Agricultural commodity price prediction model: a machine learning framework

An efficient machine learning-based framework for crop price prediction is proposed in this paper to assist the farmers in estimating their profit-loss beforehand. The proposed work is composed of four functional blocks, such as crop yield prediction, determination of supply, demand prediction and crop price prediction. The input datasets consist of the various field values, such as yield, remaining crop at the end of the year, import, demand and price of a crop. Various time series-based algorithms, such as autoregression, moving average, autoregressive moving average, autoregressive integrated moving average and exponential smoothing, are used to forecast the crop yield. The supply of the crop is determined as a sum of three variables, i.e., the predicted crop yield, residue and import values. The demand for the crop is predicted from a year alone as the demand has more correlation with year over other factors. The crop price from demand, supply and year is predicted using different approaches, which include the time series method, statistical approaches and machine learning techniques. Finally, these three techniques for price prediction are compared to determine the best model having minimum root-mean-square error value. In the proposed work, the decision tree regressor is found to be the best model, for predicting crop price, over others. The superiority of the proposed work over existing approaches, in terms of various aspects, is shown by simulation results.

Automated summary

An efficient machine learning-based framework is proposed for crop price prediction to assist farmers in estimating profit and loss in advance. The framework consists of four functional blocks: crop yield prediction, determination of supply, demand prediction, and crop price prediction. Various time series-based algorithms are utilized to forecast crop yield, while the supply is determined using predicted yield, residue, and import values. The demand is predicted solely based on the year, and crop price is predicted using different approaches including time series, statistical methods, and machine learning techniques. The decision tree regressor is identified as the best model for predicting crop price in this work, showing the superiority of the proposed framework over existing approaches.