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

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Soft Computing and Signal Processing (ICSCSP 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1118))

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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.

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References

  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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Garg, B., Aggarwal, S., Sokhal, J.: Crop yield forecasting using fuzzy logic and regression model. Comput. Electr. Eng. 67, 383–403 (2018)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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 

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Author information

Authors and Affiliations

  1. Research Scholar, JNTUA, Ananathapuramu, AP, India

    P. Chandra Shaker Reddy

  2. Department of Computer Science & Engineering, CMR College of Engineering & Technology, Hyderabad, TS, India

    P. Chandra Shaker Reddy

  3. Professor of CSE Department, JNTUA College of Engineering, Ananathapuramu, AP, India

    A. Sureshbabu

Authors
  1. P. Chandra Shaker Reddy
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  2. A. Sureshbabu
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Editor information

Editors and Affiliations

  1. Department of ECE, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India

    V. Sivakumar Reddy

  2. Department of CSE, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India

    V. Kamakshi Prasad

  3. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  4. Department of ECE, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India

    K. T. V. Reddy

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Reddy, P.C.S., Sureshbabu, A. (2020). An Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_16

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