Inicio  /  Applied Sciences  /  Vol: 12 Par: 1 (2022)  /  Artículo
ARTÍCULO
TITULO

Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing

Julyanne Braga Cruz Amaral    
Fernando Bezerra Lopes    
Ana Caroline Messias de Magalhães    
Sebastian Kujawa    
Carlos Alberto Kenji Taniguchi    
Adunias dos Santos Teixeira    
Claudivan Feitosa de Lacerda    
Thales Rafael Guimarães Queiroz    
Eunice Maia de Andrade    
Isabel Cristina da Silva Araújo and Gniewko Niedbala    

Resumen

Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples.

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