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A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

  • G. R. CHANTRE (a1), A. M. BLANCO (a2), F. FORCELLA (a3), R. C. VAN ACKER (a4), M. R. SABBATINI (a1) and J. L. GONZALEZ-ANDUJAR (a5)...

Summary

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 conventional 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.

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*To whom all correspondence should be addressed. Email: andujar@cica.es

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A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

  • G. R. CHANTRE (a1), A. M. BLANCO (a2), F. FORCELLA (a3), R. C. VAN ACKER (a4), M. R. SABBATINI (a1) and J. L. GONZALEZ-ANDUJAR (a5)...

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