A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
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.
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Published: |
Cambridge University Press
2013-01-23
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| Online Access: | http://hdl.handle.net/10261/127655 http://dx.doi.org/10.13039/501100002923 http://dx.doi.org/10.13039/501100005740 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100000780 |
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