Exploration of Convolutional Neural Network Models for Accurate Classification of Severity in Silkworms | IEEE Conference Publication | IEEE Xplore

Exploration of Convolutional Neural Network Models for Accurate Classification of Severity in Silkworms


Abstract:

The technique of raising silkworms to produce silk is sericulture. The farmed silk moth (Bombyx mori) is the species that is most frequently utilized in this sector, but ...Show More

Abstract:

The technique of raising silkworms to produce silk is sericulture. The farmed silk moth (Bombyx mori) is the species that is most frequently utilized in this sector, but Tasar, Eri, and Muga are also grown for wild silks. With greater than 60% of the world's output going to India and China, the production of silk is at its highest level. The mulberry silkworm has four phases in its life cycle: the egg, larvae, pupa, and moth. For the health and quality of the silkworm and its cocoons, proper upkeep is essential, including the supply of nourishing food and ideal ambient conditions. Just after silkworm has been slain during the pupal stage, the silk thread is extracted from the cocoon. The illnesses that plague silkworms have an influence on sericulture as well. Common diseases include Grasserie, Muscardine, and Flacherie. Numerous uses of Deep Transfer Learning in sericulture, such as the counting and categorization of silkworm eggs, illness detection, and the gender and variety identification of silkworm cocoons, have shown to be successful. In this study, we seek to present a thorough overview of the sericulture sector, including the life cycle of the silkworm, the prevalence of diseases, and the use of Deep Transfer Learning in the sector.
Date of Conference: 06-08 July 2023
Date Added to IEEE Xplore: 23 November 2023
ISBN Information:

ISSN Information:

Conference Location: Delhi, India

Contact IEEE to Subscribe

References

References is not available for this document.