首页|Convolutional neural network for apple bruise detection based on hyperspectral
Convolutional neural network for apple bruise detection based on hyperspectral
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NSTL
Elsevier
The timely detection of apple bruises caused by collision and squeeze is of great significance to reduce the economic losses of the apple industry. This study proposed a spectral analysis model (SpectralCNN) based on a one-dimensional convolutional neural network to detect apple bruises. The influences of six spectral preprocessing methods on the SpectralCNN model were firstly analyzed in this paper. Compared with traditional chemometric models, the SpectralCNN model had a better accuracy, which was demonstrated not depend on the spectral preprocessing method by experiment results. Then, 20 characteristic wavelengths could be extracted by successive projection algorithm. The SpectralCNN model could achieve an accuracy of 95.79% on the test set of characteristic wavelengths, indicating that the extracted characteristic wavelengths contain most of the features of bruised and healthy pixels.
Apple bruisesHyperspectral imagingConvolutional neural networkSpectral preprocessingCLASSIFICATIONTRANSPORTMODEL
NSTL
Elsevier
