DETECTION AND CLASSIFICATION OF EXTERNAL BOVINE DISEASES USING DEEP LEARNING

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dc.contributor.authorMuhamad Faaris Bin Jamhari
dc.date.accessioned2026-05-04T05:02:27Z
dc.date.issued2025
dc.descriptionLivestock diseases pose significant challenges to agricultural productivity, often requiring timely and accurate diagnosis to mitigate economic losses and improve animal welfare. Traditional diagnostic methods, reliant on veterinary expertise, can be time-intensive and inaccessible in remote areas. This project focuses on developing a YOLOv11-based deep learning model to detect and classify visible external bovine diseases. A curated and augmented dataset ensures diversity and robustness, while training on Google Colab leverages cloud-based computational resources to optimize precision, recall, and mean Average Precision (mAP). The resulting model aims to provide a reliable and efficient foundation for disease detection, offering potential for future system integration to support farmers with rapid and accurate diagnostics.
dc.identifier.urihttps://scholarhub.unimas.my/handle/123456789/601
dc.language.isoEnglish
dc.publisherUniversiti Malaysia Sarawak
dc.relation.ispartofseriesFaculty of Computer Science and Information Technology
dc.subjectBovine diseases, deep learning, YOLOv11, object detection, image classification
dc.titleDETECTION AND CLASSIFICATION OF EXTERNAL BOVINE DISEASES USING DEEP LEARNING
dc.typeFinal Year Project

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