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Original Article
Forecasting of Tomato Prices and Yield Based on Season and Weather Using LSTM and ARIMA Algorithms
Mohammed Tabraz khan1
Dr. Abdul Rahman2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Assistant professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 54-59
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250505010
References
1. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
2. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 5th ed., Wiley, 2015.
3. S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, vol. 13, no. 3, e0194889, 2018.
4. J. Brownlee, Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery, 2017.
5. M. Z. Hossain and M. M. Rahman, "Agricultural price forecasting using deep learning techniques: A case study of Bangladesh," Computers and Electronics in Agriculture, vol. 179, p. 105799, 2020.
6. R. Kumar and A. Jain, "Crop yield and price forecasting using time series and machine learning algorithms," Procedia Computer Science, vol. 192, pp. 1042–1050, 2021.
7. R. Adhikari and R. K. Agrawal, "An Introductory Study on Time Series Modeling and Forecasting," arXiv preprint arXiv:1302.6613, 2013.
8. A. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Computers and Electronics in Agriculture, vol. 151, pp. 61–69, 2018.
9. National Agricultural Statistics Service (NASS), "Tomato Prices and Yield Reports," U.S. Department of Agriculture, 2023.
10. V. Pandey and R. Prasad, "Weather-sensitive agricultural forecasting using hybrid LSTM-ARIMA models," Journal of Applied Artificial Intelligence, vol. 34, no. 12, pp. 915–933, 2020.
2. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 5th ed., Wiley, 2015.
3. S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, vol. 13, no. 3, e0194889, 2018.
4. J. Brownlee, Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery, 2017.
5. M. Z. Hossain and M. M. Rahman, "Agricultural price forecasting using deep learning techniques: A case study of Bangladesh," Computers and Electronics in Agriculture, vol. 179, p. 105799, 2020.
6. R. Kumar and A. Jain, "Crop yield and price forecasting using time series and machine learning algorithms," Procedia Computer Science, vol. 192, pp. 1042–1050, 2021.
7. R. Adhikari and R. K. Agrawal, "An Introductory Study on Time Series Modeling and Forecasting," arXiv preprint arXiv:1302.6613, 2013.
8. A. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Computers and Electronics in Agriculture, vol. 151, pp. 61–69, 2018.
9. National Agricultural Statistics Service (NASS), "Tomato Prices and Yield Reports," U.S. Department of Agriculture, 2023.
10. V. Pandey and R. Prasad, "Weather-sensitive agricultural forecasting using hybrid LSTM-ARIMA models," Journal of Applied Artificial Intelligence, vol. 34, no. 12, pp. 915–933, 2020.
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