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

作者：Chantre G R; Blanco A M; Forcella F; Van Acker R C; Sabbatini M R; Gonzalez Andujar J L^*

来源：Journal of Agricultural Science, 2014, 152(2): 254-262.

DOI：10.1017/S0021859612001098

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.

• 出版日期2014-4