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

S VISHNU  SHANKAR; RANJIT KUMAR  PAUL; MD  YEASIN; PATIL SANTOSH  GANAPATI

doi:10.56093/ijas.v95i8.149323

Authors

S VISHNU SHANKAR ICAR-Indian Agricultural Statistics Research Institute, New Delhi
RANJIT KUMAR PAUL ICAR-Indian Agricultural Statistics Research Institute, New Delhi
MD YEASIN ICAR-Indian Agricultural Statistics Research Institute, New Delhi
PATIL SANTOSH GANAPATI ICAR-Indian Agricultural Statistics Research Institute, New Delhi

https://doi.org/10.56093/ijas.v95i8.149323

Keywords:

Deep learning, Error metrics, Gated recurrent unit, Long short-term memory, Price volatility

Abstract

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.

Downloads

Download data is not yet available.

References

Bacanin N, Stoean C, Zivkovic M, Rakic M, Strulak-Wojcikiewicz R and Stoean R. 2023. On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting. Energies 16(3): 1434.

Badal P S, Kamalvanshi V, Goyal A, Kumar P and Mondal B. 2022. Forecasting Potato Prices: Application of ARIMA Model. Economic Affairs 67(4): 491–96.

Bal F and Kayaalp F. 2021. Review of machine learning and deep learning models in agriculture. International Advanced Researches and Engineering Journal 5(2): 309–23.

Cahuantzi R, Chen X and Guttel S. 2023. A comparison of LSTM and GRU networks for learning symbolic sequences. (In) Science and Information Conference 1: 771–85.

Coulibaly S, Kamsu-Foguem B, Kamissoko D and Traore D. 2022. Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications 16: 200102.

Garai S, Paul R K, Rakshit D, Yeasin M, Emam W, Tashkandy Y and Chesneau C. 2023. Wavelets in combination with stochastic and machine learning models to predict agricultural prices. Mathematics 11(13): 2896.

Gowthaman T, Adarsh V S, Sathees Kumar K and Manobharathi K. 2023. A review of deep learning models for price prediction in agricultural commodities. Economic Affairs 68(1): 561–68. Gu Y H, Jin D, Yin H, Zheng R, Piao X and Yoo S J. 2022.

Forecasting agricultural commodity prices using dual input attention LSTM. Agriculture 12(2): 256.

Guo H, Woodruff A and Yadav A. 2020. Improving lives of indebted farmers using deep learning: Predicting agricultural produce prices using convolutional neural networks. (In) Proceedings of the AAAI Conference on Artificial Intelligence 34(8):13294–99.

He K, Yang Q, Ji L, Pan J and Zou Y. 2023. Financial time series forecasting with the deep learning ensemble model. Mathematics 11(4): 1054.

Jaiswal R, Jha G K, Kumar R R and Choudhary K. 2022. Deep long short-term memory-based model for agricultural price forecasting. Neural Computing and Applications 34(6): 4661–76.

Kumar K S, Ilakiya T and Gowthaman T. 2023. Price instability, seasonal index and modelling for major vegetables in India. Journal of Applied Horticulture 25(2): 2–6.

Kumari P, Goswami V and Pundir R S. 2023. Recurrent neural network architecture for forecasting banana prices in Gujarat, India. Plos One 18(6): e0275702.

Manogna R L and Mishra A K. 2021. Forecasting spot prices of agricultural commodities in India: Application of deep‐ learning models. Intelligent Systems in Accounting, Finance and Management 28(1): 72–83.

Mishra E, Murugesan R and Dash D P. 2023a. Deep learning model-based approach for agricultural crop price prediction in Indian market. (In) International Conference on Women Researchers in Electronics and Computing, Singapore: Springer Nature Singapore, pp. 133–46.

Mishra P, Mohamad A B, Kuamri B, Tiwari S, Singh A P, Yadav S, Sharma D and Kumari P. 2023b. Forecasting potato production in major South Asian countries: a comparative study of machine learning and time series models. Potato Research 30(1): 1–9.

Mohan P and Patil K K. 2018. Deep learning based weighted SOM to forecast weather and crop prediction for agriculture application. International Journal of Intelligent Engineering and Systems 11: 167–76.

Nayak G H, Alam M W, Singh K N, Avinash G, Kumar R R, Ray M and Deb C K. 2024. Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India. Scientific Reports 14(1): 17203.

Paul R K, Yeasin M, Kumar P, Kumar P, Balasubramanian M, Roy M and Gupta A. 2022. Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India. Plos One 17(7): e0270553.

Paul R K, Yeasin M, Kumar P, Paul A K and Roy H S. 2023. Deep learning technique for forecasting the price of cauliflower. Current Science 124(9): 1065.

Purohit S K, Panigrahi S, Sethy P K and Behera S K. 2021. Time series forecasting of price of agricultural products using hybrid methods. Applied Artificial Intelligence 35(15): 1388–406.

Ray S, Lama A, Mishra P, Biswas T, Das S S and Gurung B. 2023. An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique. Applied Soft Computing 149: 110939.

Seabe P L, Moutsinga C R B and Pindza E. 2023. Forecasting cryptocurrency prices using LSTM, GRU, and bi-directional LSTM: A deep learning approach. Fractal and Fractional 7(2): 203.

Shankar S V, Ajaykumar R, Ananthakrishnan S, Aravinthkumar A, Harishankar K, Sakthiselvi T and Navinkumar C. 2023a. Modeling and forecasting of milk production in the western zone of Tamil Nadu. Asian Journal of Dairy and Food Research 42(3): 427–32.

Shankar S V, Chandel A, Gupta R K, Sharma S, Chand H, Kumar and Gowsar S N. 2023b. Corrigendum: Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques. Frontiers in Sustainable Food Systems 7: 1290515.

Wang J, Wang P, Tian H, Tansey K, Liu J and Quan W. 2023. A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables. Computers and Electronics in Agriculture 20(6): 107705.

Wazirali R, Yaghoubi E, Abujazar M S S, Ahmad R and Vakili A H. 2023. State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques. Electric Power Systems Research 225: 109792.

Wu S S, Du Z H, Zhang F, Zhou Y and Liu R Y. 2023. Time-series forecasting of chlorophyll-a in coastal areas using LSTM, GRU and attention-based RNN Models. Journal of Environmental Informatics 41(2): 171–82.

Zaheer S, Anjum N, Hussain S, Algarni A D, Iqbal J, Bourouis S and Ullah S S. 2023. A multi parameter forecasting for stock time series data using LSTM and deep learning model. Mathematics 11(3): 590.

Zhang S, Luo J, Wang S and Liu F. 2023. Oil price forecasting: A hybrid GRU neural network based on decomposition reconstruction methods. Expert Systems with Applications 218: 119617.

Zhao H. 2021. Futures price prediction of agricultural products based on machine learning. Neural Computing and Applications 33: 837–50.