Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging

Accurate and rapid diagnosis of nitrogen status in fruit trees on an individual tree basis is a prerequisite for precision orchard nutrient management. This study presents a rapid and non-destructive approach for estimation and mapping of nitrogen content in apple trees at both leaf and canopy levels. An ImSpector V10 system was used to collect hyperspectral images (400–1000 nm) for both apple leaves and canopies. Nitrogen content in apple leaves was measured by Vario EL cube. Raw reflectance and first derivative reflectance were used to relate to leaf nitrogen content. Partial least squares (PLS) regression and multiple linear regression (MLR) analyses were performed to estimate nitrogen content from reflectance. The results showed that both PLS and MLR models achieved reasonable predictive accuracy (PLS and MLR models based on raw reflectance: R2 = 0.7728 and 0.7843 (p < 0.001); PLS and MLR models based on first derivative reflectance: R2 = 0.7745 and 0.774 (p < 0.001)). However, the MLR model based on raw reflectance demonstrated its advantage over the PLS models as well as the MLR model based on first derivative reflectance, because it only used 4 key wavelengths (505, 560, 675 and 705 nm) while the other models were based on either the full wavelengths (132 wavelengths) or more narrowband wavelengths adjacent to the selected key wavelengths. Furthermore, nitrogen distribution maps at both leaf and canopy levels were generated based on the nitrogen contents estimated by the MLR model based on raw reflectance. This new approach may be potentially applied to precision apple orchard nutrient management.