Prediction of Crop Yield Using Machine Learning

Abstract—Major source of India’s population depends on agriculture. Researchers have been working to improve agricultural production prediction using different methods and techniques but all these methods have certain limitations. An important tool for crop yield prediction is Machine Learning. Machine Learning has turned out to be productive in data mining and agricultural studies. Some of the factors behind the reduced rate of crop production are climate and its unpredictability therefore crop yield prediction using machine learning help in increasing crop yield and production. The system takes various datasets consisting of soil moisture, physiological features of the crop, humidity, and temperature and uses various machine learning algorithms to train the model for predicting the yield. This study explains how supervised machine-learning techniques can be used to predict the yield of crops. Kaggle, a public data-gathering platform was utilized to obtain the data for this Project. With the help of Jupyter Notebook, Random Forest Regressor is used to train the model to get accurate predictions.