Crop Yield Prediction

Abstract

The agriculture sector continues to be the backbone of Indian economy. Agricultural productivity has become a problem. This makes crop yield prediction a critical task. Data Mining and Machine Learning are emerging fields of research in crop yield analysis. It depends on varied parameters such as temperature, weather, nutrients percentages, soil type, PH value, regional rainfall, etc. Incorporating these factors into crop yield predictions is important. By analyzing these data attribute sand training the data with different machine learning algorithmic rule would boost crop yield prediction under different climatic circumstances. In this work, three supervised algorithms such as Random forest algorithm, Support Vector Machine and Artificial Neural Network are used. The performance of the three is compared and then the best model is used for prediction. The main aim of this project is to achieve optimum yield and to help framers sustain the crop. It also provides the information about maintaining the crop. This work gives a better prediction for the framers to have prior knowledge about crop production.

 Keywords: Data Mining, Machine Learning, Random Forest, Support Vector Machine, Artificial Neural Network.