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Most popular emergence prediction models require species-specific population-based parameters to modulate thermal/hydrothermal accumulation. Such parameters are frequently unknown and difficult to estimate. Moreover, such models also rely on hardly available and difficult to estimate soil site-specific microclimate conditions, which in turn depend on soil heterogeneity at a field spatial level. On the other hand, modern agriculture benefits from easily
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dc.contributor.author | Chantre Balacca, Guillermo Ruben | |
dc.contributor.author | Vigna, Mario Raul | |
dc.contributor.author | Renzi Pugni, Juan Pablo | |
dc.contributor.author | Blanco, Anibal Manuel | |
dc.date.accessioned | 2018-05-04T18:33:39Z | |
dc.date.available | 2018-05-04T18:33:39Z | |
dc.date.issued | 2018-06 | |
dc.identifier.issn | 1537-5110 | |
dc.identifier.other | https://doi.org/10.1016/j.biosystemseng.2018.03.014 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1537511017306335 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/2333 | |
dc.description.abstract | Most popular emergence prediction models require species-specific population-based parameters to modulate thermal/hydrothermal accumulation. Such parameters are frequently unknown and difficult to estimate. Moreover, such models also rely on hardly available and difficult to estimate soil site-specific microclimate conditions, which in turn depend on soil heterogeneity at a field spatial level. On the other hand, modern agriculture benefits from easily available real-time information, in particular on-line meteorological data generated by forecasts and automatic local weather stations. In this context, Artificial Neural Networks (ANN) provide a flexible option for the development of prediction models, especially to study species which show a highly distributed emergence pattern along the year. In this work, an ANN approach based on easily obtainable meteorological data (daily minimum and maximum temperatures; daily precipitation) is proposed for weed emergence prediction. Relative Daily Emergence (RDE), expressed as a proportion of the total emergence, was the adopted output variable. Field emergence data recorded on a weekly basis were used to generate RDE patterns through linear interpolation. Results for three study cases from the Semiarid Pampean Region of Argentina (Lolium multiflorum, Avena fatua and Vicia villosa), which show irregular and time-distributed field emergence patterns, are reported. In all cases, ANN model selection was based on the Root Mean Square Error of the test set which showed better consistency than other typical Information Theory performance metrics. The combination of large ANN with a Bayesian Regularization Algorithm generated satisfactory estimations based on the RMSE values for independent Cumulative Emergence data. | eng |
dc.format | application/pdf | eng |
dc.language.iso | eng | |
dc.rights | info:eu-repo/semantics/restrictedAccess | eng |
dc.source | Biosystems engineering 170 : 51-60. (June 2018) | eng |
dc.subject | Malezas | es_AR |
dc.subject | Weeds | eng |
dc.subject | Emergencia | es_AR |
dc.subject | Emergence | eng |
dc.subject | Modelos | es_AR |
dc.subject | Models | eng |
dc.subject | Inteligencia Artificial | es_AR |
dc.subject | Artificial Intelligence | eng |
dc.subject.other | Redes Neuronales Artificiales | es_AR |
dc.subject.other | Modelos de Predicción | es_AR |
dc.title | A flexible and practical approach for real-time weed emergence prediction based on Artificial Neural Networks | eng |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | eng |
dc.type | info:eu-repo/semantics/publishedVersion | eng |
dc.description.origen | EEA Bordenave | es_AR |
dc.description.origen | EEA Hilario Ascasubi | |
dc.description.fil | Fil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiarida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiarida; Argentina | es_AR |
dc.description.fil | Fil: Vigna, Mario Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina | es_AR |
dc.description.fil | Fil: Renzi Pugni, Juan Pablo. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina. | es_AR |
dc.description.fil | Fil: Blanco, Aníbal M. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina | es_AR |
dc.subtype | cientifico |
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