Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion

Weng, Shizhuang; Tang, Peipei; Yuan, Hecai; Guo, Bingqing; Yu, Shan; Huang, Liusheng; Chen, Xu

doi:10.1016/j.saa.2020.118237

Cited by 74 publications

(27 citation statements)

References 28 publications

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“…The band at 995 nm represents the 2nd vibration of the NH bonds in proteins or amino acids, whereas that at 880 nm constitutes the 3rd overtone absorption of CH. Additionally, the band relates to the 2nd overtone absorption of the OH and NH bonds at 750–900 nm and 962–1000 nm, respectively [ 32 ]. Figure 5 illustrates the score scattering attributes demonstrated in the principal components (PCs) during intuitive data analysis.…”

Section: Resultsmentioning

confidence: 99%

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Seo

Kim

Lim

et al. 2021

Sensors

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Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

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Section: Resultsmentioning

confidence: 99%

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Seo

Kim

Lim

et al. 2021

Sensors

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“…Third, recent literature shows a shift from deriving spectral signals from satellite images, to working directly over the Hyperspectral images (HSI) [20]. Even using deep learning frameworks for identifying soil characteristics [21], rice varieties [22], rice phenology [23], and even focusing on data imbalance in the image of domain [24].…”

Section: Discussionmentioning

confidence: 99%

Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama

Sanchez-Galán¹,

Barranco²,

Reyes³

et al. 2021

Adv. sci. technol. eng. syst. j.

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In this article supervised classification methods for the analysis of local Panamanian rice crops using Near-Infrared (NIR) spectral signatures are assessed. Neural network (Multilayer Perceptron-MLP) and Tree based (Decision Trees-DT and Random Forest-RF) algorithms are used as regression and supervised classification of the spectral signatures by rice varieties, against other crops and by plant phenology (days after planting). Also, satellite derived spectral signature is validated with a field collected spectral model. Results suggest that MLP networks, either for regression or classification, were more efficient (RMSE of 8.78 and 0.068, respectively) than either tree based methods to regress/classify the rice spectral signature (RMSE of 19.37,19.09 and 0.979, respectively). The validation made using satellite derived spectral signatures resulted in MLP models with RMSE of 0.216 and 7.318, respectively, leaving room for further improvement of the models. This work aimed to present a practical example of the employment of recent supervised classification algorithms for the determination of regression and classification models from reflectance spectral signatures in local rice varieties.

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“…In some cases, the availability of abundant and reliable data is not available due to a number of factors. In such cases, some researchers like Weng et al. (2020) and Liu et al.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…(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.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

229

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion

Cited by 74 publications

References 28 publications

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

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