Muhammad Asim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Muhammad Asim has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bareera Anam
Department of Software & Communications Engineering, Hongik University, Sejong City 30016, Republic of Korea
2 shared publications - Byung-Seo Kim
Department of Software and Communications Engineering, Hongik University, Sejong 30016, Republic of Korea
2 shared publications - Muhammad Nadeem Ali
Department of Software & Communications Engineering, Hongik University, Sejong City 30016, Republic of Korea
2 shared publications - Ghulam Musa Raza
Department of Software and Communications Engineering, Hongik University, Sejong-si, South Korea
1 shared publication
Latest Publications
- 7 September 2026
- Published by Elsevier in Computers and Electrical Engineering
- Vol. 140
A survey of intelligent and content-aware aerial networks’ capabilities, challenges, and future directions
- 7 September 2026
- Published by Elsevier in Computers and Electrical Engineering
- Vol. 140
A survey of intelligent and content-aware aerial networks’ capabilities, challenges, and future directions
- Open Access
Cattle Farming Activity Monitoring Using Advanced Deep Learning Approach
Technological advancements have significantly improved cattle farming, particularly in sensor-based activity monitoring for health management, estrus detection, and overall herd supervision. However, such a sensor-based monitoring framework often illustrates several issues, such as high cost, animal discomfort, and susceptibility to false measurement. This study introduces a vision-based cattle activity monitoring approach deployed in a commercial Nestlé dairy farm, specifically one that is estrus-focused, where overhead cameras capture unconstrained herd behavior under variable lighting, occlusions, and crowding. A custom dataset of 2956 Images are collected and then annotated into four fine-grained behaviors—standing, lying, grazing, and estrus—enabling detailed analysis beyond coarse activity categories commonly used in prior livestock monitoring studies. Furthermore, computer vision-based deep learning algorithms are deployed on this dataset to classify the aforementioned classes. A comparative analysis of YOLOv8 and YOLOv9 is provided, which clearly illustrates that YOLOv8-L achieved a mAP of 91.11%, whereas YOLOv9-E achieved a mAP of 90.23%.
Open AccessCattle Farming Activity Monitoring Using Advanced Deep Learning Approach
Technological advancements have significantly improved cattle farming, particularly in sensor-based activity monitoring for health management, estrus detection, and overall herd supervision. However, such a sensor-based monitoring framework often illustrates several issues, such as high cost, animal discomfort, and susceptibility to false measurement. This study introduces a vision-based cattle activity monitoring approach deployed in a commercial Nestlé dairy farm, specifically one that is estrus-focused, where overhead cameras capture unconstrained herd behavior under variable lighting, occlusions, and crowding. A custom dataset of 2956 Images are collected and then annotated into four fine-grained behaviors—standing, lying, grazing, and estrus—enabling detailed analysis beyond coarse activity categories commonly used in prior livestock monitoring studies. Furthermore, computer vision-based deep learning algorithms are deployed on this dataset to classify the aforementioned classes. A comparative analysis of YOLOv8 and YOLOv9 is provided, which clearly illustrates that YOLOv8-L achieved a mAP of 91.11%, whereas YOLOv9-E achieved a mAP of 90.23%.