Optimized Flower Species Classification Using Fine-Tuned EfficientNetB3: A Five-Class Approach

Flower classification is one of the key tasks of image classification because of the huge number of applications in botany, agriculture, and environmental monitoring. In this paper, a dataset of 4,242 images of five classes of flowers was used: chamomile, tulip, rose, sunflower, and dandelion. The images were taken from different sources like Flickr, Google Images, and Yandex Images. The images are really diverse in resolution and proportion, but their size can be estimated as approximately 320x240 pixels. The task of classification in this job involves identifying the precise species of flowers with respect to the image data. It solves the classification problem using EfficientNetB3, a state-of-the-art CNN. The EfficientNetB3 models are a family of models that balance network depth, width, and resolution in a particularly efficient way, thus offering high performance while keeping the computational cost low. In the course of this study, the model could able to realize 92.82 % validation accuracy with a loss of 0.2229, hence establishing itself as effective in handling diverse characteristics of images and making accurate predictions for different categories of flowers. This further reflects the model's ability to generalize across differences that naturally occur in the visual pattern of flower images. It improves the performance of automated flower recognition systems that can find applications in many real-world domains, such as plant identification, biodiversity research, and precision agriculture. The efficiency of EfficientNetB3 here may form a ground to extend its applicability in more domains with fine-grained visual recognition tasks.