Published
Weed recognition by SVM texture feature classification in outdoor vegetable crops images
Reconocimiento de maleza por características de textura usando SVM en imágenes exteriores de cultivos de hortalizas.
Keywords:
Weed recognition, support vectors, co-occurrence matrix, PCA (en)Reconocimiento de maleza, vectores de soporte, matrices de co-ocurrencia, PCA (es)
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.
El presente trabajo expone un sistema de clasificación de maleza y hortalizas a partir de imágenes exteriores de cultivos. El clasificador está basado en la teoría de las máquinas de vectores de soporte (Support Vector Machine SVM) con su extensión para el caso no lineal, haciendo uso de la función de base radial (RBF) y optimizando su parámetro de escala σ para suavizar la región de decisión. El espacio de características es el resultado del análisis por componentes principales (PCA) de 10 medidas de textura calculadas a partir de matrices de co-ocurrencia en niveles de gris (GLCM). Los resultados indican un rendimiento del clasificador por encima del 90% calculando los índices de especificidad, sensibilidad y precisión.
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Copyright (c) 2017 Camilo Pulido Rojas, Leonardo Solaque Guzmán, Nelson Velasco Toledo
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