Baskar Ganapathysubramanian

The paper uses AI-assisted image phenotyping to analyze pod traits in mungbean, identifies SNPs associated with pod morphology, and demonstrates improved genomic prediction accuracy.

This paper compares genomic and phenomic prediction for soybean seed yield, protein, and oil content, finding phenomic prediction outperforms genomic for yield while genomic is better for protein and oil.

The paper compares spatial adjustment methods in plant breeding trials, finding that XGBoost improves yield estimates by incorporating soil feature modeling at different scales.

This study uses data-driven methods to analyze hyperspectral leaf reflectance data from maize plants, identifying genetic markers correlated with reflectance patterns and chlorophyll content.

This paper uses deep learning to predict soybean yield by integrating genotype and weather data from historical trials.