, 2021, 42(4): 43-47.
4	崔冬冬, 王晓芳, 李晨, 等. 山东省智慧果业云服务平台的设计与实现[J]. 中国果树, 2021(6): 71-76.
4	CUI D, WANG X, LI C, et al. Design and implementation of Shandong Province smart fruit cloud service platform[J]. China Fruits, 2021(6):71-76.
5	刘吉敏, 覃泽林, 方辉, 等. 广西智慧柑桔技术构成与发展建议[J]. 中国南方果树, 2021, 50(3): 180-184, 193.
5	LIU J, QIN Z, FANG H, et al. Proposal for development of intelligent citrus in Guangxi[J]. South China Fruits, 2021, 50(3): 180-184, 193.
6	李梓臻. 基于无人机多光谱影像的苹果树冠层叶面积指数估测研究[D]. 泰安: 山东农业大学, 2021.
6	LI Z. Estimation of leaf area index in apple trees canopy based on UAV multispectral image[D]. Taian: Shandong Agricultural University, 2021.
7	冯珊珊, 梁雪映, 樊风雷, 等. 基于无人机多光谱数据的农田土壤水分遥感监测[J]. 华南师范大学学报(自然科学版), 2020, 52(6): 74-81.
7	FENG S, LIANG X, FAN F, et al. Monitoring of farmland soil moisture based on unmanned aerial vehicle multispectral data[J]. Journal of South China Normal University (Natural Science Edition), 2020, 52(6): 74-81.
8	束美艳, 李世林, 魏家玺, 等. 基于无人机平台的柑橘树冠信息提取[J]. 农业工程学报, 2021, 37(1): 68-76.
8	SHU M, LI S, WEI J, et al. Extraction of citrus crown parameters using UAV platform[J]. Transactions of the CSAE, 2021, 37(1): 68-76.
9	翟肇裕, 曹益飞, 徐焕良, 等. 农作物病虫害识别关键技术研究综述[J]. 农业机械学报, 2021, 52(7): 1-18.
9	ZHAI Z, CAO Y, XU H, et al. Review of key techniques for crop disease and pest detection[J]. Transactions of the CSAM, 2021, 52(7): 1-18.
10	李海, 李谊骏, 陈诗果, 等. 苹果树病虫害智能识别系统设计与实现[J]. 科学技术与工程, 2021, 21(25): 10639-10645.
10	LI H, LI Y, CHEN S. Design and implementation of intelligent recognition system for apple tree diseases and pests[J]. Science Technology and Engineering, 2021, 21(25): 10639-10645.
11	刘伟洪, 何雄奎, 刘亚佳, 等. 果园行间3D LiDAR导航方法[J]. 农业工程学报, 2021, 37(9): 165-174.
11	LIU W, HE X, LIU Y, et al. Navigation method between rows for orchard based on 3D LiDAR[J]. Transactions of the CSAE, 2021, 37(9): 165-174.
12	WANG S, QI P, ZHANG W, et al. Development and application of an intelligent plant protection monitoring system[J]. Agronomy, 2022, 12(5): ID 1046.
13	乔大科技. 基于茎水势传感器的灌溉决策系统[EB/OL]. [2021-10-15]. .
14	戴秀, 王坚强, 任妮, 等. 智能水肥一体化管控平台的设计与实现[J]. 江苏农业科学, 2021, 49(18): 177-181.
14	DAI X, WANG J, REN N, et al. Design and implementation of intelligent water and fertilizer integrated management and control platform[J]. Jiangsu Agricultural Sciences, 2021, 49(18): 177-181.
15	宋晨, 何邦令, 杨勤民, 等. 不同类型杀虫灯对苹果园昆虫的诱控效果[J]. 中国果树, 2020(4): 59-62, 67.
15	SONG C, HE B, YANG Q, et al. Effect of different kinds of insecticidal lamps on insect induction in apple orchard[J]. China Fruits, 2020(4): 59-62, 67.
16	王荣英, 孟纯, 张九青, 等. 基于无人机扰动的果园防霜试验研究[J]. 气象与环境科学, 2021, 44(5): 105-111.
16	WANG R, MENG C, ZHANG J, et al. Experimental study on frost prevention in orchard based on unmanned aerial vehicle intervention[J]. Meteorological and Environmental Sciences, 2021, 44(5): 105-111.
17	袁鹏成, 李秋洁, 邓贤, 等. 基于LiDAR的对靶喷雾实时控制系统设计与试验[J]. 农业机械学报, 2020, 51(S1): 273-280.
17	YUAN P, LI Q, DENG X, et al. Design and experiment of real-time control system for target spraying based on LiDAR[J]. Transactions of the CSAM, 2020, 51(S1): 273-280.
18	QI P, HE X, LIU Y, et al. Design and test of target-oriented profile modeling of unmanned aerial vehicle spraying[J]. International Journal of Agricultural and Biological Engineering, 2022, 15(3): 85-91.

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[11]	YANG XuanJiang, LI Hualong, LI Miao, HU Zelin, LIAO Jianjun, LIU Xianwang, GUO Panpan, YUE Xudong. Beehive Key Parameters Online Monitoring System and Performance Test [J]. Smart Agriculture, 2020, 2(2): 115-125.
[12]	HONG Wei, XU Baohua, LIU Shengping. Design and Experimental Research of Long-Term Monitoring System for Bee Colony Multiple Features [J]. Smart Agriculture, 2020, 2(2): 105-114.
[13]	Zhang Haifeng, Li Yang, Zhang Yu, Song Lijuan, Tang Lixin, Bi Hongwen. Design and implementation of intelligent terminal service system for greenhouse vegetables based on cloud service:A case study of Heilongjiang province [J]. Smart Agriculture, 2019, 1(3): 87-99.
[14]	Li Kailiang, Shu Lei, Huang Kai, Sun Yuanhao, Yang Fan, Zhang Yu, Huo Zhiqiang, Wang Yanfei, Wang Xinyi, Lu Qiaoling, Zhang Yacheng. Research and prospect of solar insecticidal lamps Internet of Things [J]. Smart Agriculture, 2019, 1(3): 13-28.
[15]	Zhao Chunjiang. State-of-the-art and recommended developmental strategic objectivs of smart agriculture [J]. Smart Agriculture, 2019, 1(1): 1-7.

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