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dc.contributor.authorSupachaya Prathanen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSanparith Marukataten_US
dc.description.abstract© 2019 Association for Computing Machinery. Silkworm egg classification and counting are essential tasks in the silkworm industry for promotion and conservation of the silkworm gene. Normally, the egg counting process is done by human or estimated from the average weight of an egg. However, these methods have been proven to be both time-consuming and inaccurate. Therefore, in this work, we develop a silkworm counting system that can count eggs laid on the disease-free laying (DFL) sheet image. The system can count eggs in all classes that are in the fresh, all-blue, and shell period. The result shows that the system yields approximately 80 to 88%counting rate in fresh and shell period. Whereas in the all-blue period, the system can produce about 60 to 78%counting rate because of the condition of the type of DFL sheet and the similar characteristic of all-blue in the early stage and unfertilized eggs.en_US
dc.subjectComputer Scienceen_US
dc.titleImage-based silkworm egg classification and counting using counting neural networken_US
dc.typeConference Proceedingen_US
article.title.sourcetitleACM International Conference Proceeding Seriesen_US National Electronics and Computer Technology Centeren_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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