Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8881906 | Postharvest Biology and Technology | 2018 | 11 Pages |
Abstract
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 near-infrared (380-1030â¯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 R2Pâ¯=â¯0.890, RMSEPâ¯=â¯1.81â¯N and RPDPâ¯=â¯3.05 for firmness, and R2Pâ¯=â¯0.921, RMSEPâ¯=â¯0.22% and RPDPâ¯=â¯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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Authors
Xinjie Yu, Huanda Lu, Di Wu,