Nondestructive in-ovo sexing of Hy-Line Sonia eggs by EggFormer using hyperspectral imaging
2024-10
发表期刊COMPUTERS AND ELECTRONICS IN AGRICULTURE (IF:7.7[JCR-2023],8.4[5-Year])
ISSN0168-1699
EISSN1872-7107
卷号225
发表状态已发表
DOI10.1016/j.compag.2024.109298
摘要

Early identification of egg gender during incubation is crucial for animal welfare and commercial poultry production, as nowadays day-old male chicks are often culled due to low economic value. Hyperspectral imaging (HSI) recognition presents a swift, non-destructive, and cost-effective solution for in-ovo sexing compared to traditional methods such as Polymerase Chain Reaction (PCR), Volatile Organic Compounds (VOC), and Raman spectroscopy. In this study, we collected spectral images of Hy-Line Sonia chicken eggs even-numbered day from day 0 to 14, with a focus on day 10 for detailed analysis. We introduced the EggFormer model, incorporating channel attention and transformer self-attention mechanisms. To assess model performance, significant wavelengths were extracted by machine learning algorithms, including Random Forest(RF), Principal Component Analysis(PCA), Successive Projections Algorithm (SPA), and Competitive Adaptive Reweighted Sampling Algorithm (CARS). The channel images of these significant wavelengths were then employed with ViT-Base(Vision Transformer) for prediction and comparison. The EggFormer model demonstrated superior results, with accuracy of 95.4%, recall of 98.6%, F1 score of 0.958 and Kappa of 0.908. Furthermore, by interpreting the channel attention block, 22 wavelengths were selected, maintaining optimal results, with 4 bands achieving an accuracy of 94.6%. This outperformance positions it as a promisingly efficient and economical solution for industrial applications. The code of this work is available at https://github.com/quietbamboo/EggFormer for reproducibility. © 2024 Elsevier B.V.

关键词Animals Cost effectiveness Deep learning Forestry Learning algorithms Learning systems Polymerase chain reaction Principal component analysis Volatile organic compounds Animal welfare Chicken eggs Cost-effective solutions Deep learning Economic values In-ovo sexing Interpretable Non destructive Poultry production Spectral images
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收录类别SCI ; EI
语种英语
WOS研究方向Agriculture ; Computer Science
WOS类目Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS记录号WOS:001292197300001
出版者Elsevier B.V.
EI入藏号20243216844893
EI主题词Hyperspectral imaging
EI分类号461.4 Ergonomics and Human Factors Engineering ; 723.4.2 Machine Learning ; 746 Imaging Techniques ; 801.2 Biochemistry ; 804.1 Organic Compounds ; 821 Agricultural Equipment and Methods ; Vegetation and Pest Control ; 911.2 Industrial Economics ; 922.2 Mathematical Statistics
原始文献类型Journal article (JA)
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/411223
专题信息科学与技术学院
信息科学与技术学院_本科生
通讯作者Pan, Leiqing
作者单位
1.College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, Nanjing; 210095, China
2.College of Food Science and Technology, Nanjing Agricultural University, Jiangsu, Nanjing; 210095, China
3.School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
推荐引用方式
GB/T 7714
Ji, Chengming,Song, Ke,Chen, Zixin,et al. Nondestructive in-ovo sexing of Hy-Line Sonia eggs by EggFormer using hyperspectral imaging[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2024,225.
APA Ji, Chengming.,Song, Ke.,Chen, Zixin.,Wang, Shanyong.,Xu, Huanliang.,...&Huang, Junxian.(2024).Nondestructive in-ovo sexing of Hy-Line Sonia eggs by EggFormer using hyperspectral imaging.COMPUTERS AND ELECTRONICS IN AGRICULTURE,225.
MLA Ji, Chengming,et al."Nondestructive in-ovo sexing of Hy-Line Sonia eggs by EggFormer using hyperspectral imaging".COMPUTERS AND ELECTRONICS IN AGRICULTURE 225(2024).
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