Optimizing Flower Classification with EfficientNetB3: Insights and Results from a High-Performance Model

The categorization of flowers in this work is done into five primary groups: Lily, Lotus, Sunflower, Orchid, and Tulip. Each class has 1,000 images; the dataset consists of 5,000 images overall; of these, 400 for testing, 600 for validation, and 4,000 for training. We obtained accurate classification using EfficientNetB3, a recent convolutional neural network design recognized for its balance of efficiency and performance. The model was tuned to increase its ability to distinguish among the five floral categories. Using advanced techniques like data augmentation and regularization, the training strategy aimed to improve the model so preventing overfitting and enhancing generalization. Our model showed very good in discriminating and categorizing many floral species with a 98% classification accuracy on the test set. These results emphasize the opportunities of EfficientNetB3 in flower classification tasks and its applicability in more general image recognition challenges, supporting sustainable agriculture, biodiversity preservation, quality education in AI-driven techniques, and so innovation in environmental studies.