International Journal of Agricultural Engineering

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Hidden Markov Modeling for Sorghum Crop Production


Affiliations
1 Department of Community Medicine, Sri Venkateshwaraa Medical College Hospital and Research Centre, Ariyur (Puducherry), India
     

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This study presents of a hidden markov model (HMM) based on technique to classify agricultural crops time series and identify better sequence. The objective is to figure out the hidden state sequence given the observation sequence so that the trend can be analyzed using the steady state probability distribution values. The probability of Markov process generated one year difference in time series value when considered is found to give the best optimum state sequence then other difference sequence. These numerical results clearly show an improved forecasting accuracy compared to all difference fitness value and highest fitness value is well fitted sequence in sorghum production using MATLAB coding programme.

Keywords

Markov Chain, Hidden Sequence, Observation Sequence, Transition, Emission Probability Matrix.
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  • Albert, P.S. (1991). A two-state markov mixture model for a time series of epileptic seizure counts. Biometrics, 47: 1371-1381.

  • Churchill, G.A. (1992). Hidden markov chains and analysis of genome structure. Computers & Chemistry, 51 : 107-115.

  • Elliott, R.J. and Van, de Hoek J. (1997). An application of hidden markov models to asset allocation problems. Finance & Stochastic, 1: 229-238.

  • Juang, B.H. and Rabiner, L.R. (1991). Hidden markov models for speech recognition. Technometrics, 33: 251-272.

  • Krogh, A., Brown, M., Mian, I.S., Sjolander, K. and Haussler, D. (1994). Hidden markov models in computational biology: Applications to protein modeling. J. Molecular Biology, 235: 1501-1531.

  • Leroux, B.G. and Puterman, M.L. (1992).Maximum-penalized-likelihood estimation for independent and markov-dependent mixture models. Biometrics, 48: 545-558.

  • Rabiner, L.R. (1989). A tutorial on hidden markov processes and selected applications in speech recognition. Proc. IEEE, 77 : 257-286.

  • Rabiner, L. and Juang, B. (1993). Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs,NJ.

  • Turin, W. and Sondih, M.M. (1993). Modeling error sources in digital channels. IEEE J. Selected Areas in Communications, 11: 340-347.

  • Zamani, Behzad, Akbari, Ahmad, Nasersharif, Babak, Mohammadi, Mehdi and Jalalvand, Azarakhsh (2010). Discriminative transformation for speech features based on genetic algorithm and HMM likelihoods. IEICE Electronic Express, 7 (4): 247-253.


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  • Hidden Markov Modeling for Sorghum Crop Production

Abstract Views: 303  |  PDF Views: 2

Authors

M. Thirunavukkarasu
Department of Community Medicine, Sri Venkateshwaraa Medical College Hospital and Research Centre, Ariyur (Puducherry), India

Abstract


This study presents of a hidden markov model (HMM) based on technique to classify agricultural crops time series and identify better sequence. The objective is to figure out the hidden state sequence given the observation sequence so that the trend can be analyzed using the steady state probability distribution values. The probability of Markov process generated one year difference in time series value when considered is found to give the best optimum state sequence then other difference sequence. These numerical results clearly show an improved forecasting accuracy compared to all difference fitness value and highest fitness value is well fitted sequence in sorghum production using MATLAB coding programme.

Keywords


Markov Chain, Hidden Sequence, Observation Sequence, Transition, Emission Probability Matrix.

References