Archie Snr: A Robotic Platform for Autonomous Apple Fruitlet Thinning

Apple fruitlet thinning is critical in cultivating high-quality apples, requiring an expert workforce to manage the orchard. The thinning process requires precise mapping of fruitlet clusters across the tree branches to manage the desired load for each tree. This paper presents Archie Snr, which was developed to autonomously assess the current load of the tree and thin the excess apples as an expert thinner would. The platform has been extensively evaluated in a real-world commercial orchard. The results show the platform can generate an average load count accuracy of 82.1% with a recall of 93.3%. The system was then able to successfully thin 66.14% of the fruitlets from the canopy.

Authors

Institutions

Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10803063
Citations
2
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
1.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Archie Snr: A Robotic Platform for Autonomous Apple Fruitlet Thinning

Ans Qureshi, Henry Williams, Trevor Gee, Catherine Downes et al.
2 citations
Smart Agriculture and AI
1.20
article

Archie Snr: A Robotic Platform for Autonomous Apple Fruitlet Thinning

Ans Qureshi, Henry Williams, Trevor Gee, Catherine Downes, Mike Duke, Bruce A. MacDonald, David A. Smith, Ben McGuinness, R. G. Oliver, Rahul Jangali, Hin Lim, Kale Black, Scott Harvey
article en
2 citations

Abstract

Apple fruitlet thinning is critical in cultivating high-quality apples, requiring an expert workforce to manage the orchard. The thinning process requires precise mapping of fruitlet clusters across the tree branches to manage the desired load for each tree. This paper presents Archie Snr, which was developed to autonomously assess the current load of the tree and thin the excess apples as an expert thinner would. The platform has been extensively evaluated in a real-world commercial orchard. The results show the platform can generate an average load count accuracy of 82.1% with a recall of 93.3%. The system was then able to successfully thin 66.14% of the fruitlets from the canopy.

Plant & Food Research (NZ), University of Auckland (NZ), University of Waikato (NZ)
Openalex Percentile: Top 15%
Smart Agriculture and AI
1.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.