Estimating Body Weight in Captive Rabbits Based on Improved Mask RCNN

Enze Duan, Hongyun Hao, Shida Zhao, Hongying Wang and Zongchun Bai ()
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Enze Duan: Agricultural Facilities and Equipment Research Institute, Jiangsu Academy of Agriculture Science, Nanjing 210014, China
Hongyun Hao: College of Engineering, China Agriculture University, Beijing 100082, China
Shida Zhao: Agricultural Facilities and Equipment Research Institute, Jiangsu Academy of Agriculture Science, Nanjing 210014, China
Hongying Wang: College of Engineering, China Agriculture University, Beijing 100082, China
Zongchun Bai: Agricultural Facilities and Equipment Research Institute, Jiangsu Academy of Agriculture Science, Nanjing 210014, China

Agriculture, 2023, vol. 13, issue 4, 1-18

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.

Keywords: deep learning; Mask RCNN; breeding robot; weight estimation; rabbit (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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