High-Throughput Phenotyping: Application to Maize Breeding

Abstract

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.

Plant phenotyping relevance

UAV画像による植生指数・葉面積推定と、画像によるトウモロコシ穂の形質推定を手動測定と比較検証しており、フェノタイピング手法が中心的です。

Code and data availability

The preprint describes UAV-based vegetation index analysis and RGB ear-image phenotyping for maize, but provides no public dataset, image, or code deposit. The Data Availability Statement says only 'Data are contained within the article.' All referenced tools (R, FIELDimageR, OpenCV, skimage, Pix4D, AgiSoft) are third‑

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