Quantification of the percentage of total broken grain in rice (Oryza sativa L.) by digital image analysis

Authors

DOI:

https://doi.org/10.15517/am.v33iEspecial.51568

Keywords:

physical attributes, broken grain, sample plate, capture

Abstract

Introduction. Digital image analysis (DIA) can be used to evaluate the quality parameters of rice grains, such as the percentage of whole grain. Objective. To quantify percentage of total broken grain by means of digital image analysis (DIA) applied to the quantification of rice (Oryza sativa L.) quality. Materials and methods. The present work was developed in facilities of the Centro para Investigaciones en Granos y Semillas (CIGRAS) of the Universidad de Costa Rica (UCR), Costa Rica, in 2021. The work was carried out with commercial rice samples. A sample plate was developed to facilitate the arrangement of the grains and to acquire the digital images. The length parameters established in the technical regulation of Costa Rica RTCR 202:1998 were used to quantify the percentages of small broken, broken grains, and whole grains, which were quantified conventionally and by means of digital images. The DIA included the segmentation and binarization process of the objects (small broken, broken grains, and whole grain) to quantify their areas and catalog the digitally identified elements in weight values. Results. It was possible to quantified the percentage of small broken and broken grain with DIA. The correlation between the variables quantified by DIA and those evaluated conventionally was greater than 0.93 for the small broken property and 0.98 for broken grain. Conventional analysis requires 1 to 2 hours to determine the percentage of total broken grain and other quality properties in each rice sample. The digital analysis requires 7 to 23 minutes per sample plate to analyze all the properties evaluated (small broken and broken grain). Conclusions. The digital analysis method applied allowed to determine the total broken grain properties in samples composed of hundreds of grains.

Downloads

Download data is not yet available.

References

Acosta, C., Sampallo, G., Cleva, L., Cleva, D., & Liska, M. (2017). Detección e identificación de defectos en granos de arroz empleando visión artificial. In Sociedad Argentina de Informática e Investigación Operativa (Eds.), IX Congreso Argentino de AgroInformática (pp. 98–111). Sociedad Argentina de Informática e Investigación Operativa. http://sedici.unlp.edu.ar/handle/10915/62834

Abbaspour-Gilandeh, Y., Molaee, A., Sabzi, S., Nabipur, N., Shamshirband, S., & Mosavi, A. (2020). A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars. Agronomy (Basel, Switzerland), 10(1), Article 117. https://doi.org/10.3390/agronomy10010117

Aznan, A., González Viejo, C., Pang, A., & Fuentes, S. (2021). Computer vision and machine learning analysis of commercial rice grains: A potential digital approach for consumer perception studies. Sensors, 21(19), Article 6354. https://doi.org/10.3390/s21196354

Barbin, D. F., Mastelini, S. M., Barbon, S., Campos, G. F. C., Barbon, A. P. A. C., & Shimokomaki, M. (2016). Digital image analyses as an alternative tool for chicken quality assessment. Biosystems Engineering, 144, 85–93. https://doi.org/10.1016/j.biosystemseng.2016.01.015

Butardo, V. M., Sreenivasulu, N., Juliano, B. O. (2019). Improving Rice grain quality: state-of-the-art and future prospects. In N. Sreenivasulu (Ed.), Rice grain quality (pp. 19–55). Springer. https://doi.org/10.1007/978-1-4939-8914-0_2

Camelo-Méndez, G. A., Camacho-Díaz, B. H., del Villar-Martínez, A. A., Arenas-Ocampo, M. L., Bello-Pérez, L. A., & Jiménez-Aparicio, A. R. (2012). Digital image analysis of diverse Mexican rice cultivars. Journal of the Science of Food and Agriculture, 92, 2709–2714. https://doi.org/10.1002/jsfa.5693

Corporación Arrocera Nacional. (2021). Informe anual estadístico 2020/2021. Estadísticas arroceras. https://www.conarroz.com/estadisticasarroceras.php

Gayathri Devi, T. G., Neelamegam, P., & Sudha, S. (2017). Machine vision based quality analysis of rice grains. 2017. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, 2017, 1052–1055. https://doi.org/10.1109/ICPCSI.2017.8391871

Ghatkamble, R., & Vishwanatha, K. (2017). Development of novel algorithm to Determine the rice varieties using digital image processing. 1st International Conference Responsible Business and Sustainable Development, Bangalore, India. https://bit.ly/3yrbJ2c

Gudipalli, A., Prabha, A., & Reddy, P. (2016). A review on analysis and grading of rice using image processing. Journal of Engineering and Applied Sciences, 11(23), 13550–13555. https://bit.ly/3u51GwT

Herath, K. (2016). Rice grains classification using image processing technics. The Open University of Sri Lanka. https://bit.ly/3UZlnBS

Herath, H. M. K. K. M. B. (2017). Image processing analysis for the rice grain quality in Sri Lanka. The Official E-Newsletter of the Institution of Engineers Sri Lanka, 33. https://doi.org/10.6084/m9.figshare.9759290

Kang, T., Cho, B., Won, J., Kang, S., Han, C., Lee, D., & Lee, H. (2018). Milling characteristics of cutting type rice milling machine (I) – Characteristics of milling in accordance with blowing velocity. Engineering in Agriculture Environment and Food, 11(3), 91–94. https://doi.org/10.1016/j.eaef.2017.12.003

Li, X., Yuan, J., Gu, T., & Liu, X. (2011). Detection level of raisins based on image analysis and neural network. Third Pacific-Asia Conference on Circuits, Communications and System, 2011, 1–3. https://doi.org/10.1109/PACCS.2011.5990209

Mahale, B., & Korde, S. (2014). Rice quality analysis using image processing techniques. International Conference for Convergence of Technology, 2014, 1–5. https://doi.org/10.1109/I2CT.2014.7092300

Mousavirad, S. J., Akhlaghian Tab, F., & Mollazade, K. (2012). Design of an expert system for rice kernel identification using optimal morphological features and back propagation neural network. International Journal of Applied Information Systems, 3(2), 33–37. https://bit.ly/3QTQA7W

Ngampak, D., & Piamsa-nga, P. (2015). Image analysis of broken rice grains of Khao Dawk Mali rice. In IEEE (Ed.), 7th International Conference on Knowledge and Smart Technology (pp. 115–120). IEEE. https://doi.org/10.1109/KST.2015.7051471

Petrou, M., & Petrou, C. (2010). Image processing the fundamentals (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/9781119994398

Presidencia de la República, & Ministerio de Economía y Comercio (1998, mayo 20). Decreto No. 26901-MEIC. Reglamento técnico RTCR 202:1998: Arroz pilado. Especificaciones y métodos de análisis. Sistema Costarricense de Información Jurídica. http://www.pgrweb.go.cr/SCIJ/Busqueda/Normativa/Normas/nrm_texto_completo.aspx?param1=NRTC&nValor1=1&nValor2=51661&nValor3=55975&strTipM=TC

Presidencia de la República, Ministerio de Economía y Comercio, Ministerio de Agricultura y Ganadería, & Ministerio de Salud. (2008, mayo 7). Decreto No. 34802. Reforma al Decreto Ejecutivo N° 34487-MEIC-MAG-S RTCR 406-:2007 arroz en granza. Especificaciones y métodos de análisis para la comercialización e industrialización. Sistema Costarricense de Información Jurídica. http://www.pgrweb.go.cr/scij/Busqueda/Normativa/Normas/nrm_texto_completo.aspx?param1=NRTC&nValor1=1&nValor2=64180&nValor3=74318&param2=1&strTipM=TC&lResultado=1&strSim=simp

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Salas-Arias, K. M., Madriz-Quirós, C. E., Sánchez-Brenes, O., Sánchez-Brenes, M., & Hernández-Granados, J. B. (2017). Modelos de cuantificación de error humano aplicados en la industria de manufactura moderna. Revista Tecnología en Marcha, 30, 58–66. http://doi.org/10.18845/tm.v30i2.3197

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J. -Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open-source platform for biological-image analysis. Nature Methods, 9, 676–682. https://doi.org/10.1038/nmeth.2019

Shin, Y., Won, Y. J., Lee, C., Cheon, K. -S., Oh, H., Lee, G. -S., Baek, J., Yoon, I. S., Kim, S. L., Cha, Y. -S., Kim, K. -H., & Ji, H. (2022). Identification of grain size-related QTLs in Korean japonica rice using genome resequencing and high-throughput image analysis. Agriculture, 12(1), Article 51. https://doi.org/10.3390/agriculture12010051

Siddagangappa, M. R., & Kulkarni, A. H. (2014). Classification and quality analysis of food grains. IOSR Journal of Computer Engineering, 16(4), 1–10. https://doi.org/10.9790/0661-16430110

Silva, C., & Sonnadara, U. (2013). Classification of rice grains using neural networks. Proceedings of Technical Sessions, 29, 9–14. https://bit.ly/3AcJndc

Sindhu, C., Sasmitha, S., Tamilmani, P., Udaysriram, C., & Vidhya, V. (2021). Rice grain type and grading of rice grains using image processing. International Journal of Research in Engineering, Science and Management, 4(7), 2581–5792. https://journals.resaim.com/ijresm/article/view/1089

Tanabata, T., Shibaya, T., Hori, K., Ebana, K., & Yano, M. (2012). SmartGrain: High-Throughput phenotyping Software for measuring seed shape through image analysis. Plant Physiology, 160(4), 1871–1880. https://doi.org/10.1104/pp.112.205120

Tanwong, K., Suksawang, P., & Punsawad, Y. (2018, November 21-24). Using digital image to classify phenotype of the rice grain quality under agricultural standards act. 22nd International Computer Science and Engineering Conference (ICSEC), Chaing Mai, Thailand. https://doi.org/10.1109/ICSEC.2018.8712732

van Dalen, G. (2004). Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Research International, 37(1), 51–58. https://doi.org/10.1016/j.foodres.2003.09.001

Yang, W., Liang, J., Hao, Q., Luan, X., Tan, Q., Lin, S., Zhu, H., Liu, G., Liu, Z., Bu, S., Wang, S., & Zhang, G. (2021). Fine mapping of two grain chalkiness QTLs sensitive to high temperature in rice. Rice, 14, Article 33. https://doi.org/10.1186/s12284-021-00476-x

Yoshioka, Y., Iwata, H., Tabata, M., Ninomiya, S., & Ohsawa, R. (2007). Chalkiness in rice: Potential for evaluation with image analysis. Crop Science, 47(5), 2113–2120. https://doi.org/10.2135/cropsci2006.10.0631sc

Published

2022-11-30

How to Cite

Zúñiga Picado, L. A., Campos Boza, S., Mora Chaves, J. R., & Barboza-Barquero, L. (2022). Quantification of the percentage of total broken grain in rice (Oryza sativa L.) by digital image analysis. Agronomía Mesoamericana, 33(Especial). https://doi.org/10.15517/am.v33iEspecial.51568