Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network

Hongyun Hao, Peng Fang, Wei Jiang, Xianqiu Sun, Liangju Wang and Hongying Wang ()
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Hongyun Hao: College of Engineering, China Agriculture University, Beijing 100083, China
Peng Fang: College of Engineering, Jiangxi Agriculture University, Nanchang 330045, China
Wei Jiang: College of Engineering, China Agriculture University, Beijing 100083, China
Xianqiu Sun: Shandong Minhe Animal Husbandry Co., Ltd., Yantai 265600, China
Liangju Wang: College of Engineering, China Agriculture University, Beijing 100083, China
Hongying Wang: College of Engineering, China Agriculture University, Beijing 100083, China

Agriculture, 2022, vol. 12, issue 12, 1-12

Abstract: The feeding behavior of laying hens is closely related to their health and welfare status. In large-scale breeding farms, monitoring the feeding behavior of hens can effectively improve production management. However, manual monitoring is not only time-consuming but also reduces the welfare level of breeding staff. In order to realize automatic tracking of the feeding behavior of laying hens in the stacked cage laying houses, a feeding behavior detection network was constructed based on the Faster R-CNN network, which was characterized by the fusion of a 101 layers-deep residual network (ResNet101) and Path Aggregation Network (PAN) for feature extraction, and Intersection over Union (IoU) loss function for bounding box regression. The ablation experiments showed that the improved Faster R-CNN model enhanced precision, recall and F1-score from 84.40%, 72.67% and 0.781 to 90.12%, 79.14%, 0.843, respectively, which could enable the accurate detection of feeding behavior of laying hens. To understand the internal mechanism of the feeding behavior detection model, the convolutional kernel features and the feature maps output by the convolutional layers at each stage of the network were then visualized in an attempt to decipher the mechanisms within the Convolutional Neural Network(CNN) and provide a theoretical basis for optimizing the laying hens’ behavior recognition network.

Keywords: laying hens; feeding behavior; Faster R-CNN; model visualization (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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