International Journal of Electronics Signals and Systems


Sericulture is an agricultural activity that involves rearing of silkworms for the production of cocoons which is in turn used to produce raw silk. In countries like India where agriculture is pre-dominant, sericulture is considered to be one of the most important economic activities. India ranks second among the silk producing countries in the world, accounting for over 17 percent of the world’s production. The major activities of sericulture comprise of food-plant cultivation to feed the silkworms, spin silk cocoons and reel them for unwinding the silk filament for processing and weaving to produce valuable silk products. Though technology has been a boon to the agricultural sector, there is not much implementation of technological methods in disease detection in silkworms. But diseases in silkworms pose a major threat and causes a huge economic loss to farmers which in turn necessitates early identification of diseases and this is quite an arduous process. Identification and detection of diseases at an earlier stage would be helpful for a farmer to take essential precautionary measures to avoid spreading of diseases. With the advancement in technology, a variety of methods have been developed to address this issue. In this paper, different machine learning algorithms are compared for their accuracy and the best ensemble learning algorithm is adopted which can be further implemented on a hardware model for real-time applications. The developed algorithm aids the machine in decision making and hence identifies grasserie disease in Bombyx Mori silkworm.



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