MULTİSPEKTRAL VE HİPERSPEKTRAL GÖRÜNTÜLEME TEKNİKLERİNİN MEYVE - SEBZE İŞLEME TESİSLERİNDE KULLANIM OLANAKLARI

Özgür Neşe Özen; Fatih Akkoyun; Ahmet Görgüç; Fatih Mehmet Yılmaz

doi:10.17780/ksujes.1398289

EN TR

POTENTIALS OF MULTISPECTRAL AND HYPERSPECTRAL IMAGING TECHNIQUES IN FRUIT AND VEGETABLE PROCESSING PLANTS

Abstract

In this study, the potentials of advanced imaging techniques, i.e., multispectral imaging and hyperspectral imaging, in the fruit and vegetable industry were reviewed. Multispectral imaging and hyperspectral imaging techniques are used for diagnosis and intervention in many applications, such as classifying fruits and vegetables, sorting them according to maturity, separating defective products, measuring drought, and determining harvest time. In experimental studies, multispectral imaging has been shown to be successful when used for classification at visible and near wavelengths. In hyperspectral imaging, it has been seen that it is used to determine specific conditions such as color, firmness, acidity, sugar, antioxidant compound amount, total soluble solids in fruits and vegetables, as well as quality parameters such as ripeness, physiological disorder, mechanical damage, sensory quality, biological defect, and has high levels success rates have been achieved. These imaging techniques provide faster results compared to other classification methods and are environmentally friendly and nondestructive to fruits and vegetables.

Keywords

Kaynakça

Akkoyun, F. (2022). Inexpensive multispectral imaging device. Instrumentation Science & Technology, 50(5), 543-559. https://doi.org/10.1080/10739149.2022.2047061
Cen, H., Lu, R., Zhu, Q., & Mendoza, F. (2016). Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification. Postharvest Biology and Technology, 111, 352-361. https://doi.org/10.1016/j.postharvbio.2015.09.027
Chen, Q., Lin, H., & Zhao, J. (2021). Spectral imaging technology in food. Advanced nondestructive detection technologies in food, 127-160. https://doi.org/10.1007/978-981-16-3360-7
Cömert, O., Hekim, M., & Kemal, A. D. E. M. (2019). Faster R-CNN kullanarak elmalarda çürük tespiti. International Journal of Engineering Research and Development, 11(1), 335-341. DOI: 10.29137/umagd.469929
Du, Z., Zeng, X., Li, X., Ding, X., Cao, J., & Jiang, W. (2020). Recent advances in imaging techniques for bruise detection in fruits and vegetables. Trends in Food Science & Technology, 99, 133-141. https://doi.org/10.1016/j.tifs.2020.02.024
Ebner, A., Gattinger, P., Zorin, I., Krainer, L., Rankl, C., & Brandstetter, M. (2023). Diffraction-limited hyperspectral mid-infrared single-pixel microscopy. Scientific Reports, 13(1), 281. https://doi.org/10.1038/s41598-022-26718-6.
Gao, L., & Smith, R. T. (2015). Optical hyperspectral imaging in microscopy and spectroscopy–a review of data acquisition. Journal of biophotonics, 8(6), 441-456. https://doi.org/10.1002/jbio.201400051
Gracia-Romero, A., Vergara-Díaz, O., Thierfelder, C., Cairns, J. E., Kefauver, S. C., & Araus, J. L. (2018). Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe. Remote Sensing, 10(2), 349. https://doi.org/10.3390/rs10020349

Gutiérrez, S., Tardáguila, J., Fernández‐Novales, J., & Diago, M. P. (2019). On‐the‐go hyperspectral imaging for the in‐field estimation of grape berry soluble solids and anthocyanin concentration. Australian journal of grape and wine research, 25(1), 127-133. https://doi.org/10.1111/ajgw.12376
Güzel, E., & Özlüoymak, Ö. B. (2015). Elektromanyetik Spektrumun Tarım Makinaları Araştırmalarında Kullanımı. Tarım Makinaları Bilimi Dergisi, 11(4), 315-320.
Hashim, N., Onwude, D. I., & Osman, M. S. (2018). Evaluation of chilling injury in mangoes using multispectral imaging. Journal of food science, 83(5), 1271-1279. https://doi.org/10.1111/1750-3841.14127
Huang, Y., Lu, R., & Chen, K. (2020). Detection of internal defect of apples by a multichannel Vis/NIR spectroscopic system. Postharvest Biology and Technology, 161, 111065. https://doi.org/10.1016/j.postharvbio.2019.111065
Karydas, C., Iatrou, M., Kouretas, D., Patouna, A., Iatrou, G., Lazos, N., ... & Mourelatos, S. (2020). Prediction of antioxidant activity of cherry fruits from UAS multispectral imagery using machine learning. Antioxidants, 9(2), 156. https://doi.org/10.3390/antiox9020156
Khodabakhshian, R., Emadi, B., Khojastehpour, M., Golzarian, M. R., & Sazgarnia, A. (2017). Development of a multispectral imaging system for online quality assessment of pomegranate fruit. International Journal of Food Properties, 20(1), 107-118. https://doi.org/10.1080/10942912.2016.1144200
Lan, W., Jaillais, B., Renard, C. M., Leca, A., Chen, S., Le Bourvellec, C., & Bureau, S. (2021). A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices. Postharvest Biology and Technology, 175, 111497. https://doi.org/10.1016/j.postharvbio.2021.111497
Li, C., Chen, G., Zhang, Y., Tang, F., & Wang, Q. (2020). Advanced fluorescence imaging technology in the near-infrared-II window for biomedical applications. Journal of the American Chemical Society, 142(35), 14789-14804. https://doi.org/10.1021/jacs.0c07022
Li, J., Chen, L., Huang, W., Wang, Q., Zhang, B., Tian, X., ... & Li, B. (2016). Multispectral detection of skin defects of bi-colored peaches based on vis–NIR hyperspectral imaging. Postharvest Biology and Technology, 112, 121-133. https://doi.org/10.1016/j.postharvbio.2015.10.007
Li, Q., He, X., Wang, Y., Liu, H., Xu, D., & Guo, F. (2013). Review of spectral imaging technology in biomedical engineering: achievements and challenges. Journal of biomedical optics, 18(10), 100901-100901. https://doi.org/10.1117/1.JBO.18.10.100901
Li, X., Li, R., Wang, M., Liu, Y., Zhang, B., & Zhou, J. (2018a). Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables. Hyperspectral imaging in agriculture, food and environment, 27-63. DOI:10.5772/intechopen.72250.
Li, X., Wei, Y., Xu, J., Feng, X., Wu, F., Zhou, R., ... & He, Y. (2018b). SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology. Postharvest Biology and Technology, 143, 112-118. https://doi.org/10.1016/j.postharvbio.2018.05.003
Liu, C., Liu, W., Chen, W., Yang, J., & Zheng, L. (2015). Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit. Food Chemistry, 173, 482-488. https://doi.org/10.1016/j.foodchem.2014.10.052
Liu, C., Liu, W., Lu, X., Ma, F., Chen, W., Yang, J., & Zheng, L. (2014). Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PloS one, 9(2), e87818. https://doi.org/10.1371/journal.pone.0087818
Lorente, D., Aleixos, N., Gómez-Sanchis, J. U. A. N., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5, 1121-1142. DOI:10.1007/s11947-011-0725-1
Manthou, E., Lago, S. L., Dagres, E., Lianou, A., Tsakanikas, P., Panagou, E. Z., & Nychas, G. J. E. (2020). Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools. Computers and Electronics in Agriculture, 175, 105529. https://doi.org/10.1016/j.compag.2020.105529
Martínez Gila, D. M., Navarro Soto, J. P., Satorres Martínez, S., Gómez Ortega, J., & Gámez García, J. (2022). The advantage of multispectral images in fruit quality control for extra virgin olive oil production. Food Analytical Methods, 1-10. https://doi.org/10.1007/s12161-021-02099-w
Mishra, P., Chauhan, A., & Pettersson, T. (2023). Seeing through plastics: A novel combination of NIR hyperspectral imaging and spectral orthogonalization for detecting fresh fruit inside plastic packaging to support automated barcode less checkouts in supermarkets. Food Control, 150, 109762. https://doi.org/10.1016/j.foodcont.2023.109762
Montembeault, Y., Lagueux, P., Farley, V., Villemaire, A., & Gross, K. C. (2010, June). Hyper-Cam: Hyperspectral IR imaging applications in defence innovative research. In 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (pp. 1-4). IEEE. DOI: 10.1109/WHISPERS.2010.5594890
Munera, S., Gómez-Sanchís, J., Aleixos, N., Vila-Francés, J., Colelli, G., Cubero, S., ... & Blasco, J. (2021). Discrimination of common defects in loquat fruit cv. ‘Algerie’using hyperspectral imaging and machine learning techniques. Postharvest Biology and Technology, 171, 111356. https://doi.org/10.1016/j.postharvbio.2020.111356
Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M., Tona, E., ... & Ulbrich, H. (2016). Selective spraying of grapevines for disease control using a modular agricultural robot. Biosystems engineering, 146, 203-215. https://doi.org/10.1016/j.biosystemseng.2015.12.004
Ortega, S., Halicek, M., Fabelo, H., Callico, G. M., & Fei, B. (2020). Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomedical Optics Express, 11(6), 3195-3233. https://doi.org/10.1364/BOE.386338
Rajkumar, P., Wang, N., EImasry, G., Raghavan, G. S. V., & Gariepy, Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of food engineering, 108(1), 194-200. https://doi.org/10.1016/j.jfoodeng.2011.05.002
Siedliska, A., Baranowski, P., Zubik, M., Mazurek, W., & Sosnowska, B. (2018). Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Postharvest Biology and Technology, 139, 115-126. https://doi.org/10.1016/j.postharvbio.2018.01.018
Siripatrawan, U., & Makino, Y. (2018). Simultaneous assessment of various quality attributes and shelf life of packaged bratwurst using hyperspectral imaging. Meat science, 146, 26-33. https://doi.org/10.1016/j.meatsci.2018.06.024
Su, W. H., & Sun, D. W. (2018). Multispectral imaging for plant food quality analysis and visualization. Comprehensive reviews in food science and food safety, 17(1), 220-239. https://doi.org/10.1111/1541-4337.12317
Tang, T., Zhang, M., & Mujumdar, A. S. (2022). Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology. Comprehensive Reviews in Food Science and Food Safety, 21(6), 5171-5198. https://doi.org/10.1111/1541-4337.13039
Vega Diaz, J. J., Sandoval Aldana, A. P., & Reina Zuluaga, D. V. (2021). Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging. Journal of the Science of Food and Agriculture, 101(3), 897-906. https://doi.org/10.1002/jsfa.10697
Wang, B., Yang, H., Zhang, S., & Li, L. (2023). Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology. Applied Sciences, 13(5), 3279. https://doi.org/10.3390/app13053279
Wang, N. N., Sun, D. W., Yang, Y. C., Pu, H., & Zhu, Z. (2016). Recent advances in the application of hyperspectral imaging for evaluating fruit quality. Food analytical methods, 9, 178-191. DOI 10.1007/S12161-015-0153-3
Weng, S., Ma, J., Tao, W., Tan, Y., Pan, M., Zhang, Z., ... & Zhao, J. (2023). Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion. Frontiers in Plant Science, 14, 1073530. https://doi.org/10.3389/fpls.2023.1073530
Wieme, J., Mollazade, K., Malounas, I., Zude-Sasse, M., Zhao, M., Gowen, A., ... & Van Beek, J. (2022). Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. biosystems engineering, 222, 156-176. https://doi.org/10.1016/j.biosystemseng.2022.07.013
Wu, D., & Sun, D. W. (2013). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review—Part I: Fundamentals. Innovative Food Science & Emerging Technologies, 19, 1-14. https://doi.org/10.1016/j.ifset.2013.04.014
Xiao, Q., Bai, X., & He, Y. (2020). Rapid screen of the color and water content of fresh-cut potato tuber slices using hyperspectral imaging coupled with multivariate analysis. Foods, 9(1), 94. https://doi.org/10.3390/foods9010094
Yaqoob, M., Sharma, S., & Aggarwal, P. (2021). Imaging techniques in agro-industry and their applications, a review. Journal of Food Measurement and Characterization, 15, 2329-2343. https://doi.org/10.1007/s11694-021-00809-w
Zhang, H., Zhang, S., Dong, W., Luo, W., Huang, Y., Zhan, B., & Liu, X. (2020). Detection of common defects on mandarins by using visible and near infrared hyperspectral imaging. Infrared Physics & Technology, 108, 103341. https://doi.org/10.1016/j.infrared.2020.103341
Zhang, S., Wu, X., Zhang, S., Cheng, Q., & Tan, Z. (2017). An effective method to inspect and classify the bruising degree of apples based on the optical properties. Postharvest Biology and Technology, 127, 44-52. https://doi.org/10.1016/j.postharvbio.2016.12.008

Ayrıntılar

Birincil Dil

Türkçe

Konular

Görüntü İşleme , Gıda Mühendisliği

Bölüm

Derleme

Yazarlar

Özgür Neşe Özen
0009-0008-3192-5272
Türkiye

Fatih Akkoyun
0000-0002-1432-8926
Türkiye

Ahmet Görgüç
0000-0003-3018-4595
Türkiye

Fatih Mehmet Yılmaz ^*
0000-0002-1370-1231
Türkiye

Yayımlanma Tarihi

3 Haziran 2024

Gönderilme Tarihi

30 Kasım 2023

Kabul Tarihi

5 Ocak 2024

Yayımlandığı Sayı

Yıl 1970 Cilt: 27 Sayı: 2

DOI

https://doi.org/10.17780/ksujes.1398289

IZ

https://izlik.org/JA92BD75TW

Kaynak Göster

RIS / Bibtex

APA

Özen, Ö. N., Akkoyun, F., Görgüç, A., & Yılmaz, F. M. (2024). MULTİSPEKTRAL VE HİPERSPEKTRAL GÖRÜNTÜLEME TEKNİKLERİNİN MEYVE - SEBZE İŞLEME TESİSLERİNDE KULLANIM OLANAKLARI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 643-656. https://doi.org/10.17780/ksujes.1398289

POTENTIALS OF MULTISPECTRAL AND HYPERSPECTRAL IMAGING TECHNIQUES IN FRUIT AND VEGETABLE PROCESSING PLANTS

Abstract

Keywords