Abstract
Deep-Convolution Neural Network (CNN) is the branch of computer science. Deep Learning CNN is a methodology that teaches computer systems to do what comes naturally to humans. It is a method that learns by example and experience. This is a heuristic-based method to solve computationally exhaustive problems that are not resolved in a polynomial computation time like NP-Hard problems. The purpose of this research is to develop a hybrid methodology for the detection and segmentation of flower images that utilize the extension of the deep CNN. The plant, leaf, and flower image detection are the most challenging issues due to a wide variety of classes, based on their amount of texture, color distinctiveness, shape distinctiveness, and different size. The proposed methodology is implemented in Matlab with deep learning Tool Box and the dataset of flower image is taken from Kaggle with five different classes like daisy, dandelion, rose, tulip, and sunflower. This methodology takes an input of different flower images from data sets, then converts it from RGB (Red, Green, Blue) color model to the L*a*b color model. L*a*b has reduced the effort of image segmentation. The flower image segmentation has been performed by the canny edge detection algorithm which provided better results. The implemented extended deep learning convolution neural network can accurately recognize varieties of flower images. The learning accuracy of the proposed hybrid method is up to 98% that is maximizing up to + 1.89% from state of the art.
Early smoke detection of forest fires based on SVM image segmentation
