International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Deep Learning Based Method for Silkworm Egg Counting |
|---|---|
| ABSTRACT | The sericulture industry relies heavily on accurate monitoring and assessment of silkworm eggs to ensure high-quality silk production. Traditional manual counting and inspection methods are labor-intensive, time-consuming, and prone to human error, particularly when dealing with large egg sheets. This project proposes an automated solution for silkworm egg detection and counting using modern deep learning techniques. A YOLOv8 (You Only Look Once) object detection model is trained on a curated dataset of silkworm egg images, enabling accurate identification of individual eggs, even in dense clusters or under varying lighting conditions. The trained model achieves around 90% detection accuracy and is integrated into a Flask-based web application, allowing users to upload egg images and receive instant results with annotated images and precise egg counts. The system demonstrates high accuracy, real-time performance, and consistent results, effectively eliminating the limitations of manual counting. Furthermore, the modular design of the system allows for future enhancements, such as mobile deployment, egg quality classification, and clustering analysis, making it a scalable and practical tool for the sericulture industry. This work highlights the potential of AI-driven automation in modernizing traditional agricultural practices, improving productivity, and enabling data-driven decision-making. |
| AUTHOR | PROF. GOWRAMMA B H, HARICHANDRAN C, MD AWAIZ B K, VASANTH KUMAR M, VENKATESH M P Assistant Professor, Department of CS & E (Data Science), Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India U.G. Student, Department of CS&E (Data Science), Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312154 |
| pdf/154_A Deep Learning Based Method for Silkworm Egg Counting.pdf | |
| KEYWORDS | |
| References | 1. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv, 2020. 2. G. Jocher et al., “Ultralytics YOLOv8 Documentation,” Ultralytics, 2023. 3. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv, 2018. 4. W. Liu et al., “SSD: Single Shot Multibox Detector,” in ECCV, 2016. 5. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in CVPR, 2016. 6. S. Malik et al., “Image-based Object Counting Techniques: A Survey,” Journal of Imaging, 2021. 7. Sericulture Department of India, “Silkworm Egg Production Guidelines,” Govt. of India, 2020. 8. T.-Y. Lin et al., “Feature Pyramid Networks for Object Detection,” in CVPR, 2017. 9. M. Everingham et al., “The PASCAL VOC Challenge: A Retrospective,” IJCV, 2015. 10. D. Mishra and S. Saha, “Deep Learning Techniques for Agriculture: A Comprehensive Survey,” IEEE Access, 2020. 11. R.Singh and P.Lakshman, “Automation in Sericulture: Machine Vision Applications,” IJASR, 2022. 12. Bhat et al., “Small Object Detection Using Deep Learning: A Review,” IEEE Access, 2021. 13. https://demo.karnataka.gov.in/serikolar/public/uploads/media_to_upload1702037443.pdf |
