Maize grain and silage yield prediction of commercial fields using high-resolution UAS imagery

The aim was to evaluate if maize (Zea mays L.) grain and silage yield can be estimated from unmanned aerial systems (UAS) imagery. A fixed-wing UAS was used to collect imagery throughout the growing season (emergence to reproductive growth stages) at eight commercial fields, each implemented with a nitrogen-rich (NRich) strip. Exponential regression models were fitted between yield and seven vegetation indices (VIs) from NRich strip, whilst two machine learning (ML) regression models (random forest [RF] and support vector regression [SVR]) were fitted with VIs, elevation, and landform layers. The results showed that the normalised difference vegetation index (NDVI) was the most reliable indicator for yield using exponential models when the images were collected at late vegetative stages before tasseling for silage and late reproductive stage before senescence for grain. The exponential models performed better with NRich strip than those derived with the statistical reference strips (SRS) for fields without NRich strips, but the differences were minimal when sensed at a vegetative stage before tasselling. The Boruta algorithm identified elevation, landform, and three VIs as the top five important features for yield estimation. Using these five features, the RF model produced a lower error and was faster to train than the SVR. The ML models outperformed the exponential models with SRS but underperformed compared to the models with NRich strip. It was concluded that if a field does not have an NRich strip, using RF models with additional data layers can provide reliable yield estimations for maize grain and silage.