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:641-686Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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:667-709Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
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:75-85Code / dataset availability confirmedEurope PMC · checked 10 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 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:1-65