Volumentric model evaluation using digital image processing of unbroken corn kernels

In this article, three models are compared to estimate the volume of corn (Zea mays) grains from values obtained using digital image processing techniques on images of the sample. In the first model, the volume of each grain is assumed to be proportional to its cubed length. In the second model, the...

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Autores principales: Cleva, Mario S., Villaverde, Jorge E., Liska, Diego O., Duran Muñoz, Héctor A.
Formato: Artículo revista
Lenguaje:Español
Publicado: Instituto Agrotécnico "Pedro M. Fuentes Godo" - Facultad de Ciencias Agrarias (UNNE) 2025
Materias:
Best-fitting ellipse
Morphology
Binarization
ImageJ®
Elipse que mejor ajusta
Morfología
Binarización
Acceso en línea:https://revistas.unne.edu.ar/index.php/agr/article/view/8548
Aporte de:
Revistas UNNE - Universidad Nacional del Noroeste (UNNE) de Universidad Nacional del Nordeste
id I48-R154-article-8548
record_format ojs
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-154
container_title_str Revistas UNNE - Universidad Nacional del Noroeste (UNNE)
language Español
format Artículo revista
topic Best-fitting ellipse
Morphology
Binarization
ImageJ®
Elipse que mejor ajusta
Morfología
Binarización
ImageJ®
spellingShingle Best-fitting ellipse
Morphology
Binarization
ImageJ®
Elipse que mejor ajusta
Morfología
Binarización
ImageJ®
Cleva, Mario S.
Villaverde, Jorge E.
Liska, Diego O.
Duran Muñoz, Héctor A.
Volumentric model evaluation using digital image processing of unbroken corn kernels
topic_facet Best-fitting ellipse
Morphology
Binarization
ImageJ®
Elipse que mejor ajusta
Morfología
Binarización
ImageJ®
author Cleva, Mario S.
Villaverde, Jorge E.
Liska, Diego O.
Duran Muñoz, Héctor A.
author_facet Cleva, Mario S.
Villaverde, Jorge E.
Liska, Diego O.
Duran Muñoz, Héctor A.
author_sort Cleva, Mario S.
title Volumentric model evaluation using digital image processing of unbroken corn kernels
title_short Volumentric model evaluation using digital image processing of unbroken corn kernels
title_full Volumentric model evaluation using digital image processing of unbroken corn kernels
title_fullStr Volumentric model evaluation using digital image processing of unbroken corn kernels
title_full_unstemmed Volumentric model evaluation using digital image processing of unbroken corn kernels
title_sort volumentric model evaluation using digital image processing of unbroken corn kernels
description In this article, three models are compared to estimate the volume of corn (Zea mays) grains from values obtained using digital image processing techniques on images of the sample. In the first model, the volume of each grain is assumed to be proportional to its cubed length. In the second model, the volume is proportional to the square of the width, multiplied by its length. In the third model, the volume is proportional to the projected area multiplied by the length. The proportionality constants are determined experimentally using the toluene displacement method. Ten samples, each containing one hundred grains were prepared. Five samples were used to determine the proportionality constants needed in the models, while the remaining five were used to  compare the volumes obtained by the three models. Images of the samples were obtained using a desktop scanner at a resolution of 300 dpi. The operations on the images were carried out using the ImageJ® software.  The length, width, and area of each grain were determined by finding the ellipse that best fits the projected area of each grain in the red channel of each binarized image. The volumes of the control samples were determined using the values of the proportionality constants, and then compared with the experimental ones. The average percentage relative deviation (RPD) was calculated for each model using these five samples. The first model had a DRP of 3.9 % while the second and third models had DRP of 4.9 % and 2.5 %, respectively. The low margin of error and simple requirements for application make this methodology easily adaptable for determining volume using non-destructive methods. 
publisher Instituto Agrotécnico "Pedro M. Fuentes Godo" - Facultad de Ciencias Agrarias (UNNE)
publishDate 2025
url https://revistas.unne.edu.ar/index.php/agr/article/view/8548
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spelling I48-R154-article-85482025-08-21T15:11:26Z Volumentric model evaluation using digital image processing of unbroken corn kernels Evaluación de modelos volumétricos basados en procesamiento digital de imágenes para granos enteros de maíz Cleva, Mario S. Villaverde, Jorge E. Liska, Diego O. Duran Muñoz, Héctor A. Best-fitting ellipse Morphology Binarization ImageJ® Elipse que mejor ajusta Morfología Binarización ImageJ® In this article, three models are compared to estimate the volume of corn (Zea mays) grains from values obtained using digital image processing techniques on images of the sample. In the first model, the volume of each grain is assumed to be proportional to its cubed length. In the second model, the volume is proportional to the square of the width, multiplied by its length. In the third model, the volume is proportional to the projected area multiplied by the length. The proportionality constants are determined experimentally using the toluene displacement method. Ten samples, each containing one hundred grains were prepared. Five samples were used to determine the proportionality constants needed in the models, while the remaining five were used to  compare the volumes obtained by the three models. Images of the samples were obtained using a desktop scanner at a resolution of 300 dpi. The operations on the images were carried out using the ImageJ® software.  The length, width, and area of each grain were determined by finding the ellipse that best fits the projected area of each grain in the red channel of each binarized image. The volumes of the control samples were determined using the values of the proportionality constants, and then compared with the experimental ones. The average percentage relative deviation (RPD) was calculated for each model using these five samples. The first model had a DRP of 3.9 % while the second and third models had DRP of 4.9 % and 2.5 %, respectively. The low margin of error and simple requirements for application make this methodology easily adaptable for determining volume using non-destructive methods.  En este artículo se comparan tres modelos para la estimación del volumen de granos de maíz (Zea mays) mediante técnicas de procesamiento digital de imágenes. En el primer modelo, se asume que el volumen de cada grano es proporcional a su longitud al cubo. En el segundo modelo, como proporcional al cuadrado del ancho, multiplicado por la longitud del mismo. En el tercer modelo, como proporcional al área proyectada multiplicada por la longitud. Las constantes de proporcionalidad son determinadas experimentalmente por el método de desplazamiento de tolueno. Se prepararon diez muestras de cien granos cada una, de las cuales cinco se tomaron para la determinación de las constantes de proporcionalidad y el resto para la comparación de los volúmenes obtenidos por los tres modelos. Las imágenes de las muestras fueron obtenidas con un escáner de escritorio a una resolución de 300 dpi y se procesaron empleando el software ImageJ®. Con el canal rojo de cada imagen binarizada se determinó la elipse que mejor ajusta al área proyectada de cada grano para obtener largo, ancho y área. Con los valores de las constantes de proporcionalidad, se determinaron los volúmenes de las muestras de control y se compararon con los experimentales. Se calculó para estas cinco muestras la desviación relativa porcentual (DRP) promedio para cada modelo. El primer modelo tuvo una DRP del 3,9 % y, para el segundo y tercer modelo 4,9 % y 2,5 % respectivamente.  La baja desviación y la sencillez de esta metodología, permiten su adaptación para determinar volúmenes mediante métodos no destructivos. Instituto Agrotécnico "Pedro M. Fuentes Godo" - Facultad de Ciencias Agrarias (UNNE) 2025-08-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unne.edu.ar/index.php/agr/article/view/8548 10.30972/agr.368548 Agrotecnia; Núm. 36 (2025); 1-8 2545-8906 0328-4077 spa https://revistas.unne.edu.ar/index.php/agr/article/view/8548/8129 http://creativecommons.org/licenses/by-nc-sa/4.0

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