MLG-YOLO: A Model for Real-Time Accurate Detection and Localization of Winter Jujube in Complex Structured Orchard Environments.

ABSTRACT: Our research focuses on winter jujube trees and is conducted in a greenhouse environment in a structured orchard to effectively control various growth conditions. The development of a robotic system for winter jujube harvesting is crucial for achieving mechanized harvesting. Harvesting winter jujubes efficiently requires accurate detection and location. To address this issue, we proposed a winter jujube detection and localization method based on the MobileVit-Large selective kernel-GSConv-YOLO (MLG-YOLO) model. First, a winter jujube dataset is constructed to comprise various scenarios of lighting conditions and leaf obstructions to train the model. Subsequently, the MLG-YOLO model based on YOLOv8n is proposed, with improvements including the incorporation of MobileViT to reconstruct the b

SUBMITTER: Yu C 

PROVIDER: S-EPMC11418275 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature