Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/12/12/2141 |
| _version_ | 1797461908943536128 |
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| author | Hongyun Hao Peng Fang Wei Jiang Xianqiu Sun Liangju Wang Hongying Wang |
| author_facet | Hongyun Hao Peng Fang Wei Jiang Xianqiu Sun Liangju Wang Hongying Wang |
| author_sort | Hongyun Hao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T17:26:07Z |
| format | Article |
| id | doaj.art-85dd1d68cf134c01a79c826915f7a9f6 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T17:26:07Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-85dd1d68cf134c01a79c826915f7a9f62023-11-24T12:42:06ZengMDPI AGAgriculture2077-04722022-12-011212214110.3390/agriculture12122141Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural NetworkHongyun Hao0Peng Fang1Wei Jiang2Xianqiu Sun3Liangju Wang4Hongying Wang5College of Engineering, China Agriculture University, Beijing 100083, ChinaCollege of Engineering, Jiangxi Agriculture University, Nanchang 330045, ChinaCollege of Engineering, China Agriculture University, Beijing 100083, ChinaShandong Minhe Animal Husbandry Co., Ltd., Yantai 265600, ChinaCollege of Engineering, China Agriculture University, Beijing 100083, ChinaCollege of Engineering, China Agriculture University, Beijing 100083, ChinaThe 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.https://www.mdpi.com/2077-0472/12/12/2141laying hensfeeding behaviorFaster R-CNNmodel visualization |
| spellingShingle | Hongyun Hao Peng Fang Wei Jiang Xianqiu Sun Liangju Wang Hongying Wang Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network Agriculture laying hens feeding behavior Faster R-CNN model visualization |
| title | Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network |
| title_full | Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network |
| title_fullStr | Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network |
| title_full_unstemmed | Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network |
| title_short | Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network |
| title_sort | research on laying hens feeding behavior detection and model visualization based on convolutional neural network |
| topic | laying hens feeding behavior Faster R-CNN model visualization |
| url | https://www.mdpi.com/2077-0472/12/12/2141 |
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