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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2019
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| Materias: | |
| 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 |