Price Forecasting of Gram and Black Gram across Major States of India: A Comparative Analysis of Deep Learning Models
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Keywords:
Pulses price forecasting, machine learning, deep learning, gram, blackgramAbstract
Precise price forecasting of pulse crops is essential in effective market interventions, price stabilisation and informed policy decisions. This paper compares the performance of some deep learning models in predicting wholesale prices of two key pulses, gram and black gram, across the major producing states. Four deep learning models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), were employed and compared in terms of RMSE and MAPE to assess their performance. The RNN performed better than other models in most datasets. The projections indicate that the prices of the two crops will show a downward trend in 2026, with the exception of gram in Madhya Pradesh. The results highlight that deep learning models have the potential to improve the accuracy of crop price predictions and of evidence-based policy-making.
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