BMC Plant Biology

Table 6 Accuracy performance for every class using different classifiers (for plant class identifier, consider Fig. 1 for details) with the proposed model

From: Using transfer learning-based plant disease classification and detection for sustainable agriculture

Plant Class	PDDNet‑EA model (%)					PDDNet‑LVE Model (%)
identifier	NB	LR	RF	KNN	SVM	NB	LR	RF	KNN	SVM
D1	0.52	0.76	0.49	0.7	0.75	0.44	1	0.78	0.96	1
D2	0.57	0.9	0.67	0.79	0.87	0.57	0.91	0.8	0.83	0.9
D3	0.7	0.95	0.56	0.81	0.93	0.42	0.98	0.87	0.95	0.95
D4	0.79	1	0.57	0.91	0.94	0.49	0.9	0.89	0.84	0.89
D5	0.82	1	0.61	0.93	0.95	0.53	0.97	0.72	0.94	0.94
D6	0.97	0.99	0.69	0.98	0.99	0.64	0.79	0.63	0.62	0.75
D7	0.96	1	0.66	0.99	1	0.56	0.95	0.86	0.87	0.91
D8	0.67	0.81	0.73	0.79	0.83	0.71	0.83	0.82	0.65	0.78
D9	0.72	0.89	0.67	0.83	0.89	0.57	0.99	0.63	0.98	0.96
D10	0.62	0.79	0.61	0.7	0.81	0.43	1	0.81	0.96	0.99
D11	0.76	0.91	0.68	0.86	0.9	0.61	0.97	0.62	0.76	0.94
D12	0.7	0.94	0.89	0.91	0.93	0.87	1	0.78	0.95	0.99
D13	0.69	0.96	0.72	0.92	0.96	0.62	0.95	0.53	0.73	0.92
D14	0.61	0.93	0.87	0.88	0.94	0.81	0.82	0.71	0.66	0.82
D15	0.71	0.99	0.75	0.94	0.99	0.69	0.88	0.57	0.69	0.83

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ISSN: 1471-2229

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