Fast detection of the early decay in oranges using visible-LED structured-illumination imaging combined with spiral phase transform and feature-based classification model

Zhonglei Cai, Chanjun Sun, Yizhi Zhang, Ruiyao Shi, Junyi Zhang, Hailiang Zhang, Jiangbo Li

Abstract


The early decay of citrus can cause economic and serious food safety issues. The early decayed area has no obvious visual characteristics, making effective detection of this damage very difficult for the citrus industry. This study constructed a new detection system based on visible-light emitting diode (LED) structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform (SPT) algorithm for the early detection of decayed oranges. Each sample obtained three phase-shifting pattern images with phase shifts of −2π/3, 0, and 2π/3 at a spatial frequency of 0.25 cycles/mm. Three strategies (i.e., the conventional three-phase-shifting method, 2-phase SPT, and 1-phase SPT) were used to demodulate the original patterned images to recover the direct component (DC) and amplitude component (AC) images. The partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) classification models were established based on the texture features of DC, AC, and RT (i.e. the ratio of AC to DC) images. Then, the random frog (RF) algorithm was used to simplify the optimal full-featured model. Finally, the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1% for all tested samples. This study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges.
Keywords: oranges, early decay detection, structured-illumination imaging, spiral phase transform, classification model
DOI: 10.25165/j.ijabe.20241703.8614

Citation: Cai Z L, Sun C J, Zhang Y Z, Shi R Y, Zhang J Y, Zhang H L, et al. Fast detection of the early decay in oranges using visible-LED structured-illumination imaging combined with spiral phase transform and feature-based classification model. Int J Agric & Biol Eng, 2024; 17(3): 185-192.

Keywords


oranges, early decay detection, structured-illumination imaging, spiral phase transform, classification model

Full Text:

PDF

References


Zhang H L, Chen Y, Liu X M, Huang Y F, Zhan B S, Luo W. Identification of common skin defects and classification of early decayed citrus using hyperspectral imaging technique. Food Analytical Methods, 2021; 14（6）: 1176–1193.

Cubero S, Lee W S, Aleixos N, Albert F, Blasco J. Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest-a review. Food Bioprocess Technology, 2016; 9（10）: 1623–1639.

Li J B, Huang W Q, Tian X, Wang C P, Fan S X, Zhao C J. Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging. Computers and Electronics in Agriculture, 2016; 127: 582–592.

Golzarian M R, Ghooshkhaneh N G, Mamarabadi M. Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging. Journal of the Science of Food and Agriculture, 2018; 98（9）: 3542–3550.

Lorente D, Zude M, Regen C, Palou L, Gomez-Sanchis J, Blasco J. Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology, 2013; 86: 424–430.

Lorente D, Zude M, Idler C, Gómez-Sanchis J, Blasco J. Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. Journal of Food Engineering, 2015; 154: 76–85.

Tian X, Fan S X, Huang W Q, Wang Z L, Li J B. 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, 2020; 161: 111071.

Cai Z L, Huang W Q, Wang Q Y, Li J B. Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models. Frontiers in Plant Science, 2022; 13: 952942.

Mei M W, Li J B. An overview on optical non-destructive detection of bruises in fruit: technology, method, application, challenge and trend. Computers and Electronics in Agriculture, 2023; 213: 108195.

Lorente D, Escandell-Montero P, Cubero S, Gómez-Sanchis J, Blasco J. Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit. Journal of Food Engineering, 2015; 163: 17–24.

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

Slaughter D C, Obenland D M, Thompson J F, Arpaia M L, Margosan D A. Non-destructive freeze damage detection in oranges using machine vision and ul-traviolet fluorescence. Postharvest Biology and Technology, 2008; 48: 341–346.

Obenland D, Margosan D, Smilanick J L. Ultraviolet fluorescence to identify navel oranges with poor peel quality and decay. HortTechnology, 2010; 20（6）: 991–995.

Luo W, Fan G Z, Tian P, Dong W T, Zhang H L, Zhan B S. Spectrum classification of citrus tissues infected by fungi and multispectral image identification of early rotten oranges. Spectrochim Acta Part A: Molecular and Biomoleculr Spectroscopy, 2022; 279: 121412.

Fan S X, Liang, X T, Huang W Q, Zhang V J, Pang Q, He X, et al. Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network. Computers and Electronics in Agriculture, 193: 106715.

Lu Y Z, Li R, Lu R F. Structured-illumination reflectance imaging （SIRI） for enhanced detection of fresh bruises in apples. Postharvest Biology and Technology, 2016; 117: 89–93.

Cai Z L, Sun C J, Zhang H L, Zhang Y Z, Li J B. Developing universal classification models for the detection of early decayed citrus by structured-illumination reflectance imaging coupling with deep learning methods. Postharvest Biology and Technology, 2024; 210: 112788.

Sun Y, Lu R F, Lu Y Z, Tu K, Pan LQ. Detection of early decay in peaches by structured illumination reflectance imaging. Postharvest Biology and Technol, 2019; 151: 68–78.

Lu Y Z, Lu R F, Zhang Z. Detection of subsurface bruising in fresh pickling cucumbers using structured-illumination reflectance imaging. Postharvest Biology and Technology, 2021; 180: 111624.

Li J B, Lu Y Z, Lu R F. Detection of early decay in navel oranges by structured-illumination reflectance imaging combined with image enhancement and segmentation. Postharvest Biology and Technology, 2023; 196: 112162.

Barmore C R, Brown G E. Polygalacturonase from citrus fruit infected with Penicillium italicum. Phytopathology, 1981; 71: 328–331

Baranowski P, Mazurek W, Pastuszka-Wozniak J. Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data. Postharvest Biology and Technology, 2013; 86: 249–258.

Sun Y, Yuan M, Liu X Y, Su M, Wang L L, Zeng Y Z, et al. A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity. Spectrochim Acta A: Molecular and Biomolecular Spectroscopy, 2021; 258: 119870.

Lu Y Z, Lu R F. Structured-illumination reflectance imaging for the detection of defects in fruit: Analysis of resolution, contrast and depth-resolving features. Biosystems Engineering, 2019; 180: 1–15.

Li J B, Lu Y Z, Lu R F. Identification of early decayed oranges using structured-illumination reflectance imaging coupled with fast demodulation and improved image processing algorithms. Postharvest Biology and Technology, 2024; 207: 112627.

Larkin K G, Bone D J, Oldfield M A. Natural demodulation of two-dimensional fringe patterns. I. General background of the spiral phase quadrature transform. Journal of the Optical Society of America A, 2001; 18（8）: 1862–1870.

Lu Y Z, Li R, Lu R F. Fast demodulation of pattern images by spiral phase transform in structured-illumination reflectance imaging for detection of bruises in apples. Computers and Electronics in Agriculture, 2016; 127: 652–658.

Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973; SMC-3（6）: 610–621.

Hu J, Li D L, Duan Q L, Han, Y Q, Chen G F, Si X L. Fish species classification by color, texture and multi-class support vector machine using computer vision. Computers and Electronics in Agriculture, 2012; 88: 133–140.

Li H D, Xu Q S, Liang Y Z. Random frog: An efficient eversible jump Markov chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. Analytica Chimica Acta, 2012; 740: 20–26.

Wold S, Sjöström M, Eriksson L. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 2001; 58（2）: 109–130.

Zhang Y F, Wang Z L, Tian X, Yang X H, Cai Z L, Li J B. Online analysis of watercore apples by considering different speeds and orientations based on Vis/NIR full-transmittance spectroscopy. Infrared Physics & Technology, 2022; 122: 104090.

Li J B, Zhang R Y, Li J B, Wang Z L, Zhang H L, Zhan B S, et al. 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, 2019; 158: 110986.




Copyright (c) 2024 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office