Potato Plant Image Detection Based on Deep Learning

DOI

10.2991/icadme-16.2016.74How to use a DOI?

Keywords

Deep learning; Machine learning; Remote sensing; Feature analysis recognition

Abstract

Potato is one of the most important food crops in the world. The information which extraction from high resolution remote sensing image is a new way to study the potato planting distribution and growth condition. For remote sensing target detection, a lot of people were used AdaBoost algorithm, SIFT algorithm, Tamura texture feature algorithm in the past. But it's just a feature of artificial extraction. Deep learning provides an effective framework for automatic extraction of target features. The experiment uses a simple but useful deep learning method (PCANet). After image segmentation, gray, binaryzation and filtering, the 42*48 of the potato plant images are trained and tested by feature extraction. The results showed that the detection rate of potato plants could reach 82.20%, the false detection rate was 12.66%, and the detection speed is 1.22-1.31 image per second, which could be applied to high efficiency fertilization, weeding and insect pests in order to achieve the purpose of increasing potato yield.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiuyu Xia
AU  - Jingwen Xu
AU  - Junfang Zhao
AU  - Ning Li
AU  - Juncheng Wu
PY  - 2017/07
DA  - 2017/07
TI  - Potato Plant Image Detection Based on Deep Learning
BT  - Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 444
EP  - 447
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-16.2016.74
DO  - 10.2991/icadme-16.2016.74
ID  - Xia2017/07
ER  -