Biology subjects

Shenhar, I.

Publications and source records attributed to Shenhar, I..

8 recordsLinked to original sources

bioRxiv2026

Designed nutrient-gradient phenotyping enables predictive ionomics of tropical leafy greens

Accurate prediction of edible-leaf mineral composition is challenging because tissue ionomes integrate nutrient supply, plant identity, growth dilution, water-balance regulation and cumulative environmental exposure. We combined a designed nutrient-gradient experimental framework with machine learning to predict leaf ionome using 1,163 Chinese spinach (Amaranthus dubius) and Chinese broccoli (Brassica oleracea Alboglabra Group) plants grown across 16 designed nutrient-gradient experiments in a semi-controlled tropical greenhouse. Targeted perturbations of N, P, K, Ca, Mg, Fe and Mo were combined with cumulative transpiration, integrated microclimate, shoot dry weight and laboratory quantification of 12 harvested-leaf mineral targets. The resulting ionome was broad, non-Gaussian and species dependent, with coordinated off-target shifts also evident for minerals whose supplied concentrations were held constant. We benchmarked AutoGluon and TabPFN using five-fold leakage-free, distribution-balanced grouped cross-validation, in which all biological replicates from each dosing condition were withheld together. Across previously unseen nutrient conditions, pooled out-of-fold R2 ranged from 0.72 to 0.92, with broadly comparable performance between architectures. Learning curves showed rapid early gains for several targets but persistent condition-level generalization gaps for total reduced nitrogen, K, Mn and Zn. Model-agnostic Shapley additive explanations identified target-specific combinations of nutrient inputs, plant type, substrate, cumulative environment and physiological traits, while cross-model attribution agreement varied among minerals. Finally, reduced-feature models were tested on an independent grower-operated cohort of 99 plants. Calibration was closest for NO3, Mg and B, whereas Mn and Na showed substantial external bias. These results establish leakage-aware predictive ionomics as a robust framework for post-harvest lab-free crop mineral estimation while defining the calibration and sensing requirements for commercial deployment.

plant biology↗

bioRxiv2025

Wireless Sensor Network: New Concept of Spatial-Temporal Monitoring Plant-Environment Interactions

We present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant-environment interactions. Each plant carries in-canopy microclimate sensors (temperature, relative humidity, illuminance) paired with nearby ambient references, yielding real-time canopy-ambient differentials. The system is easy to install: at planting or sowing, sensors are fixed at positions that will lie within the developing canopy, and a separate ambient reference area is designated and kept free of vegetation. As plants grow, they envelop the sensors, thereby capturing growth dynamics over time. The sensors accuracy was validated against a commercial weather station and portable system that measures gas exchange, temperature and light (LI-COR 6800/6400), and the systems ability to resolve plant physiological activity was confirmed using the PlantArray functional phenotyping platform with independent whole-plant transpiration and biomass references. Under controlled growth-room conditions and across two contrasting Cannabis cultivars, daily transpiration strongly predicted biomass gain (R{superscript 2} > 0.9). Microclimate signals mirrored physiology: midday canopy air was cooler by 4-7 {degrees}C, more humid by 18-25 % RH, and increasingly shaded as biomass accumulated, with temperature, RH, and light attenuation showing saturating logarithmic relationships with growth. The network operated for months unattended with low packet loss and predictable power use. It provides 4D (x-y-z-time) coverage, where x and y denote horizontal location, z the vertical position within the canopy, and time the dynamics, enabling resolution of where changes occur and how they evolve, and supplying high-frequency labeled data. This system complements, rather than replaces, precision instruments and high-end phenotyping platforms, providing a scalable layer for continuous tracking across wide areas. We outline practical constraints and next steps toward field pilots, modest energy harvesting, expanded sensor suites, and integration with machine learning for predictive crop management.

bioengineering↗

bioRxiv2025

Effects of Drought on Inflorescence Yield, and Secondary Metabolites in Cannabis sativa L.

O_LIThe use of medical products derived from Cannabis sativa L. has increased significantly in recent years. While drought is known to negatively affect the yields of many crops, growers often recommend controlled periods of drought for cannabis cultivation to increase concentrations of secondary metabolites. This is especially pertinent when considering the relationship between medicinal effects and the secondary-metabolite profile. C_LIO_LIWe examined the effects of tightly controlled drought treatments on biochemical (111 phytocannabinoids and 132 terpenoids), physiological, and anatomical responses of three Type-I chemotype cultivars, specifically the THCA-dominant cultivars Odem, MVA, and 187. C_LIO_LIOur results revealed strong correlations between inflorescence and phytocannabinoid yields, on the one hand, and cumulative transpiration on the other (0.96 < r2 < 0.99). Drought treatment reduced canopy conductance, with inflorescence weight decreasing by 40% and total fresh weight decreasing by 48%. The concentrations of the major phytocannabinoids, THCA and CBGA, decreased over increasing levels of drought stress (by 26% and 61%, respectively). Interestingly, terpene concentrations showed greater stress-induced variation, and that variation was genotype-dependent. C_LIO_LIOur findings suggest that the decreases in inflorescence weight and concentrations of major phytocannabinoids under drought conditions are mainly due to a lack of biochemical-production processes, as opposed to metabolic degradation. C_LI

plant biology↗

bioRxiv2024

Harnessing Smartphone RGB Imagery and LiDAR Point Cloud for Enhanced Leaf Nitrogen and Shoot Biomass Assessment - Chinese Spinach as a Case Study

Accurate estimation of leaf nitrogen concentration and shoot dry-weight biomass in leafy vegetables is crucial for crop yield management, stress assessment, and nutrient optimization in precision agriculture. However, obtaining this information often requires access to reliable plant physiological and biophysical data, which typically involves sophisticated equipment, such as high-resolution in-situ sensors and cameras. In contrast, smartphone-based sensing provides a cost-effective, manual alternative for gathering accurate plant data. In this study, we propose an innovative approach for estimating leaf nitrogen concentration and shoot biomass by integrating smartphone RGB imagery with Light Detection and Ranging (LiDAR) data, using Amaranthus dubius (Chinese spinach) as a case study. The influence of varying nitrogen dosages on individual spectral and structural features derived from smartphone RGB imagery and LiDAR data was modeled. Additionally, the spectral indices from RGB imagery and structural indices from LiDAR data were combined to model both leaf nitrogen concentration and shoot biomass. The performance of crop parameter modeling was evaluated using support vector regression, random forest regression, and lasso regression. Results demonstrate that the combined use of smartphone RGB imagery and LiDAR data can accurately estimate leaf total reduced nitrogen concentration, leaf nitrate concentration, and shoot dry-weight biomass, with average relative root mean square errors as low as 0.06, 0.16, and 0.05, respectively. Furthermore, the optimal nitrogen dosage for maximizing biomass yield in Chinese spinach was also estimated using the smartphone data. This study lays the groundwork for smartphone-based estimate leaf nitrogen concentration and shoot biomass, supporting accessible precision agriculture practices.

bioinformatics↗

bioRxiv2024

Nitrogen ionome dynamics on leafy vegetables in tropical climate

Nitrogen is known to be a critical macro-nutrient influencing plant physiology, growth, and mineral composition. In tropical conditions, which are challenging for leafy vegetable farming, the nitrogen delivery effect is unclear. In this study, we aimed to investigate the effect of nitrogen application on key physiological traits and the mineral composition of the plants, the plant ionome. Experiments were conducted under tropical conditions greenhouse with varying levels of nitrogen supply to examine the effect on plant transpiration, yield, use efficiency of water and nitrogen, and nutrient uptake dynamics followed by cross-correlation analysis, trying to understand the physiological behavior-uptake dynamics relationships. The results demonstrated that transpiration, yield and WUE theoretic optimum curve, which peaking in nitrogen concentration of 120 mg/L for Chinese spinach and 200 mg/L for Chinese broccoli. Conversely, NUE reduce significantly with increasing nitrogen delivery which reflected on antagonistic increase of excess nitrogen. In terms of mineral composition, nitrogen application resulted in an increase I nitrogen content in the plant leaf tissue, while concentration of certain macronutrients and micronutrients were affected, including potassium, phosphorus, calcium, magnesium, iron, zinc, and molybdenum. Part of the minerals exhibited decreasing pattern due to potential competitive uptake mechanism, iron revealed increasing pattern that correlated with nitrogen delivery, and some minerals correlated with the measured physiological parameters. These results underscore the importance of optimizing nitrogen fertilization to balance plant growth, physiological processes, and plant nutrient homeostasis. The study offers valuable insights for sustainable nitrogen management in agricultural systems aimed at maximizing crop yield while maintaining nutritional quality.

plant biology↗

bioRxiv2021

Predicting tomato field-yield using continuous monitoring of young tomato water status

To address the challenge of predicting tomato yields in the field, we used whole-plant functional phenotyping to evaluate water relations under well-irrigated and drought conditions. The genotypes tested are known to exhibit variability in their yields in wet and dry fields. The examined lines included two lines with recessive mutations that affect carotenoid biosynthesis, zeta z2083 and tangerine t3406, both isogenic to the processing tomato variety M82. The two mutant lines were reciprocally grafted onto M82, and multiple physiological characteristics were measured continuously, before, during and after drought treatment in the greenhouse. A comparative analysis of greenhouse and field yields showed that the whole-canopy stomatal conductance (gsc) in the morning and cumulative transpiration (CT) were strongly correlated with field measurements of total yield (TY: r2 = 0.9 and 0.77, respectively) and plant vegetative weight (PW: r2 = 0.6 and 0.94, respectively). Furthermore, the minimum CT during drought and the rate of recovery when irrigation was resumed were both found to predict resilience.

physiology↗

bioRxiv2020

A High Throughput Gravimetric Phenotyping Platform for Real Time Physiological Screening of Plant Environment Dynamic Responses

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged. Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters only indirectly. This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant-environment interactions. It aims to help users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environments effect on the plants physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feedback fertigation system that enables a truly randomized experimental design with a field-like plant density. This system detects the soil-water content limiting threshold ({theta}) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement. SUMMARYThis high-throughput, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements and analysis of multiple yield-related physiological traits involved in dynamic plant-environment interactions.

plant biology↗

bioRxiv2019

A High-Throughput Physiological Functional Phenotyping System for Time- and Cost-Effective Screening of Potential Biostimulants

The improvement of crop productivity under abiotic stress is one of the biggest challenges faced by the agricultural scientific community. Despite extensive research, the research-to-commercial transfer rate of abiotic stress-resistant crops remains very low. This is mainly due to the complexity of genotype{square}x{square}environment interactions and in particular, the ability to quantify the dynamic plant physiological response profile to a dynamic environment.\n\nMost existing phenotyping facilities collect information using robotics and automated image acquisition and analysis. However, their ability to directly measure the physiological properties of the whole plant is limited. We demonstrate a high-throughput functional phenotyping system (HFPS) that enables comparing plants dynamic responses to different ambient conditions in dynamic environments due to its direct and simultaneous measurement of yield-related physiological traits of plants under several treatments. The system is designed as one-to-one (1:1) plant-[sensors+controller] units, i.e., each individual plant has its own personalized sensor, controller and irrigation valves that enable (i) monitoring water-relation kinetics of each plant-environment response throughout the plants life cycle with high spatiotemporal resolution, (ii) a truly randomized experimental design due to multiple independent treatment scenarios for every plant, and (iii) reduction of artificial ambient perturbations due to the immobility of the plants or other objects. In addition, we propose two new resilience-quantifying-related traits that can also be phenotyped using the HFPS: transpiration recovery rate and night water reabsorption.\n\nWe use the HFPS to screen the effects of two commercial biostimulants (a seaweed extract--ICL-SW, and a metabolite formula--ICL-NewFo1) on Capsicum annuum under different irrigation regimes. Biostimulants are considered an alternative approach to improving crop productivity. However, their complex mode of action necessitates cost-effective pre-field phenotyping. The combination of two types of treatment (biostimulants and drought) enabled us to evaluate the precision and resolution of the system in investigating the effect of biostimulants on drought tolerance. We analyze and discuss plant behavior at different stages, and assess the penalty and trade-off between productivity and survivability. In this test case, we suggest a protocol for the screening of biostimulants physiological mechanisms of action.

plant biology↗