Weed recognition by SVM texture feature classification in outdoor vegetable crops images
This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feat...
- Autores:
-
Pulido Rojas, Camilo
Solaque Guzmán, Leonardo
Velasco Toledo, Nelson
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/67585
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/67585
http://bdigital.unal.edu.co/68614/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Weed recognition
support vectors
co-occurrence matrix
PCA
Reconocimiento de maleza
vectores de soporte
matrices de co-ocurrencia
PCA
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feature space is the result of principal component analysis (PCA) for 10 texture measurements calculated from gray level co-occurrence matrices (GLCM). The results indicate that classifier performance is above 90%, validated with specificity, sensitivity and precision calculations. |
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