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Cattle Farming Activity Monitoring Using Advance Deep learning Approach

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dc.contributor.author Asim, Muhammad
dc.date.accessioned 2024-07-30T12:23:51Z
dc.date.available 2024-07-30T12:23:51Z
dc.date.issued 2024
dc.identifier.other 328415
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/45036
dc.description Supervisor: Dr Shehzad Younis en_US
dc.description.abstract Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have become the state-of-the-art techniques for object detection, classification, segmentation, and behavior classification. Despite their extensive application across various domains, including agriculture, cow identification in dairy farming often relies on time-consuming, costly, and inaccurate methods such as direct visual monitoring or invasive contact devices like sensors. This research evaluates the performance of different variants of the YOLOv8 and YOLOv9 models for cow detection and counting in a dairy farming context. Our dataset, consisting of 2,956 cow images extracted from videos provided by Nestlé dairy farms, was processed using the Roboflow platform to convert video frames into images. These images were augmented to simulate varying illumination conditions and annotated using the RoboFlow tool in YOLO format. Among the YOLOv8 variants, the YOLOv8l model achieved superior performance, with a mean Average Precision (mAP) of 91.11%, precision of 92%, and recall of 89%. Additionally, the YOLOv8l model demonstrated effective cow counting capabilities within individual frames. In comparison, the YOLOv9l model showed a mAP of 90%, indicating its robustness in cow detection tasks. Our findings highlight the YOLOv8l model’s potential in enhancing cow identification and counting processes in dairy farming, offering a reliable, non-invasive, and efficient alternative to traditional methods. This research underscores the practical implementation of advanced deep learning models in livestock management, contributing significantly to the future research directions of the YOLO algorithm. en_US
dc.language.iso en en_US
dc.publisher NUST School of Electrical Engineering and Computer Science (NUST SEECS) en_US
dc.title Cattle Farming Activity Monitoring Using Advance Deep learning Approach en_US
dc.type Thesis en_US


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