Dual-representation fusion of near-infrared spectra for simultaneous identification of fresh-cocoon sex and imprinted-dead cocoons during pupal metamorphosis

Automated identification of fresh-cocoon sex and imprinted-dead cocoons is essential for raw silk quality control and requires an efficient and reliable approach. Although near-infrared (NIR) spectroscopy shows potential, sex-identification performance across pupal metamorphosis remains insufficiently understood, and existing modeling approaches remain underdeveloped. This study developed a dual-representation weighted-fusion network for fresh-cocoon sex and imprinted-dead cocoon identification during pupal metamorphosis. The network comprises two ablation-optimized branches: a 1D-CNN incorporating dilated spatial attention, adaptive coordinate attention, and ConvNeXtV2 blocks for spectral-sequence feature extraction, and a 2D-CNN incorporating shuffle attention network, adaptive coordinate attention, and ResNet blocks for recurrence-plot-based structural feature extraction. The spectral-sequence and recurrence-plot branches capture complementary information that is integrated using an adaptive weighted-fusion strategy, with the optimized branch parameters used as pretrained weights for transfer learning. On the held-out test set, the dual-representation weighted-fusion network achieved the highest overall accuracy of 95.39%, outperforming the optimized 1D-CNN (93.55%) and 2D-CNN (94.59%), with an average per-sample network inference time of 0.921 ms. Sex-identification performance varied with pupal metamorphosis, showing generally lower and more variable accuracy during the early period, improved performance during the middle period, and some decline during the late period. The fusion network maintained high accuracy across the three periods and achieved its highest mean sex-identification accuracy of 97.22% during the middle period (days 5–7). Overall, the proposed dual-representation NIR framework provides a promising approach for non-destructive fresh-cocoon sex identification and imprinted-dead cocoon screening, while highlighting the pupal developmental period as an important consideration for practical sex sorting.

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Publication Details

Journal
Computers and Electronics in Agriculture
Published
2026-09-21
DOI
https://doi.org/10.1016/j.compag.2026.112431
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
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Dual-representation fusion of near-infrared spectra for simultaneous identification of fresh-cocoon sex and imprinted-dead cocoons during pupal metamorphosis

Hua Huang, Tianfu Zhao, Haibo He, Zongmeng Yang et al.
Computers and Electronics in Agriculture
Spectroscopy and Chemometric Analyses
article

Dual-representation fusion of near-infrared spectra for simultaneous identification of fresh-cocoon sex and imprinted-dead cocoons during pupal metamorphosis

Hua Huang, Tianfu Zhao, Haibo He, Zongmeng Yang, Xiaoling Tong, Mingqing Zhong, Yong Chen, Shiping Zhu, Wantong Xie, Bing Lu
article en

Abstract

Automated identification of fresh-cocoon sex and imprinted-dead cocoons is essential for raw silk quality control and requires an efficient and reliable approach. Although near-infrared (NIR) spectroscopy shows potential, sex-identification performance across pupal metamorphosis remains insufficiently understood, and existing modeling approaches remain underdeveloped. This study developed a dual-representation weighted-fusion network for fresh-cocoon sex and imprinted-dead cocoon identification during pupal metamorphosis. The network comprises two ablation-optimized branches: a 1D-CNN incorporating dilated spatial attention, adaptive coordinate attention, and ConvNeXtV2 blocks for spectral-sequence feature extraction, and a 2D-CNN incorporating shuffle attention network, adaptive coordinate attention, and ResNet blocks for recurrence-plot-based structural feature extraction. The spectral-sequence and recurrence-plot branches capture complementary information that is integrated using an adaptive weighted-fusion strategy, with the optimized branch parameters used as pretrained weights for transfer learning. On the held-out test set, the dual-representation weighted-fusion network achieved the highest overall accuracy of 95.39%, outperforming the optimized 1D-CNN (93.55%) and 2D-CNN (94.59%), with an average per-sample network inference time of 0.921 ms. Sex-identification performance varied with pupal metamorphosis, showing generally lower and more variable accuracy during the early period, improved performance during the middle period, and some decline during the late period. The fusion network maintained high accuracy across the three periods and achieved its highest mean sex-identification accuracy of 97.22% during the middle period (days 5–7). Overall, the proposed dual-representation NIR framework provides a promising approach for non-destructive fresh-cocoon sex identification and imprinted-dead cocoon screening, while highlighting the pupal developmental period as an important consideration for practical sex sorting.

Computers and Electronics in AgricultureVol. 256
Southwest University (CN)
Openalex Percentile: Top 16%
Spectroscopy and Chemometric Analyses
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