Analysis and prediction of crop yield and cost using machine learning approaches

Abstract

In world economy agriculture has own major part to improve financial stability of a country. However still we are facing many loopholes in that to develop agricultural techniques and to adopt new things to get better results. Hence still agriculture is the developing industry in many countries. Due to these problems farmers are not getting good crop productivity. To overcome from these problems first we have to predict crop yield by using latest technologies. For this here we have to use Machine Learning technology to predict crop yield from a dataset which is already stored in ( SMLT ) to get the details like Uni-variate analysis, By-vairiate analysis, Multi-vairiate analysis, Missing value treatment, Variable identification etc. To determine which algorithms are more accurate to predict crop yield, A comparative study done between Machine Learning algorithms. The best outcome shows that the effectiveness of the proposed Machine learning algorithm technique can be compared with best accuracy with calculation, precision, Recall, F1 score.