Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties

The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant (HTPP) techniques, such as aerial ground RGB images quick canopy leaf sensors, aid assessing crop status selecting tolerant varieties. This study focused on region, considering need for a precise selection research examined use affordable HTPP methods, imaging active multispectral management improved health support commercial field programs. evaluated 40 advanced lines bread (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated untreated blocks disease pressure (Septoria, brown rust, stripe rust observed). Measurements leaf-level pigments vegetation indexes were taken using portable cameras, sensors mounted unmanned vehicles (UAVs). Significant differences observed Dualex flavonoids nitrogen balance index (NBI) between treatments some (p < 0.001 Elorz Ejea). vigor color plot level showed significant all sites, highlighting green area (GA), senescence (CSI), triangular greenness (TGI) effects fungicide different cultivars. from UAV highly correlated (r = 0.817 r 0.810 TGI NGRDI). However, Greenseeker NDVI sensor was found be more effective estimating grain protein content (R2 0.61–0.7 R2 0.45–0.55, respectively) compared AgroCam GEO 0.25–0.35 0.12–0.21, respectively). We suggest practical consideration GreenSeeker user-friendly less affected by external factors. emphasized throughput benefits HTPPs with high similarity results highlighted potential supporting fungal-disease-resistant