Image-Based Silkworm Egg Classification and Counting Using Counting Neural Network
Authors: 
Supachaya Prathan, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Sanparith MarukatatAuthors Info & Claims
Supachaya Prathan, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Sanparith MarukatatAuthors Info & ClaimsPages 21 - 26
Abstract
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.
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Kiratiratanapruk, K., Methasate, I., Watcharapinchai, N. and Sinthupinyo, W. 2014. Silkworm Eggs Detection and Classification Using Image Analysis. 2014 International Computer Science and Engineering Conference (ICSEC) (2014), 340--345.
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Index Terms
- Image-Based Silkworm Egg Classification and Counting Using Counting Neural Network
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Published In
January 2019
268 pages
ISBN:9781450366120
DOI:10.1145/3310986
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Published: 25 January 2019
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ICMLSC 2019
ICMLSC 2019: 2019 the 3rd International Conference on Machine Learning and Soft Computing
January 25 - 28, 2019
Da Lat, Viet Nam
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