Flower Identification and Classification applying CNN through Deep Learning Methodologies

Convolutional neural networks play a significant role in the identification of flora species. Deep learning methodologies support us in image identification based on properties such as color and shape. Every species is distinct concerning attributes like texture, the shape of petals, and sepals. In this paper, we classify five various categories of flora named as daisy, dandelion, rose, sunflower, tulip. The methodology utilized here is the convolutional neural network where for instinctive recognition based on flora pictures. We focused on a neural network which implies an approach called skip connections and is flexible enough to perform various classification methodologies. We concentrated related to image classification by training and validating the dataset. The methodology adopted here is a residual neural network with nine deep layers, where implementation and approaches are discussed related to the classification model. We observed different existing algorithms and discussed the disadvantages, based on the drawbacks. We designed an algorithm for the classification and identification of a flower. The Experimental methodologies adopted are based on PyTorch and datasets. Finally, we presented the experimental results along with the graphical analysis. Keywords—Convolutional Neural Networks, Deep learning, Classification, Data mining, Artificial Intelligence.