Table 2.

Deep Learning (DL) applications in hyperspectral image analysis of food products.

Study	Wavelength range (nm)	Spectral pre-processing	Image processing	Deep learning characteristics				DL Computation software	Classification accuracy	References
				Deep learning network type	Network topology and features	Parameter values/Pertinent particulars	Training set: Validation set
Detection of aflatoxin in peanut	400–1000	–	Image binarization and thresholding	Convolutional Neural Network (CNN)	1st layer- input; 2nd layer-convolution; 3rd layer-sub-sampling; 4th layer- convolution; 5th layer-sub-sampling; Output layer (fully connected); epochs: 1-100	Mean Error-11.39–2.74%; Time required: 150–15000s	80:20	–	96%	Han and Gao (2019)
Detection of internal mechanical damage in blueberries	400–1000	–	Subsampling, image resizing, data augment and normalization	Two convolutional neural networks used: Residual Network (ResNet) and ResNeXt	Convolution layer filter size: 3 ​× ​3; stride:2; Activation function: Rectified Linear Unit (ReLU)	Learning rate, decay rate and decay step: 0.1, 0.1 and 32,000	80:20	MATLAB R2014a	88%	Wang et al. (2018)
Determination of chemical compositions in dry black goji berries	900–1700	–	Image thresholding	Convolutional Neural Network (CNN)	One-dimension (1D) convolution layers, max pooling layers, ReLU activations, a fully connected layer. convolution kernel size: 3 ​× ​3; stride:1; Max pooling layers: 2; stride:2	Learning rate and batch size: 0.005 and 5	65:35	MATLAB R2014b; PYTHON 3	88%	Zhang et al., 2020b, Zhang et al., 2020a
Determination of rice varieties	400–1000	Multivariate scatter correction (MSC), standard normal variate (SNV), Savitzky–Golay smoothing and Savitzky-Golay's first-order	Texture parameters calculation: gray-level gradient co- occurrence matrix (GLGCM), discrete wavelet transform (DWT) and Gaussian Markov random field (GMRF)	Principal component analysis network (PCANet) deep learning network	–	–	75:25	MATLAB R2017b; PYTHON	98.57	Weng et al. (2020)
Detection of internal defects in cucumber	400–1000	–	Image thresholding	Convolutional Neural Network -Stacked Sparse Auto-Encoder (CNN-SSAE) deep learning architecture	Greedy layer-wise unsupervised pretraining; Staking of additional output layer (softmax classifier) on pre-trained SSAE; training through gradient descent with back-propagation	Sparsity control parameter (β)-0.1 on encoding neurons; two layers: 16 encoding neurons in each layer	80:20	–	91%	Liu et al. (2018)
Prediction of firmness and soluble solid content of pear	400–1000	Multiplicative signal correction (MSC); successive projections algorithm (SPA)	Image thresholding	Stacked auto-encoders (SAE) and fully-connected neural network (FNN)	Feature extraction from hyperspectral data through SAE	Input for FNN: SAE extracted features	80:20	MATLAB 8.1; PYTHON	Firmness: 89%; Soluble solid content: 92%	Yu et al. (2018)
Identification of rice variety (single seed)	900–1700	–	Wavelet transform (Daubechies 8- basis function; decomposition level 3); Image thresholding	Convolutional neural network (CNN) adapted from Visual Geometry Group (VGG) Net	Two convolutional layers, max pooling layer, fully connected layer, dropout and dense layers (output layer). Kernel size-3x3; stride-1; padding-1; epochs:200; ReLU activation function; softmax function on output	Learning rate-0.0005	80:20	–	92%	Qiu et al. (2018)
Detection and quantification of nitrogen content in rapeseed leaf	400–1000	–	Image thresholding	Stacked auto-encoders (SAE) and fully-connected neural network (FNN)	Feature extraction from hyperspectral data through SAE	Input for FNN: SAE extracted features	80:20	MATLAB 8.1; PYTHON	90%	Yu et al. (2019)
Classification of coffee bean varieties	900–1700	Savitzky–Golay first-order derivative	Image segmentation: watershed algorithm	Two branch convolutional neural network (2B–CNN)	1st branch: 1D convolution of spectral features; 2nd branch: 2D convolution of spatial features; Fully connected layer-Absent; Training- Batch normalization and dropout strategy; epochs: 100	Trained weights: effective wavelengths indicator	80:20	–	95%	Liu et al. (2019)
Detection of bruises in strawberry	900–1700	Savitzky–Golay first-order derivative	Image segmentation: watershed algorithm	Two branch convolutional neural network (2B–CNN)	1st branch: 1D convolution of spectral features; 2nd branch: 2D convolution of spatial features; Fully connected layer-Absent; Training- Batch normalization and dropout strategy; epochs: 200	Trained weights: effective wavelengths indicator	80:20	–	99%	Liu et al. (2019)