Skip to main content
SpringerLink
Log in

Low-cost monochromatic uniform illumination HSI system for detection and classification of apple bruise

  • Original Paper
  • Published:
Journal of Food Measurement and Characterization Aims and scope Submit manuscript

Abstract

Hyperspectral imaging, as a non-destructive testing technique with the ability to acquire rich spatial and spectral information, is a potential tool for fruit bruise detection. However, the cost issue is one of the main reasons limiting the popularity of this technique. In addition, it is difficult to detect the entire surface of samples such as fruits due to the uncertainty of the damaged area. Furthermore, directional scattering on the target sample can lead to bright spots and shadows on the acquired hyperspectral image, which can affect the image quality and add additional preprocessing steps. Therefore, a monochromatic illumination model based on the combination of Xenon lamp and reflective grating is proposed. An experimental prototype of the system has realized 101 spectral channels in the 400–700 nm range. The sample is placed in an optical integrating sphere with rollers for indirect illumination to avoid spots and shadows during image acquisition, enabling multi-surface imaging. This prototype is then used to prepare hyperspectral datasets of sound apples and bruised apples. Models built using classic classification algorithms SVM (87.5%), k-NN (82.5%) AlexNet (95%), VGG16 (95%), ResNet (100%), and achieve effective results in tests. The results demonstrate that the HSI system we designed has exceptional performance in apple mechanical damage detection and classification, showing the advantages of good spectral resolution, low cost, and low thermal effect.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data will be made available on request.

References

  1. Z. Du, X. Zeng, X. Li, X. Ding, J. Cao, W. Jiang, Recent advances in imaging techniques for bruise detection in fruits and vegetables. Trends Food Sci. Technol. 99, 133–141 (2020). https://doi.org/10.1016/j.tifs.2020.02.024

    Article  CAS  Google Scholar 

  2. X. Zhu, G. Li, Rapid detection and visualization of slight bruise on apples using hyperspectral imaging. Int. J. Food Prop. 22(1), 1709–1719 (2019). https://doi.org/10.1080/10942912.2019.1669638

    Article  CAS  Google Scholar 

  3. N.K. Mahanti, R. Pandiselvam, A. Kothakota, S.P. Ishwarya, S.K. Chakraborty, M. Kumar, D. Cozzolino, Emerging non-destructive imaging techniques for fruit damage detection: image processing and analysis. Trends Food Sci. Technol. 120, 418–438 (2022). https://doi.org/10.1016/j.tifs.2021.12.021

    Article  CAS  Google Scholar 

  4. J. Blasco, S. Cubero, J. Gómez-Sanchís, P. Mira, E. Moltó, Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J. Food Eng. 90(1), 27–34 (2009). https://doi.org/10.1016/j.jfoodeng.2008.05.035

    Article  Google Scholar 

  5. G. ElMasry, S. Cubero, E. Moltó, J. Blasco, In-line sorting of irregular potatoes by using automated computer-based machine vision system. J. Food Eng. 112(1–2), 60–68 (2012). https://doi.org/10.1016/j.jfoodeng.2012.03.027

    Article  Google Scholar 

  6. U.-O. Dorj, M. Lee, S.-s Yun, An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput. Electron. Agric. 140, 103–112 (2017). https://doi.org/10.1016/j.compag.2017.05.019

    Article  Google Scholar 

  7. B. Zhang, B. Gu, G. Tian, J. Zhou, J. Huang, Y. Xiong, Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: a technical review. Trends Food Sci. Technol. 81, 213–231 (2018). https://doi.org/10.1016/j.tifs.2018.09.018

    Article  CAS  Google Scholar 

  8. J. Wieme, K. Mollazade, I. Malounas, M. Zude-Sasse, M. Zhao, A. Gowen, D. Argyropoulos, S. Fountas, J. Van Beek, Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: a review. Biosyst. Eng. 222, 156–176 (2022). https://doi.org/10.1016/j.biosystemseng.2022.07.013

    Article  CAS  Google Scholar 

  9. X. Huang, Q. Meng, Z. Wu, F. He, P. Tian, J. Lin, H. Zhu, X. Zhou, Y. Huang, Detection of early bruises in Gongcheng persimmon using hyperspectral imaging. Infrared Phys. Technol. (2022). https://doi.org/10.1016/j.infrared.2022.104316

    Article  Google Scholar 

  10. N. Vélez Rivera, J. Gómez-Sanchis, J. Chanona-Pérez, J.J. Carrasco, M. Millán-Giraldo, D. Lorente, S. Cubero, J. Blasco, Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst. Eng. 122, 91–98 (2014). https://doi.org/10.1016/j.biosystemseng.2014.03.009

    Article  Google Scholar 

  11. X. Li, Y. Liu, X. Jiang, G. Wang, Supervised classification of slightly bruised peaches with respect to the time after bruising by using hyperspectral imaging technology. Infrared Phys. Technol. (2021). https://doi.org/10.1016/j.infrared.2020.103557

    Article  Google Scholar 

  12. H. Jiang, C. Zhang, Y. He, X. Chen, F. Liu, Y. Liu, Wavelength selection for detection of slight bruises on pears based on hyperspectral imaging. Appl. Sci. (2016). https://doi.org/10.3390/app6120450

    Article  Google Scholar 

  13. X. Fu, M. Wang, Detection of early bruises on pears using fluorescence hyperspectral imaging technique. Food Anal. Methods 15(1), 115–123 (2021). https://doi.org/10.1007/s12161-021-02092-3

    Article  Google Scholar 

  14. Q. Thien Pham, N.-S. Liou, The development of on-line surface defect detection system for jujubes based on hyperspectral images. Comput. Electron. Agric. (2022). https://doi.org/10.1016/j.compag.2022.106743

    Article  Google Scholar 

  15. Q.T. Pham, N.-S. Liou, Hyperspectral imaging system with rotation platform for investigation of jujube skin defects. Appl. Sci. (2020). https://doi.org/10.3390/app10082851

    Article  Google Scholar 

  16. Z. Han, B. Li, Q. Wang, A. Yang, Y. Liu, J.S. Camara, Detection storage time of mild bruise’s loquats using hyperspectral imaging. J. Spectrosc. 2022, 1–9 (2022). https://doi.org/10.1155/2022/9989002

    Article  CAS  Google Scholar 

  17. H. Yin, B. Li, Y.D. Liu, F. Zhang, C.T. Su, A.G. Ou-Yang, Detection of early bruises on loquat using hyperspectral imaging technology coupled with band ratio and improved Otsu method. Spectrochim. Acta A 283, 121775 (2022). https://doi.org/10.1016/j.saa.2022.121775

    Article  CAS  Google Scholar 

  18. J. Qin, K. Chao, M.S. Kim, R. Lu, T.F. Burks, Hyperspectral and multispectral imaging for evaluating food safety and quality. J. Food Eng. 118(2), 157–171 (2013). https://doi.org/10.1016/j.jfoodeng.2013.04.001

    Article  CAS  Google Scholar 

  19. Y. Lu, W. Saeys, M. Kim, Y. Peng, R. Lu, Hyperspectral imaging technology for quality and safety evaluation of horticultural products: a review and celebration of the past 20-year progress. Postharvest Biol. Technol. (2020). https://doi.org/10.1016/j.postharvbio.2020.111318

    Article  Google Scholar 

  20. M.B. Stuart, M. Davies, M.J. Hobbs, T.D. Pering, A.J.S. McGonigle, J.R. Willmott, High-resolution hyperspectral imaging using low-cost components: application within environmental monitoring scenarios. Sensors (2022). https://doi.org/10.3390/s22124652

    Article  PubMed  PubMed Central  Google Scholar 

  21. D. Reese, A.M. Lefcourt, M.S. Kim, Y. Martin Lo, Using parabolic mirrors for complete imaging of apple surfaces. Bioresour. Technol. 100(19), 4499–4506 (2009). https://doi.org/10.1016/j.biortech.2008.11.059

    Article  CAS  PubMed  Google Scholar 

  22. Z. Xiao-bo, Z. Jie-wen, L. Yanxiao, M. Holmes, In-line detection of apple defects using three color cameras system. Comput. Electron. Agric. 70(1), 129–134 (2010). https://doi.org/10.1016/j.compag.2009.09.014

    Article  Google Scholar 

  23. Y. Sun, H. Xiao, S. Tu, K. Sun, L. Pan, K. Tu, Detecting decayed peach using a rotating hyperspectral imaging testbed. LWT 87, 326–332 (2018). https://doi.org/10.1016/j.lwt.2017.08.086

    Article  CAS  Google Scholar 

  24. A.A. Gowen, Y. Feng, E. Gaston, V. Valdramidis, Recent applications of hyperspectral imaging in microbiology. Talanta 137, 43–54 (2015). https://doi.org/10.1016/j.talanta.2015.01.012

    Article  CAS  PubMed  Google Scholar 

  25. X. Tian, X. Liu, X. He, C. Zhang, J. Li, W. Huang, Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm. J. Sci. Food Agric. (2023). https://doi.org/10.1002/jsfa.12764

    Article  PubMed  Google Scholar 

  26. Y. Wang, Z. Ding, J. Song, Z. Ge, Z. Deng, Z. Liu, J. Wang, L. Bian, C. Yang, Peanut defect identification based on multispectral image and deep learning. Agronomy (2023). https://doi.org/10.3390/agronomy13041158

    Article  Google Scholar 

  27. M.B. Stuart, L.R. Stanger, M.J. Hobbs, T.D. Pering, D. Thio, A.J.S. McGonigle, J.R. Willmott, Low-cost hyperspectral imaging system: design and testing for laboratory-based environmental applications. Sensors (Basel) (2020). https://doi.org/10.3390/s20113293

    Article  PubMed  Google Scholar 

  28. S. Younas, C. Liu, H. Qu, Y. Mao, W. Liu, L. Wei, L. Yan, L. Zheng, Multispectral imaging for predicting the water status in mushroom during hot-air dehydration. J. Food Sci. 85(4), 903–909 (2020). https://doi.org/10.1111/1750-3841.15081

    Article  CAS  PubMed  Google Scholar 

  29. G. Leary, G. Switzer, G. Kuntz, T. Kaiser, IEEE, Comparison of xenon lamp-based and led-based solar simulators, in 43rd IEEE Photovoltaic Specialists Conference (PVSC), Portland, OR, Jun 05–10 2016. IEEE Photovoltaic Specialists Conference (2016), pp. 3062–3067

  30. J.-Y. Song, R.-M. Zeng, D.-Y. Xu, Y. Wang, Z. Ding, C. Yang, A compact AAA-compatible multispectral solar simulator based on spherical cap chamber. Sol. Energy 220, 1053–1064 (2021). https://doi.org/10.1016/j.solener.2021.03.074

    Article  Google Scholar 

  31. S. Younas, Y. Mao, C. Liu, W. Liu, T. Jin, L. Zheng, Efficacy study on the non-destructive determination of water fractions in infrared-dried Lentinus edodes using multispectral imaging. J. Food Eng. (2021). https://doi.org/10.1016/j.jfoodeng.2020.110226

    Article  Google Scholar 

  32. N. Çetin, K. Karaman, E. Kavuncuoğlu, B. Yıldırım, A. Jahanbakhshi, Using hyperspectral imaging technology and machine learning algorithms for assessing internal quality parameters of apple fruits. Chemom. Intell. Lab. Syst. (2022). https://doi.org/10.1016/j.chemolab.2022.104650

    Article  Google Scholar 

  33. C. Zhang, C. Guo, F. Liu, W. Kong, Y. He, B. Lou, Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J. Food Eng. 179, 11–18 (2016). https://doi.org/10.1016/j.jfoodeng.2016.01.002

    Article  Google Scholar 

  34. A. Nirere, J. Sun, R. Kama, V.A. Atindana, F.D. Nikubwimana, K.D. Dusabe, Y. Zhong, Nondestructive detection of adulterated wolfberry (Lycium chinense) fruits based on hyperspectral imaging technology. J. Food Process Eng. (2023). https://doi.org/10.1111/jfpe.14293

    Article  Google Scholar 

  35. K.H. Liu, M.H. Yang, S.T. Huang, C. Lin, Plant species classification based on hyperspectral imaging via a lightweight convolutional neural network model. Front. Plant Sci. 13, 855660 (2022). https://doi.org/10.3389/fpls.2022.855660

    Article  PubMed  PubMed Central  Google Scholar 

  36. P.J. Navarro, L. Miller, A. Gila-Navarro, M.V. Díaz-Galián, D.J. Aguila, M. Egea-Cortines, 3DeepM: an ad hoc architecture based on deep learning methods for multispectral image classification. Remote Sens. (2021). https://doi.org/10.3390/rs13040729

    Article  Google Scholar 

  37. F. Chollet, Xception: deep learning with depthwise separable convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 1251–1258

  38. Y. Yang, L. Wang, M. Huang, Q. Zhu, R. Wang, Polarization imaging based bruise detection of nectarine by using ResNet-18 and ghost bottleneck. Postharvest Biol. Technol. (2022). https://doi.org/10.1016/j.postharvbio.2022.111916

    Article  Google Scholar 

  39. R. Pourdarbani, S. Sabzi, R. Zohrabi, G. García-Mateos, R. Fernandez-Beltran, J.M. Molina-Martínez, M.H. Rohban, Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection. J. Food Sci. 88(12), 5149–5163 (2023). https://doi.org/10.1111/1750-3841.16801

    Article  CAS  PubMed  Google Scholar 

  40. Y. Tang, S. Gao, J. Zhuang, C. Hou, Y. He, X. Chu, A. Miao, S. Luo, Apple bruise grading using piecewise nonlinear curve fitting for hyperspectral imaging data. IEEE Access 8, 147494–147506 (2020). https://doi.org/10.1109/access.2020.3015808

    Article  Google Scholar 

  41. X. Pan, L. Sun, Y. Li, W. Che, Y. Ji, J. Li, J. Li, X. Xie, Y. Xu, Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging. J. Sci. Food Agric. 99(4), 1709–1718 (2019). https://doi.org/10.1002/jsfa.9360

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The work was supported in part by the National Natural Science Foundation of China with Grant 62065003; in part by Guizhou Provincial Science and Technology Projects with Grant ZK [2022] Key-020; in part by Renjihe of Guizhou University (2012).

Author information

Authors and Affiliations

  1. College of Big Data and Information Engineering, Guizhou University, Guiyang, 550025, China

    Jian-Feng Chen, Zhao Ding, Jia-Yong Song, Yang Wang & Chen Yang

  2. Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China

    Li-Feng Bian

Authors
  1. Jian-Feng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jia-Yong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li-Feng Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jian-Feng Chen: methodology, investigation, writing—original draft preparation. Zhao Ding: supervision. Jia-Yong Song: investigation, formal analysis. Yang Wang: software, data curation. Li-Feng Bian: project administration. Chen Yang: conceptualization, writing—review & editing, validation.

Corresponding author

Correspondence to Chen Yang.

Ethics declarations

Conflict of interest

There are no conflicts to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, JF., Ding, Z., Song, JY. et al. Low-cost monochromatic uniform illumination HSI system for detection and classification of apple bruise. Food Measure (2024). https://doi.org/10.1007/s11694-024-02540-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11694-024-02540-8

Keywords

Search

Navigation