Corder, G. W., Foreman, D. I., & Wiley InterScience (Online service). (2009).
Nonparametric statistics for non-statisticians a step-by-step approach. Wiley.
http://dx.doi.org/10.1002/9781118165881.
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)
Gunasekaran, S., Paulsen, M. R., & Shove, G. C. (1985). Optical methods for nondestructive quality evaluation of agricultural and biological materials. Journal of Agricultural Engineering Research, 32(2), 209-241. https://doi.org/10.1016/0021-8634(85)90081-2.
Gunasekaran, S., Paulsen, M. R., & Shove, G. C. (1986). A laser optical method for detecting corn kernel defects. Transactions of the ASAE, 29(l), 294-298, 304. https://doi.org/10.13031/2013.30142.
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), https://doi.org/10.19080/AIBM.2017.07.555709.
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.
Matshuisa, T., & Hosokawa, A. (1981). Possibilities of checking cracks in brown rice using illumination by oblique ray and image data processing system. Journal of the Society of Agriculture machinery of Japan, 42(4), 515-520. https://doi.org/10.11357/jsam1937.42.515.
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., & Jokar, A. (2023). Evaluation of the Machine Vision System to Determine the Percentage of Breakage and the Number of Sorghum, Cotton and Barley Seeds.
Agricultural Mechanization and Systems Research Journal, 24(86), 99-114.
https://doi.org/10.22092/amsr.2024.365359.1484.
(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.
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. https://doi.org/10.1016/j.scienta.2009.03.014.
Yan, X., Wang, J., Liu, S., & Zhang, C. (2017). Purity identification of maize seed based on color characteristics. Proceedings of the 4th Conference on Computer and Computing Technologies in Agriculture (CCTA). https://doi.org/10.1007/978-3-642-18354-6_73.
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.