Species classification of aquatic plants using GRNN and BPNN
Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plants. This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features. Color-based segmentation using k-means clustering is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. From 405 images of flowers, color, texture and shape features are extracted. Classification of the plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into Iridaceae and Epilobium family and marsh aquatic plants into Malvaceae and Onagraceae family, the Iridaceae family is again classified into Babiana and Crocus species, the family Epilobium into Canum and Hirsutum, the family Malvaceae into Mallow and Pavonia, the family Onagraceae into Fuschia and Ludwigia species are done using general regression neural network and backpropagation neural network classifiers.
feature extraction k-means clustering texture filters cross-fold validation