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Table 1 Summary of published studies on the estimation of plant height and biomass of crops from RGB imagery acquired from unmanned aerial vehicles (UAVs)

From: Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system

Reference Crop type UAV Sensors Regression method VIs/SfM Canopy characteristic Best accuracy
Bendig et al. [21] Barley MK-Oktokopter Panasonic Lumix GX1 (RGB) ER Biomass R2 = 0.82
PH R2 = 0.92
Watanabe et al. [27] Sorghum USM-S1 Powershot ELPH 110HS GPM PH r = 0.84
Schirrmann et al. [28] Wheat Hexacopter (P-Y6) Sony Nex 7 LR Biomass r = 0.68
PCA PH r > 0.80
Iqbal et al. [22] Poppy Oktokopter Canon 550D DSLR LR SfM PH R2 = 0.71
Volume R2 = 0.71
Roth et al. [17] Winter wheat ARF Mikrokopter Okto XL Canon EOS 100D LR None Biomass R2 = 0.74
PH R2 = 0.80–0.84
Bendig et al. [6] Barley MK-Oktokopter Panasonic Lumix GX1 (RGB) + FieldSpec3 MLR VIs + SfM Biomass R2 = 0.84
Kim et al. [29] Cabbage DJI F550 Hexa-rotor Powershot S110 RGB MLR PH R2 > 0.90
Li et al. [7] Maize Rotor-wing UAV Sony A6000 MSLR VIs + SfM Biomass R2 = 0.78
RF PH R2 = 0.88
Holman et al. [30] Wheat DJI Wookong M Sony Nex 7 SfM PH RMSE = 3.00 cm
Growth rate
Madec et al. [31] Wheat Hexacopter Sony ILCE-6000 SfM PH RMSE = 3.50 cm
  1. CC Canopy cover, ER exponent regression, GPM genomic prediction modeling, LR linear regression, MLR multiple linear regression, MNLR multiple non-linear regression, MSLR multiple stepwise linear regression, PCA principal components analysis, PH plant height, RF random forest, SfM structure from motion, VIs vegetation indices