Machine Learning Algorithms and Applications. Группа авторов

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СКАЧАТЬ the training dataset to determining the class of eggs can be easily generated with minimal human effort.

Acknowledgment

      The authors would like to thank Smt. R. Latha S-B and Mr. P. B. Vijayakumar S-C of KSSRDI, KA, IN for providing silkworm egg sheets for this study.

References

      1. Xue, Y. and Ray, N., Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing, CoRR, abs/1708.03307, arXiv preprint arXiv:1708.03307, 2017.

      2. Zieliński, B., Plichta, A., Misztal, K., Spurek, P., Brzychczy-Włoch, M., Ochońska, D., Deep learning approach to bacterial colony classification. PLoS One, 12, 9, e0184554, 2017.

      3. Abe, M. and Nakayama, H., Deep learning for forecasting stock returns in the cross-section, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10937 LNAI, pp. 273–284, 2018.

      4. Kumar, D.A., Silkworm Growth Monitoring Smart Sericulture System based on Internet of Things (IOT) and Image Processing. Int. J. Comput. Appl., 180, 18, 975–8887, 2018.

      5. S.P., Rajanna, G.S., Chethan, D., Application of Image Analysis methods for Quantification of Fecundity in Silkworm Bombyx mori L, in: International Sericulture Commission, Research Papers, 2015 (http://www.inserco.org/en/previous_issue).

      6. Kiratiratanapruk, K., Watcharapinchai, N., Methasate, I., Sinthupinyo, W., Silkworm eggs detection and classification using image analysis, in: 2014 International Computer Science and Engineering Conference, ICSEC 2014, pp. 340–345, 2014.

      7. Pandit, A., Rangole, J., Shastri, R., Deosarkar, S., Vision system for automatic counting of silkworm eggs, 2014 International Conference on Information Communication and Embedded Systems, ICICES 2014, no. 978, pp. 1–5, 2015.

      8. Kiratiratanapruk, K. and Sinthupinyo, W., Worm egg segmentation based centroid detection in low contrast image, in: 2012 International Symposium on Communications and Information Technologies, ISCIT 2012, pp. 1139–1143, 2012.

      9. Pathan, S., Harale, A., Student, P.G., A Method of Automatic Silkworm Eggs Counting System. Int. J. Innovative Res. Comput. Commun. Eng., 4, 12, 25, 2016.

      10. K.P.R., Sanjeev Poojary, L., M.G.V., S.N.K., An Image Processing Algorithm for Silkworm Egg Counting. Perspect. Commun. Embedded-Syst. Signal-Process. (PiCES), 1, 4, 2566–932, 2017.

11. Matas, J., Galambos, C., Kittler, J., Robust Detection of Lines Using the Progressive Probabilistic Hough Transform. Comput. Vision Image Understanding, 78, 1, 119–137, Apr. 2000.

      12. Nikitha, R.N., Srinidhi, R.G., Harshith, R., Amar, T., Raghavendra, C.G., Reckoning the hatch rate of multivoltine silkworm eggs by differentiating yellow grains from white shells using blob analysis technique, in: Advances in Intelligent Systems and Computing, vol. 709, pp. 497–506, 2018.

      13. F. und T. des L. N.-W. Ministerium für Innovation and Wissenschaft, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in neural information processing systems, 2015.

      14. Özdener, A.E. and Rivkin, A., You Only Look Once: Unified, Real-Time Object Detection Joseph. Drug Des. Devel. Ther., abs/1506.02640, 11, 2827–2840, 2015.

      1 *Corresponding author: [email protected]

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