Best-View Pedicel Localization with YOLO-DSC for Calyx-Preserving Robotic Harvesting of Cherry Tomatoes

Abstract

Robotic harvesting of cherry tomatoes remains challenging due to dense foliage, asynchronous ripening, and the strict market requirement for calyx-preserving cuts. The calyx frequently occludes the pedicel, making precise localization indispensable. In 640 × 480 images, pedicels span only 7–32 pixels, where even minor errors can lead to miscutting the calyx. To address this challenge, we apply YOLO-DSC to localize pedicels across dynamic frames as the arm-mounted camera moves during the best-view search. This strategy maximizes the visible pedicel length, exposing it perpendicularly to the camera and ensuring clear separation from the calyx, while null-data suppresses false positives from distractors such as leaves, stems, and calyces. In 15 autonomous trials along a 28m greenhouse row, YOLO-DSC achieved the lowest pedicel localization errors, outperforming YOLO baseline model (significant under p < 0.05). This improvement directly translated into higher harvesting success, increasing from 47% with YOLO (include null data training) to 73.3% with YOLO-DSC. These results demonstrate that integrating YOLO-DSC with best-view searching enhances recall and stability under dynamic viewpoints, enabling more reliable calyx-preserving harvesting in real greenhouse conditions.