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

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...

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Bibliographic Details
Published in:Journal of food measurement & characterization 2024-06, Vol.18 (6), p.4853-4864
Main Authors: Chen, Jian-Feng, Ding, Zhao, Song, Jia-Yong, Wang, Yang, Bian, Li-Feng, Yang, Chen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Apples
Cameras
Carotenoids
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Chlorophyll
Classification
Damage detection
Engineering
Food quality
Food Science
Fruits
Hyperspectral imaging
Illumination
Image acquisition
Image quality
Integrating spheres
Light
Low cost
Nondestructive testing
Original Paper
Prototypes
Shadows
Spectral resolution
Temperature effects
Xenon
Xenon lamps
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Summary: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.
ISSN:2193-4126
2193-4134
DOI:10.1007/s11694-024-02540-8