Grape Leaf Disease Classification using LBP-derived Texture Operators and Colour

This paper proposes new feature extractors for colour texture classification based on Local Binary Patterns (LBP) operators which are invariant to rotation, illumination and change of the observation scale, being also robust to Gaussian noise. Our novel approaches are operating in the RGB colour space and are using a state-of-the-art image denoising algorithm embedded into a multiscale LBP feature extraction process. The results obtained in the experimental section for grape leaf disease classification on a public database, using Support Vector Machines, show that the proposed feature extractors bring a significant improvement in terms of accuracy, precision and recall when compared to recent grayscale LBP-based approaches.