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A Survey of Artificial Neural Network-Based Modeling in Agroecology

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Soft Computing Applications in Industry

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 226))

Introduction

Agroecological systems are difficult to model because of their high complexity and their nonlinear dynamic behavior. The evolution of such systems depends on a large number of ill-defined processes that vary in time, and whose relationships are often highly non-linear and very often unknown. According to Schultz et al. (2000), there are two major problems when dealing with modeling agroecological processes. On the one hand, there is an absence of equipment able to capture information in an accurate way, and on the other hand there is a lack of knowledge about such systems. Researchers are thus required to build-up models in rich and poor-data situations, by integrating different sources of data, even if this data is noisy, incomplete, and imprecise.

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Authors and Affiliations

  1. Faculty of BioScience Engineering: Agricultural Science, Ghent University,  

    Jiménez Daniel & Van Damme Patrick

  2. Hautes Etudes Commerciales (HEC), Institut des Systèmes d’Information (ISI), Université de Lausanne,  

    Satizábal Héctor, Barreto Miguel & Tomassini Marco

  3. REDS Institute, University of Applied Sciences of Western Switzerland (HEIG-VD),  

    Jiménez Daniel, Pérez-Uribe Andrés, Satizábal Héctor & Barreto Miguel

  4. BIOTEC. Precision Agriculture and the Construction of Field-Crop Models for Tropical Fruit Species,  

    Jiménez Daniel, Satizábal Héctor & Barreto Miguel

Authors
  1. Jiménez Daniel
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  2. Pérez-Uribe Andrés
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  3. Satizábal Héctor
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  4. Barreto Miguel
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  5. Van Damme Patrick
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  6. Tomassini Marco
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Bhanu Prasad

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Daniel, J., Andrés, PU., Héctor, S., Miguel, B., Patrick, V.D., Marco, T. (2008). A Survey of Artificial Neural Network-Based Modeling in Agroecology. In: Prasad, B. (eds) Soft Computing Applications in Industry. Studies in Fuzziness and Soft Computing, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77465-5_13

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  • DOI: https://doi.org/10.1007/978-3-540-77465-5_13

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