Crop models are frequently used in agronomy for simulating crop variables at a discrete time step. This paper describes the application of an artificial neural network in developing a model for yield forecasts in durum wheat, using back-propagation algorithms based on the mechanistic model AFRCWHEAT2 (Porter, 1993; Porter et al., 1993). Given the relevant number of inputs (16) required to operate AFRCWHEAT2, we have tried to develop a simpler model based on a neural network, in order to match AFRCWHEAT2 performance while substantially reducing the need of inputs.
Bianchi, S., Barbanti, L., Bettati, T., Rossi, V., Miglietta, F., Ranieri, R., Piacentino, G., Toscano, P., Developing a neural network to simplify durum wheat yield prediction in Italy, Abstract de <<ESA XIIIth Congress>>, (Debrecen, 25-29 August 2014 ), Péter Pepó, József Csajbók, Debrecen 2014: 257-258 [http://hdl.handle.net/10807/64254]
Developing a neural network to simplify durum wheat yield prediction in Italy
Rossi, Vittorio;
2014
Abstract
Crop models are frequently used in agronomy for simulating crop variables at a discrete time step. This paper describes the application of an artificial neural network in developing a model for yield forecasts in durum wheat, using back-propagation algorithms based on the mechanistic model AFRCWHEAT2 (Porter, 1993; Porter et al., 1993). Given the relevant number of inputs (16) required to operate AFRCWHEAT2, we have tried to develop a simpler model based on a neural network, in order to match AFRCWHEAT2 performance while substantially reducing the need of inputs.
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