Proceedings
Authors
| Filter results2 paper(s) found. |
|---|
1. Robotic Arm Tomato Harvesting System and Next Best View Algorithm DevelopmentReplacing human labor with robots is a trend for future agriculture due to its efficiency and consistency. However, in automatic fruit harvesting tasks, leaf occlusion and the dynamic orientation of fruit make it difficult for robots to directly observe the picking point. To address this problem, this research focuses on tomato harvesting, and proposes a next-best-view (NBV) algorithm based on two main structures: “tomato pose prediction” and a “target-hit-gain function”.... P. Yen |
2. Null Dataset-Based Detection Enhances Robotic Vision in Greenhouse Cherry Tomato HarvestingCluttered cherry tomato greenhouse environments with visually similar distractors often trigger False Positives (FPs) in robotic vision, misguiding the robot’s motion and reducing harvesting success. We introduce a null-dataset strategy that integrates unannotated distractor images into YOLOv8l training, with their proportion tuned through loop refinement to suppress FPs while preserving precision. Optimal null proportions were identified as 12.3% for tomato detection and 8.3% for pedicel... P. Yen |