Rong-chang JIANG, Ming-sheng GU, Qing-he ZHAO, Xin-ran LI, Jing-xin SHEN, Zhong-bin SU. Identification of Pesticide Residue Types in Chinese Cabbage Based on Hyperspectral and Convolutional Neural Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1385
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 5, 1385 (2022)
Fig. 1. Schematic diagram of hyperspectral imaging system
Fig. 2. Schematic representation of selection of ROI on Chinese cabbage sample
Fig. 3. Spectral data preprocessing by MSC
(a): Without preprocessing; (b): With MSC preprocessing
(a): Without preprocessing; (b): With MSC preprocessing
Fig. 4. CNN structure
Fig. 5. CNN hyperparameter selection
(a): TLA for different LR; (b): TLA for different BS; (c): OA for different Epochs
(a): TLA for different LR; (b): TLA for different BS; (c): OA for different Epochs
Fig. 6. Average spectra of chinese cabbage samples
Fig. 7. Low frequency portions of wavelet transform based on db1 function
(a)—(f) corresponding to 1~6 layers of DWT, respectively
(a)—(f) corresponding to 1~6 layers of DWT, respectively
Fig. 8. Process of selecting characteristic wavelength by CARS
Fig. 9. Flow chart of hyperspectral image classification based on DWT and deep learning
Fig. 10. Modeling results based on DWT, PCA and CARS
(a)—(d) Overall accuracies of CNN, MLP, KNN and SVM models based on DWT;(e) Overall accuracies of four models based on PCA; (f) OAs of four models based on CARS
(a)—(d) Overall accuracies of CNN, MLP, KNN and SVM models based on DWT;(e) Overall accuracies of four models based on PCA; (f) OAs of four models based on CARS
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Table 1. MLP network structure
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Table 2. Parameter settings of the CNN structure
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Table 3. Overall accuracies and Kappa coefficients of different algorithms
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Table 4. Overall accuracies of the various prediction algorithms
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