Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

Research on Image Acquisition and Recognition for Stored Grain Pests

Authors
Defa Wang, Huiling Zhou, Haiying Yang, Yufeng Shen, Yang Cao, Huiyi Zhao
Corresponding Author
Defa Wang
Available Online November 2016.
DOI
10.2991/aiie-16.2016.62How to use a DOI?
Keywords
stored grain pests; image dataset; MSERs; color features; shape features
Abstract

Manual ways of recognizing stored grain pests which are trapped is very time-consuming. In this study, an image dataset of 9 species of pests was built up by finding the MSERs (Maximally Stable Extremal Regions), through a trap of stored grain pests combined with a real-time imaging device. On this basis, the localization and recognition of stored grain pests were achieved. The experimental results on 3600 images showed that by the combination of shape features and color features, the average F1 score was about 0.947 by selecting the appropriate parameters of SVM (Support Vector Machines) classifier.

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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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.62How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Defa Wang
AU  - Huiling Zhou
AU  - Haiying Yang
AU  - Yufeng Shen
AU  - Yang Cao
AU  - Huiyi Zhao
PY  - 2016/11
DA  - 2016/11
TI  - Research on Image Acquisition and Recognition for Stored Grain Pests
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 268
EP  - 272
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.62
DO  - 10.2991/aiie-16.2016.62
ID  - Wang2016/11
ER  -