LIVEMOS-G: A High Throughput Gantry Monitoring System with Multi-Source Imaging and Environmental Sensing for Large-Scale Commercial Rabbit Farming
Year: 2025
Type: article
Abstract: The rising global demand for high-quality animal protein has driven the development of advanced technologies in high-density livestock farming. Rabbits, with their rapid growth, high reproductive efficiency, and excellent feed conversion, play an important role in modern animal agriculture. However, large-scale rabbit farming poses challenges in timely health inspection and environmental monitoring. Traditional manual inspections are labor-intensive, prone-to-error, and inefficient for real-time management. To address these issues, we propose Livestock Environmental Monitoring System-Gantry (LIVEMOS-G), an intelligent gantry-based monitoring system tailored for large-scale rabbit farms. Inspired by plant phenotyping platforms, the system integrates a three-axis motion module with multi-source imaging (RGB, depth, near-infrared, thermal infrared) and an environmental sensing module. It autonomously inspects around the farm, capturing multi-angle, high-resolution images and real-time environmental data without disturbing the rabbits. Key environmental parameters are collected accurately and compared with welfare standards. After training on an original dataset, which contains a total of 2325 sets of images (each set includes RGB, NIR, TIR, and depth image), the system is able to detect dead rabbits using a fusion-based object detection model during inspections. LIVEMOS-G offers a scalable, non-intrusive solution for intelligent livestock inspection, contributing to enhanced biosecurity, animal welfare, and data-driven management in high-density, modern rabbit farms. It also shows the potential to be extended to other species, contributing to the sustainable development of the animal farming industry as a whole.
Source: Animals
DOI: 10.3390/ani15213177
Authors: Yutong Han, Tai Wei, Zhaowang Chen, Hongying Wang, Liangju Wang, Congyan Li, Xiuli Mei, Liangde Kuang, Jianjun Gong
Institutions: China Agricultural University, Sichuan Animal Science Academy
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415812852
