Analysis of different Methodology behind the Crop Yield Prediction

Abstract

Different factors are affecting the crop yield prediction. Many research studies have been conducted so far by considering different circumstances of climatologically factors influencing present climate conditions in different parts of the world. These climate conditions directly affect crop yield. Different investigations have been finished to find the associations between the huge scope of climatologically factors, seed type, and Crop yield. We can consider the different methodology: Multiple linear regression model and Logistic linear regression model, also Deep Neural Network organizations to predict the crop yield prediction. Crop prediction systems are utilized to anticipate the appropriate yield by detecting different boundaries of soil and many more boundaries identified with air. Boundaries like soil, PH, nitrogen, phosphate, potassium, natural carbon, calcium, magnesium, sulfur, manganese, copper, iron, profundity, temperature, precipitation, stickiness. For that reason, we are utilizing the Deep Neural Network. The information module contains crop name, land zone, soil type, soil pH, bug subtleties, climate, water level, and seed type. The component determination module is answerable for the subset choice of a property from crop subtleties. The harvest yield forecast model is utilized to foresee plant development and plant disease