International Journal of Scientific Research and Engineering Development

Flower species classification is a fundamental task in botany with numerous applications in biodiversity preservation, horticulture, and ecological studies. This research paper intro- duces a novel approach to tackle this challenge by leveraging the power of machine learning, specifically Convolutional Neural Networks (CNNs), withinthe domain of computer science. The problem addressed inthis study pertains to the accurate identification and categorization of diverse flower species based on images of their petals, leaves, and overall morphology. The methodology employed involves a comprehensivedataset of high-resolution flower images collected from various sources. Preprocessing techniques are applied to standardize the dataset and improve the model’s performance. Our machine learning model is designed and trained to classify flower species with high precision and accuracy. The key findings of this research paper include the successful development of a flower species classification model, achieving remarkable accuracy in distinguishing between a wide range of flower species. The experimentation phase reveals the potential of deep learning, specifically CNNs, in automating the flower species identification process. We also highlight the model’s adaptability to a variety of flower species, making it a versatile tool for botanists and horticulturists.

Ramyashree S, Smt.Prof.Shruthi Modi, Dr. P Sripalreddy, "Flowers Classification Using ML" International Journal of Scientific Research and Engineering Development, V9(4): Page(897-903) July-August 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.