Analysis of Crop Yield Prediction using Machine Learning algorithms

Agriculture is the backbone of any country. Totally 58% of the population is dependent on agriculture, in 48% is directly dependent on it. India is one of the agricultural countries, its economy is primarily based on agricultural yield growth. Agriculture is largely affected by rainwater and temperature which is highly uncertain in India. Technology proved to be useful for agriculture, increasing the productivity of crops and providing better yields for the farmer. Simple parameters like State, district, temperature, and rainfall are used in this paper so that the farmer can predict the yield for the crop in the year needed. The system follows to do a comparative analysis of K-Nearest Neighbor’s Random Forest and Linear Regression algorithms to obtain this prediction for enhancing the algorithms to give a better prediction. The system follows the prediction based on parameters such as state, crop, temperature, and rainfall. In this model, K-Nearest Neighbor’s algorithm has shown 97% accuracy, the Linear Regression algorithm has shown 54% and the Random Forest algorithm has shown 75%accuracy. From these algorithms, K-Nearest Neighbors has shown the highest accuracy.