Local Rose Breeds Detection System Using Transfer Learning Techniques

要約

タイトル：
「転移学習技術を用いた地元のバラ品種検出システム」

要約：
– バングラデシュの地元の花の中でも、バラは最も人気があり需要が高い。
– バラの品種検出は、花の栽培、品種開発、花ビジネスにとって重要。
– しかし、特定の花の品種検出に関する研究は不十分であり、目的とする品種の分類とは異なる。
– この研究では、転移学習技術を用いて画像からバラの品種を検出するモデルを提案している。
– 本研究では、Inception V3、ResNet50、Xception、VGG16の4つの転移学習モデルを用いたが、VGG16が最高の正答率99%を示し、最も優れた成果を出した。
– 転移学習を用いた特定の花の品種検出に関する研究は公開されていなかった。

要約(オリジナル)

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.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI