Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System

Ayman Ibrahim, Nazeer El-Bialee, Mohsen Saad, Elio Romano

Abstract


In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.


Keywords


Potato tubers; quality inspection; image processing; automated vision systems; color analysis; texture features

Full Text:

PDF

References


FAOSTAT., 2016. Production crops, Agricultural production database. http://faostat. fao.org.

MALR., 2018. Ministry of Agriculture and Land Reclamation, Central Administration for Plant Quarantine (CAPQ). Statistical Unit and FAS Cairo office research. GAIN Report Number: EG18026, 1-15.

Khojastehnazhand, M., Omid, M., Tabatabaeefar, A., 2010. Determination of tangerine volume using image processing methods. Int. J. Food Prop. 13 (4), 760-770.

Naik, S., Patel, B., 2017. Machine vision-based fruit classification and grading-A review. Int. J. Comput. Appl. 170 (9), 22-34.

Arendse, E., Fawole, O.A., Magwaza, L.S., Opara, U.L., 2018. Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review. J. Food Eng. 217, 11-23.

Satpute, M.R., Jagdale, S., 2016. Color, size, volume, shape and texture feature extraction techniques for fruits: A review. Int. Res. J. Eng. Technol. 3, 703–708.

Pu, Y.Y., Feng, Y.Z., Sun, D.W., 2015. Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: A review. Comprehensive Reviews in Food Science and Food Safety 14, 176-188.

Ehsani, R., Toudeshki, A., Wan, P.J.T.P., 2016. New sensor technology for yield estimation and disease detection. Tomato Proceedings, 15-16.

Karlsson, P., 2016. Is Manual Harvest Really Better Than Mechanical Harvest? Retrieved from https://www.bkwine.com/features/ winemaking-viticulture/raw-truth-manualmechanical-harvest/.

Chen, Y.R., Chao, K., Kim, M.S., 2002. Machine vision technology for agricultural applications. Comput. Electron. Agric. 36, 173-191.

Moreda, G.P., Muñoz, M.A., Ruiz-Altisent, V., Perdigones, A., 2012. Shape determination of horticultural produce using two-dimensional computer vision – A review. Journal of Food Engineering 108 (2), 245-261.

Nyalala, I., Okinda, C., Nyalala, L., Makange, N., Chao, Q., Chao, L., Yousaf, K., Chen, K., 2019. Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model. Journal of Food Engineering 263, 288-298.

Ibrahim, A.A., Eisa, A.H., Alghannam, A.O., 2014. Image processing system for automated classification date fruit. International Journal of Advanced Research 2(1), 702-715.

Jadhav, T., Singh, K., Abhyankar, A.J.M.T., 2019. Volumetric Estimation Using 3D Reconstruction Method for Grading of Fruits. Multimedia Tools and Applications 78, 1613-1634.

Shafiee, S., Minaei, S., Moghaddam-Charkari, N., Barzegar, M., 2014. Honey characterization using computer vision system and artificial neural networks. Food Chem. 159, 143-150.

Arjenaki, O.O., Moghaddam, P.A., Motlagh, A.M., 2013. Online tomato sorting based on shape, maturity, size, and surface defects using machine vision. Turk. J. Agric. For. 37, 62-68.

Noordam, J.C., Otten, G.W., Timmermans, A.J.M., Zwol, B., 2000. High-speed potato grading and quality inspection based on a color vision system. In: Proceeding of SPIE – Machine Vision Application in Industrial Inspection, 206-217.

Halimi, A., Roukhe, A., Abdenabi, B., El Barbri, N., 2013. Sorting dates fruit bunches based on their maturity using camera sensor system. Journal of Theoretical and Applied Information Technology 56(2).

Moallem, P., Serajoddin, A., Pourghassem, H., 2017. Computer vision-based apple grading for golden delicious apples based on surface features. Inf. Process. Agric. 4 (1), 33-40.

Tian, D. P., 2013. A review on image feature extraction and representation techniques. International Journal of Multimedia and Ubiquitous Engineering 8, 385-396.

Haralick, R.M., 1979. Statistical and Structural Approaches to Texture. Proceeding of the IEEE 67, 786-804.

Mathworks Inc., 2013. Matlab’s Statistic Toolbox 7. Natick, MA: Mathworks Inc., Matlab.

Fongaro, L., Kvaal, K., 2013. Surface texture characterization of an Italian pasta by means of univariate and multivariate feature extraction from their texture images. Food Res. Int. 51, 693-705.

Ireri, D., Belal, E., Okinda, C., Makange, N., Ji, C., 2019. A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture 2, 28-37.

Baigvand, M., Banakar, A., Minaei, S., Khodaei, J., Behroozi-Khazaei, N., 2015. Machine vision system for grading of dried figs. Computers and Electronics in Agriculture 119, 158-165.

Weijun, X., Wang, F., Yang, D., 2019. Research on Carrot Grading Based on Machine Vision Feature Parameters. IFAC PapersOnLine 52(30), 30-35.

ElMasry, G., Cubero, S., Moltó, E., Blasco, J., 2012. In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering 112, 60-68.

Al-Mallahi, A., Kataoka, T., Okamoto, H., and Shibata, Y., 2010. An image processing algorithm for detecting in-line potato tubers without singulation. Computers and Electronics in Agriculture 70, 239-244.

Mohsenin, N.N., 1986. Physical Properties of Plant and Animal Materials, Gordon and Breach Science Publishers, New York, USA.

R Core Team., 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Poynton, C., 2003. Digital Video and HDTV. Morgan-Kaufmann. p. 226. ISBN 1-55860-792-7.

Handcock, M.S., Chaudhuri, S., Rendall, M.S., 2004. glmc: An R package for generalized linear models subject to constraints. http://www.stat.washington.edu/handcock/combining.




DOI: http://dx.doi.org/10.18517/ijaseit.10.6.13079

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development