CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms
AI Summary1 min read
TL;DR
This study introduces CRE-YOLO, a compressed deep learning model for efficient Jaboticaba tree detection using UAVs in precision agriculture, aiming to improve accuracy and speed.
CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms
Junyu Huang; Renbo Luo; Yuna Tan; Zhuowen Wu
https://doi.org/10.1109/ACCESS.2024.3520115
Volume 13
This study focuses on the detection of Jaboticaba trees in an orchard located in Nanxiong City, Guangdong Province, utilizing UAV platforms to enhance precision agriculture practices. The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their deployment on UAV platforms for rapid Jaboticaba tree identification. The proposed CRE-YOLO model i...