International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Dissertation Chapters | Agricultural Studies | India | Volume 12 Issue 3, March 2023 | Popularity: 5 / 10


     

Predictive Model for the Production of Cotton Crop of the State Telangana before and After Division

T. Rajani Devi, Dr. V V Hara Gopal


Abstract: Instability is an inherent characteristic of agriculture everywhere, being dependent on Weather conditions, area, yield and production of Crops are liable to substantial variations from year to year. Agricultural production has always involved the exploitation of resources such as soil, water, and energy. Increasing production to feed a growing world population while at the same time conserving resources for future generations has led to a search for ?sustainable? agricultural methods. This Study will be a step in identifying the Possible error and see that how much of Volatility risk is involved fitting the proper distribution of the Secondary data. Also, any study is not simply to fit the kind of Distribution fits well or not, but the estimation of the Cotton production is also evaluated by Deep learning methods such as Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The data is fewer after division of the state various curves are fitted and compared to predict the production of Cotton.


Keywords: Distribution fitting, Goodness of fit, Kolmogorav-Smirnov test, Anderson-Darling test, Chi square test, MLP, RNN, LSTM, Curve fitting


Edition: Volume 12 Issue 3, March 2023


Pages: 158 - 162


DOI: https://www.doi.org/10.21275/SR23301084114



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T. Rajani Devi, Dr. V V Hara Gopal, "Predictive Model for the Production of Cotton Crop of the State Telangana before and After Division", International Journal of Science and Research (IJSR), Volume 12 Issue 3, March 2023, pp. 158-162, https://www.ijsr.net/getabstract.php?paperid=SR23301084114, DOI: https://www.doi.org/10.21275/SR23301084114