Abstract
In this paper we consider the successful hybridation of a two modern computational schemes, Clustering and Neural Networks, for the Predictive Classification of the future value of insect infestation levels for Integrated Pest Management (IPM) of olive groves. The predictive classification techniques employed allow managers to improve their work in two ways: first, by reducing sampling demands of the variables involved, which is a costly process; and second, by recognizing potential infestation problems a up to two weeks beforehand, in order to optimize the use of pesticide chemical products and thus reduce financial costs.
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© 2001 Springer-Verlag Berlin Heidelberg
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Salmerón, M., Guidotti, D., Petacchi, R., Reyneri, L.M. (2001). Predictive Classification for Integrated Pest Management by Clustering in NN Output Space. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_72
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DOI: https://doi.org/10.1007/3-540-45723-2_72
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