Prediction of crop yield using satellite vegetation indices combined with machine learning approaches

India is the world's second-largest exporter of agricultural products. Agriculture is India's most populated sector of the economy which plays a key part in the country's overall socio-economic fabric. Crop yield prediction is crucial yet desirable for every country to ensure food security. Machine learning can be a very important decision support tool for the prediction of crop yield which can help in preparing for the future in a much more substantial way. Many studies have been conducted for crop yield prediction using multi-source data, still, the predictions of the crop of the Rajasthan region in India have not been investigated. In the present study well-known machine learning techniques, decision tree, random forest, lasso regression, and support vector regression have been implemented for crop yield prediction using multi-source data. Machine learning techniques were implemented on data set that consists of Vegetation Indices, extracted from remote sensing data and Weather data. Random forest attained the more accurate prediction with the coefficient of determination (R2) of 0.77, root mean square error of 0.39 t/ha and mean absolute error of 0.28 t/ha. The research obtained a good estimation of crop yield prior to the harvest. The crop yield is impacted by the change in environmental factors, this research would assist the farmers to have a prior estimation of the yield. The study also reveals that gradually adding more data to the model increases the overall prediction rate.