Proceedings of the IV International research conference "Information technologies in Science, Management, Social sphere and Medicine" (ITSMSSM 2017)

Comparison of object classification methods in seed stream separation

Authors
Andrey Vlasov, Alexander Fadeev
Corresponding Author
Andrey Vlasov
Available Online December 2017.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

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  - 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  - https://doi.org/10.2991/itsmssm-17.2017.38
ID  - Vlasov2017/12
ER  -