Comparative Analysis of Soil Properties to Predict Fertility and Crop Yield using Machine Learning Algorithms

Agriculture is an essential part of human lives. It is one of the major source of employment in India. More than half of the population depend upon agriculture. It is the backbone of our economy. Crop yield depends on many factors. One of the major factors which affect the yield of the crop is soil. Improvising the techniques to predict crop yield in different climatic conditions can help farmers and other stakeholders in better decision making in terms of agronomy and crop selection. Crop yield prediction includes forecasting the yield of the crop from previous historical data which consists of factors such as temperature, humidity, pH, rainfall and crop name. It gives us an idea for the finest predicted crop which will be cultivate in the field weather conditions. In the proposed work, a comparative analysis on soil properties to predict fertility and crop yield has been performed using machine learning algorithms. The analysis has been done on self -obtained dataset, for three crops - tomato, potato and chilli. The crop yield prediction has been done using K Nearest Neighbour algorithm, Naïve Bayes algorithm and Decision Trees classifier.