Background Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops. Results High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured. Conclusions New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.
Why it matches plant phenotyping methods全粒FAME分析とGridFreeによる種子形状・サイズ・重量・個数の表現型取得を統合した高スループット手法を開発しており、表現型取得ワークフローが研究の中心である。
abstractIn combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis.
Due to their sessile nature, plants are unable to escape environmental factors that negatively impact health, resulting in losses to agricultural productivity. Rapid, non-invasive tools to detect plant stress response are essential for optimizing resource efficiency and mitigating the effects of extreme environmental pressures. However, many existing methods are either invasive, incompatible with other measurement techniques, or have not been applied to a wide range of varying environmental factors. In this study, we assess the physiological responses of four week old camelina (Camelina sativa) and sorghum (Sorghum bicolor) to chitosan, cold, drought, and both acute and chronic salt stress. Several plant characteristics were measured in parallel during stress exposure, including fluorescence and gas exchange parameters (MultispeQ and LI-6800), tissue electrical impedance with wearable biosensors (Multi-PIP), and biochemical properties via Fourier-transform infrared (FTIR) spectroscopy. We compiled unique profiles for whole plant physiological changes in response to environmental stress, demonstrating that certain aspects of plant health and makeup underwent alterations on differing temporal scales. This finding emphasizes the need for a comprehensive multi-modal approach to rapidly and accurately perform remote sensing of plant health in the field. Physiological parameters such as leaf impedance were also observed to rapidly change in response to treatment and can be leveraged to detect very early signs of plant perturbation. This research establishes the utility of a holistic phenotyping approach to inform agricultural strategies aimed at enhancing crop resilience under changing environmental conditions.
Why it matches plant phenotyping methods複数のセンサー・分光法を統合した非侵襲的な植物ストレス表現型取得と、マルチモーダル表現型解析の有用性が研究の中心である。
abstractRapid, non-invasive tools to detect plant stress response are essential
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
(Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.
Why it matches plant phenotyping methods蛍光・可視画像から種子の形態・色特徴を抽出し、塩ストレス由来の状態を判定するウェブポータルと機械学習手法が研究の中心であるため。
abstractautomated, non-destructive, image-based phenotyping to assess seed quality
Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.
Why it matches plant phenotyping methods葉のハイパースペクトルデータライブラリを構築し、機械学習による複数の葉形質推定と外部データへの転移性を評価することが研究の中心であり、表現型取得・推定手法の開発および検証に該当する。
titleA leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer
Integration of plant phenotyping and irrigation is particularly advantageous for identifying genetic variation associated with crop productivity. Collecting phenotypic data and water management under controlled or open environment can be expensive and laborious. This study aims to design a cost-effective solution for high-throughput phenotyping (HTP) and automated irrigation using open-source electronics. A portable HTP system was developed using a microcontroller and a single-board computer Raspberry Pi and was extended to include soil water monitoring and water pump control. An Arduino board was integrated with a multispectral camera, mini LiDAR sensors, infrared thermometers, soil moisture sensors, water pumps, and a temperature/humidity sensor. Sensor calibration and power management enhanced the accuracy and reliability of the system. Two genotypes (CAM212 and Giessen#4) of camelina were used to evaluate the system to measure phenotypic responses to abiotic stress in growth chambers under two temperatures (25 °C and 35 °C) and two water treatments (40% and 90% water holding capacity). The HTP system monitored 24 plants periodically, and data were wirelessly accessed by a smartphone and transferred to a computer for further analyses. The system revealed that camelina genotype 1 (CAM212) showed superior resistance to heat and drought stress. The results showed that the developed HTP system offers a cost-effective and portable solution for phenotyping and water management in controlled environment and can be modified for field applications.
Why it matches plant phenotyping methods植物表現型取得システムの開発と校正・評価が研究の中心であり、センサーを統合した高スループット表現型解析基盤を構築している。
abstractThis study aims to design a cost-effective solution for high-throughput phenotyping (HTP) and automated irrigation using open-source electronics.
Abstract Alternaria leaf spot (ALS) is a major disease of Brassica crops, and it causes huge economic losses to both the cultivated oilseed-and vegetable- Brassicas . The present study was aimed to develop a non-destructive robust method for screening of wild Brassica species and to find resistant wild species against Alternaria brassicicola (Schw.) Wiltsh available in the germplasm. For this, 38 wild Brassica species were screened at adult plant stage by an in vitro detached leaf inoculation method in three consecutive years i.e. 2019-20, 2020-21 and 2021-22. The new screening protocol provides favourable environment (temperature 25±2 °C; relative humidity >90%) for the pathogen and retained the host leaves in condition (by placing sucrose 5% w/v in petiolar base) for disease development. The consistency in reactions of the species against A. brassicicola during all three years of testing indicates the robustness of the protocol. Further, the multiple parameters were recorded on leaf condition and disease response of the wild species. Complete resistance was observed in Capsella while resistance in Lepidium , Camelina and Biscutella . Both Capsella bursa pastoris (L.) Medik (early) and C. bursa pastoris (late) were symptomless resistant. Camelina sativa (L.) Crantz, Diplotaxis erucoides (L.) DC and Diplotaxis gomez-campoi Mart.-Laborde were found to be resistant against A. brassicicola . The significant correlation between disease parameters indicates the robustness and effectiveness of the screening protocol for wild Brassica species.
Why it matches plant phenotyping methods野生Brassicaの病害抵抗性という植物状態を測定する非破壊的な葉接種スクリーニング法を開発し、複数年で頑健性・有効性を検証しているため、方法が中心的である。
abstractThe present study was aimed to develop a non-destructive robust method for screening of wild Brassica species
Climate change and environmental pollution will have a great impact on food security worldwide. More than 30% of the world's irrigated areas are estimated to be perturbed by high salinity affecting the productivity of crops. Camelina sativa, also known as false flax, is a flowering plant that is mainly cultivated as an oilseed crop that has many potential economic benefits; it can be used in food products, in industrial applications, and in animal feed and converted into biofuel. However, natural disasters due to climate events have led to significant crop losses. In this work, we developed a high-throughput phenotyping protocol to analyze the effects of different concentrations of salt on C. sativa using the McGill Plant Phenomics Platform (MP3). We present an adapted protocol to be applied with phenomics facilities in a greenhouse environment and the most effective way for high-throughput phenotyping.
Why it matches plant phenotyping methods塩ストレス実験を対象とするが、温室フェノミクス施設向けのハイスループット表現型解析プロトコルを開発・適応し、その適用方法を中心に記述しているため含める。
abstractIn this work, we developed a high-throughput phenotyping protocol to analyze the effects of different concentrations of salt on C. sativa using the McGill Plant Phenomics Platform (MP3).
A field experiment was conducted in Southern Greece to assess Normalized Difference Vegetation Index (NDVI) and Red-Edge Normalized Difference Vegetation Index (NDRE) in estimating Camelina’s crop growth and yield parameters under different tillage systems (conventional and minimum tillage) and organic fertilization types (compost, vermicompost and untreated control). A proximal canopy sensor was used to measure the aforementioned Spectral Vegetation Indices (SVIs) at different days after sowing (DAS). Camelina presented the highest values of NDVI and NDRE under compost fertilization (0.63 and 0.22 accordingly) and minimum tillage system (0.50 and 0.18 accordingly). Additionally, the highest correlations between the measured crop parameters and NDVI, NDRE were achieved at leaf development to early flowering stage. Moreover, NDRE presented the highest correlation with seed yield (R2=0.60, p
Why it matches plant phenotyping methods近接型キャノピーセンサーによるNDVI/NDREでCamelinaの生育・収量形質を推定し、各形質との相関を評価することが研究の中心であるため、センサー型表現型計測の実質的応用と判断する。
titleEvaluation of Organic Camelina Crop Under Different Tillage Systems and Fertilization Types Using Proximal Remote Sensing
Fast, non-destructive methods for determining the seed composition of Camelina sativa (L.) Crantz would be beneficial in evaluating germplasm for important agronomic traits. In this study, near infrared spectroscopy (NIRS) methods were developed and evaluated for conducting non-destructive, high throughput phenotyping of seed quality traits. Crude protein and total oil content for 85 accessions (63 summer- and 22 winter-biotypes) were first determined by established wet chemistry methodology; whereas, for fatty acid profiles 173 accessions (149 summer- and 24 winter-biotypes) were determined using Gas Chromatography (GC). The wet chemistry and GC data were used to develop NIRS calibration equations for each trait. Based on the wet chemistry data obtained from 85 accessions, mean crude protein content was significantly less in summer (300 g kg−1) than in winter (315 g kg−1) biotypes (P ≤ 0.05) and total oil was greater in seeds of summer (351 g kg−1) than that of winter (326 g kg−1) biotypes. Coefficient of determination (r2 = 0.979 and 0.894, respectively) and ratio of performance to deviation (RPD = 9.15 and 4.33, respectively) for crude protein and oil content indicated a high level of confidence for predicting these traits using NIRS. Evaluation of all 173 accessions by NIRS did not appreciably change the predicted mean crude protein content of summer- and winter-biotypes; however, it did change the predicted mean total oil content of summer biotypes (260 g kg−1), which was significantly less than predicted for winter biotypes (323 g kg−1). Fatty acids contents were not significantly different between summer- and winter-biotypes. The most abundant fatty acid was linolenic acid (18:3) ranging from 22.8 to 38.4%, followed by linoleic acid (18:2) at 15.2–27.1%, eicosenoic acid (20:1) at 11.6–18.2%, and oleic acid (18:1) at 9.1–22.1%. Calibration models for the main fatty acids oleic, linoleic, linolenic, and eicosenoic acids had r2 values of 0.718, 0.790, 0.828, and 0.586, respectively. Results of this study indicate that NIRS has potential as a non-destructive, high throughput method for determining quality traits of camelina seed.
Why it matches plant phenotyping methodsCamelina種子の品質形質をNIRSで非破壊・高速推定する校正モデルを開発・評価しており、形質取得法が研究の中心である。
abstractnear infrared spectroscopy (NIRS) methods were developed and evaluated for conducting non-destructive, high throughput phenotyping of seed quality traits
The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .
Why it matches plant phenotyping methods種子形態と放出粘液を画像から同時測定する高スループット手法およびImageJツールを開発・適用しており、植物表現型取得が研究の中心である。
abstractHere, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters.
Reproduction assets foundThe paper's MuSeeQ ImageJ macro (the authors' phenotyping analysis code) is explicitly stated to be freely available, with video tutorials, at the authors' dedicated public website http://MuSeeQ.free.fr, which appears in the allowed URLs.Code · publicThe script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .Open asset ↗MuSeeQ.free.frlines:1-73Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Determining seed quality parameters is an integral part of cultivar improvement and germplasm screening. However, quality tests are often time consuming, seed destructive, and can require large seed samples. This study describes the development of near-infrared spectroscopy (NIRS) calibrations to measure moisture, oil, fatty acid profile, nitrogen, glucosinolate, and chlorophyll content in six species from the Brassicaceae family. Rapeseed and similar oilseeds are potential feedstocks for producing hydrotreated renewable jet fuel. Screening samples with NIRS would allow cultivars with desirable characteristics to be quickly identified. A total of 367 samples of six species (Brassica napus, Brassica carinata, Brassica juncea, Brassica rapa, Sinapis alba, and Camelina sativa) were scanned with NIRS. Global calibrations for all six species were developed using modified partial least squares regression with reference values obtained through wet chemistry techniques. Comparing predicted values to reference data, the coefficients of determination (r2) and ratios of performance to deviation (RPD) varied, with some calibrations performing better than others. The calibration equations for seed oil content (r2=0.98, RPD=7.3) and nitrogen (r2=0.98, RPD=5.3) performed very well while the equations for seed moisture (r2=0.93, RPD=3.8) and total glucosinolate content (r2=0.92, RPD=2.3) were more qualitative. Large variation was observed for chlorophyll content (0–390mg/kg) so two calibration equations were developed, one for the higher and one for the lower range of values. When combined, these calibrations also showed very good performance (r2=0.99, RPD=14). The performance of the calibrations for the fatty acids was more varied, with some performing very well, such as the calibration for C18:3 (r2=0.99, RPD=9.9), and others, such as C22:0 (r2=0.69, RPD=1.9), showing poor correlation.
Why it matches plant phenotyping methodsBrassicaceae種子の品質形質をNIRSで非破壊推定する校正モデルを開発し、湿式化学分析との比較で性能検証しており、形質取得法が研究の中心である。
abstractThis study describes the development of near-infrared spectroscopy (NIRS) calibrations to measure moisture, oil, fatty acid profile, nitrogen, glucosinolate, and chlorophyll content in six species from the Brassicaceae family.