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An Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop

  • P. Chandra Shaker Reddy
  • A. Sureshbabu
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)

Abstract

Rice is an essential yield amongst the most essential food crops of India and it is grown everywhere throughout the nation. Rice is a major yield in the semi-arid district as Ananthapur is the part of Andhra Pradesh (AP) state in India. Precise and early forecasting of rice yield can give valuable information to inside season alteration of yield management. Time series data has been of incredible significance to investigate the area of forecasting strategies and times series models are used for rice yield forecasting from various investigators across the world, yet the forecast has not been precise. In this investigation, we proposed an effective approach to predict seasonal rice production of coming four years based on existing data. The model was build based on rice production data, which it is collected from agricultural department Andhra Pradesh. Rice production data of two seasons (Kharif and Rabi) was gathered in the period of 2008–2014 from Ananthapur district. In this article, we introduced Seasonal Adaptive Auto-Regressive Integrated Moving Average (ARIMA) time series model for prediction for rice crop production for next four years seasonalwise and give more precise outcomes than the previous existing models.

Keywords

Rice crop prediction Forecasting ARIMA Time series data 

References

  1. 1.
    Mariappan, A.K., Austin Ben Das, J.: A paradigm for rice yield prediction In Tamil Nadu. In: International Conference on Technological Innovations in ICT for Agriculture and Rural Development, Chennai, India, pp. 1–4 (2017)Google Scholar
  2. 2.
    Kumar, R., Singh, M.P., Kumar, P., Singh, J.P.: Crop selection method to maximize crop yield rate using machine learning technique. In: International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, Chennai, India, pp. 13–145 (2015)Google Scholar
  3. 3.
    Raja, S.K.S., Rishi, R., Sundaresan, E., Srijit, V.: Demand based crop recommender system for farmers. In: International Conference on Technological Innovations in ICT for Agriculture and Rural Development, Chennai, India, pp. 1–6 (2017)Google Scholar
  4. 4.
    Hossain, M.A., Uddin, M.N., Hossain, M.A., Jang, Y.M.: Predicting rice yield for Bangladesh by exploiting weather conditions, Jeju, South Korea, pp. 1–6 (2017)Google Scholar
  5. 5.
    Mondal, P., Shit, L., Goswami, S.: Study of effectiveness of time series modeling (Arima) in forecasting stock prices. Int. J. Comput. Sci. Eng. Appl. 4(2), 1–17 (2014)Google Scholar
  6. 6.
    Rotela, Jr., P., Salomon, F.L.R., de Oliveira Pamplona, E.: ARIMA: an applied time series forecasting model for the Bovespa stock index. Appl. Math. 5(21), 3383–3391 (2014)Google Scholar
  7. 7.
    Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the ARIMA model. In: International Conference on Computer Modelling and Simulation, Sim-AMSS, UK, pp. 1–7 (2014)Google Scholar
  8. 8.
    Kaur, K., Attwal, K.S.: Effect of temperature and rainfall on paddy yield using data mining. In: International Conference on Cloud Computing, Data Science & Engineering—Confluence, Noida, India, pp. 1–6 (2017)Google Scholar
  9. 9.
    Reddy, P.C., Babu, A.S.: A novel approach to analysis district level long scale seasonal forecasting of monsoon rainfall in Andhra Pradesh and Telangana. Int. J. Adv. Res. Comput. Sci. 8(9) Nov–Dec (2017)Google Scholar
  10. 10.
    Garg, A., Garg, B.: A robust and novel regression based fuzzy time series algorithm for prediction of rice yield. In: International Conference on Intelligent Communication and Computational Techniques, Jaipur, India, pp. 1–7 (2017)Google Scholar
  11. 11.
    Garg, B., Aggarwal, S., Sokhal, J.: Crop yield forecasting using fuzzy logic and regression model. Comput. Electr. Eng. 67, 383–403 (2018)CrossRefGoogle Scholar
  12. 12.
    Reddy, P.C., Babu, A.S.: Survey on weather prediction using big data analytics. In: International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, pp. 1–6 (2017)Google Scholar
  13. 13.
    Jabjone, S., Jiamrum, C.: Artificial neural networks for predicting the rice yield in Phimai district of Thailand. Int. J. Electr. Energy 1(3), 177–181 (2013)CrossRefGoogle Scholar
  14. 14.
    Manoj, K., Madhu, A.: An application of time series Arima forecasting model for predicting sugarcane production In India. Stud. Bus. Econ. 9(1), 81–94 (2014)Google Scholar
  15. 15.
    Baruah, R.D., Bhagat, R.M.: Use of data mining technique for prediction of tea yield in the face of climate change of Assam, India. In: International Conference on Information Technology, Bhubaneswar, India, pp. 1–5 (2016)Google Scholar
  16. 16.
    Corraya, A.D., Corraya, S.: Regression based price and yield prediction of agricultural crop. Int. J. Comput. Appl. 152(5), 1–7 (2016)Google Scholar
  17. 17.
    Aggarwal, S., Sokhal, J., Garg, B.: Forecasting production values using fuzzy logic interval based partitioning in different intervals. Int. J. Adv. Comput. Sci. Appl. 8(5), 1–8 (2017)CrossRefGoogle Scholar
  18. 18.
    Zhang, Y., Qin, Q.: Winter Wheat yield estimation with ground based spectral information. In: International Geoscience and Remote Sensing Symposium, Valencia, Spain, pp. 1–4 (2018)Google Scholar
  19. 19.
    Shastry, K.A., Sanjay, H.A., Deshmukh, A.: A parameter based customized artificial neural network model for crop yield prediction. J. Artif. Intell. 9(3), 23–32 (2016)Google Scholar
  20. 20.
    Niedbała, G.: Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield. J. Integr. Agric. 18(1), 54–61 (2019)Google Scholar
  21. 21.
    Rocha, H., Dias, J.M.: Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agro-climatic data. Comput. Electron. Agric. 157(5), 427–435 (2019)Google Scholar
  22. 22.
    van der Velde, M., Nisini, L.: Performance of the MARS-crop yield forecasting system for the European Union: assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015. Agric. Syst. 168(3), 203–212 (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • P. Chandra Shaker Reddy
    • 1
    • 2
  • A. Sureshbabu
    • 3
  1. 1.Research Scholar, JNTUAAnanathapuramuIndia
  2. 2.Department of Computer Science & EngineeringCMR College of Engineering & TechnologyHyderabadIndia
  3. 3.Professor of CSE DepartmentJNTUA College of EngineeringAnanathapuramuIndia

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