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...

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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