Comparative study of neural network variants for potato (Solanum tuberosum) price modeling

The intricate nature of agricultural price data possesses a formidable challenge in the modeling process, necessitating the careful selection and fine-tuning of methodologies. Deep learning emerges as a potent tool for enhancing the predictive accuracy and understanding the complexities of agricultural prices. The effectiveness of deep learning methodologies in handling the complex patterns of agricultural price datasets was demonstrated using monthly potato (Solanum tuberosum L.) price data collected from the National Horticultural Board across four distinct markets. The study was carried out during 2023 aimed to compare the performance of deep learning models, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with feed forward Artificial Neural Networks (ANN) using the error metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The GRU model performed best for the Chandigarh (16.26% MAPE) and Delhi (6.09% MAPE) markets where LSTM model showed superior performance in the Dehradun market (17.81% MAPE) and CNN for Shimla market (12.53% MAPE). The error percentage of deep learning models were remarkably low when compared to the machine learning model.