Table 7 Comparison of classification experiments

From: Dual discriminator GAN-based synthetic crop disease image generation for precise crop disease identification

Model	Evaluation scores
Model	Augmentation type	Precision	Recall	F1-score
VGG16	NA	84.5	83.78	83.61
	CA	92.57	92.44	92.42
	FDIT	92.91	92.89	92.86
	FHWD	93.83	93.78	93.86
GoogleNet	NA	91.48	91.33	91.26
	CA	96.72	96.67	96.66
	FDIT	97.04	96.89	96.89
	FHWD	97.62	97.56	97.56
ResNet18	NA	89.49	89.33	89.23
	CA	93.93	93.78	93.75
	FDIT	94.5	94.44	94.4
	FHWD	95.38	95.33	95.32
SEResNet18	NA	86.82	86.44	86.22
	CA	92.98	92.89	92.78
	FDIT	93.9	93.56	93.48
	FHWD	94.99	94.89	94.87
PreactReSNet18	NA	88.51	88.22	88.15
	CA	93.86	93.78	93.75
	FDIT	94.35	94.22	94.21
	FHWD	95.25	95.11	95.1
Vision transformer	NA	93.96	93.78	93.75
	CA	95.51	95.11	95.1
	FDIT	95.29	95.11	95.11
	FHWD	95.54	95.33	95.33
Swin tansformer	NA	90.78	90.44	90.42
	CA	94.16	94	93.99
	FDIT	94.86	94.67	94.63
	FHWD	95.37	95.11	95.07

The best results are shown in bold
Here and in Table 7, NA No augmentation; CA Conventional augmentation

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ISSN: 1746-4811

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