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Application of Computer Vision and Image Processing Technology in Agro-Product Quality Control- Review

Yıl 2022, Cilt: 18 Sayı: 2, 105 - 113, 30.10.2022

Omsalma Alsadig Adam Gadalla Y. Benal Öztekin Geofrey Prudence Baitu

Öz

Post harvest quality evaluation processes are very important operations and play a significant role in determining the acceptability and marketability of the product. Computer vision and image processing technology has been applied wildly in food industries in order to evaluate the quality of agricultural products such as; sorting, grading, and classification processes due to the performance, low cost, and effectiveness of the technology. In this article, we aim to review to application of computer vision and image processing technology in evaluating the quality of agricultural products representing in; application of this technology in evaluation the quality of fruits, vegetables, and nut products with more attention to hazelnut product.

Anahtar Kelimeler

Hazelnut quality Fruit and vegetables Defect detection Machine vision

Kaynakça

  • Anand, S., and Priya, L. (2019). A Guide for Machine Vision in Quality Control: Applied spectroscopy, 54(3), 413–419.
  • Blasco, J., Aleixos, N., Gómez, J., and Moltó, E. (2007). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of food Engineering, 83(3), 384-393.
  • Blasco, J., Aleixos, N., and Moltó, E. (2007). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of food Engineering, 81(3), 535-543.
  • Caner, K., Gerdan, D., Emin, M. B., YegüL, U., Bulent, K., and VatandaŞ, M. (2020). Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 48(4), 2316-2327.
  • Da Costa, A. Z., Figueroa, H. E., and Fracarolli, J. A. (2020). Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 190, 131-144.
  • Delila, H. E. S., Emir Turajlic. (2017). Almonds classification using supervised learning.pdf.
  • Donis, I. R., Guyer, D. E., Leiva-Valenzuela, G. A., and Burns, J. (2013). Assessment of chestnut (Castanea spp.) slice quality using color images. Journal of food Engineering, 115(3), 407-414.
  • ElMasry, G., Cubero, S., Moltó, E., and Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of food Engineering, 112(1-2), 60-68.
  • Giraudo, A., Calvini, R., Orlandi, G., Ulrici, A., Geobaldo, F., & Savorani, F. (2018). Development of an automated method for the identification of defective hazelnuts based on RGB image analysis and colourgrams. Food Control, 94, 233-240. doi:10.1016/j.foodcont.2018.07.018
  • Guvenc, S. A., Senel, F. A., and Cetisli, B. (2015). Classification of processed hazelnuts with computer vision. Paper presented at the 2015 23nd Signal Processing and Communications Applications Conference (SIU).
  • Kang, S., East, A., and Trujillo, F. (2008). Colour vision system evaluation of bicolour fruit: A case study with ‘B74’mango. Postharvest Biology and Technology, 49(1), 77-85.
  • Kang, S., and Sabarez, H. (2009). Simple colour image segmentation of bicolour food products for quality measurement. Journal of food Engineering, 94(1), 21-25.
  • Kinrak, O., Gürbüz, M. (2019). Detection of Defective Hazelnuts by Image Processing and Machine Learning.pdf. Natural and Engineering Sciences, 4(3), 100-106.
  • Ma, J., Sun, D.W., Qu, J.H., Liu, D., Pu, H., Gao, W, H., and Zeng, X. A. (2016). Applications of computer vision for assessing quality of agri-food products: a review of recent research advances. Critical reviews in food science and nutrition, 56(1), 113-127.
  • Mathanker, S., Weckler, P., Bowser, T., Wang, N., and Maness, N. (2011). AdaBoost classifiers for pecan defect classification. Computers and Electronics in Agriculture, 77(1), 60-68.
  • Moscetti, R., Haff, R. P., Saranwong, S., Monarca, D., Cecchini, M., and Massantini, R. (2014). Nondestructive detection of insect infested chestnuts based on NIR spectroscopy. Postharvest Biology and Technology, 87, 88-94. doi:10.1016/j.postharvbio.2013.08.010
  • Naik, S., & Patel, B. (2017). Machine vision based fruit classification and grading-a review. International Journal of Computer Applications, 170(9), 22-34.
  • Norgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L., and Engelsen, S. B. (2000). Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied spectroscopy, 54(3), 413–419.
  • Pearson, T. C., Doster, M. A., and Michailides, T. J. (2001). Automated Detection of Pistachio Defects by Machine Vision. Applied Engineering in Agriculture, 17(5). doi:10.13031/2013.6905
  • Pinto, N., Cox, D. D., and DiCarlo, J. J. (2008). Why is real-world visual object recognition hard? PLoS. computational biology, 4(1), e27.
  • Quevedo, R., Díaz, O., Ronceros, B., Pedreschi, F., and Aguilera, J. M. (2009). Description of the kinetic enzymatic browning in banana (Musa cavendish) slices using non-uniform color information from digital images. Food Research International, 42(9), 1309-1314.
  • Quevedo, R., Ronceros, B., Garcia, K., Lopéz, P., and Pedreschi, F. (2011). Enzymatic browning in sliced and puréed avocado: a fractal kinetic study. Journal of food Engineering, 105(2), 210-215.
  • Razmjooy, N., Mousavi, B. S., and Soleymani, F. (2012). A real-time mathematical computer method for potato inspection using machine vision. Computers & Mathematics with Applications, 63(1), 268-279.
  • S. Kim, & Schatzki, T. (2001). Detection of Pinholes In Almonds. American Society of Agricultural Engineers ISSN, 0001–2351.
  • Sandoval, E. M., Rosas, M. E. M., Sandoval, J. R. M., Velasco, M. M. M. (2018). Machine Vision system - A Tool for Automatic Color Analysis in Agriculture. doi:10.5772/intechopen.71935
  • Solak, S., and Altinisik, U. (2018). Detection and classification of hazelnut fruit by using image processing techniques and clustering methods. Sakarya University Journal of Science, 22(1), 56-65.
  • Taner, A., Öztekin, Y. B., and Duran, H. (2021). Performance analysis of deep learning CNN models for variety classification in hazelnut. Sustainability, 13(12), 6527.
  • Timmermans, A. J. M. (1998). Computer Vision System For On-Line Sorting Of Pot Plants Based On Learning Techniques. International Society for Horticultural Science, 421, 91-98. DOI:.
  • Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M.-F., and Debeir, O. (2011). Automatic grading of Bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 75(1), 204-212.
  • Wang, C., Li, X., Wang, W., Feng, Y., Zhou, Z., and Zhan, H. (2011). Recognition of worm-eaten chestnuts based on machine vision. Mathematical and Computer Modelling, 54(3-4), 888-894.
  • Xiao-bo, Z., Jie-wen, Z., Yanxiao, L., and Holmes, M. (2010). In-line detection of apple defects using three color cameras system. Computers and Electronics in Agriculture, 70(1), 129-134.
  • Zheng, H., and Lu, H. (2012). A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.). Computers and Electronics in Agriculture, 83, 47-51.
  • Zude, M. (2008). Optical monitoring of fresh and processed agricultural crops.

Yıl 2022, Cilt: 18 Sayı: 2, 105 - 113, 30.10.2022

Omsalma Alsadig Adam Gadalla Y. Benal Öztekin Geofrey Prudence Baitu

Öz

Kaynakça

  • Anand, S., and Priya, L. (2019). A Guide for Machine Vision in Quality Control: Applied spectroscopy, 54(3), 413–419.
  • Blasco, J., Aleixos, N., Gómez, J., and Moltó, E. (2007). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of food Engineering, 83(3), 384-393.
  • Blasco, J., Aleixos, N., and Moltó, E. (2007). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of food Engineering, 81(3), 535-543.
  • Caner, K., Gerdan, D., Emin, M. B., YegüL, U., Bulent, K., and VatandaŞ, M. (2020). Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 48(4), 2316-2327.
  • Da Costa, A. Z., Figueroa, H. E., and Fracarolli, J. A. (2020). Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 190, 131-144.
  • Delila, H. E. S., Emir Turajlic. (2017). Almonds classification using supervised learning.pdf.
  • Donis, I. R., Guyer, D. E., Leiva-Valenzuela, G. A., and Burns, J. (2013). Assessment of chestnut (Castanea spp.) slice quality using color images. Journal of food Engineering, 115(3), 407-414.
  • ElMasry, G., Cubero, S., Moltó, E., and Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of food Engineering, 112(1-2), 60-68.
  • Giraudo, A., Calvini, R., Orlandi, G., Ulrici, A., Geobaldo, F., & Savorani, F. (2018). Development of an automated method for the identification of defective hazelnuts based on RGB image analysis and colourgrams. Food Control, 94, 233-240. doi:10.1016/j.foodcont.2018.07.018
  • Guvenc, S. A., Senel, F. A., and Cetisli, B. (2015). Classification of processed hazelnuts with computer vision. Paper presented at the 2015 23nd Signal Processing and Communications Applications Conference (SIU).
  • Kang, S., East, A., and Trujillo, F. (2008). Colour vision system evaluation of bicolour fruit: A case study with ‘B74’mango. Postharvest Biology and Technology, 49(1), 77-85.
  • Kang, S., and Sabarez, H. (2009). Simple colour image segmentation of bicolour food products for quality measurement. Journal of food Engineering, 94(1), 21-25.
  • Kinrak, O., Gürbüz, M. (2019). Detection of Defective Hazelnuts by Image Processing and Machine Learning.pdf. Natural and Engineering Sciences, 4(3), 100-106.
  • Ma, J., Sun, D.W., Qu, J.H., Liu, D., Pu, H., Gao, W, H., and Zeng, X. A. (2016). Applications of computer vision for assessing quality of agri-food products: a review of recent research advances. Critical reviews in food science and nutrition, 56(1), 113-127.
  • Mathanker, S., Weckler, P., Bowser, T., Wang, N., and Maness, N. (2011). AdaBoost classifiers for pecan defect classification. Computers and Electronics in Agriculture, 77(1), 60-68.
  • Moscetti, R., Haff, R. P., Saranwong, S., Monarca, D., Cecchini, M., and Massantini, R. (2014). Nondestructive detection of insect infested chestnuts based on NIR spectroscopy. Postharvest Biology and Technology, 87, 88-94. doi:10.1016/j.postharvbio.2013.08.010
  • Naik, S., & Patel, B. (2017). Machine vision based fruit classification and grading-a review. International Journal of Computer Applications, 170(9), 22-34.
  • Norgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L., and Engelsen, S. B. (2000). Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied spectroscopy, 54(3), 413–419.
  • Pearson, T. C., Doster, M. A., and Michailides, T. J. (2001). Automated Detection of Pistachio Defects by Machine Vision. Applied Engineering in Agriculture, 17(5). doi:10.13031/2013.6905
  • Pinto, N., Cox, D. D., and DiCarlo, J. J. (2008). Why is real-world visual object recognition hard? PLoS. computational biology, 4(1), e27.
  • Quevedo, R., Díaz, O., Ronceros, B., Pedreschi, F., and Aguilera, J. M. (2009). Description of the kinetic enzymatic browning in banana (Musa cavendish) slices using non-uniform color information from digital images. Food Research International, 42(9), 1309-1314.
  • Quevedo, R., Ronceros, B., Garcia, K., Lopéz, P., and Pedreschi, F. (2011). Enzymatic browning in sliced and puréed avocado: a fractal kinetic study. Journal of food Engineering, 105(2), 210-215.
  • Razmjooy, N., Mousavi, B. S., and Soleymani, F. (2012). A real-time mathematical computer method for potato inspection using machine vision. Computers & Mathematics with Applications, 63(1), 268-279.
  • S. Kim, & Schatzki, T. (2001). Detection of Pinholes In Almonds. American Society of Agricultural Engineers ISSN, 0001–2351.
  • Sandoval, E. M., Rosas, M. E. M., Sandoval, J. R. M., Velasco, M. M. M. (2018). Machine Vision system - A Tool for Automatic Color Analysis in Agriculture. doi:10.5772/intechopen.71935
  • Solak, S., and Altinisik, U. (2018). Detection and classification of hazelnut fruit by using image processing techniques and clustering methods. Sakarya University Journal of Science, 22(1), 56-65.
  • Taner, A., Öztekin, Y. B., and Duran, H. (2021). Performance analysis of deep learning CNN models for variety classification in hazelnut. Sustainability, 13(12), 6527.
  • Timmermans, A. J. M. (1998). Computer Vision System For On-Line Sorting Of Pot Plants Based On Learning Techniques. International Society for Horticultural Science, 421, 91-98. DOI:.
  • Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M.-F., and Debeir, O. (2011). Automatic grading of Bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 75(1), 204-212.
  • Wang, C., Li, X., Wang, W., Feng, Y., Zhou, Z., and Zhan, H. (2011). Recognition of worm-eaten chestnuts based on machine vision. Mathematical and Computer Modelling, 54(3-4), 888-894.
  • Xiao-bo, Z., Jie-wen, Z., Yanxiao, L., and Holmes, M. (2010). In-line detection of apple defects using three color cameras system. Computers and Electronics in Agriculture, 70(1), 129-134.
  • Zheng, H., and Lu, H. (2012). A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.). Computers and Electronics in Agriculture, 83, 47-51.
  • Zude, M. (2008). Optical monitoring of fresh and processed agricultural crops.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Omsalma Alsadig Adam Gadalla 0000-0001-6132-4672

Y. Benal Öztekin

Geofrey Prudence Baitu 0000-0002-3243-3252

Erken Görünüm Tarihi 27 Ağustos 2022
Yayımlanma Tarihi 30 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 18 Sayı: 2

Kaynak Göster

APA Gadalla, O. A. A., Öztekin, Y. B., & Baitu, G. P. (2022). Application of Computer Vision and Image Processing Technology in Agro-Product Quality Control- Review. Tarım Makinaları Bilimi Dergisi, 18(2), 105-113.
 Kapak Resmi İndir

Tarım Makinaları Bilimi Dergisi, Tarım Makinaları Derneği tarafından yılda 3 sayı olarak yayınlanan hakemli bilimsel bir dergidir.