The domestic rabbit (Oryctolagus cuniculus f. domesticus) is a very important variety in biomedical research and agricultural animal breeding. Due to the different geographical areas in which rabbit breeds originated, and the long history of domestication/artificial breeding, rabbits have experienced strong selection pressure, which has shaped many traits of most rabbit varieties, such as color and weight. An efficient genome-wide single-nucleotide polymorphism (SNP) detection strategy is genotyping-by-sequencing (GBS), which has been widely used in many organisms. This study attempted to explore bi-allelic SNPs associated with fur color and weight-related traits using GBS in five rabbit breeds. The data consisted of a total 831,035 SNPs in 150 individuals from Californian rabbits (CF), German Zika rabbits (ZK), Qixing rabbits (QX), Sichuan grey rabbits (SG), and Sichuan white rabbits (SW). In addition, these five breeds of rabbits were obviously independent populations, with high genetic differentiation among breeds and low genetic diversity within breeds. A total of 32,144 SNP sites were identified by selective sweep among the different varieties. The genes that carried SNP loci in these selected regions were related to important traits (fur color and weight) and signal pathways, such as the MAPK/ERK signaling pathway and the Hippo signaling pathway. In addition, genes related to fur color and weight were identified, such as ASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s, and they had more SNP sites. The research offers the foundation for further exploration of molecular genetic markers of SNPs that are related to traits.

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.