Winter beans: the use of an unmanned aerial vehicle for monitoring and prediction of crop performance

John R Dean, Shara Ahmed, Catherine Nicholson, Simon Rutter, John Marshall, Justin Perry


Traditional field-based techniques for phenotyping of crops are based on visual assessment which are subjective and time consuming. A high throughput automated technique using an unmanned aerial vehicle (UAV) with a multispectral image (MSI) camera was used to investigate the correlation between markers of winter bean crop development with eventual crop yield. A simplified approach has been developed using different vegetation indices i.e. NDVI, GNDVI and NDRE, coupled with an iso-cluster classification method to monitor plant characteristics across all growing stages. The UAV-MSI data could then be incorporated into a yield estimator model to estimate the winter bean seed yield.  The NDVI approach showed the greatest correlation between the modelled seed yield and the actual seed yield determined on two separate occasions (R2 = 0.84 and R2 = 0.87). In addition, GNDVI and NDRE were a better estimator of seed yield for areas with dense vegetation. These are hence shown to be able to monitor a winter bean harvest in an efficient and timely manner.


winter beans; unmanned aerial vehicle; multispectral imaging; crop yield prediction; phenotyping

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