Open Access
| Issue |
ITM Web Conf.
Volume 87, 2026
2nd International Conference on Computing Paradigms (ICCP-2026)
|
|
|---|---|---|
| Article Number | 01006 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/itmconf/20268701006 | |
| Published online | 30 June 2026 | |
- P. Kumaresan, R.G. Geetha Devi, C.K. Kamble, Determinants of mulberry silk cocoon quality. IUP J. Agric. Econ. 7, 20–29 (2010). https://ssrn.com/abstract=1543635 [Google Scholar]
- S. Vasta, S. Figorilli, L. Ortenzi, S. Violino, C. Costa, L. Moscovini, F. Tocci, F. Pallottino, A. Assirelli, A. Saviane et al., Automated prototype for Bombyx mori cocoon sorting attempts to improve silk quality and production efficiency through multi-step approach and machine learning algorithms. Sensors 23, 868 (2023). https://doi.org/10.3390/s23020868 [Google Scholar]
- C. Harish Reddy, M.R. Bhat, S.N.N. Kankanawadi, N.M.P.K. Gowda, Artificial intelligence in the new era of sericulture. J. Sci. Res. Rep. 31, 788–803 (2025). https://doi.org/10.9734/jsrr/2025/v31i83422 [Google Scholar]
- H. Zheng, X. Guo, Y. Ma, X. Zeng, J. Chen, T. Zhang, Fine-grained detection model based on attention mechanism and multi-scale feature fusion for cocoon sorting. Agriculture 14, 700 (2024). https://doi.org/10.3390/agriculture14050700 [Google Scholar]
- J. Chen, X. Guo, T. Zhang, H. Zheng, Efficient defective cocoon recognition based on vision data for intelligent picking. Electron. Res. Arch. 32, 3299–3312 (2024). https://doi.org/10.3934/era.2024151 [Google Scholar]
- A. Pal, T. Dey, P. Chopra, A. Akuli, M. Ray, N. Bhattacharva, A new method for grading of silk yarn using electronic vision, in Proc. 6th Int. Conf. Sens. Technol. (ICST), Kolkata, India (2012), pp. 387–392. https://doi.org/10.1109/ICSensT.2012.6461706 [Google Scholar]
- J. Raj, A. Noel, R. Sundaram, V.G.V. Mahesh, Z. Zhuang, A. Simeone, A multi-sensor system for silkworm cocoon gender classification via image processing and support vector machine. Sensors 19, 2656 (2019). https://doi.org/10.3390/s19122656 [Google Scholar]
- P.P. Prasobhkumar, C.R. Francis, S.S. Gorth, Cocoon quality assessment system using vibration impact acoustic emission processing. Eng. Agric. Environ. Food 12, 556–563 (2019). https://doi.org/10.1016/j.eaef.2019.11.008 [Google Scholar]
- N.S. Yogeshraj, Smart automated sericulture based on image processing technique and embedded system. J. Univ. Shanghai Sci. Technol. 24, 327–331 (2022) [Google Scholar]
- K. Kumar, N. Pavan, R. Yashas, R. Rajesh, B.G. Rakshith, Advancement in sericulture using image processing, in V.K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lect. Notes Electr. Eng. 1106 (Springer, Singapore, 2024). https://doi.org/10.1007/978-981-99-7954-7_61 [Google Scholar]
- R.N. Sharifbayev, S.S. Djurayev, A.A. Khoshimov, S.S. Sharipbayev, S. Kurambayev, AI-driven cocoon classification for silk production. AIP Conf. Proc. 3304, 030033 (2025). https://doi.org/10.1063/5.0269511 [Google Scholar]
- J. Liu, J. Zhang, Z. Ma, E. Li, J. Yuan, An online quality detection algorithm for cocoon clusters based on CDYOLO. IEEE Access (2024) [Google Scholar]
- Cohesion test method for identifying dried cocoon raw silk. China Patent CN109490123A (2019) [Google Scholar]
- G. Li, Z. Zhang, L. Huang, G. Lü, D. Li, R. Wu, Cocoon blowing type intelligent silkworm cocoon sorting machine. China Patent CN216064327U (2022) [Google Scholar]
- Z. Zheng, H. Lin, X. Zhao, L. Li, B. Yin, X. Mao, J. Xu, Pneumatic high-speed cocoon selecting device. China Patent CN112547566B (2025) [Google Scholar]
- J. Lin, G. Hu, J. Chen, A data augmentation method for computer vision task with feature conversion between class. Comput. Electron. Agric. 231, 109909 (2025) [Google Scholar]
- C. Chadebec, E. Thibeau-Sutre, N. Burgos, S. Allassonnière, Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder. IEEE Trans. Pattern Anal. Mach. Intell. 45, 2879–2896 (2022) [Google Scholar]
- H. Li, C. Wang, Y. Liu, Aircraft skin defect detection based on Fourier GAN for data augmentation, in Proc. Int. Conf. Adv. Robot. Mechatronics (ICARM) (2024), pp. 449–454 [Google Scholar]
- J. Shen, T. Wu, Learning spatially-adaptive squeezeexcitation networks for few shot image synthesis, in Proc. IEEE Int. Conf. Image Process. (ICIP) (2023), pp. 2855–2859 [Google Scholar]
- B. Kwon, Data augmentation using convolutional autoencoder for facial emotion recognition, in Proc. Int. Conf. Electron. Inf. Commun. (ICEIC), Osaka, Japan (2025), pp. 1–4. https://doi.org/10.1109/ICEIC64972.2025.10879763 [Google Scholar]
- S. Hangaragi, N. Neelima, V. Venugopal, S. Ganguly, J. Mudi, J.-H. Choi, CAE SynthImgGen: Revolutionizing cancer diagnosis with convolutional autoencoder-based synthetic image generation. Alex. Eng. J. 115, 343–354 (2025). https://doi.org/10.1016/j.aej.2024.11.117 [Google Scholar]
- A.A. Luthfie, A. Alamsyah, Identifying coconut maturity levels using CNN and YOLOv8 deep learning algorithms. J. Inf. Syst. Explor. Res. 3 (2025). https://doi.org/10.52465/joiser.v3i2.595 [Google Scholar]
- YOLOv8 for object detection: A comprehensive review of advances, techniques, and applications. Int. J. Adv. Comput. Inform. 2, 53–61 (2025). https://doi.org/10.71129/ijaci.v2i1.pp53-61 [Google Scholar]
- Intel Corporation, Intel® RealSense™ depth camera D435i specifications (2025). https://www.intel.com/content/www/us/en/products/sku/190004/intel-realsense-depth-camerad435i/specifications.html [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

