Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Table 4

Classification results of the SqueezeNet model (experiment 2).

Classifier (%)	Recall (%)	Sensitive (%)	F1 score (%)	FNR (%)	Accuracy (%)	Time (sec)
Linear SVM	99.22	99.24	99.23	0.78	99.2	301.74
Quadratic SVM	99.46	99.46	99.4	0.54	99.5	304.67
Weight KNN	98.58	98.44	98.51	1.42	98.6	362.9
Cosine KNN	97.9	97.94	97.92	2.1	97.9	387.4
Linear discriminant	99.32	99.32	99.32	0.68	99.3	72.783
Medium neural network	99.42	99.42	99.42	0.58	99.4	178.13
Narrow neural network	99.38	99.38	99.38	0.62	99.4	244.41
Wide neural network	99.41	99.4	99.41	0.58	99.4	249.99
Bilayered neural network	99.36	99.36	99.36	0.64	99.4	336.25
Trilayered neural network	99.37	99.38	99.37	0.64	99.4	458.32