Comparison of object classification methods in seed stream separation; Advances in Computer Science Research; Vol. 72 : Information technologies in Science, Management, Social sphere and Medicine (ITSMSSM 2017)

Bibliografische gegevens
Parent link:Advances in Computer Science Research
Vol. 72 : Information technologies in Science, Management, Social sphere and Medicine (ITSMSSM 2017).— 2017.— [P. 179-181]
Hoofdauteur: Vlasov A. V. Andrey Vladimirovich
Coauteur: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Andere auteurs: Fadeev A. S. Aleksandr Sergeevich
Samenvatting:Title screen
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.
Taal:Engels
Gepubliceerd in: 2017
Onderwerpen:
Online toegang:http://dx.doi.org/10.2991/itsmssm-17.2017.38
Formaat: Elektronisch Hoofdstuk
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657526

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