A Vacuum‐Suction and Rotate End Effector for Robotic Apple Fruitlet Thinning: Comparative Development, Integrated Field Evaluation, and Preliminary Harvesting

Apple fruitlet thinning and harvesting are labor‐intensive operations requiring fruit to be detached from the tree. This paper presents a robotic end effector that uses vacuum suction to grasp target fruit and rotation to detach it. The suction cup is separated from the rotation drive, allowing it to be replaced according to fruit size and damage tolerance. Because thinning is restricted to a short seasonal window, four detachment principles were developed and tested in parallel off‐season using artificial fruitlet structures, enabling year‐round testing before field trials. In comparative field trials with 50 attempts per end effector, the suction design achieved the highest success rate at 70%, compared with 44% for cutting and 26% for the paraboloid design. Off‐season screening distinguished the strongest and weakest concepts but overestimated absolute field performance, indicating that in this study the artificial structure worked better as a feasibility filter than as a predictor of orchard performance. Integrated with vision and path planning, the suction end effector achieved a 66.1% success rate over 127 attempts in a commercial New Zealand orchard, with localization error the main cause of failure. Fitted with a harvesting cup, the same device also successfully detached mature apples in a preliminary demonstration.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2026-10-19
DOI
https://doi.org/10.1002/aisy.70585
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

A Vacuum‐Suction and Rotate End Effector for Robotic Apple Fruitlet Thinning: Comparative Development, Integrated Field Evaluation, and Preliminary Harvesting

Shen Hin Lim, Benjamin J. McGuinness, Ans H. Qureshi, Chi Kit Au et al.
Advanced Intelligent Systems
Smart Agriculture and AI
article

A Vacuum‐Suction and Rotate End Effector for Robotic Apple Fruitlet Thinning: Comparative Development, Integrated Field Evaluation, and Preliminary Harvesting

Shen Hin Lim, Benjamin J. McGuinness, Ans H. Qureshi, Chi Kit Au, Henry A. Williams, Mike D. Duke, Bruce A. MacDonald, Abozar Nasirahmadi, Rahul Jangali, David Smith
article en

Abstract

Apple fruitlet thinning and harvesting are labor‐intensive operations requiring fruit to be detached from the tree. This paper presents a robotic end effector that uses vacuum suction to grasp target fruit and rotation to detach it. The suction cup is separated from the rotation drive, allowing it to be replaced according to fruit size and damage tolerance. Because thinning is restricted to a short seasonal window, four detachment principles were developed and tested in parallel off‐season using artificial fruitlet structures, enabling year‐round testing before field trials. In comparative field trials with 50 attempts per end effector, the suction design achieved the highest success rate at 70%, compared with 44% for cutting and 26% for the paraboloid design. Off‐season screening distinguished the strongest and weakest concepts but overestimated absolute field performance, indicating that in this study the artificial structure worked better as a feasibility filter than as a predictor of orchard performance. Integrated with vision and path planning, the suction end effector achieved a 66.1% success rate over 127 attempts in a commercial New Zealand orchard, with localization error the main cause of failure. Fitted with a harvesting cup, the same device also successfully detached mature apples in a preliminary demonstration.

Advanced Intelligent Systems
University of Auckland (NZ), Swedish University of Agricultural Sciences (SE), Auckland University of Technology (NZ), University of Waikato (NZ)
Openalex Percentile: Top 15%
Smart Agriculture and AI
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