Machine vision system for determination of the breakage percentage and the number of wheat seeds

Shaker, Mohammad

doi:10.22092/amsr.2022.359263.1423

Document Type : Original Article

Author

Mohammad Shaker

Assistant Professor, Agricultural Engineering Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research Education And Extention Organization, Shiraz, Iran

https://doi.org/10.22092/amsr.2022.359263.1423

Abstract

Considering the problems of visual and human evaluation, machine vision, can be used as a suitable substitute for human vision. The purpose of this research was to build and evaluate a machine vision system to determine the breakage percentage and the number of wheat seeds. The system consists of three parts: suction box, sampling box and imaging box and evaluated for two cultivars Torabi and Azar wheat. In each type of wheat, the performance of the suction device was evaluated with two seed plates and four suction values. In each amount of suction, the number of singled seeds and the seeds of sticked together on each hole were counted and their percentage was calculated. The image prepared was transferred to MATLAB software and breakage determination algorithm and wheat seed number was coded and presented. The results showed that the most suitable treatment for wheat of Torabi cultivar, was seed plate with 1 mm hole and suction of -100 mm Hg with 95.31 percent singled seeds and 4.69 percent of sticked together seeds. For Azar cultivar, seed plate with 1 mm hole and suction of -120 mm Hg with a percentage of singled seeds of 91.6 and a percentage of sticked together seeds of 8.4 was the most appropriate treatment. The validation results of the algorithm showed that its accuracy for determining the percentage of breakage and the number of wheat seeds were 85.33 and 98.76%, respectively. It is suggested that this system be evaluated for seeds of different sizes.

Keywords

References

Amiryousefi, M. R., Mohebbi, M. & Tehranifar, A. (2018). Pomegranate seed clustering by machine vision. Food science and nutrition, 6(1), 18-26.

Ghaderifar, F., & Soltani, A. (2010). Seed control and certification. Publications of Mashhad University – Jahad. (in Persian)

Granitto, M., Navone, D., Verdes, F., & Ceccatto, H. A. (2002). Weed seeds identification by machine vision. Computers and Electronics in Agriculture, 33(2), 91-103.

Gunasekaran, S., Cooper, T. M., & Berlage, A. G. (1988). Evaluating quality factors of corn and soybeans using a computer vision system. Transactions of the ASAE, 31(4), 1264-1271.

Kapadia, V. N., Sasidharan, N., & Kalyanrao, P. (2017). Seed image analysis and its application in seed science research. Advances in Biotechnology and Microbiology, 7(2), 555709.

Lurstwut, B., & Pornpanomchai, C. (2016). Application of image processing and computer vision on rice seed germination analysis. International Journal of Applied Engineering Research, 11(1), 6800-6807.

Majumdar, S., & Jayas, D. S. (2000). Classification of cereal grains using machine vision: III. Texture models. Transactions of the ASABE, 43(6), 1681-1687.

Majumdar, S., Jayas, D. S., & Bulley, N. R. (1997). Classification of cereal grains using machine vision. ASAE Paper No. 97-3105. St Joseph, Mich., ASAE.

Sakai, N., Yonekawa, S., & Matsuzaki, A. (1996). Two-dimensional image analysis of the shape of rice and its application to separating varieties. Journal of Food Engineering, 27(4), 397-407.

Sapirstein, H. D., Neuman, M., Wright, E. H., Swedyk, E., & Bushuk, W. (1987). An instrumental system for cereal grain classification using digital image analysis. Journal of Cereal Science, 6(1), 3-14.

Shaker, M., Bazrafshan, M., & Jafari, A. (2020). Determining the number of sugar beet seedling using image processing method. Sugarbeet Journal, 36(1), 71-79. (in Persian)

Shaker, M., Minaei, S., Khushtaqaza, M. H., Banakar, A., & Jafari, A. (2015). Using machine vision to improve performance and reduce waste in paddy peeling machine. Agricultural Mechanization and Systems Engineering Research Journal, 16(65), 47-64. (in Persian)

Tanabata, T., Shibaya, T., Hori, K., Ebana, K., & Yano, M. (2012). Smart Grain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiology, 160(4), 1871-1880.

Tanska, M., Rotkiewicz, D., Kozirok, W., & Konopka, I. (2005). Measurement of the geometrical features and surface colour of rapeseeds using digital image analysis. Food Research International, 38(7), 741-750.

Venora, G., Grillo, O., Ravalli, C., & Cremonini, R. (2009). Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system. Scientia Horticulturae, 121(4), 410-418.

Wan, Y. N., Lin, C. M., & Chiou, J. F. (2002). Rice quality classification using an automatic grain quality inspection system. Transactions of the ASAE, 45(2), 379-387.

Yan, X., Wang, J., Liu, S., & Zhang, C. (2010). Purity identification of maize seed based on color characteristics. Proceedings of the 4^th Conference on Computer and Computing Technologies in Agriculture (CCTA). Oct. 22-25, Nanchang, China.

Zayas, I., Converse, H., & Steele, J. (1990). Discrimination of whole from broken corn kernels with image analysis. Transactions of the ASAE, 33(5), 1642–1646.

Agricultural Mechanization and Systems Research

Machine vision system for determination of the breakage percentage and the number of wheat seeds

References

References

Volume 23, Issue 82 - Serial Number 82
September 2022
Pages 19-32

Machine vision system for determination of the breakage percentage and the number of wheat seeds

References

References

Volume 23, Issue 82 - Serial Number 82September 2022Pages 19-32

Volume 23, Issue 82 - Serial Number 82
September 2022
Pages 19-32