Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging

• Published in: Postharvest biology and technology Vol. 141; pp. 39 - 49
• Main Authors: Yu, Xinjie, Lu, Huanda, Wu, Di
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-07-2018; Elsevier BV
• Subjects: Artificial intelligence; Coders; Data imaging; Deep learning; Feature extraction; Firmness; Fruit quality; Fruits; Fully-connected neural network; Hyperspectral imaging; Mathematical models; Neural networks; Non-destructive detection; Pixel-level spectral features; Predictions; Reflectance; Stacked auto-encoders; Stacked auto-encoders; Pixel-level spectral features; Non-destructive detection; Fully-connected neural network; Fruit quality
• ISSN: 0925-5214; 1873-2356
• DOI: 10.1016/j.postharvbio.2018.02.013

•Firmness and SSC in pear were successfully predicted by deep learning method.•Deep spectral features in Vis/NIR hyperspectral image of pear were extracted by SAE.•SAE-FNN model was applied to fit the deep spectral features to firmness and SSC.
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 (380–1030 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 R2P = 0.890, RMSEP = 1.81 N and RPDP = 3.05 for firmness, and R2P = 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.