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. 2023 Jul 27;13(15):2428.
doi: 10.3390/ani13152428.

Estimating the Feeding Time of Individual Broilers via Convolutional Neural Network and Image Processing

Amin Nasiri  1 , Ahmad Amirivojdan  1 , Yang Zhao  2 , Hao Gan  1
Affiliations

Estimating the Feeding Time of Individual Broilers via Convolutional Neural Network and Image Processing

Amin Nasiri et al. Animals (Basel). .

Abstract

Feeding behavior is one of the critical welfare indicators of broilers. Hence, understanding feeding behavior can provide important information regarding the usage of poultry resources and insights into farm management. Monitoring poultry behaviors is typically performed based on visual human observation. Despite the successful applications of this method, its implementation in large poultry farms takes time and effort. Thus, there is a need for automated approaches to overcome these challenges. Consequently, this study aimed to evaluate the feeding time of individual broilers by a convolutional neural network-based model. To achieve the goal of this research, 1500 images collected from a poultry farm were labeled for training the You Only Look Once (YOLO) model to detect the broilers' heads. A Euclidean distance-based tracking algorithm was developed to track the detected heads, as well. The developed algorithm estimated the broiler's feeding time by recognizing whether its head is inside the feeder. Three 1-min labeled videos were applied to evaluate the proposed algorithm's performance. The algorithm achieved an overall feeding time estimation accuracy of each broiler per visit to the feeding pan of 87.3%. In addition, the obtained results prove that the proposed algorithm can be used as a real-time tool in poultry farms.

Keywords: YOLO; broiler; feeding time; image processing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An instance of the labeling process for training the YOLO; (a) original image and (b) labeled image.
Figure 2
Figure 2
Samples of the developed algorithm’s result. Broilers’ heads with blue dots: feeding behavior; broilers’ heads with red dots: non-feeding behavior.
Figure 3
Figure 3
The workflow of the developed algorithm for feeding time estimation.
Figure 4
Figure 4
Pseudocode of the developed algorithm for feeding time estimation. “#” shows the comments.
Figure 5
Figure 5
(a) Mean average precision (mAP) and (b) loss function for each iteration of the training process.
Figure 6
Figure 6
Results of evaluating the developed algorithm for three different 1-min videos.
See this image and copyright information in PMC

References

    1. Li G., Zhao Y., Purswell J.L., Du Q., Chesser G.D., Jr., Lowe J.W. Analysis of feeding and drinking behaviors of group-reared broilers via image processing. Comput. Electron. Agric. 2020;175:105596. doi: 10.1016/j.compag.2020.105596. - DOI
    1. Li N., Ren Z., Li D., Zeng L. Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: Towards the goal of precision livestock farming. Animal. 2020;14:617–625. doi: 10.1017/S1751731119002155. - DOI - PubMed
    1. Kashiha M., Pluk A., Bahr C., Vranken E., Berckmans D. Development of an early warning system for a broiler house using computer vision. Biosyst. Eng. 2013;116:36–45. doi: 10.1016/j.biosystemseng.2013.06.004. - DOI
    1. Nasiri A., Yoder J., Zhao Y., Hawkins S., Prado M., Gan H. Pose estimation-based lameness recognition in broiler using CNN-LSTM network. Comput. Electron. Agric. 2022;197:106931. doi: 10.1016/j.compag.2022.106931. - DOI
    1. Guo Y., Zhu W., Jiao P., Chen J. Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation. Biosyst. Eng. 2014;125:98–104. doi: 10.1016/j.biosystemseng.2014.07.002. - DOI

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