Skip to main content

Advertisement

Table 8 Accuracy assessment for the estimation of AGB in wheat from vegetation indices, plant height metrics and their combination with SMLR and three machine learning techniques

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

Input variables Technique Calibration (N = 201) Validation (N = 85)
R 2 RMSE (t/ha) AIC R 2 RMSE (t/ha) rRMSE (%)
VIs SMLR 0.69 1.52 742.63 0.70 1.58 34.49
SVR 0.69 1.53 696.27 0.64 1.75 38.13
ELM 0.69 1.52 759.25 0.64 1.73 37.27
RF 0.70 1.51 423.87 0.69 1.61 34.06
Canopy height metrics SMLR 0.68 1.53 747.72 0.72 1.54 33.61
SVR 0.68 1.56 708.20 0.70 1.55 33.71
ELM 0.65 1.65 751.30 0.71 1.55 33.63
RF 0.73 1.44 398.32 0.74 1.39 30.95
VIs and canopy height metrics SMLR 0.72 1.43 717.41 0.75 1.46 31.77
SVR 0.71 1.50 592.05 0.73 1.51 33.10
ELM 0.72 1.45 733.02 0.73 1.51 32.77
RF 0.76 1.34 369.23 0.78 1.34 28.98
  1. The accuracy metrics were calculated from calibration data and validation data separately. The number in bold for each column represents the maximum R2, minimum RMSE, minimum AIC and minimum rRMSE, respectively