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    Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review

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    [EN] Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.Cubero GarcĂ­a, S.; Lee, WS.; Aleixos BorrĂĄs, MN.; Albert Gil, FE.; Blasco Ivars, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review. Food and Bioprocess Technology. 9(10):1623-1639. https://doi.org/10.1007/s11947-016-1767-1S16231639910Adebayo, 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.Aleixos, N., Blasco, J., NavarrĂłn, F., & MoltĂł, E. (2002). Multispectral inspection of citrus in real time using machine vision and digital signal processors. Computers and Electronics in Agriculture, 33(2), 121–137.Annamalai, P., & Lee, W. S. (2003). Citrus yield mapping system using machine vision. ASAE Paper No. 031002. St. Joseph: ASAE.Annamalai, P., & Lee, W. S. (2004). Identification of green citrus fruits using spectral characteristics. ASAE Paper No. FL04–1001. St. Joseph: ASAE.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, 220–226.Bansal, R., Lee, W. S., & Satish, S. (2013). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.Barreiro, P., Zheng, C., Sun, D.-W., HernĂĄndez-SĂĄnchez, N., PĂ©rez-SĂĄnchez, J. M., & Ruiz-Cabello, J. (2008). Non-destructive seed detection in mandarins: comparison of automatic threshold methods in FLASH and COMSPIRA MRIs. Postharvest Biology and Technology, 47, 189–198.Basavaprasad, B., & Ravi, M. (2014). A comparative study on classification of image segmentation methods with a focus on graph based techniques. International Journal of Research in Engineering and Technology, 3, 310–315.Birth, G. S. (1976). How light interacts with foods. In: Gafney J.Jr.(Ed.), Quality detection in foods (pp. 6–11). St. Joseph: ASAE.Blanc, P.G.R., Blasco, J., MoltĂł, E., GĂłmez-Sanchis, J., & Cubero, S. (2010) System for the automatic selective separation of rotten citrus fruits. Patent number EP2133157 A1 CN101678405A, EP2133157A4, EP2133157B1, US20100121484Blasco, J., Aleixos, N., & MoltĂł, E. (2007a). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, 81(3), 535–543.Blasco, J., Aleixos, N., GĂłmez, J., & MoltĂł, E. (2007b). 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., GĂłmez-Sanchis, J., & MoltĂł, E. (2009). Recognition and classification of external skin damages in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145.Blasco, J., Cubero, S., & MoltĂł, E. (2016). Quality evaluation of citrus fruits. In D.-W. Sun (Ed.), Computer vision technology for food quality evaluation (2nd ed.). San Diego: Academic Press.Bulanon, D. M., Burks, T. F., & Alchanatis, V. (2009). Image fusion of visible and thermal images for fruit detection. Biosystems Engineering, 103, 12–22.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.Burks, T. F., Villegas, F., Hannan, M. W., & Flood, S. (2003). Engineering and horticultural aspects of robotic fruit harvesting: opportunities and constraints. HortTechnology, 15(1), 79–87.Campbell, B. L., Nelson, R. G., Ebel, R. C., Dozier, W. A., Adrian, J. L., & Hockema, B. R. (2004). Fruit quality characteristics that affect consumer preferences for Satsuma mandarins. Hortscience, 39(7), 1664–1669.Chinchuluun, R., Lee, W. S., & Ehsani, R. (2009). Machine vision system for determining citrus count and size on a canopy shake and catch harvester. Applied Engineering in Agriculture, 25(4), 451–458.Choi, D., Lee, W. S., Ehsani, R., & Roka, F. M. (2015). A machine vision system for quantification of citrus fruit dropped on the ground under the canopy. Transactions of the ASABE, 58(4), 933–946.Codex Alimentarius, (2011). Codex standard for oranges. Available at: http://www.codexalimentarius.org/download/standards/10372/CXS_245e.pdf . Accessed March 2016Cubero, S., Aleixos, N., Albert, A., Torregrosa, A., Ortiz, C., GarcĂ­a-Navarrete, O., & Blasco, J. (2014a). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture, 15(1), 80–94.Cubero, S., Aleixos, N., MoltĂł, E., GĂłmez-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.Cubero, S., Diago, M. P., Blasco, J., TardĂĄguila, J., MillĂĄn, B., & Aleixos, N. (2014b). A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis. Biosystems Engineering, 117, 62–72.Dong, C.-W., Ye, Y., Zhang, J.-Q., Zhu, H.-K., & Liu, F. (2014). Detection of thrips defect on green-peel citrus using hyperspectral imaging technology combining PCA and B-Spline lighting correction method. Journal of Integrative Agriculture, 13(10), 2229–2235.FAOSTAT (2012). URL: http://faostat.fao.org http://www.fao.org/fileadmin/templates/est/COMM_MARKETS_MONITORING/Citrus/Documents/CITRUS_BULLETIN_2012.pdf . Accessed March 2016.Farrell, T. J., Patterson, M. S., & Wilson, B. (1992). A diffusion-theory model of spatially resolved steady-state diffuse reflectance for the noninvasive determination of tissue optical-properties in vivo. Medical Physics, 19, 879–888.Flood, S. J., Burks, T. F., & Teixeira, A. A. (2006). Physical properties of oranges in response to applied gripping forces for robotic harvesting. Transactions of ASAE, 49(2), 341–346.Gaffney, J. J. (1973). Reflectance properties of citrus fruit. Transactions of ASAE, 16(2), 310–314.Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture, 91, 106–115.GĂłmez, J., Blasco, J., MoltĂł, E., & Camps-Valls, G. (2007). Hyperspectral detection of citrus damage with a Mahalanobis kernel classifier. Electronics Letters, 43(20), 1082–1084.GĂłmez-Sanchis, J., Blasco, J., Soria-Olivas, E., Lorente, D., Escandell-Montero, P., MartĂ­nez-MartĂ­nez, J. M., MartĂ­nez-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, 76–86.GĂłmez-Sanchis, J., GĂłmez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., MoltĂł, 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.GĂłmez-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.GĂłmez-Sanchis, J., MartĂ­n-Guerrero, J. D., Soria-Olivas, E., MartĂ­nez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785.Gong, A., Yu, J., He, Y., & Qiu, Z. (2013). Citrus yield estimation based on images processed by an android mobile phone. Biosystems Engineering, 115, 162–170.Gottwald, T. R., Graham, J. H., & Schubert, T. S. (2002). Citrus canker: the pathogen and its impact. Plant Health Progress. doi: 10.1094/PHP-2002-0812-01-RV.Hannan, M., Burks, T. F., & Bulanon, D.M. (2009). A machine vision algorithm for orange fruit detection. The CIGR Ejournal. Manuscript 1281. Vol XI. December 2009.Harrell, R. C., Adsit, P. D., & Slaughter, D. C. (1985). Real-time vision-servoing of a robotic tree-fruit harvester. ASAE Paper No (pp. 85–3550). St. Joseph: ASAE.HernĂĄndez-SĂĄnchez, N., Barreiro, P., & Ruiz-Cabello, J. (2006). On-line identification of seeds in mandarins with magnetic resonance imaging. Biosystems Engineering, 95, 529–536.Holmes, G. J., & Eckert, J. W. (1999). Sensitivity of Penicillium digitatum and P. italicum to postharvest citrus fungicides in California. Phytopathology, 89(9), 716–721.Iqbal, S. M., Gopal, A., Sankaranarayanan, P. E., & Nair, A. B. (2016). Classification of selected citrus fruits based on color using machine vision system. International Journal of Food Properties, 19, 272–288.Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley.Jafari, A., Fazayeli, A., & Zarezadeh, M. R. (2014). Estimation of orange skin thickness based on visual texture coarseness. Biosystems Engineering, 117, 73–82.JimĂ©nez-Cuesta, M. J., Cuquerella, J., & MartĂ­nez-JĂĄvega, J. M. (1981). Determination of a color index for citrus fruit degreening. In Proceedings of the International Society of Citriculture, 2, 750–753.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, 41–50.Kim, D. G., Burks, T. F., Ritenour, M. A., & Qin, J. (2014). Citrus black spot detection using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 7, 20–27.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., Kaichi, T., & Okamoto, S. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4, 7–11.Kondo, N., Kuramoto, M., Shimizu, H., Ogawa, Y., Kurita, M., Nishizu, T., Chong, V. K., & Yamamoto, K. (2009). Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Engineering in Agriculture, Environment and Food, 2, 54–59.Kurita, M., Kondo, N., Shimizu, H., Ling, P. P., Falzea, P. D., Shiigi, T., Ninomiya, K., Nishizu, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21, 533–540.Kurtulmus, F., Lee, W. S., & Vardar, A. (2011). Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 78(2), 140–149.Ladaniya, M. S. (2010). Citrus fruit: biology, technology and evaluation. San Diego: Academic Press.Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture. doi: 10.1007/s11119-016-9443-z.Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014). Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15, 162–183.Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78, 38–48.Li, J., Rao, X., & Ying, Y. (2012a). Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. Journal of the Science of Food and Agriculture, 92, 125–134.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.Li, J., Rao, X., Ying, Y., & Wang, D. (2010). Detection of navel oranges canker based on hyperspectral imaging technology. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 26, 222–228.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A., Yang, C., & Mangan, R. (2012b). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 83, 32–46.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2015). Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering, 132, 28–38.Lopes, L. B., VanDeWall, H., Li, H. T., Venugopal, V., Li, H. K., Naydin, S., Hosmer, J., Levendusky, M., Zheng, H., Bentley, M. V., Levin, R., & Hass, M. A. (2010). Topical delivery of lycopene using microemulsions: enhanced skin penetration and tissue antioxidant activity. Journal of Pharmaceutical Sciences, 99, 1346–1357.LĂłpez, J. J., Cobos, M., & Aguilera, E. (2011). Computer-based detection and classification of flaws in citrus fruits. Neural Computing and Applications, 20, 975–981.LĂłpez-GarcĂ­a, F., Andreu, 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, 189–197.Lorente, D., Aleixos, N., GĂłmez-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.Lorente, D., Aleixos, N., GĂłmez-Sanchis, J., Cubero, S., GarcĂ­a-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.Lorente, D., Blasco, J., Serrano, A. J., Soria-Olivas, E., Aleixos, N., & GĂłmez-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.Lorente, D., Zude, M., Regen, C., Palou, L., GĂłmez-Sanchis, J., & Blasco, J. (2013c). Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology, 86, 424–430.Lorente, D., Zude, M., Idler, C., GĂłmez-Sanchis, J., & Blasco, J. (2015). 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.Maf Industries. (2016). VIOTEC brochure. http://mafindustries.com/wp-content/uploads/2015/02/viotec3.pdf . Accessed March 2016.Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & NicolaĂŻ, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5(2), 425–444.Mehta, S. S., & Burks, T. F. (2014). Vision-based control of robotic manipulator for citrus harvesting. Computers and Electronics in Agriculture, 102, 146–158.MoltĂł, E., Blasco, J., & GĂłmez-Sanchis, J. (2010). Analysis of hyperspectral images of citrus fruits. In D.-W. Sun (Ed.), Hyperspectral imaging for food quality analysis and control (pp. 321–348). California: Academic Press.MoltĂł, E., PlĂĄ, F., & Juste, F. (1992). Vision systems for the location of citrus fruit in a tree canopy. Journal of Agricultural Engineering Research, 52, 101–110.Momin, A., Kondo, N., Kuramoto, M., Ogawa, Y., Yamamoto, K., & Shiigi, T. (2012). Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV-VIS spectra. Engineering in Agriculture, Environment and Food, 5, 126–132.Momin, A., Kondo, N., Ogawa, Y., Ido, K., & Ninomiya, K. (2013b). Patterns of fluorescence associated with citrus peel defects. Engineering in Agriculture, Environment and Food, 6, 54–60.Momin, A., Kuramoto, M., Kondo, N., Ido, K., Ogawa, Y., Shiigi, T., & Ahmad, U. (2013a). Identification of UV-fluorescence components for detecting peel defects of lemon and yuzu using machine vision. Engineering in Agriculture, Environment and Food, 6, 165–171.Morgan, S. P., & Stockford, I. M. (2003). Surface-reflection elimination in polarization imaging of superficial tissue. Optics Letters, 28, 114–116.Niphadkar, N. P., Burks, T. F., Qin, J., & Ritenour, M. (2013b). Edge effect compensation for citrus canker lesion detection due to light source variation—a hyperspectral imaging application. Agricultural Engineering International: CIGR Journal, 15, 314–327.Niphadkar, N. P., Burks, T. F., Qin, J. W., & Ritenour, M. A. (2013a). Estimation of citrus canker lesion size using hyperspectral reflectance imaging. International Journal of Agricultural and Biological Engineering, 6, 41–51.Obenland, D., Margosan, D., Smilanick, J. L., & Mackey, B. (2010). Ultraviolet fluorescence to identify navel oranges with poor peel quality and decay. HortTechnology, 20, 991–995.Ogawa, Y., Abdul, M. M., Kuramoto, M., Kohno, Y., Shiigi, T., Yamamoto, K., & Kondo, K. (2011). Rotten part detection on citrus fruit surfaces by use of fluorescent images. The Review of Laser Engineering, 394, 255–261.Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66(2), 201–208.Omid, M., Khojastehnazhand, M., & Tabatabaeefar, A. (2010). Estimating volume and mass of citrus fruits by image processing technique. Journal of Food Engineering, 100, 315–321.Ottavian, M., Barolo, M., & GarcĂ­a-Muñoz, S. (2013). Maintenance of machine vision systems for product quality assessment. Part I. Addressing changes in lighting conditions. Industrial & Engineering Chemistry Research, 52, 12309–12318.Ottavian, M., Barolo, M., & GarcĂ­a-Muñoz, S. (2014). Maintenance of machine vision systems for product quality assessment. Part II. Addressing camera replacement. Industrial & Engineering Chemistry Research, 53, 1529–1536.Palou, L. (2014). Penicillium digitatum, Penicillium italicum (green mold, blue mold). In S. Bautista-Baños (Ed.), Postharvest decay. Control strategies. London: Elsevier.Palou, L., Smilanick, J. L., Montesinos-Herrero, C., Valencia-Chamorro, S., & PĂ©rez-Gago, M. B. (2011). Novel approaches for postharvest preservation of fresh citrus fruits. In Slaker (Ed.), Citrus fruits: properties, consumption and nutrition. New York: Nova Science Publishers, Inc..Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: a systematic review of research. Journal of Sensors, Open Access Article ID 195308.Pourreza, A., Lee, W. S., Ehsani, R., Schueller, J. K., & Raveh, E. (2015a). An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture, 110, 221–232.Pourreza, A., Lee, W. S., Etxeberria, E., & Banerjee, A. (2015b). An evaluation of a vision based sensor performance in Huanglongbing disease identification. Biosystems Engineering, 130, 13–22.Qin, J., Burks, T. F., Kim, M. S., Chao, K., & Ritenour, M. A. (2008). Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety, 2(3), 168–177.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.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2011). Multispectral detection of citrus canker using hyperspectral band selection. Transactions of the ASABE, 54, 2331–2341.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2012). Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 108, 87–93.Sengupta, S., & Lee, W. S. (2014). Identification and determination of the number of immature green citrus fruit under different ambient light conditions.

    Robot Autonomy for Surgery

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