Table 3.

Different pixel-based crop/plant classification methods

Ref.	Data types	GSD (cm)	Altitude (m)	Method	Backbone	Target crops	Overall Accuracy (%)	Precision (%)	Recall (%)	Kappa	F1-Score (%)	Time (s)
[118]	RGB	5.3	230	SegNet	VGG-16	Rice paddy, Rice lodging		83.10	69.06		75.43	101
	RGB+ExG							71.03	89.64		79.26	108
	RGB+ExGR							73.96	83.36		78.38	109
	RGB+ExG+ExGR							87.66	57.38		69.36	106
	RGB			FCN	AlexNet			84.73	82.43		83.56	59
	RGB+ExG							84.92	80.85		82.84	65
	RGB+ExGR							77.02	88.80		82.49	66
	RGB+ExG+ExGR							82.02	84.44		83.21	72
	RGB	5.7	200	SegNet	VGG-16	Rice paddy, Rice lodging		87.55	38.65		53.63	99
	RGB+ExG							57.06	67.50		61.84	109
	RGB+ExGR							81.35	58.59		68.12	107
	RGB+ExG+ExGR							82.47	29.07		42.99	113
	RGB			FCN	AlexNet			99.12	39.59		56.58	57
	RGB+ExG							95.03	59.18		72.94	67
	RGB+ExGR							95.32	66.39		78.27	68
	RGB+ExG+ExGR							93.19	67.03		77.97	71
[102]	RGB	6.4	150	FCN		Sunflower	78
				SegNet			79
				Improved SegNet	VGG-16		81.95
	Multispectral	8.1		FCN			77.45
				SegNet			78.25
				Improved SegNet	VGG-16		80.5
	Fusion (RGB + FNIR)	9.5		FCN			81.55
				SegNet			82.65
				Improved SegNet	VGG-16		86.55
[57]	RGB	9.5		FCRN-MTL		Full-grown citrus trees	98.8	99.2	98.4		98.8
						Citrus tree seedlings	56.6	47.8	65.4		55.23
[27]	RGB		20	Deep convolutional Encoder-decoder network		Fig Plant	93.84	92	95.05		93.50
				SegNet-basic			93.82	93.49	93.22		93.35
[53]	RGB		20	U-Net		Corn	99.4
[8]	RGB		10	FCN		Beet		49.69	26.25		32.15
				SegNet				80.24	67.4		71.79
				CR-Hough-SLIC				86.58	85.67		85.40
				CRowNet				90.37	90.56		90.39
				CR-Hough-SLIC		Maize		85.14	97.74		82.13
				CRowNet				84.57	80.93		82.5
[21]	RGB	2.21		SegNet	VGG-16	Rice, Corn	89.44
				FCN	AlexNet		88.48
[23]	Multispectral			U-Net	VGG-16	Sugar beet	99
				SegNet			98
[112]	RGB			U-Net		Apple tree crown	97.1	84.2	84.5		84.2
[123]	RGB	0.37, 0.56, 0.74	20	U-Net		Purple rapeseed leaves		$\sim$94	$\sim$90		91.56
[52]	RGB	3	100	U-Net		Pinus radiata, Ulex europaeus	85.5
[66]	RGB	1.7–2		Mask R-CNN		Potato		99.72	82.50		90.3
						Lettuce		100	95.43		97.7
[81]	RGB	1	40–60	Mask Scoring R-CNN		Maize V5 growth stage	95.8	82.8
	Multispectral	2.5				Maize V4 growth stage	82.7	79.9
[25]	RGB	4	120	DeepLabv3+	ResNet-18	Amazonian palms		86	88		87
[74]	RGB			DeepLabv3+		Mauritia flexuosa	98.036	96.688	95.616		96.14
				U-Net1			95.973	91.381	92.632		92
				U-Net2			97.682	94.858	95.953		95.4
				U-Net3			96.843	92.534	94.886		93.7
				U-Net4			97.512	95.166	95.028		95.1
[128]	Hyperspectral	46.3	500	CNN-CRF		WHU-Hi-LongKou: 6 crop types	98.91			0.9857
		10.9	250			WHU-Hi-HanChuan: 7 crop types	93.95			0.9290
		4.3	100			WHU-Hi-HongHu: 17 crop types	93.74			0.9217
[60]	RGB	0.47, 0.90, 1.43, and 1.76		FDN-92		12 plants	87
				FDN-29			84
				FDN-17			86
				Inception-V1			80
				Inception-V2			79
				Inception-V3			77
				ResNet-17			73
				ResNet-50			74
				ResNet-101			76
				DenseNet-21			82
				DenseNet-36			80
				DenseNet-121			82
[43]	Hyperspectral			CNN		19 crop types	88.62			0.8557
				CNN-CRF			91.79			0.8957
[82]	RGB	25		2D-CNN		Highland Kimchi cabbage, Cabbage, Potato	86.56
[18]	RGB	3		DNN	VGG-16	Maize, Bananas, Legumes	86	86	86	0.82	86
[109]	RGB + Multi-spectral		100	LeNet	LeNet	Corn	67.7		46.4
			180				86.8		81.8
			100-180				72.6		54.5
			100				47.7		12.8
[85]	RGB	5		Hybrid CNN-HistNN		22 crops					90
[129]	UAV			ANN		Peanut, Maize, Honeysuckle, Tree	78.53			0.73
	Fused						85.72			0.81