Developing climate-resilient wheat varieties requires combining high yield with stability across diverse environments, especially under increasingly variable precipitation and rising temperatures. This study evaluated 64 post-Green Revolution durum wheat cultivars under irrigated and rainfed conditions at two contrasting Mediterranean sites in Spain. A classification framework was developed to support genotype selection based on yield and yield stability, estimated using linear mixed models and yield slopes across environments. Genotypes were classified by interquartile thresholds, and those showing either low yield or low stability were considered undesirable for selection. High-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data. VIs and TIR at anthesis and grain filling, and their differences (senescence proxies), were used to train Random Forests for yield and stability estimation including sequential feature selection. Environmental covariates (water input, reference evapotranspiration) were integrated in yield models, with strong outcomes (R² > 0.74; MAPE <23.6%). Stability predictions were based on VI stability and, though moderate (R² up to 0.56; MAPE <17.75%), outperformed previous studies. Selected features were used to evaluate seasonal reflectance phenotypes: “keep” genotypes (intermediate/high yield or/and stability) exhibited early-vigor but lower green retention by the end of grain filling, while “discard” genotypes (low yield or/and stability) showed reduced early vigor and “stay-green” behavior. This study highlights early-vigor and earlier senescence over “stay-green” for wheat selection, offering a cost-effective approach shifting the breeding focus from yield maximization to joint yield-stability evaluation, promoting sustainability.

The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen (Warrior/Ambition), against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity. RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R² = 0.581 and R² = 0.536, respectively), far surpassing the predictive ability of NDVI (R² = 0.118 and R² = 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.