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Yield Prediction Using Artificial Neural Networks

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Computer Networks and Information Technologies (CNC 2011)

Abstract

Artificial Neural Network (ANN’s) technology with PSO as optimization technique was used for the approximation and prediction of paddy yield at 3 different districts in different climatic zones based on 10 years of historical data sets of yields of paddy ,daily temperature(mean and maximum) and precipitation(rainfall).

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References

  1. Heinzow, T., Tol, S.J.: Prediction of Crop yields across four climatic zones in Germany: An Artificial Neural Network Approach. Research unit Sustainability and Global Change, Hamburg University (September 2003)

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  2. Kaul, M., Hill, L.R., Walthall, C.: Artificial neural networks for corn and soybean yield prediction. Agricultural Systems 85, 1–18 (2005)

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  3. Drummond, S.T., Sudduth, K.A., Joshi, A., Birrell, S.J., Kitchen, N.R.: Statistical and neural methods for site-specific yield prediction. Transactions of the ASAE 46(1), 5–14 (2003)

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© 2011 Springer-Verlag Berlin Heidelberg

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Baral, S., Kumar Tripathy, A., Bijayasingh, P. (2011). Yield Prediction Using Artificial Neural Networks. In: Das, V.V., Stephen, J., Chaba, Y. (eds) Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19542-6_57

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  • DOI: https://doi.org/10.1007/978-3-642-19542-6_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19541-9

  • Online ISBN: 978-3-642-19542-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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