Leaf recognition using BP-RBF hybrid neural network

Abstract

Plant recognition has great potential in forestry research and management. A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples. The process was carried out in three steps: image pretreatment, feature extraction, and leaf recognition. In the image pretreatment processing, an image segmentation method based on hue, saturation and value color space and connected component labeling was presented, which can obtain the complete leaf image without veins and background. The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recognition. The recognition accuracy of different classifiers was used to compare classification performance. The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%, highest among all the classifiers.

A Color Space Blending With Deep Learning Networks in the Identification of Plant Leaves

“…HSV color space has linear scalability, good in the separation of background, removal of noises, and extraction of contours. S and H components help in the identification of leaf veins, Yang et al (2022).…”