Evaluation of the Machine Vision System to Determine the Percentage of Breakage and the Number of Sorghum, Cotton and Barley Seeds

Shaker, Mohammad; Jokar, Akbar

doi:10.22092/amsr.2024.365359.1484

Document Type : Original Article

Authors

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.2024.365359.1484

Abstract

In this research, a machine vision system was used and evaluated for seeds of sorghum, cotton and barley. For each type of seed, the performance of the suction device, with three seed plates (with hole diameters of 1, 1.5 and 2 mm) and four suction values (-80, -100, -120 and -130 mmHg) was evaluated. In each f suction value, the total number of seeds sticked to the seed plate, the number of singled seeds and the number of sticked seeds on each hole were counted and their percentage was calculated. After that, for the three types of seeds tested, the algorithm for determining the percentage of breakage and the number of seed coding and validation of the algorithm was evaluated in 30 repetitions. The results showed that for sorghum seed, treatment No.1 (seed plate with 1 mm holes and suction value of -80 mm Hg), for cotton seed, treatment No. 5 (seed plate with 1.5 mm holes and suction value of -80 mm Hg), and for barley seed, treatment No. 2 (seed plate with 1 mm holes and suction value of -100 mm Hg), were the most suitable treatments. The validation results of the algorithm for determining the percentage of breakage and the number of seeds for the three types of seeds tested showed that the average accuracy of the algorithm was equal to 100%.

Keywords

References

Farouk, S. M., & Islam, M. N. (1995). Effect of parboiling and milling parameters on breakage of rice grains. Agricultural Mechanization in Asia, Africa and Latin America, 26(4), 33-38.

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. https://doi.org/10.1016/S0168-1699(02)00004-2.

Gunasekaran, S., Paulsen, M. R., & Shove, G. C. (1985). Optical methods for nondestructive quality evaluation of agricultural and biological materials. Journal of Agricultural Engineering, 32(2), 209-241. https://doi.org/10.1016/0021-8634(85)90081-2.

Gunasekaran, S., Cooper, T. M., Berlage, A. G., & Krishnan, P. (1987). Image processing for stress cracks in corn kernels. Transactions of the ASAE, 30(1), 266-271. https://doi.org/10.13031/2013.30438.

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.

Majumdar, S., & Jayas, D. S. (2000). Classification of cereal grains using machine vision: III. Texture models. Transactions of the ASABE, 43(6), 1681-1687. https://doi.org/10.13031/2013.3068.

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. https://doi.org/10.22092/erams.2016.105953.
(in Persian)

Shaker, M., Yazdani, S. Mohammadi, D. & Alevi Manesh S. M. (2022). Determining the percentage of breakage and impurity of wheat seed based on the image processing method with the construction and evaluation of a single device, Research Report, Agricultural Engineering Research Institute, No. 62686. (in Persian)

Shaker, M. (2022). Machine vision system to determine the percentage of breakage and the number of wheat seeds. Agricultural Mechanization and Systems Research Journal, 23(82), 19-32. https://doi.org/10.22092/amsr.2022.359263.1423. (in Persian)

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. https://doi.org/10.1016/j.foodres.2005.01.008.

Yan, X., Wang, J., Liu, S., & Zhang, C. (2017). Purity identification of maize seed based on color characteristics. Proceedings of the 4^th Conference on Computer and Computing Technologies in Agriculture (CCTA).

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. https://doi.org/10.13031/2013.31521.

Evaluation of the Machine Vision System to Determine the Percentage of Breakage and the Number of Sorghum, Cotton and Barley Seeds

References

References

Volume 24, Issue 86 - Serial Number 86May 2023Pages 99-114

Volume 24, Issue 86 - Serial Number 86
May 2023
Pages 99-114