Bloom Sense: Revolutionizing Botanical Analysis with AI Using Machine Learning

Abstract

The accurate identification of flower species remains a challenging task in computer vision due to high intra-class variability and inter-class similarity. This paper presents BloomSense, a novel flower recognition system that leverages Convolutional Neural Networks (CNNs) implemented using TensorFlow and Keras frameworks. The proposed system achieves high-precision classification across five distinct flower species: roses, tulips, daisies, dandelions, and sunflowers. To enhance model robustness against real-world variations, comprehensive data augmentation techniques including rotation, flipping, zooming, and brightness adjustment are employed. The CNN architecture consists of multiple convolutional layers for hierarchical feature extraction, max-pooling layers for dimensionality reduction, and fully connected layers for final classification. The model achieves 94.3% accuracy on the test dataset, demonstrating superior performance compared to traditional machine learning approaches. Additionally, the system integrates a Streamlit-based web interface that provides real-time predictions along with detailed botanical information about identified species. The proposed methodology has broader implications for automated botanical analysis, agricultural monitoring, and educational applications.