BlossomNet: A Deep Learning Framework for Accurate Flower Identification

This study focuses on the vital task of classifying floral photographs, essential for applications in botany, agriculture, and environmental protection. Utilizing deep learning, we propose an efficient system employing a Convolutional Neural Network (CNN) to automatically extract discriminative features from floral images. Our approach involves training and evaluating the model with a substantial dataset of labeled flower photos from diverse species. To optimize performance, we employ supervised learning techniques, including data augmentation, fine-tuning, and transfer learning, during image preprocessing for quality enhancement and data normalization. The CNN model effectively identifies and distinguishes various flower species based on visual features like color, texture, and form. Experimental results showcase the system's proficiency in accurately categorizing floral photos, with excellent accuracy on both training and testing datasets, highlighting robustness and generalization capabilities. Comparative analysis underscores the superior classification accuracy and effectiveness of our proposed strategy. The developed technique holds significant potential for real-world applications, aiding botanists, scientists, and enthusiasts in easily identifying diverse flower species. This advancement contributes to plant biodiversity study and preservation, automating tasks related to ecological assessments, species monitoring, and floral inventories. The study presents a reliable and effective deep learning-based method for floral image categorization, providing a valuable resource for various sectors requiring precise flower detection and identification.