A simple algorithm outperforms a machine learning approach for quantifying spittlebug damage in tropical grasses
In the extensive livestock systems of tropical America, host-plant resistance has proven to be the most efficient strategy for integrated pest management in forage grasses (i.e., Urochloa hybrids and Megathyrsus maximus) to spittlebug (Hemiptera: Cercopidae) attack. Precise and efficient quantificat...
| Autores principales: | , , , |
|---|---|
| Formato: | Póster |
| Lenguaje: | Inglés |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/155544 |
Ejemplares similares: A simple algorithm outperforms a machine learning approach for quantifying spittlebug damage in tropical grasses
- Vision computacional y aprendizaje profundo para el fenotipado de alto rendimiento en plantas de interés agronómico
- AI-powered tool for spidermite damage assessment in tropical grasses
- Insect damage assessment of tropical grasses under field conditions using a modular pipeline with image processing and deep learning techniques
- Digital imaging outperforms traditional scoring methods for spittlebug tolerance in Urochloa humidicola hybrids
- Imputación de genotipos faltantes mediante algoritmos de machine learning = Imputation of missing genotypes using machine learning algorithms
- Relevant loci for milk production in dairy cattle, obtained by machine learning algorithms