Deep Learning Model-Based Approach for Agricultural Crop Price Prediction in Indian Market

The Indian economy depends largely on the agricultural sector. The price fluctuation in the agricultural market is a major issue for farmers and consumers. In this work, deep learning and machine learning model were used in designing an algorithm for agricultural crop price prediction. The SVR model is largely used for time series data prediction. The SVR model with RBF and polynomial kernel was applied to predict the crop price. The SVR with polynomial kernel achieved better prediction accuracy compared to the SVR RBF model. The RNN model achieved the lowest MSE in predicting crop price. The prediction accuracy for SVR and RNN models was compared by evaluating the models’ MAE, MSE, and RMSE values. The RNN model predicted the rice crop price with the lowest MSE, i.e., 0.003. Finally, the deep learning model proved to work better compared to the machine learning model in predicting Indian crop prices.