Volumentric model evaluation using digital image processing of unbroken corn kernels
DOI:
https://doi.org/10.30972/agr.368548Keywords:
Best-fitting ellipse, Morphology, Binarization, ImageJ®Abstract
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.
References
Altuntas, Y., Kocamaz, A.F., Comert, Z., Cengiz, R., y Esmeray, M. (2018). Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques. En 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE.
Cleva, M. y Liska, D.O. (2021). Determinación de las dimensiones de granos de maíz por técnicas de procesa-miento digital de imágenes con ImageJ®. En Congreso Argentino y Latinoamericano de Ingeniería.
Cleva, M., Sampallo, G. y Cristian Acosta, A.G.T. (2013). Método para la determinación del volumen de una muestra de granos de arroz mediante el procesamiento digital de imágenes. Revista de Investiga-ciones Agropecuarias, 39(2):185–190.
Cleva, M.S., Sampallo, G.M., Rodrigues Da Fonseca, C., Aquino, D.C., Liska, D.O., y Ventre, F.R. (2017). Método para la determinación de la densidad de una muestra de granos enteros de alubia por procesamiento digital de imágenes. RIA. Revista de Investigaciones Agropecuarias, 43(2):144–148.
Kaliniewicz, Z., Choszcz, D., y Lipiński, A. (2022). Determination of seed volume based on selected seed dimensions. Applied Sciences, 12(18).
Mohsenin, N. N. (2020). Physical Properties of Plant and Animal Materials: v. 1: Physical Characteris-tics and Mechanical Properties. Routledge.
Prakasa, E., Prajitno, D.R., Nur, A., Sulistyo, K.A., y Rachmawati, E. (2021). Quality categorization of corn (zea mays) seed using feature-based classifier and deep learning on digital images. In 2021 Inter-national Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE.
Varnamkhasti, M.G., Mobli, H., Jafari, A.L.I., Rafiee, S., Heidarysoltanabadi, M., y Kheiralipour, K. (2007). Some engineering properties of paddy (var. sazandegi). 9:763–766.
Wang, R., Han, F., Jin, Y., y Wu, W. (2020). Correlation between moisture content and machine vision image characteristics of corn kernels. International Journal of Food Properties, 23(1):319–328.
Wu, A., Zhu, J., Yang, Y., Liu, X., Wang, X., Wang, L., Zhang, H., y Chen, J. (2018). Classification of corn kernels grades using image analysis and support vector machine. Advances in Mechanical Engi-neering, 10(12):168781401881764.
Wu, D., Cai, Z., Han, J., y Qin, H. (2020). Automatic kernel counting on maize ear using rgb images. Plant Methods, 16(1).
Yuanyuan, Z., Jilong, Z., y Lingshen, F. (2016). Research on quality evaluation of maize seed shape ba-sed on support vector machine. In 2016 2nd IEEE International Conference on Computer and Commu-nications (ICCC). IEEE.
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