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A High-Precision Method for 100-Day-Old Classification of Chickens in Edge Computing Scenarios Based on Federated Computing

文献类型： 外文期刊

作者： Huang, Yikang 1 ; Yang, Xinze 2 ; Guo, Jiangyi 2 ; Cheng, Jia 3 ; Qu, Hao 4 ; Ma, Jie 4 ; Li, Lin 1 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China

3.China Agr Univ, Coll Agron & Biotechnol, Beijing 100083, Peoples R China

4.Guangdong Acad Agr Sci, Inst Anim Sci, Guangzhou 510640, Peoples R China

关键词： 100-day-old classification of chickens; edge computing scenarios; federated learning; poultry; deep learning

期刊名称：ANIMALS （ 影响因子：3.231； 五年影响因子：3.312 ）

ISSN： 2076-2615

年卷期： 2022 年 12 卷 24 期

页码：

收录情况： SCI

摘要： Simple Summary Improving the accuracy of day-age detection of chickens is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. Finally, our method can achieve an accuracy of 96.1%, which can fully meet the needs of application scenarios. Due to the booming development of computer vision technology and artificial intelligence algorithms, it has become more feasible to implement artificial rearing of animals in real production scenarios. Improving the accuracy of day-age detection of chickens is one of the examples and is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. Due to the huge amount of data and the different computing power of different devices in practical application scenarios, it is important to maximize the computing power of edge computing devices without sacrificing accuracy. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. In order to accommodate different computing power in different scenarios, this paper proposes a dual-ended adaptive federated learning framework; in order to adapt to low computing power scenarios, this paper performs lightweighting operations on the mainstream model; and in order to verify the effectiveness of the model, this paper conducts a number of targeted experiments. Compared with AlexNet, VGG, ResNet and GoogLeNet, this model improves the classification accuracy to 96.1%, which is 14.4% better than the baseline model and improves the Recall and Precision by 14.8% and 14.2%, respectively. In addition, by lightening the network, our methods reduce the inference latency and transmission latency by 24.4 ms and 10.5 ms, respectively. Finally, this model is deployed in a real-world application and an application is developed based on the wechat SDK.

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