Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials

Abstract Improving selection for multiple disease resistance (MDR) and yield in maize ( Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years in near‐isogenic inbreds, near‐isogenic hybrids, and a diverse hybrid panel. VIs showed lower coefficients of variation but broad‐sense heritability ranging from 0.09 to 0.95, compared with 0.30 to 0.99 for visual disease scores and 0.21 to 0.97 for yield. Correlations between VIs and ground traits were strongest in near‐isogenic hybrids, particularly for early‐season yield prediction ( r = 0.98–0.99 in 2018; r = 0.87–0.90 in 2019), and moderate for total disease severity (e.g., r = −0.61 to −0.68 in 2018). Associations were weaker and less consistent in the diverse hybrid panel (yield r = 0.11–0.28). Disease‐specific signals were temporally structured: NLS correlated most strongly with early‐season VIs ( r = −0.59 to −0.75), whereas ATD was best detected mid‐season ( r = −0.63 to −0.66) along with NLB ( r = −0.66 to −0.75). Overall, multispectral VIs captured meaningful canopy variation related to MDR and yield, with predictive performance depending on germplasm structure and flight timing. These findings highlight the potential of drone‐based temporal phenotyping to complement visual assessments and improve selection efficiency in maize breeding programs.