Developing a neural network to simplify durum wheat yield prediction in Italy

Simone Bianchi; Lorenzo Barbanti; Tiziano Bettati; Vittorio Rossi; Franco Miglietta; Roberto Ranieri; Giuseppe Piacentino; Piero Toscano

Developing a neural network to simplify durum wheat yield prediction in Italy

Simone Bianchi, Lorenzo Barbanti, Tiziano Bettati, Vittorio Rossi, Franco Miglietta, Roberto Ranieri, Giuseppe Piacentino, Piero Toscano

Risultato della ricerca: Contributo in libro › Contributo a convegno

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.

Lingua originale	English
Titolo della pubblicazione ospite	Book of abstracts ESA XIIIth Congress
Pagine	257-258
Numero di pagine	2
Stato di pubblicazione	Pubblicato - 2014
Evento	ESA XIIIth Congress - Debrecen Durata: 25 ago 2014 → 29 ago 2014

Convegno

Convegno	ESA XIIIth Congress
Città	Debrecen
Periodo	25/8/14 → 29/8/14

Keywords

AFRCWHEAT2
Modelling
durum wheat
mechanistic model

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@inproceedings{2b07eabd4a1245cb824e212d29827205,

title = "Developing a neural network to simplify durum wheat yield prediction in Italy",

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.",

keywords = "AFRCWHEAT2, Modelling, durum wheat, mechanistic model, AFRCWHEAT2, Modelling, durum wheat, mechanistic model",

author = "Simone Bianchi and Lorenzo Barbanti and Tiziano Bettati and Vittorio Rossi and Franco Miglietta and Roberto Ranieri and Giuseppe Piacentino and Piero Toscano",

year = "2014",

language = "English",

isbn = "978-963-473-723-0",

pages = "257--258",

booktitle = "Book of abstracts ESA XIIIth Congress",

note = "ESA XIIIth Congress ; Conference date: 25-08-2014 Through 29-08-2014",

}

TY - GEN

T1 - Developing a neural network to simplify durum wheat yield prediction in Italy

AU - Bianchi, Simone

AU - Barbanti, Lorenzo

AU - Bettati, Tiziano

AU - Rossi, Vittorio

AU - Miglietta, Franco

AU - Ranieri, Roberto

AU - Piacentino, Giuseppe

AU - Toscano, Piero

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - AFRCWHEAT2

KW - Modelling

KW - durum wheat

KW - mechanistic model

KW - AFRCWHEAT2

KW - Modelling

KW - durum wheat

KW - mechanistic model

UR - http://hdl.handle.net/10807/64254

M3 - Conference contribution

SN - 978-963-473-723-0

SP - 257

EP - 258

BT - Book of abstracts ESA XIIIth Congress

T2 - ESA XIIIth Congress

Y2 - 25 August 2014 through 29 August 2014

ER -

Developing a neural network to simplify durum wheat yield prediction in Italy

Abstract

Convegno

Keywords

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