An efficient approach for automated system to identify the rice crop disease using intensity level based multi-fractal dimension and twin support vector machine

Rice is a major staple food crop for providing food security in Asian region. Rice crop mainly suffers from diseases like brown spot, leaf blast and hispa. Detecting rice crop disease in natural RGB images is a daunting task due the intricate texture of the region of the interest. The paper gives a novice approach to the detection of rice plant diseases. Here two different feature extraction methods were used one being Gray level co-occurrence matrix (GLCM) and the other is Intensity level based on the multi-fractal dimension (ILMFD) technique. The three different types of classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Twin Support Vector Machine (TWSVM) were used. Initially, the input rice crop images were processed, features extracted and finally the classification was done. The combination of ILMFD and TWSVM significantly improves the classification results as compared to existing models. The ILMFD method and TWSVM gives the highest Kernel accuracy of 100% detection of rice crop diseases (for the sample database used in this work) which validates the efficiency of the above mentioned techniques.