Weed recognition by SVM texture feature classification in outdoor vegetable crop images

Abstract

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 the nonlinear case, using the Radial Basis Function (RBF) and optimizing its scale parameter Σ to smooth the boundary decision. 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.