Predicting grain protein concentration in winter wheat (Triticum aestivum L.) based on unpiloted aerial vehicle multispectral optical remote sensing

Abstract

Prediction models for crude protein concentration (CP) in winter wheat (Triticum aestivum L.) based on multispectral reflectance data from field trials in 2019 and 2020 in southern Sweden were developed and evaluated for independent trial sites. Reflectance data were collected using an unpiloted aerial vehicle (UAV)-borne camera with nine spectral bands having similar specification to nine bands of Sentinel-2 satellite data. Models were tested for application on near-real time Sentinel-2 imagery, on the prospect that CP prediction models can be made available in satellite-based decision support systems (DSS) for precision agriculture. Two different prediction methods were tested: linear regression and multivariate adaptive regression splines (MARS). Linear regression based on the best-performing vegetation index (the chlorophyll index) was found to be approximately as accurate as the best performing MARS model with multiple predictor variables in leave-one-trial-out cross-validation (R2 = 0.71, R2 = 0.70 and mean absolute error 0.64%, 0.60% CP respectively). Models applied on satellite data explained to a small degree between-field variations in CP (R2 = 0.36), however did not reproduce within-field variation accurately. The results of the different methods presented here show the differences between methods used and their potential for application in a DSS.

Keywords

• decision support system
• multispectral
• protein
• sentinel-2
• unpiloted aerial vehicle (uav)
• wheat