Using rabbit manure to prepare growing media is an effective method to solve environmental pollution and realize resource utilization. The solution to rabbit manure management is the composting process which could produce compost suitable for seedling nursery blocks, which could improve transplanting efficiency and seedlings’ survival rate. Seedling nursery blocks were obtained by mixing rabbit manure compost, vermiculite, rice straw, and peat. The effect of cold pressing parameters, including moisture content (25–45%), binder content (1–5%), molding compression ratio (2.5–4.5:1), and strain maintenance time (0–120 s), were investigated on blocks quality (i.e., ventilatory porosity, relaxation density, compressive resistance, and specific energy consumption) through a general rotation combined experiment. These results showed there were significant interaction effects between molding compression ratio and moisture content, moisture and binder content, binder content and strain maintenance time, and molding compression ratio and binder content on block quality. The optimal parameters for manufacturing blocks were that the molding compression ratio, moisture content, binder content, and strain maintenance time were 4:1, 33.5%, 3.1%, and 60 s, and the relaxation density, ventilation porosity, and specific energy consumption were 363.31 kg/m3, 18.72%, and 0.44 J/g, which could achieve emergence performance.

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Automated body weight (BW) estimation is an important indicator to reflect the automation level of breeding, which can effectively reduce the damage to animals in the breeding process. In order to manage meat rabbits accurately, reduce the frequency of manual intervention, and improve the intelligent of meat rabbit breeding, this study constructed a meat rabbit weight estimation system to replace manual weighing. The system consists of a meat rabbit image acquisition robot and a weight estimation model. The robot stops at each cage in turn and takes a top view of the rabbit through an RGB camera. The images from the robot are automatically processed in the weight estimation model, which consists of the meat rabbit segmentation network based on improved Mask RCNN and the BW fitting network. Attention mechanism, PointRend algorithm, and improved activation function are proposed to improve the performance of Mask RCNN. Six morphological parameters (relative projected area, contour perimeter, body length, body width, skeleton length, and curvature) are extracted from the obtained mask, and are sent into the BW fitting network based on SVR-SSA-BPNN. The experiment shows that the system achieves a 4.3% relative error and 172.7 g average absolute error in BW estimation for 441 rabbits, while the meat rabbit segmentation network achieves a 99.1% mean average precision (mAP) and a 98.7% mean pixel accuracy (MPA). The system provides technical support for automatic BW estimation of meat rabbits in commercial breeding, which is helpful to promote precision breeding.

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