Table 6 Performance analysis of DAC-GAN.

CNN Models	Accuracy with DAC-GAN Synthetic Nuts Images
CNN Models	Applying Raw Nuts images	Applying Sobel Nuts images	Applying Canny Edge Nuts images	Applying Kernel Isolated Nuts images	Applying Corner Key point nuts images
DenseNet121	69.24	70.42	72.36	76.24	78.31
VGG19	69.26	72.88	73.59	77.42	79.56
Inception	70.32	73.39	74.88	78.85	80.35
XCeption	71.53	74.45	75.93	79.92	81.39
MobileNet	72.66	76.28	76.44	80.32	82.67
ResNet-50	73.93	77.98	77.53	81.98	83.82
EfficientNet-B0	70.12	75.64	77.11	78.54	81.32
EfficientNet-B4	71.26	76.23	78.64	80.12	83.15
ConvNeXt	72.48	77.54	79.22	81.08	84.76
ViT CNN	73.32	78.18	80.16	82.03	86.44
SwinT	74.15	79.24	81.08	83.55	88.21
WGAN	79.21	81.44	84.28	86.72	94.81
CGAN	80.32	82.52	85.21	87.51	93.59
DAC-GAN	81.25	84.73	87.82	89.87	99.83

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