Ittai Herrmann

The paper uses a designed nutrient-gradient framework and machine learning to predict leaf ionome in tropical crops, achieving high predictive accuracy across unseen conditions.

This study uses smartphone RGB imagery and LiDAR data to accurately estimate leaf nitrogen concentration and shoot biomass in Chinese spinach, with low error rates.

This study examines how nitrogen application affects physiological traits and mineral composition in leafy vegetables under tropical conditions, finding optimal nitrogen levels and mineral uptake dynamics.

This study uses high-throughput phenotyping and genomic models to improve yield prediction in sesame by analyzing temporal trait dynamics and multi-trait associations.