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A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network

文献类型： 外文期刊

作者： Ren, Yufei 1 ; Huang, Yikang 1 ; Wang, Yichen 1 ; Zhang, Shijie 1 ; Qu, Hao 2 ; Ma, Jie 2 ; Wang, Longhe 3 ; Li, Lin 1 ;

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

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

3.Natl Res Facil Phenotyp & Genotyp Anal Model Anim, Beijing 100083, Peoples R China

关键词： chick day-age classification; precision livestock; convolutional neural network; edge computation; attention encoder; artificial intelligence application

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 18 期

页码：

收录情况： SCI

摘要： Simple Summary Most management methods for poultry farming currently rely on human labor. Such management is labor-intensive and inefficient, especially in identifying poultry growth stages. Given the lack of a high-precision artificial method for chick's day-age detection, a high-performance day-age classification and detection model for chicks was proposed based on artificial intelligent techniques. This method can detect and classify chicks in six different living stages from 1 to 32 day-ages, and the accuracy is 95.2%, superior to other current ones. In order to apply this method in practical scenarios, it has been deployed into an application based on the IOS system, which can recoganize the day-age of chicks by capturing real-time photos. The system is currently deployed in Rizhao City, Shandong Province, China. It helps chicken farm staff automatically detect the behavior of chickens, whose excellent working effect proves the robust availibility of the proposed method. Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices.

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