Biology subjects

Herrmann, I.

Publications and source records attributed to Herrmann, I..

6 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↗

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

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sesame (Sesamum indicum) is an important oilseed crop with rising demand due to its high oil quality. To meet these future demands, there is an urgent need to develop and integrate new breeding strategies. While genomic resources have advanced genetic research in sesame, implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11 conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

plant biology↗

bioRxiv2023

Optimization of chickpea irrigation in a semi-arid climate based on morpho-physiological parameters

While the world population is steadily growing, the demand for plant-based protein in general, and chickpea in particular, is rising. Heatwaves and terminal drought are the main environmental constraints on chickpea production worldwide. Thus, developing better irrigation management for the chickpea agro-system can promote higher and more sustainable yields. Supplemental irrigation at the right timing and dose can increase yield dramatically. Here, we studied the response of a modern Kabuli chickpea cultivar to supplemental irrigation during the critical pod-filling period over three growing seasons (2019-2021) in northern Negev, Israel, under semi-arid conditions. Six irrigation treatments were applied based on irrigation factors of 0, 0.5, 0.7, 1.0, 1.2, and 1.4 of crop evapotranspiration (ET0) as measured by an on-site meteorological station. Morpho-physiological parameters and above-ground biomass accumulation were monitored throughout the cropping seasons, and the final grain yield was determined at maturation. Irrigation onset was determined based on plants leaf water potential ({Psi}LWP > 15 bar) in the field. Our results indicate that optimal water status (as reflected by pressure chamber values) was 12-14 bar during the irrigation period. Irrigation according to evapotranspiration (ET0) with an irrigation factor of 1.2 resulted in the highest grain yields over the three years. To ensure optimal water supply during the reproductive phase compatible with the crop water requirements, maintaining a 25 mm node length above the last fully developed pod and a 90 mm distance between the last fully developed pod to the stem apex is recommended. In conclusion, irrigation onset when the crop is already at mild drought stress, followed by sufficient irrigation while following the indicated morphology and water potential values, may help farmers optimize irrigation and maximize chickpea crop production. HighlightsO_LIChickpea irrigation is optimized based on meteorological data (ETc) and morphological traits. C_LIO_LIMorphological- not only physiological- traits can capture the dynamics of crop water status. C_LIO_LIIrrigation onset should be at mild drought stress (>15 bar) during the reproductive stage. C_LIO_LIExtending the reproductive phase is essential for grain yield improvement under semi-arid conditions. C_LIO_LIIF=1.2 provides the most productive irrigation regime for chickpea in Mediterranean conditions. C_LI

plant biology↗

bioRxiv2021

Particle interactions and their effect on magnetic particle imaging and spectroscopy

Tracer and thus signal stability is crucial for an accurate diagnosis via magnetic particle imaging (MPI). However, MPI-tracer nanoparticles frequently agglomerate during their in vivo applications leading to particle interactions. Here, we investigate the influence of such magnetic coupling phenomena on the MPI signal. We prepared and characterized Zn0.4Fe2.6O4 nanoparticles and controlled their interparticle distance by variying SiO2 coating thickness. The silica shell affected the magnetic properties indicating stronger particle interactions for a smaller interparticle distance. The SiO2-coated Zn0.4Fe2.6O4 outperformed the bare sample in magnetic particle spectroscopy (MPS) in terms of signal/noise, however, the shell thickness itself only weakly influenced the MPS signal. To investigate the importance of magnetic coupling effects in more detail, we benchmarked the MPS signal of the bare and SiO2-coated Zn-ferrites against commercially available PVP-coated Fe3O4 nanoparticles in water and PBS. PBS is known to destabilize nanoparticles mimicking an agglomeration in vivo. The bare and coated Zn-ferrites showed excellent signal stability, despite their agglomeration in PBS. We attribute this to their aggregated morphology formed during their flame-synthesis. On the other hand, the MPS signal of commercial PVP-coated Fe3O4 strongly decreased in PBS compared to water, indicating strongly changed particle interactions. The relevance of this effect was further investigated in a mammalian cell model. For PVP-coated Fe3O4, we could detect a strong discrepancy between the particle concentration obtained from the MPS signal and the actual concentration determined via ICP-MS. The same trend was observed during their MPI analysis; while SiO2-coated Zn-ferrites could be precisely located in water and PBS, PVP-coated Fe3O4 could not be detected in PBS at all. This drastically limits the sensitivity and also general applicability of MPI using such standard commercial tracers and highlights the advantages of our flame-made Zn-ferrites concerning signal stability and ultimately diagnostic accuracy.

bioengineering↗

bioRxiv2021

Spectral estimation of in-vivo wheat chlorophyll a/b ratio under contrasting water availabilities

To meet the ever-growing global population necessities, it is needed to identify climate change-relevant plant traits to integrate into breeding programs. Developing new tools for fast and accurate estimation of chlorophyll parameters, chlorophyll a (Chl-a), chlorophyll b (Chl-b) content, and their ratio (Chl-a/b), can promote breeding programs of wheat with enhanced climate adaptively. Spectral reflectance of leaves is affected by changes in pigments concentration and can be used to estimate chlorophyll parameters. The current study identified and validated the top spectral indices known and developed new vegetation indices (VIs) for Chl-a and Chl-b content estimation and used them to non-destructively estimate Chl-a/b values and compare them to hyperspectral estimations. Three wild emmer introgression lines, with contrasting drought stress responsiveness dynamics, were selected. Well and limited irrigation irrigation regimes were applied. The wheat leaves were spectrally measured with a handheld spectrometer to acquire their reflectance at the 330 to 790 nm range. Regression models based on calculated VIs as well as all hyperspectral curves were calibrated and validated against chlorophyll extracted values. The developed VIs resulted in high accuracy of Chl-a and Chl-b estimation allowing indirect non-destructive estimation of Chl-a/b with root mean square error (RMSE) values that could fit 6 to 10 times in the range of the measured values. They also performed similarly to the hyperspectral models. Altogether, we present here a new tool for a non-destructive estimation of Chl-a/b which can serve as a basis for future breeding efforts of climate-resilience wheat as well as other crops.

physiology↗