Modeling weed emergence: artifi cial neural networks versus non-linear regression procedures

• Autores: Torra, Joel; Royo Esnal, Aritz; Chantre Balacca, Guillermo Ruben
• Año de publicación: 2016
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: THERMAL TIME; HIDROTIME; GERMINATION; SIGMOIDAL REGRESSION
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Artificial Neural Networks (ANNs) are machines with complexfunctional relations learnable with a limited amount of trainingdata emulating data processing functions of the brain. ANNs have ahigh potential applicability in ecological systems due to their capacity to describehighly non-linear relationships among variables. In this sense, they represent promising computational tools to accurately model weed emergence and therefore, for prediction purposes to improve weed control. But they have not beenwidely used with this aim, and so far, they have been proven to be useful only for one weed species (Avena fatua). The objectives of the present work were to develop an ANN model for ripgut brome (Bromus diandrus) for emergence prediction and to compare their predictive capability against already developed non-linear regression (NLR) models. Thermal-time and hydro-time were used as independent input variablesfor developing bivariate models. The accumulated proportion of seedling emergence was the output variable. A total of 1610 input/output data pairs corresponding to three years of data collection in two different field trials wereused in this study. A total of 16 different scenarios or emergence data sets (differing in sowing dates and soil management) were modeledto compare the goodness of fit (RMSE) by the two approaches. The ANN developed had three layers: one input layer, one hidden layer with 2 neurons, and one output layer. Both procedures, ANN and NLR, were able to predict satisfactorily B. diandrus emergence patterns. However, the ANN improved the fitting accuracy in 11 of the 16 scenarios with RMSE estimates 46% lower compared to NLR models. These results confirm that ANNs are powerful tools for modeling weed emergence, thus they could help improve IWM decision support systems.
Fil: Torra, Joel. Universidad de Lleida; España
Fil: Royo Esnal, Aritz. Universidad de Lleida; España
Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
7th International Weed Science Congress
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República Checa
International Weed Science Society