Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging

Durum wheat is an important cereal that is widely grown in the Mediterranean basin. In addition to high yield, grain quality traits are of high importance for farmers. The strong influence of climatic conditions makes the improvement of grain quality traits, like protein content, vitreousness, and test weight, a challenging task. Evaluation of quality traits post-harvest is time- and labor-intensive and requires expensive equipment, such as near-infrared spectroscopes or hyperspectral imagers. Predicting not only yield but also important quality traits in the field before harvest is of high value for breeders aiming to optimize resource allocation. Implementation of efficient approaches for trait prediction, such as the use of high-resolution spectral data acquired by a multispectral camera mounted on unmanned aerial vehicles (UAVs), needs to be explored. In this study, we have acquired multispectral image data with an 11-band multispectral camera mounted on a UAV and analyzed the data with machine learning (ML) models to predict grain yield and important quality traits in breeding micro-plots. Combining 11-band multispectral data for 34 cultivars and 16 environments allowed to develop ML models with good prediction capability. Applying the trained models to test sets explained a considerable degree of phenotypic variance with good accuracy showing r squared values of 0.84, 0.69, 0.64, and 0.61 and normalized root mean squared errors of 0.17, 0.07, 0.14, and 0.03 for grain yield, protein content, vitreousness, and test weight, respectively.

Automated summary

This study uses multispectral data acquired by a UAV-mounted camera and analyzes it with machine learning models to successfully predict grain yield and quality traits. The results demonstrate the high potential of this method in improving grain quality and optimizing resource allocation.