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Original Article

Forecasting of Tomato Prices and Yield Based on Season and Weather Using LSTM and ARIMA Algorithms

  • Paper ID 235
  • Volume 5, Issue 5
  • Pages 54-59
  • Published Online: September-October 2025
  • 4 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • 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.

Article Information

Published Online
September-October 2025
Pages
54-59
Article Type
Original Article
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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.
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  • ISSN 2583-1240
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