Engineering in Agriculture, Environment and Food
Online ISSN : 1881-8366
ISSN-L : 1881-8366
Research article
An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm
Addie Ira Borja PARICO Tofael AHAMED
著者情報
ジャーナル フリー

2020 年 13 巻 2 号 p. 42-48

詳細
抄録
The real-time object detection system You Only Look Once (specifically YOLOv3) has recently shown remarkable speed, making it potentially suitable for Unmanned Aerial Vehicle (UAV) precision spraying. In this study, YOLO-WEED, a weed detection system based on YOLOv3, was developed. The dataset, derived from a five-minute UAV video, was split into a 69 : 17 : 13 ratio for training, validation, and testing, respectively. YOLO-WEED demonstrated a real-time detection speed (up to 24.4 FPS) and high performance using NVIDIA GeForce GTX 1060, with a mean average precision of 93.81 % and an F1 score of 0.94. These results successfully show the effectiveness of the YOLO-WEED system for real-time UAV weed detection, given its high speed and high accuracy in detection.
著者関連情報
© 2020 Asian Agricultural and Biological Engineering Association
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