All Issue

2026 Vol.8, Issue 1 Preview Page

Review Article

Analysis of end-effector cases for fruit-thinning robot development: A review and future prospects
Jeong Gyun Kang1
,
Jun-Woo Park1
,
Ho-Seung Jang1
,
Choung-Keun Lee1
,
Manjung Kim1*
1Department of Agricultural Engineering, National Institute of Agricultural Sciences, JeonJu 54875, Republic of Korea
*Corresponding Author
31 March 2026. pp. 10-24
PDF XML Citation
Abstract

This paper reviews end-effectors for robotic fruit thinning of fruiting vegetable crops, emphasizing that the primary separation target is the fruit attachment structure—pedicel, peduncle, or abscission zone (AZ)—rather than the fruit body. Prior studies are organized along three linked axes: target physical properties, end-effector design (mechanism/ actuation), and performance evaluation (metrics/protocols). The review shows that comparability and reproducibility are most often limited by inconsistent definitions of the target cutting/separation location, heterogeneous test conditions, and differing definitions of success. Because AZ presence and proximal–distal pedicel segments can substantially change diameter, stiffness, and required separation loads, results are difficult to compare without explicit specification of target region and measurement conditions. Design implications are discussed across cutting-based and twisting/plucking-based end-effectors, with emphasis on integrated design features that couple support–alignment–separation–retrieval. To address the limitations of single success-rate reporting, we propose a minimum reporting format including (1) target attachment structure and cutting location, (2) condition metadata (environment, cultivation, maturity, moisture), (3) stage-wise metrics (reach/alignment, grip/support, separation, damage), and (4) cycle time and the number of trials (n). We further recommend robustness and durability tests that inject alignment errors and incorporate contamination and wear, and a staged validation roadmap from repeated indoor baseline tests, to intermediate tests with controlled variations in occlusion and illumination, and finally to long-duration greenhouse/open-field pilots.

Keywords
End-effector
Fruit attachment structures
Detachment mechanics
Benchmarking protocol
References
1

Ao, J., Ji, W., Yu, X., Ruan, C., Xu, B. 2025. End-effectors for fruit and vegetable harvesting robots: A review of key technologies, challenges, and future prospects. Agronomy 15(11): 2650. https://doi.org/10.3390/agronomy15112650

10.3390/agronomy15112650
2

Arad, B., Balendonck, J., Barth, R., Ben-Shahar, O., Edan, Y., Hellström, T., Hemming, J., Kurtser, P., Ringdahl, O., Tielen, T., van Tuijl, B.A.J. 2020. Development of a sweet pepper harvesting robot. Journal of Field Robotics 37(6): 1027-1039. https://doi.org/10.1002/rob.21937

10.1002/rob.21937
3

Arima, S., Kondo, N., Nakamura, H. 1996. Development of robotic system for cucumber harvesting. Japan Agricultural Research Quarterly (JARQ) 30(4): 233-238.

4

Bac, C.W., van Henten, E.J., Hemming, J., Edan, Y. 2014. Harvesting robots for high-value crops: State-of-the-art review and challenges ahead. Journal of Field Robotics 31(6): 888-911. https://doi.org/10.1002/rob.21525

10.1002/rob.21525
5

Bac, C.W., Hemming, J., van Tuijl, B.A.J., Barth, R., Wais, E., van Henten, E.J. 2017. Performance evaluation of a harvesting robot for sweet pepper. Journal of Field Robotics 34(6): 1123-1139. https://doi.org/10.1002/rob.21709

10.1002/rob.21709
6

Bargoti, S., Underwood, J. 2017. Deep fruit detection in orchards. In 2017 IEEE International Conference on Robotics and Automation (ICRA): 3626-3633. https://doi.org/10.1109/ICRA.2017.7989417

10.1109/ICRA.2017.7989417
7

Barth, R., Hemming, J., Arad, B., Kurtser, P., Edan, Y., Ben-Shahar, O. 2016. Report on test scenarios and definition performance measures (SWEEPER Deliverable D7.1; Grant Agreement No. 644313). Wageningen University & Research.

8

Bechar, A., Vigneault, C. 2016. Agricultural robots for field operations: Concepts and components. Biosystems Engineering 149: 94-111. https://doi.org/10.1016/j.biosystemseng.2016.06.014

10.1016/j.biosystemseng.2016.06.014
9

Bu, L., Chen, J., Hu, G., Sugirbay, A., Sun, F., Chen, J. 2022. Design and evaluation of a robotic apple harvester using optimized picking patterns. Computers and Electronics in Agriculture 198: 107092. https://doi.org/10.1016/j.compag.2022.107092

10.1016/j.compag.2022.107092
10

Caracciolo, G., Cefariello, R., Corelli Grappadelli, L. 2021. A new mechanical thinner to reduce hand labor in peach. Acta Horticulturae 1304: 243-248. https://doi.org/10.17660/ActaHortic.2021.1304.34

10.17660/ActaHortic.2021.1304.34
11

Chen, J., Ma, W., Liao, H., Lu, J., Yang, Y., Qian, J., Xu, L. 2024. Balancing accuracy and efficiency: The status and challenges of agricultural multi-arm harvesting robot research. Agronomy 14(10): 2209. https://doi.org/10.3390/agronomy14102209

10.3390/agronomy14102209
12

Dimeas, F., Sako, D.V., Moulianitis, V.C., Aspragathos, N.A. 2013. Towards designing a robot gripper for efficient strawberry harvesting. In: Proceedings of the RAAD 2013 (22nd International Workshop on Robotics in Alpe-Adria-Danube Region), Portorož, Slovenia.

13

Falco, J.A., Hemphill, D., Kimble, K.E., Messina, E.R., Norton, A., Ropelato, R.F., Yanco, H.A. 2020. Benchmarking protocols for evaluating grasp strength, grasp cycle time, finger strength, and finger repeatability of robot end-effectors. IEEE Robotics and Automation Letters 5(2): 644-651. https://doi.org/10.1109/LRA.2020.2964164

10.1109/LRA.2020.2964164
14

Gongal, A., Amatya, S., Karkee, M., Zhang, Q., Lewis, K. 2015. Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture 116: 8-19. https://doi.org/10.1016/j.compag.2015.05.021

10.1016/j.compag.2015.05.021
15

Hayashi, S., Ganno, K., Ishii, Y. 2002. Robotic harvesting system for eggplants. Japan Agricultural Research Quarterly (JARQ) 36(3): 163-168. https://doi.org/10.6090/jarq.36.163

10.6090/jarq.36.163
16

He, L., Xu, W., Li, Z., Zhang, Z., Fu, J., Gong, D., Meng, L., Wang, Y. 2025. Advance on agricultural robot hand-eye coordination for complex agronomic tasks: A review. Engineering 51(8): 263-279. https://doi.org/10.1016/j.eng.2025.01.022

10.1016/j.eng.2025.01.022
17

Hemming, J., van Tuijl, B.A.J., Gauchel, W., Wais, E. 2016. Field test of different end-effectors for robotic harvesting of sweet-pepper. Acta Horticulturae 1130: 567-574. https://doi.org/10.17660/ActaHortic.2016.1130.85

10.17660/ActaHortic.2016.1130.85
18

Huang, Y., Xu, S., Chen, H., Li, G., Dong, H., Yu, J., Zhang, X., Chen, R. 2025. A review of visual perception technology for intelligent fruit harvesting robots. Frontiers in Plant Science 16: 1646871. https://doi.org/10.3389/fpls.2025.1646871

10.3389/fpls.2025.164687140904864PMC12401999
19

Ito, Y., Nakano, T. 2015. Development and regulation of pedicel abscission in tomato. Frontiers in Plant Science 6: 442. https://doi.org/10.3389/fpls.2015.00442

10.3389/fpls.2015.0044226124769PMC4462994
20

Jin, T., Huang, Z., Fu, L., Chen, R., Ge, C., Xiao, J. 2025. Performance evaluation of robotic harvester with integrated real-time perception and path planning for dwarf hedge-planted apple orchard. Agriculture 15(15): 1593. https://doi.org/10.3390/agriculture15151593

10.3390/agriculture15151593
21

Jo, Y., Park, Y., Son, H.I. 2024. A suction cup-based soft robotic gripper for cucumber harvesting: Design and validation. Biosystems Engineering 238: 143-156. https://doi.org/10.1016/j.biosystemseng.2024.01.008

10.1016/j.biosystemseng.2024.01.008
22

Jun, J., Kim, J., Seol, J., Kim, J., Son, H.I. 2021. Towards an efficient tomato harvesting robot: 3D perception, manipulation, and end-effector. IEEE Access 9: 17631-17640. https://doi.org/10.1109/ACCESS.2021.3052240

10.1109/ACCESS.2021.3052240
23

Kim, T., Lee, D.H., Kim, K.C., Kim, Y.J. 2023. 2D pose estimation of multiple tomato fruit-bearing systems for robotic harvesting. Computers and Electronics in Agriculture 211: 108004. https://doi.org/10.1016/j.compag.2023.108004

10.1016/j.compag.2023.108004
24

Koirala, A., Walsh, K.B., Wang, Z., McCarthy, C. 2019. Deep learning - Method overview and review of use for fruit detection and yield estimation. Computers and Electronics in Agriculture 162: 219-234. https://doi.org/10.1016/j.compag.2019.04.017

10.1016/j.compag.2019.04.017
25

Lee, B.K., Kam, D.H., Min, B.C., Hwa, J., Oh, S. 2019. A vision servo system for automated harvest of sweet pepper in Korean greenhouse environment. Applied Sciences 9(12): 2395. https://doi.org/10.3390/app9122395

10.3390/app9122395
26

Lei, X.-H., Yuan, Q.-C., Xyu, T., Qi, Y.-N., Zeng, J., Huang, K., Sun, Y.-H., Herbst, A., Lyu, X.-L. 2023. Technologies and Equipment of Mechanized Blossom Thinning in Orchards: A Review. Agronomy 13(11): 2753. https://doi.org/10.3390/agronomy13112753

10.3390/agronomy13112753
27

Lehnert, C., English, A., McCool, C., Tow, A.W., Perez, T. 2017. Autonomous sweet pepper harvesting for protected cropping systems. arXiv arXiv:1706.02023. https://doi.org/10.1109/LRA.2017.2655622

10.1109/LRA.2017.2655622
28

Li, T., Feng, Q., Qiu, Q., Xie, F., Zhao, C. 2022. Occluded apple fruit detection and localization with a frustum-based point-cloud- processing approach for robotic harvesting. Remote Sensing 14(3): 482. https://doi.org/10.3390/rs14030482

10.3390/rs14030482
29

López-Barrios, J.D., Escobedo Cabello, J.A., Gómez-Espinosa, A., Montoya-Cavero, L.-E. 2023. Green sweet pepper fruit and peduncle detection using Mask R-CNN in greenhouses. Applied Sciences 13(10): 6296. https://doi.org/10.3390/app13106296

10.3390/app13106296
30

Lu, R., Dickinson, N., Lammers, K., Zhang, K., Chu, P., Li, Z. 2022. Design and evaluation of end effectors for a vacuum-based robotic apple harvester. Journal of the ASABE 65(5): 963-974.https://doi.org/10.13031/ja.14970

10.13031/ja.14970
31

Mangat, A., Niyas, P. 2017. Factors affecting cutting of peduncle of tomato (Solanum lycopersicum). International Journal of Agriculture, Environment and Biotechnology 10(3): 345-348. https://doi.org/10.5958/2230-732X.2017.00042.0

10.5958/2230-732X.2017.00042.0
32

Ministry of Justice, Ministry of Agriculture, Food and Rural Affairs, Ministry of Oceans and Fisheries. 2025. Government allocates 109,000 seasonal foreign workers for 2026. (https://www.moj.go.kr Accessed Jan. 29, 2026)

33

Navas, E., Fernández, R., Sepúlveda, D., Armada, M., González-de-Santos, P. 2020. A design criterion based on shear energy consumption for robotic harvesting tools. Agronomy 10(5): 734. https://doi.org/10.3390/agronomy10050734

10.3390/agronomy10050734
34

Park, Y., Seol, J., Pak, J., Jo, Y., Kim, C., Son, H.I. 2023. Human-centered approach for an efficient cucumber harvesting robot system: Harvest ordering, visual servoing, and end-effector. Computers and Electronics in Agriculture 212: 108116. https://doi.org/ 10.1016/j.compag.2023.108116

10.1016/j.compag.2023.108116
35

Rajendran, V.S., Parsa, S., Parsons, S., Ghalamzan Esfahani, A. 2022. Peduncle gripping and cutting force for strawberry harvesting robotic end-effector design. In: Proceedings of the 2022 4th International Conference on Control and Robotics (ICCR), pp. 59-64. https://doi.org/10.1109/ICCR55715.2022.10053882

10.1109/ICCR55715.2022.10053882
36

Ringdahl, O., Kurtser, P., Edan, Y. 2019. Evaluation of approach strategies for harvesting robots: Case study of sweet pepper harvesting. Journal of Intelligent & Robotic Systems 95: 149-164. https://doi.org/10.1007/s10846-018-0892-7

10.1007/s10846-018-0892-7
37

Roberts, J.A., Elliott, K.A., González-Carranza, Z.H. 2002. Abscission, dehiscence, and other cell separation processes. Annual Review of Plant Biology 53: 131-158. https://doi.org/10.1146/annurev.arplant.53.092701.180236

10.1146/annurev.arplant.53.092701.180236
38

Rong, J., Fu, J., Zhang, Z., Yin, J., Tan, Y., Yuan, T., Wang, P. 2022. Development and evaluation of a watermelon-harvesting robot prototype: Vision system and end-effector. Agronomy 12(11): 2836. https://doi.org/10.3390/agronomy12112836

10.3390/agronomy12112836
39

Rutkowski, K., Łysiak, G. P. 2022. Thinning methods to regulate sweet cherry crops—A review. Applied Sciences 12(3): 1280. https://doi.org/10.3390/app12031280

10.3390/app12031280
40

Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., Perez, T. 2017. Peduncle detection of sweet pepper for autonomous crop harvesting—Combined colour and 3D information. IEEE Robotics and Automation Letters 2(2): 765-772. https://doi.org/10.1109/LRA.2017.2651952

10.1109/LRA.2017.2651952
41

Schupp, J. R., Baugher, T. A., Miller, S. S., Harsh, R. M., Lesser, K. M. 2011. Peach Blossom String Thinner Performance Improved with Pruning Modifications. HortScience 46(11): 1486-1492. https://doi.org/10.21273/HORTSCI.46.11.1486

10.21273/HORTSCI.46.11.1486
42

Seol, J., Lee, S., Son, H.I. 2020. A review of end-effector for fruit and vegetable harvesting robot. Journal of Korea Robotics Society 15(2): 91-99. https://doi.org/10.7746/jkros.2020.15.2.091

10.7746/jkros.2020.15.2.091
43

Shi, G., Zhang, F., Wu, X. 2025. Robust keypoint-based method for peduncle pose estimation in unstructured environments. Computers and Electronics in Agriculture 236: 110380. https://doi.org/10.1016/j.compag.2025.110380

10.1016/j.compag.2025.110380
44

Sobol, Z., Kurpaska, S., Nawara, P., Pedryc, N., Basista, G., Tabor, J., Hebda, T., Tomasik, M. 2024. Prototype of a new head grabber for robotic strawberry harvesting with a vision system. Sensors 24(20): 6628. https://doi.org/10.3390/s24206628

10.3390/s2420662839460108PMC11511573
45

Statistics Korea. 2025. Results of the 2024 agriculture, forestry and fishery survey. (https://www.kostat.go.kr Accessed Jan. 29, 2026)

46

Tang, Y., Chen, M., Wang, C., Luo, L., Li, J., Lian, G., Zou, X. 2020. Recognition and localization methods for vision-based fruit picking robots: A review. Frontiers in Plant Science 11: 510. https://doi.org/10.3389/fpls.2020.00510

10.3389/fpls.2020.0051032508853PMC7250149
47

Tavakoli, H., Mohtasebi, S.S., Jafari, A. 2009. Effects of moisture content, internode position and loading rate on the bending characteristics of barley straw. Research in Agricultural Engineering 55(2): 45-51. https://doi.org/10.17221/26/2008-RAE

10.17221/26/2008-RAE
48

UK-RAS Network (UK Robotics and Autonomous Systems Network). 2018. Agricultural robotics: The future of robotic agriculture. pp. 1-26.

49

Vrochidou, E., Tsakalidou, V.N., Kalathas, I., Gkrimpizis, T., Pachidis, T., Kaburlasos, V.G. 2022. An overview of end effectors in agricultural robotic harvesting systems. Agriculture 12(8): 1240. https://doi.org/10.3390/agriculture12081240

10.3390/agriculture12081240
50

Weng, W., He, M., Zheng, Z., Lin, T., Lai, Z., Zheng, S., Wu, X. 2024. Tomato pedicel physical characterization for fruit-pedicel separation tomato harvesting robot. Agronomy 14(10): 2274. https://doi.org/10.3390/agronomy14102274

10.3390/agronomy14102274
51

Xu, Z., Liu, J., Wang, J., Cai, L., Jin, Y., Zhao, S., Xie, B. 2023. Realtime picking point decision algorithm of trellis grape for high-speed robotic cut-and-catch harvesting. Agronomy 13(6): 1618. https://doi.org/10.3390/agronomy13061618

10.3390/agronomy13061618
52

Yaguchi, H., Hasegawa, T., Nagahama, K., Inaba, M. 2018. A research of construction method for autonomous tomato harvesting robot focusing on harvesting device and visual recognition. Journal of the Robotics Society of Japan 36(10): 693-702. https://doi.org/10.7210/jrsj.36.693

10.7210/jrsj.36.693
53

Zhang, S., Li, S., Dai, M., Lu, E., Liu, S., Ge, L., Zhang, Y., Guan, C., Xv, B., Su, W., Miao, H. 2025. Mechanical and biological evaluation of two fresh pepper varieties. Frontiers in Plant Science 16: 1542262. https://doi.org/10.3389/fpls.2025.1542262

10.3389/fpls.2025.154226240241825PMC11999842
Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 8
  • No :1
  • Pages :10-24
  • Received Date : 2026-02-25
  • Revised Date : 2026-03-06
  • Accepted Date : 2026-03-06
  • DOI :https://doi.org/10.22765/pastj.20260002
Journal Informaiton Precision Agriculture Science and Technology Precision Agriculture Science and Technology
Online Submission & Review System
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close