Research on Identification of Wild Mushroom Species Based on Improved Xception Transfer Learning

Degang Chen; Zieguli Ai; Pengbo Yin; Yanuo Lu; Shunping Li

doi:10.3788/LOP202158.0810023

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 8 >Page 0810023 > Article

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

Research on Identification of Wild Mushroom Species Based on Improved Xception Transfer Learning

Degang Chen, Zieguli Ai^*, Pengbo Yin, Yanuo Lu, and Shunping Li

Author Affiliations

School of Computer Science and Technology, Xinjiang Normal University, Urumqi, Xinjiang 830054, China

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DOI: 10.3788/LOP202158.0810023 Cite this Article Set citation alerts

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 Copy Citation Text

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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. 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

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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. 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

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Fig. 3. Structural diagram of Xception

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

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Fig. 5. Principle diagram of CBAM's realization

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

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Fig. 7. Comparison among model parameters for different training methods. (a) Accuracy; (b) average training time

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A	B	C	D
[Input 299×299×3]	Sep Conv 128, 3×3	$\{\begin{array}{l} ReLU \\ Sep Conv 256, 3 \times 3 \end{array}\}$ ×3	$\{\begin{array}{l} ReLU \\ Sep Conv 728, 3 \times 3 \end{array}\}$ ×3
Conv 32,3×3,stride of 2×2ReLU	ReLU
Conv 32,3×3,stride of 2×2ReLU	Sep Conv 128,3×3	MaxPool 3×3,stride of 2×2	MaxPool 3×3,stride of 2×2
	MaxPool 3×3,stride of 2×2

Table 1. Structures of A, B, C, and D components in Xception

E(repeated 8 times)	F	G
$\{\begin{array}{l} ReLU \\ Sep Conv 728, 3 \times 3 \end{array}\}$ ×3	ReLU	Sep Conv 1536,3×3
	Sep Conv 728,3×3	ReLU
	ReLU	Sep Conv 2048,3×3
	Sep Conv 1024,3×3	ReLU
	MaxPool 3×3,stride of 2×2	Global average pool

Table 2. Structures of E, F, and G components in Xception

Experimental number	Training structure	Top 1 /%	Top 5 /%
1#	Origin model	94.91	99.30
2#	Dropout	95.92	99. 33
3#	Disout	96.32	99.61

Table 3. Comparison among different feature map disturbance forms

Experimental number	Training method	Top 1 /%	Top 5 /%	T_Average/s
1##	Random parameters	92.10	98.10	1688.37
2##	Freezing all parameters	87.26	98.34	599.38
3##	Freezing partial parameters	96.47	99.69	647.91
4##	Training all network layers	97.02	99.67	1645.39

Table 4. Comparisonamong different training methods

Experimental number	Size of training set	Size of validation set	Top 1 /%	Top 5 /%
1-1	5	5	94.23	98.91
2-1	6	4	95.15	98.99
3-1	7	3	95.67	99.36
4-1	8	2	96.32	99.61

Table 5. Comparison among different proportions

Model	Top 1 /%	Top 5 /%	Number of parameters /10⁶
Alex	86.24	97.94	61.10
ResNet50	89.39	98.64	25.64
ResNet101	91.64	98.69	44.71
ResNet152	92.36	99.32	60.42
InceptionV1	90.64	98.85	13.02
InceptionV3	93.16	99.09	23.93
InceptionResNetV2	95.41	99.28	55.87
Xception	95.58	99.43	22.99
Dis-Xception-CBAM	96.32	99.61	24.04

Table 6. Comparison among different model experiments

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