A flexible and practical approach for real-time weed emergence prediction based on Artificial Neural Networks
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-speci...
| Autores principales: | , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | https://www.sciencedirect.com/science/article/pii/S1537511017306335 http://hdl.handle.net/20.500.12123/2333 https://doi.org/10.1016/j.biosystemseng.2018.03.014 |
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