DSpace Repository

Cattle External Disease Classification using Deep Learning Techniques

Show simple item record

dc.contributor.author Rony, Md.
dc.contributor.author Riad, Riad
dc.contributor.author Barai, Dola
dc.date.accessioned 2022-12-03T08:43:18Z
dc.date.available 2022-12-03T08:43:18Z
dc.date.issued 2022-01-02
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/9114
dc.description.abstract External cattle disorders such as Foot and Mouth Disease (FMD), Lumpy Skin Disease (LSD), and Infectious Bovine Keratoconjunctivitis (IBK) are among the most common in the sub-continent. Early detection is critical for disease control. The most widely utilized architecture in the state-of-the-art of image processing and computer vision is the typical convolutional neural network. No other method for detecting cattle diseases in a husbandry farm has been implemented, leveraging deep learning techniques to our knowledge. This suggested model uses different CNN architectures such as traditional deep CNN, Inception-V3, and VGG-16 in the area of deep learning to identify the most prevalent external illnesses at an early stage. The document details every step involved in conducting the illness detection model, from data collection through procedure and result. The suggested approach is successful, obtaining findings with a 95% accuracy rate, which may help decrease human error during the classification and aid veterinarians and livestock producers in recognizing diseases. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Foot-and-mouth disease en_US
dc.subject Diseases and pests--Control en_US
dc.title Cattle External Disease Classification using Deep Learning Techniques en_US
dc.type Other en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


My Account