Developing an Analytical Model for Predicting Mulberry Yield Using Data Mining Techniques

Due to increased computerization in the current trend of modern agriculture, in this contemporary scenario in India, sericulture plays a major role, aiding in the empowerment of the rural sector. The present investigation deals with the study of mulberry yield with varieties and various parameters that affect its growth. The current study is more pronounced, considering the fact that India is the second major silk producer in the world. The study involves analysing a large amount of data using a data mining technique to derive meaningful interpretation. Furthermore, multivariate regression analysis was performed to predict the effect of various parameters affecting the growth of the mulberry yield. An attempt has been made to develop a viable model for mulberry dynamics using data mining techniques to understand the hidden correlations (yield–variety) using multivariate regression. This led to the development of a forewarning system. Making appropriate use of the proposed system leads to considerable gains in efficiency and economic advantages.