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

Year 2024, Volume: 27 Issue: 2, 643 - 656, 03.06.2024

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

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

Abstract

Bu çalışmada ileri görüntüleme tekniklerinden olan multispektral görüntüleme ve hiperspektral görüntülemenin meyve ve sebze endüstrisinde kullanım olanakları derlenmiştir. Multispektral görüntüleme ve hiperspektral görüntüleme teknikleri; meyve sebzeleri sınıflandırma, olgunluğa göre sıralama, kusurlu ürün ayırma, kuraklık ölçümü yapma, hasat zamanını belirleme gibi birçok uygulamada teşhis ve müdahale amacıyla kullanılmaktadır. Deneysel çalışmalarda multispektral görüntülemenin görünür ve yakın dalga boylarında gıdaların sınıflandırılması amacıyla kullanıldığında yüksek oranda başarılı olduğu görülmüştür. Hiperspektral görüntülemede ise meyve ve sebzelerde renk, sıkılık, asitlik, şeker, antioksidan madde miktarı, toplam çözünür kuru madde miktarını belirlemek gibi spesifik durumların yanında olgunluk, fizyolojik bozukluk, mekanik hasar, duyusal kalite, biyolojik kusur gibi kalite parametrelerinin belirlenmesi amacıyla da kullanıldığı görülmüş ve yüksek oranlarda başarılar elde edilmiştir. Bu görüntüleme teknikleri diğer sınıflandırma yöntemlerine kıyasla hızlı sonuç veren, çevreye duyarlı, meyve ve sebzelerde tahribat yaratmayan yöntemlerdir.

Keywords

İleri görüntüleme teknikleri meyve kalitesi meyve sınıflandırma tayflı kameralar tahribatsız ölçüm

References

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

Year 2024, Volume: 27 Issue: 2, 643 - 656, 03.06.2024

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

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

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

Advanced imaging techniques fruit quality fruit sorting spectrum cameras non-destructive measurement

References

  • 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
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There are 45 citations in total.

Details

Primary Language Turkish
Subjects Image Processing, Food Engineering
Journal Section Reviews
Authors

Özgür Neşe Özen 0009-0008-3192-5272

Fatih Akkoyun 0000-0002-1432-8926

Ahmet Görgüç 0000-0003-3018-4595

Fatih Mehmet Yılmaz 0000-0002-1370-1231

Publication Date June 3, 2024
Submission Date November 30, 2023
Acceptance Date January 5, 2024
Published in Issue Year 2024Volume: 27 Issue: 2

Cite

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
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