Plant phenotyping methods.

A novel approach for monitoring the spatial distribution and quantitative analysis of micronutrients in plant tissues using laser ablation ICP-MS imaging →

Quantitative imaging of plant tissues by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is hindered by the lack of matrix-matched calibration standards. Established approaches, such as gelatine or homogenised tissue blocks, do not replicate plant matrices accurately. Here, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections, exploiting the low endogenous metal content of the endosperm to generate in situ calibration curves. Calibration performance for Mg, Mn, Cu, Zn, and Mo was assessed using LA-ICP-MS imaging and Iolite 4 data processing. The method demonstrated excellent linearity (R 2 > 0.98), reproducibility across multiple grains, and sub-ppm limits of detection. Comparative analysis with in-house homogenised blocks and NIST wheat reference material confirmed superior accuracy and reproducibility of the nano-dispenser approach. As a proof-of-concept, we have applied the method for the quantitative imaging of metals in a whole barley grain section, and the results show excellent agreement with published data obtained by conventional liquid-mode ICP MS. The technique reported here provides a robust, scalable, solution for quantitative metallomics studies of plant tissues, enabling improved assessment of nutrient distribution and supporting the development of standardised protocols for LA-ICP-MS imaging.

Why it matches plant phenotyping methods植物組織内の元素分布を定量画像化するLA-ICP-MSの校正法を開発・検証しており、植物栄養状態の測定手法が中心である。

abstractHere, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections

Finlay-Wilkinson random regression for yield and yield stability prediction in cereals. →

Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.

Why it matches plant phenotyping methodsFWRRを用いて穀類の収量安定性を推定・予測する統計的手法を検討し、環境数やデータ構成による予測性能を評価しているため、収量形質の計算的フェノタイピング手法が中心です。

abstractMethods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability.

Enhancing soluble dietary fiber prediction in barley via Boruta-based feature selection and mid-infrared spectroscopy. →

Barley ( Hordeum vulgare L.) is a major cereal crop whose soluble dietary fiber (SDF) offers significant health benefits, yet conventional SDF determination methods are time-consuming, labor-intensive, and destructive to samples. This study developed a rapid, non-destructive method for SDF quantification in barley using mid-infrared (MIR) spectroscopy combined with a Boruta-based partial least squares (PLS) hybrid approach. A total of 280 barley grain samples were subjected to Fourier-transform infrared (FTIR) spectral acquisition from 2000 to 650 cm -1 , with reference SDF values determined by the association of official analytical chemists (AOAC) 991.43 enzymatic-gravimetric method. The Boruta algorithm selected 165 informative wavenumbers out of 363, reducing the variable space by 54.5%. The developed Boruta-PLS model achieved excellent predictive performance with a coefficient of determination for the training set ( R 2 C ) of 0.9695 and for the test set ( R 2 P ) of 0.9601 and a root mean squared error for the training set (RMSE C ) of 0.7464%, and for the test set (RMSE P ) of 0.7340%, substantially outperforming the full-spectrum PLS model with an R 2 P of 0.7759 and an RMSE P of 1.7387%, as well as conventional wavelength selection methods including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE). The selected wavenumbers were predominantly located in chemically relevant regions at 1000-1200 cm -1 and 1500-1700 cm -1 , confirming model interpretability. This Boruta-PLS approach provides a rapid, non-destructive, and cost-effective alternative for SDF quantification, with strong potential for high-throughput screening and real-time quality monitoring of barley samples.

Why it matches plant phenotyping methods大麦種子の可溶性食物繊維という種子形質を、MIR分光とBoruta-PLSで非破壊・高速推定する手法の開発と性能比較が研究の中心であり、単なる生物学的実験のルーチン測定ではない。

abstractThis study developed a rapid, non-destructive method for SDF quantification in barley using mid-infrared (MIR) spectroscopy combined with a Boruta-based partial least squares (PLS) hybrid approach.

Structural failure in cereal stems under climate extremes: insights from barley head loss. →

Structural failure of cereal stems during late-season climate extremes is a critical determinant of yield stability. In barley, breakage of the stem below the spike, known as head loss, leads to major yield losses, particularly in hot and dry regions where the crop is widely grown. Despite a predicted increase in head loss risk due to global warming, current understanding of the genetic, physiological, anatomical, and environmental factors that control head loss remains limited. Overcoming these knowledge gaps is essential to providing a systems-level strategy for barley breeders to develop climate-ready cultivars that are resilient to stem breakage and suitable for industry adoption. Here, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting, and genetic modification strategies informed by studies on hormonal regulation and cell wall composition. Coupled with genotypic data, these efforts will enable the development of a genomic selection platform to facilitate future breeding programs. The framework and tools discussed here are broadly applicable to improving stem resilience in other cereal crops.

Why it matches plant phenotyping methods茎の強度・柔軟性や穂数を対象とする表現型計測手法をレビューし、機械試験とドローンによる高スループット計測を育種基盤として論じているため、表現型手法が中心的です。

abstractHere, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting

UAV-Based Classification of Crop Phenological Stages Using Deep Learning →

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育（フェノロジー）段階を自動推定する手法が研究の中心であり、植物状態の取得・分類に直接関わる。

abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.

From Field Photosynthesis to Genetic Architecture: Insights from the First Dedicated Photosynthesis Hackathon →

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

Why it matches plant phenotyping methods圃場光合成データから時間分解特徴量や遺伝的シグナルを抽出する計算手法を中心に扱っており、植物生理形質の実質的なフェノタイピング手法応用に該当する。

abstractmechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield.

Laser biospeckle imaging and mathematical modeling for comprehensive malting barley (Hordeum vulgare subsp. distichum L.) seed quality evaluation: viability, moisture and germination dynamics. →

Background and aims Laser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination. Methods Malting barley (Hordeum vulgare subsp. distichum L. cv. Sinfonia) seeds were analysed using five coefficients-Generalised Differences (GD), Fujii, Lasca, Frequent Motion Image (FMI), and Moment of Inertia (MI)-to assess their ability to discriminate between treatments and tissue regions, and to track changes during imbibition. Whole and longitudinally cut seeds from two treatments (control [untreated] and autoclaved [heat-inactivated]) were analysed, focusing on embryo/endosperm activity ratios. LBSA was also evaluated as a function of imbibition time and seed moisture content. Key results Four coefficients (GD, Fujii, FMI, and MI) successfully differentiated control and autoclaved seeds, as well as embryo and endosperm regions in control seeds, revealing distinct activity patterns. In control seeds, LBSA increased with imbibition time and was well described by polynomial models (quadratic for Fujii and MI; cubic for GD and FMI). GD, Fujii, and FMI required a minimum seed moisture content of 25% to detect activity, while MI was responsive only above 31.5%. In contrast, Lasca was exclusively sensitive to hydration level, fitting a relaxation curve independent of treatment. Conclusions LBSA constitutes a robust, non-destructive methodology for monitoring early germination processes. By combining coefficients, it is possible to infer physiological traits such as embryo specificity, hydration thresholds, and dynamic metabolic reactivation. This positions LBSA not only as a diagnostic tool for seed viability but also as a physiologically informative proxy for studying germination and tissue-level dynamics.

Why it matches plant phenotyping methodsレーザーバイオスペックル画像法を用いて種子の生存性、含水率、発芽動態を測定・識別し、複数係数の性能評価とモデル化を行う手法中心の研究である。

abstractLaser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination.

UAV-Based Classification of Crop Phenological Stages Using Deep Learning →

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育・フェノロジー段階を自動推定する方法が研究の中心であり、植物状態の抽出性能も評価している。

abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.

Utilizing Vegetation Indices Derived from VNIR-SWIR Hyperspectral Data to Characterize Growth, Maturation, and Senescence in Wheat and Barley →

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.

Why it matches plant phenotyping methodsRGB画像、赤外線サーモグラフィー、VNIR–SWIRハイパースペクトルを統合した高スループット表現型解析ワークフローが中心的に記述され、複数の植物形質・状態を定量化している。

abstractA high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence.

Enhancing Predictive Ability of Agronomic and Quality Traits in Ethiopian Malting Barley ( Hordeum vulgare L.) Using Spectral Variable Selection Methods →

ABSTRACT Phenomic selection (PS) offers a cost‐effective , breeder‐friendly approach for public breeding programmes with limited access to genotyping or restricted financial resources for laboratory infrastructure. Since PS relies on high‐throughput phenotyping data, which is often derived from near‐infrared spectroscopy (NIRS) of harvested seeds, prediction is challenged by the high dimensionality and strong intercorrelation of NIRS data, which means that only a subset of wavelengths is informative. This study evaluates spectral variable selection models for predicting key morpho‐agronomic and quality traits in malt barley ( Hordeum vulgare L.) and assesses their performance under realistic breeding scenarios. Four NIRS‐based regularized regression models (Lasso, Enet, Ridge and a heritability‐filtered Ridge model) were tested to predict 10 morphological, agronomic and quality traits measured in two malt barley trials conducted during the 2022 and 2024 cropping seasons at three locations in Ethiopia using 100 genotypes in each trial. Model performance was evaluated across four practical breeding scenarios: within‐location unseen genotype prediction (WL‐uG), leave‐one‐location‐out prediction (LOLO), target environment unseen genotype prediction (TargetEnv) and across‐location wide adaptability (RuG). Accordingly, Cross‐validation identified stable, informative spectral predictors for each trait, scenario and trial. Among the models tested, Lasso and Enet consistently outperformed Ridge regression, with Enet showing the best predictive performance across scenarios. Prediction ability (r) ranged from 0.15 to 0.85 for quality traits, 0.16 to 0.79 for agronomic traits and 0.07 to 0.89 for morphological traits across scenarios. Thus, these findings underscore the importance of spectral predictor selection in improving predictive ability and demonstrate the transferability of PS in barley breeding.

Why it matches plant phenotyping methodsNIRSを用いた植物形質予測とスペクトル変数選択モデルの比較・検証が研究の中心であり、複数の形態・農業・品質形質に対する予測性能を交差検証している。

abstractThis study evaluates spectral variable selection models for predicting key morpho‐agronomic and quality traits in malt barley ( Hordeum vulgare L.) and assesses their performance under realistic breeding scenarios.

Parameterizing steady-state C3 photosynthesis models using photosynthesis response curves: Which models to use and which parameter estimates to trust? →

Photosynthesis sustains life on Earth, yet we still lack comprehensive understanding of the biochemical and environmental factors that affect this fundamental process. Steady-state models of C3 photosynthesis provide a powerful framework but rely on reliable estimation of numerous parameters from gas-exchange data. Despite methodological advances, how model structure and data choice influence parameter accuracy and consistency remains poorly explored. Here, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements. Using synthetic photosynthesis response curves generated from the examined models with sampled parameter values, we applied Bayesian inference to quantify parameter uncertainty and estimation performance for the considered models. We showed that while key parameters of C3 photosynthesis, such as maximum rate of RuBP-saturated carboxylation and of electron transport through photosystem II, can be reliably estimated from a single A-Ci curve, other parameters, such as leaf mitochondrial respiration and CO2 compensation point, require expanded sampling of light response space. We also demonstrated the advantage of using simultaneous estimation of all model parameters over biasing the estimation by keeping some parameters fixed to prior values. Usage of barley gas-exchange data further demonstrated that parameter consistency across models can be evaluated comparing different levels of measurements and depends strongly on both model formulation and data type. Together, our study provides practical guidance for selecting photosynthesis models, designing phenotyping strategies and choosing parameterization approaches for steady-state C3 photosynthesis.

Why it matches plant phenotyping methodsガス交換応答曲線から光合成パラメータを推定するモデルとベイズ推論を体系的に比較・評価し、フェノタイピング戦略の設計を扱うため、植物表現型取得・推定法が中心である。

abstractHere, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements.

RootHairAreaFinder: an image processing method for quantifying barley root growth and root hairs simultaneously in a flat rhizotron system. →

Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of barley root system architecture and accompanied visible projected root hair area (VP root hair area) over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hairs from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for projected root area and 0.65 for VP root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm -3 ). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a training-free method for integrated analysis of root traits e.g. projected root size, root growth, root diameter and root hairs under controlled physical conditions similar to soil, facilitating studies of root-soil interactions that require both spatial resolution and temporal continuity.

Why it matches plant phenotyping methods根系形態と根毛面積を画像から定量する画像解析ワークフローおよび生育・撮像システムを開発し、アルゴリズム性能も検証しているため、植物フェノタイピング手法が中心である。

abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R

A Novel Approach for Monitoring The Spatial Distribution and Quantitative Analysis of Micronutrients in Plant Tissues Using Laser Ablation Inductively Coupled Plasma Mass Spectrometry Imaging →

Quantitative imaging of plant tissues by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is hindered by the lack of matrix-matched calibration standards. Established approaches, such as gelatine or homogenized tissue blocks, do not replicate plant matrices accurately. Here, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections, exploiting the low endogenous metal content of the endosperm to generate in situ calibration curves. Calibration performance for Mg, Mn, Cu, Zn and Mo was assessed using LA-ICP-MS imaging and Iolite data processing. The method demonstrated excellent linearity (R2 > 0.98), reproducibility across multiple grains, and sub-ppm limits of detection. Comparative analysis with in-house homogenized blocks and NIST wheat reference material confirmed superior accuracy and reproducibility of the nano-dispenser approach. As a proof-of-concept we have applied the method for the quantitative imaging of metals in a whole barley grain section and the results show excellent agreement with published data obtained by conventional liquid-mode ICP MS. The technique reported here provides a robust, scalable solution for quantitative metallomics studies of plants tissues, enabling improved assessment of nutrient distribution and supporting the development of standardised protocols for LA-ICP-MS Imaging

Why it matches plant phenotyping methods植物組織中の元素分布を定量画像化するための校正手法を開発し、直線性・再現性・精度を検証している。栄養元素という植物形質の取得法が研究の中心である。

abstractHere, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections

High-performance 3D morphometrics via deep learning and tabular foundation models: a case study on complex cereal grain classification →

This study explores the integration of advanced 3D morphometric techniques and machine/deep learning (ML/DL) for the analysis of complex shapes. In this study, cereal grains were employed to develop a series of complex 3D classification tasks, aiming to improve previous 2D-based classifications of barley grain origins, type, and landrace. Traditional geometric morphometric methods in archaeobotany, typically reliant on 2D data, are expanded here using high-resolution 3D models, spherical harmonics (SH) capturing complex shape variations, and different classification models. Using the Northern European Barley Dataset (NEBD), this work tests multiple ML/DL approaches, including gradient boosting machines, multilayer perceptron (MLP), MeshCNN and Tabular Foundation Models (TFM), to determine optimal classification methods across various attributes. Results indicate that SH-based coefficients combined with MLP and TFM achieved the highest classification accuracies, with over 90% accuracy in binary tasks and over 80% accuracy in multi-class landrace classification. While MeshCNN showed potential, performance was limited by computational constraints resulting in the use of lower mesh resolutions. While MLP classification of SH-based 3D shape representation achieved similar results, TFM allowed the direct use of 3D grains measures within a simple workflow. Our results demonstrate that these methods allow for significant shape analysis advances in archaeobotanical studies and beyond. This approach can enable identification and differentiation of, up to now, non-identifiable grain attributes, underscoring its potential for broad application in morphometrics.

Why it matches plant phenotyping methods3D穀粒形状の取得・表現と機械学習による形態分類が研究の中心であり、植物器官の形態形質を抽出・分類する方法論的研究である。

abstracthigh-resolution 3D models, spherical harmonics (SH) capturing complex shape variations, and different classification models

VNIR Hyperspectral Signatures and Machine Learning for Early Detection and Classification of Barley Diseases. →

This study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections. We examined the causal agents of widespread crop diseases, including: loose smut, head blight, fusarium head blight (FHB), stem rust, net blotch, spot blotch, common root rot. Analysing disease-specific spectral characteristics with machine learning (ML) algorithms revealed the most informative spectral ranges: the green region (~520-560 nm), the red chlorophyll absorption zone (~650-680 nm), and the red-edge region (~700 nm). These ranges accurately reflect alterations in the plant's cellular structure and pigment complexes. Spectral data were processed using five ML algorithms. Random Forest (RF) proved to be the most effective for identifying and differentiating barley diseases, achieving an accuracy of up to 90.13% (MCC = 0.86). This superior performance stems from the ensemble method's robustness to noise and its ability to extract critical features from high-dimensional hyperspectral data, particularly when distinguishing diseases with overlapping spectral signatures. Furthermore, this study highlights the potential of integrating UAV-based remote sensing to delineate reference zones, proximal hyperspectral imaging (HSI), and ML for robust plant health monitoring. This combined approach shows significant promise for early disease diagnostics, enabling site-specific treatments, curbing disease progression, and reducing pesticide application. Ultimately, these findings offer practical value for the agro-industrial sector in major grain-producing countries, especially in Central Asia, where agricultural advancement is a strategic priority for sustainable development and food security.

Why it matches plant phenotyping methodsVNIRハイパースペクトル計測と機械学習により、オオムギの病害状態を植物体のスペクトル特徴から検出・分類する方法が研究の中心であり、植物フェノタイピング手法に該当する。

abstractThis study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections.

Evaluation of a hyperspectral imaging data processing pipeline for early rust disease diagnosis in grain crops applied to wheat, rye, and barley phenotyping →

Hyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale. Hyperspectral images of healthy plants, obtained under laboratory conditions using a Cubert Ultris 20 camera (450–874 nm range, 106 channels), were utilized. The effectiveness of various preprocessing schemes was compared: full (including normalization, smoothing, calculation of derivatives, and identification of extreme features), reduced, and minimal. Machine learning models were exploited for classification: logistic regression, support vector machine, and gradient boosting, trained on averaged spectra. It is shown that the use of a full pipeline optimized for phytopathological diagnostics leads to reduced classification accuracy in phenotyping tasks. The best results (F1 = 0.97 ± 0.025) were achieved using the original averaged spectral curves without additional transformations. It is concluded that for healthy wheat, barley, and rye phenotyping, absolute reflectance levels are informative, whereas for disease diagnostics, changes in the shape of the spectral curve are more important. The obtained results clarify the applicability limits of pipelines developed for phytosanitary purposes and can inform the development of remote monitoring and phenotyping systems for cereal crops.

Why it matches plant phenotyping methods穀類の健全植物フェノタイピングに対するハイパースペクトル画像処理パイプラインの適用性を比較評価しており、前処理と分類性能の検証が研究の中心である。

abstractHyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale.

An Effective YOLOv11 Grain Detection Model Trained on Intact Barley Spikes Reveals a QTL Containing a Pivotal Regulator of Lateral Spikelet Formation. →

Grain number is a primary agronomic trait for targeted yield improvement, with the prospect of enhanced grain production leading to greater food security. Given the complex polygenic nature of the grain number trait, large sample sizes are essential for effective QTL identification. The implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation. In this study, we trained a grain detection model using Ultralytics' You Only Look Once (YOLOv11) framework. Training was completed on 1000 images of barley spikes, derived from a doubled haploid (DH) population descended from Hindmarsh and RGT Planet. The trained model, termed BarleyGC, achieved satisfactory accuracy metrics (mAP50-95 = 71.9%, recall = 96.7%, precision = 97.1%). Phenotypic characterisation of the DH population was completed with BarleyGC on a distinct collection of 973 images. The Pearson correlation coefficient (r) between model and manual-derived counts for the trait of grain number per spike was 0.895 ( p n = 153 DH lines), revealed a QTL peak at position 224.959 cM on the genetic map (LOD = 3.14), named qGN-2H. The QTL region contained 21 candidate genes-including HORVU2Hr1G092290 (HORVU.MOREX.r3.2HG0184740), encoding the six-rowed spike 1 ( Vrs1 ) gene-a well-characterised major regulator of row-type divergence and lateral spikelet development. Our study demonstrates the power of the YOLOv11 framework for grain quantification, with BarleyGC capable of grain detection directly from images of intact spikes in two-rowed barley varieties-thus achieving accelerated sample processing for the grain number trait.

Why it matches plant phenotyping methods大麦穂の画像から穀粒数を推定するYOLOv11モデルを開発・検証し、手動計数との比較およびQTL解析に応用しており、植物表現型取得法が研究の中心である。

abstractThe implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation.

A thermal time framework drives coordinated below- and above-ground development in temperate cereal crops →

Summary Cereal architecture is underpinned by the coordinated development of modular phytomer units. While above-ground phenology is well characterized by metrics such as the phyllochron, an equivalent framework for root system development is lacking. Because each phytomer node initiates both leaves and adventitious roots, root and shoot development are inherently linked. Here, we quantified this coordination in wheat, barley, and rye across contrasting temperature regimes and validated the results under field conditions. We introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes. Root development followed a highly conserved thermal sequence synchronized with shoot phenology. Across species and environments, the rhizochron averaged 146.1°C d, closely matching the phyllochron (126.6°C d). We also identified a consistent thermal offset, with nodal roots emerging approximately 185.3°C d after the corresponding leaf on the same phytomer node. The root appearance interval averaged 45.3°C d, reflecting continuous root deployment across active nodes. By integrating root phenology into a node-based framework, the rhizochron provides a predictive tool for crop modeling, trait-based breeding, and more target phenotyping aimed at improving resource acquisition and climate resilience.

Why it matches plant phenotyping methods根系と地上部の発達を定量化する新しい熱時間指標（rhizochron等）を導入し、複数種・環境および圃場条件で検証しており、表現型測定法が研究の中心である。

abstractWe introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes.

GrowScreen-Rhizo 3 - automated large-scale high throughput greenhouse phenotyping of plant root and shoot development. →

Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80x40x5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r 2 =0.52 and r 2 =0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.

Why it matches plant phenotyping methods根とシュートを自動撮像し、バイオマス・成長などの形質を抽出する大規模フェノタイピング platform の開発・検証が中心である。

abstractWe introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3.

Classification and Prediction of Growth Conditions in Food Barley Fields Using UAV Multispectral Images and Machine Learning Approaches →

Abstract (150 to 250 words) Spatial variability in barley maturation complicates preharvest classification for food-grade production and accurate conventional field-based assessments. Therefore, we classified barley growth into food-grade–oriented categories and developed a predictive framework integrating UAV-based multispectral imagery with machine learning. Grain quality metrics were first analyzed using principal component analysis and k-means clustering to define three physiologically distinct growth levels. Multi-temporal vegetation indices (NDVI, GNDVI, and NDRE) were extracted from UAV imagery, and key predictors were selected using Lasso regularization. Comparisons of Random Forest (RF), XGBoost, and Support Vector Machine models indicated that tree-based ensembles achieved high classification accuracy (>0.9), with late-season NDVI identified as the most influential predictor. Spatial mapping using the RF model revealed pronounced inter-field variability, identifying zones of incomplete maturation associated with lodging. Overall, integrating grain quality traits with UAV-based spectral monitoring allows accurate field-scale classification of food-grade barley growth and informs site-specific harvest management.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から大麦の成熟・生育状態を推定し、複数の機械学習手法を比較する予測フレームワークが研究の中心であるため、植物状態の実質的なフェノタイピング手法に該当する。

abstractwe classified barley growth into food-grade–oriented categories and developed a predictive framework integrating UAV-based multispectral imagery with machine learning.

OzBarley: A genetic and phenotypic data resource capturing the Australian barley breeding history. →

OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.

Why it matches plant phenotyping methods高スループット画像およびX線CTによる形質取得を含む、再利用可能な遺伝型・表現型データ資源であり、植物フェノタイピング手法とデータセットが中心です。

abstractThe phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology.

Rootex 2.0: Multi-head deep learning and graph-based analysis for automated barley root phenotyping →

• A fully automated pipeline for barley root extraction and characterization. • DeepRoot-3H : multi-head network for segmenting roots, tips, and sources. • Post-processing stage handling overlaps and dense root clusters. • Graph-based path analysis for RSML generation and trait extraction • High accuracy and robustness on challenging barley root image datasets Understanding plant root architecture under diverse environmental conditions is crucial for improving crop resilience and ensuring global food security. We present a fully automated method for segmenting barley root systems from high-resolution images and detecting keypoints such as tips and sources with high precision. At the core of our approach is DeepRoot-3H , a novel multi-head deep network built upon the DeepLabv3+ backbone, designed to jointly handle root segmentation and keypoint detection within a unified architecture. This integrated design enhances both the consistency and robustness of the outputs. A dedicated post-processing stage further refines keypoint localization, effectively handling challenges such as dense root clusters and variability in image quality. The resulting predictions are then structured into a graph representation, on which a path-walking algorithm identifies biologically meaningful connections between tips and sources. This enables the generation of RSML files and the extraction of critical morphological traits. To evaluate the system, we employ IoU and Dice scores for segmentation quality, alongside Euclidean and weighted distance metrics for tip and source detection. We also assess the biological consistency of the extracted traits—such as total root length, tortuosity, covered area, and outer angles—through correlation and discrepancy measures. Experimental results on a challenging benchmark dataset demonstrate significant improvements over existing techniques, confirming the effectiveness and reliability of our method for high-fidelity root system analysis.

Why it matches plant phenotyping methods根系画像から分割・キーポイント検出・形態形質抽出を行う自動フェノタイピング手法の開発と評価が中心である。

abstractA fully automated pipeline for barley root extraction and characterization.

Parameterization of steady-state models for C3 photosynthesis using photosynthesis response curves: Which models to use and which parameter estimates to trust? →

Photosynthesis sustains life on Earth, yet we still lack comprehensive understanding of the biochemical and environmental factors that affect this fundamental process. Steady-state models of C 3 photosynthesis provide a powerful framework but rely on reliable estimation of numerous parameters from gas-exchange data. Despite methodological advances, how model structure and data choice influence parameter accuracy and consistency remains poorly explored. Here, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements. Using synthetic photosynthesis response curves generated from the examined models with sampled parameter values, we applied Bayesian inference to quantify parameter uncertainty and estimation performance for the considered models. We showed that while key parameters of C 3 photosynthesis, such as maximum rate of RuBP-saturated carboxylation and of electron transport through photosystem II, can be reliably estimated from a single A-Ci curve, other parameters, such as leaf mitochondrial respiration and CO 2 compensation point, require expanded sampling of light response space. We also demonstrated the advantage of using simultaneous estimation of all model parameters over biasing the estimation by keeping some parameters fixed to prior values. Usage of barley gas-exchange data further demonstrated that parameter consistency across models can be evaluated comparing different levels of measurements and depends strongly on both model formulation and data type. Together, our study provides practical guidance for selecting photosynthesis models, designing phenotyping strategies and choosing parameterization approaches for steady-state C 3 photosynthesis.

Why it matches plant phenotyping methodsガス交換データから光合成パラメータを推定するモデルと測定設計を体系的に評価しており、植物生理形質の取得・推定手法が研究の中心である。

abstractwe systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements.

Grain geometry matters: Hyperspectral imaging challenges for moisture detection in individual kernels of barley →

This study evaluated the effects of penetration depth, grain orientation, and placement angle on hyperspectral imaging (HSI) signal quality using short-wave infrared (SWIR) HSI (1000–2500 nm). Orientation-related spectral variability was observed, primarily due to groove direction and angular placement. Penetration assessment with lead sulfide (PbS) quantum dots (QDs) at 1200 nm and 1800 nm revealed that barley husks exhibited higher transmittance, while intact kernels showed limited light penetration. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) analyses, two key moisture sensitive wavelength regions (1153 nm and 1954 nm) were identified. Deep learning models, including 2D and 3D convolutional neural network (CNN), were developed and evaluated under several configurations. The 3D CNN showed the best performance when trained solely on HSI data with selected feature wavelengths. Using 540 barley kernels for training and 132 kernels for testing, the model achieved an R² of 0.98, an RMSE of 1.1763, and an MAE of 1.2592, demonstrating that spectral–spatial information alone can provide stable and accurate moisture prediction, and that further inclusion of geometric metadata (angle and orientation) provides limited benefit. The proposed XGBoost–SHAP–3D CNN framework offers an interpretable, efficient, and cost-effective solution for rapid moisture estimation in barley, demonstrating strong potential for intelligent grain quality monitoring.

Why it matches plant phenotyping methods大麦個粒の水分という植物器官の状態を、HSIとXGBoost–SHAP–3D CNNで推定する手法を開発・評価しており、表現型取得・推定手法が研究の中心である。

abstractDeep learning models, including 2D and 3D convolutional neural network (CNN), were developed and evaluated under several configurations.

Automatic pixel-level annotation for plant disease severity estimation →

Plant disease adversely impacts food production and quality. Alongside detecting the disease, estimating its severity is important in managing the disease. Artificial intelligence deep learning-based techniques for plant disease detection are emerging. Unlike most of these techniques, which focus on disease recognition, this study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation. We propose a novel approach that learns the disease symptoms, which are then used to segment disease lesions for severity estimation. To demonstrate the work, a dataset of barley images was used. We captured the images of barley plants inoculated with diseases on test-bed paddocks at various growth stages. The dataset was automatically annotated at a pixel level using a trained vision transformer to obtain the ground truth labels. The annotated dataset was applied to train salient object detection (SOD) methods. Two top-performing lightweight SOD models were used to segment the disease lesion areas. To evaluate the performance of the SODs, we have tested them on our dataset and several other datasets, including the Coffee dataset, which has expert pixel-level labels that were unseen during the training step. Several morphological and spectral disease symptoms, including those akin to the widely used ABCD rule for human skin-cancer detection, i.e., asymmetry (A), border irregularity (B), colour variance (C), and diameter (D), are learned. To the best of our knowledge, this is the first study to incorporate these ABCD features in plant disease detection. We further extract visual and texture features using the grey level co-occurrence matrix (GLCM) and fuse them with the ABCD features. For the coffee dataset, our method achieved 82 + % detection accuracy on the severity classification task. The results demonstrate the performance of the proposed method in detecting plant diseases and estimating their severity.

Why it matches plant phenotyping methods植物病斑の画素レベル分割と病害重症度推定という、植物状態を画像から定量化する手法が研究の中心であり、データセット構築・モデル評価も行っている。

abstractthis study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation

Automatic pixel-level annotation for plant disease severity estimation →

Plant disease adversely impacts food production and quality. Alongside detecting the disease, estimating its severity is important in managing the disease. Artificial intelligence deep learning-based techniques for plant disease detection are emerging. Unlike most of these techniques, which focus on disease recognition, this study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation. We propose a novel approach that learns the disease symptoms, which are then used to segment disease lesions for severity estimation. To demonstrate the work, a dataset of barley images was used. We captured the images of barley plants inoculated with diseases on test-bed paddocks at various growth stages. The dataset was automatically annotated at a pixel level using a trained vision transformer to obtain the ground truth labels. The annotated dataset was applied to train salient object detection (SOD) methods. Two top-performing lightweight SOD models were used to segment the disease lesion areas. To evaluate the performance of the SODs, we have tested them on our dataset and several other datasets, including the Coffee dataset, which has expert pixel-level labels that were unseen during the training step. Several morphological and spectral disease symptoms, including those akin to the widely used ABCD rule for human skin-cancer detection, i.e., asymmetry (A), border irregularity (B), colour variance (C), and diameter (D), are learned. To the best of our knowledge, this is the first study to incorporate these ABCD features in plant disease detection. We further extract visual and texture features using the grey level co-occurrence matrix (GLCM) and fuse them with the ABCD features. For the coffee dataset, our method achieved 82+% detection accuracy on the severity classification task. The results demonstrate the performance of the proposed method in detecting plant diseases and estimating their severity.

Why it matches plant phenotyping methods植物病害の病変を画像から抽出し、病害重症度という植物状態を推定する手法の開発・評価が中心であるため。

titleAutomatic pixel-level annotation for plant disease severity estimation

StomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment →

ABSTRACT Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis . Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.

Why it matches plant phenotyping methods気孔の検出・密度・サイズ推定を目的とする深層学習画像解析手法を開発し、複数モデルおよび困難画像で性能比較・検証しており、植物表現型取得が研究の中心である。

titleStomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment

A multi-class deep learning segmentation approach for automated analysis of axial and lateral roots in barley plants →

Accurate segmentation and analysis of root images from soil-grown plants are critical for advancing our understanding of root growth and plasticity under varying environmental conditions. Most approaches typically rely on binary segmentation of the entire root system architecture (RSA), which limits their ability to capture the hierarchical complexity of root structures, including axial and lateral roots. To address this, our study evaluated five convolutional neural network (CNN) architectures for multi-class (i.e. axial/lateral) segmentation of 2D root images from barley plants grown in rhizoboxes: (i) U-Net, (ii) U-Net with Atrous Spatial Pyramid Pooling (UnetASPP), (iii) U-Net with Attention Block (UnetAtt), (iv) DeepLabV3+ with MobileNetV2 (DLMB), and (v) DeepLabV3+ with ResNet-50 (DLR50). Among these, the DLR50 model achieved the highest segmentation accuracy, particularly for distinguishing lateral roots within complex RSA structures. Furthermore, analysis of root traits derived from the segmented images confirmed that DLR50 produced the most reliable estimations of phenotypic traits compared to ground truth measurements. These findings highlight the strong potential of advanced multi-class CNN models—especially DLR50—for detailed and quantitative analysis of soil-root systems, providing new insights into root responses to environmental conditions.

Why it matches plant phenotyping methods根画像から軸根・側根を分割し、分割画像に基づく表現型形質推定のCNN手法を開発・比較検証しており、植物フェノタイピング手法が研究の中心です。

abstractour study evaluated five convolutional neural network (CNN) architectures for multi-class (i.e. axial/lateral) segmentation of 2D root images from barley plants grown in rhizoboxes

Application of GIS and UAS for crop condition analysis in the Sofia region Application of GIS and UAS for crop condition analysis in the Sofia region Application of GIS and UAS for crop condition analysis in the Sofia region Application of GIS and UAS for crop condition analysis in the Sofia region →

The technological advancements in recent decades have facilitated the widespread adoption of unmanned aerial systems (UAS) and remote sensing methodologies in agricultural practices. These innovative approaches enable precise crop monitoring, early stress detection, and yield optimization through the analysis of vegetation indices derived from aerial imagery. This study investigates the application of UAS-acquired multispectral data and vegetation indices for crop health assessment, with particular emphasis on barley (Hordeum vulgare L.) and sunflower (Helianthus annuus L.). Geographic Information Systems (GIS) serve as a critical platform for integrating, processing, and visualizing remote sensing data, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), and Normalized Difference Red Index (NDRI). Our research methodology employed a DJI Mavic 3M UAS equipped with multispectral sensors to conduct aerial surveys of agricultural plots in the Sofia region of Bulgaria. The acquired data were processed to generate index maps that facilitate quantitative assessment of crop physiological status.The implemented workflow demonstrates an efficient technology for rapid identification of agronomic issues and supports data-driven decision making. The results highlight the potential of UAS-GIS integration for precision agriculture applications, particularly in monitoring cereal and oilseed crops under temperate climatic conditions. This approach provides agricultural stakeholders with timely, spatially explicit information for crop management while establishing a framework for future research in precision farming technologies.

Why it matches plant phenotyping methodsUASマルチスペクトル画像と植生指数により作物の生理状態・健全性を定量評価する取得・解析ワークフローが研究の中心であり、単なる生物学的実験の routine 測定ではない。

abstractThis study investigates the application of UAS-acquired multispectral data and vegetation indices for crop health assessment

Evaluating Leaf Responses to Microbial Secondary Metabolites Using A High-Throughput Format. →

Microbes secrete structurally diverse secondary metabolites during plant infection, some of which are detected by plant cells, which trigger stress responses. In this method, the induction of ion leakage, peroxidase activity, and callose production is measured in the same leaf disk sample. First, Arabidopsis or barley leaf disks are vacuum infiltrated in a 96-well plate. After 4-6 hours, conductivity is measured, followed by peroxidase activity and callose deposition at 24 hours. The flg22 peptide induces all three responses and is an affordable positive control. Surfactin and gramillin cyclic lipopeptides induce peroxidase activity and ion leakage, respectively, while the phytotoxic T-2 trichothecene suppresses peroxidase activity. Overall, this approach enables multiple comparisons across either plant genotypes or metabolite treatments. This approach can be applied to chemical genetics or bioprotection to identify stress-modulating compounds for further study. In plant genetics, this approach can be used to compare responses across plant populations for genetic mapping and to improve our understanding of plant-microbe interactions.

Why it matches plant phenotyping methods植物葉のストレス応答（イオン漏出、ペルオキシダーゼ活性、カロース蓄積）を同一試料で高スループット測定する方法が研究の中心であり、植物表現型取得法に該当する。

abstractIn this method, the induction of ion leakage, peroxidase activity, and callose production is measured in the same leaf disk sample.

High-throughput monitoring of root diameter reveals the temporal dynamics of root decomposition. →

Background and aims Increasing C storage in cultivated soils requires a better understanding of C dynamics, particularly at depth, where root litter decomposition dynamics is expected to be slower than in ploughed layers. Methods We assessed the effect of barley root diameter on root decomposition in situ using a non-invasive method at different depths. Temporal decreases in root diameter and length were measured using images acquired by optical scanners buried at depths of 20, 50 and 90 cm from seeding and for 1.5 years. A parallel root litterbag experiment was performed to measure root mass loss. Results Root decomposition was observed on the scanned images before the flowering stage, with up to 85 % of the maximum root volume achieved being lost at harvest. Thinner roots ( Conclusions Optical scanner-based image analysis complements litterbags by enabling individual root tracking and in situ decomposition assessment without root manipulation. This method offers the opportunity to measure root decomposition at various soil depths over long periods, and could improve the estimation of root-derived soil C inputs.

Why it matches plant phenotyping methods埋設光学スキャナーと画像解析による根径・根長・根体積の非侵襲的経時測定が研究の中心で、根の分解を個別追跡する再利用可能な植物表現型取得法を実証している。

abstractTemporal decreases in root diameter and length were measured using images acquired by optical scanners buried at depths of 20, 50 and 90 cm from seeding and for 1.5 years.

Accelerated Haustoria Segmentation Enables Rapid Gene Function Analysis in Cereal-Powdery Mildew Pathosystems. →

Reliable, high-throughput quantification of early fungal infection events is crucial for gene function studies, but it remains labor-intensive. We report an openly available pipeline that automates the detection of β-glucuronidase (GUS)-stained epidermal cells and the intracellular haustoria formed by powdery mildew on barley and wheat leaves. Whole-slide images are captured with a commercial scanner, focus-projected, tiled, and analyzed by deep-learning models trained on expertly annotated datasets. A You Only Look Once ( YOLO ) network identifies GUS-positive cells, and a companion segmentation model pinpoints haustoria within each cell; automatic focus-layer selection preserves fine structural detail. The workflow runs in minutes per slide on a single workstation and maintains near-perfect agreement with manual counts in both barley and wheat, demonstrating robust cross-species transferability. By delivering single-cell readouts with minimal user input, the pipeline enables rapid functional validation screens and supports large-scale phenotyping of cereal-powdery mildew interactions. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Why it matches plant phenotyping methods深層学習と画像処理により、感染植物細胞内のハウストリアを自動検出・セグメンテーションする公開パイプラインを開発し、手動計数および種間移植性を検証しているため、植物病害表現型の取得手法が中心である。

abstractWe report an openly available pipeline that automates the detection of β-glucuronidase (GUS)-stained epidermal cells and the intracellular haustoria formed by powdery mildew on barley and wheat leaves.

Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field →

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environments to the field, and a lack of understanding of genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars in field experiments conducted across two contrasting environments. A diverse subset of 20 genotypes was subjected to ground-based root and shoot phenotyping at key growth stages, and this dataset was used in combination with unmanned aerial vehicle (UAV)-captured vegetation indices (VIs) to train machine learning models to predict root distribution and above-ground biomass for the untested panel comprising 544 genotypes across the two seasons. Unlike previous root studies that have focused on above-ground traits or indirect proxies, this approach predicts root traits in the field using machine learning. Haplotype-based mapping using predicted root and shoot traits in the broader panel revealed key genomic regions. These include novel regions, previously reported root quantitative trait loci, and EGT2-a recently cloned gene that regulates root gravitropism in barley. This scalable phenotyping approach offers opportunities to advance root research across crops and support the development of future varieties adapted to changing climates.

Why it matches plant phenotyping methodsUAV画像由来の植生指数と機械学習を組み合わせ、圃場で根系形質と地上部バイオマスを推定するスケーラブルな表現型解析手法が研究の中心である。

titlecombining UAV phenotyping and machine learning to predict barley root traits in the field

High-Throughput Phenotyping of Cereal Crops Under Stress: Unveiling Evapotranspiration and Respiration Patterns →

Addressing crop responses to drought and nitrogen stress is crucial for improving resilience and ensuring sustainable agriculture under changing climatic conditions. This study investigates the physiological responses of wheat (Videodur [DU], Sensas [SW]) and barley (Tiroler Imperial [SG1], Amidala [SG2]) cultivars to drought and nitrogen stress during early reproductive to full maturity stages (BBCH 70 to 90) using infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging. Evapotranspiration (ET) and respiration were analyzed as functions of mean plant temperature (Tplant), light intensity, plant water status (indicated by the Normalized Difference Water Index, NDWI), and air humidity. Results revealed that drought stress significantly reduced NDWI and ET while increasing Tplant, with wheat cultivars showing greater sensitivity to water deficit. Barley, particularly SG2, exhibited superior water retention and thermal regulation, highlighting its potential for drought resilience with consistently higher NDWI values and lower Tplant. Temporal analysis identified the reproductive stage as the most vulnerable to stress, with a sharp decline in NDWI and rise in Tplant, emphasizing the need for stage-specific interventions. Regression models explained 74% of ET variance and 67% of respiration variance, underscoring the predictive power of NDWI and Tplant as proxies for plant water status and metabolic activity. Real-time evapotranspiration (ET) measurements using a balance during precision watering further validated the predictive capabilities of NDWI and Tplant. These findings provide valuable insights into growth stage-specific breeding programs and sustainable crop management strategies under environmental stress conditions.

Why it matches plant phenotyping methods赤外・ハイパースペクトル画像からNDWI、植物温度、蒸発散量、呼吸を推定し、回帰モデルと実測バランスで検証しており、植物ストレス形質の取得・推定ワークフローが中心的である。

abstractusing infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging

Deep learning versus geometric morphometrics for archaeobotanical domestication study and subspecific identification →

The identification of archaeological fruits and seeds is crucial for understanding the relationships between humans and plants within the cultural and biological history of both wild and cultivated species. We compared the relative performance of a deep learning approach, namely convolutional neural networks (CNN), and outline analyses via geometric morphometrics using elliptical Fourier transforms (EFT) at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allows to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (barley, olive, date palm and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. Sample sizes ranged from 473 to 1,769 seeds per class, which constitute a relatively small dataset for training CNNs models yet typical within archaeobotanical research. On these eight datasets, we compared the performance of CNN and EFT coupled with linear discriminant analyses. Our objectives were twofold: i) to test whether CNN can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For the CNN network, we deliberately used a candid approach relying on pre-parameterised VGG19 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: our CNN used bare photographs where EFT used outline coordinates. This "pre-distilled" geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that our CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNNs for archaeobotany, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.

Why it matches plant phenotyping methods種子・果実石の画像形態を対象に、CNNと幾何学的形態計測を比較し、分類性能と必要サンプル数を検証する方法中心の研究である。植物器官の形状という観測可能な形質の抽出・識別を扱う。

abstractWe compared the relative performance of a deep learning approach, namely convolutional neural networks (CNN), and outline analyses via geometric morphometrics using elliptical Fourier transforms (EFT) at identifying pairs of plant taxa.

DyMEP: R package for weather data-based phenology prediction for ten crops →

For most digital agriculture applications, such as in-season yield predictions, information on crop phenology is a prerequisite. Phenology is largely determined by environmental factors, e.g., temperature, precipitation, and global radiation. Consequently, weather data can be used to predict phenology. Here, we introduce the R package Dynamic Multi-Environmental Phenology (DyMEP) that facilitates such predictions. DyMEP was trained for ten crops, among others winter wheat, spring wheat, barley, green peas, beans and oat, with a large dataset representing the Central European climate. DyMEP fills the gap between complex, highly parameterized crop growth models that are difficult to use by non-experts, and extremely simplified models such as the Growing Degree Day approach. By carefully selecting the environmental covariates to use for each phenological phase, the user can reach suitable prediction accuracy for most applications in DyMEP. If temperature, precipitation, relative humidity, and global radiation are available as covariates to select from, the package achieves absolute errors ranging from 0 to 6 days across all applied phenology phases and root mean square errors ranging from 7 to 17 days on an independent test set. Combining DyMEP-based phenology predictions with ground-based or remote sensing observations holds promise to facilitate digital agriculture applications such as large-scale yield forecasting or monitoring of fields for crop insurance.

Why it matches plant phenotyping methods作物の生育ステージ（フェノロジー）を気象データから予測するRパッケージを開発・評価しており、植物状態の計測・推定手法が研究の中心である。

abstractHere, we introduce the R package Dynamic Multi-Environmental Phenology (DyMEP) that facilitates such predictions.

Botanic Spectrum Analyser: A Deep Learning GUI for Plant Image Segmentation in Hyperspectral and RGB Phenotyping →

Abstract Plant phenotyping systematically quantifies plant traits such as growth, morphology, physiology, or yield, assessing genetic and environmental influences on plant performance. The integration of advanced phenotyping technologies, including imaging sensors and data analytics, facilitates the non-destructive and longitudinal acquisition of high-throughput data. Nevertheless, the sheer volume of such phenotyping data introduces significant challenges for researchers, particularly related to data processing. To overcome these challenges, researchers are turning to artificial intelligence (AI), a tool that can autonomously process and learn from large amounts of data. Despite this advantage, accurate image segmentation remains a key hurdle due to the complexity of plant morphology and environmental noise. In this study, we present the Botanical Spectrum Analyser (BSA), a user-friendly graphical user interface (GUI) that integrates a modified U-Net deep neural network for plant image segmentation. Designed for accessibility, BSA enables non-technical users to apply advanced AI segmentation to RGB and hyperspectral (VNIR and SWIR) imagery. We evaluated BSA’s performance across three case studies involving wheat, barley, and Arabidopsis, demonstrating its robustness across species and imaging modalities. Our results show that BSA achieves an average accuracy of 99.7%, with F1-scores consistently exceeding 98% and strong Jaccard and recall performance across datasets. For challenging root segmentation tasks, BSA outperformed commercial algorithms, achieving a 76% F1-score compared to 24%, representing a 50% improvement. These results highlight the adaptability of the BSA framework for diverse phenotyping scenarios, bridging the gap between advanced deep learning methods and accessible plant science applications.

Why it matches plant phenotyping methods植物画像からの表現型抽出を目的とするGUI・深層学習セグメンテーション手法を開発し、複数種・画像モダリティで性能評価しているため、方法が中心的です。

abstractwe present the Botanical Spectrum Analyser (BSA), a user-friendly graphical user interface (GUI) that integrates a modified U-Net deep neural network for plant image segmentation.

Comparing hormone dynamics in cereal crops via transient expression of hormone sensors. →

Although hormone biology is critical for plant breeding, complex phenotypic outputs make it difficult to compare across species. We used transient expression of hormone biosensors and transcriptomics to simplify this output and quantify gibberellic acid (GA) and auxin responses across multiple cereal crop genotypes and tissues. We show the GPS2 biosensor detects exogenous GA in maize, barley, sorghum, and wheat. Measuring across GA dosages, we detect tissue- and genotype-specific differences in GA sensor response with an unexpected drop in GPS2 output in the maize d1 GA biosynthesis mutant after GA treatment, likely reflecting differences in GA response across samples. We used RNA sequencing followed by ortholog prediction and Gene Ontology-term enrichment analysis to measure GA responses in leaves and floral tissues from maize wildtype, d1, and barley Golden Promise. We determine that cross-tissue, cross-genotype, and cross-species GA responses include downregulation of GA-INSENSITIVE DWARF1 and upregulation of α-Expansin1, independent of GA biosynthesis. We identify F-Box proteins, hexokinase, and AMPK/SNF1 protein kinase orthologs as unexpected cross-species GA-responsive genes. We then compared transient expression of DR5, DR5v2, and DII-mDII auxin reporters in barley and maize and find DR5v2 and DII-mDII are functional auxin reporters in both species.

Why it matches plant phenotyping methods植物ホルモン応答を定量するバイオセンサーの種・組織・遺伝子型間での機能検証と比較適用が中心であり、植物の生理状態を取得するフェノタイピング手法に該当する。

abstractWe used transient expression of hormone biosensors and transcriptomics to simplify this output and quantify gibberellic acid (GA) and auxin responses across multiple cereal crop genotypes and tissues.

The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants →

• A high-precision digital platform for plant phenotyping and cultivation was developed. • LED illumination based multispectral imaging system to study crop plant responses. • Different multispectral signatures were induced by plant abiotic and biotic factors. Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g. , multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

Why it matches plant phenotyping methods植物の画像取得・解析、ストレス定量、ソフトウェアを統合した高スループット表現型解析プラットフォームの開発が中心である。

abstractA high-precision digital platform for plant phenotyping and cultivation was developed.

Genotypic variations in sensitivity of root K+ and Ca2+ transporters to H2O2 explains differential salt tolerance in wheat and barley →

Wheat and barley are known as important staple food worldwide, but their growth and yield are severely affected by soil salinity, prompting a need for regaining their stress tolerance lost during domestication, to meet food security targets under current climate scenarios. The bottle neck in this process is plant phenotyping. In the past decades, approach to plant phenotyping for salinity stress tolerance was predominantly driven by the need for a high throughput screening and focused on the whole-plant level traits by advocating various non-destructive and/or analytical methods. This approach, though useful for assessing overall plant performance under salinity stress, fails to account for tissue- and cell-specific operation of contributing mechanisms and, as a result, lack the predictive power. In this work, we propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H₂O₂-induced K⁺ and Ca²⁺ flux responses from mature root epidermis. By screening 44 barley, 20 durum and 20 bread wheat accessions, we show that tolerant genotypes reduce sensitivity of cation (Na⁺, K⁺ and Ca²⁺) permeable ion channels to ROS and argue that such desensitization may allow plants to efficiently regulate its ionic homeostasis in a cell- and tissue-specific manner, without compromising stress-induced ROS signaling to downstream targets, for transcriptional regulation purposes. Being conducted on young (4-d old) seedlings, this cell-based phenotyping platform offer breeders a possibility to target new (previously unexplored) traits and may be instrumental for assisting breeders in engineering salinity stress tolerance in future breeding programs.

Why it matches plant phenotyping methods塩ストレス耐性を評価する新しい細胞ベースの生理フェノタイピング手法を提案・検証し、イオンフラックス応答を用いて遺伝子型をスクリーニングしているため、手法が研究の中心である。

abstractwe propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H₂O₂-induced K⁺ and Ca²⁺ flux responses from mature root epidermis

Spatial Coordination between Leaf Gradient and Temperature Response in Barley →

The linear shape of cereal leaves creates distinct longitudinal zones that coordinate tissue maturation and resource allocation. Under abiotic stress such as heat, these longitudinal zones may differentially activate protective pathways, revealing hidden heterogeneity in stress response that remains poorly understood. Barley (Hordeum vulgare), a cold-adapted crop particularly sensitive to elevated temperatures, can serve as an ideal model for studying region-specific heat responses in leaves. Using chlorophyll fluorescence imaging, we found that non-photochemical quenching (NPQ) kinetics captured additional physiological changes beyond those detected by SPAD, highlighting the added value of chlorophyll fluorescence-based assessments. NPQ kinetics traits displayed consistent spatial gradients from tip to base, with heat stress reducing NPQ induction across leaf gradients. Genome-wide association analysis of traits derived from chlorophyll fluorescence imaging across leaf gradients identified significant SNPs within multiple candidate genes, including HORVU.MOREX.r3.3HG0262630 that was consistently detected in over 90% resampling iterations under heat stress, suggesting its key role during heat responses. Transcriptomic profiling along the leaf axis revealed both conserved and region-specific heat responses between leaf regions. Conserved activations highlighted conserved heat response pathways, including the reactivation of the Arabidopsis thermomemory module FtsH6-HSP21. In contrast, the region-by-temperature interaction analysis identified 40 genes with spatial responses indicative of resource reallocation from growth to defense, including those involved in growth and transport. The integration between spatially resolved phenotyping and transcriptional profiling underscores region-specific variation in response to heat along the leaf axis, guiding targeted strategies to enhance heat resilience in barley and other cereal crops.

Why it matches plant phenotyping methods葉の空間的な熱応答を評価するため、クロロフィル蛍光イメージングとNPQ動態による生理形質取得を中心的に実施しており、空間分解フェノタイピングが研究の主要手法である。

abstractUsing chlorophyll fluorescence imaging, we found that non-photochemical quenching (NPQ) kinetics captured additional physiological changes beyond those detected by SPAD

Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches. →

By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). A 130-line F8-F9 barley population caused Badia and Kavir to grow at the Gonbad Kavous University Research Farm on three planting dates (19 November, 19 January, and 19 March), with three replicates in 2018/2019 and 2019/2020. The study employed RReliefF, MRMR, and F-Test feature selection algorithms to identify essential phenotype traits and molecular markers. Subsequently, Decision Tree, Random Forest, Neural Network, and Gaussian Process Regression models were compared using MAE, RMSE, and R2 metrics. The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models. The results indicated that the Neural Network model accurately predicted powdery mildew disease resistance in barley lines. The evaluation based on high R2 values, as well as low MAE and RMSE, highlighted the efficacy of these models in identifying significant phenotype traits and molecular markers associated with disease resistance. The findings demonstrate machine learning models' potential in accurately predicting powdery mildew disease resistance in Barley. The neural network model specifically showed excellent results in this area because it managed to identify critical phenotypic traits and molecular markers very well. This research highlights the importance of combining AI with molecular markers for improved disease resistance and other desirable crop traits during plant breeding.

Why it matches plant phenotyping methods機械学習モデルの比較・最適化により、オオムギのうどんこ病抵抗性という植物状態を予測する計算手法が研究の中心であり、単なる生物学的実験の測定結果ではない。

abstractBy employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L).

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning →

Abstract Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R 2 ≥ 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R 2 ≥ 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.

Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる高スループット表現型取得と、時系列モデルによる干ばつ状態および収穫形質の予測が研究の中心である。

abstractwe investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants

Extending genomic prediction to future climates through crop modelling. A case study on heading time in barley →

Integrating genomic prediction (GP) and biophysical crop models has the potential to support plant breeding in defining adapting strategies to climate change. However, whether this integrated approach can actually broaden the prediction domain to unexplored environments is still unclear. We showed how crop models can extend GP to new environments and capture genotype-specific response to future climate conditions. Days to heading (HD) in spring barley was used as a case study, given the primary role of phenology for crop adaptation. Phenotypic and genomic (50 K Illumina SNP array) data for 151 two-row genotypes were used, with HD determined on 17 site × season combinations spread from the Mediterranean basin to Northern Europe. A dedicated modelling solution was developed by integrating approaches from the WARM and WOFOST crop models, with optimization algorithms used to derive accession-specific values of most relevant model parameters. GP was carried out on the model parameters with the R package rr-BLUP, and the resulting markers-derived parameters were used to simulate heading date with the crop model explicitly reproducing G × E × M interactions. Prediction accuracy for unknown genotypes was evaluated through ten-fold cross validation, whereas the capability of crop models to extend GP to unexplored environments was evaluated with a leave-one site-out cross validation. Results were encouraging, with average R², RMSE and Nash-Sutcliffe efficiency for unknown genotypes in unexplored environments equal to 0.97, 9.27, and 0.94, respectively. Accession-specific HD under future climates (20 seasons centred on 2060 generated for the IPSL-CM6A-LR realization of SSP3–7.0) highlighted large G × E × M interactions, confirming the potential of integrating crop modelling and GP to supporting breeding programs targeting adaptation to climate change.

Why it matches plant phenotyping methods作物モデルとゲノム予測を統合して出穂日という植物形質を推定する手法を開発し、未知遺伝子型・未知環境で交差検証しているため、方法開発・検証が中心である。

abstractWe showed how crop models can extend GP to new environments and capture genotype-specific response to future climate conditions.

Phenotyping-based spectral signatures uncover barley cultivars’ sensitivity to combined mildew and drought treatment →

The plant’s phenotype changes under biotic and abiotic stress, reflecting its adaptations in gene expression and metabolism. For crop management, rapid detection of plant stress responses is crucial. To facilitate rapid detection of stress responses in crops, we explored the potential of UCPH’s PhenoLab for assessing barley disease resistance under both biotic and abiotic stress. We used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts. At specific wavelengths, PhenoLab spectral signatures clearly distinguished cultivars with different levels of susceptibility to the obligate biotroph pathogen Blumeria graminis (powdery mildew). Microscope phenotyping at similar reflectance and fluorescence settings parallelled the PhenoLab-derived spectral signatures. However, a specific systemic resistance response emerged three days after inoculation, detectable only by microscopy when targeting infected and non-infected leaf areas. We hypothesized that combined stresses would work additively and used phenotyping to study the response of the resistant and susceptible barley cultivar to a combination of drought with powdery mildew infection. Surprisingly, drought made the resistant cultivar less resistant and the susceptible one less susceptible according to changes in reflectance and fluorescence at defined wavelengths. The spectroscopic absorbance assay confirmed this result biochemically. This proof-of-concept study showcases the potential of holistic functional phenomics, using non-invasive imaging to identify predictive spectral signatures for barley pathogen resistance.

Why it matches plant phenotyping methodsPhenoLabの高スループット反射・蛍光イメージングを用いて、病害抵抗性と複合ストレス応答を非破壊的に評価し、スペクトルシグネチャを検証しており、表現型取得法が研究の中心です。

abstractWe used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts.

Barley Head Detection Using UAV Imagery and YOLOv10 →

Abstract Barley head detection is a crucial task for agricultural applications such as yield estimation and crop monitoring. Unlike wheat, automated barley head detection has not been extensively studied due to challenges posed by its complex head structures and the lack of annotated datasets. In this paper, we leverage YOLOv10, a state-of-the-art object detection framework, to detect barley heads from high-resolution images captured using UAVs. Our dataset, consisting of UAV-captured images and supplemented with the Global Wheat Head Dataset, provides a robust foundation for model training. The proposed approach achieves a mean Average Precision of 0.83 at Intersection of Union 0.5, setting a new benchmark for barley head detection. This work contributes to advancing automated crop monitoring systems in precision agriculture.

Why it matches plant phenotyping methodsUAV画像からオオムギ穂を検出するYOLOv10手法とデータセットを中心に開発・評価しており、植物器官の画像ベース表現型取得に該当する。

abstractIn this paper, we leverage YOLOv10, a state-of-the-art object detection framework, to detect barley heads from high-resolution images captured using UAVs.

Genotypic variations in sensitivity of root K+ and Ca2+ transporters to H2O2 explains differential salt tolerance in wheat and barley →

Wheat and barley are known as important staple food worldwide, but their growth and yield are severely affected by soil salinity, prompting a need for regaining their stress tolerance lost during domestication, to meet food security targets under current climate scenarios. The bottle neck in this process is plant phenotyping. In the past decades, approach to plant phenotyping for salinity stress tolerance was predominantly driven by the need for a high throughput screening and focused on the whole-plant level traits by advocating various non-destructive and/or analytical methods. This approach, though useful for assessing overall plant performance under salinity stress, fails to account for tissue- and cell-specific operation of contributing mechanisms and, as a result, lack the predictive power. In this work, we propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H 2 O 2 -induced K + and Ca 2+ flux responses from mature root epidermis. By screening 44 barley, 20 durum and 20 bread wheat accessions, we show that tolerant genotypes reduce sensitivity of cation (Na + , K + and Ca 2+ ) permeable ion channels to ROS and argue that such desensitization may allow plants to efficiently regulate its ionic homeostasis in a cell- and tissue-specific manner, without compromising stress-induced ROS signaling to downstream targets, for transcriptional regulation purposes. Being conducted on young (4-d old) seedlings, this cell-based phenotyping platform offer breeders a possibility to target new (previously unexplored) traits and may be instrumental for assisting breeders in engineering salinity stress tolerance in future breeding programs.

Why it matches plant phenotyping methods塩ストレス耐性を評価する細胞ベースの表現型取得法を提案・検証し、イオンフラックスを用いた再利用可能なフェノタイピング基盤として中心的に扱っている。

abstractIn this work, we propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H 2 O 2 -induced K + and Ca 2+ flux responses from mature root epidermis.

Development and quality assessment of low-cost benchtop malting protocol for laboratory-scale barley (Hordeum vulgare L.) malt quality evaluation →

High-quality malt is influenced by three primary factors: barley genotype, environmental conditions, and malting process. To effectively evaluate malting barley (Hordeum vulgare L.) breeding material and assess how environmental changes influence malt quality, it is essential to have laboratory-scale malting methods that can produce malt similar to that produced by commercial malting operations. However, existing laboratory-scale malting procedures often require large quantities of grain, specialized equipment, and are costly. To overcome these challenges, we developed a small sample-scale benchtop malting method utilizing standard laboratory equipment and components available at hardware stores. We validated the method by conducting standard malt quality tests including diastatic power, α-amylase activity, total malt protein, and wort composition (soluble protein, wort soluble/total malt protein, β-glucan, free amino nitrogen, and malt extract). Our findings indicate that the benchtop malting method yields quality metrics comparable to those obtained from established small-scale and full-scale malting protocols. Furthermore, a key innovation of this system is the use of separate Erlenmeyer flasks for malting each sample. Unlike conventional shared malting systems, this design enables precise measurement and comparison of treatment effects across samples malted simultaneously. This reliable, low-cost, and efficient method provides a valuable tool for screening malt quality traits in breeding lines with limited sample sizes and for testing malting regimes aimed at improving malt quality and efficiency. Additionally, it offers an accessible solution for producing high-quality, research-scale malt in laboratories without dedicated quality assurance facilities.

Why it matches plant phenotyping methods大麦育種材料の麦芽品質形質を測定するための低コスト小規模麦芽化法を開発し、既存法との品質指標の比較検証まで行っており、形質取得法が研究の中心である。

abstractwe developed a small sample-scale benchtop malting method utilizing standard laboratory equipment and components available at hardware stores.

Comprehensive Evaluation and Construction of Drought Resistance Index System in Hulless Barley Seedlings. →

With global climate change ongoing, the frequency and intensity of extreme weather events have increased annually. Hulless barley ( Hordeum vulgare L. var. nudum), a primary crop cultivated in the Qinghai-Tibet Plateau mountains, frequently encounters multiple abiotic stresses including low temperature, high salinity, and drought. Among these stresses, drought has emerged as a critical environmental constraint affecting sustainable agricultural development worldwide. Establishing a drought resistance evaluation system for the hulless barley germplasm during its seedling stages could provide a theoretical foundation for screening and breeding drought-tolerant cultivars to address climate change challenges. This study employed two drought-sensitive (YC85 and YC88) and two drought-tolerant (ZY1252 and ZY1100) cultivars to develop an effective drought resistance evaluation protocol for hulless barley. Our findings identified several reliable indicators for assessing drought tolerance at the seedling stage: fresh mass, chlorophyll fluorescence parameters (F v /F m , NPQ, and R FD ), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) levels. The established evaluation system was subsequently applied to three uncharacterized cultivars (ZY673, ZY1403, and KL14). The results classified all three as drought-sensitive, with ZY1403 exhibiting the highest sensitivity. Our work has established a comprehensive drought resistance evaluation framework for Tibetan hulless barley. Furthermore, this study provides valuable insights for optimizing cultivation practices and water resource management strategies, offering theoretical guidance for agricultural adaptation to climate change.

Why it matches plant phenotyping methods幼苗の乾 drought resistance を評価するための指標選定・評価プロトコルと総合評価枠組みを開発し、別品種で適用しているため、表現型取得・評価法が研究の中心である。

abstractdevelop an effective drought resistance evaluation protocol for hulless barley

From a regional to field scale - transfer learning for Earth observation based crop yield forecasts →

Climate change is threatening food security, necessitating optimized resource management to ensure food availability. Field-scale crop yield forecasts, using machine learning and Earth observation data, have great potential for adaptive farm management, but the development of such models is curbed by the scarcity of field-scale training data. This strongly limits the applicability of traditional machine-learning approaches for field-scale crop yield modeling. However, increasingly popular transfer learning techniques provide a solution to improve this, since they can learn from a different domain than the one they are applied for. Here, we explore transfer learning to forecast crop yields on a field scale by training the model on a regional scale (where we have abundant data in Europe). We use Sentinel-1 and Sentinel-2 data with an artificial neural network to forecast maize, winter wheat, and spring barley yields in southern Czechia. We compared four model setups: two classical machine learning approaches trained and tested on a regional scale and one trained and tested on a field scale as a baseline. We compared these models to two transfer learning models that are trained on a regional scale and tested on a field scale, one with and one without fine-tuning the model using field-scale data. Forecasts were calculated at four lead times (1-4 months) before harvest. We showed that transfer learning with fine-tuning demonstrates superior performance, achieving correlations of approximately 0.75 at a one-month lead time for all crops. It outperformed the field scale-trained model by 0.05-0.12. In addition, transfer learning required significantly less field-level data to achieve a performance comparable to the model trained at the field level: 50% of the data for spring barley and maize, and only 25% for winter wheat. Therefore, this transfer learning approach improves the efficiency of crop yield data utilization and enhances field-level crop yield forecasting. The work of this study was conducted in the frame of the project “Yield Prediction and Estimation from Earth Observation” (funded by ESA - Contract No. 4000141154/23/I-EF)

Why it matches plant phenotyping methods圃場スケールの作物収量という植物・圃場形質を、衛星観測と転移学習で推定する手法を開発・比較検証しており、形質推定法が研究の中心である。

abstractHere, we explore transfer learning to forecast crop yields on a field scale by training the model on a regional scale

RootEx: An automated method for barley root system extraction and evaluation →

Plant phenotyping plays a crucial role in agricultural research, especially in identifying resilient traits essential for global food security. Quantitative analysis of root growth has become increasingly vital in evaluating a plant’s resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images presents substantial challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums. Our method involves several stages, beginning with preprocessing to identify the Region of Interest (ROI). Subsequent stages utilize deep neural network-based segmentation, skeleton construction, and graph generation to produce detailed representations of root systems stored in RSML format. Notably, our dataset exclusively comprises primary roots without secondary roots or bifurcations, allowing for a focused examination of primary root characteristics and environmental adaptability. Evaluation against established methods, RootNav 1.8 and 2.0, reveals significant improvements in root system reconstruction accuracy across various performance indicators. Although RootEx may exhibit slightly lower performance due to the absence of neural network-based tip detection, its advantages include minimal losses in missing root lengths and independence from dedicated training datasets. Our approach effectively mitigates detection errors, providing a reliable tool for precise barley root analysis in agricultural research.

Why it matches plant phenotyping methods根画像からオオムギ根系の形質を自動抽出する手法を開発し、既存手法と精度比較しているため、植物フェノタイピング手法が研究の中心です。

abstractIn this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums.

Drought resistance evaluation in hulless barley seedlings: physiological markers for climate-resilient crop breeding →

Abstract Global climate change intensifies extreme weather-induced crop losses, necessitating drought-resilient crops. Qingke (Hordeum vulgare var. nudum), the staple barley of Tibet's climate-vulnerable plateau, offers genetic insights into stress adaptation. We established a seedling-stage drought evaluation system identifying four biomarkers: fresh weight, chlorophyll fluorescence (Fv/Fm, NPQ, RFD), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) accumulation. Systematic screening of the physiological traits revealed these parameters as optimal predictors of drought tolerance, enabling rapid germplasm classification. Application to three uncharacterized cultivars (ZY673, ZY1403, KL14) demonstrated weak drought resistance across all lines, with ZY1403 showing extreme sensitivity. This standardized protocol for hulless barley integrates photosynthetic efficiency and oxidative stress metrics, providing breeders with actionable thresholds for climate-resilient crop development in montane agroecosystems.

Why it matches plant phenotyping methods乾燥耐性を評価する標準化された生理フェノタイピング系を構築し、複数の形質をスクリーニングして予測指標・判定閾値を検証しているため、方法が研究の中心である。

abstractWe established a seedling-stage drought evaluation system identifying four biomarkers: fresh weight, chlorophyll fluorescence (Fv/Fm, NPQ, RFD), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) accumulation.

RootEx: An automated method for barley root system extraction and evaluation →

Plant phenotyping plays a crucial role in agricultural research, especially in identifying resilient traits essential for global food security. Quantitative analysis of root growth has become increasingly vital in evaluating a plant’s resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images presents substantial challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums. Our method involves several stages, beginning with preprocessing to identify the Region of Interest (ROI). Subsequent stages utilize deep neural network-based segmentation, skeleton construction, and graph generation to produce detailed representations of root systems stored in RSML format. Notably, our dataset exclusively comprises primary roots without secondary roots or bifurcations, allowing for a focused examination of primary root characteristics and environmental adaptability. Evaluation against established methods, RootNav 1.8 and 2.0, reveals significant improvements in root system reconstruction accuracy across various performance indicators. Although RootEx may exhibit slightly lower performance due to the absence of neural network-based tip detection, its advantages include minimal losses in missing root lengths and independence from dedicated training datasets. Our approach effectively mitigates detection errors, providing a reliable tool for precise barley root analysis in agricultural research. • RootEx: automated extraction of barley root systems from high-res images. • Improved precision in root analysis through deep learning-based segmentation. • Focus on primary roots, without the complexity of secondary root systems. • Significant accuracy improvement w.r.t. RootNav 1.8 and 2.0. • RootEx minimizes detection errors, ensuring reliabile root analysis.

Why it matches plant phenotyping methods根系画像から植物形質を抽出する自動手法を開発し、既存手法と精度比較しているため、植物フェノタイピング手法が中心である。

abstractwe introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems

Deep phenotyping platform for microscopic plant-pathogen interactions. →

The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro , a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision ’s open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

Why it matches plant phenotyping methods自動顕微鏡画像とソフトウェアを基盤とする高スループット植物病害表現型解析プラットフォームを開発し、精度・感度・再現性を検証しているため、方法が研究の中心である。

abstractwe developed BluVision Micro , a modular, machine learning-aided system designed for high-throughput microscopic phenotyping.

Sub-field detection of cereal yield losses and its causes using Sentinel-2 time series and weather data →

Weather extremes severely affect agricultural production and threaten food security worldwide. Throughout the growing season, crops can experience various degrees of weather stress whereby multiple stressors could occur simultaneously or intermittently. For large spatial extents, it is difficult to estimate actual crop damage merely through field experiments or crop models. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents, but current RS-based studies usually focus on a single stress or event. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these losses to different weather extremes. To assess our method’s potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time derived from gridded meteorological data. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. The combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. Our approach can help to improve early assessment of crop damage and understand weather causes of such damage, thus informing strategies for crop protection.

Why it matches plant phenotyping methods衛星時系列画像から作物生育異常・収量低下・気象ストレスによる作物損傷を推定する手法を提案し、予測性能を検証している。単なる農業実験ではなく、植物状態・収量関連形質の取得と推定ワークフローが中心である。

abstractWe propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these losses to different weather extremes.

LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 →

Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training.

Why it matches plant phenotyping methods葉の検出・セグメンテーション・計数を対象とする統一画像解析モデルを開発・評価しており、植物形態形質の抽出手法が研究の中心である。

abstractAccurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture.

Improving agricultural carbon monitoring with Sentinel-2 and eddy-covariance-based plant productivity estimates →

Carbon balances of croplands are often assessed using models that depend critically on accurate estimates of soil carbon inputs such as crop residues, dead roots, and root exudates. Here, we develop a method to estimate soil carbon inputs by combining satellite-based gross primary productivity (GPP) estimates with harvest yields extracted from agricultural statistics. We model the daily GPP as a statistical regression on photosynthetically active radiation and the red edge chlorophyll index measured by the Sentinel-2 satellites and train the model using data from five eddy covariance flux measurement sites. When tested with leave-one-site-out cross validation, the model predicted the yearly GPP with a root mean squared error equal to about 10 % of the mean. We furthermore show that the predicted cumulative GPP explains 60-70 % of the observed variability within a set of 135 aboveground biomass measurements collected on 40 agricultural fields. Finally, we apply the method to three Finnish regions and estimate the soil carbon inputs as the difference between the net primary productivity (NPP), assumed 50 % of the GPP, and the carbon removed in harvest. Compared to the allometric method used in the current national greenhouse gas inventory, our annual carbon inputs are within 5-30 % for wheat but 50-100 % higher for barley, 40-50 % higher for green fallows, and 100-160 % higher for forage grasses. These results highlight a discrepancy between the current national greenhouse gas inventory and carbon budgets derived from flux measurements at eddy covariance sites.

Why it matches plant phenotyping methodsSentinel-2のスペクトル指標から作物GPPを推定する手法を開発し、サイト除外交差検証と圃場バイオマスで検証しており、植物の生産性・バイオマス推定が中心的な技術要素です。

abstractWe model the daily GPP as a statistical regression on photosynthetically active radiation and the red edge chlorophyll index measured by the Sentinel-2 satellites and train the model using data from five eddy covariance flux measurement sites.

SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigor in wheat and other cereal crops using deep learning-powered dynamic phenotypic analysis. →

Background As one of the most important cereal crops, wheat (Triticum aestivum L.) production and grain quality are essential to many nations in the world. Early developmental phases such as seed germination and seedling establishment are key to wheat's growth and development as they impact directly on a crop's early performance and yield potential. Hence, it is critical to develop varieties with favorable early growth characteristics under various growing conditions. Results Here, we present SeedGerm-VIG, an automated and comprehensive pipeline developed for assessing seed vigor in wheat and other cereal crops. Building on the SeedGerm system, we integrated multiple deep learning models (i.e., YOLOv8x-Germ and optimized U-Net) and computer vision algorithms into the automated seed-level analysis pipeline to identify key germination phases and measure seed-, root-, and seedling-level phenotypic traits. Then, by using a time-series directed graph, we not only tracked root tips to reliably measure root emergence during the germination procedure (seed-lot R2 = 84.1%) but also established a new approach to examine speed and uniformity of seed germination. These resulted in the establishment of a vigor scoring matrix, through which 21 commercial genotypes' (n = 494 randomly sampled seeds, with over 29,500 seed-level images) vigor scores were summarized and evaluated at key phases such as protrusion, radicle emergence, and chloroplast biogenesis. These measures largely matched with manual assessment based on the International Seed Testing Association (ISTA) guidelines. Finally, we also demonstrated that the SeedGerm-VIG pipeline could be used to assess seed vigor for other cereal crops, including rice (n = 120 seeds) and barley (n = 240 seeds), reproducibly. Conclusions In conclusion, we believe that our work demonstrates a valuable step forward to enable a broader plant and crop research community to examine seed vigor and vigor-related phenotypic features in an automated manner, facilitating effective and scalable plant selection and relevant seed science research for crop improvement.

Why it matches plant phenotyping methods深層学習とコンピュータビジョンを統合した種子活力・発芽表現型の自動取得パイプラインを開発し、手動評価との一致および他作物で再現性を検証しており、表現型取得法が研究の中心である。

abstractwe present SeedGerm-VIG, an automated and comprehensive pipeline developed for assessing seed vigor in wheat and other cereal crops.

Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions →

The ability of laser scanning confocal microscopy to generate high-contrast 2D and 3D images has become essential in studying plant-fungal interactions. Techniques such as visualization of native fluorescence, fluorescent protein tagging of microbes, green fluorescent protein (GFP)/red fluorescent protein (RFP)-fusion proteins, and fluorescent labeling of plant and fungal proteins have been widely used to aid in these investigations. Use of fluorescent proteins has several pitfalls, including variability of expression in planta and the requirement of gene transformation. Here, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline that uses propidium iodide (PI), which stains RNA and DNA, and wheat germ agglutinin labeled with fluorescein isothiocyanate (WGA-FITC), which stains chitin, to visualize fungal colonization of plants. This pipeline relies on the use of KOH to remove the cutin layer of the leaf, increasing its permeability, allowing the different stains to penetrate and effectively bind to their targets, resulting in a consistent visualization of cellular structures. To expand the utility of this pipeline, we used the staining techniques in conjunction with machine learning to analyze fungal biomass through volume analysis, as well as quantifying nuclear breakdown, an early indicator of programmed cell death (PCD). This pipeline is simple to use, robust, consistent across host and fungal species, and can be applied to most plant-fungal interactions. Therefore, this pipeline can be used to characterize model systems as well as nonmodel interactions where transformation is not routine. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.

Why it matches plant phenotyping methods植物-真菌相互作用を可視化し、真菌バイオマスと核崩壊を画像から定量する染色・共焦点顕微鏡・機械学習パイプラインの開発と評価が中心である。

abstractHere, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline

LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 →

Abstract Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and above 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. This highlights that while the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training. This work demonstrates the potential of AI-driven models to advance automated phenotyping for large-scale precision agriculture.

Why it matches plant phenotyping methods複数作物の葉を自動検出・セグメンテーションし、統合モデルと作物別モデルの性能を比較する画像解析手法が中心で、葉の抽出・計数という植物表現型取得に直接関係する。

abstractAccurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture.

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field →

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars ( Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. Highlight Integrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

Why it matches plant phenotyping methodsUAV画像由来の植生指数と機械学習を用いて、圃場で根系形質を非破壊推定する方法が研究の中心であり、実データへの応用と技術的概念実証を含む。

abstractUAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass.

Does domestication trade-off stress tolerance for leaf growth? A search for evidence across eight Pooideae grass species →

Plant domestication is thought to create trade-offs between high yield and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. However, trade-offs can also occur when traits (such as yield in agriculture) optimized for favorable conditions perform less efficiently in stressful conditions. Deciphering the mechanisms driving these trade-offs is crucial for maintaining yield in changing environments. We examine leaf growth, a key trait influencing carbon assimilation, in eight species of grasses. We use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics, finding that domesticated plants generally have longer leaves, larger division zones and higher cell production rates. We found no clear evidence of trade-off between domestication and drought response in final leaf length. However, a trade-off is observed in development as wild species exhibited a smaller decrease in elongation zone size under drought than their domesticated counterparts. These nuanced trade-offs associated with domestication highlight the importance of examining physiological traits and mechanisms in greater detail, possibly informing breeding strategies to enhance crop resilience in the face of climate change. Highlight This study uses a high throughput pipeline to characterize leaf elongation responding to drought stress across eight species including barley, wheat, oat and wild relatives.

Why it matches plant phenotyping methods葉の顕微鏡画像から細胞寸法・位置を自動抽出する機械学習パイプラインが、葉の成長特性評価の中心的手法として用いられているため。

abstractWe use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics

Malt Barley Yield and Quality Response to Crop Water Stress Index →

Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley’s seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict TLL, while a physical model was used to estimate TUL. The neural network model accurately predicted TLL, with a strong correlation (R2 = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R2 = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI.

Why it matches plant phenotyping methodsキャノピー温度とCWSIを用いた植物水ストレス推定法が中心で、ニューラルネットワークおよび物理モデルの開発・検証と、収量・品質との関連評価を行っている。

abstractthe objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling

Barley disease recognition using deep neural networks →

Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (Hordeum vulgare L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.

Why it matches plant phenotyping methods圃場で撮影したオオムギ画像から病徴・病害状態を深層学習で検出・分類する手法の開発と比較が主目的であり、植物表現型の取得・推定が中心です。

abstractThe objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (Hordeum vulgare L.)

Evaluating waterlogging stress response and recovery in barley (Hordeum vulgare L.): an image-based phenotyping approach. →

Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.

Why it matches plant phenotyping methods画像ベース表現型解析を用いた湛水ストレス評価プロトコルの開発と、別施設・異なるセンサーによる再現性検証が中心であるため、収録対象。

abstractwe present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping.

Metabolic imaging in living plants: A promising field for chemical exchange saturation transfer (CEST) MRI →

Magnetic resonance imaging (MRI) is a versatile technique in the biomedical field, but its application to the study of plant metabolism in vivo remains challenging because of magnetic susceptibility problems. In this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI. This method enables noninvasive access to the metabolism of sugars and amino acids in complex sink organs (seeds, fruits, taproots, and tubers) of major crops (maize, barley, pea, potato, sugar beet, and sugarcane). Because of its high signal detection sensitivity and low susceptibility to magnetic field inhomogeneities, CEST analyzes heterogeneous botanical samples inaccessible to conventional magnetic resonance spectroscopy. The approach provides unprecedented insight into the dynamics and distribution of sugars and amino acids in intact, living plant tissue. The method is validated by chemical shift imaging, infrared microscopy, chromatography, and mass spectrometry. CEST is a versatile and promising tool for studying plant metabolism in vivo, with many applications in plant science and crop improvement.

Why it matches plant phenotyping methods植物の生体内代謝を非侵襲的に測定するCEST-MRI法を確立し、複数手法で検証しており、植物表現型取得法が研究の中心である。

abstractIn this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI.

Micro computed tomography analysis of barley during the first 24 hours of germination. →

Background Grains make up a large proportion of both human and animal diets. With threats to food production, such as climate change, growing sustainable and successful crops is essential to food security in the future. Germination is one of the most important stages in a plant's lifecycle and is key to the success of the resulting plant as the grain undergoes morphological changes and the development of specific organs. Micro-computed tomography is a non-destructive imaging technique based on the differing x-ray attenuations of materials which we have applied for the accurate analysis of grain morphology during the germination phase. Results Micro Computed Tomography conditions and parameters were tested to establish an optimal protocol for the 3-dimensional analysis of barley grains. When comparing optimal scanning conditions, it was established that no filter, 0.4 degrees rotation step, 5 average frames, and 2016 × 1344 camera binning is optimal for imaging germinating grains. It was determined that the optimal protocol for scanning during the germination timeline was to scan individual grains at 0 h after imbibition (HAI) and then the same grain again at set time points (1, 3, 6, 24 HAI) to avoid any negative effects from X-ray radiation or disruption to growing conditions. Conclusion Here we sought to develop a method for the accurate analysis of grain morphology without the negative effects of possible radiation exposure. Several factors have been considered, such as the scanning conditions, reconstruction, and possible effects of X-ray radiation on the growth rate of the grains. The parameters chosen in this study give effective and reliable results for the 3-dimensional analysis of macro structures within barley grains while causing minimal disruption to grain development.

Why it matches plant phenotyping methods大麦の発芽中の穀粒形態を対象に、マイクロCTの撮像条件・再構成・放射線影響を検討し、3次元形態解析プロトコルを開発・検証しているため。

abstractMicro-computed tomography is a non-destructive imaging technique based on the differing x-ray attenuations of materials which we have applied for the accurate analysis of grain morphology during the germination phase.

Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production →

Net blotch disease caused by Drechslera teres is a major fungal disease that affects barley ( Hordeum vulgare ) plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection on seedling leaves using Cascade R-CNN (region-based convolutional neural network) and U-Net (a convolutional neural network) architectures. We used a dataset of barley leaf images with annotations of net blotch disease to train and evaluate the model. The model achieved an accuracy of 95% for Cascade R-CNN in net blotch disease detection and a Jaccard index score of 0.99, indicating high accuracy in disease quantification and location. The combination of Cascade R-CNN and U-Net architectures improved the detection of small and irregularly shaped lesions in the images at 4 days postinfection, leading to better disease quantification. To validate the model developed, we compared the results obtained by automated measurement with a classical method (necrosis diameter measurement) and a pathogen detection by real-time PCR. The proposed deep learning model could be used in automated systems for disease quantification and to screen the efficacy of potential biocontrol agents to protect against disease.

Why it matches plant phenotyping methods大麦葉の病斑を画像から定量する深層学習手法を開発し、古典的測定法およびPCRと比較検証しており、植物病害状態の表現型取得が中心である。

abstractwe developed a deep learning model to quantify net blotch disease symptoms on different days postinfection on seedling leaves using Cascade R-CNN (region-based convolutional neural network) and U-Net (a convolutional neural network) architectures.

Yellow Dwarf Virus Resistance in Barley: Phenotyping, Remote Imagery, and Virus–Vector Characterization →

Yellow dwarf viruses (YDVs) spread by aphids are some of the most economically important barley ( Hordeum vulgare ) virus-vector complexes worldwide. Detection and control of these viruses are critical components in the production of barley, wheat, and numerous other grasses of agricultural importance. Genetic control of plant diseases is often preferable to chemical control to reduce the environmental and economic cost of foliar insecticides. Accordingly, the objectives of this work were to (i) screen a barley population for resistance to YDVs under natural infection using phenotypic assessment of disease symptoms, (ii) implement drone imagery to further assess resistance and test its utility as a disease screening tool, (iii) identify the prevailing virus and vector types in the experimental environment, and (iv) perform a genome-wide association study to identify genomic regions associated with measured traits. Significant genetic differences were found in a population of 192 barley inbred lines regarding their YDV symptom severity, and symptoms were moderately to highly correlated with grain yield. The YDV severity measured with aerial imaging was highly correlated with on-the-ground estimates ( r = 0.65). Three aphid species vectoring three YDV species were identified with no apparent genotypic influence on their distribution. A quantitative trait locus impacting YDV resistance was detected on chromosome 2H, albeit undetected using aerial imaging. However, quantitative trait loci for canopy cover and mean normalized difference vegetation index were successfully mapped using the drone. This work provides a framework for utilizing drone imagery in future resistance breeding efforts for YDVs in cereals and grasses, as well as in other virus-vector disease complexes.

Why it matches plant phenotyping methodsドローン画像によるYDV症状重症度の推定を実装し、地上評価との相関でスクリーニング手法として検証しており、植物病害表現型の取得が中心的です。

abstractimplement drone imagery to further assess resistance and test its utility as a disease screening tool

Yellow Dwarf Virus Resistance in Barley: Phenotyping, Remote Imagery, and Virus-Vector Characterization →

Yellow dwarf viruses (YDVs) spread by aphids are some of the most economically important barley (Hordeum vulgare) virus-vector complexes worldwide. Detection and control of these viruses are critical components in the production of barley, wheat, and numerous other grasses of agricultural importance. Genetic control of plant diseases is often preferable to chemical control to reduce the environmental and economic cost of foliar insecticides. Accordingly, the objectives of this work were to (i) screen a barley population for resistance to YDVs under natural infection using phenotypic assessment of disease symptoms, (ii) implement drone imagery to further assess resistance and test its utility as a disease screening tool, (iii) identify the prevailing virus and vector types in the experimental environment, and (iv) perform a genome-wide association study to identify genomic regions associated with measured traits. Significant genetic differences were found in a population of 192 barley inbred lines regarding their YDV symptom severity, and symptoms were moderately to highly correlated with grain yield. The YDV severity measured with aerial imaging was highly correlated with on-the-ground estimates (r = 0.65). Three aphid species vectoring three YDV species were identified with no apparent genotypic influence on their distribution. A quantitative trait locus impacting YDV resistance was detected on chromosome 2H, albeit undetected using aerial imaging. However, quantitative trait loci for canopy cover and mean normalized difference vegetation index were successfully mapped using the drone. This work provides a framework for utilizing drone imagery in future resistance breeding efforts for YDVs in cereals and grasses, as well as in other virus-vector disease complexes.

Why it matches plant phenotyping methodsドローン画像を用いたYDV抵抗性・病徴評価を疾病スクリーニング手法として実装し、地上評価との相関で検証しているため、植物フェノタイピング手法が中心的です。

abstractimplement drone imagery to further assess resistance and test its utility as a disease screening tool

Heading and maturity date prediction using vegetation indices: A case study using bread wheat, barley and oat crops →

Contemporary crop research programs involve the evaluation of numerous micro-plots spread across extensive experimental fields. As a result, there is a growing need to depart from labor-intensive manual measurements when assessing phenological data. The growing significance of high throughput phenotyping platforms (HTTP), including unmanned aerial vehicles (UAVs), has rendered these technologies essential in crop research. The overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions. To pinpoint DH and DM prediction, a total of nine UAV surveys were conducted throughout the entire crop cycle. Prediction models for DH and DM using VIs were successfully developed for various crop species, explaining 65 % of the variance in bread wheat and 75 % in oats. The highest percentages of variance explained were achieved when models were developed separately for the two irrigation conditions (well-irrigated and rainfed). However, the percentage of variance explained by these models decreased when applied to barley (R²<0.5 for DH). Notably, including final plant height as a predictor increased the percentage of variance explained by the models only for irrigated bread wheat. Furthermore, the utilization of multi-temporal equations, which amalgamated data from diverse UAV surveys, notably enhanced the percentage of variance explained by the model (+160.71 % improvement in DH predictions), particularly those tailored to each specific crop species and irrigation condition. The investigation additionally established a thorough protocol for modeling the phenological aspects of cereal crops utilizing data acquired from UAVs, thereby enhancing the accessibility of this technology for measurements of phenology in large crop research programs. • Low-cost UAVs enable adequate phenology estimates using VIs in wheat and oats. • VIs near maturity and plant height are key to phenology prediction. • Improved models for heading date combine multiple UAV surveys during grain filling. • Single flight near maturity suffices for accurate prediction of days to maturity. • Phenology prediction models for barley were less robust.

Why it matches plant phenotyping methodsUAVのRGB画像から植生指数を抽出し、出穂日・成熟日という植物表現型を予測する手法を開発・検証しており、測定ワークフローが研究の中心です。

abstractThe overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions.

Independent estimates of net carbon uptake in croplands: UAV-LiDAR and machine learning vs. eddy covariance →

Understanding the sequestration of organic carbon (C) in agroecosystems is of primary importance for greenhouse gas (GHG) accounting in managed lands, to reduce the environmental footprint of land use, and inform crediting programs. However, a broader application of precise C accounting is currently constrained by a limited number of direct flux measurements. Aside well-studied ecosystems via the eddy covariance technique (EC), many still bear significant uncertainty. In this study, we propose and evaluate a method for estimating accumulated C stocks in agricultural sites, by assessing the plant aboveground carbon (AGC) throughout two growing seasons using unstaffed aerial vehicles (UAV) and machine learning (ML) regression methods. Then, we used these estimates to assess total plant C, and benchmarked it with CO₂ fluxes derived from the eddy covariance method from the ICOS DK-Vng site in Denmark. We utilized a light detection and ranging (LiDAR) sensor onboard a UAV to derive the structural characteristics of crops, and we conducted in parallel destructive field-based measurements of AGC. Then, we designed a ML pipeline to provide estimates of AGC as a supervised regression problem, using the LiDAR-derived point cloud data to extract predictive features and the AGC labels as ground-truth target values. The best performing ML model attained predictions of R2 = 0.71 and R2 = 0.93 at spatial resolutions of 1 m² and 2 m², respectively. The C content in the aboveground plant components was assessed via laboratory analysis (46.6 ± 0.3% of C-to-biomass in barley and 47.7 ± 0.3% in wheat), while the belowground components (root allocation and rhizodeposition) were estimated based on a phenology-dependent allometric ratio. The cumulative value of C uptake along the growing season (i.e. net primary productivity) was compared with the difference of C predictions between UAV-LiDAR survey dates, finding an optimal disagreement between methods below ± 9% in two different cereal crops. The plant carbon budget in croplands, determined through UAV-LiDAR and machine learning regression, aligns with the carbon ecosystem uptake estimated through the eddy covariance technique, showcasing comparable results. Thereby, the proposed method also demonstrates the potential to estimate cumulative CO₂ fluxes in areas lacking direct eddy covariance measurements. Various experimental setups are evaluated as well as the sources of uncertainty resulting from the sampling design.

Why it matches plant phenotyping methodsUAV-LiDARと機械学習により作物の地上部炭素量を推定する手法を開発し、破壊測定および渦相関法と比較検証しており、植物形質取得が研究の中心である。

abstractwe propose and evaluate a method for estimating accumulated C stocks in agricultural sites, by assessing the plant aboveground carbon (AGC) throughout two growing seasons using unstaffed aerial vehicles (UAV) and machine learning (ML) regression methods.

Classification and prediction of drought and salinity stress tolerance in barley using GenPhenML. →

Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience. Considering the research gap in the use of molecular markers in predicting abiotic stresses, this paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models. GenPhenML uses feature selection algorithms to determine the most important molecular markers. It then identifies the best model that predicts atmospheric resistance with lower MAE, RMSE, and higher R 2 . The results showed that GenPhenML with a neural network model predicted the salinity stress resistance score with MAE, RMSE and R 2 values of 0.1206, 0.0308 and 0.9995, respectively. Also, the NN model predicted drought stress scores with MAE, RMSE and R 2 values of 0.0727, 0.0105 and 0.9999, respectively. The GenPhenML approach was also used to classify barley genotypes as resistant and stress-sensitive. The results showed that the accuracy, accuracy and F1 score of the proposed approach for salinity and drought stress classification were higher than 97%.

Why it matches plant phenotyping methodsGenPhenMLという機械学習手法を開発し、分子マーカーと表現型形質から大麦遺伝子型の乾燥・塩ストレス耐性という植物状態を予測・分類しており、表現型推定手法が中心である。

abstractthis paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models.

Non-invasive exploration of malting barley (Hordeum vulgare) in vitro germination and varietal effects using short wave infrared spectral imaging and ANOVA simultaneous component analysis. →

ANOVA-simultaneous component analysis (ASCA) was applied to short-wave infrared spectral fingerprints of 5 malting barley varieties collected using a hyperspectral imaging system to determine the effect of germination, the influence of time and the influence of barley by means of a full factorial experimental design. ASCA indicated that there was a significant (p < 0.0001) effect of the germination status, the germination time and interaction on the spectral data for all varieties. The biochemical and physiological modification of the samples were characterised by visualisation of the longitudinal scores obtained from simultaneous component analysis for the germination time factor. This resulted in the visualisation and explanation of biochemical change over the course of barley germination as a factor of time. The relevant loadings indicated a significant change to the proteome, lipid and starch structure as driven by the uptake of water over time. The ASCA model were extrapolated to include the effect of barley variety to the already mentioned germination status and germination time factors, resulting once again in all the effects being significant (p < 0.0001). Here it was shown that all the barley varieties are significantly different from one another pre- and post-modification, based on the molecular vibrations observed in the short wave-infrared (SWIR) spectra, suggesting that the detection of biotic stress factors, such as pre-harvest germination, also differ for each variety, by indicating that the germination profile of each barley variety varies as a function of germination time. Thus, also the malting performance, germinative energy and chemical profile of each barley variety tested will vary before, during and after imbibition and germination - indicating the importance of malting commercial barley malt true to variety. These results indicate that (SWIR) spectral imaging instrumentation can possibly be used to monitor controlled germination of barley grain. Due to the shown ability of SWIR spectral imaging to detect small biochemical changes over time of barley grain during germination.

Why it matches plant phenotyping methodsSWIRスペクトル画像とASCAを用いて大麦種子の発芽状態・時間変化を非侵襲的に推定・監視する方法が中心であり、植物状態の取得手法として適格。

titleNon-invasive exploration of malting barley (Hordeum vulgare) in vitro germination and varietal effects using short wave infrared spectral imaging and ANOVA simultaneous component analysis.

LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction →

Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource use, farm sustainability, and informed decision-making. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, deepening our understanding of genetic variation and enhancing desirable crop traits to optimize performance in various environments. There is increasing interest in using machine learning (ML) and deep learning (DL) algorithms for genotype-to-phenotype prediction due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we propose a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically for flowering time and grain yield estimation, which could potentially help optimize yields and management practices. Our model outperformed the other baseline methods, demonstrating its potential in handling complex high-dimensional agricultural datasets and enhancing crop phenotype prediction performance.

Why it matches plant phenotyping methods大麦の開花期・穀粒収量という植物形質を予測するLSTMオートエンコーダモデルを新規開発し、ベースライン比較で性能検証しており、計算的な形質推定法が中心である。

abstractIn this work, we propose a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically for flowering time and grain yield estimation

Barley variety Quest exhibits broad-spectrum, moderate quantitative resistance against diverse Xanthomonas translucens pathovar translucens →

Abstract Barley ( Hordeum vulgare subspp. vulgare ) is the fourth most widely produced cereal today and is valuable for both animal and human consumption. Bacterial leaf streak (BLS) of barley is caused primarily by X. translucens pv. translucens (Xtt) and can lead to significant yield losses. There are currently no available control methods for BLS pathogens, but there are ongoing efforts to identify and characterize sources of resistance to Xtt in available barley germplasm. These screening projects require field trials which are time-consuming and challenged due to variables introduced by environmental and weather conditions, field conditions and other organisms that may be present. Reliable greenhouse phenotyping techniques are needed to accelerate screening of barley germplasm against more Xtt strains. In this study, we established a rapid greenhouse spray inoculation protocol for pathogen-plant phenotyping. This provides a framework for rapid germplasm screening before time-consuming and limited field nursery trials. Our method confirmed the moderate quantitative resistance phenotype discovered in field trials for the cultivars Quest and Tradition against the Xtt strain CIX95. One week old seedlings of the previously characterized resistant line PI329000 were not resistant relative to the susceptible check line MW14-5371-013. The cultivar Quest then demonstrated moderate quantitative resistance against a diverse panel of Xtt strains. Xtt subgroup did not influence BLS outcomes on tested barley lines. We found evidence to support the hypothesis that the virulence of modern strains is increasing, though this does not improve their ability to cause disease on Quest.

Why it matches plant phenotyping methods温室での病害表現型取得プロトコルを確立し、圃場試験前の迅速な抵抗性スクリーニングに適用しているため、植物フェノタイピング手法が中心的です。

abstractReliable greenhouse phenotyping techniques are needed to accelerate screening of barley germplasm against more Xtt strains.

The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch →

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.

Why it matches plant phenotyping methodsオオムギ葉の病徴と網斑病重症度をRGB画像と機械学習で自動推定するスクリーニング手法を開発・評価しており、植物病害表現型の取得が中心である。

abstractThis study presents a novel screening method for evaluating NB severity in barley.

Re-Evaluation of Vascular Histogenesis in the Root Tips of Selected Species in the Poaceae Using New Methods: Analysis of the Plerome, Vascular Initials, Pericycle and Late-Maturing Metaxylem Vessels. →

Serial sectioning and 3D image reconstruction methods were applied to elucidate the structures of the apices of root vascular cylinders (VCs) in taxa of the Poaceae: Zea mays "Honey Bantam", Z. mays ssp. mexicana , Hordeum vulgare and Oryza sativa . The primary and nodal roots were investigated. Observations were performed using high-quality sectioning and 3D image-processing techniques improved and developed by the authors. We found that a quiescent uniseriate plerome was located at the most distal part of each VC. Vascular initials were located immediately basipetally to the plerome as a specific uniseriate layer that could be classified into central and peripheral initials that produced all the cells in the VC. No supplying of cells from the plerome to the vascular initials was observed. Numerical analysis revealed a "boundary point" along the root axis where the rate of increase of the vascular cell number markedly declined, and the VC diameter, number of vascular cells, and number of late-maturing metaxylem vessels (LMXs) at that point showed a similar relationship among the taxa and the types of roots examined (primary vs. nodal). The plerome and vascular initials layer can be considered independent after seed germination in these taxa. A boundary point at which procambial cell proliferation sharply declined was identified. The diameters of the VCs, number of LMXs, and number of vascular cells at the boundary point were found to be strongly related to each other.

Why it matches plant phenotyping methods根端の血管組織を対象に、改良・開発した連続切片作製と3D画像再構成を用いて細胞構造や血管径・細胞数を定量化しており、植物形態の取得・解析手法が研究の中心的要素です。

abstractSerial sectioning and 3D image reconstruction methods were applied to elucidate the structures of the apices of root vascular cylinders (VCs)

Evaluation of Leaf Chlorophyll Content from Acousto-Optic Hyperspectral Data: A Multi-Crop Study →

Chlorophyll plays a crucial role in the process of photosynthesis and helps to regulate plants’ growth and development. Timely and accurate evaluation of leaf chlorophyll content provides valuable information about the health and productivity of plants as well as the effectiveness of agricultural treatments. For non-contact and high-performance chlorophyll content mapping in plants, spectral imaging techniques are the most widely used. Due to agility and rapid random-spectral-access tuning, acousto-optical imagers seem to be very attractive for the detection of vegetation indices and chlorophyll content assessment. This laboratory study demonstrates the capabilities of an acousto-optic imager for evaluation of leaf chlorophyll content in six crops with different biophysical properties: Ribes rubrum, Betula populifolia, Hibiscus rosa-sinensis, Prunus padus, Hordeum vulgare and Triticum aestivum. The experimental protocol includes plant collecting, reference spectrophotometric measurements, hyperspectral imaging data acquisition, processing and analysis and building a multi-crop chlorophyll model. For 90 inspected samples of plant leaves, the optimal vegetation index and model were found. Obtained values of chlorophyll concentrations correlate well with reference values (determination coefficient of 0.89 and relative error of 15%). Applying a multi-crop model to each pixel, we calculated chlorophyll content maps across all plant samples. The results of this study demonstrate that acousto-optic imagery is very promising for fast chlorophyll content assessment and other laboratory spectral-index-based measurements.

Why it matches plant phenotyping methodsアコースト光学ハイパースペクトル画像から葉のクロロフィル含量を推定する取得・解析手法を開発および検証しており、植物表現型測定が中心である。

abstractFor non-contact and high-performance chlorophyll content mapping in plants, spectral imaging techniques are the most widely used.

Automated Seminal Root Angle Measurement with Corrective Annotation →

Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify SNPs significantly associated with angle and length, shedding light on the genetic basis of root architecture.

Why it matches plant phenotyping methods根の表現型である根角度を画像から自動抽出する手法・オープンソースツールを開発し、手動測定との妥当性検証も行っているため、方法が研究の中心である。

abstractWe introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images.

Rapid nondestructive hardness detection of black highland Barley Kernels via hyperspectral imaging →

The objective of this study was to propose a rapid and nondestructive method for quantitatively detecting the hardness of black highland barley kernels using hyperspectral imaging. Initially, a regression model was established to predict hardness based on β-glucan content. Spectral reflectance within the 400–1000 nm wavelength range was gathered for black highland barley, and six preprocessing techniques were applied. Once preprocessing was completed, three characteristic wavelength screening methods were employed. Finally, three different models were utilized to construct a dependable prediction model for β-glucan content. The results indicated that the one-dimensional convolutional neural network (1D-CNN), in combination with the moving average (MA) preprocessing method, exhibited the best performance. To validate the hardness prediction model, the β-glucan content prediction model was integrated with the hardness regression model. The hardness prediction model attained a coefficient of determination (R²) value of 0.8093 and root mean square error (RMSE) of 0.2643 kg. The visual images exhibit characteristics feature of hardness in different varieties of black highland barley. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of black highland barley.

Why it matches plant phenotyping methods黒ハイランド大麦粒の硬度という植物器官形質を、ハイパースペクトル画像と回帰・1D-CNNで非破壊推定する手法を開発・検証しており、表現型取得が研究の中心である。

abstractThe objective of this study was to propose a rapid and nondestructive method for quantitatively detecting the hardness of black highland barley kernels using hyperspectral imaging.

Back to the future for drought tolerance. →

Global agriculture faces increasing pressure to produce more food with fewer resources. Drought, exacerbated by climate change, is a major agricultural constraint costing the industry an estimated US$80 billion per year in lost production. Wild relatives of domesticated crops, including wheat (Triticum spp.) and barley (Hordeum vulgare L.), are an underutilized source of drought tolerance genes. However, managing their undesirable characteristics, assessing drought responses, and selecting lines with heritable traits remains a significant challenge. Here, we propose a novel strategy of using multi-trait selection criteria based on high-throughput spectral images to facilitate the assessment and selection challenge. The importance of measuring plant capacity for sustained carbon fixation under drought stress is explored, and an image-based transpiration efficiency (iTE) index obtained via a combination of hyperspectral and thermal imaging, is proposed. Incorporating iTE along with other drought-related variables in selection criteria will allow the identification of accessions with diverse tolerance mechanisms. A comprehensive approach that merges high-throughput phenotyping and de novo domestication is proposed for developing drought-tolerant prebreeding material and providing breeders with access to gene pools containing unexplored drought tolerance mechanisms.

Why it matches plant phenotyping methodsハイパースペクトル・熱画像から画像ベース蒸散効率指標（iTE）を抽出する高スループット表現型解析が中心であり、乾燥耐性選抜への技術適用を提案している。

abstracta novel strategy of using multi-trait selection criteria based on high-throughput spectral images

Co 3 O 4 Nanostructured Sensor for Electrochemical Detection of H 2 O 2 as a Stress Biomarker in Barley: Fe 3 O 4 Nanoparticles-Mediated Enhancement of Salt Stress Tolerance. →

This research investigates the enhancement of barley's resistance to salt stress by integrating nanoparticles and employing a nanostructured Co 3 O 4 sensor for the electrochemical detection of hydrogen peroxide (H 2 O 2 ), a crucial indicator of oxidative stress. The novel sensor, featuring petal-shaped Co 3 O 4 nanostructures, exhibits remarkable precision and sensitivity to H 2 O 2 in buffer solution, showcasing notable efficacy in complex analytes like plant juice. The research establishes that the introduction of Fe 3 O 4 nanoparticles significantly improves barley's ability to withstand salt stress, leading to a reduction in detected H 2 O 2 concentrations, alongside positive impacts on morphological parameters and photosynthesis rates. The developed sensor promises to provide real-time monitoring of barley stress responses, providing valuable information on increasing tolerance to crop stressors.

Why it matches plant phenotyping methods植物ストレス状態の指標であるH2O2を検出する電気化学センサーを開発し、緩衝液および植物汁液で性能を示しているため、植物生理フェノタイピング手法が中心です。

abstractemploying a nanostructured Co 3 O 4 sensor for the electrochemical detection of hydrogen peroxide (H 2 O 2 ), a crucial indicator of oxidative stress

A new multispectral index for canopy nitrogen concentration applicable across growth stages in ryegrass and barley →

Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R² = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R² = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart.

Why it matches plant phenotyping methods作物キャノピーの窒素濃度を推定するマルチスペクトル指標を開発し、独立データセットで検証しており、植物形質取得手法が研究の中心である。

abstractThis study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions.

Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision →

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.

Why it matches plant phenotyping methodsRGBステレオ画像から3D点群を再構成し、植生高や成長率を自動抽出するワークフローの開発・検証が研究の中心であるため、植物フェノタイピング手法として含める。

abstractwe present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images

A Transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery →

Artificial intelligence (AI) and deep learning (DL) for plant disease detection are emerging research areas. DL methods generally require a large amount of annotated data for training, which is often costly, time-consuming, and infeasible. This article addresses the data scarcity problem and proposes a few-shot learning (FSL) method for barley plant disease detection. To prepare a dataset, we captured images from outdoor test-bed trials (at two different growth stages of plants across multiple paddocks) under various weather conditions, such as sunny and cloudy. The images are divided into patches and manually labelled into five classes: no-disease, net form net blotch (NFNB) (which is classified into two stages: early and severe), spot form net blotch (SFNB), and scald. We name this as the Barley dataset. We also used the publicly available cassava dataset, which has five classes. The datasets are then applied to the proposed FSL pipeline, which only uses as few as five images for each class in training. We use the Swin transformer as the network backbone. The method with the Swin-B variant as the feature extractor achieved a detection accuracy of 91.80% and 97.93% on the barley disease and cassava datasets, respectively. The result indicates that our FSL model can efficiently perform and classify barley disease with small training data.

Why it matches plant phenotyping methods圃場画像からオオムギ葉の病徴を検出・分類する少数ショット画像解析パイプラインを提案し、データセット上で精度検証しているため、植物表現型取得手法が中心である。

abstractproposes a few-shot learning (FSL) method for barley plant disease detection

Measuring residual transpiration in plants: a comparative analysis of different methods. →

Residual transpiration (RT) is defined as a loss of water through the leaf cuticle while stomata are closed. Reduced RT might be a potentially valuable trait for improving plant performance under water deficit conditions imposed by either drought or salinity. Due to the presence of stomata on the leaf surface, it is technically challenging to measure RT. RT has been estimated by the water loss through either astomatous leaf surface or isolated astomatous cuticular layers. This approach is not suitable for all species (e.g. not applicable to grasses) and is difficult and too time consuming for large-scale screening in breeding programs. Several alternative methods may be used to quantify the extent of RT; each of them comes with its own advantages and limitations. In this study, we have undertaken a comparative assessment of eight various methods of assessing RT, using barley (Hordeum vulgare ) plants as a model species. RT measured by water retention curves and a portable gas exchange (infrared gas analyser; IRGA) system had low resolution and were not able to differentiate between RT rates from young and old leaves. Methods based on quantification of the water loss at several time-points were found to be the easiest and least time-consuming compared to others. Of these, the 'three time-points water loss' method is deemed as the most suitable for the high throughput screening of plant germplasm for RT traits.

Why it matches plant phenotyping methods残存蒸散という植物生理形質の測定法8種を比較評価し、高スループットスクリーニングに適した方法を検討しており、表現型取得法が中心である。

abstractIn this study, we have undertaken a comparative assessment of eight various methods of assessing RT, using barley (Hordeum vulgare ) plants as a model species.

Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods. →

The pigment composition of plant seed coat affects important properties such as resistance to pathogens, pre-harvest sprouting, and mechanical hardness. The dark color of barley (Hordeum vulgare L.) grain can be attributed to the synthesis and accumulation of two groups of pigments. Blue and purple grain color is associated with the biosynthesis of anthocyanins. Gray and black grain color is caused by melanin. These pigments may accumulate in the grain shells both individually and together. Therefore, it is difficult to visually distinguish which pigments are responsible for the dark color of the grain. Chemical methods are used to accurately determine the presence/ absence of pigments; however, they are expensive and labor-intensive. Therefore, the development of a new method for quickly assessing the presence of pigments in the grain would help in investigating the mechanisms of genetic control of the pigment composition of barley grains. In this work, we developed a method for assessing the presence or absence of anthocyanins and melanin in the barley grain shell based on digital image analysis using computer vision and machine learning algorithms. A protocol was developed to obtain digital RGB images of barley grains. Using this protocol, a total of 972 images were acquired for 108 barley accessions. Seed coat from these accessions may contain anthocyanins, melanins, or pigments of both types. Chemical methods were used to accurately determine the pigment content of the grains. Four models based on computer vision techniques and convolutional neural networks of different architectures were developed to predict grain pigment composition from images. The U-Net network model based on the EfficientNetB0 topology showed the best performance in the holdout set (the value of the "accuracy" parameter was 0.821).

Why it matches plant phenotyping methods大麦粒の画像から種皮のアントシアニン・メラニン組成を推定する画像解析・機械学習法を開発し、化学分析で検証しているため、植物表現型取得法が中心である。

abstractwe developed a method for assessing the presence or absence of anthocyanins and melanin in the barley grain shell based on digital image analysis using computer vision and machine learning algorithms.

Comparing hormone dynamics in cereal crops via transient expression of hormone sensors →

ABSTRACT Plant hormones are small molecules which elicit profound physiological responses. Although plant hormone biosynthesis and response genes have been critical for agricultural improvement, it has been difficult to experimentally compare hormone biology across species because of complex phenotypic outputs. We used transient expression of genetic hormone sensors and transcriptomics to quantify tissue-specific gibberellic acid (GA) and auxin responses across tissues and genotypes in cereal crops. We found that the FRET-based GPS2 biosensor detects exogenous GA treatments in maize, barley, sorghum, and wheat, in both vegetative and floral tissues. Measuring GPS2 output across GA dosages revealed tissue- and genotype-specific differences in GA sensor response. We observed marked differences in maize vs barley leaves and floral tissues and an unexpected drop in GPS2 output in the maize d1 GA biosynthesis mutant after GA treatment, likely reflecting differences in bioactive GA content, GA transport, and mechanisms of GA response. We then used RNAseq to measure transcriptional responses to GA treatment in leaves from maize wildtype, d1 , and barley as well as floral tissues from maize and barley for a cross-tissue, cross-genotype, and cross-species GA-response comparison. After orthology prediction and analysis of within- and cross-species GO-term enrichment, we identified core sets of GA-responsive genes in each species as well as maize- barley orthogroups. Our analysis suggests that downregulation of GA-INSENSITIVE DWARF1 ( GID1 ) and upregulation of α -Expansin1 ( EXPA1 ) orthologs comprises a universal GA-response mechanism that is independent of GA biosynthesis, and identifies F-Box proteins, hexokinase, and AMPK/SNF1 protein kinase orthologs as unexpected cross-tissue, cross-genotype, and cross-species GA-responsive genes. We then compared the transient expression of the DR5, DR5v2, and DII-mDII auxin reporters in barley and maize and find that although DR5 did not respond to exogenous auxin in barley, DR5v2 responded to auxin treatment with a similar magnitude as in maize. Both species display auxin-mediated DII degradation that requires the 26S proteasome.

Why it matches plant phenotyping methods遺伝子型・組織・種をまたいでホルモンセンサーの応答を検証・比較し、植物の生理状態を定量化する手法が研究の中心である。

abstractWe used transient expression of genetic hormone sensors and transcriptomics to quantify tissue-specific gibberellic acid (GA) and auxin responses across tissues and genotypes in cereal crops.

A review on common root rot of wheat and barley in Australia →

Common root rot (CRR) caused by the soilborne pathogen Bipolaris sorokiniana (teleomorph Cochliobolus sativus) is becoming increasingly prevalent worldwide. Identification of CRR is difficult and time‐consuming for human assessors due to the non‐distinctive above‐ground symptoms, with browning of subcrown internodes and roots the most distinguishing symptom of infection. CRR disease has been recognized as a significant disease for cereal crops in many countries. In 2009, CRR in Australia was estimated to cause $30 million average annual yield loss for wheat and $13 million for barley. Recent evidence indicates CRR may be more prevalent than expected in Australian wheat cropping areas due to lack of research on this disease. Low levels of B. sorokiniana survive in the soil for up to 10 years and attack plants at early stages of growth. Therefore, mitigating CRR in wheat and barley may not be practical at the late stages of infection due to lack of effective methods; however, early detection might be viable to alleviate the impact of this disease. A comprehensive overview of CRR caused by B. sorokiniana, including disease background, worldwide economic losses, management methods, potential CRR detection using multispectral and hyperspectral sensors and the research focus over the past 50 years is provided in this article. This review paper is expected to provide thorough supplemental information for current studies about CRR and proposes recommendations for whole‐of‐field disease scouting methods to farmers, enabling reduced time and cost for CRR management and increasing wheat and barley production worldwide.

Why it matches plant phenotyping methodsCRRの植物症状を対象に、マルチスペクトル・ハイパースペクトルセンサーによる早期病害検出を扱うレビューであり、植物表現型計測手法のレビューとして中心的です。

abstractA comprehensive overview of CRR caused by B. sorokiniana, including disease background, worldwide economic losses, management methods, potential CRR detection using multispectral and hyperspectral sensors and the research focus over the past 50 years is provided in this article.

Excised leaves show limited and species-specific effects on photosynthetic parameters across crop functional types →

Abstract Photosynthesis is increasingly becoming a recognized target for crop improvement. Phenotyping photosynthesis-related traits on field-grown material is a key bottleneck to progress here due to logistical barriers and short measurement days. Many studies attempt to overcome these challenges by phenotyping excised leaf material in the laboratory. To date there are no demonstrated examples of the representative nature of photosynthesis measurements performed on excised leaves relative to attached leaves in crops. Here, we tested whether standardized leaf excision on the day prior to phenotyping affected a range of common photosynthesis-related traits across crop functional types using tomato (C3 dicot), barley (C3 monocot), and maize (C4 monocot). Potentially constraining aspects of leaf physiology that could be predicted to impair photosynthesis in excised leaves, namely leaf water potential and abscisic acid accumulation, were not different between attached and excised leaves. We also observed non-significant differences in spectral reflectance and chlorophyll fluorescence traits between the treatments across the three species. However, we did observe some significant differences between traits associated with gas exchange and photosynthetic capacity across all three species. This study represents a useful reference for those who perform measurements of this nature and the differences reported should be considered in associated experimental design and statistical analyses.

Why it matches plant phenotyping methods切除葉を用いた光合成フェノタイピングの代表性を付着葉と比較検証しており、測定条件・実験設計への影響を評価する方法検証が中心です。

abstractHere, we tested whether standardized leaf excision on the day prior to phenotyping affected a range of common photosynthesis-related traits across crop functional types using tomato (C3 dicot), barley (C3 monocot), and maize (C4 monocot).

Scales of development for wheat and barley specific to either single culms or a population of culms →

Accurate assessment of plant development is essential for agronomic management and scientific research, and crop development scales with repeatable and reproducible protocols are required to achieve this. Development scales currently in use are ambiguous, subjective and qualitative, they do not describe all stages in a crop's lifecycle, they do not explicitly distinguish between culm-level and crop-level development, and they are incompatible with modern analytical and computational technologies. Here we propose two new scales of wheat and barley development: the Single Culm Development Scale (SCDS) to define progression through the lifecycle of an individual culm, and the Population of Culms Development Scale (PCDS) to identify the timing of stages and duration of phases within a crop canopy. These development scales merge and fill gaps within existing scales currently in use, describe development in terms that are unambiguous, objective and quantitative, and better interface with crop simulation models, automated image analysis and other computational tools and analytical methods. The SCDS and PCDS are paired with definitive protocols to measure each stage that were developed and tested using different operators in controlled environment and field experiments.

Why it matches plant phenotyping methodsコムギ・オオムギの個体茎および群落の発育段階を客観的・定量的に測定する新しい発育スケールとプロトコルを開発し、異なる操作者および環境で検証しており、植物表現型測定法が研究の中心です。

abstractHere we propose two new scales of wheat and barley development: the Single Culm Development Scale (SCDS) to define progression through the lifecycle of an individual culm, and the Population of Culms Development Scale (PCDS) to identify the timing of stages and duration of phases within a crop canopy.

Assessment of deep learning methods for classification of cereal crop growth stage pre and post canopy closure →

Growth stage (GS) is an important crop growth metric commonly used in commercial farms. We focus on wheat and barley GS classification based on in-field proximal images using convolutional neural networks (ConvNets). For comparison purposes, use of a conventional machine learning algorithm was also investigated. The research includes extensive data collection of images of wheat and barley crops over a 3-year period. During data collection, videos were recorded during field walks at two camera views: downward looking and 45 deg angled. The resulting dataset contains 110,000 images of wheat and 106,000 of barley taken over 34 and 33 GS classes, respectively. Three methods were investigated as candidate technologies for the problem of GS classification. These methods were: (I) feature extraction and support vector machine, (II) ConvNet with learning from scratch, and (III) ConvNet with transfer learning. The methods were assessed for classification accuracy using test images taken (a) in fields on days imagined in the training data (i.e., seen field-days GS classification) and (b) in fields on days not imagined in the training data (i.e., unseen field-days principal GS classification). Of the three methods investigated, method III achieved the best accuracy for both classification tasks. The model achieved 97.3% and 97.5% GS classification accuracy for seen field-day test data for wheat and barley, respectively. The model also achieved accuracies of 93.5% and 92.2% for the principal GS classification task for wheat and barley, respectively. We provide a number of key research contributions: the collection curation and exposure of a unique GS labeled proximal image dataset of wheat and barley crops, GS classification, and principal GS classification of cereal crops using three different machine learning methods as well as a comprehensive evaluation and comparison of the obtained results.

Why it matches plant phenotyping methodsコムギ・オオムギの生育段階という植物状態を近接画像から推定する手法を開発・比較検証し、評価済みデータセットも提供しており、フェノタイピング手法が中心である。

abstractWe focus on wheat and barley GS classification based on in-field proximal images using convolutional neural networks (ConvNets).

A bench-top Dark-Root device built with LEGO® bricks enables a non-invasive plant root development analysis in soil conditions mirroring nature →

Roots are the hidden parts of plants, anchoring their above-ground counterparts in the soil. They are responsible for water and nutrient uptake and for interacting with biotic and abiotic factors in the soil. The root system architecture (RSA) and its plasticity are crucial for resource acquisition and consequently correlate with plant performance while being highly dependent on the surrounding environment, such as soil properties and therefore environmental conditions. Thus, especially for crop plants and regarding agricultural challenges, it is essential to perform molecular and phenotypic analyses of the root system under conditions as near as possible to nature (#asnearaspossibletonature). To prevent root illumination during experimental procedures, which would heavily affect root development, Dark-Root (D-Root) devices (DRDs) have been developed. In this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box). The DRD-BIBLOX consists of one or more 3D-printed rhizoboxes, which can be filled with soil while still providing root visibility. The rhizoboxes sit in a scaffold of secondhand LEGO® bricks, which allows root development in the dark and non-invasive root tracking with an infrared (IR) camera and an IR light-emitting diode (LED) cluster. Proteomic analyses confirmed significant effects of root illumination on barley root and shoot proteomes. Additionally, we confirmed the significant effect of root illumination on barley root and shoot phenotypes. Our data therefore reinforces the importance of the application of field conditions in the lab and the value of our novel device, the DRD-BIBLOX. We further provide a DRD-BIBLOX application spectrum, spanning from investigating a variety of plant species and soil conditions and simulating different environmental conditions and stresses, to proteomic and phenotypic analyses, including early root tracking in the dark.

Why it matches plant phenotyping methodsLEGO製の暗所ルートボックスと赤外線カメラによる非侵襲的な根系発達・表現型追跡手法を開発し、その応用範囲を示した研究であり、植物フェノタイピング手法が中心である。

abstractwe describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box).

Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs. →

This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination ( R 2 ) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からLAI、fAPAR、fCover、LCCなどの植物形質を推定し、収量予測・育種選抜への有用性を評価する手法応用研究であり、形質取得・推定ワークフローが中心的です。

abstractutilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments

Monitoring crop phenology with street-level imagery using computer vision →

Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds, maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley, winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.

Why it matches plant phenotyping methods街路画像とコンピュータビジョンを用いて作物のフェノロジーを抽出する枠組みを開発し、独立データで性能評価しており、表現型取得手法が中心である。

abstractWe present a framework to collect and extract crop type and phenological information from street level imagery using computer vision.

Leaf excision has minimal impact on photosynthetic parameters across crop functional types →

Photosynthesis is increasingly becoming a recognised target for crop improvement. Phenotyping photosynthesis-related traits on field-grown material is a key bottleneck to progress here due to logistical barriers and short measurement days. Many studies attempt to overcome these challenges by phenotyping excised leaf material in the laboratory. To date there are no demonstrated examples of the representative nature of photosynthesis measurements performed on excised leaves relative to intact leaves in crops. Here, we tested whether standardised leaf excision on the day prior to phenotyping impacted a range of common photosynthesis-related traits across crop functional types using tomato (C3-dicot), barley (C3-monocot), and maize (C4-monocot). Potentially constraining aspects of leaf physiology that could be forecasted to impair photosynthesis in excised leaves, namely leaf water potential and abscisic acid accumulation, were not different between intact and excised leaves. We also observed non-significant differences in spectral reflectance and chlorophyll fluorescence traits between the treatments across the three species. However, we did observe some significant differences between gas exchange and photosynthetic capacity associated traits across all three species. This study represents a useful reference for those who perform measurements of this nature and the differences reported should be considered in associated experimental design and statistical analyses. HighlightAcross the main photosynthesis functional types (C3-dicot, C3-monot, and C4-monot) of major crops (tomato, barley, and maize), measurements of photosynthetic parameters demonstrate few, but important, differences when measured on excised relative to intact leaves.

Why it matches plant phenotyping methods切除葉と intact 葉で光合成関連形質の測定結果を比較し、葉切除を用いた表現型取得手法の代表性・妥当性を検証しているため、単なる生物学的実験ではなく方法検証が中心である。

abstractHere, we tested whether standardised leaf excision on the day prior to phenotyping impacted a range of common photosynthesis-related traits across crop functional types

Retrieving canopy nitrogen concentration and aboveground biomass with deep learning for ryegrass and barley: comparing models and determining waveband contribution →

Real-time crop canopy nitrogen concentration (CNC) and aboveground biomass (AGB) sensing capability can enable precision agriculture with significant economic and ecological benefits. Canopy spectral response in visible near-infrared (VNIR, 400–979 nm) has been widely used to estimate CNC and AGB of canopies but is often confounded by the soil background, crop type, phenological changes and environmental conditions. Machine (ML) and deep learning (DL) have emerged as promising techniques for mapping CNC and AGB using canopy spectral reflectance in VNIR. However, ML and DL models’ performance relies on extensive labelled training datasets, which are often limited due to being expensive and labour intensive. We estimated CNC and AGB using state-of-the-art hyperspectral (HS) modeling approaches using partial least squares regression (PLSR), random forest (RF), and one- and two-dimensional convolutional neural network (1D and 2D CNN). These models, together with multispectral (MS) approaches, were evaluated in the context of a small (422) dataset for model development collected from diverse fertilization and contrasting growth conditions. A handheld field spectroradiometer was used to collect spectral reflectance samples across a wide range (more than ten-fold difference in AGB) of canopy development stages in two seasons (winter and summer) in ryegrass, and across phenological stages (from GS-25 to GS-60) in barley. Results can be summarized in four components: (1) the median values of performance metrics in 1D CNN (R²ₜₑₛₜ=0.83, NRMSEₜₑₛₜ=0.15 for CNC; R²ₜₑₛₜ=0.79 (ryegrass), 0.61 (barley), NRMSEₜₑₛₜ= 0.29 (ryegrass), 0.30 (barley) for AGB) and PLSR (R²ₜₑₛₜ=0.71, NRMSEₜₑₛₜ= 0.20 for CNC; R²ₜₑₛₜ=0.62 (ryegrass), 0.70 (barley), NRMSEₜₑₛₜ= 0.38 (ryegrass), 0.27 (barley) for AGB) showed the best performance for CNC and AGB prediction in the test data; (2) when evaluated with the independent measurements, 1D CNN demonstrated the best (up to 56% decrease in NRMSE) performance for AGB and was comparable to PLSR for CNC (up to 25% decrease in NRMSE than their HS and MS counterparts); (3) predictive models underperformed at low canopy cover for CNC estimation and were adversely affected by the high biomass saturation effects for AGB prediction; and (4) visible spectrum is relatively more informative to CNC models whereas the red edge to NIR spectrum is more informative to AGB models. This study demonstrates the potential and limitations of commonly available field VNIR spectral reflectance data for CNC and AGB mapping across contrasting conditions in a small dataset (<500).

Why it matches plant phenotyping methodsVNIR分光センシングと複数の機械学習・深層学習モデルにより、作物キャノピーの窒素濃度と地上部バイオマスを推定し、性能比較・限界評価を行うことが研究の中心である。

abstractWe estimated CNC and AGB using state-of-the-art hyperspectral (HS) modeling approaches using partial least squares regression (PLSR), random forest (RF), and one- and two-dimensional convolutional neural network (1D and 2D CNN).

Large-scale screening of diverse barely lignocelluloses for simultaneously upgrading biomass enzymatic saccharification and plant lodging resistance coupled with near-infrared spectroscopic assay →

Barley is an important economical crop for food and beer industry with abundant lignocellulose residues convertible for biofuels and biochemicals. Although lignocellulose recalcitrance provides mechanic strength to maintain plant lodging resistance for biomass production, it also leads to a costly process for biofuel production. By collecting total 94 barely samples with diverse genetic backgrounds, this study examined large variations of lignocellulose levels, sugars yields and lodging index values, and further identified that the cellulose level was a crucial factor either positively accounting for biomass enzymatic saccharification or negatively affecting plant lodging resistance. Despite lignin level showed slight impact on lodging resistance, it could negatively affect biomass saccharification in barley samples examined. Regarding normal distributions of barley samples, this study generated optimal near-infrared spectroscopy (NIRS) equations applicable for rapid assessment of lignocellulose levels, sugars yields and lodging-related parameters with high determination coefficients for external validation (0.71–0.97), cross-validation (0.89–0.95) and calibration (0.89–0.95), and acceptable ratio performance deviation (2.85–4.35). Notably, the established NIRS models could be employed for selection of the desirable barley samples that are simultaneously of better lodging resistance and higher biomass saccharification. Therefore, this study has established a powerful approach applicable for both rapidly screening lignocellulose-related traits in crop breeding and precisely selecting the ideal lignocellulose substrates for green-like biomass process into cost-effective biofuels and value-added bioproducts.

Why it matches plant phenotyping methods近赤外分光法（NIRS）によるリグノセルロース量、糖収量、倒伏関連形質の迅速推定モデルを開発し、外部検証・交差検証・較正まで実施している。植物形質の取得・選抜手法が研究の中心である。

abstractthis study generated optimal near-infrared spectroscopy (NIRS) equations applicable for rapid assessment of lignocellulose levels, sugars yields and lodging-related parameters with high determination coefficients for external validation (0.71–0.97), cross-validation (0.89–0.95) and calibration (0.89–0.95)

Visualizing Early Infection Sites of Rice Blast Disease (Magnaporthe oryzae) on Barley (Hordeum vulgare) Using a Basic Microscope and a Smartphone. →

Understanding how plants and pathogens interact, and whether that interaction culminates in defense or disease, is required to develop stronger and more sustainable strategies for plant health. Advances in methods that more effectively image plant-pathogen samples during infection and colonization have yielded tools such as the rice leaf sheath assay, which has been useful in monitoring infection and early colonization events between rice and the fungal pathogen, Magnaporthe oryzae. This hemi-biotrophic pathogen causes severe disease loss in rice and related monocots, including millet, rye, barley, and more recently, wheat. The leaf sheath assay, when performed correctly, yields an optically clear plant section, several layers thick, which allows researchers to perform live-cell imaging during pathogen attack or generate fixed samples stained for specific features. Detailed cellular investigations into the barley-M. oryzae interaction have lagged behind those of the rice host, in spite of the growing importance of this grain as a food source for animals and humans and as fermented beverages. Reported here is the development of a barley leaf sheath assay for intricate studies of M. oryzae interactions during the first 48 h post-inoculation. The leaf sheath assay, regardless of which species is being studied, is delicate; provided is a protocol that covers everything, from barley growth conditions and obtaining a leaf sheath, to inoculation, incubation, and imaging of the pathogen on plant leaves. This protocol can be optimized for high-throughput screening using something as simple as a smartphone for imaging purposes.

Why it matches plant phenotyping methodsバーレイ葉鞘アッセイとスマートフォン／顕微鏡 imaging を用いて、感染初期の植物・病原体相互作用を観察するプロトコルを開発しており、植物病徴・感染状態の取得法が中心である。

abstractReported here is the development of a barley leaf sheath assay for intricate studies of M. oryzae interactions during the first 48 h post-inoculation.

A bench-top dark-root device built with LEGO bricks enables a non-invasive plant root development analysis in soil conditions mirroring nature →

Roots are the hidden parts of plants, anchoring their above ground counterparts in the soil. They are responsible for water and nutrient uptake, as well as for interacting with biotic and abiotic factors in the soil. The root system architecture (RSA) and its plasticity are crucial for resource acquisition and consequently correlate with plant performance, while being highly dependent on the surrounding environment, such as soil properties and therefore environmental conditions. Thus, especially for crop plants and regarding agricultural challenges, it is essential to perform molecular and phenotypic analyses of the root system under conditions as near as possible to nature (#asnearaspossibletonature). To prevent root illumination during experimental procedures, which would heavily affect root development, dark-root (D-Root) devices (DRDs) have been developed. In this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box). The DRD-BIBLOX consists of one or more 3D-printed rhizoboxes which can be filled with soil, while still providing root visibility. The rhizoboxes sit in a scaffold of secondhand LEGO® bricks, which allows root development in the dark as well as non-invasive root-tracking with an infrared (IR) camera and an IR light emitting diode (LED) cluster. Proteomic analyses confirmed significant effects of root illumination on barley root and shoot proteome. Additionally, we confirmed the significant effect of root illumination on barley root and shoot phenotypes. Our data therefore reinforces the importance of the application of field conditions in the lab and the value of our novel device, the DRD-BIBLOX. We further provide a DRD-BIBLOX application spectrum, spanning from investigating a variety of plant species and soil conditions as well as simulating different environmental conditions and stresses, to proteomic and phenotypic analyses, including early root tracking in the dark.

Why it matches plant phenotyping methods根系を暗所で非侵襲的に追跡・解析するオープンハードウェア装置を開発し、その構成と応用を示しており、植物表現型取得法が研究の中心である。

abstractIn this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box).

A simple and cost-effective method for studying anoxia tolerance in plants. →

Premise We developed a novel, cost-effective protocol that facilitates testing anoxia tolerance in plants without access to specialized equipment. Methods and results Arabidopsis thaliana and barley ( Hordeum vulgare ) seedlings were treated in airtight 2-L Kilner jars. An anoxic atmosphere was generated using Oxoid AnaeroGen 2.5-L sachets placed on in-house, custom-built wire stands. The performed experiments confirmed a higher sensitivity to low oxygen stress previously observed in anac017 A. thaliana mutants and the positive effect of exogenous sucrose on anoxia tolerance reported by previous studies in A. thaliana . Barley seedlings displayed typical responses to anoxia treatment, including shoot growth cessation and the induction of marker genes for anaerobic metabolism and ethylene biosynthesis in root tissue. Conclusions The results validate the novel method as an inexpensive, simple alternative for testing anoxia tolerance in plants, where access to an anaerobic workstation is not possible. The novel protocol requires minimum investment and is easily adaptable.

Why it matches plant phenotyping methods植物の無酸素耐性を評価するための新規・低コストな実験プロトコルを開発し、植物の成長停止などの表現型応答で妥当性を検証しており、方法が研究の中心です。

abstractWe developed a novel, cost-effective protocol that facilitates testing anoxia tolerance in plants without access to specialized equipment.

Detecting variation in starch granule size and morphology by high-throughput microscopy and flow cytometry →

Starch forms semi-crystalline, water-insoluble granules, the size and morphology of which vary according to biological origin. These traits, together with polymer composition and structure, determine the physicochemical properties of starch. However, screening methods to identify differences in starch granule size and shape are lacking. Here, we present two approaches for high-throughput starch granule extraction and size determination using flow cytometry and automated, high-throughput light microscopy. We evaluated the practicality of both methods using starch from different species and tissues and demonstrated their effectiveness by screening for induced variation in starch extracted from over 10,000 barley lines, yielding four with heritable changes in the ratio of large A-granules to small B-granules. Analysis of Arabidopsis lines altered in starch biosynthesis further demonstrates the applicability of these approaches. Identifying variation in starch granule size and shape will enable identification of trait-controlling genes for developing crops with desired properties, and could help optimise starch processing.

Why it matches plant phenotyping methods植物由来デンプン顆粒のサイズ・形態という形質を対象に、フローサイトメトリーと自動ハイスループット顕微鏡による抽出・測定法を開発し、大規模スクリーニングで実証しているため、方法が中心的である。

abstractHere, we present two approaches for high-throughput starch granule extraction and size determination using flow cytometry and automated, high-throughput light microscopy.

Site‐specific estimation of spatiotemporal crop evapotranspiration through field‐level sensing data →

This study integrated field‐level sensor data into the FAO‐56 Penman-Monteith algorithm to provide a site‐specific estimate of crop evapotranspiration. This was carried out at two contrasting sites for pea and bean (Manawatū) and barley (Hawke's Bay) crops managed within two irrigation management zones, at each site, under variable‐rate irrigation systems in New Zealand. Daily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height). In addition, calibrated soil moisture data were used with a soil water balance model to compare estimations of daily crop evapotranspiration with those estimated using the crop sensing method. The results indicated that variable crop responses to different irrigation strategies and soil types provided a good opportunity to quantify different levels of spectral reflectance, canopy temperature, and consequently the estimation of crop water use. The statistical comparisons revealed that the modified FAO‐56 Penman-Monteith using crop sensor data compared well with the more conventional soil water balance approach using soil moisture data (R² = 0.70, 0.83, 0.91 for barley, pea, and bean, respectively). Overall, the results from this study indicated that crop sensing approaches combined with the FAO‐56 Penman-Monteith model have potential to provide a more easily determined site‐specific field estimation of crop evapotranspiration than other methods, and it can take into consideration the spatiotemporal variability of crop growth in a field.

Why it matches plant phenotyping methods圃場レベルの作物センシング（分光反射、群落温度、草丈）を用いて作物蒸発散量を推定し、土壌水分収支法と比較検証している。単なる生物学的実験のルーチン測定ではなく、作物の水利用状態を取得・推定するセンシング手法が中心である。

abstractDaily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height).

In vivo surface-enhanced Raman scattering nanosensor for the real-time monitoring of multiple stress signalling molecules in plants. →

When under stress, plants release molecules to activate their defense system. Detecting these stress-related molecules offers the possibility to address stress conditions and prevent the development of diseases. However, detecting endogenous signalling molecules in living plants remains challenging due to low concentrations of these analytes and interference with other compounds; additionally, many methods currently used are invasive and labour-intensive. Here we show a non-destructive surface-enhanced Raman scattering (SERS)-based nanoprobe for the real-time detection of multiple stress-related endogenous molecules in living plants. The nanoprobe, which is placed in the intercellular space, is optically active in the near-infrared region (785 nm) to avoid interferences from plant autofluorescence. It consists of a Si nanosphere surrounded by a corrugated Ag shell modified by a water-soluble cationic polymer poly(diallyldimethylammonium chloride), which can interact with multiple plant signalling molecules. We measure a SERS enhancement factor of 2.9 × 10 7 and a signal-to-noise ratio of up to 64 with an acquisition time of ~100 ms. To show quantitative multiplex detection, we adopted a binding model to interpret the SERS intensities of two different analytes bound to the SERS hot spot of the nanoprobe. Under either abiotic or biotic stress, our optical nanosensors can successfully monitor salicylic acid, extracellular adenosine triphosphate, cruciferous phytoalexin and glutathione in Nasturtium officinale, Triticum aestivum L. and Hordeum vulgare L.-all stress-related molecules indicating the possible onset of a plant disease. We believe that plasmonic nanosensor platforms can enable the early diagnosis of stress, contributing to a timely disease management of plants.

Why it matches plant phenotyping methods生きた植物内のストレス関連シグナル分子を非破壊・リアルタイムに測定するSERSナノセンサーを開発し、複数植物・ストレス条件で定量監視しており、植物状態の取得法が中心です。

abstractHere we show a non-destructive surface-enhanced Raman scattering (SERS)-based nanoprobe for the real-time detection of multiple stress-related endogenous molecules in living plants.

NAPPN Annual Conference Abstract: Using Deep Learning (DL) to Improve Segmentation from RGB and Hyperspectral Imaging Data →

To study how plants respond to their environment researchers use imaging phenotyping technologies. The use of image-based phenotyping has enabled researchers to analyse plants and produce data at a large scale. However, this large influx of data has created a 'big data' problem to emerge causing researchers to search for new innovative ways to tackle the challenges of processing their data in a reasonable timeframe. To address such issues, deep learning and data science techniques are being used to perform a comprehensive analysis. Here we use a Plant Screen™ compact system to image a series of barley plants using two different imaging sensors. This compact system contains an RGB top and side view camera and a hyperspectral visible near infrared (VNIR) camera. To streamline the processing and analysis of RGB and hyperspectral imaging, we are building a pipeline using a lightweight implementation of the U-Net architecture to improve the accuracy of semantic segmentation based on the raw images captured via the compact system. Several models were designed and developed, each of which was tailored to either the type of imaging sensor being used or the angle for which the images been provided were taken (e.g., top-down, side-view). Results showed that each model regardless of sensor or perspective produced an accuracy greater than 90% and could accurately segment cereal crops regardless of their size, shape or colour. These results demonstrate the feasibility of using DL models to semantically segment cereal crops imaged using either RGB or hyperspectral imaging sensors.

Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像から植物を分割する深層学習パイプラインの開発と精度評価が中心であり、植物表現型取得基盤に直接関係する。

abstractwe are building a pipeline using a lightweight implementation of the U-Net architecture to improve the accuracy of semantic segmentation based on the raw images captured via the compact system

NAPPN Annual Conference Abstract: Application of hyperspectral imaging to predict alpha amylase content and starch breakdown in pregerminated barley seeds →

Malting is the controlled germination of a cereal grain. In barley, malted grains provide the fermentable sugars necessary for brewing and distilling processes. Harvested barley must adhere to strict industry quality standards to be considered for malting including robust hydrolytic enzyme content, low protein content and high rates of germination. Failure to meet quality metrics results in significant loss of market value and presents a risk to growers considering malting barley. Modern cultivars of malting barley are susceptible to preharvest sprout (PHS), or germination of the seed prior to harvest, resulting in premature endosperm modification, reduced enzyme content and poor malthouse germination. Seeds with PHS damage fail quality assessments and are sold for feed at reduced prices. Most presprouted grain shows no visual signs of damage and accepted methods to assess PHS damage including Hagberg falling number and stirring number (Rapid Visco Analyzer), which are costly and destructive to the seed. To address this need, we applied time-series hyperspectral imaging of barley seeds with varying levels of PHS damage and used a deep neural network to predict stirring number and alpha amylase values, which are indicators of sprouting. Prediction models were generated for each of the seven genotypes tested individually and also for all genotypes when combined. Our prediction models had mean average errors from 10.5 to 23.9 and root mean square errors from 19.0 to 35.1 demonstrating the applicability of hyperspectral imaging as a high-throughput, nondestructive method for predicting levels PHS damage in malting barley.

Why it matches plant phenotyping methods大麦種子のPHS損傷・発芽状態を、時系列ハイパースペクトル画像と深層ニューラルネットワークで非破壊・高スループットに推定する手法の開発と性能評価が中心である。

abstractwe applied time-series hyperspectral imaging of barley seeds with varying levels of PHS damage and used a deep neural network to predict stirring number and alpha amylase values, which are indicators of sprouting.

X-Ray Computed Tomography Imaging for Rapid and Automated 3D Cereal Spike Phenotyping →

The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.

Why it matches plant phenotyping methodsX線CTと自動解析アルゴリズムを開発し、穀類の穂・粒形質を高速かつ非破壊で抽出する手法が研究の中心で、精度・効率も検証している。

abstractwe developed a high-throughput and non-destructive method to analyse cereal spikes.

Comparison of plant proximal sensing approaches for nitrogen supply detection in crops →

Nondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory‐determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time‐intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best‐performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor‐N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD.

Why it matches plant phenotyping methods作物の窒素状態という植物生理形質を対象に、複数の近接セン​​サーの測定精度を比較・評価し、センサー情報と実験室測定値の関係を解析しているため、センシング手法の技術評価が中心である。

abstractThe aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level.

High-throughput phenotyping of the core European Heritage Collection (ExHIBiT) under waterlogging →

Barley is the most produced crop in Ireland; it is essential as a fodder crop and a key ingredient for the malting industry, contributing to Irish national identity. In Ireland, climate change is bringing more extreme rain; leading to an increase in flooding and waterlogging events. Barley is particularly sensitive to waterlogging urging the need to harness genetic diversity and breed barley with increased waterlogging tolerance to maintain current agricultural production. One of the limiting factors in breeding is the drawbacks of traditional phenotyping. This project relies on modern high-throughput phenotyping, which allow non-destructive, continuous and quantitative data collection. Using imaging sensors in controlled conditions, we phenotyped a collection of barley accessions under controlled and waterlogged conditions using RGB, fluorescent and hyperspectral cameras (VNIR and SWIR). We used the core European Barley Heritage collection (ExHIBiT); made up of 230 diverse 2-row spring barley accessions. This collection was assembled, genotyped, agronomically characterized and its application for association mapping has been established by our team. We observed that 14 days of waterlogging lead to a significant reduction in pixel count (Project Shoot Area) and Quantum yield, showing a large impact on several hyperspectral indices. We also observed that 7 days of recovery after stress are fundamental for differentiate stress resilience. Work is ongoing to optimize hyperspectral image analysis, and establish parameters to distinguish plant performance, enabling to discriminate between resilient and sensitive accessions. These data will be used for association mapping to identify genetic regions contributing to waterlogging tolerance in spring barley.

Why it matches plant phenotyping methods複数の画像センサーを用いた非破壊・連続・定量的な植物表現型取得が研究の中心であり、水logging耐性評価のためのハイスループット表現型解析プラットフォームを適用している。

abstractThis project relies on modern high-throughput phenotyping, which allow non-destructive, continuous and quantitative data collection.

Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities →

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring.

Why it matches plant phenotyping methodsリモートセンシングと機械学習によるLAI推定手法を開発・比較検証し、作物モデルへの統合まで扱っており、植物形質取得が研究の中心である。

abstractThis study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches.

Improved breeding for Fusarium pseudograminarium (Fusarium crown rot) using qPCR measurement of infection in multi-species winter cereal experiments →

Fusarium crown rot (FCR) causes significant grain yield loss in winter cereals around the world. Breeding for resistance and/or tolerance to FCR has been slow with relatively limited success. In this study, multi-species experiments were used to demonstrate an improved method to quantify FCR infection levels at plant maturity using qPCR, as well as the genotype yield retention using residual regression deviation. Using qPCR to measure FCR infection allowed a higher degree of resolution between genotypes than traditional visual stem basal browning assessments. The results were consistent across three environments with different levels of disease expression. The improved measure of FCR infection along with genotype yield retention allows for partitioning of both tolerance and partial resistance. Together these methods offer new insights to FCR partial resistance and its relative importance to tolerance in bread wheat and barley. This new approach offers a more robust, cost-effective way to select for both FCR traits within breeding programs. Key messageGenetic gain for tolerance and partial resistance against Fusarium crown rot (FCR) in winter cereals has been impeded by laborious and variable visual measures of infection severity. This paper presents results of an improved method to quantify FCR infection that are strongly correlated to yield loss and reveal previously unrecognised partial resistance in barley and wheat varieties.

Why it matches plant phenotyping methodsqPCRによる植物体のFusarium冠腐病感染レベル定量法の改良・検証が中心であり、品種間の感染程度と抵抗性・耐性を評価する植物フェノタイピング手法に該当する。

abstractdemonstrate an improved method to quantify FCR infection levels at plant maturity using qPCR

Analyses of the Impact of Soil Conditions and Soil Degradation on Vegetation Vitality and Crop Productivity Based on Airborne Hyperspectral VNIR–SWIR–TIR Data in a Semi-Arid Rainfed Agricultural Area (Camarena, Central Spain) →

Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring.

Why it matches plant phenotyping methods航空ハイパースペクトルデータからLAI、作物水分ストレス、収量を推定し、回帰性能も検証しているため、植物形質の取得・推定が主要な技術的内容に含まれる。

abstractSpecifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data

A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images →

In recent years, various techniques have been developed to generate crop-type maps based on remote sensing data. Wheat and barley are two major cereal crops cultivated as the first and fourth largest grain crops across the globe. The variations in spectral temporal profile of both crops are generally insignificant at small scales and therefore the two crops are phenologically fairly clearly separated; however, at large scale areas the variance of phenological parameters increases for both crops due to the effects of various climatic and orographic factors which adversely influences discrimination of wheat and barley. Additionally, wheat and barley are usually cultivated as both spring and winter or early and late season crops in some areas, making it more difficult to distinguish them. Therefore, developing a new method based on remote sensing data for effective discrimination of wheat and barley is an important necessity in the field of precision agriculture. To this end, this research presents a new phenology-based method to discriminate barley from wheat. In this study, Sentinel-2 (S2) time-series data of a study site in Iran (Markazi) and two sites in the USA (Idaho and North California), are employed. Spectral reflectance values of wheat and barley are examined during the growing season and a new spectral-temporal feature is successfully developed for automatic identification of the barley heading date. The Relief-f algorithm is then employed to select appropriate spectral features of S2 to distinguish wheat from barley at the heading date. Finally, generated spectral features at the heading date are used as input to Support Vector Machine (SVM) and Random Forest (RF) to produce barley and wheat maps. The Kappa coefficient and overall accuracy (OA) obtained for the three study sites are more than 0.67 and 76%, respectively. The findings of this study demonstrate the potential of remote sensing data to identify the phenological growth stages of barley and distinguish it successfully from wheat.

Why it matches plant phenotyping methodsSentinel-2時系列から作物の生育段階（barley heading date）を自動抽出する新しいスペクトル・時間特徴量を開発しており、単なる作物分類ではなく植物フェノタイプ取得手法が中心である。

abstracta new spectral-temporal feature is successfully developed for automatic identification of the barley heading date

Improving winter barley adaptation to freezing and heat stresses in the U.S. Midwest: bottlenecks and opportunities →

Continental cropping systems are increasingly exposed to extreme and opposing trends of temperatures over the same growing season. This situation is epitomized by winter barley grown in the Upper Midwest, which is subject to temperatures that can be as low as −30 °C during the winter and over 30 °C during summer. This interaction severely limits the potential of this emerging crop, by threatening the winter survival of the crown which is often exposed to lethal freezing stress and by exposing reproductive organs to high temperature (HT) stress, to which this cool-season grass is highly sensitive. This poses a unique challenge that requires the discovery and capture of well-defined sets of traits enabling adaptation to extreme tail ends of a stressor, with limited trade-offs, while minimizing costs. Here, based on a critical literature review, we propose a framework integrating i) environmental characterization (envirotyping), ii) envirotype-relevant scouting of genetic resources, iii) ecophysiology-informed trait identification and phenotyping and iv) breeding pipelines. We outline propositions and guidelines for implementing these steps and discuss their feasibility, with an emphasis on i) identifying novel genetic resources and eco-physiological traits relevant to this problem, ii) navigating their physiological trade-offs and iii) leveraging this information to develop ad hoc phenotyping methods for deployment in breeding programs. Our review indicates that the eco-physiological and genetic bases for improving tolerance to both stresses in the same organism likely exists based on evidence from crop relatives and extremophile species, with smaller vasculature (freezing tolerance) and transpirational cooling (HT tolerance) being prime examples. Our findings indicate that such traits could be captured with minimal trade-offs, and that is possible to use similar phenotyping concepts (thermal imaging) and infrastructure (cold/heat tents) to screen for these traits and accelerate breeding to enhance adaptation to temperature extremes.

Why it matches plant phenotyping methods耐凍性・高温耐性に関わる形質の同定とフェノタイピング手法をレビューの中心的枠組みに含み、熱画像や低温・高温環境設備を用いたスクリーニング概念を具体的に論じているため、方法論的レビューとして採用。

abstractHere, based on a critical literature review, we propose a framework integrating i) environmental characterization (envirotyping), ii) envirotype-relevant scouting of genetic resources, iii) ecophysiology-informed trait identification and phenotyping and iv) breeding pipelines.

Detecting variation in starch granule size and morphology by high-throughput microscopy and flow cytometry. →

Starch forms semi-crystalline, water-insoluble granules, the size and morphology of which vary according to biological origin. These traits, together with polymer composition and structure, determine the physicochemical properties of starch. However, screening methods to identify differences in starch granule size and shape are lacking. Here, we present two approaches for high-throughput starch granule extraction and size determination using flow cytometry and automated, high-throughput light microscopy. We evaluated the practicality of both methods using starch from different species and tissues and demonstrated their effectiveness by screening for induced variation in starch extracted from over 10,000 barley lines, yielding four with heritable changes in the ratio of large A-granules to small B-granules. Analysis of Arabidopsis lines altered in starch biosynthesis further demonstrates the applicability of these approaches. Identifying variation in starch granule size and shape will enable identification of trait-controlling genes for developing crops with desired properties, and could help optimise starch processing.

Why it matches plant phenotyping methods植物由来デンプン顆粒のサイズ・形態という形態形質を、フローサイトメトリーと自動高スループット顕微鏡で取得・解析する方法を開発し、大規模スクリーニングで実証しており、フェノタイピング手法が中心である。

abstractHere, we present two approaches for high-throughput starch granule extraction and size determination using flow cytometry and automated, high-throughput light microscopy.

Sentinel‐2 Accurately Estimated Wheat Yield in a Semi-arid Region Compared With Landsat‐8 →

Wheat and barley are among the primary food resources of the world population; therefore, their growth and observation are essential in farms to enhance food security worldwide. On top of that, careful observation of the product is essential to find solutions for the issues faced during their production and to reduce the impacts of weather changes. With the advancement of Remote Sensing technology, the observation and estimation process has increased. In this study, numbers of spectral vegetation indices was used along with canopy biophysical properties ( LAI ) and biochemical properties (chlorophyll), there calculated from (Landsat 8 and Sentinel-2) satellite data. The wheat and barley samples were collected before were be ready for harvest, and a relation with the vegetarian indices was established using the Multi-Linear Regression module, in which the equations used in predicting the harvest were developed and used to create a graph for expected harvest. The result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R2 of 0.77 and 0.71, respectively. The results show that the strongest correlation is observed between the LAI data obtained from Sentinel data and cereal yield data, with an R2 0.68, and the highest correlation for the indices of Landsat images is observed in the NDWI with R2 0.59 and the lowest degree of error was in the root mean square error (RMSE) for the Sentinel-2 and Landsat 8 with 0.57 and 1.54. In addition, this study also showed that the least relationship for grain yield prediction was observed between the NDRI for Sentinel-2 (R2 0.1) and SAVI for Landsat image (R2 0.47).

Why it matches plant phenotyping methods衛星リモートセンシングと回帰モデルにより穀物収量を推定し、Sentinel-2とLandsat-8の精度を比較・評価しており、植物形質の取得・推定手法が中心である。

abstractThe result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R2 of 0.77 and 0.71, respectively.

Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France →

Crop supply and management is a global issue, particularly in the context of global climate change and rising urbanization. Accurate mapping and monitoring of specific crop types are crucial for crop studies. In this study, we proposed: (1) a methodology to map two main winter crops (winter wheat and winter barley) in the northern region of Finistère with high-resolution Sentinel-2 data. Different classification approaches (the hierarchical classification and the classical direct extraction), and classification methods (pixel-based classification (PBC) and object-based classification (OBC)) were performed and evaluated. Subsequently, (2) a further study that involved monitoring the phenology of the winter crops was carried out, based on the previous results. The aim is to understand the temporal behavior from sowing to harvesting, identifying three important phenological statuses (germination, heading, and ripening, including harvesting). Due to the high frequency of precipitation in our study area, crop phenology monitoring was performed using Sentinel-1 C-band SAR backscatter time series data using the Google Earth Engine (GEE) platform. The results of the classification showed that the hierarchical classification achieved a better accuracy when it is compared to the direct extraction, with an overall accuracy of 0.932 and a kappa coefficient of 0.888. Moreover, in the hierarchical classification process, OBC reached a better accuracy in cropland mapping, and PBC was proven more suitable for winter crop extraction. Additionally, in the time series backscatter coefficient of winter wheat, the germination and ripening (harvesting) phases can be identified at VV and VH/VV polarizations, and heading can be identified in both VV and VH polarizations. Secondly, we were able to detect the germination phase of winter barley in VV and VH, ripening with both polarizations and VH/VV, and finally, heading in VV and VH polarizations.

Why it matches plant phenotyping methodsSentinel-1時系列とGEEを用いて冬作物の発芽・出穂・成熟（収穫）という植物の生育状態を推定し、分類・抽出手法も精度評価しているため、衛星センシングによるフェノタイピング手法の実質的応用と判断する。

abstracta further study that involved monitoring the phenology of the winter crops was carried out

Capability of the TrueColor Sensor Array for Determining the Nitrogen Supply in Winter Barley ( Hordeum vulgare L.). →

In agriculture, efforts are being made to reduce pesticides and fertilizers because of the possible negative environmental impacts, high costs, political requirements, and declining social acceptance. With precision farming, significant savings can be achieved by the site-specific application of fertilizers. In contrast to currently available single sensors and camera-based systems, arrays or line sensors provide a suitable spatial resolution without requiring complex signal processing and promise significant potential regarding price and precision. Such systems comprise a cost-effective and compact unit that can be extended to any working width by cascading into arrays. In this study, experiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth. This sensor is based on recording the reflectance values in various channels of the CIELab color space: luminosity, green-red, and blue-yellow. The unique selling point of this sensor is the detection of luminosity because only the CIELab color space provides this opportunity. Strong correlations were found between the different reflection channels and the nitrogen level (R² = 0.959), plant coverage (R² = 0.907), and fresh mass yield (R² = 0.866). The fast signal processing allows this sensor to meet stringent demands for the operating speed, spatial resolution, and price structure.

Why it matches plant phenotyping methods冬オオムギの窒素状態・被覆率・生体重を推定するTrueColorセンサーアレイの適用性を評価しており、植物形質取得とセンサー性能評価が研究の中心である。

abstractexperiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth.

Detection of Fusarium infected seeds of cereal plants by the fluorescence method. →

Infection of seeds of cereal plants with fusarium affects their optical luminescent properties. The spectral characteristics of excitation (absorption) in the range of 180-700 nm of healthy and infected seeds of wheat, barley and oats were measured. The greatest difference in the excitation spectra of healthy and infected seeds was observed in the short-wave range of 220-450 nm. At the same time, the excitation characteristics of infected seeds were higher than those of healthy ones, and the integral parameter Η in the entire range was 10-56% higher. A new maximum appeared at the wavelength of 232 nm and the maximum value increased by 362 nm. The spectral characteristics were measured when excited by radiation at wavelengths of 232, 362, 424, 485, 528 nm and the luminescence fluxes were calculated. It is established that the photoluminescence fluxes Φ in the short-wave ranges of 290-380 nm increase by 1.58-3.14 times and 390-550 nm-by 1.44-2.54 times. The fluxes in longer wavelength ranges do not change systematically and less significantly: for wheat, they decrease by 12% and increase by 19%, for barley, they decrease by 10% and increase by 33%. The flux decreases by 43-71% for oats. Based on the results obtained for cereal seeds, it is possible to further develop a method for detecting fusarium infection with absolute measurements of photoluminescence fluxes in the range of 290-380 nm, or when measuring photoluminescence ratios: for wheat seeds when excited with wavelengths of 424 nm and 232 nm (Φ424/Φ232); for barley seeds-when excited with wavelengths of 485 nm and 232 nm (Φ485/Φ232) and for oat seeds-when excited with wavelengths of 424 nm and 362 nm (Φ424/Φ362).

Why it matches plant phenotyping methods穀物種子のフザリウム感染状態を蛍光スペクトル・フォトルミネッセンスで検出する測定法を検討し、判別指標と今後の検出法を提示しており、植物状態の取得法が中心である。

titleDetection of Fusarium infected seeds of cereal plants by the fluorescence method.

Indirect Measurement of β-Glucan Content in Barley Grain with Near-Infrared Reflectance Spectroscopy. →

β-Glucan is a component of barley grains with functional properties that make it useful for human consumption. Cultivars with high grain β-glucan are required for industrial processing. Breeding for barley genotypes with higher β-glucan content requires a high-throughput method to assess β-glucan quickly and cheaply. Wet-chemistry laboratory procedures are low-throughput and expensive, but indirect measurement methods such as near-infrared reflectance spectroscopy (NIRS) match the breeding requirements (once the NIR spectrometer is available). A predictive model for the indirect measurement of β-glucan content in ground barley grains with NIRS was therefore developed using 248 samples with a wide range of β-glucan contents (3.4%-17.6%). To develop such calibration, 198 unique samples were used for training and 50 for validation. The predictive model had R 2 = 0.990, bias = 0.013% and RMSEP = 0.327% for validation. NIRS was confirmed to be a very useful technique for indirect measurement of β-glucan content and evaluation of high-β-glucan barleys.

Why it matches plant phenotyping methodsオオムギ穀粒のβ-グルカン含量という植物形質をNIRSで間接推定する予測モデルを開発し、独立検証しており、表現型取得・推定法が研究の中心である。

abstractA predictive model for the indirect measurement of β-glucan content in ground barley grains with NIRS was therefore developed using 248 samples with a wide range of β-glucan contents (3.4%-17.6%).

Development of a near‐infrared spectroscopy calibration for Hagberg falling number assessment of barley (Hordeum vulgare): A comparison of methods →

Climate change leads to increased risks of reduced Hagberg falling numbers (HFN). The objectives of this study were to (i) compare partial least square (PLS) with deep learning methods with respect to establishing near‐infrared spectroscopy (NIRS) calibrations for HFN in spring barley, (ii) compare the accuracy of NIRS calibrations for metric versus categorical response variables and (iii) discuss the usefulness of the developed NIRS calibrations in the context of barley breeding programmes. This study was based on 560 samples from the preapplication trial of a commercial spring barley breeding programme and the double round robin population of barley. Across all the examined preprocessing combinations, the lowest root mean square error of prediction (RMSEP) in the calibration set was observed with 59.95 s for PLS regression where that of the best deep learning method was higher. The accuracies to predict HFN from NIR spectra observed in our study indicated that they cannot replace the HFN reference method, but their use for selection in early generations of barley breeding programmes are promising.

Why it matches plant phenotyping methods大麦のHagberg falling numberという穀粒形質をNIRSから推定する較正法を開発・比較し、精度を検証しているため、表現型取得法が研究の中心である。

abstractThe objectives of this study were to (i) compare partial least square (PLS) with deep learning methods with respect to establishing near‐infrared spectroscopy (NIRS) calibrations for HFN in spring barley

Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model. →

Accurate prediction of root growth and related resource uptake is crucial to accurately simulate crop growth especially under unfavorable environmental conditions. We coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential. The model was tested with field data collected during two growing seasons of spring barley and winter wheat on Haplic Luvisol. In that experiment, mechanical strip-wise subsoil loosening (30-60 cm) (DL treatment) was tested, and effects on root and shoot growth at the melioration strip as well as in a control treatment were evaluated. At most soil depths, strip-wise deep loosening significantly enhanced observed root length densities (RLDs) of both crops as compared to the control. However, the enhanced root growth had a beneficial effect on crop productivity only in the very dry season in 2018 for spring barley where the observed grain yield at the strip was 18% higher as compared to the control. To understand the underlying processes that led to these yield effects, we simulated spring barley and winter wheat root and shoot growth using the described field data and the model. For comparison, we simulated the scenarios with the simpler 1D conceptual root model. The coupled model showed the ability to simulate the main effects of strip-wise subsoil loosening on root and shoot growth. It was able to simulate the adaptive plasticity of roots to local soil conditions (more and thinner roots in case of dry and loose soil). Additional scenario runs with varying weather conditions were simulated to evaluate the impact of deep loosening on yield under different conditions. The scenarios revealed that higher spring barley yields in DL than in the control occurred in about 50% of the growing seasons. This effect was more pronounced for spring barley than for winter wheat. Different virtual root phenotypes were tested to assess the potential of the coupled model to simulate the effect of varying root traits under different conditions.

Why it matches plant phenotyping methods土壌条件に応じた根の伸長・根系形質を推定する結合モデルを開発し、圃場データで検証するとともに仮想根表現型を評価しており、表現型推定手法が中心である。

abstractWe coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential.

Monitoring crop phenology with street-level imagery using computer vision →

Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds, maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley, winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.

Why it matches plant phenotyping methods街路画像とコンピュータビジョンで作物の生育段階を抽出する枠組みを開発・評価しており、植物状態の取得手法が研究の中心である。

abstractWe present a framework to collect and extract crop type and phenological information from street level imagery using computer vision.

Quantitative monitoring of paramagnetic contrast agents and their allocation in plant tissues via DCE-MRI →

Abstract Background Studying dynamic processes in living organisms with MRI is one of the most promising research areas. The use of paramagnetic compounds as contrast agents (CA), has proven key to such studies, but so far, the lack of appropriate techniques limits the application of CA-technologies in experimental plant biology. The presented proof-of-principle aims to support method and knowledge transfer from medical research to plant science. Results In this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI). The new approach has been applied in situ to a cereal crop ( Hordeum vulgare ). The pDCE-MRI allows non-invasive investigation of CA allocation within plant tissues. In our experiments, gadolinium-DTPA, the most commonly used contrast agent in medical MRI, was employed. By acquiring dynamic T 1 -maps, a new approach visualizes an alteration of a tissue-specific MRI parameter T 1 (longitudinal relaxation time) in response to the CA. Both, the measurement of local CA concentration and the monitoring of translocation in low velocity ranges (cm/h) was possible using this CA-enhanced method. Conclusions A novel pDCE-MRI method is presented for non-invasive investigation of paramagnetic CA allocation in living plants. The temporal resolution of the T 1 -mapping has been significantly improved to enable the dynamic in vivo analysis of transport processes at low-velocity ranges, which are common in plants. The newly developed procedure allows to identify vascular regions and to estimate their involvement in CA allocation. Therefore, the presented technique opens a perspective for further development of CA-aided MRI experiments in plant biology.

Why it matches plant phenotyping methods植物組織内の造影剤分布と輸送を非侵襲的に測定するpDCE-MRI手法を設計・試験した研究であり、植物の生理状態・輸送過程の取得方法が中心である。

abstractIn this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI).

Visualising the effect of freezing on the vascular system of wheat in three dimensions by in‐block imaging of dye‐infiltrated plants →

Infrared thermography has shown after roots of grasses freeze, ice spreads into the crown and then acropetally into leaves initially through vascular bundles. Leaves freeze singly with the oldest leaves freezing first and the youngest freezing later. Visualising the vascular system in its native 3-dimensional state will help in the understanding of this freezing process. A 2 cm section of the crown that had been infiltrated with aniline blue was embedded in paraffin and sectioned with a microtome. A photograph of the surface of the tissue in the paraffin block was taken after the microtome blade removed each 20 μm section. Two hundred to 300 images were imported into Adobe After Effects and a 3D volume of the region infiltrated by aniline blue dye was constructed. The reconstruction revealed that roots fed into what is functionally a region inside the crown that could act as a reservoir from which all the leaves are able to draw water. When a single root was fed dye solution, the entire region filled with dye and the vascular bundles of every leaf took up the dye; this indicated that the vascular system of roots was not paired with individual leaves. Fluorescence microscopy suggested the edge of the reservoir might be composed of phenolic compounds. When plants were frozen, the edges of the reservoir became leaky and dye solution spread into the mesophyll outside the reservoir. The significance of this change with regard to freezing tolerance is not known at this time. Thermal cameras that allow visualisation of water freezing in plants have shown that in crops like wheat, oats and barley, ice forms first at the bottom of the plant and then moves upwards into leaves through water conducting channels. Leaves freeze one at a time with the oldest leaves freezing first and then younger ones further up the stem freeze later. To better understand why plants freeze like this, we reconstructed a 3-dimensional view of the water conducting channels. After placing the roots of a wheat plant in a blue dye and allowing it to pull the dye upwards into leaves, we took a part of the stem just above the roots and embedded it in paraffin. We used a microtome to slice a thin layer of the paraffin containing the plant and then photographed the surface after each layer was removed. After taking about 300 images, we used Adobe After Effects software to re-construct the plant with the water conducting system in three dimensions. The 3D reconstruction showed that roots fed into a roughly spherical area at the bottom of the stem that could act as a kind of tank or reservoir from which the leaves pull up water. When we put just one root in dye, the entire reservoir filled up and the water conducting channels in every leaf took up the dye. This indicates that the water channels in roots were not directly connected to specific leaves as we had thought. When plants were frozen, the dye leaked out of the reservoir and spread into cells outside. Research is continuing to understand the significance of this change during freezing. It is possible that information about this effect can be used to help breeders develop more winter-hardy crop plants.

Why it matches plant phenotyping methods植物体内の血管系を連続画像から3次元再構成し、凍結時の漏出状態を可視化する画像計測法が研究の中心であり、植物の構造・生理状態を定量的に観察している。

abstractVisualising the vascular system in its native 3-dimensional state will help in the understanding of this freezing process.

Multi-environment genome-wide association mapping of culm morphology traits in barley →

In cereals with hollow internodes, lodging resistance is influenced by morphological characteristics such as internode diameter and culm wall thickness. Despite their relevance, knowledge of the genetic control of these traits and their relationship with lodging is lacking in temperate cereals such as barley. To fill this gap, we developed an image-analysis based protocol to accurately phenotype culm diameter and culm wall thickness across 261 barley accessions. Analysis of culm trait data collected from field trials in 7 different environments revealed genetic control as supported by high heritability values, as well as genotype-by-environment interactions. The collection was structured mainly according to row-type, which had a confounding effect on culm traits as evidenced by phenotypic correlations. In addition, culm traits showed strong negative correlations with lodging but weak correlations with plant height across row-types, indicating the possibility of improving lodging resistance independent of plant height. Using 50k iSelect SNP genotyping data, we conducted multi-environment genome-wide association studies using mixed model approach across the whole panel and row-type subsets: we identified a total of 192 QTLs for the studied traits, including subpopulation-specific QTLs and several main effect loci for culm traits showing negative effects on lodging without impacting plant height. Providing first insights into the genetic architecture of culm morphology in barley and the possible role of candidate genes involved in hormone and cell wall related pathways, this work supports the potential of loci underpinning culm features to improve lodging resistance and increase barley yield stability under changing environments. One-sentence summary Genetic analysis of a diverse collection of European barleys reveals genomic regions underpinning stem morphological features associated with lodging resistance.

Why it matches plant phenotyping methods大麦の稈直径と稈壁厚を測定する画像解析プロトコルを開発し、多環境・多数アクセッションで適用しており、植物形質取得法が研究の中心的要素である。

abstractwe developed an image-analysis based protocol to accurately phenotype culm diameter and culm wall thickness across 261 barley accessions.

An Improved Inoculation Method to Detect Wheat and Barley Genotypes for Resistance to Fusarium Crown Rot. →

Fusarium crown rot (FCR), caused by Fusarium species, is a serious soilborne fungal disease in many wheat growing regions in the world. A reliable FCR assessment method is essential for germplasm screening and host resistance studies. Here, we report a new assay in which we inoculated wheat seedlings grown in a glasshouse for FCR by injecting spore suspensions into the seedling stems. The effects of inoculum concentration and injection time points on disease severity were investigated. Of different treatments, the injection of 10 7 macroconidia/ml suspension at one leaf and one heart stage gave best results. A collection of 92 emmer-derived hexaploid bread wheats, 43 barley germplasms, and four wheat genotypes with known resistance levels to FCR was used to validate this new method. Repeatability of the two trials in the validation experiments was high ( r = 0.97, P < 0.01). Two emmer-derived hexaploid bread wheat and three Chinese barley germplasms showed consistent resistance to FCR in multiple rounds of selection. The short timeframe of this assay for phenotypic screening makes it a valuable tool to eliminate germplasms with undesirable susceptibility to FCR at seedling stage before costly field assays.

Why it matches plant phenotyping methodsFCRの病害重症度を測定する新規接種・評価法を開発し、複数の遺伝資源で再現性と妥当性を検証しており、表現型取得法が研究の中心である。

abstractA reliable FCR assessment method is essential for germplasm screening and host resistance studies.

Deep phenotyping platform for microscopic plant-pathogen interactions →

1O_LIThe initial phases of plant-pathogen interactions are critical since they are often decisive for the successful infection. However, these early stages of interaction are typically microscopic, making it challenging to study on a large scale. C_LIO_LIFor this reason, using the powdery mildew fungi of cereals as a model, we have developed an automated microscopy pipeline coupled with deep learning-based image analysis for the high-throughput phenotyping of plant-pathogen interactions. C_LIO_LIThe system can quantify fungal microcolony count and density, the precise area of the secondary hyphae of each colony, and different morphological parameters. Moreover, the high throughput and sensitivity allow quantifying rare microscopic phenotypes in a large sample size. One of these phenotypes is the cryptic infection of non-adapted pathogens, marking the hidden transition stages of pathogen adaptation and breaking the nonhost barrier. Thus, our tool opens the nonhost resistance phenomenon to genetics and genomics studies. C_LIO_LIWe have developed an open-source high-throughput automated microscopy system for phenotyping the initial stages of plant-pathogen interactions, extendable to other microscopic phenotypes and hardware platforms. Furthermore, we have validated the systems performance in disease resistance screens of genetically diverse barley material and performed Genome-wide associations scans (GWAS), discovering several resistance-associated loci, including conferring nonhost resistance. C_LI

Why it matches plant phenotyping methods植物病原体相互作用の顕微鏡画像を自動取得・深層学習解析し、感染関連の形態・状態を定量化する高スループット表現型解析基盤を開発・検証しており、方法が研究の中心である。

abstractwe have developed an automated microscopy pipeline coupled with deep learning-based image analysis for the high-throughput phenotyping of plant-pathogen interactions

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets →

Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.

Why it matches plant phenotyping methods植物画像から病徴を分類する少数ショット手法の開発と、従来法との大規模な性能比較・検証が研究の中心であるため、植物病害状態の画像ベースフェノタイピングに該当する。

abstractThis study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm).

High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings. →

In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive × additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement.

Why it matches plant phenotyping methods根数・根角度・蒸散速度という植物形質を対象に、ゲノム情報に基づく予測モデルを構築し、交差検証して予測性能を評価することが研究の中心であるため。

abstractSecond, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits.

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation →

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.

Why it matches plant phenotyping methods航空ハイパースペクトル画像から収量・バイオマスを推定するAutoML解析ワークフローを構築・評価しており、植物形質推定手法が中心である。

abstractThis research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass

Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany →

Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop developmental stages. All-weather availability and sensitivity to crop structure, dielectric properties make synthetic aperture radar (SAR) images an excellent resource for yield estimation. Both types of data provide complementary information about crop conditions. A random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation. We utilized the crop harvest and quality survey (BEE) yield data set collected by the Hessisches Statistisches Landesamt (HSL), Wiesbaden, Germany, over 490 fields. We prepared 20 m resolution yield maps for winter wheat, winter barley, winter rye and winter rapeseed. Input features for the yield estimation model are selected based on the prior knowledge of remote sensing of vegetation. Baseline random forest regression models are developed for all the four crop types with optical and SAR input features. An optimized random forest regression model with genetic algorithm-based feature selection results in performance improvement. Dissimilarity in genetic algorithm selected image features highlights the significance of crop-specific feature selection for yield estimation. The optimized models reliably estimate yield by achieving correlation coefficient (r) of 0.65–0.86, mean absolute error 0.93–1.16 t ha–1 and root mean square error 1.12–1.56 t ha–1 with BEE yield on testing data set. The proposed models could estimate the intra-field yield variation when winter wheat, winter barley, winter rye were in the shooting phase to the beginning of ear-shifting, and winter rapeseed began to flower or was already flowering. These results demonstrate the merits of our model for early-stage crop yield estimation at the field level with mono-temporal image and adaptability for the cropping season with high cloud cover.

Why it matches plant phenotyping methods衛星光学・SAR画像から圃場内の作物収量を推定するモデルを開発・検証しており、植物の収量という形質の取得・推定手法が研究の中心である。

abstractA random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation.

Inhibition of peroxidases and oxidoreductases is crucial for avoiding false-positive reactions in the localization of reactive oxygen species in intact barley root tips. →

Main conclusion NBT and HE may be efficiently used for the detection of superoxide, while DCDHF-DA and DHR123 for the detection of peroxynitrite in intact barley root tips, only if PRXs and oxidoreductases are inhibited to avoid false-positive reactions. Strong peroxidase (PRX) and oxidoreductase activities were observed in the barley root tips that were markedly inhibited by NaN 3 . Rapid and strong nitro-blue tetrazolium chloride (NBT) reduction is associated mainly with the vital functions of root cells but not with superoxide formation. In turn, the inhibition of root surface redox activity by NaN 3 strongly reduced the formation of formazan, but its slight accumulation, observed in the root elongation zone, was a result of NADPH oxidase-mediated apoplastic superoxide formation. A longer staining time period with NBT was required for the detection of antimycin A-mediated superoxide formation inside the cells. This antimycin A-induced superoxide was clearly detectable by hydroethidine (HE) after the inhibition of PRXs by NaN 3 , and it was restricted into the root transition zone. TEMPOL, a superoxide scavenger, strongly inhibited both NBT reduction and HE oxidation in the presence of NaN 3 . Similarly, the DCDHF-DA and DHR123 oxidation was markedly reduced after the inhibition of apoplastic PRXs by NaN 3 and was detectable mainly in the root transition zone. This fluorescence signal was not influenced by the application of pyruvate but was strongly reduced by urea, a peroxynitrite scavenger. The presented results suggest that if the root PRXs and oxidoreductases are inhibited, both NBT and HE detect mainly superoxide, whereas both DCDHF-DA and DHR123 may be efficiently used for the detection of peroxynitrite in intact barley root tips. The inhibition of PRXs and oxidoreductases is crucial for avoiding false-positive reactions in the localization of reactive oxygen species in the intact barley root tip.

Why it matches plant phenotyping methodsオオムギ根端における活性酸素種の局在検出法を比較・検証し、ペルオキシダーゼ等による偽陽性を抑制する条件を評価しているため、植物状態の取得法が中心である。

abstractNBT and HE may be efficiently used for the detection of superoxide, while DCDHF-DA and DHR123 for the detection of peroxynitrite in intact barley root tips, only if PRXs and oxidoreductases are inhibited to avoid false-positive reactions.

SAI: Fast and automated quantification of stomatal parameters on microscope images →

Using microscopy to investigate stomatal behaviour is a common technique in plant physiology research. Manual inspection and measurement of stomatal features is a low throughput process in terms of time and human effort, which relies on expert knowledge to identify and measure stomata accurately. This process represents a significant bottleneck in research pipelines, adding significant researcher time to any project that requires it. To alleviate this, we introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision. We evaluated the reliability of predicted measurements: SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. Hence, SAI boosts the number of images that biologists can evaluate in a fraction of the time so is capable of obtaining more accurate and representative results.

Why it matches plant phenotyping methods気孔画像から形質を自動抽出する深層コンピュータビジョンツールを開発し、専門家測定および既報データとの一致性を検証しており、植物表現型取得法が研究の中心である。

abstractwe introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision.

Detection of crop diseases using enhanced variability imagery data and convolutional neural networks →

The timely detection of crop diseases is critical for securing crop productivity, lowering production costs, and minimizing agrochemical use. This study presents a crop disease identification method that is based on Convolutional Neural Networks (CNN) trained on images taken with consumer-grade cameras. Specifically, this study addresses the early detection of wheat yellow rust, stem rust, powdery mildew, potato late blight, and wild barley net blotch. To facilitate this, pictures were taken in situ without modifying the scene, the background, or controlling the illumination. Each image was then split into several patches, thus retaining the original spatial resolution of the image while allowing for data variability. The resulting dataset was highly diverse since the disease manifestation, imaging geometry, and illumination varied from patch to patch. This diverse dataset was used to train various CNN architectures to find the best match. The resulting classification accuracy was 95.4 ± 0.4%. These promising results lay the groundwork for autonomous early detection of plant diseases. Guidelines for implementing this approach in realistic conditions are also discussed.

Why it matches plant phenotyping methods植物画像から病害状態をCNNで推定する画像ベース手法を開発・評価しており、病害識別精度も検証しているため、植物フェノタイピング手法が研究の中心である。

abstractThis study presents a crop disease identification method that is based on Convolutional Neural Networks (CNN) trained on images taken with consumer-grade cameras.

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning →

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

Why it matches plant phenotyping methodsハイパースペクトル画像とPLS機械学習により、葉の15種類のミネラル濃度を大規模に推定する手法を評価しており、植物形質の取得・推定が研究の中心である。

abstractHere, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients

Evaluation of Individual Plant Growth Estimation in an Intercropping Field with UAV Imagery →

Agriculture practices in monocropping need to become more sustainable and one of the ways to achieve this is to reintroduce intercropping. However, quantitative data to evaluate plant growth in intercropping systems are still lacking. Unmanned aerial vehicles (UAV) have the potential to become a state-of-the-art technique for the automatic estimation of plant growth. Individual plant height is an important trait attribute for field investigation as it can be used to derive information on crop growth throughout the growing season. This study aimed to investigate the applicability of UAV-based RGB imagery combined with the structure from motion (SfM) method for estimating the individual plants height of cabbage, pumpkin, barley, and wheat in an intercropping field during a complete growing season under varying conditions. Additionally, the effect of different percentiles and buffer sizes on the relationship between UAV-estimated plant height and ground truth plant height was examined. A crop height model (CHM) was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The results showed that the overall correlation coefficient (R2) values of UAV-estimated and ground truth individual plant heights for cabbage, pumpkin, barley, and wheat were 0.86, 0.94, 0.36, and 0.49, respectively, with overall root mean square error (RMSE) values of 6.75 cm, 6.99 cm, 14.16 cm, and 22.04 cm, respectively. More detailed analysis was performed up to the individual plant level. This study suggests that UAV imagery can provide a reliable and automatic assessment of individual plant heights for cabbage and pumpkin plants in intercropping but cannot be considered yet as an alternative approach for barley and wheat.

Why it matches plant phenotyping methodsUAV画像とSfMによる個体植物高の自動推定を評価・検証しており、植物形質の取得手法が研究の中心です。

abstractThis study aimed to investigate the applicability of UAV-based RGB imagery combined with the structure from motion (SfM) method for estimating the individual plants height

Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat →

Abstract Barley yellow dwarf (BYD) is one of the major viral diseases of cereals. Phenotyping BYD in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to, i) evaluate the use of high-throughput phenotyping (HTP) from unmanned aerial systems to improve BYD assessment and selection, ii) identify genomic regions associated with BYD resistance, and iii) evaluate genomic prediction models ability to predict BYD resistance. Up to 107 wheat lines were phenotyped during each of five field seasons under both insecticide treated and untreated plots. Across all seasons, BYD severity was lower with the insecticide treatment and plant height (PTHTM) and grain yield (GY) showed increased values relative to untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying resistance gene Bdv2 on chromosome 7DL. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for resistance on chromosome 5AS. Given the variable heritability of the trait (0.211 – 0.806), we obtained relatively good predictive ability for BYD severity ranging between 0.06 – 0.26. Including Bdv2 on the predictive model had a large effect for predicting BYD but almost no effect for PTHTM and GY. This study was the first attempt to characterize BYD using field-HTP and apply GS to predict the disease severity. These methods have the potential to improve BYD characterization and identifying new sources of resistance will be crucial for delivering BYD resistant germplasm.

Why it matches plant phenotyping methodsUASによるフィールド高スループットフェノタイピングを用いたBYD病徴評価が研究目的の中心であり、植物の病害状態を測定・予測する方法を実質的に適用している。

abstractThis study was the first attempt to characterize BYD using field-HTP and apply GS to predict the disease severity.

Use of Imaging as Non-Destructive Tool for Water Stress Tolerance in Spring Barley (Hordeum vulgare) →

Climate change poses a major threat on agriculture, thus on food security.Drought stress, a factor in climate change, is a major problem for barley production, since it simultaneously affects morphological, physiological and biochemical traits.The present work was conducted to provide comprehensive information regarding barley genotypes response and adaptation to drought stress by using a high throughput phenotyping approach.Different barley genotypes were grown in a controlled environment greenhouse.Control plants were kept fully irrigated at 100% field capacity (FC), while the treated plants were stressed by reducing irrigation to 50% of FC.The effects of water deficit on barley genotypes development in terms of early detection of plant response to stress.Morpho-physiological parameters were recorded using Scanalyzer 3D High Throughput Phenotyping platform together with more conventional phenotyping methods to identify and select a set of putative drought tolerant genotypes.The results showed significant differences among genotypes in drought stress response based on digital and traditional indices.Among the selected tolerant genotypes, the best performer was a doubled haploid line derived by a cross Roho×Ardhaoui.

Why it matches plant phenotyping methodsScanalyzer 3D高スループット表現型解析プラットフォームを用いた画像ベースの干ばつストレス表現型取得とデジタル指標による遺伝子型評価が研究の中心であるため。

abstractThe present work was conducted to provide comprehensive information regarding barley genotypes response and adaptation to drought stress by using a high throughput phenotyping approach.

The Use of Imaging as Non-Destructive Tool for Water Stress Tolerance in Spring Barley (Hordeum Vulgare L.) →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods春大麦の水ストレス耐性評価における画像利用を主題とする非破壊的フェノタイピング手法の応用研究であり、画像取得が中心的役割を担う。

titleThe Use of Imaging as Non-Destructive Tool for Water Stress Tolerance in Spring Barley (Hordeum Vulgare L.)

Improving Nitrogen Status Estimation in Malting Barley Based on Hyperspectral Reflectance and Artificial Neural Networks →

Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.

Why it matches plant phenotyping methods大麦の窒素状態という植物生理形質を、ハイパースペクトル反射とニューラルネットワークで推定する方法の開発・精度評価が研究の中心である。

abstractdevelop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning. →

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

Why it matches plant phenotyping methodsハイパースペクトル画像とPLSモデルで葉の無機栄養濃度を高スループット推定する手法が研究の中心であり、植物形質の取得・予測方法として実質的に評価されている。

abstractHere, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients

Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods →

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.

Why it matches plant phenotyping methods穀類スパイクの画像検出・セグメンテーション手法を比較評価し、SpikeAppとして植物画像の自動フェノタイピングを提供しており、方法開発・検証が中心である。

abstractwe performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images

Untargeted metabotyping to study phenylpropanoid diversity in crop plants →

Plant genebanks constitute a key resource for breeding to ensure crop yield under changing environmental conditions. Because of their roles in a range of stress responses, phenylpropanoids are promising targets. Phenylpropanoids comprise a wide array of metabolites; however, studies regarding their diversity and the underlying genes are still limited for cereals. The assessment of barley diversity via genotyping‐by‐sequencing is in rapid progress. Exploring these resources by integrating genetic association studies to in‐depth metabolomic profiling provides a valuable opportunity to study barley phenylpropanoid metabolism; but poses a challenge by demanding large‐scale approaches. Here, we report an LC‐PDA‐MS workflow for barley high‐throughput metabotyping. Without prior construction of a species‐specific library, this method produced phenylpropanoid‐enriched metabotypes with which the abundance of putative metabolic features was assessed across hundreds of samples in a single‐processed data matrix. The robustness of the analytical performance was tested using a standard mix and extracts from two selected cultivars: Scarlett and Barke. The large‐scale analysis of barley extracts showed (1) that barley flag leaf profiles were dominated by glycosylation derivatives of isovitexin, isoorientin, and isoscoparin; (2) proved the workflow's capability to discriminate within genotypes; (3) highlighted the role of glycosylation in barley phenylpropanoid diversity. Using the barley S42IL mapping population, the workflow proved useful for metabolic quantitative trait loci purposes. The protocol can be readily applied not only to explore the barley phenylpropanoid diversity represented in genebanks but also to study species whose profiles differ from those of cereals: the crop Helianthus annuus (sunflower) and the model plant Arabidopsis thaliana.

Why it matches plant phenotyping methods植物の代謝状態を大規模に取得するLC-PDA-MS metabotypingワークフローの開発・技術検証が中心であり、単なる生物学的実験のルーチン測定ではない。

abstractHere, we report an LC‐PDA‐MS workflow for barley high‐throughput metabotyping.

Assessing a Removable Mini-Lysimeter for Monitoring Crop Evapotranspiration Using a Well-Established Large Weighing Lysimeter: A Case Study for Barley and Potato →

Further research is required on the measurement of crop evapotranspiration (ETc) to produce new or updated crop coefficients for a large number of crops using accurate weighing lysimeters. However, large weighing lysimeters are sometimes expensive and are not portable, and different prototypes of small-sized lysimeters may be a feasible alternative. This study evaluated the performance of a removable mini-lysimeter model to measure ETc and derive crop coefficients using a long-established large precision weighing lysimeter over a two-year period. The study was conducted during the 2017 and 2018 barley and potato growing seasons, respectively, at a lysimeter facility located in Albacete (southeast Spain). ETc values were determined using daily mass change in the lysimeters. Irrigation was managed to avoid any water stress. In the barley season, the mini-lysimeter underestimated the seasonal ETc by 2%, the resulting errors in barley ETc estimation were an MBE of −0.070 mm d−1 and an RMSE of ±0.289 mm d−1. In the potato season, the mini-lysimeter overestimated the cumulative ETc by 5%, the resulting errors in potato ETc measurement were an MBE of 0.222 mm d−1 and an RMSE of ±0.497 mm d−1. The goodness of fit indicators showed a good agreement between the large and mini-lysimeter barley and potato ETc measurements at daily time step. Single (Kc) and dual crop coefficients (Kcb, crop transpiration + Ke, soil evaporation) were derived from the lysimeter measurements, the grass reference evapotranspiration (ETo) and the FAO56 dual Kc approach; after temperate standard climate adjustment, mid-season values were Kc mid (std) = 1.05 and Kcb mid (std) = 1.00 for barley; and Kc mid (std) = 1.06 and Kcb mid (std) = 1.02 for potato. The good agreement found between Kcb values and fc will allow barley and potato water requirements to be accurately estimated.

Why it matches plant phenotyping methods作物蒸発散量（ETc）を測定する可搬型ミニライシメータの性能を大型精密ライシメータと比較検証しており、植物の生理状態・水利用に関わる測定法の技術的妥当性評価が中心である。

abstractThis study evaluated the performance of a removable mini-lysimeter model to measure ETc and derive crop coefficients using a long-established large precision weighing lysimeter over a two-year period.

Identification of a bio-signature for barley resistance against Pyrenophora teres infection based on physiological, molecular and sensor-based phenotyping. →

Necrotic and chlorotic symptoms induced during Pyrenophora teres infection in barley leaves indicate a compatible interaction that allows the hemi-biotrophic fungus Pyrenophora teres to colonise the host. However, it is unexplored how this fungus affects the physiological responses of resistant and susceptible cultivars during infection. To assess the degree of resistance in four different cultivars, we quantified visible symptoms and fungal DNA and performed expression analyses of genes involved in plant defence and ROS scavenging. To obtain insight into the interaction between fungus and host, we determined the activity of 19 key enzymes of carbohydrate and antioxidant metabolism. The pathogen impact was also phenotyped non-invasively by sensor-based multireflectance and -fluorescence imaging. Symptoms, regulation of stress-related genes and pathogen DNA content distinguished the cultivar Guld as being resistant. Severity of net blotch symptoms was also strongly correlated with the dynamics of enzyme activities already within the first day of infection. In contrast to the resistant cultivar, the three susceptible cultivars showed a higher reflectance over seven spectral bands and higher fluorescence intensities at specific excitation wavelengths. The combination of semi high-throughput physiological and molecular analyses with non-invasive phenotyping enabled the identification of bio-signatures that discriminates the resistant from susceptible cultivars.

Why it matches plant phenotyping methodsセンサー型マルチリフレクタンス・蛍光イメージングによる感染植物の非侵襲的表現型評価が、抵抗性識別の主要な技術要素として明示されているため。

titleIdentification of a bio-signature for barley resistance against Pyrenophora teres infection based on physiological, molecular and sensor-based phenotyping.

Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction →

In recent studies, the potential of hyperspectral sensors for the analysis of plant–pathogen interactions was expanded to the ultraviolet range (UV; 200–380 nm) to monitor stress processes in plants. A hyperspectral imaging set‐up was established to highlight the influence of early plant–pathogen interactions on secondary plant metabolites. In this study, the plant–pathogen interactions of three different barley lines inoculated with Blumeria graminis f. sp. hordei (Bgh, powdery mildew) were investigated. One susceptible genotype (cv. Ingrid, wild type) and two resistant genotypes (Pallas 01, Mla1‐ and Mla12‐based resistance and Pallas 22, mlo5‐based resistance) were used. During the first 5 days after inoculation (dai) the plant reflectance patterns were recorded and plant metabolites relevant in host–pathogen interactions were studied in parallel. Hyperspectral measurements in the UV range revealed that a differentiation between barley genotypes inoculated with Bgh is possible, and distinct reflectance patterns were recorded for each genotype. The extracted and analysed pigments and flavonoids correlated with the spectral data recorded. A classification of noninoculated and inoculated samples with deep learning revealed that a high performance can be achieved with self‐attention networks. The subsequent feature importance identified wavelengths as the most important for the classification, and these were linked to pigments and flavonoids. Hyperspectral imaging in the UV range allows the characterization of different resistance reactions, can be linked to changes in secondary plant metabolites, and has the advantage of being a non‐invasive method. It therefore enables a greater understanding of plant reactions to biotic stress, as well as resistance reactions.

Why it matches plant phenotyping methodsUVハイパースペクトル画像と深層学習による植物の病害抵抗反応・遺伝型の非侵襲的判別が研究の中心であり、植物状態の表現型取得・解析手法を評価している。

abstractA hyperspectral imaging set‐up was established to highlight the influence of early plant–pathogen interactions on secondary plant metabolites.

The use of the data derived from the PlanetScope satellite and unmanned aerial vehicles to estimate crop yield as dependent on the amount of nitrogen fertilizer applied →

Abstract The purpose of the present study is to show the usefulness of the satellite data and the data derived from unmanned aerial vehicles (UAVs) for estimating the relationship between cereal grain crop yield and the amount of nitrogen fertilizer applied. The study was conducted on the land of the Kuraginskoye Research Farm. The study material was spring barley cv. Biom. Three test plots were studied; mineral fertilizer, urea, was used in different quantities for foliar application in June; applications were performed at equal intervals. Multispectral images were based on PlanetScope satellite data, with the 3 m spatial resolution, and the data derived from the DJI Phantom 4 Multispectral UAV, with the 10 cm resolution. The satellite and UAV data were used to calculate spectral vegetation index (NDVI) (Normalized Difference Vegetation Index). A high correlation was obtained between the NDVI values calculated using satellite data and UAV data. The satellite data provided the basis for assessing barley crop yield as dependent on the amount of foliar-applied urea during the growing season. Maps of the spatial distribution of barley NDVI were constructed using the Phantom UAV data; they showed that the third foliar application of the fertilizer was not economically justified.

Why it matches plant phenotyping methods衛星・UAV画像からNDVIを算出し、作物収量を推定するセンシング手法の有用性と一致性を検討しており、施肥試験の単なる routine 測定を超えて手法適用が中心です。

abstractThe purpose of the present study is to show the usefulness of the satellite data and the data derived from unmanned aerial vehicles (UAVs) for estimating the relationship between cereal grain crop yield and the amount of nitrogen fertilizer applied.

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley. →

Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.

Why it matches plant phenotyping methods大麦粒の画像から汚染・損傷を抽出し品質を評価する画像解析手順の開発が中心であり、植物器官の状態を測定するフェノタイピング手法に該当する。

abstractThe paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels.

Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping →

Cereal plant density is a relevant agronomic trait in agriculture and high-throughput phenotyping of plant density is important for the decision-making process in precision farming and breeding. It influences the water as well as the fertilization requirements, the intraspecific competition, and the occurrence of weeds or pathogens. Recent studies have determined plant density using machine-learning approaches and feature extraction. This requires spatially very highly resolved images (0.02 cm) because the accuracy distinctly decreased when images had lower resolution. In this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV). We assumed that at an early seedling stage fractional cover is closely related to the number of plants. Spring barley and spring wheat experiments, each with three genotypes and four different sowing densities, were examined. The practicability and repeatability of the methodology were evaluated with an independent experiment consisting of 42 winter wheat genotypes. This experiment mainly differed for genotypes, sowing density and season.The empirical regression models that make us of multispectral images having a GSD of 0.69 cm were able to determine plant density with a high prediction accuracy for barley and wheat (R² > 0.91, mean absolute error (MAE) < 28 plants). In addition, prediction accuracy only slightly declines for multispectral image data having 1.4 cm GSD or RGB image data having 0.6 cm GSD (MAE < 35 plants m⁻²). BBCH stage 13 was identified as the ideal growth stage in which the plants were large enough to accurately determine fractional cover even from the lower resolution image data. Moreover, a developed empirical regression model was transferred to an independent experimental field verifying its robustness across different conditions. The prediction accuracy of UAV estimated plant density showed an R² value of 0.83 and an MAE of less than 21 plants m⁻². Furthermore, manual measurements of 11 randomly selected plots proved sufficient for a user-based training of the regression model (R² = 0.83, MAE < 23 plants m⁻²) adapted to the independent experimental field.The method and the use of UAV image data enable high-throughput phenotyping of cereal plant density with uncertainties of less than 10 %.The practicability, repeatability and robustness of the developed approach were demonstrated in this study.

Why it matches plant phenotyping methodsUAV画像から作物個体密度を推定する手法を開発し、精度・再現性・独立圃場での頑健性を検証しており、植物フェノタイピング手法が研究の中心である。

abstractIn this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV).

Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction →

Abstract In recent studies, the potential of hyperspectral sensors for the analysis of plant–pathogen interactions was expanded to the ultraviolet range (UV; 200–380 nm) to monitor stress processes in plants. A hyperspectral imaging set‐up was established to highlight the influence of early plant–pathogen interactions on secondary plant metabolites. In this study, the plant–pathogen interactions of three different barley lines inoculated with Blumeria graminis f. sp. hordei (Bgh, powdery mildew) were investigated. One susceptible genotype (cv. Ingrid, wild type) and two resistant genotypes (Pallas 01, Mla1 ‐ and Mla12 ‐based resistance and Pallas 22, mlo5 ‐based resistance) were used. During the first 5 days after inoculation (dai) the plant reflectance patterns were recorded and plant metabolites relevant in host–pathogen interactions were studied in parallel. Hyperspectral measurements in the UV range revealed that a differentiation between barley genotypes inoculated with Bgh is possible, and distinct reflectance patterns were recorded for each genotype. The extracted and analysed pigments and flavonoids correlated with the spectral data recorded. A classification of noninoculated and inoculated samples with deep learning revealed that a high performance can be achieved with self‐attention networks. The subsequent feature importance identified wavelengths as the most important for the classification, and these were linked to pigments and flavonoids. Hyperspectral imaging in the UV range allows the characterization of different resistance reactions, can be linked to changes in secondary plant metabolites, and has the advantage of being a non‐invasive method. It therefore enables a greater understanding of plant reactions to biotic stress, as well as resistance reactions.

Why it matches plant phenotyping methodsUVハイパースペクトル画像を用いて植物病害ストレスおよび抵抗性反応を非侵襲的に特徴づけ、深層学習分類と波長重要度解析も検証しており、表現型取得・解析法が中心である。

abstractA hyperspectral imaging set‐up was established to highlight the influence of early plant–pathogen interactions on secondary plant metabolites.

Fully-automated root image analysis (faRIA). →

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

Why it matches plant phenotyping methods根画像から形態を自動抽出するCNNベースの画像解析ツールを開発し、既存ツールとの性能比較・複数画像モダリティと植物種で検証しており、植物フェノタイピング手法が中心である。

abstractHere, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture.

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing →

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

Why it matches plant phenotyping methodsUAV画像とランダムフォレストを用いて、オオムギの乾物バイオマス収量とLAIを推定・検証する手法が研究の中心であり、精度評価と空間マッピングも実施している。

abstractUAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI)

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding →

With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像を用いた作物形質抽出、センサー比較、再現性評価、収量予測を中心に扱う高スループット表現型解析研究である。

abstractwe present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction

The importance of spectral correction of UAV-based phenotyping with RGB cameras →

Spectral correction of colour (RGB) cameras mounted on unmanned aerial vehicles (UAV) is considered important to produce accurate data for quantitative studies in agricultural field research and breeding nurseries. This study investigated the extent to which a simplified spectral correction improves the precision and accuracy of early vigour assessments in winter wheat and winter barley genotypes based on the excess green vegetation Index (nExG) from a pairwise comparison of colour-corrected orthomosaics produced with two different RGB cameras. Two methods of spectral correction were compared, the empirical line method (ELM) and the spectral correction performed in a commercial photogrammetry software package (Pix4D Mapper). Both methods improved the accuracy, with the ELM spectral correction performing better than the Pix4D Mapper spectral correction. Despite high precision and improved accuracy after spectral correction, there were still statistically significant camera effects on vigour assessments because of the minute differences in nExG between genotypes. However, camera effects were evaluated as marginal and with little practical and agronomical relevance under field conditions and in breeding nurseries, as the minute differences between genotypes is overall difficult to reproduce in outdoor field experiments.

Why it matches plant phenotyping methodsUAV-RGB画像のスペクトル補正法を比較・評価し、コムギとオオムギの初期生育形質（nExG）推定精度への影響を検証しており、植物表現型取得法が中心である。

abstractThis study investigated the extent to which a simplified spectral correction improves the precision and accuracy of early vigour assessments in winter wheat and winter barley genotypes based on the excess green vegetation Index (nExG) from a pairwise comparison of colour-corrected orthomosaics produced with two different RGB cameras.

Temperature-based prediction of harvest date in winter and spring cereals as a basis for assessing viability for growing cover crops →

Cover crops are an important component of sustainable cereal cropping systems by providing nutrient retention and improving soil quality. However, the successful establishment and growth of cover crops depend on a timely harvest of the preceding annual crops. To assess the viability of growing cover crops in Denmark, a phenology model was developed, calibrated, cross-validated and applied to predict the harvest date of spring barley and winter wheat in Denmark using data from field experiments in Denmark during 1991 to 2018. A simple phenology model was used for the period from sowing to maturity, and this was extended to simulate the duration from maturity to harvest. This extended model was used to predict the harvest date in the ongoing season, and it provides the capability for assessing the performance of cover crops as a basis for improved cover crop management. The model uses temperature sums and day length to calculate the developmental rate of spring barley from sowing to harvest and for winter wheat from 1st January to harvest. The standard deviation of the estimated harvest date based on the uncertainty of the model parameters was 10-14 days and 1-4 days for spring barley and winter wheat, respectively. The model was evaluated using historically recorded temperature data (20 years, 1999–2018) to simulate harvest date, which was within the range of observed harvest dates. The interannual range of estimated harvest dates is 7–10 days for spring barley and 5–10 days for winter wheat. This interannual variation of harvest date was lower than the spatial variation across the country. The model can be used for forecasting cereal harvest time and thus readiness for autumn field operations, including the establishment of cover crops. Simulations of growing degree days from date of harvest to 1 November shows that cover crops can be reliably established in southern parts of Denmark to ensure low nitrate leaching. Advancing the harvest date to the date of physiological maturity of the cereals would allow the establishment of efficient cover crops in all years in the entire country.

Why it matches plant phenotyping methods穀類の成熟から収穫までの発育状態を推定する温度・日長ベースのフェノロジーモデルを開発、較正、交差検証しており、収穫日という植物状態の推定法が研究の中心である。

abstracta phenology model was developed, calibrated, cross-validated and applied to predict the harvest date of spring barley and winter wheat in Denmark

Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses. →

Key message Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers. Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

Why it matches plant phenotyping methodsゲノム予測モデルを用いて未表現型の遺伝資源についてウイルス抵抗性という植物状態を推定する計算的フェノタイピング手法が研究の中心であり、予測精度の評価も行っている。

abstractOur results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections.

Comparative Analysis of Phenology Algorithms of the Spring Barley Model in APSIM 7.9 and APSIM Next Generation: A Case Study for High Latitudes. →

Phenology algorithms in crop growth models have inevitable systematic errors and uncertainties. In this study, the phenology simulation algorithms in APSIM classical (APSIM 7.9) and APSIM next generation (APSIM-NG) were compared for spring barley models at high latitudes. Phenological data of twelve spring barley varieties were used for the 2014-2018 cropping seasons from northern Sweden and Finland. A factorial-based calibration approach provided within APSIM-NG was performed to calibrate both models. The models have different mechanisms to simulate days to anthesis. The calibration was performed separately for days to anthesis and physiological maturity, and evaluations for the calibrations were done with independent datasets. The calibration performance for both growth stages of APSIM-NG was better compared to APSIM 7.9. However, in the evaluation, APSIM-NG showed an inclination to overestimate days to physiological maturity. The differences between the models are possibly due to slower thermal time accumulation mechanism, with higher cardinal temperatures in APSIM-NG. For a robust phenology prediction at high latitudes with APSIM-NG, more research on the conception of thermal time computation and implementation is suggested.

Why it matches plant phenotyping methods春播きオオムギの開花期・生理成熟期という植物フェノタイプを予測する作物モデルのアルゴリズムを比較、較正、独立データで評価しており、フェノタイプ推定手法の技術的検証が中心である。

abstractPhenology algorithms in crop growth models have inevitable systematic errors and uncertainties.

A development guide for evaluating the maximum yield potential stage in barley →

Determining the grain yield potential contributed by grain number is a step towards advancing cereal crops yield. To achieve this aim, it is pivotal to recognize the maximum yield potential (MYP) of the crop. In barley (Hordeum vulgare L.), the MYP is defined as the maximum spikelet primordia number of a spike. Previous barley studies often assumed the awn primordium (AP) stage as the MYP stage regardless of genotypes and growth conditions. From our spikelet-tracking experiments using the two-rowed cultivar Bowman, we found that the MYP stage can be different from the AP stage. Importantly, we find that the occurrence of inflorescence meristem (IM) deformation and its loss of activity coincided with the MYP stage, indicating the end of further spikelet initiation. Thus, we recommend validating the barley MYP stage with the IMs shape and propose this approach (named Spikelet Stop) for MYP staging. Following this approach, we compared the MYP stage and the MYP in 27 two- and six-rowed barley accessions grown in the greenhouse and field. Our results reveal that the MYP stage can be reached at various developmental stages, which majorly depend on the genotype and growth conditions. Furthermore, we found that two-rowed barleys MYP and the duration reaching the MYP stage may determine their yield potential. Based on our findings, we suggest key steps for the identification of the MYP in barley that can also be applied in a related crop such as wheat. HighlightWe show that the maximum yield potential stage in barley can be different from the awn primordium stage as proposed in earlier studies and it varies depending on the genotype and growth conditions. We suggest key steps to identify maximum yield potential in barley that might apply to related cereals.

Why it matches plant phenotyping methodsBarleyの穂の形態（IM形状）を用いて最大収量ポテンシャル段階を判定する「Spikelet Stop」手法を提案・検証しており、植物表現型の取得方法が研究の中心です。

abstractwe find that the occurrence of inflorescence meristem (IM) deformation and its loss of activity coincided with the MYP stage, indicating the end of further spikelet initiation. Thus, we recommend validating the barley MYP stage with the IMs shape and propose this approach (named Spikelet Stop) for MYP staging.

The Use of Imaging as Non-destructive Tool for Water Stress Tolerance in Spring Barley (Hordeum vulgare L.) →

Recent trends show a reduction in crop productivity worldwide due to severe climatic change. Water stress is a serious problem for barley production, because it affects simultaneously many morphological, physiological and biochemical traits. The present work attempts to provide comprehensive information related to barley plant response and adaptation to drought stress by using a high throughput phenotyping approach. Morpho-physiological parameters of barley plants were collected using the Scanalyzer 3D high-throughput phenotyping platform (LemnaTec, GmbH), associated to more conventional phenotyping methods. With this approach, we could study the effects of water deficit on barley plant development in terms of early detection of plant physiological stress responses. Our results showed that some barley genotypes cultivated under water stress were comparable to the control in terms of productivity, as well as in digital indices. The doubled haploid line ‘DH2’ and the genotype ‘Safra’ were the most tolerant to water stress.

Why it matches plant phenotyping methodsScanalyzer 3D高スループット表現型解析プラットフォームを用いて乾燥ストレス応答を画像・デジタル指標で評価しており、植物表現型取得が研究の中心的手法である。

abstractThe present work attempts to provide comprehensive information related to barley plant response and adaptation to drought stress by using a high throughput phenotyping approach.

"Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification. →

Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.

Why it matches plant phenotyping methodsコムギ・オオムギのうどんこ病症状を画像セグメンテーションで自動定量する方法と高スループット基盤の開発が中心であり、植物病害状態の表現型測定に該当する。

titleAn Automated Segmentation-Based System for Powdery Mildew Disease Quantification.

Effect of cadmium stress on certain physiological parameters, antioxidative enzyme activities and biophoton emission of leaves in barley (Hordeum vulgare L.) seedlings. →

Biophoton emission is a well-known phenomenon in living organisms, including plant species; however, the underlying mechanisms are not yet well elucidated. Nevertheless, non-invasive stress detection is of high importance when in plant production and plant research. Therefore, the aim of our work was to investigate, whether biophoton emission is suitable for the detection of cadmium stress in the early phase of stress evolution and to identify certain stress-related events that occur rapidly upon cadmium exposure of barley seedlings parallel to biophoton emission measurements. Changes of biophoton emission, chlorophyll content estimation index, ascorbate level, the activity of ascorbate- and guaiacol peroxidase enzymes and lipid oxidation were measured during seven days of cadmium treatment in barley (Hordeum vulgare L.) seedlings. The results indicate that the antioxidant enzyme system responded the most rapidly to the stress caused by cadmium and the lipid oxidation-related emission of photons was detected in cadmium-treated samples as early as one day after cadmium exposure. Furthermore, a concentration-dependent increase in biophoton emission signals indicating an increased rate of antioxidative enzymes and lipid oxidation was also possible to determine. Our work shows evidence that biophoton emission is suitable to identify the initial phase of cadmium stress effectively and non-invasively.

Why it matches plant phenotyping methods植物のカドミウムストレスを非侵襲的な生物フォトン発光で検出する手法の適用可能性を中心に評価しており、単なる生理測定ではなくストレス状態の表現型取得法が主要な貢献です。

abstractour work was to investigate, whether biophoton emission is suitable for the detection of cadmium stress in the early phase of stress evolution

Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening. →

Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R 2 P ) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 feature variables yielded R 2 P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-R CV ) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.

Why it matches plant phenotyping methods大麦粒のDON含量・汚染状態を推定するハイパースペクトル画像法と特徴波長選択・回帰/分類モデルを開発し、精度評価しており、植物状態の非破壊表現型取得が中心である。

abstractwe explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels.

Online crop height and density estimation in grain fields using LiDAR →

Crop height and density estimation ahead of the combine harvester have been investigated over the last 30 years, but they remain a challenge. LiDAR technology is increasingly being investigated for phenotyping and monitoring of cereals. However, so far, little has been published about the influence of laser mounting position and robust online measurement of height and density from a single LiDAR scan. Therefore, the influence of the angle and height of the LiDAR mounting on crop height and density estimation in wheat and barley was investigated in this study. Tests were conducted in different crop heights, densities, moisture levels and varieties. The crop height was estimated with a root mean squared error of 0.082 m at an angle of 90° between the LiDAR scanning plane and the horizontal. Two methods were compared for crop density estimation. The inter-percentile and the transversal variance method both successfully predicted the crop density, but the variance based method performed better with a coefficient of determination (R²) of 0.77 and a root mean squared error of 82 g [dry ears] m⁻². When considering only one variety, the performance improved to reach an R² of 0.8 and a root mean squared error of 44 g [dry ears] m⁻². Variation in mounting angle of the sensor had less effect on the prediction accuracy than the mounting height.

Why it matches plant phenotyping methodsLiDARによる作物高・密度という植物形質のオンライン推定法を開発・比較し、異なる条件で精度検証しているため、フェノタイピング手法が研究の中心である。

abstractLiDAR technology is increasingly being investigated for phenotyping and monitoring of cereals.

Assessing frost damage in barley using terahertz imaging. →

Frost is estimated to cost Australian grain growers $ 360 million in direct and indirect losses every year. Assessing frost damage manually in barley is labor intensive and involves destructive sampling. To mitigate against significant economic loss, it is crucial that assessment decisions on whether to cut for hay or continue to harvest are made soon after frost damage has occurred. In this paper, we propose a non-destructive technique by using raster-scan terahertz imaging. Terahertz waves can penetrate the spike to determine differences between frosted and unfrosted grains. With terahertz raster-scan imaging, conducted in both transmission and reflection at 275 GHz, frosted and unfrosted barley spikes show significant differences. In addition, terahertz imaging allows to determine individual grain positions. The emergence of compact terahertz sources and cameras would enable field deployment of terahertz non-destructive inspection for early frost damage.

Why it matches plant phenotyping methodsテラヘルツ画像を用いて大麦穂の凍霜害と穀粒位置を非破壊評価する手法が研究の中心であり、植物状態の取得・判定方法を提案している。

abstractIn this paper, we propose a non-destructive technique by using raster-scan terahertz imaging.

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination →

Summary Efficient seed germination and establishment are important traits for field and glasshouse crops. Large‐scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large‐scale germination scoring. Here, we present the SeedGerm system, which combines cost‐effective hardware and open‐source software for seed germination experiments, automated seed imaging, and machine‐learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination‐ and establishment‐related traits, in both comma‐separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists’ scoring of radicle emergence. Germination curves were produced based on seed‐level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large‐scale seed phenotyping and testing, for both research and routine seed technology applications.

Why it matches plant phenotyping methods種子発芽・定着形質の画像取得と機械学習による自動抽出を行うハードウェア／ソフトウェア基盤を詳細に開発・検証しており、フェノタイピング手法が研究の中心である。

abstractIn this article, we describe the hardware and software design in detail.

Image Phenotyping of Spring Barley ( Hordeum vulgare L.) RIL Population Under Drought: Selection of Traits and Biological Interpretation. →

Image-based phenotyping is a non-invasive method that permits the dynamic evaluation of plant features during growth, which is especially important for understanding plant adaptation and temporal dynamics of responses to environmental cues such as water deficit or drought. The aim of the present study was to use high-throughput imaging in order to assess the variation and dynamics of growth and development during drought in a spring barley population and to investigate associations between traits measured in time and yield-related traits measured after harvesting. Plant material covered recombinant inbred line population derived from a cross between European and Syrian cultivars. After placing the plants on the platform (28th day after sowing), drought stress was applied for 2 weeks. Top and side cameras were used to capture images daily that covered the visible range of the light spectrum, fluorescence signals, and the near infrared spectrum. The image processing provided 376 traits that were subjected to analysis. After 32 days of image phenotyping, the plants were cultivated in the greenhouse under optimal watering conditions until ripening, when several architecture and yield-related traits were measured. The applied data analysis approach, based on the clustering of image-derived traits into groups according to time profiles of statistical and genetic parameters, permitted to select traits representative for inference from the experiment. In particular, drought effects for 27 traits related to convex hull geometry, texture, proportion of brown pixels and chlorophyll intensity were found to be highly correlated with drought effects for spike traits and thousand grain weight.

Why it matches plant phenotyping methods乾ばつ下のオオムギを対象に、高スループット画像取得、画像処理による376形質の抽出、および形質選択・解析ワークフローを中心的に適用しており、植物フェノタイピング手法の実質的な応用研究である。

abstractImage-based phenotyping is a non-invasive method that permits the dynamic evaluation of plant features during growth

Can metabolic prediction be an alternative to genomic prediction in barley? →

Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.

Why it matches plant phenotyping methods代謝プロファイリングを用いて植物の農業形質を予測し、ゲノム予測と性能比較・検証しており、予測手法が研究の中心である。

abstractgenomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits

Visualization of spatial gene expression in plants by modified RNAscope fluorescent in situ hybridization. →

Background In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the spatial distribution pattern of HvGAPDH and the low expressed disease resistance gene Rpg1 in leaf tissue sections of barley and discuss precautions that should be followed during image analysis. Results We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell associated subsidiary cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAscope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. Conclusions RNAscope multiplex fluorescent RNA-ISH detection is well described and adapted for animal tissue samples, however due to morphological and structural differences in the plant tissues the standard protocol is deficient and required optimization. Utilizing barley specific HvGAPDH and Rpg1 RNA probes we report an optimized method which can be used for RNAscope detection to determine the spatial expression and semi-quantification of target RNAs. This optimized method will be immensely useful in other plant species such as the widely utilized Arabidopsis.

Why it matches plant phenotyping methods植物組織における空間的遺伝子発現の蛍光RNA-ISH法を、サンプル前処理・切片保持・画像解析を含めて最適化した研究であり、植物の状態を取得する方法が中心である。

abstractHere we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley.

Visualization of spatial gene expression in plants by modified RNAscope fluorescent in situ hybridization →

In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAcope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. Thus, we optimized the RNAscope detection method in plants to visualize the spatial expression and semi-quantification of target RNAs which can be employed in other plants such as the widely utilized model dicot plant Arabidopsis.

Why it matches plant phenotyping methods植物組織内のRNA発現を空間的に可視化・半定量するRNAscope法を、植物組織向けに最適化・実証した方法開発研究であり、表現型取得法が中心である。

abstractHere we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley.

A novel approach to estimating the competitive ability of Cirsium arvense in cereals using unmanned aerial vehicle imagery →

Eight experiments were carried out in Denmark to determine the yield loss of spring barley due to Cirsium arvense in farmers' fields and to suggest and evaluate a novel approach for quantifying C. arvense infestation in large plots. Literature about the competitive ability of C. arvense is old, scattered and inconclusive, and existing models for estimating crop yield loss are based on data from North America. This study showed that C. arvense coverage could be quantified from unmanned aerial vehicle imagery using a manual image analysis procedure. This gave similar results as scoring the coverage. Yield loss of spring barley due to C. arvense infestation assessed at harvest was given by Y = 100·(1−exp(−0.00170·X)) where Y is the percentage of crop yield loss and X is the percentage of C. arvense coverage. The yield loss was much lower than estimates from models that have been developed in North America. It is speculated that the main reason for this is the later emergence of C. arvense than the crop due to lower soil temperatures in spring. Grain moisture increased linearly with C. arvense coverage: M = 0.0310·X where M is the proportional (%) increase in grain moisture and X is the proportion (%) of C. arvense coverage. Automated image analysis procedures are needed to estimate C. arvense coverage on field scales, and further experiments are needed to reveal whether the low competitive ability of C. arvense found in this study is representative for Northern Europe.

Why it matches plant phenotyping methodsUAV画像からC. arvenseの圃場被覆率を定量する手法を提案・評価しており、植物状態の取得・抽出が研究の中心である。

abstracta novel approach for quantifying C. arvense infestation in large plots

'Macrobot' - an automated segmentation-based system for powdery mildew disease quantification →

Plant diseases, as one of the perpetual problems in agriculture, is increasingly difficult to manage due to intensifying of the field production, global trafficking, reduction of genetic variability of crops, climatic changes-driven expansion of pests, redraw and loss of effectiveness of pesticides and rapid breakdown of the disease resistance in the field. The substantial progress in genomics of both plants and pathogens, achieved in the last decades has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. In this respect, phenotyping is and will remain an essential element of any comprehensive functional genomics study. We have developed a set of methods and equipment and combined them into a “Macrophenomics pipeline”. The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible disease symptoms, typically 5-7 days after inoculation (dai) in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a throughput of the image acquisition module of up to 10 000 individual samples per day, making it appropriate for phenotyping of large germplasms collections and crossing populations.

Why it matches plant phenotyping methodsうどんこ病の葉面感染面積を自動画像 segmentation で定量する方法・装置・パイプラインの開発と精度再現性評価が中心であり、植物病害表現型の取得手法に該当する。

abstractWe have developed a set of methods and equipment and combined them into a “Macrophenomics pipeline”.

Tracking wireworm burrowing behaviour in soil over time using 3D X‐ray computed tomography →

Background Wireworms (larvae of the click beetle, Elateridae) are a significant agricultural pest, causing crop damage and reducing yields globally. Owing to the complex nature and opacity of the soil environment, research to investigate wireworm behaviour in situ has been scarce. X-ray computed tomography (CT) has previously been demonstrated as a powerful tool to independently visualise the 3D root system architecture, macroinvertebrate movement and distribution of burrow systems in soil, but not simultaneously within the same sample. In this study, we apply X-ray CT to visualise and quantify wireworms, their burrow systems and the root architecture of two contrasting crop species (Hordeum vulgare and Zea mays) in a soil pot experiment scanned at different time intervals. Results The majority of wireworm burrows were produced within the first 20 h post inoculation, suggesting that burrow systems are established quickly and persist at a similar volume. There was a significant difference in the volume of burrow systems produced by wireworms between the two crop species suggesting differences in wireworm behaviour elicited by crop species. There was no significant correlation between burrow volume and either root volume or surface area, indicating this behavioural difference is caused by factor(s) other than the mass of root systems. Conclusion X-ray CT shows potential as a non-destructive technique to quantify the interaction of wireworms in the natural soil environment with crop roots, and aid the development of effective pest management strategies to minimise their negative impact on crop production. © 2020 Society of Chemical Industry.

Why it matches plant phenotyping methodsX線CTを用いて作物の根系構造を非破壊に可視化・定量し、根と土壌中の掘孔の相互作用を評価する手法適用が中心である。

abstractIn this study, we apply X-ray CT to visualise and quantify wireworms, their burrow systems and the root architecture of two contrasting crop species

Rapid On-Site Phenotyping via Field Fluorimeter Detects Differences in Photosynthetic Performance in a Hybrid—Parent Barley Germplasm Set →

Crop productivity can be expressed as the product of the amount of radiation intercepted, radiation use efficiency and harvest index. Genetic variation for components of radiation use efficiency has rarely been explored due to the lack of appropriate equipment to determine parameters at the scale needed in plant breeding. On the other hand, responses of the photosynthetic apparatus to environmental conditions have not been extensively investigated under field conditions, due to the challenges posed by the fluctuating environmental conditions. This study applies a rapid, low-cost, and reliable high-throughput phenotyping tool to explore genotypic variation for photosynthetic performance of a set of hybrid barleys and their parents under mild water-stress and unstressed field conditions. We found differences among the genotypic sets that are relevant for plant breeders and geneticists. Hybrids showed lower leaf temperature differential and higher non-photochemical quenching, resembling closer the male parents. The combination of traits detected in hybrids seems favorable, and could indicate improved photoprotection and better fitness under stress conditions. Additionally, we proved the potential of a low-cost, field-based phenotyping equipment to be used routinely in barley breeding programs for early screening for stress tolerance.

Why it matches plant phenotyping methods低コストの野外フルオリメータを用いた高スループット表現型計測が研究の中心であり、光合成性能やストレス耐性関連形質への適用可能性も評価している。

abstractThis study applies a rapid, low-cost, and reliable high-throughput phenotyping tool to explore genotypic variation for photosynthetic performance

A Modular Tray Growth System for Barley. →

Determining when a barley plant starts and finishes meiosis is not trivial as when the spikelets undergo meiosis, the spike is not visible as it is still well within the leaf sheath on the developing tiller. This is a general constraint for any experiment involving meiosis, such as cytology, RNA extractions, or abiotic stress treatments aiming to target such a developmental stage. The lack of synchronicity between barley tillers within the same plant exacerbates the difficulty to determine the overall meiotic stage of a plant at a certain time.Given the lack of a nondestructive staging system for predicting the entry into meiosis and the problems of working with large pot plant systems, a modular plant growing is proposed. This system enables the growth of a high number of plants in a small surface, each producing a single tiller. The modular tray system was used to generate a nondestructive prediction tool for meiosis by using external morphological features. As an example, the system is used here for heat treating F 1 plants in early meiosis stages to modify recombination.

Why it matches plant phenotyping methods外部形態から大麦の減数分裂開始時期を非破壊予測する方法を開発しており、植物フェノタイピング手法が中心的な貢献である。

abstractThe modular tray system was used to generate a nondestructive prediction tool for meiosis by using external morphological features.

Use of genetic markers for the detection of off-types for DUS phenotypic traits in the inbreeding crop, barley →

Detection of crop off-types is of interest for multiple uses, including the assessment of uniformity for new plant variety applications during distinctness, uniformity and stability (DUS) testing for the awarding of plant breeders’ rights (PBR). Here, we investigate whether genetic markers, in this case Kompetitive Allele-Specific PCR (KASP), can be used for the identification off-types for phenotypes assessed for DUS in the inbreeding cereal crop, barley (Hordeum vulgare). To demonstrate proof of principle, KASP markers diagnostic for phenotypic expression of nine DUS phenotypes, and DNA from two barley varieties (‘Pelican’ and ‘Felicie’) carrying contrasting alleles at each marker were used. We found that for the majority of markers, it was possible to robustly call alleles down to template DNA concentrations of 2 ng, but not ≤ 0.2 ng. When used in mixtures of DNA consisting of ‘Felicie’ DNA spiked with different concentrations of ‘Pelican’ DNA, robust allele calling was possible in DNA mixtures down to 18 ng:2 ng. Collectively, this demonstrates that where diagnostic markers are available, molecular identification of a single off-type for a given DUS trait within a bulk of ten individuals should be possible. We validated this assumption, with all of the diagnostic genetic markers investigated found to robustly detect DUS off-types at a frequency of 10% in DNA extracted from tissue collected from pools of 10 individuals. Ultimately, this work demonstrates that, where diagnostic polymorphisms are known for DUS traits, KASP markers should be able to robustly detect off-types or cross-contamination within DNA samples from a diploid inbred species down to 10%. While just two varieties that contrasted for the eight DUS targeted were investigated in this study, as the markers used are diagnostic for their relevant phenotype (or a proportion of the variation observed for that phenotype), in theory the approach should be valid for any variety studied—although the introduction of novel alleles via spontaneous mutation or more exotic germplasm pools may mean that marker sets would need to be periodically added to or updated. However, we nevertheless demonstrate the principle that, for a subset of DUS traits, molecular markers can now be robustly used as a tool towards determining all three components of the DUS testing process in barley. These results are relevant for the assessment of varietal uniformity by crop breeders, crop testing authorities and germplasm maintenance, as well as highlighting the potential use of bulk samples rather than individual plant samples for assessment of distinctness by molecular methods.

Why it matches plant phenotyping methodsDUS形質に対応する分子マーカーを用いてオフタイプを検出する手法を開発・検証しており、品種均一性評価という植物表現型判定への技術的貢献が中心である。

abstractHere, we investigate whether genetic markers, in this case Kompetitive Allele-Specific PCR (KASP), can be used for the identification off-types for phenotypes assessed for DUS in the inbreeding cereal crop, barley (Hordeum vulgare).

Following the Formation of Synaptonemal Complex Formation in Wheat and Barley by High-Resolution Microscopy. →

Wheat and barley have large genomes of 15 Gb and 5.1 Gb, respectively, which is much larger than the human genome (3.3 Gb). The release of their respective genomes has been a tremendous advance the understanding of the genome organization and the ability for deeper functional analysis in particular meiosis. Meiosis is the cell division required during sexual reproduction. One major event of meiosis is called recombination, or the formation of crossing over, a tight link between homologous chromosomes, ensuring gene exchange and faithful chromosome segregation. Recombination is a major driver of genetic diversity but in these large genome crops, the vast majority of these events is constrained at the end of their chromosomes. It is estimated that in barley, about 30% of the genes are located within the poor recombining centromeric regions, making important traits, such as resistance to pest and disease for example, difficult to access. Increasing recombination in these crops has the potential to speed up breeding program and requires a good understand of the meiotic mechanism. However, most research on recombination in plant has been carried in Arabidopsis thaliana which despite many of the advantages it brings for plant research, has a small genome and more spread out of recombination compare to barley or wheat. Advance in microscopy and cytological procedures have emerged in the last few years, allowing to follow meiotic events in these crops. This protocol provides the steps required for cytological preparation of barley and wheat pollen mother cells for light microscopy, highlighting some of the differences between the two cereals.

Why it matches plant phenotyping methods小麦・オオムギの減数分裂過程を可視化する細胞学的調製と高解像度顕微鏡法のプロトコルが中心であり、植物の生殖細胞状態を観察する測定手法に該当する。

abstractThis protocol provides the steps required for cytological preparation of barley and wheat pollen mother cells for light microscopy

Learning from Synthetic Dataset for Crop Seed Instance Segmentation →

Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training data must be prepared, which requires a time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization , where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. Importantly, we show that our approach is effective not only for barley seeds but also for various crops including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural domain.

Why it matches plant phenotyping methods合成画像で学習したインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発・検証し、複数作物への適用性能も評価しているため、方法が中心的である。

abstractan instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset.

Testing deep placement of an 15N tracer as a method for in situ deep root phenotyping of wheat, barley and ryegrass →

Abstract Background Deep rooting is one of the most promising plant traits for improving crop yield under water-limited conditions. Most root phenotyping methods are designed for laboratory-grown plants, typically measuring very young plants not grown in soil and not allowing full development of the root system. Results This study introduced the 15 N tracer method to detect genotypic variations of deep rooting and N uptake, and to support the minirhizotron method. The method was tested in a new semifield phenotyping facility on two genotypes of winter wheat, seven genotypes of spring barley and four genotypes of ryegrass grown along a drought stress gradient in four individual experiments. The 15 N labeled fertilizer was applied at increasing soil depths from 0.4 to 1.8 m or from 0.7 to 2.8 m through a subsurface tracer supply system, and sampling of aboveground biomass was conducted to measure the 15 N uptake. The results confirm that the 15 N labeling system could identify the approximate extension of the root system. The results of 15 N labeling as well as root measurements made by minirhizotrons showed rather high variation. However, in the spring barley experiment, we did find correlations between root observations and 15 N uptake from the deepest part of the root zone. The labeled crop rows mostly had significantly higher 15 N enrichment than their neighbor rows. Conclusion We concluded that the 15 N tracer method is promising as a future method for deep root phenotyping because the method will be used for phenotyping for deep root function rather than deep root growth. With some modifications to the injection principle and sampling process to reduce measurement variability, we suggest that the 15 N tracer method may be a useful tool for deep root phenotyping. The results demonstrated that the minirhizotrons observed roots of the tested rows rather than their neighboring rows.

Why it matches plant phenotyping methods15Nトレーサー法を深根系の伸長・機能を推定するフェノタイピング手法として導入・試験し、ミニライゾトロン測定との比較も行っており、方法開発・検証が中心である。

abstractThis study introduced the 15 N tracer method to detect genotypic variations of deep rooting and N uptake, and to support the minirhizotron method.

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging. →

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts.

Why it matches plant phenotyping methodsオオムギ穀粒の栄養形質を対象に、ハイパースペクトル画像と検量モデルの回帰手法、校正セット設計、環境間転移性を評価しており、形質推定法の技術的検証が中心である。

abstractthe optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary

Machine Vision System for 3D Plant Phenotyping →

Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views. The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber. An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations. The whole system, including the programmable growth chamber, robot, scanner, data transfer, and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention. As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana (L.), and the monocot barley (Hordeum vulgare L.) plants under their diurnal light/dark cycles.

Why it matches plant phenotyping methods3Dレーザースキャン、ロボットによる自動取得、3D再構成から植物の表面積・体積・成長を定量化する統合フェノタイピングシステムが研究の中心である。

titleMachine Vision System for 3D Plant Phenotyping

A cell death assay in barley and wheat protoplasts for identification and validation of matching pathogen AVR effector and plant NLR immune receptors. →

Background Plant disease resistance to host-adapted pathogens is often mediated by host nucleotide-binding and leucine-rich repeat (NLR) receptors that detect matching pathogen avirulence effectors (AVR) inside plant cells. AVR-triggered NLR activation is typically associated with a rapid host cell death at sites of attempted infection and this response constitutes a widely used surrogate for NLR activation. However, it is challenging to assess this cell death in cereal hosts. Results Here we quantify cell death upon NLR-mediated recognition of fungal pathogen AVRs in mesophyll leaf protoplasts of barley and wheat. We provide measurements for the recognition of the fungal AVRs AvrSr50 and AVR a1 by their respective cereal NLRs Sr50 and Mla1 upon overexpression of the AVR and NLR pairs in mesophyll protoplast of both, wheat and barley. Conclusions Our data demonstrate that the here described approach can be effectively used to detect and quantify death of wheat and barley cells induced by overexpression of NLR and AVR effectors or AVR effector candidate genes from diverse fungal pathogens within 24 h.

Why it matches plant phenotyping methodsコムギ・オオムギのプロトプラストにおけるNLR誘導細胞死を定量するアッセイを開発・実証しており、植物の病害応答状態を取得する方法が研究の中心である。

titleA cell death assay in barley and wheat protoplasts for identification and validation of matching pathogen AVR effector and plant NLR immune receptors.

A Novel Image-Based Screening Method to Study Water-Deficit Response and Recovery of Barley Populations Using Canopy Dynamics Phenotyping and Simple Metabolite Profiling →

Plant phenotyping platforms offer automated, fast scoring of traits that simplify the selection of varieties that are more competitive under stress conditions. However, indoor phenotyping methods are frequently based on the analysis of plant growth in individual pots. We present a reproducible indoor phenotyping method for screening young barley populations under water stress conditions and after subsequent rewatering. The method is based on a simple read-out of data using RGB imaging, projected canopy height, as a useful feature for indirectly following the kinetics of growth and water loss in a population of barley. A total of 47 variables including 15 traits and 32 biochemical metabolites measured (morphometric parameters, chlorophyll fluorescence imaging, quantification of stress-related metabolites; amino acids and polyamines, and enzymatic activities) were used to validate the method. The study allowed the identification of metabolites related to water stress response and recovery. Specifically, we found that cadaverine (Cad), 1,3-aminopropane (DAP), tryptamine (Tryp), and tyramine (Tyra) were the major contributors to the water stress response, whereas Cad, DAP, and Tyra, but not Tryp, remained at higher levels in the stressed plants even after rewatering. In this work, we designed, optimized and validated a non-invasive image-based method for automated screening of potential water stress tolerance genotypes in barley populations. We demonstrated the applicability of the method using transgenic barley lines with different sensitivity to drought stress showing that combining canopy height and the metabolite profile we can discriminate tolerant from sensitive genotypes. We showed that the projected canopy height a sensitive trait that truly reflects other invasively studied morphological, physiological, and metabolic traits and that our presented methodological setup can be easily applicable for large-scale screenings in low-cost systems equipped with a simple RGB camera. We believe that our approach will contribute to accelerate the study and understanding of the plant water stress response and recovery capacity in crops, such as barley.

Why it matches plant phenotyping methodsRGB画像による投影キャノピー高を用いた非侵襲的な水ストレス表現型スクリーニング手法を設計・最適化・検証しており、植物表現型取得法が研究の中心である。

abstractWe present a reproducible indoor phenotyping method for screening young barley populations under water stress conditions and after subsequent rewatering.

High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery →

Bioethanol production obtained from cereal straw has aroused great interest in recent years, which has led to the development of breeding programs to improve the quality of lignocellulosic material in terms of the biomass and sugar content. This process requires the analysis of genotype-phenotype relationships, and although genotyping tools are very advanced, phenotypic tools are not usually capable of satisfying the massive evaluation that is required to identify potential characters for bioethanol production in field trials. However, unmanned aerial vehicle (UAV) platforms have demonstrated their capacity for efficient and non-destructive acquisition of crop data with an application in high-throughput phenotyping. This work shows the first evaluation of UAV-based multi-spectral images for estimating bioethanol-related variables (total biomass dry weight, sugar release, and theoretical ethanol yield) of several accessions of wheat, barley, and triticale (234 cereal plots). The full procedure involved several stages: (1) the acquisition of multi-temporal UAV images by a six-band camera along different crop phenology stages (94, 104, 119, 130, 143, 161, and 175 days after sowing), (2) the generation of ortho-mosaicked images of the full field experiment, (3) the image analysis with an object-based (OBIA) algorithm and the calculation of vegetation indices (VIs), (4) the statistical analysis of spectral data and bioethanol-related variables to predict a UAV-based ranking of cereal accessions in terms of theoretical ethanol yield. The UAV-based system captured the high variability observed in the field trials over time. Three VIs created with visible wavebands and four VIs that incorporated the near-infrared (NIR) waveband were studied, obtaining that the NIR-based VIs were the best at estimating the crop biomass, while the visible-based VIs were suitable for estimating crop sugar release. The temporal factor was very helpful in achieving better estimations. The results that were obtained from single dates [i.e., temporal scenario 1 (TS-1)] were always less accurate for estimating the sugar release than those obtained in TS-2 (i.e., averaging the values of each VI obtained during plant anthesis) and less accurate for estimating the crop biomass and theoretical ethanol yield than those obtained in TS-3 (i.e., averaging the values of each VI obtained during full crop development). The highest correlation to theoretical ethanol yield was obtained with the normalized difference vegetation index ( R 2 = 0.66), which allowed to rank the cereal accessions in terms of potential for bioethanol production.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像、OBIA、植生指数を用いて、穀類のバイオマス・糖放出・理論エタノール収量を推定する高スループット表現型解析手法が研究の中心である。

abstractThis work shows the first evaluation of UAV-based multi-spectral images for estimating bioethanol-related variables (total biomass dry weight, sugar release, and theoretical ethanol yield) of several accessions of wheat, barley, and triticale (234 cereal plots).

Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range →

Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping.

Why it matches plant phenotyping methodsUV hyperspectralイメージングセンサーを植物フェノタイピング向けに導入・評価し、再現性のある測定条件と塩ストレス検出を示しており、表現型取得法が研究の中心である。

abstractan imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping

QUANTIFYING LODGING PERCENTAGE, LODGING DEVELOPMENT AND LODGING SEVERITY USING A UAV-BASED CANOPY HEIGHT MODEL →

Abstract. Unmanned Aerial Vehicles (UAVs) are increasingly used, and open new opportunities, in agriculture and phenotyping because of the flexible data acquisition. In this study the potential of ultra-high spatially resolved UAV image data was investigated to quantify lodging percentage, lodging development and lodging severity of barley using Structure from Motion techniques. The term lodging is defined as the permanent displacement of a plant from the upright position. Traditionally lodging quantification is based on observations that need, and vary with observers in the field. An objective threshold approach was proposed in this study to improve the accuracy in lodging determination. Across breeding trials, manual reference measurements and UAV based lodging percentage showed a very high correlation (R2 = 0.96). In addition, the multi-temporal lodging percentage development was used to estimate the recovery rate and to determine the influence of different lodging events. Based on the parameter lodging percentage an approach was developed that allowed the assessment of lodging severity, an information that is important to estimate the yield impairment. Lodging severity can be used for insurance applications, precision farming and breeder research. This trait, together with differentiated recovery are novel traits next to lodging severity that will aid the selection for genetic lines.

Why it matches plant phenotyping methodsUAV画像とStructure from Motionによる作物倒伏形質の定量化手法を提案し、手動測定との相関で検証しているため、植物表現型取得が中心です。

abstractIn this study the potential of ultra-high spatially resolved UAV image data was investigated to quantify lodging percentage, lodging development and lodging severity of barley using Structure from Motion techniques.

Probing functional and optical cross-sections of PSII in leaves during state transitions using fast repetition rate light induced fluorescence transients. →

Plants adjust the relative sizes of PSII and PSI antennae in response to the spectral composition of weak light favouring either photosystem by processes known as state transitions (ST), attributed to a discrete antenna migration involving phosphorylation of light-harvesting chlorophyll-protein complexes in PSII. Here for the first time we monitored the extent and dynamics of ST in leaves from estimates of optical absorption cross-section (relative PSII antenna size; aPSII). These estimates were obtained from in situ measurements of functional absorption cross-section (σPSII) and maximum photochemical efficiency of PSII (φPSII); i.e. aPSII = σPSII/φPSII (Kolber et al. 1998) and other parameters from a light induced fluorescence transient (LIFT) device (Osmond et al. 2017). The fast repetition rate (FRR) QA flash protocol of this instrument monitors chlorophyll fluorescence yields with reduced QA irrespective of the redox state of plastoquinone (PQ), as well as during strong ~1 s white light pulses that fully reduce the PQ pool. Fitting this transient with the FRR model monitors kinetics of PSII → PQ, PQ → PSI, and the redox state of the PQ pool in the 'PQ pool control loop' that underpins ST, with a time resolution of a few seconds. All LIFT/FRR criteria confirmed the absence of ST in antenna mutant chlorina-f2 of barley and asLhcb2-12 of Arabidopsis, as well as STN7 kinase mutants stn7 and stn7/8. In contrast, wild-type barley and Arabidopsis genotypes Col, npq1, npq4, OEpsbs, pgr5 bkg and pgr5, showed normal ST. However, the extent of ST (and by implication the size of the phosphorylated LHCII pool participating in ST) deduced from changes in a'PSII and other parameters with reduced QA range up to 35%. Estimates from strong WL pulses in the same assay were only ~10%. The larger estimates of ST from the QA flash are discussed in the context of contemporary dynamic structural models of ST involving formation and participation of PSII and PSI megacomplexes in an 'energetically connected lake' of phosphorylated LHCII trimers (Grieco et al. 2015). Despite the absence of ST, asLhcb2-12 displays normal wild-type modulation of electron transport rate (ETR) and the PQ pool during ST assays, reflecting compensatory changes in antenna LHCIIs in this genotype. Impaired LHCII phosphorylation in stn7 and stn7/8 accelerates ETR from PSII →PQ, over-reducing the PQ pool and abolishing the yield difference between the QA flash and WL pulse, with implications for photochemical and thermal phases of the O-J-I-P transient.

Why it matches plant phenotyping methodsLIFT/FRRセンサーによるPSII吸収断面積と光合成状態遷移の測定・推定が研究の中心であり、複数の遺伝子型で技術的に検証されている。

abstractHere for the first time we monitored the extent and dynamics of ST in leaves from estimates of optical absorption cross-section (relative PSII antenna size; aPSII).

Quantifying cereal crop movement through hemispherical video analysis of agricultural plots. →

BACKGROUND: Violent movement of crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly detrimental to cool-season cereals such as oat, barley, and wheat; contributing to yield and economic losses. Phenotyping the movement of cereal crops in real-time could aid in the breeding and selecting of lodging resistant cereals. Since no methods exist to quantify dynamic, real time plant responses in an agricultural setting, we devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system. RESULTS: We present both the image analysis method for identifying predefined regions of a 2D field design as they appear on 360° field of view video, as well as a signal processing pipeline to quantify movement from time varying color signals from plot canopies within these predefined field regions. We detected significant differences in the natural frequency and amplitude of plant movement from video of 16 cereal cultivars planted in a randomized complete block design on five different windy days. Natural frequencies quantified by this method averaged 1.37 Hz, while over 2.5-fold differences in amplitude within similar frequency ranges were detected across the 16 cereal cultivars. CONCLUSIONS: This method is sensitive enough to systematically differentiate small frequency and amplitude differences in cultivar movement, and shows promise for investigating the physiological basis for differences in cereal movement and lodging resistance. The relative accuracy of the plot demarcation protocol suggests it could be used for other high-throughput phenotyping applications that require both high image resolution and a large field of view.

Why it matches plant phenotyping methods360度動画と信号処理により作物群落の動的な動き（周波数・振幅）を定量する表現型計測法を開発・実証しており、方法が研究の中心である。

abstractwe devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system

Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population. →

Enhancing the accumulation of essential mineral elements in cereal grains is of prime importance for combating human malnutrition. Biofortification by breeding holds great potential for improving nutrient accumulation in grains. However, conventional breeding approaches require element analysis of many grain samples, which causes high costs. Here we applied hyperspectral imaging to estimate the concentration of 15 grain elements (C, B, Ca, Cd, Cu, Fe, K, Mg, Mn, Mo, N, Na, P, S, Zn) in high-throughput in the wild barley nested association mapping (NAM) population HEB-25, comprising 1,420 BC 1 S 3 lines derived from crossing 25 wild barley accessions with the cultivar 'Barke'. Nutrient concentrations varied largely with a multitude of lines having higher micronutrient concentration than 'Barke'. In a genome-wide association study (GWAS), we located 75 quantitative trait locus (QTL) hotspots, whereof many could be explained by major genes such as NO APICAL MERISTEM-1 (NAM-1) and PHOTOPERIOD 1 (Ppd-H1). The GWAS approach revealed exotic alleles that were able to increase grain element concentrations. Remarkably, a QTL linked to GIBBERELLIN 20 OXIDASE 2 (HvGA20ox 2 ) significantly increased several grain elements without yield loss. We conclude that introgressing promising exotic alleles into elite breeding material can assist in improving the nutritional value of barley grains.

Why it matches plant phenotyping methodsハイパースペクトル画像から穀粒中15元素濃度を推定する高スループット表現型取得法を大規模集団に適用しており、画像による形質推定が研究の主要な技術的基盤です。

abstractHere we applied hyperspectral imaging to estimate the concentration of 15 grain elements

High-throughput phenotyping for crop improvement in the genomics era →

Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.

Why it matches plant phenotyping methods植物フェノタイピング手法・プラットフォームに関するレビューであり、画像解析、分光、ロボティクス、計算基盤などによる形質取得を中心に扱っている。

abstractThese modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution.

Optimized qualitative and quantitative methods for barley viability testing using triphenyl tetrazolium chloride staining →

BACKGROUND AND OBJECTIVES: Barley viability is an important agronomic trait and has a direct influence on subsequent beer production. Traditional triphenyl tetrazolium chloride (TTC) test evaluating seed viability still has some drawbacks, for example, excessive instability and time consumption. In the present study, we optimized both the qualitative and quantitative TTC seed viability test methods, and established more accurate and convenient assays to evaluate barley seed viability. FINDINGS: In the qualitative evaluation method, we established the vigor index according to the proportion of staining embryos, allowing quick evaluation of both germination and mortality rates in <12 hr. In the quantitative evaluation method, we optimized TTC incubation time, extraction temperature, and solvent volume to tissue ratios making results more reliable. Finally, we developed a high‐throughput method by combining freeze drying and tissue lysing instead of the time‐consuming method of grinding on liquid nitrogen. There was a positive significant statistical correlation between germination rate and formazan content by the optimized quantitative evaluation method (R² = 0.907; p < 0.01). CONCLUSIONS: Both the optimized qualitative and quantitative TTC seed viability test methods allow a quick and reliable seed vigor evaluation for different varieties or different aged barley seeds. SIGNIFICANCE AND NOVELTY: The optimized seed vigor evaluation method can be used to effectively screen out low vigor barley seeds. The qualitative evaluation method is appropriate to quickly evaluation for a small number of samples in <12 hr. The high‐throughput quantitative evaluation method is fit for a large number of sample evaluations as much as 80 samples at the same time.

Why it matches plant phenotyping methods大麦種子の生存性・活力という植物状態を測定するTTC法を最適化し、定量法を検証して高スループット化しているため、表現型取得法が研究の中心です。

abstractwe optimized both the qualitative and quantitative TTC seed viability test methods, and established more accurate and convenient assays to evaluate barley seed viability.

Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley →

Precision nutrient management requires accurate assessment of crop nutrient status. This is common for assessing N status, but much less so for other nutrients. Because fluorescence can indicate crop stress, the robustness of different fluorescence indices was assessed to predict crop nutrient status (K, Mg and Ca). The hypothesis was that crop nutrition limitations, especially K, can be detected using fluorescence proximal sensing to quantify crop response with a high degree of spatial resolution. A factorial experiment was imposed with four treatment factors: crop, K fertilizer rate, lime and row management. The soil at the experimental site was K deficient and the crop variables showed significant treatment effects (e.g. yield, protein). Fluorescence sensing identified a significant positive K response for three chlorophyll related indices (SFR_G, SFR_R and CHL), but not for FLAV; while wheat was significantly different from barley. Using a k-fold cross-validation method promising predictive relationships were found. The strongest predictions were for SFR_R to predict crop biomass, for SFR_G to predict crop K content of inter-row wheat, for CHL to predict crop Ca content of inter-row wheat and for FLAV with barley grain protein in the windrow treatment. The fluorescence indices produced more significant crop variable predictions than measuring NDVI using an active sensor. This study illustrates the utility of fluorescence sensing to measure chlorophyll related signals for capturing the nutritional status of barley and wheat crops. These results show encouraging potential to rapidly detect crop nutrient status for non-N nutrients using fluorescence sensing.

Why it matches plant phenotyping methods小麦・オオムギの栄養状態、バイオマス、収量等を蛍光近接センシングで推定し、交差検証およびNDVIとの比較で性能を評価しており、フェノタイピング手法が中心である。

abstractUsing a k-fold cross-validation method promising predictive relationships were found.

Construction of a large-scale semi-field facility to study genotypic differences in deep root growth and resources acquisition. →

Background Roots are vital organs for plants, and the effective use of resources from the soil is important for yield stability. However, phenotypic variation in root traits among crop genotypes is mostly unknown and field screening of root development is costly and labour demanding. As a consequence, new methods are needed to investigate root traits of fully grown crops under field conditions, particularly roots in the deeper soil horizons. Results We developed a new phenotyping facility (RadiMax) for the study of root growth and soil resource acquisition under semi-field conditions. The facility consists of 4 units each covering 400 m 2 and containing 150 minirhizotrons, allowing root observation in the 0.4 m-1.8 m or 0.7 m-2.8 m soil depth interval. Roots are observed through minirhizotrons using a multispectral imaging system. Plants are grown in rows perpendicular to a water stress gradient created by a multi-depth sub-irrigation system and movable rainout shelters. The water stress gradient allows for a direct link between root observations and the development of stress response in the canopy. Conclusion To test the concept and technical features, selected spring barley ( Hordeum vulgare L.) cultivars were grown in the system for two seasons. The system enabled genotypic differences for deep root growth to be observed, and clear aboveground physiological response was also visible along the water stress gradient. Although further technical development and field validation are ongoing, the semi-field facility concept offers novel possibilities for characterising genotypic differences in the effective use of soil resources in deeper soil layers.

Why it matches plant phenotyping methods根系形態を深層土壌で観察する新規半圃場フェノタイピング施設とマルチスペクトル画像システムを開発・技術評価しており、方法が研究の中心である。

abstractWe developed a new phenotyping facility (RadiMax) for the study of root growth and soil resource acquisition under semi-field conditions.

Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew. →

Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley-powdery mildew interactions. Significant synergistic effects between the hyperspectral signal and the corresponding gene activities has been shown using the linear discriminant analysis (LDA). Combining the data sets of hyperspectral signatures and gene expression profiles allowed a more precise differentiation of the three investigated barley-Bgh interactions independent from the time after inoculation. This shows significant synergistic effects between the hyperspectral signal and the corresponding gene activities. To analyze this coherency between spectral reflectance and seven different gene expression profiles, relevant wavelength bands and reflectance intensities for each gene were computed using the Relief algorithm. Instancing, xylanase activity was indicated by relevant wavelengths around 710 nm, which are characterized by leaf and cell structures. HvRuBisCO activity underlines relevant wavebands in the green and red range, elucidating the coherency of RuBisCO to the photosynthesis apparatus and in the NIR range due to the influence of RuBisCO on barley leaf cell development. These findings provide the first insights to links between gene expression and spectral reflectance that can be used for an efficient non-invasive phenotyping of plant resistance and enables new insights into plant-pathogen interactions.

Why it matches plant phenotyping methods植物病害抵抗性を非侵襲的に推定するため、ハイパースペクトル反射、LDA、Reliefによる波長特徴抽出を中心的に評価しており、単なる生物学的測定ではない。

abstractHyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions.

Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants →

The analysis of root system growth, root phenotyping, is important to inform efforts to enhance plant resource acquisition from soils. However, root phenotyping remains challenging due to soil opacity and requires systems that optimize root visibility and image acquisition. Previously reported systems require costly and bespoke materials not available in most countries, where breeders need tools to select varieties best adapted to local soils and field conditions. Here, we present an affordable soil-based growth container (rhizobox) and imaging system to phenotype root development in greenhouses or shelters. All components of the system are made from commodity components, locally available worldwide to facilitate the adoption of this affordable technology in low-income countries. The rhizobox is large enough (~6000 cm 2 visible soil) to not restrict vertical root system growth for at least seven weeks after sowing, yet light enough (~21 kg) to be routinely moved manually. Support structures and an imaging station, with five cameras covering the whole soil surface, complement the rhizoboxes. Images are acquired via the Phenotiki sensor interface, collected, stitched and analysed. Root system architecture (RSA) parameters are quantified without intervention. RSA of a dicot (chickpea, Cicer arietinum L.) and a monocot (barley, Hordeum vulgare L.) species, which exhibit contrasting root systems, were analysed. The affordable system is relevant for efforts in Ethiopia and elsewhere to enhance yields and climate resilience of chickpea and other crops for improved food security. Significance Statement An affordable system to characterize root system architecture of soil-grown plants was developed. Using commodity components, this will enable local efforts world-wide to breed for enhanced root systems.

Why it matches plant phenotyping methods土壌中植物の根系形態を取得・画像解析する低コストな容器、撮像システム、解析ワークフローを開発しており、フェノタイピング手法が研究の中心である。

abstractHere, we present an affordable soil-based growth container (rhizobox) and imaging system to phenotype root development in greenhouses or shelters.

Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach →

Unmanned aerial vehicles (UAVs) open new opportunities in precision agriculture and phenotyping because of their flexibility and low cost. In this study, the potential of UAV imagery was evaluated to quantify lodging percentage and lodging severity of barley using structure from motion (SfM) techniques. Traditionally, lodging quantification is based on time-consuming manual field observations. Our UAV-based approach makes use of a quantitative threshold to determine lodging percentage in a first step. The derived lodging estimates showed a very high correlation to reference data (R2 = 0.96, root mean square error (RMSE) = 7.66%) when applied to breeding trials, which could also be confirmed under realistic farming conditions. As a second step, an approach was developed that allows the assessment of lodging severity, information that is important to estimate yield impairment, which also takes the intensity of lodging events into account. Both parameters were tested on three ground sample distances. The lowest spatial resolution acquired from the highest flight altitude (100 m) still led to high accuracy, which increases the practicability of the method for large areas. Our new lodging assessment procedure can be used for insurance applications, precision farming, and selecting for genetic lines with greater lodging resistance in breeding research.

Why it matches plant phenotyping methodsUAV-SfM画像と客観的閾値を用いて、オオムギの倒伏率・倒伏程度という植物形質を定量化する手法を開発・検証しており、方法が研究の中心である。

abstractIn this study, the potential of UAV imagery was evaluated to quantify lodging percentage and lodging severity of barley using structure from motion (SfM) techniques.

High‐throughput 3D modelling to dissect the genetic control of leaf elongation in barley ( Hordeum vulgare ) →

To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.

Why it matches plant phenotyping methods個葉の伸長を少数画像と3Dシュートモデリングで大規模測定するコンピュータビジョン手法を開発し、形質抽出とQTL解析に中心的に用いているため。

abstractHere, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images.

Developing a high-throughput phenotyping method for oxidative stress tolerance in barley roots. →

More than 20% of the world’s agricultural land is affected by salinity, resulting in multibillion-dollar penalties and jeopardising food security. While the recent progress in molecular technologies has significantly advanced plant breeding for salinity stress tolerance, accurate plant phenotyping remains a bottleneck of many breeding programs. We have recently shown the existence of a strong causal link between salinity and oxidative stress tolerance in cereals (wheat and barley). Using the microelectrode ion flux estimation (MIFE) method, we have also found a major QTL conferring ROS control of ion flux in roots that coincided with the major QTL for the overall salinity stress tolerance. These findings open new (previously unexplored) prospects of improving salinity tolerance by pyramiding this trait alongside with other (traditional) mechanisms. In this work, two high-throughput phenotyping methods—viability assay and root growth assay—were tested and assessed as a viable alternative to the (technically complicated) MIFE method using barley as a check species. In viability staining experiments, a dose-dependent H 2 O 2 -triggered loss of root cell viability was observed, with salt sensitive varieties showing significantly more damage to root cells. In the root growth assays, relative root length (RRL) was measured in plants grown in paper rolls under different H 2 O 2 concentrations. The biggest difference in RRL between contrasting varieties was observed for 1 mM H 2 O 2 treatment. Under these conditions, a significant negative correlation in the reduction in RRL and the overall salinity tolerance is reported. These findings offer plant breeders a convenient high throughput method to screen germplasm for oxidative stress tolerance, targeting root-based genes regulating ion homeostasis and thus conferring salinity stress tolerance in barley (and potentially other species).

Why it matches plant phenotyping methodsオオムギ根の酸化ストレス耐性を評価する高スループット表現型解析法を開発・評価しており、表現型取得法が研究の中心です。

abstractIn this work, two high-throughput phenotyping methods—viability assay and root growth assay—were tested and assessed as a viable alternative to the (technically complicated) MIFE method using barley as a check species.

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images. →

Ear density, or the number of ears per square meter (ears/m 2 ), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R 2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

Why it matches plant phenotyping methods圃場RGB画像から穂密度を自動推定する画像取得・画像処理・計数アルゴリズムを開発し、手動計数と比較検証した研究であり、植物フェノタイピング手法が中心です。

abstractAn automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements.

Early Detection of Magnaporthe oryzae -Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging. →

Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae -infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.

Why it matches plant phenotyping methodsハイパースペクトル画像から感染時期と病斑領域を抽出・可視化する手法が研究の中心であり、植物病徴のフェノタイピングに該当する。

abstractThis research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae -infected barley leaves at four consecutive infection periods.

Probing functional and optical cross-sections of PSII in leaves during state transitions using fast repetition rate light induced fluorescence transients →

Plants adjust the relative sizes of PSII and PSI antennae in response to the spectral composition of weak light favouring either photosystem by processes known as state transitions (ST), attributed to a discrete antenna migration involving phosphorylation of light-harvesting chlorophyll-protein complexes in PSII. Here for the first time we monitored the extent and dynamics of ST in leaves from estimates of optical absorption cross-section (relative PSII antenna size; aPSII). These estimates were obtained from in situ measurements of functional absorption cross-section (σPSII) and maximum photochemical efficiency of PSII (φPSII); i.e. aPSII = σPSII/φPSII (Kolber et al. 1998) and other parameters from a light induced fluorescence transient (LIFT) device (Osmond et al. 2017). The fast repetition rate (FRR) QA flash protocol of this instrument monitors chlorophyll fluorescence yields with reduced QA irrespective of the redox state of plastoquinone (PQ), as well as during strong ~1 s white light pulses that fully reduce the PQ pool. Fitting this transient with the FRR model monitors kinetics of PSII → PQ, PQ → PSI, and the redox state of the PQ pool in the ‘PQ pool control loop’ that underpins ST, with a time resolution of a few seconds. All LIFT/FRR criteria confirmed the absence of ST in antenna mutant chlorina-f2 of barley and asLhcb2–12 of Arabidopsis, as well as STN7 kinase mutants stn7 and stn7/8. In contrast, wild-type barley and Arabidopsis genotypes Col, npq1, npq4, OEpsbs, pgr5 bkg and pgr5, showed normal ST. However, the extent of ST (and by implication the size of the phosphorylated LHCII pool participating in ST) deduced from changes in aʹPSII and other parameters with reduced QA range up to 35%. Estimates from strong WL pulses in the same assay were only ~10%. The larger estimates of ST from the QA flash are discussed in the context of contemporary dynamic structural models of ST involving formation and participation of PSII and PSI megacomplexes in an ‘energetically connected lake’ of phosphorylated LHCII trimers (Grieco et al. 2015). Despite the absence of ST, asLhcb2-12 displays normal wild-type modulation of electron transport rate (ETR) and the PQ pool during ST assays, reflecting compensatory changes in antenna LHCIIs in this genotype. Impaired LHCII phosphorylation in stn7 and stn7/8 accelerates ETR from PSII →PQ, over-reducing the PQ pool and abolishing the yield difference between the QA flash and WL pulse, with implications for photochemical and thermal phases of the O-J-I-P transient.

Why it matches plant phenotyping methodsLIFT/FRR蛍光センサーを用いて葉のPSII吸収断面積、光化学効率、電子伝達などの生理形質を定量し、複数の変異体・野生型でstate transitionの検出基準を評価しているため、測定法の適用・技術評価が中心的です。

abstractHere for the first time we monitored the extent and dynamics of ST in leaves from estimates of optical absorption cross-section (relative PSII antenna size; aPSII).

Volatilomics: a non-invasive technique for screening plant phenotypic traits. →

BACKGROUND: Climate change represents a grand challenge for agricultural productivity. Understanding complex plant traits such as stress tolerance, disease resistance or crop yield is thus essential for breeding and the development of sustainable agriculture strategies. When screening for the most robust plant phenotypes, fast, high-throughput phenotyping represents the means of choice. RESULTS: We have developed a plant phenotyping platform to measure the emission of volatile organic compounds (VOCs), photosynthetic gas exchange and transpiration under ambient, or abiotic and biotic stress conditions. These parameters are highly suitable markers to non-invasively and dynamically study plant growth and plant stress status, making them perfect test variables for long-term, online plant monitoring. Here we introduce the new phenotyping platform, termed VOC-SCREEN, and present results of a first case study with three barley cultivars, demonstrating that the plant's volatilome can be successfully applied to discriminate different barley varieties. CONCLUSION: Volatilomics is a promising technique to non-invasively screen for plant phenotypic traits.

Why it matches plant phenotyping methodsVOC放出、光合成ガス交換、蒸散を測定する植物フェノタイピング・プラットフォームを開発し、品種識別への適用も示しており、表現型取得法が研究の中心である。

abstractWe have developed a plant phenotyping platform to measure the emission of volatile organic compounds (VOCs), photosynthetic gas exchange and transpiration under ambient, or abiotic and biotic stress conditions.

Investigating the microstructure of plant leaves in 3D with lab-based X-ray computed tomography. →

Background Leaf cellular architecture plays an important role in setting limits for carbon assimilation and, thus, photosynthetic performance. However, the low density, fine structure, and sensitivity to desiccation of plant tissue has presented challenges to its quantification. Classical methods of tissue fixation and embedding prior to 2D microscopy of sections is both laborious and susceptible to artefacts that can skew the values obtained. Here we report an image analysis pipeline that provides quantitative descriptors of plant leaf intercellular airspace using lab-based X-ray computed tomography (microCT). We demonstrate successful visualisation and quantification of differences in leaf intercellular airspace in 3D for a range of species (including both dicots and monocots) and provide a comparison with a standard 2D analysis of leaf sections. Results We used the microCT image pipeline to obtain estimates of leaf porosity and mesophyll exposed surface area (S mes ) for three dicot species (Arabidopsis, tomato and pea) and three monocot grasses (barley, oat and rice). The imaging pipeline consisted of (1) a masking operation to remove the background airspace surrounding the leaf, (2) segmentation by an automated threshold in ImageJ and then (3) quantification of the extracted pores using the ImageJ 'Analyze Particles' tool. Arabidopsis had the highest porosity and lowest S mes for the dicot species whereas barley had the highest porosity and the highest S mes for the grass species. Comparison of porosity and S mes estimates from 3D microCT analysis and 2D analysis of sections indicates that both methods provide a comparable estimate of porosity but the 2D method may underestimate S mes by almost 50%. A deeper study of porosity revealed similarities and differences in the asymmetric distribution of airspace between the species analysed. Conclusions Our results demonstrate the utility of high resolution imaging of leaf intercellular airspace networks by lab-based microCT and provide quantitative data on descriptors of leaf cellular architecture. They indicate there is a range of porosity and S mes values in different species and that there is not a simple relationship between these parameters, suggesting the importance of cell size, shape and packing in the determination of cellular parameters proposed to influence leaf photosynthetic performance.

Why it matches plant phenotyping methods植物葉の微細構造をmicroCTと画像解析で定量化するパイプラインを開発し、2D手法との比較検証も行っており、表現型取得法が研究の中心である。

abstractHere we report an image analysis pipeline that provides quantitative descriptors of plant leaf intercellular airspace using lab-based X-ray computed tomography (microCT).

Azodyn-Barley, a winter-barley crop model for predicting and ranking genotypic yield, grain protein and grain size in contrasting pedoclimatic conditions →

Increasing societal demand for a more ecological agriculture stimulates demand for farm management systems adapted to non-standard situations such as low-nitrogen systems. However, trade-offs between traits of interest could make difficult to reach high agronomic and environment performances. For malting barley, premium prices depend on grain protein content and grain size with strong trade-offs with yield, such traits being highly dependent on practices and genotypes. Here we assume that such trade-offs can be implicitly embedded in models taking into account varietal traits. Starting with an existing wheat-crop model, we developed a parsimonious barley-crop model on how genotypic traits, crop management practices and pedo-climatic environment determine yield, grain protein content and grain size. Grain size predictions required the development of a new module. We parameterized the model with published and new experimental data using 200 genotype-by-environment combinations of high- and low-input management situations. The model was then assessed on 280 other situations, including high and low level of nitrogen input. The relative root mean square error of prediction of the model on hold-out data was below 15% for grain yield, grain protein content and grain retention fraction in high and low-input situations. In addition, the model correctly ranked 72% of the genotype pairs for grain yield, 55% for grain protein content and 85% for grain retention fraction. Our model can be used to predict grain protein content and retention fraction, and rank yield and calibrated yield performance of existing combinations of genotype by management by environment combinations. It also could help to explore new combinations, to support breeding.

Why it matches plant phenotyping methods大麦の収量・穀粒タンパク質・粒径を遺伝型×管理×環境から推定・順位付けする作物モデルを開発し、独立データで予測性能を評価しており、植物形質推定手法が研究の中心である。

abstractwe developed a parsimonious barley-crop model on how genotypic traits, crop management practices and pedo-climatic environment determine yield, grain protein content and grain size.

Prompt chlorophyll fluorescence as a tool for crop phenotyping: an example of barley landraces exposed to various abiotic stress factors →

The study examined photosynthetic efficiency of two barley landraces (cvs. Arabi Abiad and Arabi Aswad) through a prompt fluorescence technique under influence of 14 different abiotic stress factors. The difference in the behavior of photosynthetic parameters under the same stress factor in-between cv. Arabi Abiad and cv. Arabi Aswad indicated different mechanisms of tolerance and strategies for the conversion of light energy into chemical energy for both the landraces. This study confirmed the suitability of some chlorophyll fluorescence parameters as reliable biomarkers for screening the plants at the level of photosynthetic apparatus.

Why it matches plant phenotyping methodsプロンプトクロロフィル蛍光を作物表現型解析に用い、光合成パラメータのスクリーニング用バイオマーカーとして適用・妥当性確認しており、表現型取得法が中心です。

titlePrompt chlorophyll fluorescence as a tool for crop phenotyping

Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging. →

Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic profiles could establish efficient identification and quantification of relevant genotypes within breeding processes. Experiments were conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0-8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink specific enzymes that irreversibly cleave the transport sugar sucrose into the hexose monomers, were profiled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed specific dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0-120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early influence of Blumeria graminis f.sp. hordei (Bgh) conidia on the specific invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reflectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an increased reflectance at 680 and 710 nm and a decreased reflectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identification of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide a deeper understanding of optical data and its implementation into disease resistance screening.

Why it matches plant phenotyping methodsムギうどんこ病抵抗性を対象に、ハイスループット multispectral imaging とSVMによる病斑・過敏感反応の自動定量を開発・検証しており、植物フェノタイピング手法が中心である。

abstractApplying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established.

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features →

The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy.

Why it matches plant phenotyping methodsUAV・航空機の分光および3D特徴量を用いて作物バイオマスと窒素量を推定する方法を開発・評価しており、植物形質の取得・推定が研究の中心である。

abstractOur objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras.

Monitoring ratio of carbon to nitrogen (C/N) in wheat and barley leaves by using spectral slope features with branch-and-bound algorithm. →

Ratio of carbon to nitrogen concentration (C/N) that can illuminate metabolic status of C and N in crop leaves is one valuable indicator for crop nutrient diagnosis. This study explored the feasibility of using spectral slope features from hyperspectral measurements with Branch-and-Bound (BB) algorithm to monitor leaf C/N in wheat and barley. Experimental data from barley in 2010 and wheat in 2012 were collected and used. The analyses prove that leaf C/N is closely related to leaf N concentration (LNC), which implies that it is feasible to apply spectral technology to monitor leaf C/N in that LNC may have been effectivly estimated by hyperspectral measurements. The results also show that many spectral slope features proposed in this study exhibit the significant correlations with leaf C/N. The best slope feature could evaluate changes of leaf C/N well, with R 2 of 0.63 for wheat, 0.68 for barley and 0.65 for both species combined, respectively. using BB algorithm with input of optiaml four slope features can improve the accuracy of leaf C/N estimations with R 2 over 0.81. It is concluded that using the spectral slope new features with BB method appears very promising and potential for remotely monitoring leaf C/N in crops.

Why it matches plant phenotyping methods小麦・オオムギ葉のC/N比という植物形質を、ハイパースペクトル測定、スペクトル傾斜特徴量、Branch-and-Bound法で推定する手法が研究の中心である。

abstractThis study explored the feasibility of using spectral slope features from hyperspectral measurements with Branch-and-Bound (BB) algorithm to monitor leaf C/N in wheat and barley.

Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. →

Background Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size. Results Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well. Conclusion The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.

Why it matches plant phenotyping methods hyperspectral画像を用いた自動フェノタイピングシステムを開発し、画像解析でオオムギうどんこ病の症状・病勢を定量化することが中心であるため、収録対象。

abstractThis study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment.

Two Inexpensive and Non-destructive Techniques to Correct for Smaller-Than-Gasket Leaf Area in Gas Exchange Measurements. →

The development of technology, like the widely-used off-the-shelf portable photosynthesis systems, for the quantification of leaf gas exchange rates and chlorophyll fluorescence offered photosynthesis research a massive boost. Gas exchange parameters in such photosynthesis systems are calculated as gas exchange rates per unit leaf area. In small chambers ( 2 ), the leaf area used by the system for these calculations is actually the internal gasket area (A G ), provided that the leaf covers the entire A G . In this study, we present two inexpensive and non-destructive techniques that can be used to easily quantify the enclosed leaf area (A L ) of plant species with leaves of surface area much smaller than the A G , such as that of cereal crops. The A L of the cereal crop species studied has been measured using a standard image-based approach ( i A L ) and estimated using a leaf width-based approach ( w A L ). i A L and w A L did not show any significant differences between them in maize, barley, hard and soft wheat. Similar results were obtained when the w A L was tested in comparison with i A L in different positions along the leaf in all species studied. The quantification of A L and the subsequent correction of leaf gas exchange parameters for A L provided a precise quantification of net photosynthesis and stomatal conductance especially with decreasing A L . This study provides two practical, inexpensive and non-destructive solutions to researchers dealing with photosynthesis measurements on small-leaf plant species. The image-based technique can be widely used for quantifying A L in many plant species despite their leaf shape. The leaf width-based technique can be securely used for quantifying A L in cereal crop species such as maize, wheat and barley along the leaf. Both techniques can be used for a wide range of gasket shapes and sizes with minor technique-specific adjustments.

Why it matches plant phenotyping methods小葉の葉面積を画像法・葉幅法で非破壊推定し、ガス交換測定を補正する手法の開発・比較検証が研究の中心である。

abstractwe present two inexpensive and non-destructive techniques that can be used to easily quantify the enclosed leaf area (A L )

Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement →

Yellow rust and fusarium head blight cause significant losses in wheat and barley yields. Mapping the spatial distribution of these two fungal diseases at high sampling resolution is essential for variable rate fungicide application (in case of yellow rust) and selective harvest (in case of fusarium head blight). This study implemented a hyperspectral line imager (spectrograph) for on-line measurement of these diseases in wheat and barley in four fields in Bedfordshire, the UK. The % coverage was assessed based on two methods, namely, infield visual assessment (IVA) and photo interpretation assessment (PIA) based on 100-point grid overlaid RGB images. The spectral data and disease assessments were subjected to partial least squares regression (PLSR) analyses with leave-one-out cross-validation. Results showed that both diseases can be measured with similar accuracy, and that the performance is better in wheat, as compared to barley. For fusarium, it was found that PIA analysis was more accurate than IVA. The prediction accuracy obtained with PIA was classified as good to moderately accurate, since residual prediction deviation (RPD) values were 2.27 for wheat and 1.56 for barley, and R2 values were 0.82 and 0.61, respectively. Similar results were obtained for yellow rust but with IVA, where model performance was classified as moderately accurate in barley (RPD = 1.67, R2 = 0.72) and good in wheat (RPD = 2.19, R2 = 0.78). It is recommended to adopt the proposed approach to map yellow rust and fusarium head blight in wheat and barley.

Why it matches plant phenotyping methodsハイパースペクトルラインイメージャと画像評価・PLSRを用いて、作物上の病害被覆率を測定・検証する方法が研究の中心であり、植物病害状態の表現型計測に該当する。

abstractThis study implemented a hyperspectral line imager (spectrograph) for on-line measurement of these diseases in wheat and barley in four fields in Bedfordshire, the UK.

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery →

Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m2 are reached for the validation of predicted vs. observed dry biomass, while Willmott’s refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.

Why it matches plant phenotyping methods低コストのRGB画像とSfM/MVSによるCSMから作物バイオマスを推定する取得・解析手法を開発し、検証しており、表現型計測が研究の中心である。

abstractCrop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this.

Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection →

Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.

Why it matches plant phenotyping methods携帯型ハイパースペクトルカメラの導入、測定パイプライン、既存装置との性能比較を中心に扱い、植物の病害・ストレス状態を評価するため、植物フェノタイピング手法として適格。

abstractThe present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland).

Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study →

This paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions. Scanning of crop canopy in trays occurred between anthesis growth stage 60, and hard dough growth stage 87. Visual assessment was made at four levels, namely, at the head, at the flag leaves, at 2nd and 3rd leaves, and at the lower canopy. Partial least squares regression (PLSR) analyses were implemented separately on data captured at four growing stages to establish separate calibration models to predict the percentage coverage of yellow rust and fusarium head blight infection. Results showed that the standard deviation between 500 and 650 nm and the squared difference between 650 and 700 nm wavelengths were found to be significantly different between healthy and infected canopy particularly for yellow rust in both crops, whereas the effect of water-stress was generally found to be unimportant. The PLSR yellow rust models were of good prediction capability for 6 out of 8 growing stages, a very good prediction at early milk stage in wheat and a moderate prediction at the late milk development stage in barley. For fusarium, predictions were very good for seven growing stages and of good performance for anthesis growing stage in wheat, with best performing for the milk development stages. However, the root mean square error of predictions for yellow rust were almost half of those for fusarium, suggesting higher prediction accuracies for yellow rust measurement under laboratory conditions.

Why it matches plant phenotyping methods植物キャノピーの病害感染率をハイパースペクトル画像とPLSRで推定する手法を開発・較正・評価しており、病害表現型の取得が研究の中心である。

abstractThis paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions.

Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application →

With the increasing availability of spectral sensors and consumer-grade data processing software, a democratization of imaging spectroscopy is taking place. In particular, novel lightweight 2D spectral imagers in combination with UAVs are increasingly being adapted for imaging spectroscopy. In contrast to traditional line-scanners, these sensors capture spectral information as a 2D image within every exposure. With computer vision algorithms embedded in consumer grade software packages, these data can be processed to hyperspectral digital surface models that hold spectral and 3D spatial information in very high resolution. To understand the spectral signal, however, one must comprehend the complexity of the capturing and data processing process in imaging spectroscopy with 2D imagers.This study establishes the theoretical background to comprehend the properties of spectral data acquired with 2D imagers and investigates how different data processing schemes influence the data. To improve the interpretability of a spectral signal derived for an area of interest (AOI), the specific field of view is introduced as a concept to understand the composition of pixels and their angular properties used to characterize a specific AOI within a remote sensing scene.These considerations are applied to a multi-temporal field study carried out under different illumination conditions in a barley field phenotyping experiment. It is shown that data processing significantly affects the angular properties of the spectral data and influences the apparent spectral signature. The largest differences are found in the red domain, where the signal differs by approximately 10% relative to a single nadir image. Even larger differences of approximately 14% are found in comparison with ground-based non-imaging field spectrometer measurements. The differences are explained by investigating the interaction between the angular properties of the data and canopy anisotropy, which are wavelength and growth stage dependent. Additionally, it is shown that common vegetation indices cannot normalize the differences and that the retrieval of chlorophyll is affected. In conclusion, this study helps to understand the process of imaging spectroscopy with 2D imagers and provides recommendations for future missions.

Why it matches plant phenotyping methods2Dイメージング分光の取得・処理特性を理論的に検討し、処理手法がスペクトル、植生指数、クロロフィル推定に与える影響を圃場フェノタイピングで検証しており、植物形質取得法が中心です。

abstractThis study establishes the theoretical background to comprehend the properties of spectral data acquired with 2D imagers and investigates how different data processing schemes influence the data.

Application of Near Infrared Reflectance Spectroscopy for Rapid and Non-Destructive Discrimination of Hulled Barley, Naked Barley, and Wheat Contaminated with Fusarium. →

Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium . Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175-2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified.

Why it matches plant phenotyping methodsNIR分光とPLS-DAにより、穀粒のFusarium汚染状態を非破壊推定する手法を開発・検証しており、植物器官の病態測定が研究の中心である。

abstracta near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium

Detection and attribution of drought and oil-induced plant stress using hyperspectral remote sensing →

This study investigates the potential use of hyperspectral remote sensing in detecting stress in vegetation caused by both drought stress and hydrocarbon contamination of the soil. Spectral and plant biochemical measurements were taken from an in-situ field experiment and a glasshouse experiment at the James Hutton Institute, Invergowrie, U.K. The in-situ field experiment was part of an ongoing root restriction study of 10 barley genotypes, whilst the glasshouse experiment consisted of both drought and oil contamination of plants grown in pots. Two main biophysical parameters (chlorophyll and plant leaf water content) were calculated and spectral response measured with a spectroradiometer. Statistical analysis showed variation in spectral response between genotypes that were responding differently to treatments. The 1st derivative of reflectance at wavelength 705 nm, the ratio of the wavelength of the maximum 1st derivative reflectance at the green region (GEP) and the wavelength of maximum 1st derivative reflectance (REP) all showed a strong correlation with chlorophyll concentration. The ratio between normalized difference vegetation index NDVI (800,680) and the index GEP/REP was used to discriminate between ... (continues)

Why it matches plant phenotyping methods植物の乾燥・油汚染ストレスを対象に、ハイパースペクトル計測とスペクトル指標からクロロフィルおよび葉水分量を推定・識別する方法が中心であり、単なる生物学的実験の測定ではない。

titleDetection and attribution of drought and oil-induced plant stress using hyperspectral remote sensing

UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices →

In the context of plant breeding, high-throughput phenotyping is an assessment of plant phenotypes on a scale and with a level of speed and precision not achievable with traditional methods, through the application of emerging technologies such as automation and robotics, new sensors, and imaging technologies (hardware and software). In the present work, high-resolution digital images have been acquired with an unmanned aerial vehicle (UAV) prototype platform on an experimental phenotyping barley field. Six vegetation indices generated from the red–green–blue and near-infrared-based images were calculated for 912 experimental barley plots and provided high correlation with the indices determined from hyperspectral data taken at the ground (gt); the indices performance in discriminating the vigour of genotypes was finally assessed.

Why it matches plant phenotyping methodsUAV画像と可視・近赤外植生指数を用いてオオムギ遺伝子型の生育勢を識別し、地上ハイパースペクトル測定と比較検証する手法研究であり、表現型取得・抽出が中心である。

titleUAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices

High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness →

To optimize plant architecture (e.g., photosynthetic active leaf area, leaf-stem ratio), plant physiologists and plant breeders rely on destructively and tediously harvested biomass samples. A fast and non-destructive method for obtaining information about different plant organs could be vehicle-based spectral proximal sensing. In this 3-year study, the mobile phenotyping platform PhenoTrac 4 was used to compare the measurements from active and passive spectral proximal sensors of leaves, leaf sheaths, culms and ears of 34 spring barley cultivars at anthesis and dough ripeness. Published vegetation indices (VI), partial least square regression (PLSR) models and contour map analysis were compared to assess these traits. Contour maps are matrices consisting of coefficients of determination for all of the binary combinations of wavelengths and the biomass parameters. The PLSR models of leaves, leaf sheaths and culms showed strong correlations ( R 2 = 0.61-0.76). Published vegetation indices depicted similar coefficients of determination; however, their RMSEs were higher. No wavelength combination could be found by the contour map analysis to improve the results of the PLSR or published VIs. The best results were obtained for the dry weight and N uptake of leaves and culms. The PLSR models yielded satisfactory relationships for leaf sheaths at anthesis ( R 2 = 0.69), whereas only a low performance for all of sensors and methods was observed at dough ripeness. No relationships with ears were observed. Active and passive sensors performed comparably, with slight advantages observed for the passive spectrometer. The results indicate that tractor-based proximal sensing in combination with optimized spectral indices or PLSR models may represent a suitable tool for plant breeders to assess relevant morphological traits, allowing for a better understanding of plant architecture, which is closely linked to the physiological performance. Further validation of PLSR models is required in independent studies. Organ specific phenotyping represents a first step toward breeding by design.

Why it matches plant phenotyping methods車載型スペクトル近接センシングを用いてオオムギ器官の乾物重・窒素吸収量などを推定し、VI、PLSR、センサー性能を比較・検証した研究であり、表現型取得法が中心である。

abstractthe mobile phenotyping platform PhenoTrac 4 was used to compare the measurements from active and passive spectral proximal sensors

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley →

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

Why it matches plant phenotyping methodsUAV・地上センサーによる高スループット表現型取得と、FIJIを用いた画像解析・データ抽出を比較・実証しており、表現型測定ワークフローが研究の中心です。

abstractHere we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI

Mid-season prediction of grain yield and protein content of spring barley cultivars using high-throughput spectral sensing →

The ability to forecast grain yields and protein contents of spring barley is of particular interest for the malting and brewing industry, as well as for plant breeding. However, methods for early prediction of grain yield and protein content should ideally be timesaving, non-destructive and inexpensive. In this 3-year study using the mobile phenotyping platform PhenoTrac 4, proximally sensed reflectance data of 34 cultivars were used to develop vegetation indices and to calibrate PLSR models, followed by subsequent validation in independent field trials. A comparison among PLSR, the NDVI and REIP indices and an optimized vegetation index indicated that PLSR and REIP (R2=0.71-0.95) gave superior predictions of grain yield. Furthermore, it was possible to distinguish the performance of different cultivars. In contrast, protein content could not be predicted reliably. As an alternative, a PLSR model of leaf N uptake at anthesis was tested to predict grain protein content. Satisfactory correlations were obtained with R2=0.61, but protein content was considerably overestimated. The results show that tractor-based proximal sensing is a high-throughput, non-destructive and precise method to predict the grain yield of spring barley and could be a suitable tool to deliver information for the brewing industry and plant breeders.

Why it matches plant phenotyping methods移動式表現型プラットフォームによる近接分光センシングとPLSR・植生指数を開発し、収量およびタンパク質含量予測を独立圃場で検証しており、植物形質取得・推定法が研究の中心である。

abstractusing the mobile phenotyping platform PhenoTrac 4, proximally sensed reflectance data of 34 cultivars were used to develop vegetation indices and to calibrate PLSR models, followed by subsequent validation in independent field trials.

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis. →

The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium -infected hulled barley. Both normal hulled barley and Fusarium -infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium -infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium -infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method.

Why it matches plant phenotyping methodsNIR分光とPLS-DAを組み合わせ、感染した穀粒という植物状態を非破壊・迅速に識別する方法を開発・評価しており、測定手法が研究の中心である。

abstractThe purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium -infected hulled barley.

Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei →

Differences in early plant-pathogen interactions are mainly characterized by using destructive methods. Optical sensors are advanced techniques for phenotyping host-pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens. A microscope with a hyperspectral camera was used to study interactions between Blumeria graminis f. sp. hordei and barley (Hordeum vulgare) genotypes with high susceptibility or resistance due to hypersensitive response (HR) and papilla formation. Qualitative and quantitative assessment of pathogen development was used to explain changes in hyperspectral signatures. Within 48 h after inoculation, genotype-specific changes in the green and red range (500 to 690 nm) and a blue shift of the red-edge inflection point were observed. Manual analysis indicated resistance-specific reflectance patterns from 1 to 3 days after inoculation. These changes could be linked to host plant modifications depending on individual host-pathogen interactions. Retrospective analysis of hyperspectral images revealed spectral characteristics of HR against B. graminis f. sp. hordei. For early HR detection, an advanced data mining approach localized HR spots before they became visible on the RGB images derived from hyperspectral imaging. The link among processes during pathogenesis and host resistance to changes in hyperspectral signatures provide evidence that sensor-based phenotyping is suitable to advance time-consuming and cost-expensive visual rating of plant disease resistances.

Why it matches plant phenotyping methodsハイパースペクトル画像とデータマイニングにより、オオムギの病害抵抗性反応を早期検出・定量化するセンサーベース表現型計測法が中心である。

abstractOptical sensors are advanced techniques for phenotyping host-pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens.

A major QTL on chromosome 7HS controls the response of barley seedling to salt stress in the Nure × Tremois population. →

Background Seedling establishment is a crucial and vulnerable stage in the crop life cycle which determines further plant growth. While many studies are available on salt tolerance at the vegetative stage, the mechanisms and genetic bases of salt tolerance during seedling establishment have been poorly investigated. Here, a novel and accurate phenotyping protocol was applied to characterize the response of seedlings to salt stress in two barley cultivars (Nure and Tremois) and their double-haploid population. Results The combined phenotypic data and existing genetic map led to the identification of a new major QTL for root elongation under salt stress on chromosome 7HS, with the parent Nure carrying the favourable allele. Gene-based markers were developed from the rice syntenic genomic region to restrict the QTL interval to Bin2.1 of barley chromosome 7HS. Furthermore, doubled haploid lines with contrasting responses to salt stress revealed different root morphological responses to stress, with the susceptible genotypes exhibiting an overall reduction in root length and volume but an increase in root diameter and root hair density. Conclusions Salt tolerance at the seedling stage was studied in barley through a comprehensive phenotyping protocol that allowed the detection of a new major QTL on chromosome 7HS.

Why it matches plant phenotyping methods塩ストレス下の根形態を測定する新規・包括的なフェノタイピングプロトコルが研究の主要な技術的要素として明示されている。

abstracta novel and accurate phenotyping protocol was applied to characterize the response of seedlings to salt stress

An optimised clearing protocol for the quantitative assessment of sub-epidermal ovule tissues within whole cereal pistils. →

Background Seed development in the angiosperms requires the production of a female gametophyte (embryo sac) within the ovule. Many aspects of female reproductive development in cereal crops are yet to be described, largely due to the technical difficulty in obtaining phenotypic information at the cellular or sub-cellular level. Hoyer's solution is currently well established as a solution for clearing thin tissues samples, such as sections or whole tissues of bryophytes, mycorrhizal fungi, and small model organisms (e.g. Arabidopsis thaliana ). Results Here we report a Hoyer's solution-based clearing method to facilitate clearing of the whole barley pistil, with high reproducibility. The clearing process takes 10 days from fixation to visualisation, whereupon tissue is sufficiently clear to obtain multiple phenotypic measurements from sub-epidermal tissues and cells within the ovule. Conclusion Visualisation of cereal ovules that have not been dissected from the pistil allows an unprecedented capability to collect quantitative morphological information from the developing ovule, integument, nucellus and embryo sac. This will enable comparisons with genetic data to reveal the contribution of pre-fertilisation ovule tissues towards downstream seed development.

Why it matches plant phenotyping methods全体のオオムギ雌ずいを透明化し、胚珠組織の定量的形態情報を取得する手法の開発が中心であり、植物フェノタイピング手法に該当する。

abstractHere we report a Hoyer's solution-based clearing method to facilitate clearing of the whole barley pistil, with high reproducibility.

Long wavelength near-infrared transmission spectroscopy of barley seeds using a supercontinuum laser: Prediction of mixed-linkage beta-glucan content. →

A supercontinuum laser was used to perform the first transmission measurements on intact seeds with long wavelength near-infrared spectroscopy. A total of 105 barley seeds from five different barley genotypes (Bomi, lys5.f, lys5.g, lys16 and lys95) were measured from 2275 to 2375 nm. The mixed-linkage (1→3,1→4)-β-D-glucan (BG) and protein content was measured with wet chemical analysis for each single seed. A partial least squares model correlated the BG % (w/w) with the spectral measurements with a R 2 CV and R 2 PRED of 0.83 and 0.90, respectively. The predictive model for BG could be improved by averaging spectra from the same seed and by replacing the individual seed BG content with the average BG of each barley genotype.

Why it matches plant phenotyping methods大麦種子のβ-グルカン含量という植物器官形質を、近赤外透過分光とPLSモデルで非破壊推定する手法の開発・検証が研究の中心である。

abstractA supercontinuum laser was used to perform the first transmission measurements on intact seeds with long wavelength near-infrared spectroscopy.

Thermal phenotyping of stomatal sensitivity in spring barley →