Distinguishing High- and Low-Yielding Durum Wheat Genotypes Using UAV Spectral and Textural Data

Plant breeding trials often involve a large number of genotypes, making field-based evaluation of agronomic traits labor-intensive, expensive, and time-consuming. Pre-harvest identification of superior genotypes using remote sensing could substantially improve breeding efficiency. This study evaluated the potential of unsupervised (clustering) and supervised (regression) methods based on unmanned aerial vehicle (UAV) multispectral imagery to differentiate winter durum wheat genotypes according to yield, grain protein content (GPC), and protein yield (PY). A three-year field experiment involving 26 genotypes was conducted at the Institute of Field Crops (Chirpan, Bulgaria). UAV data acquired at the end of flowering (BBCH 69) and the beginning of grain filling (BBCH 71) with a DJI Phantom 4 Multispectral were used to derive spectral vegetation indices (SVIs) and texture features (TFs). In the supervised approach, machine learning regression models were used to predict the target traits before grouping genotypes into low-, medium-, and high-performance classes, whereas Ward’s hierarchical clustering was applied directly to the UAV-derived features in the unsupervised approach. The resulting genotype groups were compared with genotype groupings based on the means and standard deviations derived from the field measurements. Both approaches successfully identified high- and low-performing genotypes for yield and PY, achieving accuracies of 50–100%. In contrast, both methods showed limited performance for GPC. ANOVA revealed that agronomic traits and the best-performing SVI, the Normalized Difference Red-Edge Index (NDRE), were strongly influenced by environmental variability, whereas TFs appeared to capture more genotype-specific structural characteristics. These findings demonstrate that both supervised and unsupervised UAV-based approaches can support early identification of superior wheat genotypes, with texture features showing particular promise for genotype discrimination across breeding trials.