首页|Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale
Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale
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NSTL
Elsevier
Increasing nitrogen (N) diagnosis efficiency and accuracy is crucial for optimizing wheat N management. We aimed to establish a spatially and temporally explicit model for the diagnosis of winter wheat N status on small scale farms using multivariate information. To determine the most accurate approach, seven field experiments involving different cultivars and N treatments were conducted in east China over five years. A fixed-wing unmanned aerial vehicle (UAV) mounted multispectral camera was used to acquire canopy spectral data of winter wheat at the jointing and booting stages, while agronomic indicators of plant dry matter (PDM), plant N accumulation (PNA) and N nutrition index (NNI), as well as agrometeorological (AM) and field management (FM) data, were measured synchronously. Direct and indirect strategies of NNI estimation were applied for N diagnosis at the jointing and booting stages. Four machine learning (ML) algorithms were used to characterize the relationships between agronomic variables and UAV remote sensing, AM and FM data. The results demonstrated the random forest (RF) model that integrated UAV remote sensing, AM and FM data achieved the higher accuracy for predicting NNI (R2 = 0.82-0.87, RMSE = 0.11-0.12 and RE = 12.94%-15.57%) amongst the four ML models based on the direct strategy at the jointing and booting stages. Similarly, the RF model performed most accurate estimation for PDM (R2 = 0.69-0.78, RMSE = 0.43-0.61 t ha- 1 and RE = 12.74%-24.49%) and PNA (R2 = 0.83-0.84, RMSE = 13.00-17.53 kg ha- 1 and RE = 17.03%-25.44%), then NNI (R2 = 0.54-0.55, RMSE = 0.09-0.13 and RE = 8.34-12.65%) was further calculated using the indirect diagnosis strategy. Based on the optimal NNI diagnosis interval derived from the relationships between relative yield (RY) and plant NNI at the jointing (0.92-1.04) and booting (0.97-1.15) stages, the two diagnosis strategies obtained similar diagnostic accuracies in the three study farms and performed more accurately at the booting (areal agreement = 0.70-0.90, Kappa = 0.49-0.82) than jointing (areal agreement = 0.54-0.71, Kappa = 0.36-0.53) stages. The combination of fixed-wing UAV remote sensing with AM and FM information using the RF algorithm can significantly increase the accuracy and efficiency of in-season wheat N diagnosis at the farm scale.
N diagnosisFixed-wing UAVMultivariate dataMachine learningIN-SEASON FERTILIZATIONNUTRITION INDEXOPTIMIZATION ALGORITHMSOIL PROPERTIESREFLECTANCEYIELDCORNRESOLUTIONPLANTMANAGEMENT
NSTL
Elsevier
