Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition.

Recognition is an extremely high-level computer vision evaluating task that primarily involves categorizing objects by identifying and evaluating their key distinguishing characteristics. Categorization is important in botany because it makes comprehending the relationships between various flower species easier to organize. Since there is a great deal of variability among flower species and some flower species may resemble one another, classifying flowers may become difficult. An appropriate technique for classification that uses deep learning technology is vital to categorize flower species effectively. This leads to the design of proposed Sobel Restricted Boltzmann VGG19 (SRB-VGG19), which is highly effective at classifying flower species and is inspired by VGG19 model. This research primarily contributes in three ways. The first contribution deals with the dataset preparation by means of feature extraction through the use of the Sobel filter and the Restricted Boltzmann Machine (RBM) neural network approach through unsupervised learning. The second contribution focuses on improving the VGG19 and DenseNet model for supervised learning, which is used to classify species of flowers into five groups. The third contribution overcomes the issue of data poisoning attack through Fast Gradient Sign Method (FGSM) to the input data samples. The FGSM attack was addressed by forming the Adversarial Noise Layer in the dense block. The Flowers Recognition KAGGLE dataset preprocessing was done to extract only the important features using the Sobel filter that computes the image intensity gradient at every pixel in the image. The Sobel filtered image was then applied to RBM to generate RBM Component Vectorized Flower images (RBMCV) which was divided into 3400 training and 850 testing images. To determine the best CNN, the training pictures are fitted with the existing CNN models. According to experiment results, VGG19 and DenseNet can classify floral species with an accuracy of above 80%. So, VGG19 and DenseNet were fine tuned to design the proposed SRB-VGG19 model. The Novelty of this research was explored by designing two sub models SRB-VGG FCL model, SRB-VGG Dense model and validating the security countermeasure of the model through FGSM attack. The proposed SRB-VGG19 initially begins by forming the RBMCV input images that only includes the essential flower edges. The RBMCV Flower images are trained with SRB-VGG FCL model, SRB-VGG Dense model and the performance analysis was done. When compared to the current deep learning models, the implementation results show that the proposed SRB-VGG19 Dense Model classifies the flower species with a high accuracy of 98.65%.