Comparison of object classification methods in seed stream separation
- 10.2991/itsmssm-17.2017.38How to use a DOI?
- image processing, seeds sorting, classification, feature extraction, convolutional neural network, automatic detection, grains, agriculture.
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.
- © 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/).
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 - 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 -