Quantification of the percentage of total broken grain in rice (Oryza sativa L.) by digital image analysis
DOI:
https://doi.org/10.15517/am.v33iEspecial.51568Keywords:
physical attributes, broken grain, sample plate, captureAbstract
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.
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Copyright (c) 2022 Luis Arley Zúñiga Picado, Stefanny Campos Boza, Juan Roberto Mora Chaves, Luis Barboza-Barquero
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