CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 320; p. 124569
• Main Authors: Wang, Dongqiao, Wang, Qiaohua, Chen, Zhuoting, Guo, Juncai, Li, Shijun
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 05.11.2024
• Subjects: Convolutional variational autoencoder; Fertilization; Improved deep forest; Pre-incubation duck eggs; Visible/near-infrared spectroscopy; Fertilization; Improved deep forest; Pre-incubation duck eggs; Visible/near-infrared spectroscopy; Convolutional variational autoencoder
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2024.124569

[Display omitted] •A Convolutional Variational Autoencoder was built for better feature extraction.•The base classifiers of Deep Forest were upgraded to improve the detection ability.•The performance of the hybrid deep learning method was superior to baselines.•The proposed method can initially detect unfertilized duck eggs before hatching.•The spectral differences of fertilized and unfertilized duck eggs were interpreted. Unfertilized duck eggs not removed prior to incubation will deteriorate quickly, posing a risk of contaminating the normally fertilized duck eggs. Thus, detecting the fertilization status of breeding duck eggs as early as possible is a meaningful and challenging task. Most existing work usually focus on the characteristics of chicken eggs during mid-term hatching. However, little attention has been paid to the detection for duck eggs prior to incubation. In this paper, we present a novel hybrid deep learning detection framework for the fertilization status of pre-incubation duck eggs, termed CVAE-DF, based on visible/near-infrared (VIS/NIR) transmittance spectroscopy. The framework comprises the encoder of a convolutional variational autoencoder (CVAE) and an improved deep forest (DF) model. More specifically, we first collected transmittance spectral data (400–1000 nm) of 255 duck eggs before hatching. The multiplicative scatter correction (MSC) method was then used to eliminate noise and extraneous information of the raw spectral data. Two efficient data augmentation methods were adopted to provide sufficient data. After that, CVAE was applied to extract representative features and reduce the feature dimension for the detection task. Finally, an improved DF model was employed to build the classification model on the enhanced feature set. The CVAE-DF model achieved an overall accuracy of 95.94 % on the test dataset. These experimental results in terms of four metrics demonstrate that our CVAE-DF method outperforms the traditional methods by a significant margin. Furthermore, the results also indicate that CVAE holds great promise as a novel feature extraction method for the VIS/NIR spectral analysis of other agricultural products. It is extremely beneficial to practical engineering.