  Flower breed detection and giving details of that breed with the suggestion
of cultivation processes and the way of taking care is important for flower
cultivation, breed invention, and the flower business. Among all the local
flowers in Bangladesh, the rose is one of the most popular and demanded
flowers. Roses are the most desirable flower not only in Bangladesh but also
throughout the world. Roses can be used for many other purposes apart from
decoration. As roses have a great demand in the flower business so rose breed
detection will be very essential. However, there is no remarkable work for
breed detection of a particular flower unlike the classification of different
flowers. In this research, we have proposed a model to detect rose breeds from
images using transfer learning techniques. For such work in flowers, resources
are not enough in image processing and classification, so we needed a large
dataset of the massive number of images to train our model. we have used 1939
raw images of five different breeds and we have generated 9306 images for the
training dataset and 388 images for the testing dataset to validate the model
using augmentation. We have applied four transfer learning models in this
research, which are Inception V3, ResNet50, Xception, and VGG16. Among these
four models, VGG16 achieved the highest accuracy of 99%, which is an excellent
outcome. Breed detection of a rose by using transfer learning methods is the
first work on breed detection of a particular flower that is publicly available
according to the study.
