Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields

Abstract Crop nitrogen (N) needs to be accurately predicted to allow farmers to effectively match the N supply to the crop N demand during crop growth in order to minimize environmental impacts as excess N could seep into the water supplies around the field. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral MicaSense imagery validated with ground hyperspectral measurements to predict canopy nitrogen weight (g/m2) of wheat and cornfields in Ontario. A simple linear regression was established to predict the canopy nitrogen weight from various vegetation indices (VI). Ratio Vegetation Index (RVI) performed the best out of all the tested vegetation indices, with an R 2 of 0.93 for the wheat fields and 0.83 for the corn fields. RVI estimation was also consistent throughout the growing season, which is optimal in precision agriculture. Once applied the RVI-based regression model to the UAV imagery, the best RMSE was 0.95 g/m2 for the wheat McColl field using the image of May 24th and 0.66 g/m2 for the corn Jack North field using the image of June 7th. Such information for accurately predicting nitrogen is important for farmers as it will lead to a more efficient fertilizer application program.

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