Abstract
Hyperspectral imaging technology was applied to develop a rapid, accurate and non-destructive detection method for honeysuckle mildew degree levels. The original spectral data were analyzed by three pretreatment methods with Savitzky-Golay (SG) convolution smoothing, Multiple Scatter Correct (MSC) and SG-MSC. A comparison was made among SG, MSC and SG-MSC based on Partial Least Squares (PLS), of which the best pretreatment method was SG-MSC. The Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to extract the characteristic wavelengths after SG-MSC pretreatment. Partial Least Square Discriminant Analysis (PLS-DA) and Last Squares Support Vector Machine (LS-SVM) were applied to build discriminant analysis models based on characteristic wavelengths. The results showed that the LS-SVM model based on CARS performed the optimal discriminant performance for honeysuckle’s mildew degree levels, with the accuracy of 100% for training set and validation set. Therefore, hyperspectral imaging technology can be used to identify mildew degree in honeysuckle effectively and non-destructively based on characteristic wavelengths.
Publication Date
8-28-2018
First Page
60
Last Page
64,78
DOI
10.13652/j.issn.1003-5788.2018.08.013
Recommended Citation
Jie, FENG; Yunhong, LIU; Qingqing, WANG; Huichun, YU; and Xiaowei, SHI
(2018)
"Detection models of mildew degree in honeysuckle based on hyperspectral imaging technology,"
Food and Machinery: Vol. 34:
Iss.
8, Article 13.
DOI: 10.13652/j.issn.1003-5788.2018.08.013
Available at:
https://www.ifoodmm.cn/journal/vol34/iss8/13
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