Itamar Shenhar

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.

The paper introduces a wireless sensor network for continuous monitoring of plant-environment interactions, showing strong correlation between transpiration and biomass gain.

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 controlled drought affects inflorescence yield and secondary metabolites in three Cannabis sativa cultivars, finding strong correlations between transpiration and yield, and genotype-dependent terpene variation.

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.