Predicting rice blast disease: machine learning versus process-based models

BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rul...

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Detalles Bibliográficos
Autores principales: Nettleton, David F., Katsantonis, Dimitrios, Kalaitzidis, Argyris, Sarafijanovic-Djukic, Natasa, Puigdollers, Pau, Confalonieri, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806664/
https://www.ncbi.nlm.nih.gov/pubmed/31640541
http://dx.doi.org/10.1186/s12859-019-3065-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806664/
https://www.ncbi.nlm.nih.gov/pubmed/31640541
http://dx.doi.org/10.1186/s12859-019-3065-1

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