Estimation of macro and micronutrient concentrations in ‘valencia’ orange leaves at different phenological stages using multispectral image analysis and multiple regression

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:31:11Z
Fecha de publicación2024-07-26
ResumenThis study aimed at estimating the macro- and micronutrient concentrations in leaves of Valencia oranges (Citrus sinensis [L.] Osbeck) through multiple regression analysis using multispectral images and vegetation indices (VIs) selected through the stepwise regression method. The data were collected in a commercial orchard of Valencia oranges in El Barretal, Tamaulipas, Mexico. The leaf samples were analysed in a laboratory to determine the concentrations of N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn using traditional chemical methods; the relative chlorophyll values were obtained using a chlorophyll metre (SPAD502 Plus). Multi-spectral images of the tree canopy were acquired using a UAV equipped with a multispectral camera with five wavelengths (blue, green, red, red edge, and near-infrared); based on these data, 85 VIs were calculated. To select the predictor variables that were most relevant for the model, 90 candidate variables were divided into three groups with 30 VIs grouped by nutrients based on the highest and lowest absolute values found in the correlation matrix. Heteroscedasticity and autocorrelation were also assessed through the Breusch-Pagan and Durbin-Watson tests, respectively. The selected variables were used in multiple regression analysis. The resulting models were validated by mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The prediction models showed satisfactory results, with values R2 between 0.79 and 0.95 and relatively low (<10) MAE and RMSE for all nutrients, except Fe (<30) in all phenological stages; MAPE values were lower than 0.5% for all nutrients. This study showed that multispectral imaging combined with multiple regression analysis effectively predicts nutrient levels in the leaves of Valencia oranges in various phenological stages. This study presents a new perspective on agricultural systems, offering an effective alternative to assess the nutritional conditions of crops.es_ES
Doihttps://doi.org/10.1080/01431161.2024.2377835es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/2860
Idiomaenes_ES
EditorialTaylor & Francises_ES
RelaciónInternational Journal of Remote Sensinges_ES
URL relacionadohttps://doi.org/10.1080/01431161.2024.2377835es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteInternational Journal of Remote Sensing
Palabra claveMean squared errores_ES
Palabra claveMathematicses_ES
Palabra claveLinear regressiones_ES
Palabra clavePartial least squares regressiones_ES
Palabra claveStatisticses_ES
Palabra claveCoefficient of determinationes_ES
Palabra claveMean absolute percentage errores_ES
Palabra claveMultispectral imagees_ES
Palabra claveRegression analysises_ES
Palabra claveEnvironmental sciencees_ES
Palabra claveRemote sensinges_ES
Palabra claveGeographyes_ES
ClasificaciónLeaf Properties and Growth Measurementes_ES
TítuloEstimation of macro and micronutrient concentrations in ‘valencia’ orange leaves at different phenological stages using multispectral image analysis and multiple regressiones_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorOliveira, Pedro Paulo Gomes de
AutorSilva-Junior, C. A. Da
AutorBarrón-Zambrano, José Hugo
AutorNeri-Ramírez, Efraín
AutorDiaz-Manríquez, Alan
AutorOsorio-Hernández, Eduardo
AutorDelgado-Martínez, Rafael
AutorOliveira, Pedro Paulo Gomes dees_ES
AutorSilva-Junior, C. A. Daes_ES
AutorBarrón-Zambrano, José Hugoes_ES
AutorNeri-Ramírez, Efraínes_ES
AutorDiaz-Manríquez, Alanes_ES
AutorOsorio-Hernández, Eduardoes_ES
AutorDelgado-Martínez, Rafaeles_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número16es_ES
Rango de páginas5506-5529es_ES
URL relacionadahttps://doi.org/10.1080/01431161.2024.2377835
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen45es_ES

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