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Sensors 2018, 18(4), 1295; https://doi.org/10.3390/s18041295

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis

College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing 210095, China
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Received: 6 March 2018 / Revised: 8 April 2018 / Accepted: 12 April 2018 / Published: 23 April 2018
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Abstract

Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels. View Full-Text
Keywords: hyperspectral imaging; deep learning; postharvest diseases; peaches; decayed levels hyperspectral imaging; deep learning; postharvest diseases; peaches; decayed levels
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, Y.; Wei, K.; Liu, Q.; Pan, L.; Tu, K. Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis. Sensors 2018, 18, 1295.

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