The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations
Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination...
- Autores:
-
San‐Blas, Ernesto
Paba, Gabriel
Cubillán, Néstor
Portillo, Edgar
Casassa-Padrón, Ana M.
González‐González, César
Guerra, Mayamarú
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9555
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9555
https://bsppjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/ppa.13246
- Palabra clave:
- Artificial intelligence
Fourier transformed infrared spectroscopy – attenuated total reflectance (FTIR‐ATR)
Genetic algorithms
Genetic algorithms
Meloidogyne enterolobii
Plant parasitic nematodes
Support vector machine
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb