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Revolutionizing Local Duck Species Classification: An Integrated Approach using Image Processing and Transfer Learning

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dc.contributor.author Das, Joy
dc.date.accessioned 2025-09-14T07:26:03Z
dc.date.available 2025-09-14T07:26:03Z
dc.date.issued 2024-06-24
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14499
dc.description Project Report en_US
dc.description.abstract Ducks in Bangladesh are an integral part of the country’s biodiversity. Ducks play significant role in both ecological balance and local livelihood. Duck meat is a rich source of protein. There are somewhere 6-7 duck species available in Bangladesh. Each species holds different characteristics. Duck species classification includes the implementation of transfer learning and image processing techniques which leverages some pre-trained models, ResNet50, ResNet152, DenseNet121, DenseNet201 and MobileNet-v2. Accurate classification of duck species like Campbell Female, Campbell Male, Deshi Female, Deshi Male, Runner Female, Runner Male, Zinding Female and Zinding Male is the main motivation of this study. The methodology includes proper data collection from a hatchery in Bikrampur and been proceed through some pre-processing methods for ensuring the data quality. The performance of the implemented model followings: ResNet50 achieved 75.00% of accuracy with test data loss of 0.795, ResNet152 achieved 60.50 % of accuracy with test data loss of 1.043. DenseNet121 achieved 98.25% and test loss is 0.081, and MobileNet-v2 achieved 97.75% accuracy with 0.070 data loss. The proposed model, DenseNet201 abled to achieve 99.25% accuracy with test loss of 0.027. However, many researchers had some limitations of limited classes, real-time images and other aspects after model training. The study impacts on society, environment by providing ecosystem balance, enhanced monitoring techniques and better identification of the ducks. System’s potential is also been considered by emphasizing ethical aspects which recommends some guidelines for responsible use of technology. The research participates on development of a precise and automated system of duck species classification. Future study will focus into enhanced model’s decision making, exploring real-life monitoring for continuous health monitoring and lifestyle of the ducks of various species. en_US
dc.description.sponsorship DIU en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Duck species en_US
dc.subject Image Processing en_US
dc.subject Deep Learning en_US
dc.subject Computer vision en_US
dc.title Revolutionizing Local Duck Species Classification: An Integrated Approach using Image Processing and Transfer Learning en_US
dc.type Other en_US


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