Menachem Moshelion

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 shows that adventitious roots in tomato only partially compensate for transpiration loss under waterlogging, and that oxygen deficiency alone does not fully replicate waterlogging effects.

This study shows that knocking out the vacuolar invertase gene in potato plants improves drought tolerance by enhancing water-use efficiency and altering metabolic and hormonal responses.

This study uses machine learning models trained on gravimetric and environmental data to predict daily plant transpiration with high accuracy.

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 uses water-relations measurements to detect and quantify Fusarium wilt in tomato plants earlier and more objectively than visual assessments.

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.

The paper identifies a missense mutation in HvCHLI causing reduced chlorophyll content in barley, which shows potential for breeding crops adapted to arid conditions.

The paper identifies a third drought response strategy in barley, distinct from isohydric and anisohydric, showing unique transpiration dynamics and growth recovery traits.

This study evaluates the accuracy of the FAO-56 Penman-Monteith equation in a greenhouse using microclimatic data and machine learning to predict evapotranspiration.

This paper demonstrates that the iPhone 13-Pro camera with LiDAR can accurately estimate maize leaf surface area and correlate it with transpiration rates, offering a cost-effective tool for plant analysis.