期刊
POSTHARVEST BIOLOGY AND TECHNOLOGY
卷 141, 期 -, 页码 39-49出版社
ELSEVIER
DOI: 10.1016/j.postharvbio.2018.02.013
关键词
Stacked auto-encoders; Fully-connected neural network; Pixel-level spectral features; Fruit quality; Non-destructive detection
资金
- National Natural Science Foundation of China [31402352]
- Ningbo Science and Technology Special Project of China [2017C110002]
- Ningbo Natural Science Foundation [2015A610131]
The objective of this research was to develop a deep learning method which consisted of stacked auto-encoders (SAE) and fully-connected neural network (FNN) for predicting firmness and soluble solid content (SSC) of postharvest Korla fragrant pear (Pyrus brestschneideri Rehd). Firstly, deep spectral features in visible and nearinfrared (380-1030 nm) hyperspectral reflectance image data of pear were extracted by SAE, and then these features were used as input data to predict firmness and SSC by FNN. The SAE-FNN model achieved reasonable prediction performance with R-P(2) = 0.890, RMSEP = 1.81 N and RPDP = 3.05 for firmness, and R-P(2) = 0.921, RMSEP = 0.22% and RPDP = 3.68 for SSC. This research demonstrated that deep learning method coupled with hyperspectral imaging technique can be used for rapid and nondestructive detecting firmness and SSC in Korla fragrant pear, which would be useful for postharvest fruit quality inspections.
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