TY - GEN
T1 - Enhancing Precision Livestock Farming Management with AI-Driven Ear Tag Detection and OCR Recognition
AU - Alomair, Reem
AU - Al-Amoudi, Abrar
AU - Javaid, Abdulrahman
AU - Alnaser, Mustafa
AU - Al Binali, Shiekhah
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Automated cow identification is crucial for Precision Livestock Farming (PLF) which aims to improve livestock production, reproduction, health, welfare, and impact on the environment positively. Automating identification of individual cows using sensor systems such as Radio-Frequency Identification (RFID) and Quick-Response (QR) code is effective but costly, particularly for medium and large farms. To ensure sustainable cow management, cost-effective, plug-and-play systems are needed. In this context, we introduce an accurate, low-cost method for cow identification using a single RGB camera. Utilizing state-of-the-art real-time object detection with YOLOv8, our approach detects and recognizes cows by their ear tag numbers. The cow IDs images are pre-processed and then read by an Optical Character Recognition (OCR) algorithm. The experimental results demonstrate the effectiveness of our method, achieving an ear tag detection [email protected] of 91.60% and a 92% improvement in number recognition accuracy through the proposed image preprocessing.
AB - Automated cow identification is crucial for Precision Livestock Farming (PLF) which aims to improve livestock production, reproduction, health, welfare, and impact on the environment positively. Automating identification of individual cows using sensor systems such as Radio-Frequency Identification (RFID) and Quick-Response (QR) code is effective but costly, particularly for medium and large farms. To ensure sustainable cow management, cost-effective, plug-and-play systems are needed. In this context, we introduce an accurate, low-cost method for cow identification using a single RGB camera. Utilizing state-of-the-art real-time object detection with YOLOv8, our approach detects and recognizes cows by their ear tag numbers. The cow IDs images are pre-processed and then read by an Optical Character Recognition (OCR) algorithm. The experimental results demonstrate the effectiveness of our method, achieving an ear tag detection [email protected] of 91.60% and a 92% improvement in number recognition accuracy through the proposed image preprocessing.
KW - Computer Vision
KW - Cow Ear Tag
KW - Cow Identification
KW - Optical Char- acter Recognition (OCR)
KW - YOLOv
UR - https://www.scopus.com/pages/publications/86000023860
U2 - 10.1109/ICTMOD63116.2024.10878196
DO - 10.1109/ICTMOD63116.2024.10878196
M3 - Conference contribution
AN - SCOPUS:86000023860
T3 - 2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024
BT - 2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024
Y2 - 4 November 2024 through 6 November 2024
ER -