Comparison of object classification methods in seed stream separation

DOI

10.2991/itsmssm-17.2017.38How to use a DOI?

Keywords

image processing, seeds sorting, classification, feature extraction, convolutional neural network, automatic detection, grains, agriculture.

Abstract

The paper presents a study of machine learning approaches to detect and classify seeds of a grain crop in order to enhance agricultural seed purification line. The main features of seeds that are hard to recognize during a separation with mechanical methods are resolved with the help of machine learning approach. The main machine learning methods used in research was traditional machine learning and deep learning based on neural networks. A special training image database was retrieved in order to check if the stated approaches are reasonable to use and develop. A set of tests is provided to show the effectiveness of the machine learning applied to solve the stated problem.

Copyright

© 2017, 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  - Andrey Vlasov
AU  - Alexander Fadeev
PY  - 2017/12
DA  - 2017/12
TI  - Comparison of object classification methods in seed stream separation
BT  - Proceedings of the IV International research conference "Information technologies in Science, Management, Social sphere and Medicine" (ITSMSSM 2017)
PB  - Atlantis Press
SP  - 179
EP  - 181
SN  - 2352-538X
UR  - https://doi.org/10.2991/itsmssm-17.2017.38
DO  - 10.2991/itsmssm-17.2017.38
ID  - Vlasov2017/12
ER  -