Silkworm Yield Prediction in Attibele Region using Machine Learning Technique
Manoj S M
Manoj S M, MSc Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka.
Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 1172-1177 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1587059120/2020©BEIESP | DOI: 10.35940/ijrte.A1587.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sericulture is the processes of cultivation of silkworms to produce cocoons which are used for the production of silk or to produce eggs. This research work is carried out with respect to the Attibele region (Karnataka State in India). There are various species of silkworms that are grown in the world, and the yield of silk varies with climatic change. Why climatic changes important for rearing of silkworms? Because they are very sensitive for temperature and humidity fluctuations. For example if the temperature is high and humidity is low or the temperature is low and humidity is high, the silkworms become unhealthy. In this paper we have calculated the climatic conditions that is to be maintained in the future for obtaining the optimal yield of the silkworms. The work also aims to provide the remedies to be taken for the betterment of the production, both in terms of farm-land and cocoons.
Keywords: Silkworm Yield Prediction, Decision Tree Regression, Black Box, Savayava krishi.
Scope of the Article: Machine Learning