An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm

被引：0

作者：

Parico A.I.B. ^{[1
]}

Ahamed T. ^{[2
]}

机构：

[1] Graduate School of Life and Environmental Sciences, University of Tsukuba

[2] Faculty of Life and Environmental Sciences, University of Tsukuba

来源：

Engineering in Agriculture, Environment and Food | 2020年 / 13卷 / 02期

关键词：

convolutional neural network (CNN); deep learning; real time weed detection; unmanned aerial vehicle; you only look once (YOLO);

D O I：

10.37221/eaef.13.2_42

中图分类号：

学科分类号：

摘要：

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. © 2020 Elsevier B.V.. All rights reserved.

引用

页码：42 / 48

页数：6

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