• Shalini Bhaskar Bajaj

The advancement in artificial intelligence and computer vision has changed the management of livestock in a way where manual monitoring is inconsistent and is always needed. This research introduces a deep learning-based framework for cow hump areas and hoof angle detection, an AI-driven system which uses YOLOv8 model for real-time and efficient detection by analyzing images of cow. This model accurately identifies and measures the hump area as well as the hoof angle with the surface in contact as these two components are important in indicating the breed, posture and health of a cow. A dataset of 200 annotated images was used to train the model so that it detects the features with high precision, using image processing techniques to estimate the real hump area and the hoof angle. Results show that the model can detect the images with high precision and measurements which help in precision in management of livestock.

Hump Area Detection, Hoof Angle Estimation, Computer Vision, YOLOv8, Deep Learning, Image Processing

"Deep Learning Framework for Detecting Cow Hump Area and Hoof Angle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.d446-d451, September-2025, Available :http://www.jetir.org/papers/JETIR2509361.pdf

"Deep Learning Framework for Detecting Cow Hump Area and Hoof Angle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppd446-d451, September-2025, Available at : http://www.jetir.org/papers/JETIR2509361.pdf