Identification of early decayed oranges using structured-illumination reflectance imaging coupled with fast demodulation and improved image processing algorithms
Effective detection of decayed oranges (Citrus genus) at the early stage is challenging because there are no or few visual symptoms on the infected fruit. Structured-illumination reflectance imaging (SIRI) has been proven effective for enhanced detection of subsurface defects in fruit. Amplitude com...
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| Veröffentlicht in: | Postharvest biology and technology Jg. 207; S. 112627 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.01.2024
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| ISSN: | 0925-5214, 1873-2356 |
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| Abstract | Effective detection of decayed oranges (Citrus genus) at the early stage is challenging because there are no or few visual symptoms on the infected fruit. Structured-illumination reflectance imaging (SIRI) has been proven effective for enhanced detection of subsurface defects in fruit. Amplitude component (AC) images retrieved from the original SIRI patterned images are useful for defect detection, but generally require acquiring three phase-shifted pattern images, which limits the imaging and detection speed. Moreover, the AC images may also suffer from noticeable uneven brightness due to fruit curvature, which affects the identification of decayed areas. This study was therefore aimed to explore faster image demodulation, enhancement and processing algorithms, based on a specially developed SIRI technology, for effective identification of early decayed oranges. Pattern images were acquired, using three phase-shifted sinusoidal illumination patterns at the wavelength of 810 nm and a spatial frequency of 0.20 cycles mm−1, from the orange samples infected with Penicillium digitatum fungus, the most serious and devastating pathogen for orange fruit. Two-dimensional spiral phase transform was used to obtain AC images from one or two pattern images. The acquired AC images were then processed by using simple brightness adjustment and integral image-based fast average filtering for brightness correction, and improved watershed algorithm and global threshold for segmentation of decayed areas. Three different combinations of these image processing procedures for single and two pattern images were proposed to distinguish decayed oranges from sound ones. The three methodologies all achieved high overall identification rates of 97.5%, 95.0% and 95.3%, when the stem-end effect was also considered. This study showed that accurate detection of early decayed orange fruit can be achieved by using one or two phase-shifted pattern images, which would be beneficial for real-time implementation of the technique.
•Pattern images were acquired from sound and early decayed orange by a SIRI system.•Improved image demodulation, enhancement and segmentation methods were proposed.•Three image processing strategies were proposed and showed superior detection results.•This study represents an important step towards fast detection of early decayed oranges. |
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| AbstractList | Effective detection of decayed oranges (Citrus genus) at the early stage is challenging because there are no or few visual symptoms on the infected fruit. Structured-illumination reflectance imaging (SIRI) has been proven effective for enhanced detection of subsurface defects in fruit. Amplitude component (AC) images retrieved from the original SIRI patterned images are useful for defect detection, but generally require acquiring three phase-shifted pattern images, which limits the imaging and detection speed. Moreover, the AC images may also suffer from noticeable uneven brightness due to fruit curvature, which affects the identification of decayed areas. This study was therefore aimed to explore faster image demodulation, enhancement and processing algorithms, based on a specially developed SIRI technology, for effective identification of early decayed oranges. Pattern images were acquired, using three phase-shifted sinusoidal illumination patterns at the wavelength of 810 nm and a spatial frequency of 0.20 cycles mm⁻¹, from the orange samples infected with Penicillium digitatum fungus, the most serious and devastating pathogen for orange fruit. Two-dimensional spiral phase transform was used to obtain AC images from one or two pattern images. The acquired AC images were then processed by using simple brightness adjustment and integral image-based fast average filtering for brightness correction, and improved watershed algorithm and global threshold for segmentation of decayed areas. Three different combinations of these image processing procedures for single and two pattern images were proposed to distinguish decayed oranges from sound ones. The three methodologies all achieved high overall identification rates of 97.5%, 95.0% and 95.3%, when the stem-end effect was also considered. This study showed that accurate detection of early decayed orange fruit can be achieved by using one or two phase-shifted pattern images, which would be beneficial for real-time implementation of the technique. Effective detection of decayed oranges (Citrus genus) at the early stage is challenging because there are no or few visual symptoms on the infected fruit. Structured-illumination reflectance imaging (SIRI) has been proven effective for enhanced detection of subsurface defects in fruit. Amplitude component (AC) images retrieved from the original SIRI patterned images are useful for defect detection, but generally require acquiring three phase-shifted pattern images, which limits the imaging and detection speed. Moreover, the AC images may also suffer from noticeable uneven brightness due to fruit curvature, which affects the identification of decayed areas. This study was therefore aimed to explore faster image demodulation, enhancement and processing algorithms, based on a specially developed SIRI technology, for effective identification of early decayed oranges. Pattern images were acquired, using three phase-shifted sinusoidal illumination patterns at the wavelength of 810 nm and a spatial frequency of 0.20 cycles mm−1, from the orange samples infected with Penicillium digitatum fungus, the most serious and devastating pathogen for orange fruit. Two-dimensional spiral phase transform was used to obtain AC images from one or two pattern images. The acquired AC images were then processed by using simple brightness adjustment and integral image-based fast average filtering for brightness correction, and improved watershed algorithm and global threshold for segmentation of decayed areas. Three different combinations of these image processing procedures for single and two pattern images were proposed to distinguish decayed oranges from sound ones. The three methodologies all achieved high overall identification rates of 97.5%, 95.0% and 95.3%, when the stem-end effect was also considered. This study showed that accurate detection of early decayed orange fruit can be achieved by using one or two phase-shifted pattern images, which would be beneficial for real-time implementation of the technique. •Pattern images were acquired from sound and early decayed orange by a SIRI system.•Improved image demodulation, enhancement and segmentation methods were proposed.•Three image processing strategies were proposed and showed superior detection results.•This study represents an important step towards fast detection of early decayed oranges. |
| ArticleNumber | 112627 |
| Author | Lu, Yuzhen Li, Jiangbo Lu, Renfu |
| Author_xml | – sequence: 1 givenname: Jiangbo surname: Li fullname: Li, Jiangbo organization: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China – sequence: 2 givenname: Yuzhen surname: Lu fullname: Lu, Yuzhen organization: Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA – sequence: 3 givenname: Renfu surname: Lu fullname: Lu, Renfu email: renfu.lu@usda.gov organization: United States Department of Agriculture Agricultural Research Service, East Lansing, MI 48824, USA |
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| Cites_doi | 10.1109/TMI.2004.824224 10.1016/j.tifs.2021.12.021 10.1016/j.postharvbio.2022.112162 10.1016/j.jfoodeng.2015.01.004 10.1201/b20220-4 10.1016/j.compag.2016.07.012 10.1016/j.jfoodeng.2007.06.036 10.1117/12.159642 10.1002/jsfa.8865 10.1364/JOSAA.18.001862 10.1016/j.postharvbio.2019.111071 10.1109/TSMC.1979.4310076 10.1016/j.postharvbio.2013.02.011 10.13031/trans.12158 10.1016/j.postharvbio.2019.110986 10.1016/j.chemolab.2016.05.005 10.1016/j.compag.2016.07.016 10.1364/OL.37.000443 10.1016/j.compag.2018.07.025 10.1016/j.postharvbio.2021.111624 10.13031/trans.12243 10.17221/55/2016-PPS 10.1016/j.biosystemseng.2019.01.014 10.1016/j.postharvbio.2013.02.016 10.1094/PHYTO-97-11-1491 10.1016/j.compag.2017.03.021 10.1016/j.jfoodeng.2007.03.027 10.1007/s11947-012-0951-1 10.1016/j.postharvbio.2016.02.005 10.1016/j.postharvbio.2019.01.011 10.1016/B978-0-12-411552-1.00002-8 10.1016/j.patcog.2019.01.026 10.1111/jam.15769 |
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| Keywords | Brightness transformation Defect segmentation Citrus decay Image enhancement Classification |
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| References | Grau, Mewes, Alcaniz, Kikinis, Warfield (bib9) 2004; 23 Macarisin, Cohen, Eick, Rafael, Belausov, Wisniewski, Droby (bib28) 2007; 97 Li, Huang, Tian, Wang, Fan, Zhao (bib13) 2016; 127 Lu, Lu (bib23) 2018; 152 Mohammadi, Tozlu, Kotan, Kotan (bib29) 2017; 53 Li, Rao, Wang, Wu, Ying, Y.B (bib12) 2013; 82 Li, Lu, Lu (bib15) 2018; 61 Vargas, Quiroga, Sorzano, Estrada, Carazo (bib40) 2012; 37 Lu, R., 2016. Chapter 3 - Theory of light transfer in food and biological materials, in: Lu, R., (Ed.), Light Scattering Technology for Food Property, Quality and Safety Assessment. CRC Press, Taylor & Francis Group, New York, pp. 43–78. Ghooshkhaneh, Golzarian, Mamarabadi (bib5) 2018; 98 Li, Zhang, Li, Wang, Zhang, Zhan, Jiang (bib14) 2019; 158 Larkin, Bone, Oldfield (bib10) 2001; 18 Li, Lu, Lu (bib11) 2023; 196 Lu, Lu (bib24) 2018; 2018 Gómez-Sanchis, Moltó, Camps-Valls, Gómez-Chova, Aleixos, Blasco (bib7) 2008; 85 Du, Li, Tian, Cheng, Long (bib2) 2022 Lu, Li, Lu (bib20) 2016; 117 Lorente, Escandell-Montero, Cubero, Gomez-Sanchis, Blasco (bib16) 2015; 163 Lu, Lu, Zhang (bib26) 2021; 180 Lorente, Zude, Idler, Gomez-Sanchis, Blasco (bib17) 2015; 154 Momin, Kondo, Kuramoto, Ogawa, Yamamoto, Shiigi, T (bib30) 2012; 5 Gomez-Sanchis, Blasco, Soria-Olivas, Lorente, Escandell-Montero, Martinez-Martinez, Martinez-Sober, Aleixos (bib6) 2013; 82 Palou, L., 2014. Penicillium digitatum, Penicillium italicum (green mold, blue mold). Pages 45–102 in: Postharvest Decay: Control Strategies. S. Bautista Banos, ed. Academic Press, Elsevier, London, UK. Eckert, Eaks (bib3) 1989 Lu, Li, Lu (bib21) 2016; 127 Mahanti, Pandiselvam, Kothakota, Ishwarya, Chakraborty, Kumar, Cozzolino (bib27) 2022; 120 Otsu (bib31) 1979; 9 Lu, Lu (bib25) 2019; 180 Rong, Ying, Rao (bib34) 2017; 138 Qureshi, Uzair, Khurshid, Yan (bib33) 2019; 90 Sun, Lu, Lu, Tu, Pan (bib38) 2019; 151 Lu, Lu (bib22) 2017; 60 Tian, Fan, Huang, Wan, Li (bib39) 2020; 161 Folch-Fortuny, Prats-Montalban, Cubero, Blasco, Ferrer (bib4) 2016; 156 Schreiber, Bruning (bib36) 2007 Soille (bib37) 2004 Rivest, Soille, Beucher (bib35) 1993; 2 Lorente, Blasco, Serrano, Soria-Olivas, Aleixos, Gomez-Sanchis (bib18) 2013; 6 Blasco, Aleixos, Gomez, Molto (bib1) 2007; 83 Gonzalez, R.C., Woods, R.E., 2010. Digital Image Processing (third ed). Publishing house of electronics industry, Beijing, China. Lorente (10.1016/j.postharvbio.2023.112627_bib17) 2015; 154 Lu (10.1016/j.postharvbio.2023.112627_bib24) 2018; 2018 Mahanti (10.1016/j.postharvbio.2023.112627_bib27) 2022; 120 Li (10.1016/j.postharvbio.2023.112627_bib11) 2023; 196 Lu (10.1016/j.postharvbio.2023.112627_bib25) 2019; 180 Schreiber (10.1016/j.postharvbio.2023.112627_bib36) 2007 Lorente (10.1016/j.postharvbio.2023.112627_bib18) 2013; 6 Soille (10.1016/j.postharvbio.2023.112627_bib37) 2004 Li (10.1016/j.postharvbio.2023.112627_bib14) 2019; 158 Otsu (10.1016/j.postharvbio.2023.112627_bib31) 1979; 9 Macarisin (10.1016/j.postharvbio.2023.112627_bib28) 2007; 97 Lorente (10.1016/j.postharvbio.2023.112627_bib16) 2015; 163 Rong (10.1016/j.postharvbio.2023.112627_bib34) 2017; 138 Lu (10.1016/j.postharvbio.2023.112627_bib20) 2016; 117 Momin (10.1016/j.postharvbio.2023.112627_bib30) 2012; 5 Folch-Fortuny (10.1016/j.postharvbio.2023.112627_bib4) 2016; 156 Li (10.1016/j.postharvbio.2023.112627_bib13) 2016; 127 Qureshi (10.1016/j.postharvbio.2023.112627_bib33) 2019; 90 Du (10.1016/j.postharvbio.2023.112627_bib2) 2022 10.1016/j.postharvbio.2023.112627_bib8 Gómez-Sanchis (10.1016/j.postharvbio.2023.112627_bib7) 2008; 85 Rivest (10.1016/j.postharvbio.2023.112627_bib35) 1993; 2 Lu (10.1016/j.postharvbio.2023.112627_bib26) 2021; 180 Tian (10.1016/j.postharvbio.2023.112627_bib39) 2020; 161 Lu (10.1016/j.postharvbio.2023.112627_bib21) 2016; 127 Lu (10.1016/j.postharvbio.2023.112627_bib22) 2017; 60 Lu (10.1016/j.postharvbio.2023.112627_bib23) 2018; 152 Gomez-Sanchis (10.1016/j.postharvbio.2023.112627_bib6) 2013; 82 Eckert (10.1016/j.postharvbio.2023.112627_bib3) 1989 Grau (10.1016/j.postharvbio.2023.112627_bib9) 2004; 23 Mohammadi (10.1016/j.postharvbio.2023.112627_bib29) 2017; 53 Li (10.1016/j.postharvbio.2023.112627_bib12) 2013; 82 Li (10.1016/j.postharvbio.2023.112627_bib15) 2018; 61 Ghooshkhaneh (10.1016/j.postharvbio.2023.112627_bib5) 2018; 98 Vargas (10.1016/j.postharvbio.2023.112627_bib40) 2012; 37 10.1016/j.postharvbio.2023.112627_bib19 Sun (10.1016/j.postharvbio.2023.112627_bib38) 2019; 151 Blasco (10.1016/j.postharvbio.2023.112627_bib1) 2007; 83 10.1016/j.postharvbio.2023.112627_bib32 Larkin (10.1016/j.postharvbio.2023.112627_bib10) 2001; 18 |
| References_xml | – volume: 120 start-page: 418 year: 2022 end-page: 438 ident: bib27 article-title: Emerging non-destructive imaging techniques for fruit damage detection: image processing and analysis publication-title: Trends Food Sci. Technol. – volume: 156 start-page: 241 year: 2016 end-page: 248 ident: bib4 article-title: VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits publication-title: Chemom. Intell. Lab. Syst. – start-page: 547 year: 2007 end-page: 666 ident: bib36 article-title: Phase shifting interferometry publication-title: Opitcal Shop Testing – volume: 151 start-page: 68 year: 2019 end-page: 78 ident: bib38 article-title: Detection of early decay in peaches by structured-illumination reflectance imaging publication-title: Postharvest Biol. Technol. – volume: 82 start-page: 76 year: 2013 end-page: 86 ident: bib6 article-title: 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 publication-title: Postharvest Biol. Technol. – volume: 61 start-page: 809 year: 2018 end-page: 819 ident: bib15 article-title: Structured illumination reflectance imaging for enhanced detection of subsurface tissue bruising in apples publication-title: Trans. ASABE, 2018 – volume: 85 start-page: 191 year: 2008 end-page: 200 ident: bib7 article-title: Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits publication-title: J. Food Eng. – volume: 9 start-page: 62 year: 1979 end-page: 66 ident: bib31 article-title: Threshold selection method from gray-level histograms publication-title: IEEE Trans. Syst., Man Cybern. – volume: 154 start-page: 76 year: 2015 end-page: 85 ident: bib17 article-title: Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model publication-title: J. Food Eng. – volume: 97 start-page: 1491 year: 2007 end-page: 1500 ident: bib28 article-title: Penicillium digitatum suppresses production of hydrogen peroxide in host tissue during infection of citrus fruit publication-title: Phytopathology – volume: 180 start-page: 1 year: 2019 end-page: 15 ident: bib25 article-title: Structured-illumination reflectance imaging for the detection of defects in fruit: analysis of resolution, contrast and depth-resolving features publication-title: Biosyst. Eng. – volume: 37 start-page: 443 year: 2012 end-page: 445 ident: bib40 article-title: Two-step demodulation based on the Gram-Schmidt orthonormalization method publication-title: Opt. Lett. – volume: 83 start-page: 384 year: 2007 end-page: 393 ident: bib1 article-title: Citrus sorting by identification of the most common defects using multispectral computer vision publication-title: J. Food Eng. – start-page: 179 year: 1989 end-page: 260 ident: bib3 article-title: Postharvest disorders and diseases of citrus fruits publication-title: The Citrus Industry – volume: 53 start-page: 134 year: 2017 end-page: 143 ident: bib29 article-title: Potential of some bacteria for biological control of postharvest citrus green mould caused by penicillium digitatum publication-title: Plant Prot. Sci. – volume: 82 start-page: 59 year: 2013 end-page: 69 ident: bib12 article-title: Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods publication-title: Postharvest Biol. Technol. – volume: 6 start-page: 3613 year: 2013 end-page: 3619 ident: bib18 article-title: Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images publication-title: Food Bioprocess Technol. – volume: 60 start-page: 1379 year: 2017 end-page: 1389 ident: bib22 article-title: Development of a multispectral structured illumination reflectance imaging (SIRI) system and its application to bruise detection of apples publication-title: Trans. ASABE – volume: 127 start-page: 582 year: 2016 end-page: 592 ident: bib13 article-title: Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging publication-title: Comput. Electron. Agric. – volume: 117 start-page: 89 year: 2016 end-page: 93 ident: bib20 article-title: Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples publication-title: Postharvest Biol. Technol. – volume: 23 start-page: 447 year: 2004 end-page: 458 ident: bib9 article-title: Improved watershed transform for medical image segmentation using prior information publication-title: IEEE Trans. Med. Imaging – volume: 98 start-page: 3542 year: 2018 end-page: 3550 ident: bib5 article-title: Detection and classification of citrus green mold caused by publication-title: J. Sci. Food Agric. – volume: 163 start-page: 17 year: 2015 end-page: 24 ident: bib16 article-title: Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit – reference: Palou, L., 2014. Penicillium digitatum, Penicillium italicum (green mold, blue mold). Pages 45–102 in: Postharvest Decay: Control Strategies. S. Bautista Banos, ed. Academic Press, Elsevier, London, UK. – volume: 158 year: 2019 ident: bib14 article-title: Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method publication-title: Postharvest Biol. Technol. – year: 2022 ident: bib2 article-title: Natamycin as a safe food additive to control postharvest green mould and sour rot in citrus publication-title: J. Appl. Microbiol. – volume: 2 start-page: 326 year: 1993 end-page: 336 ident: bib35 article-title: Morphological gradients publication-title: J. Electron. Imaging – volume: 152 start-page: 314 year: 2018 end-page: 323 ident: bib23 article-title: Fast Bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection publication-title: Comput. Electron. Agric. – volume: 90 start-page: 12 year: 2019 end-page: 22 ident: bib33 article-title: Hyperspectral document image processing: applications, challenges and future prospects publication-title: Pattern Recognit. – reference: Gonzalez, R.C., Woods, R.E., 2010. Digital Image Processing (third ed). Publishing house of electronics industry, Beijing, China. – volume: 138 start-page: 48 year: 2017 end-page: 59 ident: bib34 article-title: Embedded vision detection of defective orange by fast adaptive lightness correction algorithm publication-title: Comput. Electron. Agric. – volume: 2018 year: 2018 ident: bib24 article-title: Detection of surface and subsurface defects of apples using structured-illumination reflectance imaging with machine learning algorithm. 2018 ASABE Annual International Meeting, etroit publication-title: Mich., July 29-August 1 – reference: Lu, R., 2016. Chapter 3 - Theory of light transfer in food and biological materials, in: Lu, R., (Ed.), Light Scattering Technology for Food Property, Quality and Safety Assessment. CRC Press, Taylor & Francis Group, New York, pp. 43–78. – volume: 18 start-page: 1862 year: 2001 end-page: 1870 ident: bib10 article-title: Natural demodulation of two dimensional fringe patterns. I. General background of the spiral phase quadrature transform publication-title: J. Opt. Soc. Am., A. Opt., Image Sci., Vis. – volume: 180 year: 2021 ident: bib26 article-title: Detection of subsurface bruising in fresh pickling cucumbers using structured-illumination reflectance imaging publication-title: Postharvest Biol. Technol. – volume: 5 start-page: 126 year: 2012 end-page: 132 ident: bib30 article-title: Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV–VIS spectra publication-title: Eng. Agric. Environ. Food – volume: 127 start-page: 652 year: 2016 end-page: 658 ident: bib21 article-title: Fast demodulation of pattern images by spiral phase transform in structured-illumination reflectance imaging for detection of bruises in apples publication-title: Comput. Electron. Agric. – volume: 196 year: 2023 ident: bib11 article-title: Detection of early decay in navel oranges by structured-illumination reflectance imaging combined with image enhancement and segmentation publication-title: Postharvest Biol. Technol. – year: 2004 ident: bib37 article-title: Morphological image analysis: Principles and applications, 2nd ed – volume: 161 year: 2020 ident: bib39 article-title: Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms publication-title: Postharvest Biol. Technol. – volume: 23 start-page: 447 issue: 4 year: 2004 ident: 10.1016/j.postharvbio.2023.112627_bib9 article-title: Improved watershed transform for medical image segmentation using prior information publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2004.824224 – start-page: 179 year: 1989 ident: 10.1016/j.postharvbio.2023.112627_bib3 article-title: Postharvest disorders and diseases of citrus fruits – volume: 120 start-page: 418 year: 2022 ident: 10.1016/j.postharvbio.2023.112627_bib27 article-title: Emerging non-destructive imaging techniques for fruit damage detection: image processing and analysis publication-title: Trends Food Sci. Technol. doi: 10.1016/j.tifs.2021.12.021 – volume: 196 year: 2023 ident: 10.1016/j.postharvbio.2023.112627_bib11 article-title: Detection of early decay in navel oranges by structured-illumination reflectance imaging combined with image enhancement and segmentation publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2022.112162 – volume: 154 start-page: 76 year: 2015 ident: 10.1016/j.postharvbio.2023.112627_bib17 article-title: Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2015.01.004 – ident: 10.1016/j.postharvbio.2023.112627_bib19 doi: 10.1201/b20220-4 – volume: 127 start-page: 652 year: 2016 ident: 10.1016/j.postharvbio.2023.112627_bib21 article-title: Fast demodulation of pattern images by spiral phase transform in structured-illumination reflectance imaging for detection of bruises in apples publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.07.012 – volume: 85 start-page: 191 issue: 2 year: 2008 ident: 10.1016/j.postharvbio.2023.112627_bib7 article-title: Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2007.06.036 – volume: 2 start-page: 326 issue: 4 year: 1993 ident: 10.1016/j.postharvbio.2023.112627_bib35 article-title: Morphological gradients publication-title: J. Electron. Imaging doi: 10.1117/12.159642 – volume: 98 start-page: 3542 issue: 9 year: 2018 ident: 10.1016/j.postharvbio.2023.112627_bib5 article-title: Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging publication-title: J. Sci. Food Agric. doi: 10.1002/jsfa.8865 – volume: 18 start-page: 1862 issue: 8 year: 2001 ident: 10.1016/j.postharvbio.2023.112627_bib10 article-title: Natural demodulation of two dimensional fringe patterns. I. General background of the spiral phase quadrature transform publication-title: J. Opt. Soc. Am., A. Opt., Image Sci., Vis. doi: 10.1364/JOSAA.18.001862 – volume: 161 year: 2020 ident: 10.1016/j.postharvbio.2023.112627_bib39 article-title: Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2019.111071 – volume: 9 start-page: 62 year: 1979 ident: 10.1016/j.postharvbio.2023.112627_bib31 article-title: Threshold selection method from gray-level histograms publication-title: IEEE Trans. Syst., Man Cybern. doi: 10.1109/TSMC.1979.4310076 – volume: 82 start-page: 76 year: 2013 ident: 10.1016/j.postharvbio.2023.112627_bib6 article-title: 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 publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2013.02.011 – volume: 60 start-page: 1379 issue: 4 year: 2017 ident: 10.1016/j.postharvbio.2023.112627_bib22 article-title: Development of a multispectral structured illumination reflectance imaging (SIRI) system and its application to bruise detection of apples publication-title: Trans. ASABE doi: 10.13031/trans.12158 – volume: 158 year: 2019 ident: 10.1016/j.postharvbio.2023.112627_bib14 article-title: Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2019.110986 – volume: 156 start-page: 241 year: 2016 ident: 10.1016/j.postharvbio.2023.112627_bib4 article-title: VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2016.05.005 – volume: 127 start-page: 582 year: 2016 ident: 10.1016/j.postharvbio.2023.112627_bib13 article-title: Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.07.016 – volume: 37 start-page: 443 issue: 3 year: 2012 ident: 10.1016/j.postharvbio.2023.112627_bib40 article-title: Two-step demodulation based on the Gram-Schmidt orthonormalization method publication-title: Opt. Lett. doi: 10.1364/OL.37.000443 – volume: 152 start-page: 314 year: 2018 ident: 10.1016/j.postharvbio.2023.112627_bib23 article-title: Fast Bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.07.025 – volume: 163 start-page: 17 year: 2015 ident: 10.1016/j.postharvbio.2023.112627_bib16 article-title: Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit – volume: 180 year: 2021 ident: 10.1016/j.postharvbio.2023.112627_bib26 article-title: Detection of subsurface bruising in fresh pickling cucumbers using structured-illumination reflectance imaging publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2021.111624 – year: 2004 ident: 10.1016/j.postharvbio.2023.112627_bib37 – ident: 10.1016/j.postharvbio.2023.112627_bib8 – volume: 61 start-page: 809 issue: 3 year: 2018 ident: 10.1016/j.postharvbio.2023.112627_bib15 article-title: Structured illumination reflectance imaging for enhanced detection of subsurface tissue bruising in apples publication-title: Trans. ASABE, 2018 doi: 10.13031/trans.12243 – volume: 53 start-page: 134 issue: 3 year: 2017 ident: 10.1016/j.postharvbio.2023.112627_bib29 article-title: Potential of some bacteria for biological control of postharvest citrus green mould caused by penicillium digitatum publication-title: Plant Prot. Sci. doi: 10.17221/55/2016-PPS – volume: 2018 year: 2018 ident: 10.1016/j.postharvbio.2023.112627_bib24 article-title: Detection of surface and subsurface defects of apples using structured-illumination reflectance imaging with machine learning algorithm. 2018 ASABE Annual International Meeting, etroit publication-title: Mich., July 29-August 1 – volume: 180 start-page: 1 year: 2019 ident: 10.1016/j.postharvbio.2023.112627_bib25 article-title: Structured-illumination reflectance imaging for the detection of defects in fruit: analysis of resolution, contrast and depth-resolving features publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2019.01.014 – volume: 82 start-page: 59 year: 2013 ident: 10.1016/j.postharvbio.2023.112627_bib12 article-title: Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2013.02.016 – volume: 97 start-page: 1491 issue: 11 year: 2007 ident: 10.1016/j.postharvbio.2023.112627_bib28 article-title: Penicillium digitatum suppresses production of hydrogen peroxide in host tissue during infection of citrus fruit publication-title: Phytopathology doi: 10.1094/PHYTO-97-11-1491 – volume: 138 start-page: 48 year: 2017 ident: 10.1016/j.postharvbio.2023.112627_bib34 article-title: Embedded vision detection of defective orange by fast adaptive lightness correction algorithm publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.03.021 – volume: 5 start-page: 126 issue: 4 year: 2012 ident: 10.1016/j.postharvbio.2023.112627_bib30 article-title: Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV–VIS spectra publication-title: Eng. Agric. Environ. Food – volume: 83 start-page: 384 issue: 3 year: 2007 ident: 10.1016/j.postharvbio.2023.112627_bib1 article-title: Citrus sorting by identification of the most common defects using multispectral computer vision publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2007.03.027 – volume: 6 start-page: 3613 issue: 12 year: 2013 ident: 10.1016/j.postharvbio.2023.112627_bib18 article-title: Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images publication-title: Food Bioprocess Technol. doi: 10.1007/s11947-012-0951-1 – volume: 117 start-page: 89 year: 2016 ident: 10.1016/j.postharvbio.2023.112627_bib20 article-title: Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2016.02.005 – volume: 151 start-page: 68 year: 2019 ident: 10.1016/j.postharvbio.2023.112627_bib38 article-title: Detection of early decay in peaches by structured-illumination reflectance imaging publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2019.01.011 – start-page: 547 year: 2007 ident: 10.1016/j.postharvbio.2023.112627_bib36 article-title: Phase shifting interferometry – ident: 10.1016/j.postharvbio.2023.112627_bib32 doi: 10.1016/B978-0-12-411552-1.00002-8 – volume: 90 start-page: 12 year: 2019 ident: 10.1016/j.postharvbio.2023.112627_bib33 article-title: Hyperspectral document image processing: applications, challenges and future prospects publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.01.026 – year: 2022 ident: 10.1016/j.postharvbio.2023.112627_bib2 article-title: Natamycin as a safe food additive to control postharvest green mould and sour rot in citrus publication-title: J. Appl. Microbiol. doi: 10.1111/jam.15769 |
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| SubjectTerms | algorithms Brightness transformation Citrus Citrus decay Classification Defect segmentation fruits fungi genus Image enhancement lighting pathogens Penicillium digitatum reflectance wavelengths |
| Title | Identification of early decayed oranges using structured-illumination reflectance imaging coupled with fast demodulation and improved image processing algorithms |
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