Artificial Intelligence-Based Decision Automation for Apple Orchard Thinning

Aotearoa New Zealand is a leading apple producer and is projected to export NZ$ 2 billion worth of high-quality pip fruit by 2030. Apple thinning is essential to quality but is also seasonal, leading to a high demand for skilled labour. However, labour shortages have been experienced globally due to the labour-intensive nature of hand-thinning and low wages. Apple thinning involves removing a proportion of fruitlets from clusters to ensure the remaining apples get adequate sunlight, nutrients, and water. Automating this task requires a system capable of accurately detecting clusters and deciding which fruitlets to thin based on physical metrics. This thesis aimed to develop a fruitlet mapping system that extracts key metrics and guides a robot in making precise decisions for accurately removing fruitlets from tree branches. This thesis presents a novel vision system capable of mapping the load and clustering information of apple fruitlets as accurately as a human expert. Accurate mapping of fruitlets along each branch is a complex task which involves quality stereo data collection, point cloud data generation, instance segmentation, fruitlet 3D geometrical representation, and fruitlet tracking. Fruitlet maps have been validated against data collected from a real-world commercial apple orchard. These maps provide the spatial information of the fruitlet clusters and the decisions required to thin them. The best results are achieved using an over-the-row robot platform called “Archie Senior”, which scans both sides of the fruitlet branches, yielding an 87.3% precision and 93.3% recall for load estimates, a 2.06 mm mean absolute error (MAE) for size estimates, an 80.8% orientation success rate, 66.14% thinning accuracy, and 66.7% correct decision-making. These results were obtained after validation using actual fruitlet data in a comprehensive set of experiments conducted during field trials in orchards.

Name thesis:degree_name

PhD

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Systems Engineering

Grantor dc:publisher

ResearchSpace@Auckland

Year dc:date.issued

2025

Author dc:creator

• Qureshi, Ans Hussain

Advisors dc:contributor.advisor

• Ahn, Ho Seok
• MacDonald, Bruce

• Agricultural Robotics
• Orchard Automation
• Robot Vision

Statement dc:rights

• Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Licence dc:rights.uri

• https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm

Handle dc:identifier.uri

https://hdl.handle.net/2292/72880

OAI identifier oai:identifier

oai:researchspace.auckland.ac.nz:2292/72880