• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 11, 3626 (2022)
Guang-lai WANG*, En-feng WANG, Cong-cong WANG, and Da-yang LIU*;
Author Affiliations
  • College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150000, China
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    DOI: 10.3964/j.issn.1000-0593(2022)11-3626-05 Cite this Article
    Guang-lai WANG, En-feng WANG, Cong-cong WANG, Da-yang LIU. Early Bruise Detection of Crystal Pear Based on Hyperspectral Imaging Technology and Transfer Learning[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3626 Copy Citation Text show less

    Abstract

    Pear in storage, packaging and transportation processes may occur in different degrees of mechanical damage. If not removed in time, the damage may gradually become serious and rot, resulting in serious economic losses. In order to establish a rapid and non-destructive detection method for early bruise detection and bruise time assessment of pears, hyperspectral imaging technology combined with a transfer learning model was used to identify crystal pears with an early bruise. The hyperspectral images of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h were acquired by the hyperspectral imaging system, and 80 hyperspectral images of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h were obtained. Principal component analysis was performed on the hyperspectral images, and principal component images 4, 5 and 6 (PC4, PC5 and PC6) were selected as the feature images for detecting crystal pear bruises. After the data expansion of the stitched images with three principal components, 160 images of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h were obtained. The training sample set and test sample set were divided according to the ratio of 9∶1, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and transfer learning bruise recognition models based on the ResNet50 network were established, respectively. The results showed that the overall recognition accuracy of SVM, k-NN and ResNet50 network-based transfer learning models for the test set was 83.33%, 85.42% and 93.75%, respectively. The transfer learning model based on the ResNet50 network had the best recognition results, and its correct recognition rates for the test set of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h reached 100%, 83% and 95%, respectively. The results of this study indicate that hyperspectral imaging technology combined with the transfer learning method based on the ResNet50 network can achieve early bruise detection of crystal pears and have a great prediction performance for bruise time, and the longer the bruise time, the higher the recognition accuracy.
    Guang-lai WANG, En-feng WANG, Cong-cong WANG, Da-yang LIU. Early Bruise Detection of Crystal Pear Based on Hyperspectral Imaging Technology and Transfer Learning[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3626
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