Weed classification using one class support vector machine

Abstract

Weed classification a necessity in identifying species of weeds to control management practice in agricultural systems, which are essential for maintaining crop productivity and quality. Many classification techniques were used to identify weeds based on images, and most of the techniques using a binary Support Vector Machine (SVM) for measuring the percentage of accuracy. No visualization of decision boundary is illustrated to prove the best performances. To analyzing weed pattern images using One Class Support Vector Machine (SVM), feature vectors of weed images extracted using Gabor Wavelet and Fast Fourier Transform (FFT) were applied. The decision boundaries of the combination extracted feature vectors are visualized and optimal feature vectors are identified. The proposed method also improve the accuracy rate in weed classification task.