DDD Conference

Dr. Tarin Paz-Kagan

Predicting of Canopy Nitrogen Content based on UAVs and Satellites Data Fusion in Citrus Orchards

Volcani Institute, Agricultural Research Organization, Israel

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.

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