Stored-grain Pests Detection Based on SVM

DOI

10.2991/mmsta-19.2019.34How to use a DOI?

Keywords

stored-grain pests; SVM; classification

Abstract

Stored grain pests detection is essential for grain management. In this paper, we have proposed a machine learning method for stored-grain pest detection. We focus on crustacean pests detection using SVM. The 20 pixels width and 20 pixels height pests and background images are directly utilized for SVM training and classification. According to the experiment results, accuracy of SVM classifier is 99.40%, which outperforms LSSVM and PLS. We then conducted an interesting experiment using synthetic pest images. We employ these synthesized data as pest samples for training SVM classifier. According to the results, the SVM classifier trained via synthetic pest images is able to detect pests in images in some cases because synthetic pest images are quite different from real pest images.

Copyright

© 2019, 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  - Fengqi Cui
AU  - Haoran Xiao
AU  - Cuiping Zhou
AU  - Yi Mou
AU  - Long Zhou
PY  - 2019/12
DA  - 2019/12
TI  - Stored-grain Pests Detection Based on SVM
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 161
EP  - 164
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.34
DO  - 10.2991/mmsta-19.2019.34
ID  - Cui2019/12
ER  -