Texture-Based Weed Classification Using Gabor Wavelets and Neural Network for Real-time Selective Herbicide Applications

A novel texture-based weed classification method was developed. The method comprised a low-level Gabor wavelets-based feature extraction algorithm and a high-level neural network-based pattern recognition algorithm. The design strategy simulated the function of the human visual system, which uses low-level receptors for early stage vision processing and high-level cognition for pattern recognition and image understanding. This model was specifically developed to classify images into broadleaf and grass categories for real-time selective herbicide application. The results showed that the method is capable of performing texture-based broadleaf and grass classification effectively and with 100 percent classification accuracy over 40 sample images with 20 samples from each class. Based on the elapsed time to do weed classification, the method meets real-time constraints.