Year: 2025
Type: article
Abstract: As the smart livestock industry continues to evolve, traditional methods of measuring cattle weight are increasingly inadequate for the demands of large-scale farms. Conventional approaches rely on manual weighing or bulky equipment, which are inefficient and lack precision, failing to provide real-time, accurate weight measurements required in modern breeding facilities. To address this challenge, this paper proposes an automatic cattle weight measurement system based on deep learning. The system employs deep learning technology for keypoint detection on cattle and uses these detection results to predict weight, enabling non-contact, high-precision measurement. For keypoint detection, MobileNetV3 is utilized as the backbone architecture, while calibration object recognition technology extracts depth information from images to perform depth correction, thereby enhancing the accuracy of image scaling. Weight prediction is conducted through a regression model that integrates keypoint coordinates with geometric features such as body length, shoulder height, and chest girth to estimate the cattle’s weight. Experimental results demonstrate that the proposed system achieves a high level of accuracy in both keypoint detection and weight prediction tasks, highlighting its potential for automated weight estimation in modern livestock management.
DOI: 10.54097/q5fkjz34
Authors: Lihao Qin, Zhongyu Ma, Zhanshuo Zhang, Yongfeng Zuo, Dan He
Institution: Xinjiang University of Science and Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4413824173