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Score-Based Harvesting Order Planning and False Detection Handling for Robotic Tomato Harvesting

Aug 2026 · International Conference on Advanced Mechatronic Systems · pp. 43-48 · 0 citations · 13 references

Abstract

Robotic tomato harvesting is crucial for addressing agricultural labor shortages. However, existing systems struggle with optimal order planning in dense clusters and erroneous motions caused by false detections. We propose a robotic harvesting system evaluated in a controlled setting with artificial tomatoes, making three main contributions: (i) a score-based harvesting order planning algorithm that integrates isolation, height, clustercentroid distance, and depth scores; (ii) a data augmentation strategy for YOLOv8, evaluated through an ablation study of 16 augmentation combinations; and (iii) a system-level false detection handling mechanism. In horizontally adjacent settings, our order planning achieves a 65.0% success rate, outperforming random (30.0%) and nearest-neighbor (45.0%) baselines. HSV augmentation alone achieves the best mAP@0.5:0.95 of 0.9250, and the integrated false detection handling mechanism reduces false positives by 87.9% without requiring detector retraining.

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