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

Abstract

ABSTRACT This 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 ( R 2 ). The prediction models showed satisfactory results, with values R 2 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.