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AI helps robots figure out how to grab apples right

Crop Improvement

The next time you see an autonomous harvester rolling through an orchard, this is the kind of vision system deciding which apples it can actually grab without bruising them or fumbling the branch.

Picking apples sounds simple, but a robot arm needs to know not just where a fruit is, but which way it's facing so it can grip it correctly without damaging the tree or the fruit. This study trained a two-stage AI system: one part finds and outlines apples even when they're partly hidden by leaves or other fruit, and a second part spots key points on each apple to figure out its orientation, essentially drawing an invisible line through the fruit's stem to its bottom. The result was over 92% accuracy in estimating that orientation, which could make automated apple harvesting more reliable.

Key Findings

1

The YOLO-SD segmentation model achieved 95.2% average accuracy (mAP50) for identifying pickable apples while cutting model size to just 2.47 million parameters

2

The YOLO-RSM keypoint detection model reached 90.3% accuracy, outperforming YOLOv8n, YOLO11n, and YOLO12n by 2.4%, 2.6%, and 4.7% respectively

3

The 2D axis estimation method achieved 92.68% accuracy with an average angular error of just 7.23 degrees

chevron_right Technical Summary

Researchers built an AI vision system that helps orchard robots figure out exactly which apples on a tree are actually reachable to pick, and how each fruit is oriented, by combining fruit segmentation with keypoint detection to estimate the apple's growth axis.

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Abstract Preview

Original paper

A two-dimensional axis estimation method for pickable canopy apples based on YOLO cascade network

Introduction In a complex orchard environment, canopy apples are obscured by various factors, making it hard for apple harvesting robots to accurately determine which apples can be directly harvest...

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Abstract copyright held by the original publisher.

hub This connects to 9 other discoveries — Apple crop-improvement, urban-ecology, food-forest 5 related articles

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