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Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review

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Abstract

Fungal diseases that may occur before or after harvest are the main challenges of the postharvest citrus industry. Some fungal diseases are slight and only drastically reduce the marketability of the product, while some postharvest fungal diseases of citrus grow rapidly and also infect their adjacent fruits. Infected fruits can neither be stored for a long time nor be exported. For this reason, early detection of fungal infections is very essential. In this paper, all optical methods used to identify citrus fungal diseases, including imaging-based methods and spectroscopic-based methods, are investigated. The application of these methods in detecting citrus fungal diseases is also reviewed.

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References

  • Abdelsalam, A. M., & Sayed, M. S. (2016). Real-time defects detection system for orange citrus fruits using multi-spectral imaging. Paper presented at the 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS).

  • Adebayo, S. E., Hashim, N., Abdan, K., & Hanafi, M. (2016). Application and potential of backscattering imaging techniques in agricultural and food processing – A review. Journal of Food Engineering, 169, 155–164. https://doi.org/10.1016/j.jfoodeng.2015.08.006

    Article  Google Scholar 

  • Adedeji, A. A., Ekramirad, N., Rady, A., Hamidisepehr, A., Donohue, K. D., Villanueva, R. T., Parrish, C. A., & Li, M. (2020). Non-destructive technologies for detecting insect infestation in fruits and vegetables under postharvest conditions: A critical review. Foods, 9(7), 927. https://doi.org/10.3390/foods9070927

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Adenaike, O., & Abakpa, G. O. (2021). Antioxidant compounds and health benefits of citrus fruits. European Journal of Nutrition & Food Safety, 13(2), 65–74. https://doi.org/10.9734/ejnfs/2021/v13i230376

    Article  Google Scholar 

  • Alander, J. T., Bochko, V., Martinkauppi, B., Saranwong, S., & Mantere, T. (2013). A review of optical nondestructive visual and near-infrared methods for food quality and safety. International Journal of Spectroscopy, 2013, 341402. https://doi.org/10.1155/2013/341402

    Article  CAS  Google Scholar 

  • Amigo, J. M., & Grassi, S. (2020). Chapter 1.2 - Configuration of hyperspectral and multispectral imaging systems. In J. M. Amigo (Ed.), Data Handling in Science and Technology (Vol. 32, pp. 17–34). Elsevier.

    Google Scholar 

  • Anlar, H. G. (2020). Chapter 23 - Cinnamic acid as a dietary antioxidant in diabetes treatment. In V. R. Preedy (Ed.), Diabetes (2nd ed., pp. 235–243). Academic Press.

    Chapter  Google Scholar 

  • Anwar, U., Mubeen, M., Iftikhar, Y., Zeshan, M. A., Shakeel, Q., Sajid, A., Umer, M., & Abbas, A. (2021). Efficacy of different fungicides against citrus melanose disease in Sargodha, Pakistan. Pakistan Journal of Phytopathology, 33(1), 67–74.

    Article  Google Scholar 

  • Baiano, A. (2017). Applications of hyperspectral imaging for quality assessment of liquid based and semi-liquid food products: A review. Journal of Food Engineering, 214, 10–15. https://doi.org/10.1016/j.jfoodeng.2017.06.012

    Article  CAS  Google Scholar 

  • Balasundaram, D., Burks, T. F., Bulanon, D. M., Schubert, T., & Lee, W. S. (2009). Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biology and Technology, 51(2), 220–226. https://doi.org/10.1016/j.postharvbio.2008.07.014

    Article  Google Scholar 

  • Batuman, O., Ritenour, M., Vicent, A., Li, H., Hyun, J.-W., Catara, V., & Cano, L. M. (2020). Chapter 17 - Diseases caused by fungi and oomycetes. In M. Talon, M. Caruso, & F. G. Gmitter (Eds.), The Genus Citrus (pp. 349–369). Woodhead Publishing.

    Chapter  Google Scholar 

  • Blanc, P. G. R., Blasco, J., Moltó, E., Gómez-Sanchis, J., & Cubero, S. (2009). System for the automatic selective separation of rotten citrus fruits. Patent US9174245B2.

  • Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393. https://doi.org/10.1016/j.jfoodeng.2007.03.027

    Article  Google Scholar 

  • Blasco, J., Aleixos, N., Gómez-Sanchís, J., & Moltó, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145. https://doi.org/10.1016/j.biosystemseng.2009.03.009

    Article  Google Scholar 

  • Blasco, J., Cubero, S., & Moltó, E. (2016a). Chapter 12 - quality evaluation of citrus fruits. In D. -W. Sun (Ed.), Computer vision technology for food quality evaluation (2nd ed., pp. 305–325). San Diego: Academic Press. https://doi.org/10.1016/B978-0-12-802232-0.00012-8

    Chapter  Google Scholar 

  • Blasco, J., Lorente, D., Cortes, V., Talens, P., Cubero, S., Munera, S., & Aleixos, N. (2016b). Application of Near Infrared spectroscopy to the quality control of citrus fruits and mango. NIR News, 27(7), 4–7. https://doi.org/10.1255/nirn.1637

    Article  Google Scholar 

  • Bulanon, D. M., Burks, T. F., Kim, D. G., & Ritenour, M. A. (2013). Citrus black spot detection using hyperspectral image analysis. Agricultural Engineering International: CIGR Journal, 15(3), 171–180.

    Google Scholar 

  • Caggia, C., Palmeri, R., Russo, N., Timpone, R., Randazzo, C. L., Todaro, A., & Barbagallo, S. (2020). Employ of citrus by-product as fat replacer ingredient for bakery confectionery products. Frontiers in Nutrition, 7, 46. https://doi.org/10.3389/fnut.2020.000466

    Article  PubMed  PubMed Central  Google Scholar 

  • Cai, Z., Huang, W., Wang, Q., & Li, J. (2022). Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models. Frontiers in Plant Science, 13, 952942. https://doi.org/10.3389/fpls.2022.952942

    Article  PubMed  PubMed Central  Google Scholar 

  • Caporaso, N., ElMasry, G., & Gou, P. (2021). Chapter 13 - Hyperspectral imaging techniques for noncontact sensing of food quality. In C. M. Galanakis (Ed.), Innovative Food Analysis (pp. 345–379). Academic Press.

    Chapter  Google Scholar 

  • Cavaco, A. M., Passos, D., Pires, R. M., Antunes, M. D., & Guerra, R. (2021). Nondestructive assessment of citrus fruit quality and ripening by visible–near infrared reflectance spectroscopy. In M. S. Khan (Ed.), Citrus. IntechOpen.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Chandrasekaran, I., Panigrahi, S. S., Ravikanth, L., & Singh, C. B. (2019). Potential of near-infrared (NIR) spectroscopy and hyperspectral imaging for quality and safety assessment of fruits: An overview. Food Analytical Methods, 12(11), 2438–2458. https://doi.org/10.1007/s12161-019-01609-1

    Article  Google Scholar 

  • Cheng, Y., Lin, Y., Cao, H., & Li, Z. (2020). Citrus postharvest green mold: Recent advances in fungal pathogenicity and fruit resistance. Microorganisms, 8(3), 449. https://doi.org/10.3390/microorganisms8030449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Croce, A. C. (2021). Light and autofluorescence, multitasking features in living organisms. Photochem, 1(2), 67–124. https://doi.org/10.3390/photochem1020007

    Article  Google Scholar 

  • Cruz-Lagunas, B., Ortega-Acosta, S. Á., Reyes-García, G., Toribio-Jiménez, J., Juárez-López, P., Guillén-Sánchez, D., & Dami´ an-Nava, A., Romero-Ramírez, Y., & Palemón-Alberto, F. (2020). Colletotrichum gloeosporioides causes anthracnose on grapefruit (Citrus paradisi) in Mexico. Australasian Plant Disease Notes, 15(1), 31. https://doi.org/10.1007/s13314-020-00401-z

    Article  CAS  Google Scholar 

  • Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504. https://doi.org/10.1007/s11947-010-0411-8

    Article  Google Scholar 

  • Deng, B., Wang, W., Deng, L., Yao, S., Ming, J., & Zeng, K. (2018). Comparative RNA-seq analysis of citrus fruit in response to infection with three major postharvest fungi. Postharvest Biology and Technology, 146, 134–146. https://doi.org/10.1016/j.postharvbio.2018.08.012

    Article  CAS  Google Scholar 

  • Dukare, A. S., Singh, R. K., Jangra, R. K., & Bhushan, B. (2020). Non-fungicides-based promising technologies for managing post-production Penicillium induced spoilage in horticultural commodities: A comprehensive review. Food Reviews International, 38(3), 227–267. https://doi.org/10.1080/87559129.2020.1727497

    Article  CAS  Google Scholar 

  • El-Mesery, H. S., Mao, H., & Abomohra, A. E. -F. (2019). Applications of non-destructive technologies for agricultural and food products quality inspection. Sensors, 19(4), 846. First published 18 February 2019. Retrieved September 23, 2021, from https://www.mdpi.com/1424-8220/19/4/846

  • Ellouze, I. (2022). Citrus bio-wastes: A source of bioactive, functional products and non-food uses. In M. F. Ramadan & M. A. Farag (Eds.), Mediterranean Fruits Bio-wastes: Chemistry, Functionality and Technological Applications (pp. 221–260). Springer International Publishing. https://doi.org/10.1007/978-3-030-84436-3_9

    Chapter  Google Scholar 

  • Elmasry, G., Kamruzzaman, M., Sun, D. W., & Allen, P. (2012). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A review. Critical Reviews in Food Science and Nutrition, 52(11), 999–1023. https://doi.org/10.1080/10408398.2010.543495

    Article  PubMed  Google Scholar 

  • Folch-Fortuny, A., Prats-Montalbán, J. M., Cubero, S., Blasco, J., & Ferrer, A. (2016). VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemometrics and Intelligent Laboratory Systems, 156, 241–248. https://doi.org/10.1016/j.chemolab.2016.05.005

    Article  CAS  Google Scholar 

  • García-Plazaola, J. I., Fernández-Marín, B., Duke, S. O., Hernández, A., López-Arbeloa, F., & Becerril, J. M. (2015). Autofluorescence: Biological functions and technical applications. Plant Science, 236, 136–145. https://doi.org/10.1016/j.plantsci.2015.03.010

    Article  CAS  PubMed  Google Scholar 

  • Ghanei Ghooshkhaneh, N., Golzarian, M. R., & Mamarabadi, M. (2018). Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging. Journal of the Science of Food and Agriculture, 98(9), 3542–3550. https://doi.org/10.1002/jsfa.8865

    Article  CAS  PubMed  Google Scholar 

  • Ghanei Ghooshkhaneh, N., Golzarian, M. R., & Mollazade, K. (2023). VIS-NIR spectroscopy for detection of citrus core rot caused by Alternaria alternata. Food Control, 144, 109320. https://doi.org/10.1016/j.foodcont.2022.109320

    Article  Google Scholar 

  • Giovanelli, S., Ciccarelli, D., Giusti, G., Mancianti, F., Nardoni, S., & Pistelli, L. (2020). Comparative assessment of volatiles in juices and essential oils from minor Citrus fruits (Rutaceae). Flavour and Fragrance Journal, 35, 639–652. https://doi.org/10.1002/ffj.3603

    Article  CAS  Google Scholar 

  • Gomes, J. F. S., & Leta, F. R. (2012). Applications of computer vision techniques in the agriculture and food industry: A review. European Food Research and Technology, 235(6), 989–1000. https://doi.org/10.1007/s00217-012-1844-2

    Article  CAS  Google Scholar 

  • Gómez-Sanchis, J., Blasco, J., Soria-Olivas, E., Lorente, D., Escandell-Montero, P., Martínez-Martínez, J. M., Martínez-Sober, M., & Aleixos, N. (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology, 82(Supplement C), 76–86. https://doi.org/10.1016/j.postharvbio.2013.02.011

  • Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., & Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86. https://doi.org/10.1016/j.jfoodeng.2008.04.009

    Article  Google Scholar 

  • Gómez-Sanchis, J., Lorente, D., Soria-Olivas, E., Aleixos, N., Cubero, S., & Blasco, J. (2014). Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food and Bioprocess Technology, 7, 1047–1056. https://doi.org/10.1007/s11947-013-1158-9

    Article  Google Scholar 

  • Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785. https://doi.org/10.1016/j.eswa.2011.07.073

    Article  Google Scholar 

  • Hahn, F. (2009). Actual pathogen detection: Sensors and Algorithms - A review. Algorithms, 2(1), 301–338. https://doi.org/10.3390/a2010301

    Article  Google Scholar 

  • Hassoun, A. (2021). Exploring the potential of fluorescence spectroscopy for the discrimination between fresh and frozen-thawed muscle foods. Photochem, 1(2), 247–263. https://doi.org/10.3390/photochem1020015

    Article  Google Scholar 

  • He, Y., Xiao, Q., Bai, X., Zhou, L., Liu, F., & Zhang, C. (2021). Recent progress of nondestructive techniques for fruits damage inspection: A review. Critical Reviews in Food Science and Nutrition, 62(20), 5476–5494. https://doi.org/10.1080/10408398.2021.1885342

    Article  PubMed  Google Scholar 

  • Hussain, A., Pu, H., & Sun, D.-W. (2018). Innovative nondestructive imaging techniques for ripening and maturity of fruits – A review of recent applications. Trends in Food Science & Technology, 72, 144–152. https://doi.org/10.1016/j.tifs.2017.12.010

    Article  CAS  Google Scholar 

  • Hussain, A., Pu, H., & Sun, D.-W. (2019). Measurements of lycopene contents in fruit: A review of recent developments in conventional and novel techniques. Critical Reviews in Food Science and Nutrition, 59(5), 758–769. https://doi.org/10.1080/10408398.2018.1518896

    Article  CAS  PubMed  Google Scholar 

  • Hussain Hassan, N. M., & Nashat, A. A. (2019). New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques. Multidimensional Systems and Signal Processing, 30(2), 571–589. https://doi.org/10.1007/s11045-018-0573-5

    Article  Google Scholar 

  • Kahramanoğlu, İ, Nisar, M. F., Chen, C., Usanmaz, S., Chen, J., & Wan, C. (2020). Light: An alternative method for physical control of postharvest rotting caused by fungi of citrus fruit. Journal of Food Quality, 2020, 8821346. https://doi.org/10.1155/2020/8821346

    Article  CAS  Google Scholar 

  • Kim, D., Burks, T. F., Ritenour, M. A., & Qin, J. (2014). Citrus black spot detection using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 7(6), 20–27.

    Google Scholar 

  • Kim, D. G., Burks, T. F., Qin, J., & Bulanon, D. M. (2009). Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering, 2(3), 41–50. https://doi.org/10.13031/2013.24555

    Article  Google Scholar 

  • Kumar, G., & Bhatia, P. K. (2014). A detailed review of feature extraction in image processing systems. Paper presented at the 2014 Fourth International Conference on Advanced Computing & Communication Technologies. https://doi.org/10.1109/ACCT.2014.74

  • Kumar, M., Pratap, V., Gour, J. K., & Singh, M. K. (2022). Chapter 4.22 - Vitamin C. In S. M. Nabavi & A. S. Silva (Eds.), Antioxidants Effects in Health (pp. 535–546). Elsevier. https://doi.org/10.1016/B978-0-12-819096-8.00065-3

    Chapter  Google Scholar 

  • Kurita, M., Kondo, N., Shimizu, H., Ling, P., Falzea, P. D., Shiigi, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21(4), 533–540.

    Article  Google Scholar 

  • Ladaniya, M. S. (2008). 3 - Postharvest losses. In M. S. Ladaniya (Ed.), Citrus Fruit Biology, Technology and Evaluation (pp. 67–78). Academic Press.

    Chapter  Google Scholar 

  • Li, J., Huang, W., Tian, X., Wang, C., Fan, S., & Zhao, C. (2016a). Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging. Computers and Electronics in Agriculture, 127, 582–592.

    Article  Google Scholar 

  • Li, J., Li, Z., Wang, N., Raghavan, G. S. V., Pei, Y., Song, C., & Zhu, G. (2020a). Novel sensing technologies during the food drying process. Food Engineering Reviews, 12(2), 121–148. https://doi.org/10.1007/s12393-020-09215-2

    Article  CAS  Google Scholar 

  • Li, J., Luo, W., Han, L., Cai, Z., & Guo, Z. (2022). Two-wavelength image detection of early decayed oranges by coupling spectral classification with image processing. Journal of Food Composition and Analysis, 111, 104642. https://doi.org/10.1016/j.jfca.2022.104642

    Article  CAS  Google Scholar 

  • Li, J., Rao, X., Wang, F., Wu, W., & Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69. https://doi.org/10.1016/j.postharvbio.2013.02.016

    Article  Google Scholar 

  • Li, J., Tian, X., & Huang, W. (2016b). Multispectral imaging for early decay detection in citrus fruit. Paper presented at the 2016 ASABE Annual International Meeting. https://doi.org/10.13031/aim.20162457020

  • Li, J., Zhang, R., Li, J., Wang, Z., Zhang, H., Zhan, B., & Jiang, Y. (2019). Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method. Postharvest Biology and Technology, 158, 110986. https://doi.org/10.1016/j.postharvbio.2019.110986

    Article  CAS  Google Scholar 

  • Li, Q., Qi, J., Qin, X., Dou, W., Lei, T., Hu, A., & He, Y. (2020b). CitGVD: A comprehensive database of citrus genomic variations. Horticulture Research, 7(1), 12. https://doi.org/10.1038/s41438-019-0234-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu, H., Lee, S.-H., & Chahl, J. S. (2017). A review of recent sensing technologies to detect invertebrates on crops. Precision Agriculture, 18(4), 635–666. https://doi.org/10.1007/s11119-016-9473-6

    Article  Google Scholar 

  • López-García, F., Andreu-García, G., Blasco, J., Aleixos, N., & Valiente, J.-M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71(2), 189–197. https://doi.org/10.1016/j.compag.2010.02.001

    Article  Google Scholar 

  • Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2013a). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology, 6(2), 530–541. https://doi.org/10.1007/s11947-011-0737-x

    Article  Google Scholar 

  • Lorente, D., Blasco, J., Serrano, A. J., Soria-Olivas, E., Aleixos, N., & Gómez-Sanchis, J. (2013b). Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food and Bioprocess Technology, 6(12), 3613–3619. https://doi.org/10.1007/s11947-012-0951-1

    Article  CAS  Google Scholar 

  • Lorente, D., Escandell-Montero, P., Cubero, S., Gómez-Sanchis, J., & Blasco, J. (2015a). Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit. Journal of Food Engineering, 163(Supplement C), 17–24. https://doi.org/10.1016/j.jfoodeng.2015.04.010

    Article  CAS  Google Scholar 

  • Lorente, D., Zude, M., Idler, C., Gómez-Sanchis, J., & Blasco, J. (2015b). Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. Journal of Food Engineering, 154, 76–85. https://doi.org/10.1016/j.jfoodeng.2015.01.004

    Article  Google Scholar 

  • Lorente, D., Zude, M., Regen, C., Palou, L., Gómez-Sanchis, J., & Blasco, J. (2013c). Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology, 86, 424–430. https://doi.org/10.1016/j.postharvbio.2013.07.021

    Article  Google Scholar 

  • Lu, R., Van Beers, R., Saeys, W., Li, C., & Cen, H. (2020a). Measurement of optical properties of fruits and vegetables: A review. Postharvest Biology and Technology, 159, 111003. https://doi.org/10.1016/j.postharvbio.2019.111003

    Article  Google Scholar 

  • Lu, Y., Saeys, W., Kim, M., Peng, Y., & Lu, R. (2020b). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biology and Technology, 170, 111318. https://doi.org/10.1016/j.postharvbio.2020.111318

    Article  CAS  Google Scholar 

  • Luo, W., Fan, G., Tian, P., Dong, W., Zhang, H., & Zhan, B. (2022). Spectrum classification of citrus tissues infected by fungi and multispectral image identification of early rotten oranges. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 279, 121412. https://doi.org/10.1016/j.saa.2022.121412

    Article  CAS  PubMed  Google Scholar 

  • Ma, G., Zhang, L., Sugiura, M., & Kato, M. (2020). Chapter 24 - Citrus and health. In M. Talon, M. Caruso, & F. G. Gmitter (Eds.), The Genus Citrus (pp. 495–511). Woodhead Publishing.

    Chapter  Google Scholar 

  • Ma, J., Sun, D.-W., Pu, H., Cheng, J.-H., & Wei, Q. (2019). Advanced techniques for hyperspectral imaging in the food industry: Principles and recent applications. Annual Review of Food Science and Technology, 10(1), 197–220. https://doi.org/10.1146/annurev-food-032818-121155

    Article  CAS  PubMed  Google Scholar 

  • Makhaik, M. S., Shakya, A. K., & Kale, R. (2021). Dietary phytochemicals: As a natural source of antioxidants. In V. Y. Viduranga (Ed.), Antioxidants (p. Ch. 21). IntechOpen. https://doi.org/10.5772/intechopen.99159

  • Malik, A. U., Hasan, M. U., Khalid, S., Mazhar, M. S., Shafique Khalid, M., Khan, M. N., Saleem, B. A., & Anwar, R. (2021). Biotic and abiotic factors causing rind blemishes in citrus and management strategies to improve the cosmetic quality of fruits. International Journal of Agriculture and Biology, 25(2), 298–318. https://doi.org/10.17957/IJAB/15.1670

    Article  CAS  Google Scholar 

  • Mollazade, K., Omid, M., Tab, F. A., & Mohtasebi, S. S. (2012). Principles and applications of light backscattering imaging in quality evaluation of agro-food products: A review. Food and Bioprocess Technology, 5(5), 1465–1485. https://doi.org/10.1007/s11947-012-0821-x

    Article  Google Scholar 

  • Moltó, E., Blasco, J., & Gómez-Sanchís, J. (2010). Chapter 10 - Analysis of hyperspectral images of citrus fruits A2. In D. -W. Sun (Ed.), Hyperspectral Imaging for Food Quality Analysis and Control (pp. 321–348). Academic Press.

    Chapter  Google Scholar 

  • Momin, A., Kondo, N., Makoto, K., Ogawa, Y., Yamamoto, K., Shiigi, T., & Ninomiya, K. (2011). Evaluation of the reasons why freshly appearing citrus peel fluorescence during automatic inspection by fluorescent imaging technique. Paper Presented at the Proceedings of the SPIE, Tenth International Conference on Quality Control by Artificial Vision. https://doi.org/10.1117/12.890118

  • Momin, M. A., Kondo, N., Ogawa, Y., Ido, K., & Ninomiya, K. (2013). Patterns of fluorescence associated with citrus peel defects. Engineering in Agriculture, Environment and Food, 6(2), 54–60. https://doi.org/10.11165/eaef.6.54

    Article  Google Scholar 

  • Niu, Y. H., Wang, L., Wan, X. G., Peng, Q. Z., Huang, Q., & Shi, Z. H. (2021). A systematic review of soil erosion in citrus orchards worldwide. CATENA, 206, 105558. https://doi.org/10.1016/j.catena.2021.105558

    Article  Google Scholar 

  • Obenland, D., Margosan, D., Smilanick, J. L., & Mackey, B. (2010). Ultraviolet fluorescence to identify navel oranges with poor peel quality and decay. HortTechnology, 20(6), 991–995. https://doi.org/10.21273/HORTSCI.20.6.991

    Article  Google Scholar 

  • Olabiyi, D., Shrestha, B., Zaka, S. M., & Neupane, S. (2023). Insect pests of citrus production. In S. Hussain, M. Khalid, M. A. Ali, N. Ahmed, M. Hasanuzzaman, & S. Ahmad (Eds.), Citrus Production: Technological Advancements and Adaptation to Climate Change (1st ed.). CRC Press. https://doi.org/10.1201/9781003119852

    Chapter  Google Scholar 

  • Ozaki, Y., Huck, C., Tsuchikawa, S., & Engelsen, S. B. (2021). Near-infrared spectroscopy. Springer Singapore. https://doi.org/10.1007/978-981-15-8648-4

    Book  Google Scholar 

  • Palou, L. (2014). Chapter 2 - Penicillium digitatum, Penicillium italicum (Green Mold, Blue Mold). In S. Bautista-Baños (Ed.), Postharvest Decay (pp. 45–102). Academic Press.

    Chapter  Google Scholar 

  • Pasquini, C. (2018). Near Infrared spectroscopy: A mature analytical technique with new perspectives - A review. Analytica Chimica Acta, 1026, 8–36. https://doi.org/10.1016/j.aca.2018.04.004

    Article  CAS  PubMed  Google Scholar 

  • Patel, K. K., Kar, A., Jha, S. N., & Khan, M. A. (2012). Machine vision system: A tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 49(2), 123–141. https://doi.org/10.1007/s13197-011-0321-4

    Article  PubMed  Google Scholar 

  • Pieczywek, P. M., Cybulska, J., Szymańska-Chargot, M., Siedliska, A., Zdunek, A., Nosalewicz, A., Baranowski, P., & Kurenda, A. (2018). Early detection of fungal infection of stored apple fruit with optical sensors – Comparison of biospeckle, hyperspectral imaging and chlorophyll fluorescence. Food Control, 85, 327–338. https://doi.org/10.1016/j.foodcont.2017.10.013

    Article  CAS  Google Scholar 

  • Pourreza, A., Lee, W. S., Ritenour, M. A., & Roberts, P. (2016). Spectral characteristics of citrus black spot disease. HortTechnology hortte, 26(3), 254–260. https://doi.org/10.21273/HORTTECH.26.3.254

    Article  Google Scholar 

  • Qin, J., Burks, T. F., Ritenour, M. A., & Gordon Bonn, W. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93, 183–191. https://doi.org/10.1016/j.jfoodeng.2009.01.014

    Article  Google Scholar 

  • Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72(1), 1–13. https://doi.org/10.1016/j.compag.2010.02.007

    Article  Google Scholar 

  • Sharma, A., Iqbal, M. N., & Singha, S. (2018). An experimental review of nondestructive testing methods for fruits and vegetables. In B. A. Bhanvase, R. P. Ugwekar, & R. B. Mankar (Eds.), Novel water treatment and separation methods (pp. 293–311). Apple Academic Press Inc.

    Google Scholar 

  • Siedliska, A., Baranowski, P., Zubik, M., & Mazurek, W. (2017). Detection of pits in fresh and frozen cherries using a hyperspectral system in transmittance mode. Journal of Food Engineering, 215, 61–71. https://doi.org/10.1016/j.jfoodeng.2017.07.028

    Article  Google Scholar 

  • Sighicelli, M., Colao, F., Lai, A., & Patsaeva, S. (2008). Monitoring post-harvest orange fruit disease by fluorescence and reflectance hyperspectral imaging. Paper presented at the I International Symposium on Horticulture in Europe 817. https://doi.org/10.17660/ActaHortic.2009.817.29

  • Smilanick, J. L., Erasmus, A., & Palou, L. (2020). Citrus fruits. In L. Palou & J. L. Smilanick (Eds.), Postharvest pathology of fresh horticultural produce. CRC Press. Retrieved September 23, 2021, from https://books.google.com/books?id=_Dy5DwAAQBAJ

  • Stegmayer, G., Milone, D. H., Garran, S., & Burdyn, L. (2013). Automatic recognition of quarantine citrus diseases. Expert Systems with Applications, 40(9), 3512–3517. https://doi.org/10.1016/j.eswa.2012.12.059

    Article  Google Scholar 

  • Strano, M. C., Altieri, G., Admane, N., Genovese, F., & Renzo, G. C. D. (2017). Advance in citrus postharvest management: Diseases, cold storage and quality evaluation. In H. Gill & H. Garg (Eds.), Citrus Pathology. IntechOpen. https://doi.org/10.5772/66518

    Chapter  Google Scholar 

  • Tan, A., Zhou, G., & He, M. (2021). Surface defect identification of citrus based on KF-2D-Renyi and ABC-SVM. Multimedia Tools and Applications, 80(6), 9109–9136. https://doi.org/10.1007/s11042-020-10036-y

    Article  Google Scholar 

  • Tian, X., Fan, S., Huang, W., Wang, Z., & Li, J. (2020). Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms. Postharvest Biology and Technology, 161, 111071. https://doi.org/10.1016/j.postharvbio.2019.111071

    Article  CAS  Google Scholar 

  • Tian, X., Zhang, C., Li, J., Fan, S., Yang, Y., & Huang, W. (2021). Detection of early decay on citrus using LW-NIR hyperspectral reflectance imaging coupled with two-band ratio and improved watershed segmentation algorithm. Food Chemistry, 360, 130077. https://doi.org/10.1016/j.foodchem.2021.130077

    Article  CAS  PubMed  Google Scholar 

  • Troncoso-Rojas, R., & Tiznado-Hernández, M. E. (2014). Chapter 5 - Alternaria alternata (Black Rot, Black Spot). In S. Bautista-Baños (Ed.), Postharvest Decay (pp. 147–187). Academic Press. https://doi.org/10.1016/B978-0-12-411552-1.00005-3

    Chapter  Google Scholar 

  • Vashpanov, Y., Heo, G., Kim, Y., Venkel, T., & Son, J. -Y. (2020). Detecting green mold pathogens on lemons using hyperspectral images. Applied Sciences, 10(4), 1209. First published 11 February 2020. Retrieved September 23, 2021, from https://www.mdpi.com/2076-3417/10/4/1209

  • Vijayarekha, K. (2012). External defect classification of citrus fruit images using linear discriminant analysis clustering and ANN classifiers. Research Journal of Applied Sciences, Engineering and Technology, 4(24), 5484–5491.

    Google Scholar 

  • Walsh, K. B., Blasco, J., Zude-Sasse, M., & Sun, X. (2020). Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biology and Technology, 168, 111246. https://doi.org/10.1016/j.postharvbio.2020.111246

    Article  CAS  Google Scholar 

  • Wang, A., Zhang, W., & Wei, X. (2019). A review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture, 158, 226–240. https://doi.org/10.1016/j.compag.2019.02.005

    Article  Google Scholar 

  • Wang, H., Peng, J., Xie, C., Bao, Y., & He, Y. (2015). Fruit quality evaluation using spectroscopy technology: A review. Sensors (basel), 15(5), 11889–11927. https://doi.org/10.3390/s150511889

    Article  PubMed  Google Scholar 

  • Wasnik, P., Menon, R., & Meshram, B. (2017). Digital image analysis: Tool for food quality evaluation. In A. K. Agrawal & M. R. Goyal (Eds.), Processing Technologies For Milk and Milk Products (pp. 65–104). Apple Academic Press Inc.

    Chapter  Google Scholar 

  • Wills, R. B. H., & Golding, J. (2016). Postharvest: An introduction to the physiology and handling of fruit and vegetables (6th ed.). CABI. First published 9 Nov 2016. Retrieved September 23, 2021, from https://books.google.com/books?id=lRhWvgAACAAJ

  • Wu, D., & Sun, D.-W. (2013). Hyperspectral imaging technology: A nondestructive tool for food quality and safety evaluation and inspection. In S. Yanniotis, P. Taoukis, N. G. Stoforos, & V. T. Karathanos (Eds.), Advances in Food Process Engineering Research and Applications (pp. 581–606). Springer, US.

    Chapter  Google Scholar 

  • Xie, C., & Lee, W. S. (2021). Detection of citrus black spot symptoms using spectral reflectance. Postharvest Biology and Technology, 180, 111627. https://doi.org/10.1016/j.postharvbio.2021.111627

    Article  Google Scholar 

  • Yin, S., Bi, X., Niu, Y., Gu, X., & Xiao, Y. (2017). Hyperspectral classification for identifying decayed oranges infected by fungi. Emirates Journal of Food and Agriculture, 29(8), 601–609. https://doi.org/10.9755/ejfa.2017-05-1074

    Article  Google Scholar 

  • Zacarias, L., Cronje, P. J. R., & Palou, L. (2020). Chapter 21 - Postharvest technology of citrus fruits. In M. Talon, M. Caruso, & F. G. Gmitter (Eds.), The Genus Citrus (pp. 421–446). Woodhead Publishing.

    Chapter  Google Scholar 

  • Zahir, S. A. D. M., Omar, A. F., Jamlos, M. F., Azmi, M. A. M., & Muncan, J. (2022). A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection. Sensors and Actuators A: Physical, 338, 113468. https://doi.org/10.1016/j.sna.2022.113468

    Article  CAS  Google Scholar 

  • Zhang, B., Liu, L., Gu, B., Zhou, J., Huang, J., & Tian, G. (2018a). From hyperspectral imaging to multispectral imaging: Portability and stability of HIS-MIS algorithms for common defect detection. Postharvest Biology and Technology, 137, 95–105. https://doi.org/10.1016/j.postharvbio.2017.11.004

    Article  Google Scholar 

  • Zhang, H., Chen, Y., Liu, X., Huang, Y., Zhan, B., & Luo, W. (2021). Identification of common skin defects and classification of early decayed citrus using hyperspectral imaging technique. Food Analytical Methods, 14(6), 1176–1193. https://doi.org/10.1007/s12161-020-01960-8

    Article  Google Scholar 

  • Zhang, H., Zhan, B., Pan, F., & Luo, W. (2020a). Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models. Postharvest Biology and Technology, 163, 111148. https://doi.org/10.1016/j.postharvbio.2020.111148

    Article  CAS  Google Scholar 

  • Zhang, H., Zhang, S., Dong, W., Luo, W., Huang, Y., Zhan, B., & Liu, X. (2020b). Detection of common defects on mandarins by using visible and near infrared hyperspectral imaging. Infrared Physics and Technology, 108, 103341. https://doi.org/10.1016/j.infrared.2020.103341

    Article  CAS  Google Scholar 

  • Zhang, Y., Lee, W. S., Li, M., Zheng, L., & Ritenour, M. A. (2018b). Non-destructive recognition and classification of citrus fruit blemishes based on ant colony optimized spectral information. Postharvest Biology and Technology, 143, 119–128. https://doi.org/10.1016/j.postharvbio.2018.05.004

    Article  Google Scholar 

  • Zhao, C., Lee, W. S., & He, D. (2015). Citrus black spot detection based on selected wavelengths using hyperspectral images. Paper presented at the 2015 ASABE Annual International Meeting, St. Joseph, MI. https://doi.org/10.13031/aim.20152181190

  • Zhao, X., Burks, T., Qin, J., & Ritenour, M. (2009). Digital microscopic imaging for citrus peel disease classification using color texture features. Applied Engineering in Agriculture, 25(5), 769–776.

    Article  Google Scholar 

  • Zhao, X., Burks, T. F., Qin, J., & Ritenour, M. A. (2010). Effect of fruit harvest time on citrus canker detection using hyperspectral reflectance imaging. Sensing and Instrumentation for Food Quality and Safety, 4(3), 126–135. https://doi.org/10.1007/s11694-010-9103-3

    Article  Google Scholar 

  • Zhong, G., & Nicolosi, E. (2020). Citrus origin, diffusion, and economic importance. In A. Gentile, S. La Malfa, & Z. Deng (Eds.), The Citrus Genome. Compendium of Plant Genomes. Cham: Springer. https://doi.org/10.1007/978-3-030-15308-3_2

    Chapter  Google Scholar 

  • Ziv, C., & Fallik, E. (2021). Postharvest storage techniques and quality evaluation of fruits and vegetables for reducing food loss. Agronomy, 11(6), 1133. https://doi.org/10.3390/agronomy11061133

    Article  Google Scholar 

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Acknowledgements

We would like to thank Dr. Mahmood Reza Golzarian, from the Department of Biosystems Engineering, for reviewing initial manuscript.

Funding

This work was supported by Ferdowsi University of Mashhad.

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  1. Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Narges Ghanei Ghooshkhaneh

  2. Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

    Kaveh Mollazade

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Narges Ghanei Ghooshkhaneh: visualization, writing–original draft; Kaveh Mollazade: conceptualization, visualization, writing–review and editing.

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Ghanei Ghooshkhaneh, N., Mollazade, K. Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review. Food Bioprocess Technol 16, 1668–1689 (2023). https://doi.org/10.1007/s11947-023-03005-4

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