Diseased leaves using texture and leaf vein features in fluorescence image

Abstract

Abstract. Tomato yellow leaf curl disease spreads very fast and often causes huge yield losses. It does not represent obvious symptoms on leaves until the disease becomes serious. This paper investigated the possibility of discriminating tomato yellow leaf curl disease by texture and leaf vein features in fluorescence image. A fluorescence imaging system was used to collect fluorescence images of healthy and diseased tomato leaves. It includes a high-speed camera (30 frames per second), filters (center wavelength: 690 nm), and excitation LEDs (center wavelength: 430 nm). By automatic iterative threshold selection, the leaf pixels were separated from back ground. After region filling, the binary mask images were acquired. Then AND operator was used to extract leaf vein images with gray information. Sub-images were segmented into 40X40 pixels. Leaf vein images were extracted by hit-or-miss transform (HMT) from sub-images. 8 texture features and 8 leaf vein features were calculated by gray level co-occurrence matrix (GLCM). The discrimination model was evaluated by support vector machine (SVM). The results showed that the infected leaf represented a clear leaf vein in sub-images and leaf vein images, especially in the main vein. All texture features and leaf vein features had significant dispersion. The differences between infected and healthy leaves were obvious for 5 features (T_ENT_MEAN, T_ENT_DEV, T_INE_DEV, L_ASM_DEV, and L_ENT_DEV). The method obtained good classification accuracy of 82.61%.