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 improe 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 tins reduce financial costs. © Springer-Verlag Berlin Heidelberg 2001.
Predictive Classification for Integrated Pest Management by Clustering in NN Output Space
PETACCHI, Ruggero;
2001-01-01
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 improe 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 tins reduce financial costs. © Springer-Verlag Berlin Heidelberg 2001.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.