Acta Scientific Agriculture (ASAG)(ISSN: 2581-365X)

Research Article Volume 6 Issue 9

Prediction of Mango Production Using Machine Intelligence Techniques: A Case Study from Karnataka, India

Santosha Rathod1, Vijayakumar S1*, Nirmala Bandumula2 and Gayathri Chitikela3

1Scientist, ICAR-Indian Institute Rice Research, Hyderabad, Telangana, India
2Senior Scientist, ICAR-Indian Institute Rice Research, Hyderabad, Telangana, India
3Professor Jayashankar Telangana State Agricultural University, Hyderabad, Telangana, India

*Corresponding Author: Vijayakumar S, Scientist, ICAR-Indian Institute Rice Research, Hyderabad, Telangana, India.

Received: July 15, 2022; Published: August 05, 2022

Abstract

Mango is the largest producing fruit crop in India. On the other hand, Karnataka is called the horticultural state of India, where mango is the highest producing fruit crop. A developing economy relies heavily on forecasting for effective planning and long-term sustainable growth. The most common technique used for forecasting in several fields for many years is autoregressive integrated moving average (ARIMA). The assumptions of linearity and stationarity are major key flaws in this model. As many time series phenomenon in the real world are not purely linear, therefore there is an opportunity to enhance the prediction ability of ARIMA models by employing nonlinear machine intelligence techniques like Autoregressive Neural Network (NAR: Neural Network Autoregressive) and non-linear support vector regression (NLSVR) model. In this study, an attempt is made to forecast the mango production of Karnataka using ARIMA, NAR and NLSVR. According to empirical evidence, the predicting accuracy of the time series machine intelligence technique is clearly superior than the traditional ARIMA model. 

 

Keywords: Mango Production; Time Series; ARIMA; NAR; NLSVR

References

  1. Pardhi R., et al. “Effect of Price of Other Seasonal Fruits on Mango Price in Uttar Pradesh”. Economic Affairs4 (2016): 1-5.
  2. Vijayakumar S., et al. “Artificial Intelligence (AI) and its Application in Agriculture”. Chronicle of Bioresource Management1 (2022): 25-31.
  3. Vijayakumar S., et al. “Rainfall and temperature projections and their impact assessment using CMIP5 models under different RCP scenarios for the eastern coastal region of India”. Current Science2 (2021): 222.
  4. Box GEP and Jenkins G. “Time series analysis, Forecasting and control, 1970”. Holden-Day, San Francisco, CA.
  5. Iquebal MA and C Chattopadhyay. "Modelling and forecasting of pigeonpea (Cajanus cajan) production using autoregressive integrated moving average methodology”. Indian Journal of Agricultural Sciences6 (2011): 520-523.
  6. Kumari Prity GC., et al. “Forecasting of productivity and pod damage by Helicoverpa armigera using artificial neural network model in pigeonpea (Cajanus Cajan)”. International Journal of Agriculture, Environment and Biotechnology 2 (2013): 335-340.
  7. Mishra GC and A Singh. "A study on forecasting prices of groundnut oil in Delhi by ARIMA methodology and artificial neural networks”. Agris on-line Papers in Economics and Informatics665-2016-44959 (2013): 25-34.
  8. Naveena K., et al. Forecasting of coconut production in India: A suitable time series model. International Journal of Agricultural Engineering7.1 ( 2014): 190-193.
  9. Kumari Prity., et al. "Comparison of forecasting ability of different statistical models for productivity of rice (Oryza sativa) in India”. The Ecoscan 8.3 (2014): 193-198.
  10. Kumari Prity., et al. "Autoregressive integrated moving average (arima) approach for prediction of rice (Oryza sativa) yield in India”. The Bioscan 9.3 (2014): 1063-1066.
  11. Alam Wasi., et al. "Improved ARIMAX modal based on ANN and SVM approaches for forecasting rice yield using weather variables”. Indian Journal of Agricultural Sciences12 (2018): 1909-1913.
  12. Rathod Santosha., et al. "Forecasting maize yield using ARIMA-Genetic Algorithm approach”. Outlook on Agriculture4 (2017): 265-271.
  13. Rathod Santosha and G C Mishra. "Weather based modeling for forecasting area and production of mango in Karnataka”. International Journal of Agriculture, Environment and Biotechnology1 (2017): 149.
  14. Rathod SANTOSHA., et al. "Modeling and forecasting of oilseed production of India through artificial intelligence techniques”. Indian Journal of Agricultural Sciences1 (2018): 22-27.
  15. Mahalingaraya, S., et al. "Statistical modeling and forecasting of Total fish production of India: a time series perspective”. International Journal of Current Microbiology and Applied Sciences 3 (2018): 1698-1707.
  16. Chitikela Gayathri., et al. "Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India”. Agronomy9 (2021): 1878.
  17. Patil R., et al. "Drought modelling and forecasting using arima and neural networks for ballari district, Karnataka”. Journal of the Indian Society of Agricultural Statistics 74 (2020): 149-157.
  18. Gorlapalli Amuktamalyada., et al. "Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models”. Sustainability11 (2022): 6690.
  19. Rathod S and V Paramesha V. “Time Series Analysis using Machine Learning Techniques”. In Vadivel, A., Paramesh, V., Uthappa, A. and Kumar, R.P. (Ed.). Ecosystem service analysis: concepts and applications in diversifed coconut and arecanut gardens. ICAR- Central Coastal Agricultural Research Institute, India (2022): 262-292.
  20. Vapnik Vladimir., et al. "Support vector method for function approximation, regression estimation and signal processing”. Advances in Neural Information Processing Systems 9 (1996).
  21. Kumar TL. "Development of hybrid models for forecasting time-series data using nonlinear SVR enhanced by PSO”. Journal of Statistical Theory and Practice 4 (2015): 699-711.
  22. Alonso Jaime., et al. "Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter”. Computers and Electronics in Agriculture 91 (2013): 116-120.
  23. Chen Kuan-Yu and Cheng-Hua Wang. "Support vector regression with genetic algorithms in forecasting tourism demand”. Tourism Management 1 (2007): 215-226.
  24. Alam Wasi., et al. "Hybrid linear time series approach for long term forecasting of crop yield”. International Journal of Agriculture Sciences 88 (2018): 1275-1279.
  25. Naveena K., et al. "Hybrid ARIMA-ANN modelling for forecasting the price of robusta coffee in India”. International Journal of Current Microbiology and Applied Sciences 7 (2017): 1721-1726.
  26. Naveena K and Singh Subedar. "Hybrid time series modelling for forecasting the price of washed coffee (Arabica Plantation Coffee) in India”. International Journal of Agriculture Sciences (2017): 0975-3710.
  27. Rathod Santosha., et al. "Hybrid time series models for forecasting banana production in Karnataka State, India”. Journal of the Indian Society of Agricultural Statistics3 (2017): 193-200.
  28. Rathod S and GC Mishra. "Statistical models for forecasting mango and banana yield of Karnataka, India”. Journal of Agricultural Science and Technology4 (2018): 803-816.
  29. Rathod Santosha., et al. "Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction and Advanced Agroecosystem Management”. Agronomy12 (2021): 2502.
  30. Rathod Santosha., et al. "Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches”. Agronomy1 (2021): 22.
  31. Saha Amit., et al. "A hybrid spatio-temporal modelling: An application to space-time rainfall forecasting”. Theoretical and Applied Climatology3 (2020): 1271-1282.

Citation

Citation: Santosha Rathod., et al. “Prediction of Mango Production Using Machine Intelligence Techniques: A Case Study from Karnataka, India". Acta Scientific Agriculture 6.9 (2022): 16-22.

Copyright

Copyright: © 2022 Vijayakumar S., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate32%
Acceptance to publication20-30 days
Impact Factor1.014

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is December 15, 2022.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of “Best Article of the Issue”.
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.
  • Contact US