Analysis of soil and prediction of crop yield (Rice) using Machine Learning approach

Abstract

Agriculture sector is backbone of Indian Economy .However Agriculture sector in India is facing severe problem of maximising the crop productivity. Farmers lack in basic knowledge of nutrient content of soil, selection of crop best suited for soil and they also lack in efficient method of prediction of crop well in advance so that appropriate methods can be used to improve crop productivity and to make arrangements for storage, marketing well before harvest. This work presents an approach which uses different Machine Learning techniques in order to predict the category of the yield based on macro-nutrients and micro- nutrients status in dataset. The dataset considered for the crop yield prediction was obtained from Krishi Bhawan (Talab-Tillo) Jammu. The parameters present in the data are Macro-Nutrients(ph,Oc,Ec,N,P,K,S) and Micro Nutrients(Zn,Fe,Mn,Cu) present in samples collected from different regions of Jammu District .After analysis Machine learning algorithms are applied to predict the category of yield . The category, thus predicted will specify the yield of crops. The problem of predicting the crop yield is formulated as Classification where different classifier algorithms are used.