3. Dr. Tarin Paz-Kagan

Nitrogen (N) is often regarded as the most critical nutrient and the growth-limiting factor in soil for ‎‎plant growth and often needs to be supplemented by N-fertilization to minimize yield ‎‎loss. However, over-application of N fertilizers may cause nutrient imbalance, ‎contribute ‎to groundwater ‎contamination by nitrate (NO3-) leaching. Today the ‎evaluation of plant ‎nutritional status (including ‎N content) is mainly based on chemical ‎analysis of leaf ‎samples in the lab’. These are expansive time consuming, and not ‎representing the spatial ‎variability in the agricultural system. Remote sensing ‎applications extracted from satellites ‎and unmanned aerial vehicle (UAV) images and ‎machine learning algorithms have been ‎proved effective in assisting nutritional analysis ‎in plants. However, each platform has its ‎pros and cons, and therefore, the fusion of ‎UAV data with satellite may overcome part ‎of these limitations. This study suggests ‎investigating the ability to combine Sentinel-2 ‎and VENµS data with UAV-derived ‎canopy data to assess canopy nitrogen content ‎‎(CNC) in citrus orchards. A new ‎framework to infer the nitrogen content in citrus-tree at ‎canopy-level using spectral data ‎and vegetation indices with the ML algorithm is ‎suggested. The framework includes six ‎steps (1) leaf sampling for N content data, (2) ‎image preprocessing of UAV (3) and ‎satellite data preprocessing, (4) feature extraction, (5) model ‎calibration and validation based on ML, ‎and (6) the development of site-specific N ‎management model. The suggested model was proven flexible and could include different or additional variables, enabling the delineation of fine site-specific nitrogen management (SSNM) zones in orchards. The model can be used to ‎reduce the need for chemical analysis of the leaf tissue and optimizes the CNC monitoring ‎, by taking into account the spatial and temporal variability in the citrus ‎orchard.