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

被引:6
作者
Hao, Hongyun [1 ]
Fang, Peng [2 ]
Jiang, Wei [1 ]
Sun, Xianqiu [3 ]
Wang, Liangju [1 ]
Wang, Hongying [1 ]
机构
[1] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
[2] Jiangxi Agr Univ, Coll Engn, Nanchang 330045, Jiangxi, Peoples R China
[3] Shandong Minhe Anim Husb Co Ltd, Yantai 265600, Peoples R China
来源
AGRICULTURE-BASEL | 2022年 / 12卷 / 12期
关键词
laying hens; feeding behavior; Faster R-CNN; model visualization; PLUMAGE CONDITION;
D O I
10.3390/agriculture12122141
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 29 条
[1]   A real-time monitoring tool to automatically measure the feed intakes of multiple broiler chickens by sound analysis [J].
Aydin, A. ;
Bahr, C. ;
Berckmans, D. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2015, 114 :1-6
[2]  
Bochkovskiy A., 2020, ARXIV 200410934
[3]   Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect [J].
Cheng, Man ;
Yuan, Hongbo ;
Wang, Qifan ;
Cai, Zhenjiang ;
Liu, Yueqin ;
Zhang, Yingjie .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 198
[4]   Unrest index for estimating thermal comfort of poultry birds (Gallus gallus domesticus) using computer vision techniques [J].
Del Valle, Jose Eduardo ;
Pereira, Danilo Florentino ;
Mollo Neto, Mario ;
Almeida Gabriel Filho, Luis Roberto ;
Salgado, Douglas D'Alessandro .
BIOSYSTEMS ENGINEERING, 2021, 206 :123-134
[5]   Pose estimation and behavior classification of broiler chickens based on deep neural networks [J].
Fang, Cheng ;
Zhang, Tiemin ;
Zheng, Haikun ;
Huang, Junduan ;
Cuan, Kaixuan .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 180
[6]  
[方鹏 Fang Peng], 2021, [农业机械学报, Transactions of the Chinese Society for Agricultural Machinery], V52, P300
[7]   A machine vision system to detect and count laying hens in battery cages [J].
Geffen, O. ;
Yitzhaky, Y. ;
Barchilon, N. ;
Druyan, S. ;
Halachmi, I. .
ANIMAL, 2020, 14 (12) :2628-2634
[8]   EFFECT OF REARING DENSITY ON PECKING BEHAVIOR AND PLUMAGE CONDITION OF LAYING HENS IN 2 TYPES OF AVIARY [J].
HANSEN, I ;
BRAASTAD, BO .
APPLIED ANIMAL BEHAVIOUR SCIENCE, 1994, 40 (3-4) :263-272
[9]   Segmentation of body parts of cows in RGB-depth images based on template matching [J].
Jia, Nan ;
Kootstra, Gert ;
Koerkamp, Peter Groot ;
Shi, Zhengxiang ;
Du, Songhuai .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 180
[10]   An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation [J].
Jiang, Kailin ;
Xie, Tianyu ;
Yan, Rui ;
Wen, Xi ;
Li, Danyang ;
Jiang, Hongbo ;
Jiang, Ning ;
Feng, Ling ;
Duan, Xuliang ;
Wang, Jianjun .
AGRICULTURE-BASEL, 2022, 12 (10)