Estimating Body Weight in Captive Rabbits Based on Improved Mask RCNN

Abstract

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.

Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network

“…While previous studies have made significant contributions to cattle weight prediction in different ways, there is still a need for a comprehensive system that integrates low-cost hardware resources and enables accurate and automatic real-time weight estimation. The advancements in deep learning, particularly in semantic segmentation and instance segmentation technologies, present new opportunities for precise body image segmentation, thus facilitating the application of computer vision technology in livestock weight measurement (Borges Oliveira et al, 2021 ; Dohmen et al, 2021 ; Witte et al, 2021 ; Duan et al, 2023 ; Hou et al, 2023 ).…”

Division of Cow Production Groups Based on SOLOv2 and Improved CNN-LSTM

“…The neural network can fit nonlinear relationships to analyze and predict unknown data attributes [20] of production groups. Therefore, we discuss the importance of instance segmentation algorithms (SOLOv2, Mask R-CNN (Region-based Convolutional Neural Network) [21,22]), as well as neural network algorithms (CNN-LSTM, BPNN (Back Propagation Neural Network)), for the division of high-and low-yielding cows.…”