Table 5.

Quantitative results of rice disease and deficiency disorder classification after applying DeepBatch showing precision (P), recall (R), F1, and accuracy (A) scores

Models	Rice disease				Rice nutrient deficiency disorder
	P	R	F1	A	P	R	F1	A
TL0: InceptionResNetV2	94.33	94.33	94.33	94.21	93.67	92.67	92.67	92.67
TL1: VGG19	92.33	92.33	92	92.16	86	83	82	83
TL2: XceptionNet	93.33	93	93	93.31	85.67	78	77.33	78
TL3: DenseNet201	94	93	93.33	93.22	88	85	85	85
TL4: MobileNetV2	79	53.67	49.67	53.59	77	48.67	44.33	48.67
EM2.0: InceptionResNetV2 + DenseNet201	93.33	96	96	96.20	94	93.33	93.33	93.33
EM2.1: InceptionResNetV2 + VGG19	94.67	94.67	94.67	94.47	92.67	92	92	92
EM2.2: InceptionResNetV2 + XceptionNet	95.67	95.67	95.67	95.62	94	93.33	93.33	93.33
EM2.3: XceptionNet + VGG19	95	94.67	95	94.72	89.67	88	87.67	88
EM2.4: DenseNet201 + VGG19	95.67	95.67	96	95.70	86.33	85.33	85	85.33
EM2.5: DenseNet201 + XceptionNet	94.67	95	94.67	94.79	91.33	89.33	89.67	89.33
EM3.0: EM2.2 + VGG19	96.33	96.33	96.33	96.28	92.33	91.33	91	91.33
EM3.1: EM2.2 + DenseNet201	97	96.67	97	96.94	94.67	94	93.67	94
EM3.2: EM2.3 + DenseNet201	96	95.67	96	95.62	90.67	90	89.67	90
EM3.3: EM2.5 + DenseNet201	96	96.33	96.33	96.20	90.67	90	89.67	90
EM4.0: EM2.2 + VGG19 + DenseNet201	96.67	96.67	96.67	96.61	93.33	92.67	92.33	92.67