Identifying different types of roses using deep learning approaches

Abstract

In horticulture, botanical study, and landscaping, accurate rose identification is critical. The ability to tell the difference between red, yellow, and white rose varieties benefits in the creation of visually pleasing gardens, contributes to botanical databases, and aids biodiversity conservation efforts. In the floral sector, precise classification is also useful in agricultural techniques, ensuring optimal production, disease management, and informed decision-making. The emphasis on deep learning models in this study emphasizes the essential role that technology plays in improving the efficiency and reliability of rose identification operations. This study explores the effectiveness of four deep learning models—EfficientNet, ResNet50, MobileNetV2, and FNet—in classifying roses by color (red, yellow, and white). Advanced computer vision algorithms help with rose identification, which is important for agriculture and botanical research. Each model is rigorously trained and evaluated using a varied collection of high-resolution rose photos. For identifying between rose colors, performance parameters such as accuracy, precision, recall, and F1 score are examined. The most promising model is FNet, which achieves an astonishing 98.17% accuracy, demonstrating the efficacy of transformer-based topologies in rose color recognition. The study emphasizes the importance of selecting proper deep learning models and positions FNet as a reliable option for accurate rose color detection. These findings add important insights to computer vision in botanical research, aiding academics and practitioners in the selection of best models for rose identification based on color features. The success of FNet motivates further investigation of transformer architectures, which could lead to advances in plant species recognition via deep learning approaches.