Browsing by Author "Sarkar, Tanu"

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Identification of Local Flowers Through Image Processing
(Daffodil International University, 2021-06-03) Sarkar, Tanu; Masum, Md. Maruf Khan; Robin, Tanvir Ahamed
Flowers are one of the most beautiful creation of almighty. Bangladesh is the land of flowers. There are lots of flower in our country. There are many different kinds of flower in our country. Every flowers have its own color, shape and name. There are many seasonal flowers in this country. Now a days flowers are used in every occasion like wedding, birthday party etc. In our everyday life, we can see many different kinds of flowers around us while we walking beside roads, rail line even in our garden. But most of our people don’t have any knowledge about that flowers. Even they don’t know the flowers name. But many of them wants to know about the flowers. For that reason we choose this topic to research and develop our system. Through our system people will be known about that unknown flowers which they see but don’t know about that flowers. Our system based on neural networks to create an image classification by Tensorflow. CNN is the most popular platform of machine learning and it is extensively used for image classification. We used some CNN models like Inception-v3, VGG-19, MobileNet and ResNet-50 for detecting and classifying flowers. From all of the model we got best accuracy (98.76%) by using MobileNet model. We have collected six types of flower for our project. Each flower have 400 images and in total 2400 image in our dataset. We kept 60% image in training set for train the dataset. 20% kept in validation set and another 20% in test set for testing purpose. In this system we use six types of flower but in future we have plan to add more flower for upgrading our system
Local Flower Detection Through Deep Learning Approach
(Daffodil International University, 2024-07-06) Sarkar, Tanu
Flowers are some of the most beautiful creations of the Almighty. There are numerous seasonal flowers in Bangladesh. Each flower has its unique color, shape, and name. In our daily lives, we can see a variety of flowers when walking along highways, rail lines, and even in our gardens. However, the majority of our population is unaware of the significance of this beautiful creation. The identification and classification of local flower species using deep learning techniques has important implications for botanical research, biodiversity protection, and ecological monitoring. In this paper, we present an in-depth analysis of multiple deep learning models for detecting and classifying flowers from a dataset of 2400 photos of various flower species the tested models consist of VGG-16, MobileNet, M-1, and ResNet-50. Each model was selected based on its distinct architecture and established efficacy in image recognition tasks. The results of our study indicate that although all models work well, there are significant variations in their abilities. Specifically, the VGG16 model demonstrates great potential for real-time applications and achieving high accuracy of this dataset, with a rate of 97.73%.Six different kinds of flowers have been collected for our research. Our dataset consists of 2450 photos overall and 410 images per flower. We allocated 60% of the images to the training set in order to train the dataset. The validation set is assigned 20% of the total data, while another 20% is allocated to the test set for the purpose of testing. To improve the models' robustness and generalizability, we apply preprocessing techniques like resizing, normalization, and data augmentation. Six different kinds of flowers are used in this system. However, we plan to add more flowers in the future to make our system better.
Local Flower Detection Through Deep Learning Approach
(DAFFODIL INTERNATIONAL UNIVERSITY, 2024-07-14) Sarkar, Tanu
Flowers are some of the most beautiful creations of the Almighty. There are numerous seasonal flowers in Bangladesh. Each flower has its unique color, shape, and name. In our daily lives, we can see a variety of flowers when walking along highways, rail lines, and even in our gardens. However, the majority of our population is unaware of the significance of this beautiful creation. The identification and classification of local flower species using deep learning techniques has important implications for botanical research, biodiversity protection, and ecological monitoring. In this paper, we present an in-depth analysis of multiple deep learning models for detecting and classifying flowers from a dataset of 2400 photos of various flower species the tested models consist of VGG-16, MobileNet, M-1, and ResNet-50. Each model was selected based on its distinct architecture and established efficacy in image recognition tasks. The results of our study indicate that although all models work well, there are significant variations in their abilities. Specifically, the VGG16 model demonstrates great potential for real-time applications and achieving high accuracy of this dataset, with a rate of 97.73%.Six different kinds of flowers have been collected for our research. Our dataset consists of 2450 photos overall and 410 images per flower. We allocated 60% of the images to the training set in order to train the dataset. The validation set is assigned 20% of the total data, while another 20% is allocated to the test set for the purpose of testing. To improve the models' robustness and generalizability, we apply preprocessing techniques like resizing, normalization, and data augmentation. Six different kinds of flowers are used in this system. However, we plan to add more flowers in the future to make our system better.