Image-Based Silkworm Egg Classification and Counting Using Counting Neural Network

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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