Current status of orchard mechanization technologies and key development priorities in the 15th Five-Year Plan period

Authors

  • Song Mei 1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
  • Jing He 2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • Xiaolan Lyu 3. Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
  • Zhiyu Song 1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
  • Guangqiao Cao Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, China
  • Cheng Shen 4. Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China http://orcid.org/0000-0001-6540-4522

DOI:

https://doi.org/10.25165/ijabe.v18i6.9314

Keywords:

orchard mechanization, intelligent, production management, main problems, development focus

Abstract

Orchard mechanization technologies are a key driver of progress in the fruit industry. Their advancement enhances operational efficiency, reduces labor intensity, and minimizes the waste of agricultural inputs. This study reviewed the current regional status of orchard mechanization in China and introduced advanced technologies for mechanized orchard production. The whole mechanized production technology for orchard was analyzed across four core dimensions, including digital orchard scenario reconstruction, autonomous navigation, under-canopy mechanized operations, and tree-oriented mechanization technologies. Furthermore, a comparative analysis was conducted on cutting-edge technologies used in similar types of equipment across four critical operational stages (power platforms, weeding, plant protection, and harvesting), and their respective advantages and limitations in various application contexts were also analyzed. Then, several key problems hindering further development were identified, including limited standardization for mechanization compatibility, significant equipment shortages in hilly and mountainous areas, insufficient integration of advanced technologies into orchard machinery, an aging and undereducated rural workforce, and an underdeveloped system of socially-supported agricultural machinery services. Finally, in view of these problems, six strategic development priorities for the orchard mechanization in the 15th five-year plan period were put forward, including promoting the integration of standardized orchard construction and agricultural machinery systems, overcoming bottlenecks of mechanized equipment, upgrading intelligent equipment in gentle slope orchards, strengthening the application of intelligent technologies in modern standardized orchards, attracting and training highly educated fruit professionals, and strengthening the construction of socialized agricultural machinery service systems. Key words: orchard mechanization; intelligent; production management; main problems; development focus DOI: 10.25165/j.ijabe.20251806.9314 Citation: Mei S, He J, Lyu X, Song Z Y, Cao G Q, Shen C. Current status of orchard mechanization technologies and key development priorities in the 15th Five-Year Plan period. Int J Agric & Biol Eng, 2025; 18(6): 12–23.

References

Liu Y, Liu Y Y, Chen J C, Li J B, Lin X W, Lin G Z. Research status and trends of key technologies for multifunctional operation platforms in standardized orchards. Journal of Intelligent Agricultural Equipment, 2024; 5(1): 31–39. (in Chinese)

Fu Z Y, Liu D W, Xie F P, Li X, Xiao X. Research status and prospects of fruit and vegetable transportation equipment in hilly and mountainous areas. Modern Agricultural Equipment, 2023; 44(3): 11–18. (in Chinese)

Wu C Y, Wang J H, Yang Y, Li D Y, Li X. Current situation and mechanization trends of China’s cash crop industry. Journal of Chinese Agricultural Mechanization, 2024; 45(1): 1–13.

Liu C X. Discussion on mechanized production technologies for key orchard operations. Hebei Agricultural Machinery, 2021; 7: 10–11.

Hu J T, Gao L, Bai X P, Li T C, Liu X G. Research progress on automatic navigation technologies for agricultural machinery. Transactions of the CSAE, 2015; 31(10): 1–10. (in Chinese)

Lepej P, Rakun J. Simultaneous localisation and mapping in a complex field environment. Biosystems Engineering, 2016; 150: 160–9.

Habibie N, Nugraha A M, Anshori A Z, Ma’sum M A, Jatmiko W. Fruit mapping mobile robot on simulated agricultural area in Gazebo simulator using simultaneous localization and mapping (SLAM). 2017 International Symposium on Micro-Nano Mechatronics and Human Science (MHS), 2017.

Marden S, Whitty M. GPS-free localisation and navigation of an unmanned ground vehicle for yield forecasting in a vineyard. Recent Advances in Agricultural Robotics, International Workshop Collocated with the 13th International Conference on Intelligent Autonomous Systems (IAS-13), 2014.

Kohlbrecher S, Meyer J, Graber T. Hector open source modules for autonomous mapping and navigation with rescue robots. Springer Berlin Heidelberg. Springer Berlin Heidelberg, 2013.

Liu L W, Zhu X K, Li X H, Zhao Z J. Map evaluation study of 2D SLAM algorithms for low-cost mobile robots. Computer Simulation, 2021; 38(4): 291–295.

Ji P. Research and design of an autonomous navigation control system for orchard operation robots. Nanjing: Nanjing University of Science and Technology, 2019. DOI: 10.27241/d.cnki.gnjgu.2019.001445.

Wu C L. Research on autonomous driving system for orchard tractors based on multi-source information fusion. Nanjing: Southeast University, 2019. DOI: 10.27014/d.cnki.gdnau.2019.000405. (in Chinese)

Ocer N E, Kaplan G, Erdem F, Kucuk Matci D, Avdan U. Tree extraction from multi-scale UAV images using Mask R-CNN with FPN. Remote Sensing Letters, 2020; 11(9): 847–856.

Onishi M, Ise T. Explainable identification and mapping of trees using UAV RGB image and deep learning. Scientific Reports, 2021; 11(1): 903.

Liang J M. Development of a self-propelled electric chassis for winter jujube orchard operations. Jinan: Shandong Jiaotong University, 2019. DOI: 10.27864/d.cnki.gsjtd.2019.000002. (in Chinese)

Zeng H. Overall design and experimental study of an electric tractor driven by hub motors. Hangzhou: Zhejiang A&F University, 2020. DOI: 10.27756/d.cnki.gzjlx.2020.000254. (in Chinese)

Han J H, Park C H, Park Y J, Kwon J H. Preliminary Results of the development of a single-frequency GNSS RTK-based autonomous driving system for a speed sprayer. Journal of Sensors, 2019; 2019(1): 4687819.

Huang Y, Fu J, Xu S, Han T, Liu Y. Research on integrated navigation system of agricultural machinery based on RTK-BDS/INS. Agriculture, 2022; 12(8): 1169.

Opiyo S, Okinda C, Zhou J, Mwangi E, Makange N. Medial axis-based machine-vision system for orchard robot navigation. Computers and Electronics in Agriculture, 2021; 185: 106153.

Radcliffe J, Cox J, Bulanon D M. Machine vision for orchard navigation. Computers in Industry, 2018; 98: 165–171.

Nie S, Wang B L, Hao H H, Chen J. Research on centerline extraction algorithm for orchard navigation based on machine vision. Journal of Agricultural Mechanization Research, 2016; 12: 86–89.

Mengoli D, Tazzari R, Marconi L. Autonomous robotic platform for precision orchard management: architecture and software perspective. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 2020. DOI: 10.1109/MetroAgriFor50201.2020.9277555

Xu Y W, Yan W X, Wu W. Improvement of laser SLAM front-end algorithm based on local maps under similar scenarios. Robot, 2022; 44(2): 176–185.

Velasquez A E B, Higuti V A H, Gasparino M V, Sivakumar A N V, Becker M, Chowdhary G. Multi-sensor fusion based robust row following for compact agricultural robots. Field robotics, 2022; 2: 1291–1319.

Lv M, Wei H, Fu X, Wang W, Zhou D. A loosely coupled extended Kalman filter algorithm for agricultural scene-based multi-sensor fusion. Frontiers in Plant Science, 2022; 13: 849260.

Ni Z K, Li M H, Lin R, Sun L N, Liu S Q. Research on SLAM based on fusion of 3D LiDAR and RTK. Manufacturing Automation, 2020; 42(7): 51–54.

Dong H, Lou C R, Wang X J. Analysis of material and equipment selection for integrated water and fertilizer systems in protected fruit and vegetable production. Liaoning Agricultural Sciences, 2017; 5: 54–58. (in Chinese)

He Q H, Zheng L, Chu Y H, Dou Q Q, Ci W L, Sun Y T. Research status and prospects of head-end units in water and fertilizer integrated systems. Journal of Chinese Agricultural Mechanization, 2021; 42(1): 122.

Luo H J, Li Y, Xie Q B, Zhong L Y. Design of a cost-effective water and fertilizer integration system. Modern Agricultural Equipment, 2020; 41(6): 41–47. (in Chinese)

Huang Y Y, Wang T, Liu X, Zheng H Y, Wu B Y, Chen Y K. Design and experiment of a circulating irrigation system for water and fertilizer integration. Water Saving Irrigation, 2019; 8: 94–97, 101. (in Chinese)

Zhang B F, Chen X, Zhu J C, Kang J, Pan W, Zhao Y. Design and experiment of a water and fertilizer integrated system based on the Internet of Things. Journal of Intelligent Agricultural Equipment, 2021; 2(1): 57. (in Chinese)

Zhao P F, Wang X F, Hu C, Xu C, Guo W S, He X W, et al. Design and experiment of water and fertilizer integrated equipment for smart greenhouses. Journal of Agricultural Mechanization Research, 2022; 44(9): 224–228.

Zhang X, Cheng C Q, Hao X Y, Li J S, Hu P. A dynamic path planning algorithm for robots considering both global and local characteristics. Journal of Geomatics Science and Technology, 2018; 35(3): 315–320.

Mccool C S, Beattie J, Firn J. Efficacy of mechanical weeding tools: A study into alternative weed management strategies enabled by robotics. IEEE Robotics and Automation Letters, 2018: pp.1184–1190.

Hub A, Schmitz B. Addition of RFID-based initialization and object recognition to the navigation system TANIA. Proceedings of the 24th Annual International Technology and Persons with Disabilities Conference (CSUN), 2009.

Berk P, Stajnko D, Lakota M, Belsak A. Real time fuzzy logic system for continuous control solenoid valve in the process of applying the plant protection product. Agricultural Engineering, 2015; 15(1): 1–9.

Seol J, Kim J, Son H I J P A. Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agriculture, 2022; 23(2): 712–732.

Zhang J, Yin H, Zhou L, Gu C, Qiu W, Lv X, et al. Variable rate airassisted spray based on real-time disease spot identification. Pest Management Science, 2023; 79(1): 402–414.

Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Tona E, et al. Selective spraying of grapevines for disease control using a modular agricultural robot. Biosystems Engineering, 2016; 146: 203–215.

Rincón V J, Grella M, Marucco P, Alcatrão L, Sanchez-Hermosilla J, BalsarI P. Spray performance assessment of a remote-controlled vehicle prototype for pesticide application in greenhouse tomato crops. Science of The Total Environment, 2020; 726: 138509.

Ma C, Li G L, Li X D, Huang X Y, Song J, Yang S H. Development of a multi-directional automatic spraying device for citrus orchards in hilly and mountainous areas. Transactions of the CSAE, 2019; 35(3): 31–41. (in Chinese)

Jiang S J, Ma H T, Yang S H, Zhang C, Su D B L G, et al. Target detection and tracking system of orchard spraying robot. Transactions of the CSAE, 2021; 37(9): 31–39. (in Chinese)

Xu H, Yi L, Li C, Sun Y, Hou L, Bai J, et al. Design and experiment of ecological plant protection UAV based on ozonated water spraying. Drones, 2023; 7(5): 291.

Ishigure Y, Hirai K, Kawasaki H. A pruning robot with a power-saving chainsaw drive. 2013 IEEE International Conference on Mechatronics and Automation (ICMA), 2013 . DOI: 10.1109/ICMA.2013.6618088

Botterill T, Paulin S, Green R, Williams S, Lin J, Saxton V, et al. A robot system for pruning grape vines. Journal of field robotics, 2016; 34(6): 1100–1122.

You A, Sukkar F, Fitch R, Karkee M, Davidson J R. An efficient planning and control framework for pruning fruit trees. 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. DOI: 10.1109/ICRA40945.2020.9197551

Zahid A, Mahmud M S, He L, Choi D, Heinemann P, Schupp J. Development of an integrated 3R end-effector with a cartesian manipulator for pruning apple trees. Computers and Electronics in Agriculture, 2020; 179: 105837.

Xu Z X, Wu G P, Liu X Z. Research on a tree-branch pruning robot for overhead high-voltage transmission line corridors. Mechanical Design and Manufacturing, 2015; 9: 204–207.

Assirelli A, Giovannini D, Cacchi M, Alberto A, Daniela G, Mattia C, et al. Evaluation of a new machine for flower and fruit thinning in stone fruits. Sustainability, 2018; 10(11): 4088.

Wouters N, Ketelaere D B, Deckers T, Baerdemaeker J D, Wouter S. Multispectral detection of floral buds for automated thinning of pear. Computers and Electronics in Agriculture, 2015; 113: 93–103.

Lei X H, Lü X L, Zhang M N. Development and experiment of a three-section arm airborne blossom thinner. Transactions of the CSAE, 2019; 35(24): 31–38. (in Chinese)

Chechetka S A, Yu Y, Tange M, Li X, Chang Y H. Materially engineered artificial pollinators. Chem, 2017; 2(2): 224–239.

Shen Y, Zhu H, Liu H, Chen Y, Ozkan E. Development of a laser-guided, embedded-computer-controlled, air-assisted precision sprayer. Transactions of the ASABE, 2017; 60(6): 1827–1838.

Tacconi G, Michelotti V, Caciopp O, Vittone G. Kiwifruit pollination: the interaction between pollen quality, pollination systems and flowering stage. Journal of berry research, 2016; 6(4): 417–426.

Williams H, Nejati M, Hussein S, Penhall N, Lim Y J, Jones M H, et al. Autonomous pollination of individual kiwifruit flowers: toward a robotic kiwifruit pollinator. Journal of Field Robotics, 2020; 37(2): 246–262.

Guo H M. Development of a spraying-type end-effector for kiwifruit pollination. Yangling: Northwest A&F University, 2016.

Liu S H. Research on target-oriented pollination method and control system design for kiwifruit. Yangling: Northwest A&F University, 2019.

Arad B, Balendonck J, Barth R, Shahar O B, Edan Y, Hellstrom T, et al. Development of a sweet pepper harvesting robot. Journal of Field Robotics, 2020; 37(6): 1027–1039.

Onishi Y, Yoshida T, Kurita H, Fukao T, Rihara H, Wai A. An automated fruit harvesting robot by using deep learning. Robomech Journal, 2019; 6(1): 1–8.

Trinidad J B. Machine for automatically harvesting fruits cultivated in rows. United States patent application: US 15/366, 091, 2018-06-07.

Avigad G, Salomon S, Kahani A, Koster Y. Robotic fruit harvesting machine with fruit-pair picking and hybrid motorized-pneumatic robot arms. United States patent: US 11, 477, 942, 2022-10-25.

Williams H A, Jones M H, Nejati M, Seabright M J, Bell J, Penhall N, et al. Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering, 2019; 181: 140–156.

Wang P F, Liu J F, Qian J, Hao Y J. Design of a static anti-rollover system for orchard operation platforms. Journal of Agricultural Mechanization Research, 2013; 35(3): 137–139.

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Published

2025-12-26

How to Cite

Mei, S., He, J., Lyu, X., Song, Z., Cao, G., & Shen, C. (2025). Current status of orchard mechanization technologies and key development priorities in the 15th Five-Year Plan period. International Journal of Agricultural and Biological Engineering, 18(6), 12–23. https://doi.org/10.25165/ijabe.v18i6.9314

Issue

Vol. 18 No. 6 (2025): IJABE

Section

Overview Articles

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