A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Table 13

Comparison with existing systems.

Source	Method	Dataset	Accuracy (%)
OTAMNet	Log-gabor filter and DenseNet201	MyDataset	98; 99; 100; 97; 99
		Flavia
		Swedish
		Folio
		MD2020
[82]	Modified AlexNet	MD2020	99
[73]	AlexNet, GoogLeNet, VGG-19, ResNet50, and MobileNetV2	Leafsnap	92
[65]	Binarized Neural Network (BNN)	Swedish leaf	77
[68]	CNN	Flavia; Swedish leaf	98
[74]	Histogram of oriented gradient (HoG) and deep convolutional neural network	Flavia; Swedish leaf dataset	96
[81]	VGG19 with LR	Folio; Flavia; Swedish leaf dataset	96
			96
			99
[102]	AlexNet and VGG16 with LDA	Swedish leaf dataset	99
[80]	17-Layer CNN architecture	LeafSnap	97
		Flavia
		Foliage datasets
[79]	AlexNet and GoogLeNet	Flavia; Swedish leaf dataset	94
			99
[75]	GoogLeNet, VGGNet, and AlexNet	LifeClef 2015 dataset	80
[103]	26-Layer CNN architecture	BJFU100 dataset	91
[78]	7-Layer CNN architecture	Flavia dataset	94
[77]	ResNet152 and Inception-ResNetv2 with LBP	Swedish leaf dataset	99