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

AuthorsChantre, Guillermo R.; Blanco, Antonio M.; Forcella, Frank; Acker, Rene C. van; Sabbatini, M. R.; González-Andújar, José Luis CSIC ORCID
Issue Date23-Jan-2013
PublisherCambridge University Press
CitationJournal of Agricultural Science 152(2): 254- 262 (2014)
AbstractNon-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.
Publisher version (URL)
Identifiersdoi: 10.1017/S0021859612001098
issn: 1469-5146
Appears in Collections:(IAS) Artículos

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