Suchismita Mondal

This study evaluates aerial high-throughput phenotyping for indirect selection of grain yield in wheat breeding programs, finding vegetation indices predictive of yield and more effective than visual selection.

The paper develops regularized selection indices to improve breeding value prediction using hyper-spectral image data, showing higher accuracy for grain yield than traditional methods.

This study uses hyperspectral reflectance-derived relationship matrices in genomic selection to predict grain yield in wheat, showing improved accuracy when combined with marker and pedigree data.