Deep learning approach for detecting tomato flowers and buds in greenhouses on 3P2R gantry robot.

ABSTRACT: In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and robotics for pollinating flowers. Robotic pollination offers several benefits, including reduced labor requirements and preservation of costly pollen through artificial tomato pollination. However, previous studies have primarily focused on the labeling and detection of tomato flowers alone. Therefore, the objective of this study was to develop a comprehensive methodology for simultaneously labeling, training, and detecting tomato flowers specifically tailored for robotic pollination. To achieve this, transfer learning techniques were employed using well-known models, namely YOLOv5 and the recently introduced YOLOv8, for tomato flower detection.

SUBMITTER: Singh R 

PROVIDER: S-EPMC11374987 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature