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.

翻译：花卉品种检测及该品种的栽培建议与养护方式，对花卉培育、品种创新及花卉产业具有重要意义。在孟加拉国所有本土花卉中，玫瑰是最受欢迎且需求量最大的品种。玫瑰不仅在南亚地区风靡全球，更广泛应用于装饰之外的诸多领域。由于玫瑰在花卉贸易中需求旺盛，其品种检测变得至关重要。然而，与普通花卉分类研究不同，目前尚无针对特定花卉品种检测的突出成果。本研究提出了一种基于迁移学习技术的图像玫瑰品种检测模型。由于此类花卉研究在图像处理与分类方面资源有限，我们需要构建包含海量图像的大型数据集来训练模型。我们采用了五个不同品种的1939张原始图像，通过数据增强技术生成9306张训练图像和388张测试图像以验证模型。本研究应用了四种迁移学习模型：Inception V3、ResNet50、Xception和VGG16。其中VGG16模型取得99%的最高准确率，展现了卓越性能。根据研究综述，利用迁移学习方法进行玫瑰品种检测，是首个公开可用的特定花卉品种检测研究成果。