Plant species classification using deep convolutional neural network

被引：378

作者：

Dyrmann, Mads ^{[1
]}

Karstoft, Henrik ^{[2
]}

Midtiby, Henrik Skov ^{[1
]}

机构：

[1] Univ Southern Denmark, Maersk Mc Kinney Moller Inst, Odense, Denmark

[2] Aarhus Univ, Dept Engn, Aarhus, Denmark

来源：

BIOSYSTEMS ENGINEERING | 2016年 / 151卷

关键词：

Plant classification; Deep learning; Convolutional Neural; Networks; Weed control; WEED; IDENTIFICATION;

D O I：

10.1016/j.biosystemseng.2016.08.024

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained and tested on a total of 10,413 images containing 22 weed and crop species at early growth stages. These images originate from six different data sets, which have variations with respect to lighting, resolution, and soil type. This includes images taken under controlled conditions with regard to camera stabilisation and illumination, and images shot with hand-held mobile phones in fields with changing lighting conditions and different soil types. For these 22 species, the network is able to achieve a classification accuracy of 86.2%. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.

引用

页码：72 / 80

页数：9