Journal of Agronomy and Field Innovations

Water scarcity has become a major limiting factor in maize production worldwide, making it necessary to create maize genotypes with higher water use efficiency (WUE). Traditional phenotyping techniques provide limited identification of traits, hence limiting the accuracy of drought-tolerant maize selection. The present study employed high-throughput phenotyping (HTP) technologies based on unmanned aerial vehicles (UAVs) and including multispectral and thermal imaging, as well as genomic prediction models and advanced statistical tools for identifying and selecting water-efficient maize genotypes. Three hundred twelve maize lines and hybrids were tested in both well-watered and water-limited fields of two different geographic locations. High throughput phenotyping technology measured parameters reflecting canopy growth, indicators of water stress, biomass, and physiological responses of maize at different stages of development. Genotyping data obtained based on the use of 50,000 SNP genotyping facilitated genomic prediction of grain yield and WUE. Genomic prediction showed 0.68–0.72 accuracy while traditional phenotypic selection led to 0.35–0.42 accuracy. Integrating multiple traits in genomic prediction involving physiological and agronomic traits enhanced predictive ability by 18-22% versus using single trait methods. Machine learning techniques provided insight into phenotypic trends associated with better WUE, specifically that canopy cooling, NDVI changes, and biomass accumulation rate were strong phenotypic indicators of grain yield during drought conditions. Advanced maize genotypes detected through integrated phenotyping and genomics achieved the yield increase by 23% when compared to sensitive checks during drought stress, while performing at par with controls during well-watered conditions. The combined use of measure phenotyping and genomic prediction can significantly facilitate breeding efforts designed to generate climate-resilient maize cultivars available for sustainable agriculture systems.