Artículos de revistas
A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
Fecha
2013-01Registro en:
Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Forcella, F.; Van Acker, R. C.; Sabbatini, Mario Ricardo; et al.; A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence; Cambridge University Press; Journal Of Agricultural Science; 152; 2; 1-2013; 254-262
0021-8596
Autor
Chantre Balacca, Guillermo Ruben
Blanco, Anibal Manuel
Forcella, F.
Van Acker, R. C.
Sabbatini, Mario Ricardo
González Andújar, J. L.
Resumen
Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the con- ventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.