How About Them Apples: 3D Pose and Cluster Estimation of Apple Fruitlets in a Commercial Orchard

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Ans Qureshi, David Smith, Trevor Gee, Ho Seok Ahn, Ben McGuinness, Catherine Downes, Rahul Jangali, Kale Black, Hin Lim, Mike Duke, Bruce A. MacDonald, Henry Williams

Published: 2025, Last Modified: 24 Mar 2026ICRA 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Aotearoa's apple industry struggles to maintain the skilled workforce required for fruitlet thinning each year. Skilled labourers play a pivotal role in managing crop loads by precisely thinning fruitlets to a desired number to achieve the desired spacing for high-quality apple growth. This complex task requires accurate mapping of the fruitlets along each branch. This paper presents a novel vision system capable of mapping the orientation and clustering information of apple fruitlets. Fruitlet pose estimation has been validated against data collected from a real-world commercial apple orchard. The results show an improved counting accuracy of 83.97% on prior implementations, an orientation estimate accuracy of 88.1%, and a clustering accuracy of 94.3%. Future work will utilise this information to determine which fruitlets to remove and then robotically thin them from the canopy.