Degang Chen, Zieguli Ai, Pengbo Yin, Yanuo Lu, Shunping Li. Research on Identification of Wild Mushroom Species Based on Improved Xception Transfer Learning[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810023

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- Laser & Optoelectronics Progress
- Vol. 58, Issue 8, 0810023 (2021)

Fig. 1. Sample data of wild mushroom images. (a) Amanita exitalis; (b) Amanita fuliginea; (c) Amanita neoovoidea; (d) Amanita parvipantherina; (e) Amanita rubrovolvata; (f) Entoloma quadratum; (g) Panaeolus sphinctrinus; (h) Psilocybe coprophila; (i) Gyromitra infula; (j) Lonomidotis frondosa

Fig. 2. Effects of different data enhancement methods. (a) Origin image; (b) random rotation; (c) horizontal flip; (d) color dither; (e) Gaussian noise; (f) histogram equalization; (g) random cut

Fig. 3. Structural diagram of Xception

Fig. 4. Experimental flow chart of wild mushroom species identification model

Fig. 5. Principle diagram of CBAM's realization

Fig. 6. Comparison of three kinds of neural network structures. (a) Traditional neural network; (b) Dropout neural network; (c) Disout neural network

Fig. 7. Comparison among model parameters for different training methods. (a) Accuracy; (b) average training time
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Table 1. Structures of A, B, C, and D components in Xception
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Table 2. Structures of E, F, and G components in Xception
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Table 3. Comparison among different feature map disturbance forms
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Table 4. Comparisonamong different training methods
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Table 5. Comparison among different proportions
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Table 6. Comparison among different model experiments

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