In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production.

The objective was to estimate the correlation between VIs and grain yield and identify the optimal timing and VIs for precise corn grain yield estimation. Furthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in CRB with three rep-lications at three locations. Vegetation indices and green leaf area were estimated throughout the cycle using an unmanned aerial vehicle (UAV) and subsequently corre-lated with grain productivity. In addition, photographs were taken of the corn ear to esti-mate their length, width and total number of kernels and compare these values with manual measurements. The experiments consistently demonstrated significant experi-mental quality across sites, with accuracy ranging from 79.07% to 95.94%. UAV flights carried out at the beginning of the crop cycle revealed a positive correlation between grain productivity and the evaluated indices (NGRDI, VARI, GLI). Regarding the phenotyping of corn ears, the regression coefficients for width, length and TNG were 0.92, 0.88 and 0.62, respectively, indicating an association with manual measurements. However, stage V5 in the localities of Lavras and Ijaci and stage V8 in the locality of Nazareno showed a posi-tive correlation with productivity. The use of images for ear phenotyping is promising as a method for measuring corn components.