Local Rose Breeds Detection System Using Transfer Learning Techniques
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Transfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity
Microservices have transformed software architecture through the creation of modular and independent services. However, they introduce operational complexities in service integration and system management that makes swif…
Anomaly DetectionCausal InferenceGAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
The transition to microservices has revolutionized software architectures, offering enhanced scalability and modularity. However, the distributed and dynamic nature of microservices introduces complexities in ensuring sy…
Anomaly DetectionGraph AttentionRoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection
Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common …
Computational EfficiencyPhysical SimulationsOutlier DetectionAnomaly DetectionA new dataset of dog breed images and a benchmark for fine-grained classification
In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including130 dog breeds…
BenchmarkingClassificationDiversityFine-Grained Image Classification+4Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
As modern microservice systems grow increasingly complex due to dynamic interactions and evolving runtime environments, they experience failures with rising frequency. Ensuring system reliability therefore critically dep…