Deep Learning Blossoms: Classifying Flowers with Precision

The objective of this project is to develop a system for identifying the type of flower using image classification techniques. Convolutional Neural Networks (CNN) will be utilized to automatically extract image features, allowing for efficient processing of large quantities of flower images collected from the internet and directly clicking photos. To ensure accuracy, the images will be labeled according to their species before being fed into the deep CNN. The research field of domain-specific image classification will be explored to enhance the system's ability to classify flower images within a specific domain. The project will also investigate the challenges associated with flower image classification, such as the variations in flower types and the different flowering phases. The training and evaluation processes will involve creating a distinct model for each flower and comparing newly acquired test flower models against existing models in the database. The project will utilize a statistical procedure to form a set of basis features, which can be used to represent any flower image as a combination of these standard flowers.