Counting and Separating Damaged Seeds of Soybean Seeds using Image Processing

Monlica Wattana, Buris Siriluk, Suwanan Khotwit

Abstract


This research proposed an image processing technique for counting soybean seeds and separating damaged seeds. The technique used was to adjust the image to black and white so that the soybean seeds differ from the background color. The second part, of the research, was the separation of soybean seeds using the distance transform method and the region growing method, to count soybean seeds. In the third part, it focused on the separation of damaged seeds by the size, the circle shape and HSV of soybean seeds. From 30 soybean seed images, the percentage of accuracy of counting and separating damaged seeds by naked eyes was 100 percent, and the average time spent was 13.70 seconds. The percentage of accuracy of counting the seeds by the developed program was 100 percent and the accuracy of the separation of damaged seeds was 99.80 percent. The average time spent was 6.49 seconds. The experimental results showed that the developed program took 2 times less to count the soybean seeds than the naked eyes. Therefore, the proposed algorithm of the program can help save the time for counting the soybean seeds and separate the damaged seeds.

Keywords


image processing; seed count; damaged seeds; soybean seed quality inspection

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References


T. Eksomtramage, “Soybean: Glycine max,†2011. [Online]. Available: http://www.natres.psu.ac.th/Department/PlantScience/510-211/pages/soybean.htm.

R. C. Gonzalez and R. E. Woods, Digital Image Processing. U.S.A: Prentice-Hall, 2002.

A. A. Khule, M. S. Nagmode, and R. D. Komati, “Automated Object Counting for Visual Inspection Applications,†in 2015 International Conference on Information Processing (ICIP), 2015.

B. K. Toh, Y. H. Ng, T.M. Liew, “Automated fish counting using image processing,†in International Conference on Computational Intelligence and Software Engineering, 2009(CiSE2009), 2009.

N. Tidmerng, W. Songpan, and M. Wattana, “Solving bird image overlapping for automatic population counts of birds using image processing,†in 2016 Management and Innovation Technology International Conference (MITicon), 2016.

O. Sarrafzadeh, A. M. Dehnavi, H. Rabbani, and A. Talebi, “A simple and accurate method for white blood cells segmentation using K-means algorithm,†in IEEE Workshop on Signal Processing Systems (SiPS), 2015.

M. Wattana and T. Boonsri, “Improvement of Complete Malaria Cell Image Segmentation,†in 2017 Twelfth International Conference on Digital Information Management (ICDIM), 2017.

S. S. Pathan and A. D. Harale, “Automated silkworm eggs count,†in 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), 2016.

C. Nampol, “The Verifying of the Red Rice Seed Mixed in the White Rice Seed by Digital Image Processing,†J. Ind. Technol. Ubon Ratchathani Rajabhat Univ. , 1 (January - June 2015), pp. 60–70, 2015.

A. Paisal and T. Kasetkasem, “Separation the mingling varieties of the mungbean seeds by image processing,†Khon Kaen Agric. J. 39, 3, pp. 240–247, 2011.

L. Pengfei, M. Qi, and Z. Weihong, “Estimation of the number of crop seeds based on image identi.â€

J. G. A. Barbedo, “Counting Clustered Soybean Seeds,†in 12th International Conference on Computational Science and Its Applications(ICCSA), 2012.

D. Liu, X. Ning, Z. Li, D. Yang, H. Li, and L. Gao, “Discriminating and elimination of damaged soybean seeds based on image characteristics,†J. stored Prod. Res. v.60, pp. 67–74, 2015.

B. Siriluk, S. Khotwit, and M. Wattana, “A Soybean Seeds Counting and Sort out Using Size of Soybean Seeds,†in The 5th ASEAN Undergraduate conference in Computing ( AUC2), 2017.




DOI: http://dx.doi.org/10.18517/ijaseit.8.4.6513

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