Identification of Flower Types Using Deep Learning Neural Network

Abstract

Flowers play a significant role in various aspects of human life, renowned for their visual appeal and vibrant colors. The identification and classification of flowers are essential for ecological studies, agricultural practices, and conservation efforts. Previous work on flower identification has faced several challenges, including variations in lighting, viewpoint, scale, and performance. This study explores the feasibility of using deep learning algorithms, particularly Convolutional Neural Network (CNN), to automate the flower identification process. By reviewing existing literature, the study investigates the methodologies, datasets, and models employed in prior research to train deep neural networks for flower recognition. CNN are especially effective in learning intricate patterns and features from large datasets, making them well-suited for flower classification. This research improves the speed and accuracy of flower recognition by automatically categorizing them into types and varieties, eliminating the need for human-mediated identification systems. It also classifies flowers into their respective varieties, supporting conservation efforts and ecological research.