Using UAV-based multispectral remote sensing imagery combined with DRIS method to diagnose leaf nitrogen nutrition status in a fertigated apple orchard

Monitoring plant growth information by timely and effective approaches, such as UAV remote sensing, is crucial to achieve the precise nutrient management. The study investigated that the effects of irrigation levels (Full irrigation and deficit irrigation) and nitrogen rates (0, 120, 240 and 360 kg N ha −1 ) on leaf nutrient (NPK) content and mainly assessed the capacity of UAV-based multispectral imagery to quantify the leaf NPK content. The UAV multi-spectral indices were used to perform ordinary linear regression (OLR), multivariate stepwise regression (MSR) and ridge regression (RR) inversion model on leaf NPK content, and further combining the diagnosis and recommendation integrated system (DRIS) with the optimal inversion model of leaf NPK content to diagnose the leaf nitrogen (N) nutrition status. The results indicated that three inversion models of LNC at a single stage had indicated that it was suitable to use UAV-based multispectral imagery to assess LNC before the early fruit expansion stage ( R 2 = 0.52–0.76), but not stages after that ( R 2 < 0.5). the MSR and RR inversion models with pooling data from multiple stages of the LPC ( R 2 = 0.67 and 0.69) and LKC ( R 2 = 0.76 and 0.76) produced better performance, while that of LNC was poor ( R 2 = 0.49 and 0.50). The DRIS analysis shown that the LNC of 360 kg ha −1 under two irrigation levels was not deficient in shoot growth stage and early fruit enlargement stage, while it was deficient in young fruit. Moreover, the LNC of other treatments was deficient in three growth stages. Combining the remote-sensed models for assessing leaf NPK content and DRIS method to diagnose leaf N nutrition status has the potential to guide precise N application in fertigated apple orchards.