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Improving Peanut Maturity Prediction Using a Hybrid Artificial Neural Network and Fuzzy Inference System

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Intelligent Problem Solving. Methodologies and Approaches (IEA/AIE 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1821))

Abstract

The goal of this research was to improve the prediction of maturity in peanuts through the development of an adaptive-network-based fuzzy inference (ANFIS) model. There were three specific objectives. The first two were to develop models for comparison with previous research results using an artificial neural network (ANN) and fuzzy inference system (FIS) separately. The third objective was to expand the research by determining the robustness of the developed ANFIS model in predicting maturity for a season not used in model development. While the hybrid model was able to improve on the results of a FIS, the hybrid model was unable to improve on the results of an ANN. The developed model was relatively robust.

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References

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Author information

Authors and Affiliations

  1. Artificial Intelligence Center, University of Georgia, Athens, GA

    H. L. Silvio

  2. Biological and Agricultural Engineering, University of Georgia, Athens, GA

    R. W. McClendon & E. W. Tollner

Authors
  1. H. L. Silvio
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  2. R. W. McClendon
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  3. E. W. Tollner
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Editor information

Editors and Affiliations

  1. The Center for Advanced Computer Studies, University of Lousiana, 2 Rex Street, Lafayette, LA, 70504-4330, USA

    Rasiah Logananthara

  2. Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, 89069, Ulm, Germany

    Günther Palm

  3. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2000 Springer-Verlag Berlin Heidelberg

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Silvio, H.L., McClendon, R.W., Tollner, E.W. (2000). Improving Peanut Maturity Prediction Using a Hybrid Artificial Neural Network and Fuzzy Inference System. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_64

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  • DOI: https://doi.org/10.1007/3-540-45049-1_64

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67689-8

  • Online ISBN: 978-3-540-45049-8

  • eBook Packages: Springer Book Archive

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