ผู้วิจัย

Wisit Lumchanao and Sakol Udomsiri

บทคัดย่อ

This paper presents a set of procedures for detecting the primary embryo development of chicken eggs using Self-Organizing Mapping (SOM) technique and K-means clustering algorithm. Our strategy consists of preprocessing of an acquired color image with color space transformation, grouping the data by Self-Organizing Mapping technique and predicting the embryo development by K-means clustering method. In our experiment, the results show that our method is more efficient. Processing with this algorithm can indicate the period of chicken embryo in on hatching. By the accuracy of the algorithm depends on the adjustment the optimum number of iterative learning. For experiment the learning rate using the example of number 4 eggs, found that the optimum learning rate to be in the range of 0.1 to 0.5. And efficiency the optimum number of iterative learning to be in the range of 250 to 300 rounds.

บรรณานุกรม

1] L. Liu, and M. O. Ngadi, “Detecting fertility and early embryo development of chicken eggs using near-infrared hyperspectral imaging,” Food and Bioprocess Technology, vol. 6, 2013, pp. 2503–2513. [2] Kurt C. Lawrence, Douglas P. Smith, William R. Windham and P.Bosoon, “Egg embryo development detection with hyperspectral imaging,” International Journal of Poultry Science, vol.5, no.10, 2006, pp. 964-969. [3] D.P. Smith, K.C. Lawrence and G.W. Heitschmidt, “Fertility and embryo development of broiler hatching eggs evaluated with a hyperspectral imaging and predictive modeling system,” International Journal of Poultry Science, vol.7, no.10, 2008, pp.1001-1004. [4] W. Zhang, L. Pan, K. Tu, Q. Zhang, and M. Liu, “Comparison of spectral and image morphological analysis for egg early hatching property detection based on hyperspectral imaging,” Journal PLoS ONE, vol.9, no.2, 2014, pp. 1-10. [5] J. Kim and T. Chen, “Segmentation of image sequences using SOFM networks”, IEEE International Conference on Pattern Recognition, vol.3, 2000, pp.869-872. [6] M. Carrasco Kind and R. J Brunner, “SOMz : photometric redshift PDFs with self organizing maps and random atlas”, Monthly Notices of the Royal Astronomical Society, vol.438, no.4, 2014, pp. 3409-3421. [7] V. Jumb, M. Sohani and A. Shrivas, “Color Image Segmentation Using K-Means Clustering and Otsu‘s Adaptive Thresholding,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol.3, Issue.9, February 2014, pp.72-76.

ไฟล์แนบ

pdf DS021-iEECON2017

ขนาดไฟล์ 511 KB | จำนวนดาวน์โหลด 282 ครั้ง

ความคิดเห็น