Feature extraction and classification of Tilletia diseases based on image recognition

The identification of three types of diseases of Tilletia caries (DC.) Tul., Tilletia indica Mitra and Tilletia controversa Kühn are important in the imports and exports inspection and quarantine for their harm to wheat production and human health. Three diseases were recognized and classified based on image analysis and pattern recognition techniques by using Tilletia diseases micrographs. Six typical patterns in sixteen features of shape and texture in the images of the disease infected spores were extracted. Minimum distance method, BP neural network and support vector machine (SVM) were used for the recognition and classification of 96 samples of Tilletia diseases infected spores images. The experimental results showed that the classification performance of SVM was superior to that of minimum distance method and BP neural network, the overall recognition accuracy reached up to 82.9%. Therefore, it is practicable to recognize and classify three types of Tilletia diseases by image analysis and SVM.