Understanding elemental distributions in plants is critical for agricultural productivity, nutritional quality, and limiting the transfer of toxic elements into the food chain. Conventional elemental mapping techniques typically require thin sectioning or complex tomographic reconstructions, making three-dimensional analysis labor-intensive and destructive. Here, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1]. This method defines a localized 3D detection volume within the sample, enabling the generation of elemental virtual cross-sections without physical sectioning while preserving native spatial relationships. We demonstrate the capability of this technique by mapping Fe distributions in carrot (Daucus carota) roots and shoots and Cd distributions in root tips of near-isogenic wheat (Triticum aestivum) lines. Integration of multiple virtual sections enabled three-dimensional reconstructions that reveal distinct Cd translocation pathways between accumulating and non-accumulating wheat lines, tracing elemental movement from the epidermis through cortical layers into vascular tissues. The method is applicable to diverse plant morphologies, including cylindrical roots and irregular leaf and stem tissues. This approach enables high-sensitivity, non-destructive 3D elemental imaging, providing a powerful tool for studying elemental transport in plants with direct relevance to crop breeding, food safety, and agricultural sustainability [2].
Why it matches plant phenotyping methods植物組織内の元素分布という生理状態を、非破壊3D XRFで取得・再構成する手法の開発と植物試料での実証が中心である。
abstractHere, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1].
Objective This study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence. Methods Six commercial varieties, including orange, yellow, white, and purple genotypes, were evaluated under field and laboratory conditions using multispectral drone imagery, high-resolution spectroradiometric signatures, red green blue (RGB) images, and CIELAB color measurements. A hierarchical modeling framework was developed across two phases: (i) spectral modeling using uncrewed aerial vehicle (UAV)-based multispectral indices, textural and geometric metrics, and laboratory-generated hyperspectral signatures; and (ii) a colorimetric index from RGB images. Results Using UAV-based multispectral field data, phenological prediction indices achieved high classification performance (F1-scores > 0.90) when modeled with a Random Forest classifier, supported by distinct spectral signatures associated with canopy development and senescence. In parallel, carotenoid content estimation using a Random Forest regression model demonstrated strong predictive accuracy ( R 2 = 0.897; RMSE = 0.584), with the Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI) identified as the most influential predictors. A complementary laboratory-based Random Forest regression model using high-resolution spectral signatures achieved near-perfect predictive performance ( R 2 = 0.987). SHapley Additive exPlanations (SHAP) analysis identified physiologically relevant wavelengths in the green (540-550 nm) and red-edge (∼700 nm) regions as the primary drivers of carotenoid concentration. Likewise, a novel colorimetric index (ICarot), derived from CIELAB parameters, enabled accurate image-based carotenoid estimation ( R 2 = 0.85). Conclusion This study introduces an innovative multi-sensor framework for precision agriculture and automated postharvest quality control, enabling rapid, objective, and scalable phenotyping in carrot production systems. Through the integration of spectral, colorimetric, and AI-based approaches, the proposed methodology effectively captures both internal nutritional attributes and external quality traits within a unified, non-destructive assessment pipeline.
Why it matches plant phenotyping methods複数センサー画像・分光計測とAIを統合し、ニンジンの生育段階およびカロテノイド含量を非破壊推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence.
Reproduction assets foundThe paper's Data Availability section explicitly deposits the study's data (and project materials) on GitHub and Zenodo, both with authors' public URLs matching allowed_urls. These qualify as paper-specific public assets for the carrot phenotyping measurements and analysis.Dataset · publicThe data is available at GitHub and Zenodo:
- https://github.com/agrocompuepidemlab/Carrot-value-chain-proyect/tree/mainOpen asset ↗github.com/agrocompuepidemlab/Carrot-value-chain-proyectlines:184-307Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Colorimetric analysis of food using the CIELab/Ch colour space (i.e., from digital images of samples) is an accessible, non-destructive method for carotenoid and anthocyanin content prediction. Literature presents very well-fit, but rudimentary, models for pigment estimation (e.g., single/multiple linear regressions). However, standardised methods that statistically account for the high multicollinearity between CIELab/Ch colour parameters, varying light conditions and colour calibration, and samples with high genotypic variability are lacking. Methods An image analysis optimisation was developed for the prediction of carotenoid and anthocyanin content of 16 carrot genotypes of different colours. Samples were photographed under six light conditions with a digital camera and image colour was calibrated before analysis with the CIELab/Ch colour space. Total pigment contents and individual carotenoid contents were analysed chemically via spectrophotometry and high-performance liquid chromatography, respectively. Partial least squares (PLS) regressions were used to assess the colour-pigment relationships to correct for high multicollinearity amongst the independent variables (CIELab/Ch colour parameters). Results/discussion The PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions. The two models are suggested as robust approaches to total pigment prediction with multi-dimensional colour spaces, varying light conditions, and for a sample group of high genotypic variability. The carrot samples proved to have very high genetic diversity within each cultivar, resulting in unsatisfactory models for prediction of individual carotenoids ( ca. R 2 = 0.45) under the default light condition. However, all the results can be used to expand databases (towards artificial intelligence) and aid breeding programmes in search for higher concentrations of these interesting antioxidants for human health.
Why it matches plant phenotyping methodsニンジン試料の画像色解析を最適化し、化学分析値を用いてカロテノイド・アントシアニン含量を予測する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractThe PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions.
Reproduction assets foundThe authors deposited the paper's data and protocols in public repositories (DOI links in the Data availability statement). The anthocyanin quantification protocol is explicitly linked (10.34894/BTPTSV), and the other two DOIs (10.34894/P37WCL, 10.34894/OUURRH) are stated to hold the paper's data. No separate author's'Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/P37WCLlines:641-686Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/OUURRHlines:641-686Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published16 Apr 20262026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)Cited by 0 · OpenAlex ↗
The productivity and sustainability of agriculture depend on the early diagnosis of plant diseases, particularly for root crops such as potatoes, tomatoes, and carrots. The hybrid deep model proposed in this study employs a Convolutional Neural Network (CNN) architecture to provide precise and realtime multi-categorization of several leafy tuber crop diseases. A complete dataset of 20,657 labeled photos from 16 diseases was used to train the model, and regular classes were employed. Our goal is to build a scalable deep learning model that can diagnose multiple tuber crop diseases in real time, reduce the need for manual surveys through a cost-effective web tool, and support farmers with early detection for smarter and more sustainable crop management. The proposed CNN architecture, which uses convolutional, pooling, and fully connected layers that are modified by the Adam optimizer, was developed using TensorFlow and Keras. The constructed model was highly successful in detecting widespread illnesses such as early blight, late blight, and other tomato and carrot leaf diseases, as demonstrated by its 92.2% total accuracy rate. Being developed as a web application later on, the system gave farmers and agri-parties an efficient and economical diagnosis tool. Based on knowledge, early detection of disease, lower reliance on manual surveys, and crop management decisions, this work encourages precision agriculture.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractThe hybrid deep model proposed in this study employs a Convolutional Neural Network (CNN) architecture to provide precise and realtime multi-categorization of several leafy tuber crop diseases.
Published1 Jun 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Carrots are a widely consumed vegetable around the world. They come in several varieties, each with their distinct quality traits. This study uses hyperspectral imaging to identify and predict soluble solids content (SSC) in three carrot cultivars using machine learning-based spectral analysis. After preprocessing the raw spectra, we constructed a partial least squares regression (PLSR) model to predict SSC using the preprocessed full spectra. iPLS and competitive adaptive reweighted sampling (CARS) methods were compared for variable selection. To differentiate carrots from three orchards, we developed four classification models: CARS-LDA, iPLS-LDA, iPLS-KNN, and CARS-KNN. Results indicated that both CARS-LDA and iPLS achieved effective wavelength selection for the Fuzhou A carrot cultivar, with the CARS-LDA model demonstrating the highest predictive capability, reflected by a relative prediction deviation (RPD) value of 2.72. In terms of accuracy, specificity, sensitivity, and precision, the CARS-KNN model was the best. This study underscores the potential of hyperspectral imaging coupled with machine learning methodologies to reliably predict and distinguish between various carrot cultivars, thereby contributing significantly to improvements in food safety and quality control standards in the agricultural sector.
Why it matches plant phenotyping methodsニンジンの可溶性固形分という器官形質をハイパースペクトル画像と機械学習で推定し、品種識別モデルも比較しており、表現型取得・抽出手法が中心である。
abstractThis study uses hyperspectral imaging to identify and predict soluble solids content (SSC) in three carrot cultivars using machine learning-based spectral analysis.
Abstract Background Seed quality analysis using X-rays is increasingly explored due to its invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of soft X-rays on a wide range of plant materials. Results The baseline of three germination categories was established across seven species before the application of soft X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of X-rays. A slight stimulative effect was observed on most of the species (bean, carrot, fennel, maize, radish, and ryegrass), notably, with a repeated reduction in ungerminated seeds. Intrinsic physical quality holds a crucial value where the minor negative impact observed in soybean originated from its degraded physical quality and not from X-ray exposure, hence, no destructive effects were detected. To understand whether seed size plays a significant role in a seed's response to exposure, linear regression models were built to predict 3D seed traits (volume) from 2D X-ray images. Yet, seed size did not explain the variation in responses to soft X-rays. However, the average density of the seven species explained both their natural germination ( p p Conclusion Soft X-ray exposure is non-destructive with a beneficial effect on germination but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This study adopted internationally-standardised germination procedures and tested the effect of soft X-rays across diverse botanical, genetic and seed quality profiles. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.
Why it matches plant phenotyping methods軟X線ラジオグラフィーによる非破壊的な種子品質・3D形質推定プロトコルの検討と検証が中心的に含まれており、単なる発芽試験ではない。
abstractSeed quality analysis using X-rays is increasingly explored due to its invasive and rapid nature.
In this study, we explore spectral heterogeneity within plant canopies, a characteristic often observed in stressed plants where certain leaves or intra-leaf regions exhibit stress symptoms while others remain unaffected. Considering this variability in spectral signatures holds promise for enhancing remote sensing methodologies aimed at plant stress detection. Typically, remote sensing techniques analyze the plant as a whole, potentially overlooking stress-related spectral signatures due to the inclusion of unaffected pixels. We used a clustering-based technique, which incorporates semi-supervised learning elements for tuning hyper-parameters, to differentiate spectral patterns associated with and unique to pixels from broomrape-infected (Orobanche spp. and Phelipanche spp.) carrots from unrelated patterns. Ground-based hyperspectral (400–1000 nm) images of broomrape-infected and non-infected carrot canopies were used in an agglomerative clustering procedure followed by spectral angle mapper (SAM) analysis to identify a spectral endmember indicative of broomrape infection symptoms. Pixels from this cluster constituted an average of 8.5–11.5 % from the canopies of infected plants. Subsequently, we: (a) examined the relationship between carrot leaf mineral content and the percentage of symptomatic pixels to explore stress-induced alterations creating the unique spectral signatures of infected plants; and (b) utilized the inverse mode of PROSPECT, a radiative transfer model (RTM), to derive primary plant traits from the distinct spectral data of each cluster. We found that deficits in two macro elements, phosphorous and potassium, along with two pigments, chlorophyll and carotenoid, were correlated with the symptomatic cluster in infected plants. The methodology presented in this study paves the way for further research into broomrape detection in various crop species, as well as other plant stressors.
Why it matches plant phenotyping methods感染症状を示す植物画素のスペクトルクラスタリングと、そこから植物形質を推定する手法が研究の中心であり、単なる病害実験での測定ではない。
abstractWe used a clustering-based technique, which incorporates semi-supervised learning elements for tuning hyper-parameters, to differentiate spectral patterns associated with and unique to pixels from broomrape-infected (Orobanche spp. and Phelipanche spp.) carrots from unrelated patterns.
The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis and three commercial entries. The entries were evaluated agronomically and two flights with Remotely Piloted Aircraft (RPA) were conducted. Clustering was performed to validate the existence of genetic variability among the entries using an artificial neural network to produce a Kohonen’s self-organizing map. The genotype–ideotype distance index was used to verify the best entries. Genetic variability among the tropical carrot entries was evidenced by the formation of six groups. The Brightness Index (BI), Primary Colors Hue Index (HI), Overall Hue Index (HUE), Normalized Green Red Difference Index (NGRDI), Soil Color Index (SCI), and Visible Atmospherically Resistant Index (VARI), as well as the calculated areas of marketable, unmarketable, and total roots, were correlated with agronomic characters, including leaf blight severity and root yield. This indicates that tropical carrot materials can be indirectly evaluated via remote sensing. Ten entries were selected using the genotype–ideotype distance (2, 15, 16, 22, 34, 37, 39, 51, 52, and 53), confirming the superiority of the entries.
Why it matches plant phenotyping methodsRGBセンサーとRPA画像を用いた画像フェノタイピングの検証・適用が研究の中心であり、画像指標や根面積から植物形質を推定している。
abstractvalidate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms
Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha−1 (R2 ≥ 0.82 with Scenario 5) to 26.2 t ha−1 (R2 ≤ 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha−1 (i.e., 12.4%) and 11.4 t ha−1 (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production.
Why it matches plant phenotyping methodsSentinel-2画像とランダムフォレストを用いて、ニンジンの収量という植物・作物形質を推定する手法を開発・評価し、最適なモニタリング時期も検証しているため、手法が中心的です。
abstractTo monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data.
Accurate, non-destructive forecasting of carrot yield is difficult due to its subterranean growing habit. Furthermore, the timing of forecasting usually occurs when the crop is mature, limiting the opportunity to implement alternative management decisions to improve yield (during the growing season). This study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches. Using Sentinel-2 satellite imagery in three Australian vegetable regions, we established a time series of carrot phenological stages (PhS) from ‘days after sowing’ (DAS) to enhance prediction timing. Numerous vegetation indices (VIs) were analyzed to derive temporal growth patterns. Correlations with yield at different PhS were established. Although the average root yield (t ha⁻¹) did not significantly differ across the regions, the temporal VI signatures, indicating different regional crop growth trends, did vary as well as the PhS at when the maximum correlation with yield occurred (PhSR2max) with two of the regions producing a delayed PhSR2max (i.e. 90–130 DAS). The best multivariate model was identified at 70 DAS, extending the forecasting window before harvest between 20 to 60 days. The performance of this model was validated with new crops producing an average error of 16.9 t ha⁻¹ (27% of total yield). These results demonstrate the potential of the model at such early stage under varying growing conditions offering growers and stakeholders the chance to optimize farming practices, make informed decisions on selling, harvesting, and labor planning, and adopt precision agriculture methods.
Why it matches plant phenotyping methodsSentinel-2時系列画像と植生指数からニンジンの根収量を推定する予測手法を開発し、新規作物で性能検証しており、植物形質の取得・推定が研究の中心である。
abstractThis study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches.
Uprooting caused by flood events is a significant disturbance factor that affects the establishment, growth, and mortality of riparian vegetation. If the hydraulic drag force acting on riparian plants exceeds the peak uprooting force originate from their below-ground portion, it may result in the uprooting of these plants. Despite previous studies have documented and investigated the uprooting processes and factors influencing the peak uprooting force of plants, most of these studies have focused on how the root morphological traits of tree and shrub seedlings affect peak uprooting force or mainly collected data in indoor experiments, which may limit the extrapolation of the results to natural environments. To address these limitations, we assume that the peak uprooting force can be estimated by the morphological traits of the above-ground portion of the vegetation. In this study, we conducted in-situ vertical uprooting tests on three locally dominant species: Conyza canadensis , Daucus carota , and Leonurus sibiricus , in a typical riverine environment. The three species were found to have the highest abundance based on the outcomes of the quadrat method. We measured the peak uprooting force, plant height, stem basal diameter, shoot and root wet biomass, and shoot and root dry biomass of each plant and compared them between species. Furthermore, we quantified the influence of morphology on peak uprooting force. Our results showed significant differences in morphological traits and peak uprooting force among the three species. We found a significant positive correlation between peak uprooting force and the morphological traits of the three species. The peak uprooting force increases with plant size following a power law function which is analogous to allometric equations. The allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants, which may help to overcome the limitations of measuring root morphological traits.
Why it matches plant phenotyping methods植物の地上部形態から地下部に由来する最大引抜抵抗力を推定する非破壊的なアロメトリック手法を開発・提示しており、形質取得・推定が研究の中心である。
abstractThe allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.6476708) containing the datasets from this study (in-situ uprooting tests and morphological trait measurements of three riparian species). This is a paper-specific, publicly accessible phenotype dataset. No author analysis代码或补Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accessionnumber(s) can be found below: https://doi.org/10.5281/zenodo.6476708 .Open asset ↗zenodo · 10.5281/zenodo.6476708lines:667-709Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Currently, the concept of plant capture efficiency is not quantitatively considered in the evaluation of off-target drift for the purposes of pesticide risk assessment in the United States. For on-target pesticide applications, canopy capture efficiency is managed by optimizing formulations or tank-mixing with adjuvants to maximize retention of spray droplets. These efforts take into consideration the fact that plant species have diverse morphology and surface characteristics, and as such will retain varying levels of applied pesticides. This work aims to combine plant surface wettability potential, spray droplet characteristics, and plant morphology into describing the plant capture efficiency of drifted spray droplets. In this study, we used wind tunnel experiments and individual plants grown to 10-20 cm to show that at two downwind distances and with two distinct nozzles capture efficiency for sunflower (Helianthus annuus L.), lettuce (Lactuca sativa L.), and tomato (Solanum lycopersicum L.) is consistently higher than rice (Oryza sativa L.), peas (Pisum sativum L). and onions (Allium cepa L.), with carrots (Daucus carota L.) showing high variability and falling between the two groups. We also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants. The mean simulated drift capture efficiency rates were within the same order of magnitude of the mean observed rates of sunflower and lettuce, and differed by one to two orders for rice and onion. We identify simulating the effects of surface roughness on droplet behavior, and the effects of wind flow on plant movement as potential model improvements requiring further species-specific data collection.
Why it matches plant phenotyping methods植物の形態をフォトグラメトリで3次元モデル化し、ドリフト散布液の植物捕捉効率を推定・検証する手法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採用する。
abstractWe also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants.
Root biomass is one of the most relevant root parameters for studies of plant response to environmental change. In this work, a dynamic and adjustable electrode array sensor system is designed for developing a cost-effective, high-speed data acquisition system based on electrical impedance tomography (EIT). The developed EIT system is found to be suitable for in situ measurements and capable of monitoring the changes in root growth and development with three-dimensional imaging by measuring impedances in multiple frequencies with the help of an EIT sensor. The designed EIT sensor system is assessed and calibrated by the inhomogeneities in both water and soil media. The impedances are measured for multiple tap roots using an electrical impedance spectroscopy (EIS) tool connected to the sensor at frequencies ranging from 1 kHz to 100 kHz. The changes in conductivity are calculated by obtaining the boundary voltages from the measured impedances for a given stimulation current. A non-invasive imaging method is utilized, and the spectral changes are observed accordingly to evaluate the growth of the roots. A further root analysis helps us estimate the root biomass non-destructively in real-time. The root size (such as, weight, length) is correlated with the measured impedances. A regression analysis is performed using the least square method, and more than 97% correlation is found for the biomass estimation of carrot roots with an RMSE of 4.516. The obtained models are later validated using a new and separate set of carrot root samples and the accuracy of the predicted models is found to be 93% or above. A complete electrode model is utilized, and the reconstruction analysis is performed and optimized by utilizing the impedance imaging technique in difference method. The tomography of the root is reconstructed with finite element method (FEM) modeling considering one-step Gauss-Newton (GN) algorithm which is carried out using an open source software known as electrical impedance and diffuse optical tomography reconstruction software (EIDORS).
Why it matches plant phenotyping methodsEITセンサーと画像再構成を開発し、根の成長・バイオマスを非破壊推定する手法を検証しており、植物表現型取得が研究の中心です。
abstracta dynamic and adjustable electrode array sensor system is designed for developing a cost-effective, high-speed data acquisition system based on electrical impedance tomography (EIT)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 8 Sept 2026
CarrotRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.
Why it matches plant phenotyping methods根のサイズ・本数を非破壊で推定する超広帯域マイクロ波画像化法を開発し、数値モデルと実験で検証しており、植物表現型取得法が研究の中心である。
abstractwe propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively.
In radioecological studies, there is a significant need for understanding the plant uptake of radionuclides on a cellular level. The present work applies mass spectrometry to image the radionuclide distribution within the cellular structures of plants at varying concentrations. In a first step, plants of Daucus carota and Pisum sativum labelled with iodine and rhenium were examined, at concentrations in the range of 10 mM. Cross sections of several plant parts were imaged by secondary ion mass spectrometry (SIMS) after cryogenation in order to preserve cell structure. In a second step, the distribution of 99 Tc in the two plant species was determined. For radiological reasons, a concentration three orders of magnitude lower was used, rendering measurements with SIMS impossible. Therefore, resonant laser secondary neutral mass spectrometry (rL-SNMS) was used for the first time to image 99 Tc with suppression of molecular isobaric interferences. The measurement of only about 10 10 atoms of 99 Tc atoms is demonstrated and the distribution of 99 Tc within a single epidermal cell is imaged.
Why it matches plant phenotyping methods植物細胞内の放射性核種分布・取り込み状態を画像化するSIMS/rL-SNMS手法が研究の中心であり、99Tcの高感度イメージングを技術的に実証している。
abstractThe present work applies mass spectrometry to image the radionuclide distribution within the cellular structures of plants at varying concentrations.
CarrotRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
), which ranges from long and tapered to short and blunt, has been used for at least several centuries to classify carrot cultivars. The subjectivity involved in determining market class hinders the establishment of metric-based standards and is ill-suited to dissecting the genetic basis of such quantitative phenotypes. Advances in digital image acquisition and analysis has enabled new methods for quantifying sizes of plant structures and shapes, but in order to dissect the genetic control of the shape features that define market class in carrot, a tool is required that quantifies the specific shape features used by humans in distinguishing between classes. This study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature. This latter approach is of particular interest, as it enabled the detection of a novel and significant quantitative trait, defined here as root fill, which accounts for 85% of the variation in root shape. Curvature analysis was demonstrated to be an effective method for precise measurement of the broadness of the carrot shoulder, and degree of tip fill; the first principal component of the respective curvature profiles captured 87% and 84% of the total variance. This platform's performance was validated in two experimental panels. First, a diverse, global collection of germplasm was used to assess its capacity to identify market classes through clustering analysis. Second, a diallel mating design between inbred breeding lines of differing market classes was used to estimate the heritability of the key phenotypes that define market class, which revealed significant variation in the narrow-sense heritability of size and shape traits, ranging from 0.14 for total root size, to 0.84 for aspect ratio. These results demonstrate the value of high-throughput digital phenotyping in characterizing the genetic control of complex quantitative phenotypes.
Why it matches plant phenotyping methodsニンジン根形状の画像取得・輪郭解析・曲率解析を行うデジタル表現型解析プラットフォームを開発し、複数パネルで性能検証しており、表現型取得手法が研究の中心である。
abstractThis study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature.
Reproduction assets foundThe paper explicitly provides two public author repositories containing the phenotyping analysis code: a Python image-acquisition/mask-generation platform and MATLAB algorithms for mask straightening and contour/curvature PCA. No standalone phenotype dataset deposit is stated; the supplementary material link is genericCode · publicAs such, this metric ranges from 0 (in the case of all variance being attributed to SCA) to 1 (in the case of all variance being attributed to GCA) ( Baker, 1978 ).
Software Availability
Python code for the image acquisition platform and scripts for producing binary masks are available at: https://github.com/shbrainard/carrot-phenotyping . MATLAB algorithms for straightening binary masks and performing PCA on contours or curvature values are available at: https://github.com/jbustamante35/carrotsweeper .
Results
Accuracy of Image-Derived Phenotypes
Prior to a rigorous evaluation of any experimental populations, it is critical to confirm that a newly developed phOpen asset ↗https://github.com/shbrainard/carrot-phenotypinglines:75-85Code · publicttributed to GCA) ( Baker, 1978 ).
Software Availability
Python code for the image acquisition platform and scripts for producing binary masks are available at: https://github.com/shbrainard/carrot-phenotyping . MATLAB algorithms for straightening binary masks and performing PCA on contours or curvature values are available at: https://github.com/jbustamante35/carrotsweeper .
Results
Accuracy of Image-Derived Phenotypes
Prior to a rigorous evaluation of any experimental populations, it is critical to confirm that a newly developed phenotyping platform produces accurate and reliable phenotypes. Scatter plots of the root phenotypes obtained from digital images vs. hand measurements confirms thOpen asset ↗https://github.com/jbustamante35/carrotsweeperlines:75-85Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.
Why it matches plant phenotyping methods種子の生理的品質・活力を推定する蛍光およびマルチスペクトル画像法を開発・比較・検証しており、植物表現型取得が研究の中心です。
abstractIn this research, a novel approach for seed quality prediction is presented.
Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R² < 0.1) than similar measures from the multispectral sensors (R² < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions.
Why it matches plant phenotyping methods近接ハイパースペクトルおよび衛星マルチスペクトルによるニンジン根収量推定を比較・検証し、予測精度を評価しているため、センサー型植物表現型計測が中心である。
abstractThis study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield
RobHortic is a remote-controlled field robot that has been developed for inspecting the presence of pests and diseases in horticultural crops using proximal sensing. The robot is equipped with colour, multispectral, and hyperspectral (400–1000 nm) cameras, located looking at the ground (towards the plants). To prevent the negative influence of direct sunlight, the scene was illuminated by four halogen lamps and protected from natural light using a tarp. A GNSS (Global Navigation Satellite System) was used to geolocate the images of the field. All sensors were connected to an on-board industrial computer. The software developed specifically for this application captured the signal from an encoder, which was connected to the motor, to synchronise the acquisition of the images with the advance of the robot. Upon receiving the signal, the cameras are triggered, and the captured images are stored along with the GNSS data. The robot has been developed and tested over three campaigns in carrot fields for the detection of plants infected with ‘Candidatus Liberibacter solanacearum’. The first two years were spent creating and tuning the robot and sensors, and data capture and geolocation were tested. In the third year, tests were carried out to detect asymptomatic infected plants. As a reference, plants were analysed by molecular analysis using a specific real-time Polymerase Chain Reaction (PCR), to determine the presence of the target bacterium and compare the results with the data obtained by the robot. Both laboratory and field tests were done. The highest match was obtained using Partial Least Squares-Discriminant Analysis PLS-DA, with a 66.4% detection rate for images obtained in the laboratory and 59.8% for images obtained in the field.
Why it matches plant phenotyping methods植物の病害状態を近接画像・マルチスペクトル／ハイパースペクトルセンシングで検出するロボットと解析手法の開発・検証が中心であり、単なる病害試験ではない。
abstracta remote-controlled field robot that has been developed for inspecting the presence of pests and diseases in horticultural crops using proximal sensing
Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
Why it matches plant phenotyping methodsニンジンのシュート・根の形態を画像から自動抽出する解析プラットフォームを開発し、手動測定との相関や反復性を検証しており、表現型取得手法が研究の中心である。
abstractwe developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis scripts on GitHub and the carrot images plus unfiltered F2 SNP calls on FigShare, both with public URLs.Code · publicScripts for data processing, visualization, and QTL mapping are available on GitHub at https://github.com/mishaploid/carrot-image-analysis .Open asset ↗mishaploid/carrot-image-analysislines:960-975Dataset · publicUnfiltered SNPs from the F 2 mapping population (variant call format) and images are deposited on FigShare at https://doi.org/10.6084/m9.figshare.c.4300439.v1 .Open asset ↗10.6084/m9.figshare.c.4300439.v1lines:960-975Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
Why it matches plant phenotyping methodsニンジンのシュート・根の形態形質を画像から自動抽出する高スループット解析基盤を開発し、手動測定との相関や反復性で検証しているため、表現型取得法が研究の中心である。
abstractwe developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots
Reproduction assets foundThe paper's Data Availability statement provides public, paper-specific assets: carrot plant images via a CyVerse download link, and authors' scripts for data processing, visualization, and QTL mapping on GitHub. Both are directly tied to this paper's phenotyping measurements and analysis.Dataset · publicAutomated image analysis for genetic studies of carrot shoot and root shape
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5 Data Availability
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All images, scripts, and sequence data used in this study are publicly available. Images are available
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at https://de.cyverse.org/dl/d/2F1B4398-9D2E-4BF4-BFFF-65F507DB6865/sampleCarrotImages.zip
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and will also be deposited in the Dryad digital repository (https://datadryad.org/). Custom algorithms
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for image analysis are accessible on CyVerse as part of the PhytoMorph ToolKit. Scripts for data
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processing, visualization, and QTL mapping are available on GitHub at
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https://github.com/mishaploid/carrot-image-Open asset ↗CyVersepdf-raw-page:14 lines:1-65Code · publicF4-BFFF-65F507DB6865/sampleCarrotImages.zip
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and will also be deposited in the Dryad digital repository (https://datadryad.org/). Custom algorithms
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for image analysis are accessible on CyVerse as part of the PhytoMorph ToolKit. Scripts for data
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processing, visualization, and QTL mapping are available on GitHub at
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https://github.com/mishaploid/carrot-image-analysis. SNPs from the F2 mapping population will be
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deposited as VCF files on FigShare.
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6 Conflict of Interest
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The authors declare that the research was conducted in the absence of any commercial or financial
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relationships that could be construed as a potential conflict of interest.
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7 Author ContributionOpen asset ↗GitHub · mishaploid/carrot-image-analysispdf-raw-page:14 lines:1-65Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
In order to detect the redox states of fruits and vegetables simply, a SERS (surface-enhanced Raman scattering) method was developed based on a redox-sensitive probe and a pH-sensitive probe. The two probes were dropped onto the flesh of the fresh incision of fruits and vegetables, respectively. The SERS spectra of redox-sensitive probes were used to indicate their redox states, and the SERS spectra of pH-sensitive probes were used to indicate their pH values to discount the influence of pH on the redox states. The order of redox states (redox potential) of the detected fruits and vegetables is as follows: carrot < Green delicious apple < Xinjiang kuerle fragrant pear < Chinese royal pear < Fuji apple < crystal pear < Golden marshall apple < potato. Compared with traditional methods based on the detection of extracts, the developed method is simple without any pretreatments and consumption of additional chemicals, which would become a popular evaluation methodology of the redox states of fruits and vegetables during their growth and storage stages.
Why it matches plant phenotyping methods果実・野菜の組織に対するSERSで酸化還元状態とpHを非破壊的に評価する手法を開発しており、植物器官の生理状態取得が研究の中心である。
abstracta SERS (surface-enhanced Raman scattering) method was developed based on a redox-sensitive probe and a pH-sensitive probe.
Three non-destructive and complementary techniques, Raman imaging, Atomic Force Microscopy and Scanning Near-field Optical Microscopy were used simultaneously to show for the first time chemical and structural differences of carotenoid crystals. Spectroscopic and microscopic scanning probe measurements were applied to the released crystals or to crystals accumulated in a unique, carotenoids rich callus tissue growing in vitro that is considered as a new model system for plant carotenoid research. Three distinct morphological crystal types of various carotenoid composition were identified, a needle-like, rhomboidal and helical. Raman imaging using 532 and 488 nm excitation lines provided evidence that the needle-like and rhomboidal crystals had similar carotenoid composition and that they were composed mainly of β-carotene accompanied by α-carotene. However, the presence of α-carotene was not identified in the helical crystals, which had the characteristic spatial structure. AFM measurements of crystals identified by Raman imaging revealed the crystal topography and showed the needle-like and rhomboidal crystals were planar but they differed in all three dimensions. Combining SNOM and Raman imaging enabled indication of carotenoid rich structures and visualised their distribution in the cell. The morphology of identified subcellular structures was characteristic for crystalline, membraneous and tubular chromoplasts that are plant organelles responsible for carotenoid accumulation in cells.
Why it matches plant phenotyping methods植物細胞内のカロテノイド結晶について、Raman imaging、AFM、SNOMを組み合わせた画像・分光計測を中心に、結晶形態、組成、細胞内分布を抽出しているため、植物状態の技術的表現型計測として含める。
abstractThree non-destructive and complementary techniques, Raman imaging, Atomic Force Microscopy and Scanning Near-field Optical Microscopy were used simultaneously to show for the first time chemical and structural differences of carotenoid crystals.
Published1 Jan 2018Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data SystemsCited by 12 · OpenAlex ↗
Carotenoid deposition in gac fruit aril, carrot root, and tomato fruit was investigated in situ using a combination of light microscopy, UV/Vis transmission spectroscopy, and diffuse reflectance spectroscopy. Reflectance data was transformed to absorbance data using the Kubelka-Munk function. As shown by light microscopy, carotenoids of gac fruit aril were deposited in round-shaped chromoplasts, whereas tomato and carrot were characterized by needle-shaped, crystalloid chromoplasts. Crystalline carotenoid deposition in tomato and carrot could be confirmed via polarized light microscopy. By identical means, crystalline deposition of lycopene and β-carotene in gac fruit aril was revealed, most likely in form of microcrystalline aggregates. This hypothesis was supported by a simple solubility estimation based on carotenoid and lipid contents in gac fruit aril. By comparison of UV/Vis absorption spectra recorded from aggregated genuine carotenoid extracts, authentic standards, and transformed reflectance spectra, an H-aggregated form of lycopene in tomato, J-aggregated forms of carotenoids in carrot, and a combination of H-aggregated lycopene, J-aggregated β-carotene, and possibly lipid-dissolved carotenoids in gac fruit aril was confirmed. The present approach was used to gain insights into the aggregate forms of carotenoids in planta, representing a low-cost, non-destructive in situ-characterization method.
Why it matches plant phenotyping methods植物組織内のカロテノイド沈着状態を、顕微鏡・分光法で非破壊に取得・解析する手法自体が中心的に開発されているため。
titleNon-destructive approach for the characterization of the in situ carotenoid deposition in gac fruit aril
Respiration traits allow calculating temperature-dependent carbon use efficiency and prediction of growth rates. This protocol aims (1) to enable validation of respiration traits as non-DNA biomarkers for breeding on robust plants in support of sustainable and healthy plant production; (2) to provide an efficient, novel way to identify and predict functionality of DNA-based markers (genes, polymorphisms, edited genes, transgenes, genomes, and hologenomes), and (3) to directly help farmers select robust material appropriate for a specified region. The protocol is based on applying isothermal calorespirometry and consists of four steps: plant tissue preparation, calorespirometry measurements, data processing, and final validation through massive field-based data.The methodology can serve selection and improvement for a wide range of crops. Several of them are currently being tested in the author's lab. Among them are important cereals, such as wheat, barley, and rye, and diverse vegetables. However, it is critical that the protocol for measuring respiration traits be well adjusted to the plant species by considering deep knowledge on the specific physiology and functional cell biology behind the final target trait for production. Here, Daucus carota L. is chosen as an advanced example to demonstrate critical species-specific steps for protocol development. Carrot is an important global vegetable that is grown worldwide and in all climate regions (moderate, subtropical, and tropical). Recently, this species is also used in my lab as a model for studies on alternative oxidase (AOX) gene diversity and evolutionary dynamics in interaction with endophytes.
Why it matches plant phenotyping methods植物の呼吸形質を取得・検証するイソサーマル熱量測定プロトコルが研究の中心であり、育種用の植物ロバスト性形質を測定・予測する方法論を扱っている。
abstractThis protocol aims (1) to enable validation of respiration traits as non-DNA biomarkers for breeding on robust plants
Carrot, a highly profitable crop in Israel, is severely damaged by Phelipanche aegyptiaca parasitism. Herbicides can effectively control the parasite and prevent damage, but for optimal results, knowledge about the soil-subsurface phenological stage of the parasite is essential. Parasitism dynamics models have been successfully developed for the parasites P. aegyptiaca, Orobanche cumana , and Orobanche minor in the summer crops, tomato, sunflower, and red clover, respectively. However, these models, which are based on a linear relationship between thermal time and the parasitism dynamics, may not necessarily be directly applicable to the P. aegyptiaca -carrot system. The objective of the current study was to develop a thermal time model to predict the effect of P. aegyptiaca parasitism dynamics on carrot growth. For development and validation of the models, data was collected from a temperature-controlled growth experiment and from 13 plots naturally infested with P. aegyptiaca in commercial carrot fields. Our results revealed that P. aegyptiaca development is related to soil temperature. Moreover, unlike P. aegyptiaca parasitism in sunflower and tomato, which could be predicted both a linear model, P. aegyptiaca parasitism dynamics on carrot roots required a nonlinear model, due to the wider range of growth temperatures of both the carrot and the parasite. Hence, two different nonlinear models were developed for optimizing the prediction of P. aegyptiaca parasitism dynamics. Both models, a beta function model and combined model composed of a beta function and a sigmoid curve, were able to predict first P. aegyptiaca attachment. However, overall P. aegyptiaca dynamics was described more accurately by the combined model (RMSE = 14.58 and 10.79, respectively). The results of this study will complement previous studies on P. aegyptiaca management by herbicides to facilitate optimal carrot growth and handling in fields infested with P. aegyptiaca .
Why it matches plant phenotyping methodsニンジン根における寄生状態の動態を予測する熱時間モデルを開発・検証しており、植物の病害・寄生状態の定量的推定が研究の中心である。
abstractThe objective of the current study was to develop a thermal time model to predict the effect of P. aegyptiaca parasitism dynamics on carrot growth.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Rapid developments in remote‐sensing of vegetation and high‐throughput precision plant phenotyping promise a range of real‐life applications using leaf optical properties for non‐destructive assessment of plant performance. Use of leaf optical properties for assessing plant performance requires the ability to use photosynthetic pigments as proxies for physiological properties and the ability to detect these pigments fast, reliably and at low cost. We describe a simple and cost‐effective protocol for the rapid analysis of chlorophylls, carotenoids and tocopherols using high‐performance liquid chromatography (HPLC). Many existing methods are based on the expensive solvent acetonitrile, take a long time or do not include lutein epoxide and α‐carotene. We aimed to develop an HPLC method which separates all major chlorophylls and carotenoids as well as lutein epoxide, α‐carotene and α‐tocopherol. Using a C30‐column and a mobile phase with a gradient of methanol, methyl‐tert‐butyl‐ether (MTBE) and water, our method separates the above pigments and isoprenoids within 28 min. The broad applicability of our method is demonstrated using samples from various plant species and tissue types, e.g. leaves of Arabidopsis and avocado plants, several deciduous and conifer tree species, various crops, stems of parasitic dodder, fruit of tomato, roots of carrots and Chlorella algae. In comparison to previous methods, our method is very affordable, fast and versatile and can be used to analyze all major photosynthetic pigments that contribute to changes in leaf optical properties and which are of interest in most ecophysiological studies.
Why it matches plant phenotyping methods植物の生理状態・性能に関係する色素を迅速かつ低コストに定量するHPLC法の開発が中心で、植物フェノタイピングおよび葉の光学特性評価への利用を明示している。
titleFast detection of leaf pigments and isoprenoids for ecophysiological studies, plant phenotyping and validating remote‐sensing of vegetation
In order to speed up the breeding of orange carrots for high carotenoid content it is imperative to develop a fast and non-destructive technique. 332 roots from 86 carrot varieties grown in 2014 at the experimental farm in Høje Taastrup (DK) form the basis of this study. All roots were measured by Raman spectroscopy. The carotenoid content of the very same roots was estimated through a wet chemistry method coupled with UV-VIS at 447nm and 540nm. For the Raman spectroscopy, measurements were made on a cross section disk approximately 10cm from the root top at three different positions in the phloem. Since the top of the carrot is intact, it may still be used for growing. The final calibration model shows an uncertainty (RMSECV) of 20.5ppm, and a R(2)=0.86. It has thus proven to be well suited for prediction of carotenoids in orange carrots, and especially for ranking them according to the content.
Why it matches plant phenotyping methodsニンジン根のカロテノイド含量という植物形質を、Raman分光とPLS校正で非破壊推定する手法の開発・検証が中心であり、交差検証性能も評価している。
abstractit is imperative to develop a fast and non-destructive technique