4.7 Article

Spatial Frequency Domain Imaging System Calibration, Correction and Application for Pear Surface Damage Detection

期刊

FOODS
卷 10, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/foods10092151

关键词

spatial frequency domain imaging (SFDI); projector-camera calibration; optical properties; pears; damage detection; linear discriminant analysis (LDA)

资金

  1. National Natural Science Fund of China [32071904]
  2. Natural Science Fund of Zhejiang Province [LY20C130008]
  3. Science Foundation of Zhejiang Sci-Tech Univ. (ZSTU) [16022177-Y]

向作者/读者索取更多资源

Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique used for optical property detection. This study successfully established an SFDI system to measure optical properties of pears with different damage types, achieving high classification accuracy using linear discriminant analysis. The results suggest that SFDI has great potential in agricultural product quality inspection.
Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient mu(a) and the reduced scattering coefficient mu'(s) were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears' quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research.

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