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
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JOURNAL FREE ACCESS

2020 Volume 13 Issue 2 Pages 42-48

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Abstract

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.

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© 2020 Asian Agricultural and Biological Engineering Association
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