Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)-derived metrics provide a potential high-throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot-level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV-derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest root mean square error (RMSE=16-18%), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.
Why it matches plant phenotyping methodsUAV画像と写真測量点群からソルガム区画の倒伏程度を推定する取得・解析フレームワークを開発し、実データで精度評価しており、植物表現型測定法が研究の中心である。
abstractThis study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots.
Manual inspection of grain plant leaves for defects is subjective and labor-intensive. Few studies have compared deep learning methods on a combined multi-crop dataset. The study collected locally 5,640 leaf images from rice, maize, and guinea corn farms in Nigeria and grouped them into six classes representing defective and healthy leaves for each crop. Three models were trained: YOLOv8 for end-to-end detection and classification, EfficientNetB0 for standalone image classification, and a hybrid that used YOLOv8 for leaf detection followed by EfficientNetB0 for patch classification. The hybrid achieved 99.85% accuracy on the test set, slightly above EfficientNetB0 (99.82%) and YOLOv8 (mAP 0.995). The hybrid also supplies bounding box locations, helping farmers identify exactly where damage appears. This system offers a reliable, field-deployable tool for monitoring grain crop health.
Why it matches plant phenotyping methods穀物葉の健全・欠損状態を画像から検出・分類する深層学習システムの開発とモデル比較が中心であり、植物の病害・損傷状態を直接推定するため、植物フェノタイピング手法に該当する。
abstractManual inspection of grain plant leaves for defects is subjective and labor-intensive.
Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.
Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。
abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.
Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。
abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.
Abstract Background Sorghum ( Sorghum bicolor ) is a versatile C4 crop used for food and feed and as biomass for bioproducts and energy. Improving nitrogen use efficiency (NUE) in sorghum is important because fertilizer is costly and excessive fertilizer use has negative environmental impacts. Leaf senescence mediates nutrient recycling, but its dynamic progression is difficult to quantify at scale. We evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum and link these phenotypes to gene expression. Sorghum Tx430 plants were grown under four N treatments (6, 9, 12, and 15 mM), imaged from vegetative growth through grain fill, and destructively sampled for RNA-seq at four developmental stages. Results A supervised support vector machine with a radial basis function kernel classified pixels from a hyperspectral image of sorghum plants grown under different N levels into green leaf, yellow leaf, dry leaf, stalk, panicle, and background classes with 0.93 accuracy. We defined the senescence ratio as the sum of yellow and dry leaf areas divided by the green leaf area and computed it across multiple growth stages and nitrogen levels. The senescence ratio did not differ among N treatments during vegetative growth, but it declined with increasing N during boot, anthesis, and grain fill, indicating earlier senescence under N limitation. Among the genes whose expression positively correlated with senescence ratio were 13 putative transcription factors, including SbiRTX430.02G247100, a WRKY1/ZAP1 homolog and a WRKY4 homolog. Gene regulatory network analysis of the top 1% of genes associated with SbiRTX430.02G247100 showed enrichment for processes associated with leaf senescence and chlorophyll catabolism. In contrast, the network associated with the WRKY4 homolog was enriched for autophagy-related terms. Conclusions Our study shows that automated hyperspectral imaging is highly effective for monitoring dynamic plant phenotypes, such as stress-induced senescence, that are difficult to visually score with the naked eye. Here, nitrogen deficiency served as the stress condition. Still, this approach supports large-scale phenotypic data collection for any such stressor and enables analyses with greater statistical power, yielding more robust conclusions and the potential for new insights that can be applied to engineering and breeding better crops.
Why it matches plant phenotyping methodsソルガムの動的な老化表現型を高スループットに取得する hyperspectral imaging と、SVMによる画像分類・senescence ratio算出が研究の中心であり、植物状態の定量化手法を実証している。
abstractWe evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum
Reproduction assets foundThe paper's availability statement points to a public GitHub repository containing the authors' image-processing, machine-learning classification, transcriptomic analysis, and figure-generation scripts. The 148 GB hyperspectral image data is only promised 'upon acceptance' (not yet public), and the RNA-seq deposit is aCode · publicle in the NCBI SRA repository,
552
under BioProject PRJNA1452908 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1452908)
553
(RNA-seq raw reads SRR38119224 to SRR38119282). Scripts used for image processing,
554
machine-learning classification, transcriptomic analyses, and figure generation will be
555
accessible through GitHub (https://github.com/belafif2/TX430_Senescence). Image data (148
556
GB) will be made available in a data repository upon acceptance. Other relevant processed data
557
files and supporting figures are available as supplementary data documents.
558
559
Competing interests
560
The authors declare that they have no competing interests.
561
Funding
562
This work was funded Open asset ↗belafif2/TX430_Senescencepdf-raw-page:22 lines:1-54Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
The fraction of absorbed photosynthetically active radiation (FAPAR) is critical for characterizing crop photosynthetic capacity and growth status. Remote sensing technology based on unmanned aerial vehicles (UAVs) enables efficient estimation of FAPAR, but multiple scattering and transmission in the complex and dynamically changing crop canopy and background limit the accuracy of vegetation index (VI)-based methods. This study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios, comprising three modules: (1) Variable Endmember Extraction , building a spectral library of foreground (crop) and background endmembers, by extracting pure pixels on the R-NIR feature space and reducing redundancy using k-means and iterative endmember selection algorithm; (2) Iterative Unmixing , iterating over foreground-background endmember combinations as input of the multilinear mixing model (MLM) pixel by pixel; (3) Optimal Selection , selecting the optimal combination according to RMSE and outputting corresponding canopy abundance A f . Taking sorghum and rice as study objects, this study collected UAV multispectral images and field-measured FAPAR at multiple periods to validate the advantages of VE-MLM. The results demonstrated that compared to fixed-endmembers and linear/bilinear mixing models, VE-MLM always achieved excellent unmixing performance, effectively quantifying canopy contributions. The derived A f mitigated the saturation and background interference that commonly existed in VI-based regression models and exhibited a higher correlation with FAPAR (sorghum: R 2 = 0.900, rRMSE = 7.753%; rice: R 2 = 0.807, rRMSE = 2.200%). In conclusion, VE-MLM has a great potential to address spectral variability, dynamic changes, and scene complexity in crop growth scenarios, providing a more accurate and generalizable approach for sorghum and rice FAPAR estimation in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物キャノピーのFAPARを推定するスペクトルアンミキシング手法を開発し、ソルガムとイネで実測値により検証しており、植物表現型取得が中心である。
abstractThis study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Abstract Plant breeding is essential for crop improvement, yet progress is often hindered by slow, laborious, and subjective field phenotyping methods. High‐throughput phenotyping (HTP), particularly image‐based methodologies powered by machine learning, offers a pathway to overcome these limitations. However, achieving robustness and generalization when analyzing diverse genotypes within a crop and across reproductive stages remains challenging and can affect model performance and the accurate extraction of phenotypic features. This study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages, using wheat ( Triticum aestivum L.), sorghum ( Sorghum bicolor L.), and corn ( Zea mays L.) as case studies. The primary objectives were to analyze (i) the overall prediction performance on the aggregated dataset for each crop, (ii) the stratified performance by genotype and collection date, and (iii) the temporal and genotypic transferability across growth stages and unseen genotypes. Four distinct smartphone cameras were used to collect images of the reproductive structure across crop growth stages (different collection dates) from 160 corn, 80 sorghum, and 40 wheat genotypes. The total number of images per crop was 2000 for wheat, 4000 for sorghum, and 3840 for corn. Five semantic segmentation models were tested in this study—DeepLabv3+, MaskFormer, SegFormer, SegNet, and U‐Net—using the images and respective binary masks for training and testing. The SegFormer model achieved the highest intersection over union (IoU) values for corn (0.90) and sorghum (0.92), while the U‐Net model performed best for wheat (0.89). A minor performance decline, with IoU differences up to 0.1, was observed when testing the same model across different genotypes. However, the temporal transferability drops up to 0.5 IoU when training and inferring on different crop growth stages. The main reason for those changes may lie in the natural color and organ architecture temporal changes between the trained and tested datasets when transferring the models across growth stages. These results highlight the urgent need to prioritize robustness and transferability when developing reliable in‐field HTP methodologies.
Why it matches plant phenotyping methods植物の生殖器官画像からの表現型抽出に用いるセマンティックセグメンテーション手法を、作物・遺伝子型・生育段階間で性能と転移性の観点から比較検証しており、方法論が研究の中心である。
abstractThis study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages
Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。
abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.
Accurate estimation of sorghum (Sorghum bicolor [L.] Moench) grain yield remains a major bottleneck in breeding programs across sub-Saharan West Africa, where traditional methods rely on manual counting and weighing of grains. These approaches are labor-intensive, time-consuming, and prone to-induced variability, limiting throughput and reproducibility. This study presents an end-to-end high-throughput phenotyping pipeline for automated grain detection and mass estimation using computer vision. The workflow integrates smartphone-based image acquisition, automated foreground extraction, and grain detection using YOLOv11 models, followed by count-to-mass calibration. Three YOLOv11 architectures, small, medium, and large, were evaluated under identical training conditions using transfer learning from COCO pre-trained weights. Among the tested models, the medium configuration provided the best trade-off between precision and detection performance, with precision values reaching up to 0.861. A preprocessing step based on automatic background masking was applied prior to detection, significantly improving robustness under heterogeneous field conditions. Grain count was converted to mass using a linear calibration model. For two locally relevant sorghum elite lines, Faourou and Payenne, strong linear relationships were observed between detected grain number and measured grain weight (R² > 0.998), with mass coefficients k ≈ 0.026 g/grain. To facilitate adoption, a user-friendly R Shiny application was developed, allowing users to upload images, perform automated grain detection, and estimate grain mass using user-defined calibration coefficients. This pipeline provides a scalable and reproducible approach for rapid grain phenotyping and offers strong potential to accelerate selection decisions in sorghum breeding programs under field conditions.
Why it matches plant phenotyping methodsソルガム粒の画像取得、検出、質量推定を統合した高スループット表現型解析パイプラインを開発・評価しており、方法が研究の中心である。
abstractThis study presents an end-to-end high-throughput phenotyping pipeline for automated grain detection and mass estimation using computer vision.
Phenomic prediction (PP) is a genetic value prediction method based on near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, the choice of pre-processing is usually done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative, orthogonal and interpretable features of spectra instead of many correlated, uninformative wavelengths. We tested this pre-processing method on five datasets representing four plant species (maize, rice, sorghum and grapevine). Results show that estimating genetic values on components of raw spectra, that are not weighted by their eigenvalues, performs as well as doing it on spectra pre-processed with the best classical chemometrics methods in most cases, while requiring less parameter optimization. Moreover, this SVD step opens up possibilities for better understanding and selecting parts of the spectral information that are relevant for PP. Plain language summary Cultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimating these values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput method that is increasingly being used for this purpose. It often uses, as predictors, near infrared spectroscopy measurements that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a pre-processing approach that performs as well as the best chemometrics pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core Ideas The SVD-based pre-processing performs as well as the best performing classical chemometrics pre-processing in most cases Using the SVD-based pre-processing reduces computing time of genetic value estimation and requires less parameter optimization than using classical chemometrics pre-processing Spectra are composed of chemical and physical information and classical pre-processing methods remove the physical part of the signal It is likely that chemical information is the most important for phenomic prediction even though physical information remains valuable Performance of the SVD-based pre-processing is likely due to a good estimation of the genetic part of spectra and the conservation of physical information of spectra
Why it matches plant phenotyping methods植物のNIRSスペクトルから遺伝的価値を推定するフェノミック予測について、SVDベースの前処理法を提案し、複数植物種のデータセットで既存法と比較検証しているため、フェノタイピング手法が中心である。
abstractIn this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths.
Protein content is an important quality trait in sorghum that influences breeding approaches, end-use applications, and market value. Influenced by genetic, agronomic, and environmental variability, sorghum is characterized by its wide variation in composition, which may also be evident in kernels from the same sample. This study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR). Applying different pre-processing techniques to the spectra collected from intact kernels, the calibration models were developed using partial least squares regression and the reference protein content values obtained from the LECO combustion method. The best model was obtained using multiplicative scatter correction as pre-processing, resulting in a standard error of prediction of 0.83% and a relative predictive determinant of 3.40. These were indicative of the good predictive ability of the model and the instrument to be applied in quality control and sorting applications. These results highlight the potential of SKNIR to capture the inter-kernel variability in sorghum protein content and enhance screening for grain quality in breeding and grain processing.
Why it matches plant phenotyping methods単一穀粒NIRによるソルガム種子のタンパク質含量推定法を開発・評価しており、植物器官の形質取得が研究の中心である。
abstractThis study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR).
Reproduction assets foundThe paper's Data Availability Statement deposits the original single-kernel NIR spectra and reference protein data openly in Ag Data Commons, a paper-specific public dataset directly reproducing this study's measurements.Dataset · publicThe original data presented in the study is openly available in Ag Data Commons [https://doi.org/10.15482/USDA.ADC/31316725].Open asset ↗Ag Data Commons · 10.15482/USDA.ADC/31316725html-lines:226-278Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic improvement, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, digital terrain models (DTMs), and digital surface models (DSMs). Manual plant height measurements were used for correlation and simple linear regression analyses, while biomass was predicted using random forest regression. Results. The DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE = 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE = 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. The DTMs and DSMs derived from multispectral imagery accurately predicted plant height during later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into predictive models enhanced biomass yield prediction. The findings demonstrate that sUAS-mounted sensors and multispectral indices are valuable tools for phenotyping in sorghum breeding programs in Costa Rica.
Why it matches plant phenotyping methodssUASマルチスペクトル画像とフォトグラメトリから草丈・バイオマスを推定し、実測値との相関・回帰および予測精度を評価する手法中心の研究である。
abstractObjective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Bioavailability of iron, an essential micronutrient to plants, is low in alkaline or calcareous soils, which are prevalent across semi-arid production regions. Breeding efforts to increase tolerance to iron deficiency chlorosis (IDC) in sorghum, a major crop of semi-arid regions, are confounded by spatial variation of stress severity in field trials. Here we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability. In the field, severity and uniformity of stress are highly predictive of genetic signals for IDC tolerance ( R 2 > 0.6 for soil pH metrics and H 2 ). Plot-level data filtering for stress conditions based on control genotypes successfully addresses field spatial variation (unfiltered H 2 = 0.18 vs. filtered H 2 = 0.4). The controlled-environment assay proxies field stress using iron sources with differential bioavailability, evidenced by high heritability ( H 2 = 0.98) and phenotypic differential for hybrid control genotypes that matches field performance. Finally, we show that assay phenotypes are suitable for genome-wide association studies in global germplasm. Together, these field and lab phenomic approaches can be deployed to understand genetics of IDC tolerance and develop crops resilient to alkaline soils. HIGHLIGHT Stress severity and uniformity greatly impact detection of genetic signals underlying iron deficiency chlorosis tolerance in sorghum. A controlled-environment assay reduces spatial heterogeneity and improves assessment of tolerance genetics.
Why it matches plant phenotyping methods鉄欠乏性クロロシス耐性を評価するため、圃場マルチスペクトル空撮と管理環境アッセイという2つのハイスループット表現型計測法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractHere we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability.
Centralizing valuable community data and resources into a user-friendly interface and accessible repository has become essential for agricultural science; embracing Findable Accessible, Interoperable, and Reusable (FAIR) principles is now standard for effective databases. SorghumBase (https://www.sorghumbase.org) is a knowledgebase designed for the sorghum research community. The SorghumBase team curates genomic, transcriptomic, variation, and phenotypic information and aggregates community events, providing rich visualizations and bulk data access. The modular framework of the database is built with open-access software to yield a robust, modifiable, and sustainable data infrastructure. Release 9 of SorghumBase includes: (i) 88 sorghum reference genomes and an updated pan-gene index, (ii) over 100 million variants have been mapped onto the 2 genomes, BTx623 and Tx2783, (iii) assignment of 41 million Reference Cluster SNP identifiers (rsIDs) from BTx623 across the pan-genome, (iv) updated gene search homology, gene expression, and germplasm visualizations and features, (v) added and standardized 234 phenotypic data from 40 community-generated GWAS studies and 148 traits from the Sorghum QTL Atlas (Oz Sorghum), (vi) improved news, funding, and a research content management system for community access and interaction, (vii) outreach materials including training documents and videos, and (viii) community engagement initiatives through training and working groups. SorghumBase serves as a hub for sorghum data and stakeholder engagement while promoting community standards to drive research and multi-omics breeding approaches.
Why it matches plant phenotyping methodsソルガムの表現型データを標準化・統合し、可視化とアクセスを提供する研究基盤であり、表現型情報のデータ基盤として中心的です。
abstractSorghumBase (https://www.sorghumbase.org) is a knowledgebase designed for the sorghum research community.
Abstract Root system architecture plays a critical role in water and nutrient acquisition, particularly in semi‐arid environments where drought stress limits crop productivity. Despite advances in three‐dimensional (3D) root phenotyping, no dedicated low‐cost imaging platform currently exists for sorghum ( Sorghum bicolor (L.) Moench) in the United States. The objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework. The system consists of a rotating aluminum frame equipped with eight high‐resolution digital cameras controlled by Raspberry Pi microcomputers, uniform LED lighting, and background reference markers to ensure accurate image alignment. Approximately 2000–3000 overlapping images are captured in under 5 min and processed using structure‐from‐motion algorithms to generate colorized 3D point clouds. The total system cost was approximately $6000, substantially lower than commercial imaging technologies such as computed tomography or magnetic resonance imaging. Initial assembly demonstrated strong geometric alignment and minimal distortion, enabling measurement of key root traits including volume, nodal root angle, and whorl spacing. This platform provides a reproducible and scalable approach for sorghum root phenotyping and addresses a critical gap in crop research tools for semi‐arid production systems. The system also offers educational value by integrating engineering design, programming, and plant science, supporting interdisciplinary training and future genotype‐phenotype studies aimed at improving drought resilience.
Why it matches plant phenotyping methodsソルガム根の形態形質を取得する低コスト3D画像プラットフォームの設計・構築が研究の中心であり、根体積や根角度などの測定法を提供している。
abstractThe objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework.
Reliable agricultural statistics support food security monitoring and evidence-based decision making. In Mozambique, official agricultural statistics are primarily derived from the Integrated Agricultural Survey (IAI), an enumerator-based field survey that provides essential contextual information on agricultural production but remains labour-intensive, costly and spatially and temporally constrained, particularly in remote rural areas. While satellite remote sensing offers complementary, wall-to-wall coverage, its spatial resolution is often insufficient to directly capture the fragmented fields, mixed and intercropping patterns, shifting cultivation and strong sub-field variability typical of smallholder farming systems. Consequently, consistent estimation of crop area and crop type derived from enumerator-based crop cover assessments remains challenging in these landscapes.This study investigates the potential of high-resolution multispectral data acquired with Uncrewed Aerial Vehicles (UAVs) to complement field surveys by providing spatially explicit and internally consistent crop cover and crop fraction estimates at the field and sub-field scale. By resolving individual crops and dominant intercropping systems, UAV-based observations support the interpretation of farmer-reported crop cover proportions, improve consistency across enumerators, and enable post-survey correction of crop area estimates, while providing a basis for future integration with coarser-resolution satellite remote sensing. High-resolution RGB and multispectral imagery (green, red, red edge, and near-infrared; ≤5 cm ground sampling distance) was collected using a DJI Mavic 3M with RTK over 30 sampling areas of 500 × 500 m in Manica Province during the 2025 agricultural season. In parallel, a field survey recorded standardized observations of agricultural activity, including crop type (of most field and tree crops), intercropping combinations and enumerator-based estimates of fractional crop cover. UAV images were processed using a workflow tailored to heterogeneous smallholder landscapes to produce orthomosaics, digital surface models (DSMs), and vegetation indices. These products were linked to field observations through segments representing relatively homogeneous land units, enabling direct comparison between UAV-derived and survey-based crop cover estimates.For crop classification, training polygons were delineated on RGB orthomosaics for single-crop fields (e.g. maize, beans, sorghum and cassava) and common intercropping combinations (e.g. maize–beans). Annotated mosaics were tiled and augmented and used to train convolutional neural network models (e.g. UNet++), incorporating multispectral vegetation indices and DSM-derived height information as additional input channels. Model performance was evaluated using Intersection over Union, Dice coefficients, and regression metrics for fractional cover accuracy.A comparison framework was implemented to relate UAV-derived crop type, crop combinations and fractional cover to field survey observations while explicitly accounting for measurement uncertainty. Model II regression quantified systematic bias and proportional differences between the two methods. Initial results indicate that UAV-derived estimates provide spatially consistent crop cover information in fields with complex intercropping structures. Ongoing work focuses on refining segmentation accuracy, analysing residual discrepancies and assessing how UAV-derived crop cover information can be integrated to expand the spatial coverage and reliability of agricultural statistics in smallholder landscapes.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物種・混植構造・作物被覆率を抽出し、CNN分類と分画被覆推定を検証する方法が研究の中心である。
abstractUAV-based observations support the interpretation of farmer-reported crop cover proportions, improve consistency across enumerators, and enable post-survey correction of crop area estimates
MilletSorghumRootTissueSegmentationRoot system architecture
Root anatomical features are critical for plant performance characterization, yet phenotyping at the anatomical scale remains limited by the extreme annotation burden of cellular segmentation. We present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions. Our approach decomposes multi-class segmentation into species-agnostic tissue identification followed by tissue type classification. By designing robust input representations invariant to imaging artifacts and morphological variations, our framework enables rapid adaptation to new species with fewer than 40 labeled images. Additionally, the first stage automatically generates tissue boundaries, transforming tedious manual tracing into simple tissue labeling. We validate our method on pearl millet, and sorghum root cross-sections from different imaging protocols, achieving state-of-the-art performance while dramatically reducing deployment time. This efficiency breakthrough enables scalable root phenotyping across diverse crop species, accelerating the development of climate-resilient varieties for global food security.
Why it matches plant phenotyping methods植物根の解剖学的形質を対象とする画像セグメンテーション手法を開発し、複数種・撮像条件で検証しているため、方法が研究の中心である。
abstractWe present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the annotated root image dataset (Zenodo 17726414), trained segmentation models (Zenodo 17737703), and the authors' source code (GitHub janetkok/Root-Segmentation-Beyond-Species-Boundaries), all directly reproducing this paper's root anatomical phenotyping andDataset · publicThe dataset and models are available at https://doi.org/10.5281/zenodo.17726414 and https://doi.org/10.5281/zenodo.17737703 , respectively.Open asset ↗Zenodo · 10.5281/zenodo.17726414lines:242-251Code · publicThe source code is hosted at https://github.com/janetkok/Root-Segmentation-Beyond-Species-Boundaries .Open asset ↗GitHub · janetkok/Root-Segmentation-Beyond-Species-Boundarieslines:242-251Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.
Why it matches plant phenotyping methods植物画像から形態・スペクトル・テクスチャ形質を抽出する統合パイプラインの開発と評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe developed an integrated multispectral phenotyping framework
Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic breeding, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, DTMs (digital terrain models), and DSMs (digital surface models). Manual plant height measurements were used for correlation and simple linear regression analysis, while biomass was predicted using random forest regression. Results. DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE= 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE= 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. DTMs and DSMs derived from multispectral imagery predicted plant height accurately in later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into models enhanced biomass prediction. The findings showed that sUAS-mounted sensors and multispectral indices are promising tools for phenotyping in sorghum breeding programs in Costa Rica.
Why it matches plant phenotyping methodssUASマルチスペクトル画像と写真測量からソルガムの草丈・バイオマスを推定し、手測定との相関、回帰、精度評価を行う方法中心の研究である。
abstractTo predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.
Lignin plays a central role in the formation and function of secondary cell walls in vascular plants. However, the structural consequences of lignin modification for cell wall properties and cellular function in grasses remain poorly understood. Here, we investigated how cinnamyl alcohol dehydrogenase (CAD) deficiency alters vascular cell architecture in Sorghum bicolor, using the brown midrib-6 (bmr6) mutant as a model system. Biochemical and histochemical analyses confirmed altered lignin chemistry in bmr6, including increased incorporation of hydroxycinnamaldehyde residues and reduced tricin levels. We applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution. PXCT enabled measurements of wall thickness distribution and lumen shape along tracheary elements. Analyses revealed no significant differences in wall thickness between wild-type and bmr6 plants. However, three-dimensional morphometric descriptors indicated reduced lumen convexity in bmr6, suggesting localized modifications not detectable by conventional two-dimensional imaging. Water flow numerical simulations through PXCT-derived images indicated reduced vessel permeability and simulated hydraulic conductivity in bmr6, suggesting that subtle geometric changes may influence performance. These findings highlight the value of three-dimensional imaging for resolving cell wall organization and provide new insight into the architectural resilience of grass xylem in response to targeted lignin modification. HighlightThree-dimensional X-ray nano-imaging reveals alterations in the cell wall architecture that affect simulated hydraulic performance under reduced CAD activity in sorghum.
Why it matches plant phenotyping methods植物の木部細胞壁形状をナノスケール3D画像から定量化するPXCT手法が研究の中心であり、壁厚・内腔形状・形態記述子を抽出しているため、植物表現型計測の実質的な適用に該当する。
abstractWe applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution.
Sorghum is a globally important crop. Under the breeding goals of high yield and stress resistance, the precise selection of elite germplasm is crucial. Phenotypic parameters such as plant height and leaf area at the seedling stage are core indicators for evaluating growth vitality. However, traditional manual measurement is inefficient and error-prone, making it difficult to meet the needs of high-throughput research. To address this, this study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters and explores the regulatory effects of different gibberellin (GA 3 ) concentrations. In this study, videos of sorghum seedlings were collected using the relevant system of Nanjing Agricultural University, and reconstructed into.ply format 3D point cloud files via the open-source software Colmap. The core optimizations of the PTV2-Fr model are as follows: Firstly, it proposes a Multi-Radius Dual-Coordinate Attention (MRDCA) mechanism to address the problems of leaf overlap and uneven point cloud density, thereby enhancing feature discrimination ability; Secondly, it introduces a Point-Graph Invariant Feature Refinement (PG-InvFR) module to improve the sensitivity of the segmentation head to local geometric details; Thirdly, it constructs a composite loss function (EL Loss) combining class-weighted cross-entropy loss and Lovász loss to alleviate class imbalance and boost segmentation accuracy. We selected 50 valid datasets from 112 video groups, annotated into three categories: Stem, Leaf, and Pot. The results show that PTV2-Fr outperforms PTV2 by 2.5% in accuracy, with significant improvements in Recall and mean F1-score (mF1). Ablation experiments confirm the positive effects of MRDCA, PG-InvFR, and EL Loss. Furthermore, PTV2-Fr demonstrates good robustness in analyzing GA concentrations, revealing that 50-100 mg/L GA concentrations promote seedling growth, while concentrations exceeding 200 mg/L inhibit growth. The PTV2-Fr model provides an efficient solution for the automatic determination of sorghum seedling phenotypes, and the revealed GA 3 regulatory mechanism can offer theoretical references for high-quality seedling cultivation and hormone management.
Why it matches plant phenotyping methods3D点群分割ネットワークを開発・検証し、ソルガム幼苗の草丈や葉面積などの表現型形質を自動抽出する方法が研究の中心である。ジベレリン処理の解析は付加的な応用であり、方法論的貢献が明確。
abstractthis study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Why it matches plant phenotyping methods穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
Reproduction assets foundThe article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicith weather data acquisition.
Edited by: Filipe Matias , University of Wisconsin-Madison, United States
Reviewed by: Xiaolong Yang , Nantong University, China
David Mojaravscki , State University of Campinas, Brazil
Data availability statement
The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material .
Author contributions
BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data Open asset ↗Figsharelines:471-515Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R2 = 0.92–0.95) in 2019 and 0.07–0.08 m (R2 = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications.
Why it matches plant phenotyping methodsUAS画像からの点群・DSM生成と草丈推定について、飛行条件および処理パラメータの影響を体系的に評価・検証しており、植物表現型取得法が研究の中心である。
abstractThis study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation.
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from imagery. Such advancements have led to phenotypic digitization and made rapid yield forecasting possible. Yield predictions are critical to assess the merit of genotypes to propel cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 m above using a DJI M300 drone at 90° nadir and 45° oblique angles. This research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN in detecting sorghum panicles, achieving a mean average precision at 50 % IoU (mAP@0.50) scores of 0.92-0.98, compared to 0.61-0.89 for Faster R-CNN. Panicle detection from field imagery showed a linear correlation of 0.86 with ground truth field panicle counts. Lab imagery analyses measured panicle area, seed counts, and seed area with correlation coefficients of 0.79, 0.94, and 0.25 with respective ground truth observations. Support Vector Regression (SVR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) were used to predict yield with correlation coefficients of 0.74, 0.71, and 0.78, respectively, and SHapley Additive exPlanation (SHAP) analysis revealed panicle seed count as the primary driver of yield prediction. We observed YOLO models are well-suited for extracting yield-predictive features from pertinent images. Such features can then be incorporated into ML regression models to predict yield per se performance with greater accuracy. The GitHub link is provided in the Data availability section.
Why it matches plant phenotyping methodsUAS・実験室画像から穂数、穂面積、種子数・面積などの植物形質を深層学習で抽出し、検出精度を検証して収量予測へ利用する方法が研究の中心である。
abstractThis research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
Reproduction assets foundThe authors explicitly state that scripts, fine-tuned models, datasets, and sample images for this sorghum yield-forecasting study are publicly available on GitHub, matching the allowed URL exactly.Code · publicThe scripts, fine-tuned models, datasets, and sample images pertinent to this manuscript are available on GitHub at https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.git .Open asset ↗https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.gitlines:244-299Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Stomata are pores in the leaf epidermis that regulate the trade-off between CO2 uptake for photosynthesis and water vapor loss to the atmosphere. Stomatal patterning therefore influences water use efficiency and is a target for engineering to avoid drought stress. However, there is limited understanding of how internal leaf anatomy is coordinated with stomatal development, in part due to the technical challenges of assessing three-dimensional anatomy with sufficient resolution. C4 grasses are understudied, and this is a significant knowledge gap given their file-like stomatal distribution and unique mesophyll organization. In this study, wild-type sorghum and a low-stomatal density transgenic line expressing a synthetic Epidermal Patterning Factor (EPFsyn) were studied. High-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace, which together determine gias. Sorghum internal leaf airspace is an arrangement of large sub-stomatal airspaces with thin air passageways. Adaxial and abaxial surfaces differed in stomatal patterning relative to mesophyll structures, sub-stomatal crypts and airspace CO2 conductance (gias). Surprisingly, adaxial stomata were consistently located above rather than between vascular bundles. Unexpectedly, gias was not significantly different in wild-type versus EPFsyn. EPFsyn plants had larger crypts and shifts in internal leaf anatomy, indicating a potential compensation mechanism for predicted impacts of reduced stomatal density on gias. These findings provide a new understanding of the interplay between leaf surface specific anatomy and internal structural patterning of the mesophyll in a C4 species, and provides knowledge relevant to engineering water use efficiency in crop species.
Why it matches plant phenotyping methods高解像度microCTと機械学習を組み合わせ、葉の三次元形態・気腔などの植物形質を抽出する手法が研究の主要部分であり、単なる生物学的測定ではない。
abstractHigh-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace
Abstract Background and Aims: Portable X-ray fluorescence spectrometry (pXRF) has emerged as a robust analytical approach for elemental determination in plant tissues, enabling rapid, non-destructive, and reagent-free measurements. This study developed and validated an empirical calibration of pXRF for quantifying silicon (Si) in plants, using autoclave-induced digestion (AID) as the reference method. Methods A total of 374 samples from seven plant species (rice, maize, soybean, cowpea, sorghum, lettuce, and beet) were analyzed. Silicon concentrations obtained via AID ranged from 1.07 to 19.23 g kg − ¹ (mean = 4.48 g kg − ¹; coefficient of variation = 67%), reflecting substantial interspecific variability. Each sample was also analyzed by pXRF under optimized instrumental conditions, and a calibration model was constructed using 75% of the dataset to predict Si concentrations relative to AID values. Results The pXRF calibration exhibited a strong linear relationship with AID results (R² = 0.94; R = 0.97; p
Why it matches plant phenotyping methods植物組織中のケイ素濃度を測定するpXRF法の開発と、基準法との校正・検証が研究の中心であり、植物形質の測定法に該当する。
abstractThis study developed and validated an empirical calibration of pXRF for quantifying silicon (Si) in plants, using autoclave-induced digestion (AID) as the reference method.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
SorghumRootRoot system architectureStress response / tolerance
Climate-induced challenges, such as drought and nutrient depletion, are increasingly constraining global crop production, threatening food and nutritional security. Sorghum bicolor (L.), a climate-resilient cereal, demonstrates strong adaptive potential under resource-limited conditions due to its robust root system architecture (RSA). While above-ground improvements have received significant attention, the role of RSA in enhancing resource-use efficiency (RUE), particularly water use efficiency (WUE) and nitrogen use efficiency (NUE), remains underexploited in breeding programs. This review explores the physiological and molecular roles of sorghum RSA traits (e.g., root depth, density, branching pattern, and root angle) in improving RUE under abiotic stress. It highlights advances in multi-omics approaches, including transcriptomics, proteomics, and genome-wide association studies (GWAS), which provide insights into the genetic regulation of root development. High-throughput phenotyping platforms, including 2D, 3D, and emerging 4D imaging techniques, are evaluated for their effectiveness in capturing dynamic root traits and informing selection strategies. Sorghum's RSA offers a functional model for developing climate-resilient cultivars with improved WUE and NUE. The integration of modern phenotyping techniques with molecular insights and multi-omics strategies will expedite the identification of critical genetic and physiological determinants of RSA characteristics. This synthesis underscores the potential of RSA-targeted breeding strategies to enhance crop productivity and sustainability in water-and nutrient -constrained environments, aiding sustainable intensification and global food security in the face of climate change challenges.
Why it matches plant phenotyping methodsソルガム根系形態の表現型計測を扱うレビューであり、2D・3D・4D画像による高スループット表現型解析手法を評価しているため、方法レビューとして中心的です。
abstractHigh-throughput phenotyping platforms, including 2D, 3D, and emerging 4D imaging techniques, are evaluated for their effectiveness in capturing dynamic root traits and informing selection strategies.
This study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK. Reliable wet chemistry procedures for quantifying TS and AA in single sorghum kernel (SSK) were established, which achieved high accuracy with test errors below 1.0 %. The partial least squares (PLS) model with 2 latent variables (LVs) for AA prediction had coefficients of determination of 0.91 (R²cal) and 0.85 (R²cv), and root mean square errors (RMSE) of 1.90 % and 2.47 % for calibration (RMSEC) and cross-validation (RMSECV), respectively. It showed an R²pred of 0.83 and RMSE of 2.58 % for prediction (RMSEP) when validated with the independent validation set. The optimal SSK-TS NIR PLS calibration model was built from 187 calibration sorghum kernels with 10 LVs, which had a R²cal of 0.79, RMSEC of 2.76 % and RMSECV of 4.93 % and showed a R²pred of 0.72 and RMSEP of 3.19 % when applied to an independent validation set of 93 samples. Overall, this study successfully developed wet chemistry methods for measuring AA and TS contents in SSK and established NIR models for nondestructive prediction and sorting of sorghum kernels by their TS or AA content, serving as useful tools for sorghum breeding and application research.
Why it matches plant phenotyping methods単一ソルガム種子のデンプン・アミロース含量という植物器官形質を、NIR分光とPLSモデルで非破壊推定する手法を開発し、独立検証しているため、方法中心の研究として採用。
abstractThis study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK.
Flow cytometry (FCM) and genome sequencing are complementary methods for estimating plant genome size (GS). However, discrepancies between the GS estimates derived from genome assemblies and FCM create ambiguity regarding the accuracy of these approaches. Approximately 12,000 plant GS measurements have been reported, with hardly any of them based on genome assemblies. Currently, FCM is the most frequently used method. Accurate GS estimation by FCM relies on internal standards with known GS values. However, previous GS calibrations, often based on incomplete reference genome assemblies, have led to significant discrepancies in GS estimates. Historically, the GS of a diploid plant species was estimated by doubling the size of a consensus genome assembly. However, consensus assemblies collapse homologous chromosomes into a single sequence, typically favouring the larger haplotype and potentially overestimating GS, especially in highly heterozygous species. Here, we applied haplotype-resolved genome assemblies to accurately recalibrate the reference standards. We utilized a recent gapless, telomere-to-telomere (T2T) consensus and the most complete phased genome assemblies of the Nipponbare rice as a primary standard to recalibrate five commonly used plant standards. Using the consensus genome as a reference revealed an overestimation of over 30% in widely used previous GS estimates for Pisum sativum and Nicotiana benthamiana , approximately 18% for Arabidopsis thaliana , and 5% for Sorghum bicolor and Gossypium hirsutum . The GS estimates based on phased haplotype assemblies suggested an additional 6%–7% overestimation. Haplotype-resolved genome assemblies allow the recalibration of GS estimates with the potential to yield more accurate values by capturing haplotype-specific variations previously missed in consensus assemblies.
Why it matches plant phenotyping methods植物のゲノムサイズ推定に用いるフローサイトメトリー標準の再校正が研究の中心であり、測定精度の検証・改善に該当する。
titleRe-calibration of flow cytometry standards for plant genome size estimation
Introduction Frequent droughts and climate fluctuations pose significant challenges to stabilizing and increasing the yields of drought-tolerant crops like sorghum. Accurate, detailed, and spatially explicit yield predictions are essential for precision irrigation, variable fertilization, and food security assessment. Methods This study was conducted in the Lifang dryland experimental area in Jinzhong, Shanxi Province, using a sorghum planting experiment. Multispectral imagery and meteorological data were collected simultaneously using a DJI Mavic 3M UAV during key growth stages (seedling emergence, jointing, flowering, and maturity). A "spectral-meteorological-spatial" three-dimensional prediction framework was developed using eight machine learning algorithms. SHAP values and Partial Dependency Plots were used to assess variable importance. Results Ensemble learning algorithms performed best, with the Gradient Boosting model achieving an R 2 of 0.9491 and Random Forest reaching 0.9070. SHAP analysis revealed that DVI and NDGI were the most important predictors. The jointing stage contributed most to prediction accuracy (R 2 = 0.9454), followed by maturity (R² = 0.9215) and flowering (R 2 = 0.9075). Yield spatial distribution ranged from 4,291 to 4,965 kg haR -1 , with a global Moran's I index of 0.5552 indicating moderate positive spatial autocorrelation. Discussion Integrating UAV multispectral data with machine learning methods enables efficient sorghum yield prediction, with the jointing stage identified as the optimal monitoring period. This study provides crucial technical support for precision planting and efficient sorghum management in arid regions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習によりソルガムの収量という植物形質を推定する技術開発・評価が中心であり、予測性能も検証している。
abstractA "spectral-meteorological-spatial" three-dimensional prediction framework was developed using eight machine learning algorithms.
Accurate assessment of crop health and yield potential facilitates precise estimation of above-ground biomass (AGB). Traditional AGB estimation methods are often limited by their destructive, labor-intensive nature. This study developed a rapid, non-destructive approach to estimate sorghum AGB using high-resolution unmanned aerial vehicle (UAV) data and machine learning (ML). A two-year field experiment tested four irrigation strategies: full irrigation at 100% of crop evapotranspiration (S1), partial deficits at 75% and 50% of S1, and a rain-fed (S4). This gradient assessed the ML model’s robustness across diverse conditions, yielding 216 AGB measurements reflecting variable plant responses to water stress. Multispectral and canopy height data were derived from UAV imagery collected during the sorghum growing season. Three ML algorithms—Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN)—were applied. RF outperformed others with an R² of 0.80, RMSE of 0.78 kg m⁻², and MAE of 0.58 kg m⁻², followed by SVM (R² = 0.64, RMSE = 1.08 kg m⁻², MAE = 0.77 kg m⁻²), while K-NN showed the lowest accuracy (R² = 0.50, RMSE = 1.26 kg m⁻², MAE = 0.96 kg m⁻²). Optimal RF hyperparameters were identified, and estimated AGB aligned closely with ground measurements, showing no significant differences. Spatial AGB maps effectively highlighted variability across treatments. This study demonstrates that UAV-based remote sensing combined with ML offers a reliable, non-destructive method for sorghum AGB estimation, enhancing precision agriculture applications such as irrigation management, crop monitoring, and yield prediction.
Why it matches plant phenotyping methodsUAV画像と機械学習によりソルガムの地上部バイオマスを推定する手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。
abstractThis study developed a rapid, non-destructive approach to estimate sorghum AGB using high-resolution unmanned aerial vehicle (UAV) data and machine learning (ML).
SorghumTobaccoTomatoLiDAR / point cloudLeafStem / branchSegmentation
Accurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes, as it provides the foundational data for both trait measurement and structural modeling. Although existing studies have made significant progress, plant semantic segmentation (stems and leaves) across multiple species remains underexplored. To this end, this paper introduces an edge-aware downsampling algorithm and a novel network for segmenting plant point clouds at multiple scales, named MSPlantSegNet. Experimental results on a dataset of tobacco, tomato, and sorghum demonstrate that MSPlantSegNet attained superior performance across all four key metrics-precision (97.13 %), recall (95.63 %), F1-score (96.20 %), and IoU (93.14 %). MSPlantSegNet surpasses a set of leading models, including PointNet++, PointNet, ASIS, DGCNN, PlantNet, PSegNet, and PointNeXt. This research has valuable implications for plant phenotyping, the development of smart agriculture, and ideal type selection.
Why it matches plant phenotyping methods植物点群から茎・葉を分割する新規ネットワークとダウンサンプリング法を開発し、複数種データで性能比較しており、表現型抽出の基盤手法が中心である。
abstractAccurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
In recent years, phenotyping approaches in plant breeding have expanded in both methodology and data collection capacity. One such tool, Near-Infrared Spectroscopy (NIRS) generates a wealth of reflectance values for biological samples. To test the potential of NIRS-based predictions, a hundred grain sorghum hybrids generated from a 10 × 10 factorial mating design were evaluated across eight environments. Hybrids were phenotyped for grain yield, days to anthesis, plant height, kernel hardness index, kernel diameter, and kernel weight. Hybrid grain samples were scanned with NIRS to generate phenomic data while parental lines were genotyped using genotyping by sequencing. Three different predictive models: genomic prediction (GP), phenomic prediction (PP), and GP + PP were fitted. Three different cross-validation schemes of untested hybrids in characterized environments (CV1), tested hybrids in uncharacterized environments (CV2), and untested hybrids in uncharacterized environments (CV3) were completed. GP + PP significantly improved over GP for days to anthesis, kernel hardness index, kernel diameter, and kernel weight for CV1. Prediction accuracy of GP + PP was also significantly improved for the kernel hardness index and kernel weight for CV2 and CV3. Depending on logistics, phenomic prediction has the potential to complement or supplement genomic data for predictive strategies in sorghum.
Why it matches plant phenotyping methodsNIRSによる穀粒の表現型データ取得と、それを用いた予測モデルおよび交差検証が研究の中心であり、農業形質・品質形質の推定性能を評価している。
abstractTo test the potential of NIRS-based predictions
Background Accurate sorghum spike detection is critical for monitoring growth conditions, accurately predicting yield, and ensuring food security. Deep learning models have improved the accuracy of spike detection thanks to advances in artificial intelligence. However, the dense distribution of sorghum spikes, variable sizes and complex background information in UAV images make detection and counting difficult. Methods We propose a multiscale and oriented sorghum spike detection and counting model in UAV images (MOSSNet). The model creates a Deformable Convolution Spatial Attention (DCSA) module to improve the network's ability to capture small sorghum spike features. It also integrated Circular Smooth Labels (CSL) to effectively represent morphological features. The model also employs a Wise IoU-based localization loss function to improve network loss. Results Results show that MOSSNet accurately counts sorghum spike under field conditions, achieving mAP of 90.3%. MOSSNet shows excellent performance in predicting spike orientation, with RMSEa and MAEa of 14.6 and 12.5 respectively, outperforming other directional detection algorithms. Compared to general object detection algorithms which output horizonal detection boxes, MOSSNet also demonstrates high efficiency in counting sorghum spikes, with RMSE and MAE values of 9.3 and 8.1, respectively. Discussion Sorghum spikes have a slender morphology and their orientation angles tend to be highly variable in natural environments. MOSSNet 's ability has been proved to handle complex scenes with dense distribution, strong occlusion, and complicated background information. This highlights its robustness and generalizability, making it an effective tool for sorghum spike detection and counting. In the future, we plan to further explore the detection capabilities of MOSSNet at different stages of sorghum growth. This will involve implementing object model improvements tailored to each stage and developing a real-time workflow for accurate sorghum spike detection and counting.
Why it matches plant phenotyping methodsUAV画像からソルガム穂の検出・計数・向き推定を行う深層学習手法を開発し、精度評価も実施しており、植物形態形質の取得手法が中心である。
abstractWe propose a multiscale and oriented sorghum spike detection and counting model in UAV images (MOSSNet).
This study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK. Reliable wet chemistry procedures for quantifying TS and AA in single sorghum kernel (SSK) were established, which achieved high accuracy with test errors below 1.0 %. The partial least squares (PLS) model with 2 latent variables (LVs) for AA prediction had coefficients of determination of 0.91 (R 2 cal ) and 0.85 (R 2 cv ), and root mean square errors (RMSE) of 1.90 % and 2.47 % for calibration (RMSEC) and cross-validation (RMSECV), respectively. It showed an R 2 pred of 0.83 and RMSE of 2.58 % for prediction (RMSEP) when validated with the independent validation set. The optimal SSK-TS NIR PLS calibration model was built from 187 calibration sorghum kernels with 10 LVs, which had a R 2 cal of 0.79, RMSEC of 2.76 % and RMSECV of 4.93 % and showed a R 2 pred of 0.72 and RMSEP of 3.19 % when applied to an independent validation set of 93 samples. Overall, this study successfully developed wet chemistry methods for measuring AA and TS contents in SSK and established NIR models for nondestructive prediction and sorting of sorghum kernels by their TS or AA content, serving as useful tools for sorghum breeding and application research.
Why it matches plant phenotyping methods単一ソルガム種子のデンプン・アミロース含量をNIR分光とPLSモデルで非破壊推定する測定法を開発し、独立検証している。単なる生物学的実験のルーチン測定ではなく、育種に再利用可能な形質取得法が中心である。
abstractThis study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK.
Abstract The rapid advancement of digital technologies in agriculture has transformed crop monitoring, particularly through the use of remote sensing and Geographic Information Systems (GIS). These tools have proven indispensable in assessing crop growth dynamics, especially in resource-constrained, semi-arid regions. Among climate-resilient crops, sorghum plays a pivotal role in ensuring food and nutritional security under erratic rainfall and limited irrigation conditions. Monitoring its growth using satellite-derived vegetation indices enables real-time, large-scale assessments that are critical for informed decision-making. This study employed the Google Earth Engine (GEE) platform for the retrieval and processing of Sentinel-2 NDVI data to evaluate its effectiveness in estimating Leaf Area Index (LAI), a key biophysical parameter closely linked to crop vigor and productivity. The primary objective was to determine the most appropriate regression model to establish the relationship between NDVI and LAI. A total of 160 field-observed LAI measurements were collected across two districts of Maharashtra, India—Solapur and Ahmednagar—representing varied agro-ecological conditions. Regression analysis revealed that second-order polynomial models outperformed linear, logarithmic, exponential, and power models, with higher R² values (> 0.40) and lower RMSE (0.83–0.89) in district-level analysis. The combined dataset showed moderate performance (R² >0.25, RMSE = 1.20), reflecting the influence of spatial variability. NDVI-based crop area classification showed high accuracy, with Kappa coefficients exceeding 0.70, and sorghum areas were estimated at 2,58,925 ha in Solapur and 1,48,475 ha in Ahmednagar, aligning within 5% of official government statistics. These results highlight the value of integrating NDVI and LAI through polynomial regression models for accurate, real-time crop monitoring, supporting climate-smart agriculture, precision farming, and policy-level planning in semi-arid regions.
Why it matches plant phenotyping methodsSentinel-2 NDVIとGEEを用いてソルガムのLAIという植物形質を推定し、回帰モデルを比較・検証しているため、形質取得手法が中心である。
abstractThis study employed the Google Earth Engine (GEE) platform for the retrieval and processing of Sentinel-2 NDVI data to evaluate its effectiveness in estimating Leaf Area Index (LAI)
Trait-based breeding has been shown to enhance and sustain yield potential of various crops under current and future changing climate. To be successful, trait-based breeding requires extensive phenotyping of plants in large-scale field trials that may include hundreds of genotypes. Computer vision approaches have been used extensively for image-based high-throughput phenotyping of diverse traits. However, studies focused on estimating grain count, a trait that directly influences the overall yield, are limited due to a lack of benchmark grain image datasets. In this work, we focus on grain count estimation in sorghum, a crop that holds immense significance for both food and energy production. We introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes (i.e., 4 panicles per genotype), for a total of approximately 5000 images, as well as approximately 12,500 images containing the corresponding threshed grains, together with machine counts per panicle. To develop baseline models, we manually annotated grains in images from 100 genotypes using bounding boxes. We used the manually annotated images to train baseline models for small object detection and counting. We also trained regression-based models for grain count estimation. The best overall model for count estimation from panicle images was a regression model, which achieved a mean absolute percent error of 29.97 and an R 2 value of 0.75. We make our dataset and baselines publicly available to facilitate further research on grain count estimation in sorghum and other crops. • We curated the Sorghum-Grain-Count dataset, which includes ∼17,500 panicle and threshed grain images covering 316 genotypes. • This is the largest dataset for grain count estimation and can help advance research on small object count estimation. • We trained strong baseline object detection models, as well as regression-based models for grain count estimation. • Our best model for grain count estimation from panicle images was a regression model that achieved an R 2 value of 0.75. • Our models provide a foundation for non-destructive yield estimation tools, which are greatly needed by breeding programs.
Why it matches plant phenotyping methodsソルガムの穂・粒画像から粒数という植物収量関連形質を推定する大規模データセット、ベンチマーク、検出・回帰モデルを開発・公開しており、表現型取得・推定手法が研究の中心です。
abstractWe introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from Uncrewed Aerial System (UAS)-derived imagery. Such advancement leads to phenotypic digitization and sorghum yield forecasting. Yield analytics are critical for breeding programs to assess the genetics and breeding potential of genotypes to enhance cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 meters above using a DJI M300 drone equipped with the P1 sensor at nadir (90 degrees) and oblique (45 degrees) angles. This research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN model in detecting sorghum panicles, achieving a mean average precision at 50% Intersection over Union (IoU) ranging from 0.92 to 0.98, compared to 0.61 to 0.89. Panicle detection from field imagery correlated at 0.86 with ground truth. Lab imagery analyses measured panicle size, seed counts, and seed area with correlation coefficients of 0.71, 0.95, and 0.25, respectively. Three machine learning models: Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) are used to predict yield with correlation coefficients of 0.58, 0.76, and 0.70, respectively. We observed that YOLO models are well-suited for extracting yield-attributing traits from images, which are then incorporated into ML regression models to improve yield prediction performance.
Why it matches plant phenotyping methodsUAS・実験室画像からソルガム穂の検出、サイズ・種子数・面積などの形質抽出と収量予測を行い、複数の物体検出モデルを比較検証しているため、表現型取得手法が中心である。
abstractThis research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
The study of sodium content in plants is crucial for the improvement of saline-alkali soil. Existing metal element detection methods pose challenges because they are complicated and time-consuming. In this study, we propose a quantitative detection model, FusionNet, that integrates Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Hyperspectral Imaging (NIR-HSI) to realize the detection of Na element content in sorghum roots. To address the small-sample dataset, A Generative Adversarial Network (GAN) was employed to increase the diversity of the samples. The results indicated that data augmentation effectively enhanced the diversity of the original dataset and improved model performance. The modeling results from the FusionNet network achieved R 2 cv of 0.9915 and RMSECV of 0.7418, while R 2 p and RMSEP were 0.9808 and 0.6693. Compared to training with LIBS data alone, FusionNet achieved improvements of 4.94 % in R 2 cv and 5.61 % in R 2 p. This study provides a new method for detecting metal elements in plants.
Why it matches plant phenotyping methods植物根のNa含量という形質を対象に、LIBSとNIR-HSIを融合した定量検出モデルを開発し、データ拡張と性能評価まで行っており、フェノタイピング手法が中心である。
abstractwe propose a quantitative detection model, FusionNet, that integrates Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Hyperspectral Imaging (NIR-HSI) to realize the detection of Na element content in sorghum roots.
ABSTRACT Current efforts to detect and evaluate crop resistance to insect pests are limited by traditional phenotyping methods, which are time‐consuming and highly variable. Sugarcane aphid (SCA; Melanaphis sacchari ) is a major pest of sorghum in North America that has emerged over the last decade and negatively impacts plant growth and development. The spectral reflectance data in visible, near infrared and shortwave infrared range (VIS–NIR–SWIR; 400–2500 nm) have been used to measure plant traits related to stress responses, nutrient dynamics, and physiological status. We examined the potential of spectral features (VIS–NIR–SWIR) to improve the current phenotyping methods in monitoring sorghum resistance mechanisms to SCA. We used eight sorghum lines that displayed varied levels of resistance to SCA and collected data from control and aphid‐infested plants. Spectral feature data were collected using a leaf spectrometer, while plant physiological and chlorophyll fluorescence parameters were measured with LICOR and MultispeQ devices. The random forest classifier model differentiated the control and aphid‐infested plants with a high accuracy of 87.4% with important spectral features in the VIS–NIR spectral range, particularly from 508 to 573 nm and 715 to 728 nm. The spectral indices exhibit significant difference in Greenness Index and Plant Senescence Reflectance Index in aphid‐infested susceptible lines (BTx623, SC1345) compared with control plants. In addition, plant physiological parameters, such as stomatal conductance and chlorophyll fluorescence, showed significantly higher value for aphid‐infested resistant line (Tx2783) compared with susceptible line (BTx623) in both treatments. Further, a partial least square regression model demonstrated medium predictive capability for plant physiological parameters related to fluorescence. In summary, spectral features at VIS–NIR range demonstrated promising results in differentiating aphid‐infested sorghum plants. This is a proof‐of‐concept study on potential of spectral sensing to develop an effective monitoring and phenotyping plant resistance to aphids.
Why it matches plant phenotyping methodsアブラムシ抵抗性という植物状態を対象に、VIS–NIR–SWIR分光センシングと機械学習による識別・生理形質推定を開発的に評価しており、表現型取得法が中心である。
abstractWe examined the potential of spectral features (VIS–NIR–SWIR) to improve the current phenotyping methods in monitoring sorghum resistance mechanisms to SCA.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Genomic prediction (GP) is an essential tool in the field of plant breeding to accelerate the cultivar development pipeline by predicting the performance of unphenotyped lines. The precision of prediction is constrained by the heritability of the target trait when applying a single-trait genomic prediction model. To overcome this limitation, a multi-trait genomic prediction model leveraging high-heritability secondary traits co-heritable with the target trait can boost predictive ability for the target trait. However, this is practically challenging because it requires additional phenotyping effort and prior knowledge of trait co-heritability. This study aimed to assess the efficiency of multi-trait genomic prediction models powered by secondary traits derived from high-throughput phenotyping data when predicting important leaf functional target traits, i.e., nitrogen (N) content and specific leaf area (SLA) in diverse sorghum accessions. Since these traits can be predicted from hyperspectral reflectance data, there is significant potential for other wavelengths within the existing dataset to meet the criteria needed to improve prediction accuracy using multi-trait approaches. Therefore, experiments were performed on traditional direct measures of leaf N content and SLA, plus partial least squares regression predictions of them (Leaf N-PLSR, SLA-PLSR), i.e., four target traits in total. Three secondary, “synthetic traits” (S1, S2, S3), each a ratio of two wavelengths within the hyperspectral data, were identified based on high co-heritability with a given target trait. Single-trait GBLUP (Genomic Best Linear Unbiased Predictor) was fitted as a baseline model, followed by three multi-trait GBLUP models using synthetic traits and target traits together. Model performance was assessed using k-fold (k=5) cross-validation (CV), which consisted of single-trait, CV1, and CV2 schemes. The synthetic traits’ high genetic correlation and heritability met the requirements for their use as secondary traits. There was a significant increase in accuracy when synthetic traits were used in the multi-trait genomic prediction model compared to a single trait alone for all four target traits. It improved prediction accuracy while using secondary traits derived from hyperspectral high-throughput phenotyping data in the multi-trait genomic prediction model, suggesting that this approach could be broadly applied in a post-hoc fashion to many datasets without any additional phenotyping effort. Our analysis highlights a practical approach to improve multi-trait genomic prediction model performance using synthetic traits with no intrinsic biological meaning selected through co-heritability estimation.
Why it matches plant phenotyping methodsハイパースペクトル高スループット表現型データから合成形質を抽出し、PLSR予測と遺伝相関に基づいてゲノム予測へ利用する解析手法が研究の中心であるため。
abstractsecondary traits derived from high-throughput phenotyping data
Sorghum canopy architecture in field trials is determined by various phenotypic traits, such plant and panicle count, leaf density and angle and panicle morphology, and canopy height. These traits together affect light capture and biomass production as well as conversion of photosynthates to grain yield. Panicle morphology exhibits considerable variation as influenced by genetics, environmental conditions and management practices. This study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology. First, we developed a scalable, low-altitude Unmanned Aerial Vehicle (UAV)-based protocol that leverages videos for efficient data acquisition, combined with Neural Radiance Fields (NeRF)s to generate high-quality 3D point cloud reconstructions of sorghum canopies. Next, a 3D model was built to simulate 3D sorghum canopies to create annotated datasets for training deep learning-based semantic segmentation and panicle detection algorithms. Finally, we propose SegVoteNet, a novel multi-task deep learning model that integrates VoteNet and PointNet++ within a shared backbone architecture. Designed for semantic segmentation and 3D detection on pure point cloud data, SegVoteNet incorporates a voting and sampling module that leverages segmentation results to optimize object proposal generation. SegVoteNet is robust, achieving 0.986 Mean Average Precision (mAP) @ 0.5 Intersection Over Union (IOU) on synthetic datasets, and 0.850 mAP @ 0.5 IOU on real point cloud datasets for sorghum panicle detection, without fine-tuning. This set of pipelines provides a robust scalable method for phenotyping sorghum panicles in field trials in breeding and commercial applications. Further work is developing a capability to estimate grain number per panicle, which would provide breeders with additional phenotypes to select.
Why it matches plant phenotyping methodsUAV・NeRF・点群再構成と深層学習によるソルガム穂形態の取得・推定パイプラインを開発し、実データで性能評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Sorghum canopy architecture in field trials is determined by various phenotypic traits, such plant and panicle count, leaf density and angle and panicle morphology, and canopy height. These traits together affect light capture and biomass production as well as conversion of photosynthates to grain yield. Panicle morphology exhibits considerable variation as influenced by genetics, environmental conditions and management practices. This study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology. First, we developed a scalable, low-altitude Unmanned Aerial Vehicle (UAV)-based protocol that leverages videos for efficient data acquisition, combined with Neural Radiance Fields (NeRF)s to generate high-quality 3D point cloud reconstructions of sorghum canopies. Next, a 3D model was built to simulate 3D sorghum canopies to create annotated datasets for training deep learning-based semantic segmentation and panicle detection algorithms. Finally, we propose SegVoteNet, a novel multi-task deep learning model that integrates VoteNet and PointNet++ within a shared backbone architecture. Designed for semantic segmentation and 3D detection on pure point cloud data, SegVoteNet incorporates a voting and sampling module that leverages segmentation results to optimize object proposal generation. SegVoteNet is robust, achieving 0.986 Mean Average Precision (mAP) @ 0.5 Intersection Over Union (IOU) on synthetic datasets, and 0.850 mAP @ 0.5 IOU on real point cloud datasets for sorghum panicle detection, without fine-tuning. This set of pipelines provides a robust scalable method for phenotyping sorghum panicles in field trials in breeding and commercial applications. Further work is developing a capability to estimate grain number per panicle, which would provide breeders with additional phenotypes to select.
Why it matches plant phenotyping methodsUAV、3D点群、NeRF、深層学習を統合したソルガム穂形態の取得・抽出パイプラインを開発し、実データで性能評価しており、植物表現型計測手法が研究の中心である。
abstractThis study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology.
Modern plant phenomics leverages advanced digital technologies to derive qualitative and quantitative traits that define plant phenotypes, offering crucial insights for breeders and farmers in precision agriculture.However, real-field conditions, with their complexity and lack of flexibility, pose significant challenges for machine vision algorithm initially developed in controlled laboratory settings.The objective of this study was to explore the potential of human aided 3D point cloud analysis for phenotyping crop under near real-field conditions using a custom-built desktop application, with sorghum and soybean plants as the case study.Light detection and ranging (LiDAR) data acquisition was performed using a Leica BLK360 imaging laser scanner (Leica Geosystems AG, USA).Coordinate difference measurements were employed in extracting various phenotypic traits from plant point clouds.The sphere outlier removal (SOR) was fundamental in macro-noise reduction, while color-based scatter plot matrix were used for micro-noise isolation.The correlation between point cloud-derived traits and manually measured values was strong, with root mean square error (RMSE) of 17.84 mm for sorghum plant height, 16.28 mm for soybean plant height, 11.65 mm for sorghum panicle height, and 0.967 mm for sorghum stem diameter, and corresponding R-squared values between 0.7334 and 0.9492.However, measuring more complex traits like crown diameter, which are influenced by overlap and occlusion, was less accurate, with an RMSE of 102.4 mm and an R-squared value of 0.3702.While 3D phenotyping in near real-field environment reliably captures linear plant structures, complex morphological traits require improved occlusionhandling algorithms.Future work should prioritize high resolution sensors to capture finer details.Likewise, automated workflows are poised to improve not only throughput but the reliability and reproducibility of the 3D phenotyping approach.
Why it matches plant phenotyping methodsLiDAR点群とカスタムアプリケーションを用いた3D植物形態表現型の取得・解析手法を開発し、手動測定との精度検証も行っており、表現型測定法が研究の中心である。
abstractThe objective of this study was to explore the potential of human aided 3D point cloud analysis for phenotyping crop under near real-field conditions using a custom-built desktop application
Nonphotochemical quenching (NPQ) is a critical photoprotective mechanism in plants, safeguarding photosystem II (PSII) and PSI from photodamage under abiotic stress. However, it is unclear if different stressors lead to similar NPQ phenotypes, and the magnitude of natural variation (between and within plant species) in NPQ response to abiotic stress is unknown. Testing a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes. Our results show substantial variation in NPQ phenotypes across species, genotypes and treatments. In C3 crops, tobacco and soybean, multiple NPQ parameters generally increased under chilling and drought, while in C4 crops, maize and sorghum, NPQ traits were more variable including a decrease of multiple NPQ parameters. Low-N stress revealed genotype- and developmental stage-specific effects on NPQ, potentially reflecting distinct adaptive strategies and regulatory changes in NPQ stress response. A significant effect of ecotype and stress treatment was detected on most NPQ kinetics traits in Arabidopsis thaliana , however, the interaction between ecotype and treatment was stronger in drought than in chilling. Differential regulation of NPQ could be associated with a combination of changes in proton motive, ATPase synthase activity, and PSI redox state. Our findings highlight that interpreting relative changes in NPQ under abiotic stress is inherently complex and demands a broader integration of physiological data across multiple regulatory layers.
Why it matches plant phenotyping methods半高速スループットの葉ディスク法を用いてNPQ動態形質を測定し、複数種・遺伝子型・ストレス条件で適用しているため、植物生理フェノタイピング手法の実質的応用と判断します。
abstractTesting a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Rapid detection of crop grain components is crucial for effective production and energy conversion. We used the sample set division method to divide multiple sample sets and optimize NIRS models for rapid prediction of protein and fat content. 1243 and 415 crop grain samples were screened and divided into 5 and 4 sets, respectively. The aim was to establish NIRS models for protein and fat content prediction. The best modeling methods for protein were N (Norris Derivative)+D (detrending)-C (CARS)-P (PLS) and N+M (MC-UVE)-C-P, while those for fat were N+M-C-P and N+S (Savitzky-Golay)-C-P. The SS (Soybean Set), KS (Sorghum Set), and FS (Full Samples Set) data sets provided accurate protein content analysis, while the FS and SS data sets were suitable for both protein content prediction and evaluation. For fat, the FS, SS, and CS (Cereal Set) models met content analysis requirements, with the FS model suitable for external validation. It compared and analyzed the fitness, robustness, and accuracy of different NIRS set models, employing various division methods in this study, which provided a new idea of green method theoretical and technical support for major component rapid detection of biomass raw materials.
Why it matches plant phenotyping methods作物穀粒のタンパク質・脂肪という種子形質をNIRSで迅速推定するモデルを開発し、適合性・頑健性・精度を比較評価しており、形質取得法が研究の中心です。
abstractThe aim was to establish NIRS models for protein and fat content prediction.
SorghumLeafTissuePhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
A non-destructive methodology for monitoring impedance changes in sorghum leaves was developed and recorded irrigation-dependent responses that differed between leaf tissues. Metal microneedles were used as impedance probes and were shown to cause minimal damage to the plant. The needles were placed on either the abaxial or adaxial side of the leaf midrib using small clamps and re-used hundreds of times with minimal signs of wear. Cross-sectional images verified the precision of microneedle placement near vascular bundles on the abaxial surface and in non-vascular hydrenchyma on the adaxial surface. Impedance measurements with microneedles displayed a significant decrease in resistance compared to planar electrodes due to bypassing the epidermal layer. A tissue-specific impedance response was seen in relation to irrigation where the non-vascular adaxial surface remained largely stable throughout a day of measurement, while impedance increased in the vascular abaxial surface during exposure to light and decreased following watering. Impedance data were also compared with simultaneous gas exchange measurements of photosynthesis and transpiration.
Why it matches plant phenotyping methodsソルガム葉の組織特異的な水分・生理応答を非破壊インピーダンス測定で取得する手法を開発し、電極比較や配置精度、再利用性も検証しているため、植物フェノタイピング手法が中心である。
abstractA non-destructive methodology for monitoring impedance changes in sorghum leaves was developed
Published1 Apr 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Innovative approaches to seed classification for breeding and quality assessment simplify the process, reduce labor time, and inspire the design of grading machines and planters. This study aims to investigate machine learning-based binary classification for five sorghum genotypes based on their color attributes. Six machine learning models (multilayer perceptron, MLP; support vector machine, SVM; k-nearest neighbors kNN, random forest, RF; extreme gradient boosting, XGBoost and light gradient boosting machine, LightGBM) were created to evaluate the classification performance. As a result, the most successful models were k-nearest neighbors (k-NN) and Multilayer Perceptron (MLP), with an accuracy of 95.2 % for all color channels. Although the accuracy results of these two models were similar, the PRC Area and ROC Area values of MLP were higher. In all pairs, the sorghum seed genotypes of PI-2 from the other genotypes were discriminated, with the most outstanding accuracies being 100.0 % for all models. According to the confusion matrix, PI-4 followed the genotype pairs of PI-3 (99 out of 100 in the true class). The lowest accuracy was PI-1 and PI-5, with a value of 86.5 % by the k-NN model. In the k-NN model, the TPR was obtained as 0.920 for PI-1 and 0.810 for PI-5, and ROC Area was determined as 0.865 for both genotypes. The findings indicate that MLP and k-NN models are suitable and unbiased methods for classifying different genotypes of sorghum seeds. The results of this study contribute to the design of automatic classification machinery, seed breeding studies, improving feed quality efficiency and food safety by increasing the traceability of seeds.
Why it matches plant phenotyping methods画像の色特徴からソルガム種子の遺伝型を分類する機械学習手法が研究の中心であり、種子の観察可能な形質を用いた再利用可能な分類ワークフローに該当する。
titleMachine learning approaches for binary classification of sorghum (Sorghum bicolor L.) seeds from image color features
Background Plant stem structural characteristics are crucial factors determining plant lodging resistance, while high throughput methods for rapid surveys of these traits are still lacking in sorghum. Results Among 103 sorghum accessions, two kinds of stem powders (dry and water-washed) were subject to visible and near-infrared spectra acquisition, and 16 models (combinations) for stem structural characteristics were generated, revealing that the support vector machine regression model has significant positive effects on the prediction of stem structural characteristics while powder type and pretreatment of spectra has minor effects on the prediction of stem structural characteristics. In addition, we found that stem structure characteristics were positively correlated with agronomic traits but negatively correlated with lodging index which is the criterion that negatively accounts for plant lodging resistance. Conclusion This study for the first time provided a precise and high throughput method for the prediction of sorghum stem structural characteristics based on spectra, which could facilitate the improvement of lodging resistance in crop breeding.
Why it matches plant phenotyping methodsソルガム茎の構造形質を可視・近赤外スペクトルと回帰モデルで高スループット推定する手法を開発しており、植物形質取得が研究の中心である。
abstracthigh throughput methods for rapid surveys of these traits are still lacking in sorghum
SorghumField / plotRootMorphology / geometry measurementRoot system architectureWater status / transpirationYield / yield components
Even though availability of water and nutrients are the main limitations for grain production globally, little is known about the rooting system, the critical plant organ involved in accessing soil water and nutrients. We know that the crop’s genetic background (G), crop management (M), and the environment (E) interact to alter the architecture of the rooting system. However, root traits are hard to measure, and the lack of quick, cheap, accurate, and functional root phenotyping approaches in the field has limited the capacity of breeding, agronomy, and precision agriculture to develop traits and services for farmers. Recent advances in high-resolution root-zone soil moisture monitoring show potential to reveal genotypic and management differences in crop root systems across contrasting environments. This paper describes novel approaches for the high-throughput phenotyping of functional root traits of value for yield and yield stability. First, we introduce the phenotyping approach for in-situ 3D characterisation of sorghum water use and the root system in soil profiles. Second, we demonstrate its application to characterise two functional root traits, i.e., maximum rooting depth (MxRD), and an index of root activity (RAindex), and their phenotypic plasticities. The experiment results show that the proposed root phenotyping method could capture G´E´M effects at different crop growing stages. The plasticity of functional root traits was associated with the stability of grain yield traits. Hybrids with high root plasticity tend to have more stable grain numbers and grain weights. There is valuable genetic diversity in the mean value and plasticity of root traits that could be used to match root phenotypes to target production environments. The root phenotyping approach can be a valuable tool for understanding the dynamic interactions between root function, root architecture and yield traits in the field under variable environments.
Why it matches plant phenotyping methods電磁誘導センサーを用いた圃場での根系機能形質の高速フェノタイピング手法を開発・適用しており、形質取得法が研究の中心である。
abstractThis paper describes novel approaches for the high-throughput phenotyping of functional root traits of value for yield and yield stability.
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Stomatal conductance (g s ) quantifies the rate of exchange of carbon dioxide for photosynthesis and water vapor for transpiration between plant leaves and the atmosphere. g s is usually measured by handheld devices like porometers , and readings are manually taken in the field, which is time-consuming and labor-intensive. In this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling. The experiment was conducted in a research field equipped with an HTP platform in 2020 and 2021 involving maize, sorghum, soybean, sunflower , and winter wheat . Weather variables including dew point temperature, wind speed , air temperature, solar radiation, and relative humidity were collected by an onsite weather station . Plot-level canopy temperature, soil temperature , and seven vegetation indices were acquired using a thermal infrared camera, a multispectral camera, and a visible near-infrared spectrometer integrated on the HTP platform. Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for g s , and model performance was evaluated by Coefficient of Determination (R 2 ) and Root Mean Squared Error (RMSE). The result showed that RFR and SVR outperformed PLSR in g s modeling. The RFR model achieved R 2 of 0.63 and RMSE of 0.16 mol m −2 ·s −1 with the combination of phenotyping data and weather data. It outperformed the model using only the weather data (R 2 =0.35 and RMSE=0.21 mol m −2 ·s −1 ), or the model using only the phenotyping data (R 2 =0.46 and RMSE=0.19 mol m −2 ·s −1 ). This result suggested that high-throughput plant phenotyping data effectively complement weather data in estimating g s rapidly and non-destructively through ML. With the wide adoption of HTP technologies in aerial and ground-based platforms, this research provides a practical framework to estimate g s at large scale for crop breeding and irrigation management .
Why it matches plant phenotyping methodsHTPセンサーデータと機械学習を用いて、植物の生理形質である気孔コンダクタンスを大規模・非破壊推定する方法が研究の中心であり、モデル性能も評価している。
abstractIn this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling.
Monitoring sorghum during the flowering stage is essential for effective fertilization management and improving yield quality, with spike identification serving as the core component of this process. Factors such as varying heights and weather conditions significantly influence the accuracy of sorghum spike detection models, and few comparative studies exist on model performance under different conditions. YOLO (You Only Look Once) is a deep learning object detection algorithm. In this research, images of sorghum during the flowering stage were captured at two heights (15 m and 30 m) in 2023 via a UAV and utilized to train and evaluate variants of YOLOv5, YOLOv8, YOLOv9, and YOLOv10. This investigation aimed to assess the impact of dataset size on model accuracy and predict sorghum flowering stages. The results indicated that YOLOv5, YOLOv8, YOLOv9, and YOLOv10 achieved mAP@50 values of 0.971, 0.968, 0.967, and 0.965, respectively, with dataset sizes ranging from 200 to 350. YOLOv8m performed best on 15 sunny and 15 cloudy clouds and, overall, exhibited superior adaptability and generalizability. The predictions of the flowering stage using YOLOv8m were more accurate at heights between 12 and 15 m, with R 2 values ranging from 0.88 to 0.957 and rRMSE values between 0.111 and 0.396. This research addresses a significant gap in the comparative evaluation of models for sorghum spike detection, identifies YOLOv8m as the most effective model, and advances flowering stage monitoring. These findings provide theoretical and technical foundations for the application of YOLO models in sorghum spike detection and flowering stage monitoring. These findings provide a technical means for the timely and efficient management of sorghum flowering.
Why it matches plant phenotyping methodsUAV画像とYOLOモデルを用いたソルガム穂の検出および開花期推定を中心に、複数モデルの精度比較・検証を行っており、植物状態の画像ベース計測手法として中核的です。
abstractThis research addresses a significant gap in the comparative evaluation of models for sorghum spike detection, identifies YOLOv8m as the most effective model, and advances flowering stage monitoring.
Plant breeding efficiency is crucial to develop varieties able to cope with climate change and support food and feed value chains. Genomic prediction (GP) has been a major step in increasing this efficiency and is now routinely used in breeding programs. Recently, phenomic prediction (PP) has gained attention as a promising complementary approach to GP, further increasing the breeding programs’ efficiency. Factors impacting the predictive ability (PA) of PP have been studied on many species but are not fully clarified. In this context, we studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection on a large multi-parental sorghum population including 2498 genotypes. Our results show that PP can compete with GP, that it is less affected by population structure, and can reach its maximal PA with smaller training sets than GP, but its performances are trait dependant. We also show that NIRS can be acquired in a reference environment to perform prediction in other environments and that it is possible to randomly select as little as 10 wavelengths to perform predictions. Finally, we show that spectra pre-processing, and statistical methods have a limited and unclear impact on PA. Our study confirms that PP is a relevant trait prediction method that deserves attention to optimize breeding schemes. The main challenges for the future will be to better understand the information contained in the spectra and disentangle their genetic and proxy components to optimize the use of PP in breeding programs. Key message Phenomic prediction is promising for sorghum breeding. Geneticists’ methods may not be suited to optimally extract spectral information.
Why it matches plant phenotyping methodsNIRSスペクトルから植物形質を予測するフェノミック予測手法を、前処理・予測法・集団構造・学習セット規模・取得環境・波長選択の観点で比較検証しており、手法が研究の中心です。
abstractwe studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicWe determined a unique genetic consensus map by projecting the physical distance of the 51,545
markers on a high-quality genetic consensus map (Guindo et al., 2019) using the R package ziplinR
(https://github.com/jframi/ziplinR).Open asset ↗jframi/ziplinRpdf-page:6 lines:1-43Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Feb 2025Computers and Electronics in Agriculture.
Accurate counting of plant organs at various growth stages is crucial for crop growth monitoring, phenotypic assessment, and yield prediction. Traditional plant counting models, typically designed for specific plant types, often exhibit poor generalization capabilities. In this research, an improved model, Field-CounTR, was proposed to accurately count multiple plant organs. The Field-Count dataset, utilizing a Few-Shot method, was introduced to enhance accuracy in plant counting. Additionally, a mixed salient module was developed to improve the feature fusion capability of sample images by exploiting their high similarity in Few-Shot counting tasks. During the downsampling process, Shuffle Attention was integrated to reduce redundant information in the feature fusion process. Furthermore, a hybrid convolution module combining Do-Conv and dilated convolutions was developed to increase the speed of convolutional inference and expand the receptive field through over-parameterized and dilation operations. To assess the effectiveness of the proposed approach, tests were conducted using the Field-Count dataset, which includes Sorghum, Wheat, Maize, and Rice. The results demonstrated a mean absolute error (MAE) of 14.49 and a root mean square error (RMSE) of 21.14. Compared with the CounTR model, the Field-CounTR model reduced the MAE and RMSE by 2.01 and 1.33, respectively. The enhanced Field-CounTR model exhibited superior feature extraction performance, high detection accuracy, and excellent generalization capabilities. This model can accurately count multiple plant types in complex field or orchard conditions and offers a wide range of applications.
Why it matches plant phenotyping methods植物器官数という観測可能な形質を画像から推定するFew-Shot計数モデルとデータセットを開発し、複数作物で性能検証しており、表現型取得・抽出手法が中心です。
abstractAccurate counting of plant organs at various growth stages is crucial for crop growth monitoring, phenotypic assessment, and yield prediction.
Abstract As climate change continues to influence global weather patterns, the frequency and severity of drought conditions are expected to increase, posing a significant challenge to crop production. In sorghum ( Sorghum bicolor L. Moench), a key cereal crop, the stay‐green trait is of particular importance as a measure of how well a genotype can tolerate post‐anthesis drought conditions, which are critical for harvestable yield. Despite its importance, there is a pressing need for a more efficient, accurate, and precise method to phenotype stay‐green in sorghum to enhance breeding efforts. To address this need, this study explores the application of random forest and XGBoost machine learning models for phenotyping the stay‐green trait in sorghum. These models provide quantitative measurements that have the potential to enhance genomic studies and offer additional benefits. Although correlations with vegetation indices were occasionally high, they were not sufficiently reliable to be used exclusively. The machine learning models, in contrast, showed high percentages of genetic variation explained and had high repeatability. The values generated by these algorithms enable plant breeders to efficiently make selections in their stay‐green breeding programs. Further research is needed to assess the robustness of these models across different environments and genetic material. Additionally, comparing these models with other machine learning approaches will help determine if decision tree‐based models are the most effective for this application. Overall, the models presented in this study serve as a promising foundation for improving the efficiency of stay‐green breeding programs in sorghum, but they require further validation and comparison with alternative approaches.
Why it matches plant phenotyping methodsソルガムのstay-green形質をUAV画像と機械学習で定量化する手法を開発・評価しており、表現型取得・抽出が研究の中心である。反復性や遺伝的変異の説明率も評価している。
abstractthere is a pressing need for a more efficient, accurate, and precise method to phenotype stay‐green in sorghum
Reproduction assets foundThe paper's data availability statement explicitly releases the raw tabular stay-green phenotyping data and the authors' Python machine learning scripts in a public GitHub repository, directly supporting this paper's phenotyping measurements and analysis.Code · publicwould like to thank Bruce Spinhirne for
his assistance with the management of the experiment.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The raw tabular data and Python machine learning scripts
used in this study are available at: https://github.com/AcePugh/staygreen-prediction.git.O RC I D
N. AcePugh https://orcid.org/0000-0001-7129-6556
R E F E R E N C E S
Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., &
Younis, I. (2022). A review of the global climate change impacts,
adaptation, and sustainable mitigation measures. Environmental Sci-
ence and Pollution Research, 29(28), 42539–Open asset ↗AcePugh/staygreen-predictionpdf-raw-page:18 lines:1-81Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The study of sodium content in plants is crucial for the improvement of saline-alkali soil. Existing metal element detection methods pose challenges because they are complicated and time-consuming. In this study, we propose a quantitative detection model, FusionNet, that integrates Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Hyperspectral Imaging (NIR-HSI) to realize the detection of Na element content in sorghum roots. To address the small-sample dataset, A Generative Adversarial Network (GAN) was employed to increase the diversity of the samples. The results indicated that data augmentation effectively enhanced the diversity of the original dataset and improved model performance. The modeling results from the FusionNet network achieved R²cv of 0.9915 and RMSECV of 0.7418, while R²p and RMSEP were 0.9808 and 0.6693. Compared to training with LIBS data alone, FusionNet achieved improvements of 4.94 % in R²cv and 5.61 % in R²p. This study provides a new method for detecting metal elements in plants.
Why it matches plant phenotyping methods植物根のNa含量という形質を、LIBSとNIR-HSIのデータ融合およびFusionNetで定量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。
abstractwe propose a quantitative detection model, FusionNet, that integrates Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Hyperspectral Imaging (NIR-HSI) to realize the detection of Na element content in sorghum roots.
Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades. However, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plants, and phenotypic traits result from the cumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. Previous studies have selected a set of genes potentially affecting Sorghum nitrogen responsiveness to be characterized. The knockout mutants of these genes are generated using the CRISPR-Cas9 technique. Using a LemnaTec plant imaging system, this study obtained time series imagery data from 29 to 130 d after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, temporal pixel count and greenness index traits were extracted as a proxy of plant growth and N responses, which, subsequently, were modeled by mathematical functions, allowing us to estimate seven key parameters from the growth curves. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters, with the Edit 1 showing especially reduced sensitiveness to use the available N resources. This high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.
Why it matches plant phenotyping methodsLemnaTec画像を用いた時系列の植物表現型取得と、画素数・緑色度および成長曲線パラメータの抽出から成る高スループット表現型パイプラインが研究の中心である。
abstractUsing a LemnaTec plant imaging system, this study obtained time series imagery data from 29 to 130 d after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the raw LemnaTec imagery datasets and the extracted phenotypic data (pixel count and greenness index time series) in a public GitHub repository, which directly reproduces this paper's plant-phenotyping measurements.Dataset · publicJC, Yang J; experimental data generation: Jin H,
Park A, Li G; data analysis and interpretation of results: Jin H,
Sreedasyam A. All authors reviewed the results and approved the final
version of the manuscript.
Data availability
The raw imagery datasets and the extracted phenotypic data are
available in the GitHub repository: https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping.Acknowledgments
This project was supported by the US Department of Energy (Grant No.
DE-SC0023138), and the National Science Foundation under the award
number OIA-1826781.
N responses of sorghum mutants
Page6of8 Jin et al.GenomicsCommunications 2025, 2: e010Open asset ↗Sorghum-edits-N-Phenotypingpdf-raw-page:6 lines:77-121Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades; however, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plant, phenotypic traits result from the accumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. To characterize the targeting N-responsiveness and growth trajectory, we employed CRISPR-Cas9 technique to generate sorghum mutants using CRISPR technology. Using a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, we extracted a number of morphological and greenness index traits as a proxy of plant growth and N responses. Subsequently, we employed two different methods to model the temporal N-responsive traits, allowing us to estimate seven key parameters from the growth curve. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters. The high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.
Why it matches plant phenotyping methodsLemnaTec画像による時系列形質取得と成長曲線モデリングを組み合わせた高スループット表現型解析パイプラインが、研究の主要な技術的要素として記述されています。
abstractUsing a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions.
Reproduction assets foundThe paper's Supporting Information section links four public GitHub-hosted supplementary data files containing the paper-specific phenotypic values (fitted pixel count and ExG curve parameters) and statistical contrasts, directly reproducing this study's sorghum N-response phenotyping measurements and analysis outputs.Supplement · publicSupporting Information
Supporting Tables
Table S1. The phenotypic values calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitpx.csv)
Table S2. The phenotypic values calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitexg.csv)
Table S3. The contrasts of the phenotypes calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/PXcontrasts.xlsx)
Table S4. The contrastOpen asset ↗JIN-HY/Sorghum-edits-N-Phenotyping · fitpx.csvpdf-raw-page:11 lines:1-21Supplement · publicSupporting Information
Supporting Tables
Table S1. The phenotypic values calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitpx.csv)
Table S2. The phenotypic values calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitexg.csv)
Table S3. The contrasts of the phenotypes calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/PXcontrasts.xlsx)
Table S4. The contrasts of the phenotypes calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/ExGcontrast.xlsx)
11/14Open asset ↗JIN-HY/Sorghum-edits-N-Phenotyping · fitexg.csvpdf-raw-page:11 lines:1-21Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published11 Dec 2024BERHAN INTERNATIONAL RESEARCH JOURNAL OF SCIENCE AND HUMANITIESCited by 0 · OpenAlex ↗
Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.
Why it matches plant phenotyping methodsSentinel-2衛星画像を用いて作物の健康状態と生育フェノロジーを圃場・区画レベルで推定し、現地データで検証する監視手法が研究の中心であるため、植物状態のリモートセンシング型フェノタイピングとして含める。
abstractremote sensing and GIS play a crucial role for monitoring crop health
Abstract India's extensive agrarian and aerospace research systems offer numerous potential technologies suited for the crop insurance sector. This study investigates the application of technology in domains such as yield prediction, loss assessment, and product design. The research was conducted in the Vijayapura, Karnataka by integrating an intrinsic bio-physical model with remote sensing. As the agricultural statistics play major role in shaping the strategies and plans under food security, an area of 51,482.3 ha was estimated using SAR data, achieving an accuracy of 87.2% and a kappa index of 0.74. Indigenously developed InfoCrop and SORGHUM- CERES were employed to estimate yield for selected monitoring locations, with simulated LAI and yield ranging from 2.6 to 4.65 and 749 to 1097 kg ha − 1 , respectively. When compared with observed data, results showed an agreement above 90% and RMSE less than 10% and R 2 above 70%. Using regression (R 2 > 0.75) equations [CSM’s LAI and backscattering (Db)] and [spatial LAI and CSM yield], LAI and yield maps were developed.
Why it matches plant phenotyping methodsSARリモートセンシングと回帰モデルにより作物のLAI・収量を推定し、観測値との精度評価およびマッピングを行っており、植物形質の取得・推定法が実質的な中心である。
abstractThis study investigates the application of technology in domains such as yield prediction, loss assessment, and product design.
Genomic prediction (GP) has proven to be an essential tool to accelerate the cultivar development pipeline by predicting the performance of un-phenotyped lines in the field of plant breeding. However, the prediction accuracy is bounded by the heritability of the target trait, limiting the efficiency of the single-trait GP model. To overcome the limitation, a multi-trait genomic prediction model using high-throughput phenotyping (HTP) data can be used to leverage secondary traits to boost the predictive ability of the target trait. This study aimed to assess the efficiency of the multi-trait (MT)- GP model powered by HTP derived secondary traits in predicting nitrogen leaf area (Narea), specific leaf area (SLA), partial least square regression (PLSR)-Narea, and PLSR-SLA in sorghum. Three secondary traits (S1, S2, S3) were identified by using whole spectra of hyperspectral data using co heritability measures and were named synthetic traits. As a baseline model, single-trait GBLUP(GenomicBestLinearUnbiasedPredictor) was fitted, followed by three MT-GBLUP models using synthetic traits and target traits together. Model performances were assessed using k-fold (k=5) cross-validation (CV) schemes which consisted of single-trait, CV1, and CV2 schemes. The heritability of Narea, SLA, PLSR-Narea, and PLSR-SLA was 0.32,0.34, 0.40, and 0.26, respectively. Additionally, the heritability ranged from 0.61– 0.68, the genetic correlation ranged from 0.7– 0.9, and the co-heritability ranged from 0.46-0.57 across synthetic traits selected for four target traits. The high genetic correlation and heritability of synthetic traits met the requirements for their use as a secondary trait. The use of synthetic traits in the MT-GP model enhanced the accuracy of prediction by 6 %, 10 %, 10.87%, and 7.5% compared to a single trait alone in Narea, SLA, PLSR-Narea, PLSR-SLA respectively. Furthermore, the study demonstrated an improvement in prediction accuracy while using secondary traits derived from HTP in the MT-GP model compared to a single trait using Narea and SLA alone. Overall, our analysis highlights a practical approach to leverage high throughput phenotyping data to improve the performance of genomic prediction models.
Why it matches plant phenotyping methods高スループット・ハイパースペクトルデータから合成形質を抽出し、交差検証でゲノム予測への有効性を評価しており、表現型取得・推定ワークフローが中心である。
abstracta multi-trait genomic prediction model using high-throughput phenotyping (HTP) data can be used to leverage secondary traits to boost the predictive ability of the target trait
Abstract Sorghum ( Sorghum bicolor (L.) Moench) is an important cereal crop cultivated around the globe. To identify the genomic regions responsible for plant height and stem diameter, a subset of the sorghum association panel was phenotyped using manual, ground‐robot, and drone‐based phenotyping approaches during 2019 and 2020 at Tifton, GA. Manual and ground‐robot‐based plant heights had a lower correlation ( r = 0.55) than the manual and drone‐based measurements ( r = 0.61). Tukey's mean difference plot analysis indicated that both high‐throughput methods were less accurate with taller accessions. In genome‐wide association studies using single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and copy number variants, a total of 136 and 18 SNP loci associated with plant height were identified using mixed linear model and fixed and random model circulating probability unification models. For the first time, we report eight significant SNPs associated with stem diameter using a ground‐based robot. We were able to detect all four major dwarfing genes ( Dw1 through Dw4 ) using manual phenotyping; in contrast, Dw2 was identified using both manual and ground‐robot‐based phenotyping. Sorbi.3004G093400 , a gene located 19 kb upstream of the SNP locus SBI‐04_7987371 and a member of the glycoside hydrolase superfamily 35, plays a role in stem diameter, plant height, and is involved in cellular processes. Novel associations (eight for stem diameter), SNPs, and structural variants identified in this study can be used for manipulating plant height and stem diameter in sorghum.
Why it matches plant phenotyping methods手動・地上ロボット・ドローンによる植物形質測定を比較し、相関と精度を評価しているため、表現型取得法の技術的検証がGWASの中で実質的に扱われている。
abstractphenotyped using manual, ground‐robot, and drone‐based phenotyping approaches
Abstract Non-destructive, rapid, and accurate detection of the nutritional compositions in sorghum is of great significance to the application of sorghum in agricultural production and food industry. In the process of sorghum nutrition detection, it can obtain good effect by extracting the corresponding characteristic wavelengths and selecting the suitable detection model for different nutrients. In this study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the research object. Firstly, the visible near-infrared(Vis-NIR) hyperspectral curves of sorghum were measured by the Starter Kit indoor mobile scanning platform (Starter Kit, Headwall Photonics, USA). Secondly, the nutritional components were determined using chemical methods in order to analyze the differences in nutritional composition among different varieties. Thirdly, the original spectral curves were de-noised by Standard normal variate(SNV), Detrending, and Multiplicative Scatter Correction (MSC) algorithms, and the Competitive adaptive reweighted sampling (CARS) and Bootstrapping soft shrinkage (BOSS) algorithms were used to coarse extract the characteristic variables, then Iteratively retains informative variables (IRIV) was used to judge the importance of the characteristic variables, and the optimal wavelength sets of crude protein, tannin and crude fat were obtained respectively. Finally, Partial least squares(PLS), Back propagation(BP) and Extreme learning machine(ELM) were used to establish the non-destructive detection models of crude protein, tannin and crude fat content respectively. The results showed the following: (1) The optimal variable sets of crude protein, tannin and crude fat contain 41, 38 and 22 wavelength variables, respectively. (2) The CARS-IRIV-PLS model was suitable for detecting crude protein, the prediction set exhibits R 2 , RMSE and RPD values of 0.6913, 0.7996% and 1.7998. The BOSS-IRIV-PLS model achieved good results in tannin detection, the prediction set exhibits R 2 , RMSE and RPD values of 0.8760, 0.2169% and 2.8398. The BOSS-IRIV-ELM model achieved the best results in crude fat detection, the prediction set exhibits R 2 , RMSE and RPD values of 0.6145, 0.3208% and 1.6106. (3) Linear PLS model is suitable for crude protein and tannin detection, and nonlinear ELM model is suitable for crude fat detection. These detection models can be used for the effective estimation of the nutritional compositions in sorghum with Vis-NIR spectral data, and can provide an important basis for the application of food nutrition assessment.
Why it matches plant phenotyping methodsソルガムの栄養成分含量をVIS-NIRハイパースペクトル画像と機械学習で非破壊推定する測定・解析手法が研究の中心であり、複数モデルの性能評価も行っている。
titleUsing VIS-NIR hyperspectral imaging and machine learning for non-destructive detection of nutrient contents in sorghum
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
Identification of high carotenoid germplasm is crucial to assist breeders in provitamin-A biofortification of sorghum (Sorghum bicolor [L.] Moench). High-performance liquid chromatography is the gold standard for carotenoid quantification, however, it is not feasible for large scale phenotyping due to its high cost and low throughput. In this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding. We hypothesized that visual, color-based selection can be an effective strategy to identify high-carotenoid accessions. Yellow grain had significantly higher carotenoid content than red, brown, and white grain. The degree of yellowness could distinguish the presence or absence of carotenoids, but could not distinguish carotenoid concentrations within yellow-only accessions. The degree of luminosity of the grain, however, was able to better predict carotenoid concentrations within yellow-only accessions. Genome-wide association studies identified significant marker-trait associations for qualitative and quantitative grain color traits and carotenoid concentrations near carotenoid pathway genes-ZEP, PDS, CYP97A, NCED, CCD, and LycE-three of which were common between grain color and carotenoid traits. These findings suggest that using grain color as a method for screening germplasm may be an effective high-throughput selection tool for prebreeding and early-stage breeding in carotenoid biofortification.
Why it matches plant phenotyping methods穀粒色を用いたカロテノイド含量推定・高スループット選抜法の実現可能性を検証しており、植物形質取得法が研究の中心である。
abstractIn this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding.
Reproduction assets foundThe paper's grain-color/carotenoid phenotyping data are in public supplementary files (Supplementary Data S1–S3: GRIN color traits, visual scores, colorimeter measurements), and the authors' analysis code is publicly deposited on GitHub with an explicit availability statement.Code · publicAll other data files are available in the supplemental files and code is available at: https://github.com/rmcdower/sorghumbiofortification/tree/a8457f87068867eb687235c255a6222863102e1aOpen asset ↗rmcdower/sorghumbiofortification · a8457f87068867eb687235c255a6222863102e1alines:134-147Dataset · publicThree grains each per accession were scored independently by two individuals and classified as white, yellow, red, or brown (Supplementary Data S2).Open asset ↗lines:71-78Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングによりソルガムの葉序を自動抽出し、手動測定との比較および反復性を評価しており、表現型取得手法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's Data Availability section publicly deposits the raw sorghum images on Zenodo and the phenotypic data, GWAS result files, and analysis/figure code on GitHub (jdavis-132/phyllotaxy). The reconstruction/skeletonization code (cropsinsilico/SorghumVoxelCarving) is also mentioned but its URL has no exact match inDataset · publicuction and skeletonization is available at GitHub: https://github.com/
401 cropsinsilico/SorghumVoxelCarving
402 The raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstructiOpen asset ↗Zenodo · 10.5281/zenodo.4426620pdf-layout-page:14 lines:1-50Code · publicare available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstruction and trait value extraction. JMD NS and
410 RJG annotated image data and employed domain expertise to reconcile extracted trait values and
411 true plaOpen asset ↗GitHub · jdavis-132/phyllotaxypdf-layout-page:14 lines:1-50Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Accurately counting the number of sorghum seedlings from images captured by unmanned aerial vehicles (UAV) is useful for identifying sorghum varieties with high seedling emergence rates in breeding programs. The traditional method is manual counting, which is time-consuming and laborious. Recently, UAV have been widely used for crop growth monitoring because of their low cost, and their ability to collect high-resolution images and other data non-destructively. However, estimating the number of sorghum seedlings is challenging because of the complexity of field environments. The aim of this study was to test three models for counting sorghum seedlings rapidly and automatically from red-green-blue (RGB) images captured at different flight altitudes by a UAV. The three models were a machine learning approach (Support Vector Machines, SVM) and two deep learning approaches (YOLOv5 and YOLOv8). The robustness of the models was verified using RGB images collected at different heights. The R 2 values of the model outputs for images captured at heights of 15 m, 30 m, and 45 m were, respectively, (SVM: 0.67, 0.57, 0.51), (YOLOv5: 0.76, 0.57, 0.56), and (YOLOv8: 0.93, 0.90, 0.71). Therefore, the YOLOv8 model was most accurate in estimating the number of sorghum seedlings. The results indicate that UAV images combined with an appropriate model can be effective for large-scale counting of sorghum seedlings. This method will be a useful tool for sorghum phenotyping.
Why it matches plant phenotyping methodsUAV画像からソルガム幼苗数という植物形質を自動推定するモデルを開発・比較し、異なる飛行高度で頑健性を検証しており、表現型取得手法が中心である。
abstractThe aim of this study was to test three models for counting sorghum seedlings rapidly and automatically from red-green-blue (RGB) images captured at different flight altitudes by a UAV.
During the last decade, the sorghum aphid (Melanaphis sorghi), previously identified as sugarcane aphid (Melanaphis sacchari), became a serious pest of sorghum, spreading to all sorghum-producing regions in the United States, Mexico, and South America, where crop losses of 50%-100% have been reported. Developing sorghum cultivars with resistance to this insect is the most sustainable strategy for long-term pest management. To design cultivars with aphid resistance, comprehensively understanding the mechanisms underlying aphid survival, host plant resistance, and aphid-sorghum interactions is critical. In this review, we summarize the comprehensive efforts to characterize the aphid populations as well as their interaction with sorghum plants via hormonal pathways that trigger various genes including leucine rich repeats, WRKY transcription factors, lipoxygenases, calmodulins, and others. We discuss efforts made during the last decade to identify specific genomic regions and candidate genes that confer aphid resistance, as well as describe recent successes and potential challenges in breeding for aphid resistance. Furthermore, we discuss the use of disruptive technologies like high-throughput phenotyping, artificial intelligence, or machine learning for developing aphid resistant sorghum cultivars. Integration of these new technologies has the potential to accelerate the development and design of novel traits that confer durable aphid resistance in new sorghum cultivars to defend sorghum against new aphid genotype development.
Why it matches plant phenotyping methodsソルガムのアブラムシ抵抗性研究を総説し、高スループット表現型解析・AI・機械学習の利用を明示的に扱うため、植物フェノタイピング手法のレビューとして中心性があります。
abstractFurthermore, we discuss the use of disruptive technologies like high-throughput phenotyping, artificial intelligence, or machine learning for developing aphid resistant sorghum cultivars.
High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops. Specifically for sorghum ( Sorghum bicolor L.), rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle. In this context, the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time. Therefore, this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions. A preharvest benchmark dataset was collected at field scale (2023 season, Kansas, USA), with 648 images of sorghum panicles retrieved via smartphone device, and grain number counted. Each sorghum panicle image was manually labeled, and the images were augmented. Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation, with an average precision of 75% and 89%, respectively. For the grain number, 3 models were trained: MCNN (multiscale convolutional neural network), TCNN-Seed (two-column CNN-Seed), and Sorghum-Net (developed in this study). The Sorghum-Net model showed a mean absolute percentage error of 17%, surpassing the other models. Lastly, a simple equation was presented to relate the count from the model (using images from only one side of the panicle) to the field-derived observed number of grains per sorghum panicle. The resulting framework obtained an estimation of grain number with a 17% error. The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.
Why it matches plant phenotyping methodsスマートフォン画像からソルガム穂の検出・分割と粒数推定を開発・検証しており、植物形質取得手法が研究の中心である。
abstractthis study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.
Reproduction assets foundThe paper's authors explicitly state that the code used to train, test, and analyze the data is publicly available on GitHub. The phenotype image datasets are only available upon request.Code · publicThe code used to train, test, and analyze the data is available at https://github.com/GustavoSantiago113/Sorghum_Grain_Counter .Open asset ↗GustavoSantiago113/Sorghum_Grain_Counterlines:169-296Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract A sorghum sudangrass (SSG) cover crop grown after a cash crop could take up residual nitrogen (N) before it is lost. As in‐field monitoring of SSG properties is laborious, predicting biomass and N concentrations with spectral sensors could be useful. At two sites in Live Oak, Florida, we evaluated the response of SSG to residual N from previous N fertilization and the performance of handheld and satellite sensors in estimating SSG properties. We quantified aboveground biomass, plant N, leaf greenness (NDVI), net potential N mineralization (PNM), and soil permanganate oxidizable carbon (POXC). Residual N did not affect SSG properties, PNM was highest at the highest N input rate in one site, and soil POXC was correlated with SSG properties (biomass and plant N). NDVI measured from a handheld sensor better predicted SSG properties than satellite imagery in these small plots, suggesting a greater potential to be a useful management tool.
Why it matches plant phenotyping methodsスペクトルセンサーによる作物バイオマスと植物N濃度の推定性能を評価・比較しており、植物形質の取得手法が研究の中心です。
abstractpredicting biomass and N concentrations with spectral sensors could be useful.
Plant counting plays an important role in evaluating planter effectiveness, assessing seed quality, devising agricultural management plans, and estimating crop yields. Given its significance and the ease of acquiring agricultural images, the development of an end-to-end image-based plant counting model applicable across diverse agricultural settings is crucial. The proposed TasselNetV2++, an improved version of TasselNetV2+ for plant counting, introduces notable enhancements to its encoder and counter while maintaining the existing normalizer. In the encoder, we designed a dual-branch architecture, with one branch being a customized YOLOv5s backbone and the other branch being the original encoder equipped with an attention mechanism. It is precisely the branch-level transfer learning, coupled with multilayer fusion, within the dual-branch architecture that significantly enhances the feature extraction capability of the network across a wide range of scenarios. Moreover, the counter has been enhanced with an attention mechanism that recalibrates its focus on crucial spatial locations and channel-wise features following average pooling. Experimental results demonstrate that TasselNetV2++ outperforms its predecessor across multiple counting tasks. Compared to TasselNetV2+, TasselNetV2++ achieves a substantial reduction in relative root mean squared error (rRMSE). Specifically, it brings a 33.3% relative decrease of rRMSE on the soybean seedlings counting dataset, 8.4% on the wheat ears detection dataset, 28.6% on the maize tassels counting dataset, and 18.0% on the sorghum heads counting dataset. Notably, ablation experiment demonstrates the indispensability of the branch-level transfer learning in achieving precise plant counting. Branch-level transfer learning achieves a notable relative decrease in rRMSE of 31.4% for soybean seedlings, 7.9% for wheat tassels, 36.5% for maize tassels, and 2.0% for sorghum heads. The proposed TasselNetV2++ attains remarkable advancements and introduces a straightforward yet highly effective branch-level transfer learning strategy.
Why it matches plant phenotyping methods植物個体・器官の画像ベース計数モデルを開発し、複数データセットで性能比較とアブレーション評価を行っており、表現型取得・抽出法が中心である。
abstractThe proposed TasselNetV2++, an improved version of TasselNetV2+ for plant counting, introduces notable enhancements to its encoder and counter while maintaining the existing normalizer.
Abstract This study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.
Why it matches plant phenotyping methodsUASマルチスペクトル画像と植生指数を用いて、ソルガムの鉄欠乏クロロシスという植物状態を検出・評価する手法を開発・比較しており、表現型取得が研究の中心である。
abstractThe objective of this project is to find the best VI to detect and monitor IDC.
Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.
Why it matches plant phenotyping methods圃場画像からソルガムの炭腐病症状を分類・セグメンテーションにより検出・定量する手法を開発・評価しており、植物病害状態の取得が研究の中心である。
abstractThe objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection.
In this study, we introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures. Our work addresses the challenges of instance segmentation of plant point clouds, including the difficulty of annotating and labeling point clouds, the loss of local structural information in neural network components, and the generation of large numbers of incorrect small clusters due to poor choices of the loss function. One of the key contributions of our approach is a digital twin of sorghum, i.e., a procedural sorghum model, which was used to generate point clouds of sorghum fields. This allowed us to create a large-scale, annotated, synthetic dataset of sorghum plants that we used to train our PlantSegNet model. We demonstrated the effectiveness of our method in segmenting instances of sorghum leaves grown in outdoor field settings. To the best of our knowledge, this is the first study to address this specific instance segmentation problem for plants grown in such a setting. We compared our proposed method with other state-of-the-art methods for indoor settings, including SGPN and TreePartNet, on both synthetic and real data. Our results show that PlantSegNet outperforms these methods regarding accuracy, robustness, and efficiency.
Why it matches plant phenotyping methods植物葉の点群から器官インスタンスを抽出するニューラルネットワークを開発し、合成データセット作成、実データでの比較検証まで行っており、植物表現型取得手法が中心である。
abstractwe introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures.
Reproduction assets foundThe authors publicly release their PlantSegNet analysis code (PyTorch models and TreePartNet wrapper) together with their labeled synthetic and real sorghum point cloud datasets, and separately state the datasets (synthetic/real sorghum and Tree Dataset) are publicly available on their GitHub page.Dataset · publicobjects. To
that end, according to PlantSegNet input format requirements, we
developed a modified version of the TreePartNet paper’s dataset named
the Tree Dataset. The Tree Dataset includes 3,521, 440, and 440 point
clouds in the training, validation, and test sets. These datasets are now
publicly available on our GitHub page https://github.com/ariyanzri/PlantSegNet.3.2. Data augmentation
To enhance the diversity of our artificially generated dataset and
make it more resilient to the noises present in the real data, we intro-
duce a common noise to each point coordinate in all three dimensions
of the 3D space separately. The noise has a mean of zero and a standard
deviation of 0.01. FurtherOpen asset ↗ariyanzri/PlantSegNet.3.2pdf-raw-page:7 lines:1-145Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Societal Impact Statement Parasitic plants that deprive crops of water and nutrients are an increasingly concerning food security issue, affecting the livelihood of millions of subsistence, small‐ and mid‐scale farmers. An in‐depth understanding of parasite–host interactions is required to develop species‐specific and ecologically sustainable parasite management methods. The non‐invasive visualization of herbaceous contact zones, applicable to diverse parasite–host pathosystems presented in this study, brings methodological advance to the research of biotic interactions between crops and plant parasites belonging to the most devastating parasitic plant family (Orobanchaceae). This work also provides first insights into how the parasites' feeding organ displaces host tissue beyond the direct parasite–host interface. Summary High‐resolution X‐ray computed tomography (HRXCT) enables sectioning‐free two‐dimensional imaging of biological structures and reconstruction of three‐dimensional objects. Although its application is common in many areas of biomedicine and despite its flexibility regarding resolution levels, the technology remains underutilized in the plant sciences. Here, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution. We tested various sample preparation methods and contrast stains for their efficiency to improve the imaging of haustorium samples. In doing so, we achieved cellular resolution with the visible cellular organization of haustorial structures, especially of the vascular system. Fresh stained and dehydrated sample preparation of soft haustoria enables the highest spatial resolution with fine‐cellular discrimination of haustorium versus host cells. Application of cell‐level resolved HRXCT to five pathosystems: Alectra ‐cowpea, Phelipanche ‐tomato, Phtheirospermum ‐tomato, Rhamphicarpa ‐tomato, and Striga ‐sorghum highlighted a life history‐specific organization and uncovered an as yet undescribed internal displacement of host tissue at parasite–host interfaces. Following image‐based training, our HRXCT approach could invoke AI‐based cell recognition for automated parasite cell–host cell differentiation. Superseding extensive microsectioning for 3D imaging, the newly established HRXCT protocol for 2D‐ and 3D‐visualization of herbaceous plant–plant contact zones and the first insights gained from it, is useful for mid‐throughput, comparative studies of parasitic plant–host interactions.
Why it matches plant phenotyping methodsHRXCTによる植物組織の2D・3D画像取得プロトコルを開発し、試料調製・染色を比較検証したうえで、寄生植物と宿主の接触領域を細胞レベルで可視化する方法が中心である。
abstractHere, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution.
Introduction: is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods: We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results: The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion: The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
Why it matches plant phenotyping methods日次の非破壊画像計測を用いて成長関連形質とWUEを時系列で抽出し、早期表現型を解析しており、画像ベースの植物表現型取得が研究の主要な方法として記述されている。
abstractWe conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment.
Reproduction assets foundThe paper's image-derived phenotypic measurements and analysis tables (accession list with phenotypic data, germination data, heritability, trait rankings, SNP-trait correlations, candidate genes) are stated to be included in the article's Supplementary Materials, publicly available at the Frontiers supplementary URL. Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2024.1278802/full#supplementary-material
Supplementary File S1
Table of Bioenergy Association Panel accessions used in this study (adapted from Brenton et al., 2016 ) with image-derived phenotypic data.
Supplementary File S2
Heatmap of a kinship matrix showing correlation analysis among the 369 BAP accessions. The coloOpen asset ↗lines:229-258Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.
Why it matches plant phenotyping methods根内AMF構造をMask R-CNNで画素単位に分割し、AMF定着を高スループット推定する画像解析手法が研究の中心である。
abstractThe high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations
Reproduction assets foundThe paper's authors publicly release their segmentation/analysis code and summary data on GitHub, and the paper uses the public Cambridge AMF image dataset from Zenodo as training data. The >20,000 raw Georgia images are only available upon request.Code · publicCodes are available in GitHub: https://github.com/Arnold-Lab/image_seg_sorghum_am .Open asset ↗Arnold-Lab/image_seg_sorghum_amlines:222-293Dataset · publicThe publicly available Cambridge dataset (zenodo ID https://doi.org/10.5281/zenodo.5118948 ) included 15 whole slide scanning imagesOpen asset ↗10.5281/zenodo.5118948lines:185-204Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 14 Sept 2026
ABSTRACT Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red-green-blue (RGB) images of sorghum plants exhibiting symptoms of infection. EfficientNet-B3 and a fully convolutional network (FCN) emerged as the top-performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet-B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their processing time decreased exponentially. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a base for drone-based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web-based application where users can easily analyze their own images. Core ideas Automated phenotyping tools are required for the efficient detection and quantification of charcoal rot of sorghum. Classification and segmentation models can distinguish between concurrent plant stresses with similar symptoms. Larger image patch sizes generally improve model performance and reduce processing time.
Why it matches plant phenotyping methodsRGB画像からソルガムの炭腐病症状を検出・定量する分類／セグメンテーション手法を開発・評価しており、植物病害状態の表現型取得が研究の中心です。
abstractThe objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red-green-blue (RGB) images of sorghum plants exhibiting symptoms of infection.
Abstract CONTEXT. Phenotypic plasticity is one of four strategies for coping with environmental heterogeneity, and can be valuable for crop adaptation. OBJECTIVE. With a perspective of phenotypic plasticity, we focus on root traits associated to water uptake and yield formation in field-grown sorghum aiming to study: (1) How do genetic (G), environmental (E) and management (M) factors and their interactions, affect functional root traits? (2) How does plasticity in root traits affect crop yield and yield stability?; and (3) How can plasticity in root traits be introduced in functional crop models? METHODS. A new high-throughput functional root phenotyping approach, that uses time-lapsed electromagnetic induction (EMI) surveys, was used in field G´E´M trials to quantify maximum rooting depth – RD, and a root activity index– RA. Phenotypic plasticity was determined using a reaction norm method. RESULTS. The root phenotyping approach captured G´E´M effects on RA and RD. There was a hierarchy of plasticities for above and below ground traits, i.e., grain number traits > root traits > grain weight traits. The plasticity of root traits was associated to the stability in grain yield traits. Hybrids with high plasticity in root traits tended to stabilise grain numbers and grain weights. Useful diversity in the mean value and plasticity of root traits amongst commercial sorghum hybrids was found here, that could be used to match root phenotypes to target production environments. CONCLUSIONS. The developed high-throughput root phenotyping approach can be a useful tool in breeding and agronomy to increase crop adaptation to drought stress.
Why it matches plant phenotyping methods時系列電磁誘導調査を用いた高スループット根系フェノタイピング手法を開発・適用し、根深度と根活性を定量化しているため、フェノタイピング手法が中心である。
abstractA new high-throughput functional root phenotyping approach, that uses time-lapsed electromagnetic induction (EMI) surveys, was used in field G´E´M trials to quantify maximum rooting depth – RD, and a root activity index– RA.
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.
Why it matches plant phenotyping methods低コストで非破壊的に植物全体の生理形質を経時測定する手法とシステムを開発し、ソルガムで実証しているため、フェノタイピング手法が中心である。
abstractWe developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers.
Plant phenotyping relevance match · UnverifiedbioRxiv · OpenAlex · Europe PMC · checked 7 Sept 2026
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to non-invasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically-stressed sorghum accessions. We identified relationships between root zone acidification and photosynthetic efficiency on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and is suitable for any non-aquatic vascular plant species.
Why it matches plant phenotyping methods低コストでモジュール型の非破壊・全植物表現型計測システムを開発し、成長、吸水、WUEなどを経時測定しており、フェノタイピング手法が研究の中心である。
abstractWe developed a low-cost and versatile approach to non-invasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Grain count is an important trait in sorghum because it is highly correlated to the potential yield. By accurately phenotyping the number of grains per panicle, farmers and agronomists can better monitor crop development. Additionally, mapping the spatial variability of grain count can help identify areas of the field with higher or lower potential yields, allowing for targeted management strategies. This study introduces a method for predicting grain count for sorghum panicles by employing a deep learning-based regression framework for point clouds and Red Green Blue (RGB) images. The framework integrates global features derived from a point cloud model of the panicle and grain counts detected from a sequence of RGB images. The models were evaluated on a paired dataset of point cloud models and RGB images collected for 147 sorghum panicles, which included a variety of panicle structures and grain counts. The point cloud models were constructed via a proximal structure-from-motion-based photogrammetry workflow. The model uses PointNet as the backbone network for processing the point clouds and YoloV5 for detecting grains from RGB images. Following the grain detection step, a scaled dot product attention module is integrated into the network to process the grain counts obtained from the RGB image sequence. Finally, the global features for the point cloud model and the grain counts are combined to predict the total grain count for the panicle. Furthermore, the models are also evaluated on downscaled low-resolution point clouds to assess their potential to be adapted in the future for point cloud models for panicles acquired in the field. The models were able to predict grain counts for the high-resolution point cloud dataset with a mean absolute percent error of 6.5% and 6.8% for the low-resolution point cloud dataset. The results serve as a proof of concept to demonstrate the viability of using a multimodal approach based on point clouds and RGB images to estimate grain count per panicle. Additional enhancements to the model like the inclusion of a module to register the point cloud and the RGB images, and evaluating more point cloud backbone networks can help further strengthen the method.
Why it matches plant phenotyping methodsソルガム穂の粒数という植物形質を、点群とRGB画像の深層学習で非侵襲推定する手法を開発・評価しており、表現型取得・抽出が研究の中心である。
abstractThis study introduces a method for predicting grain count for sorghum panicles by employing a deep learning-based regression framework for point clouds and Red Green Blue (RGB) images.
Abstract Canopy architecture traits are associated with productivity in sorghum [Sorghum bicolor (L.) Moench], and they are commonly measured at the time of flowering or harvest. Little is known about the dynamics of canopy architecture traits through the growing season. Utilizing the ground‐based high‐throughput phenotyping system Phenobot 1.0, we collected stereo images of a photoperiod‐sensitive and a photoperiod‐insensitive population over time to generate three‐dimensional (3D) representations of the canopy. Four descriptors were automatically extracted from the 3D point clouds: plot‐based plant height (PBPH), plot‐based plant width (PBPW), plant surface area (PSA), and convex hull volume (CHV). Additionally, genotypic growth rates were estimated for each canopy descriptor. Genome‐wide association analysis was performed on individual timepoints and the growth rates in both populations. We detected genotypic variation for each of the four canopy descriptors and their growth rates and discovered novel genomic regions associated with growth rates on chromosomes 1 (PBPH, CHV), 3 (PBPH), 4 (PBPH, PBPW), 5 (PBPH), 8 (PSA), and 9 (PBPW). These results provide new knowledge about the genetic control of canopy architecture, highlighting genomic regions that can be targeted in plant breeding programs.
Why it matches plant phenotyping methodsPhenobot 1.0によるステレオ画像取得、3D再構成、キャノピー形質の自動抽出が研究の主要な測定ワークフローであり、植物表現型解析への実質的な適用に該当する。
abstractUtilizing the ground‐based high‐throughput phenotyping system Phenobot 1.0, we collected stereo images of a photoperiod‐sensitive and a photoperiod‐insensitive population over time to generate three‐dimensional (3D) representations of the canopy.
Summary Radiation use efficiency (RUE) is a key crop adaptation trait that quantifies the potential amount of aboveground biomass produced by the crop per unit of solar energy intercepted. But it is unclear why elite maize and grain sorghum hybrids differ in their RUE at the crop level. Here, we used a non‐traditional top‐down approach via canopy photosynthesis modelling to identify leaf‐level photosynthetic traits that are key to differences in crop‐level RUE. A novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure, incorporating a C 4 leaf photosynthesis model, to infer cohesive sets of photosynthetic parameters by simultaneously fitting responses to CO 2 , light, and temperature. Statistically significant differences between leaf photosynthetic parameters of elite maize and grain sorghum hybrids were found across a range of leaf temperatures, in particular for effects on the quantum yield of photosynthesis, but also for the maximum enzymatic activity of Rubisco and PEPc. Simulation of diurnal canopy photosynthesis predicted that the leaf‐level photosynthetic low‐light response and its temperature dependency are key drivers of the performance of crop‐level RUE, generating testable hypotheses for further physiological analysis and bioengineering applications.
Why it matches plant phenotyping methods新規の光合成応答測定法とベイズモデルを開発し、葉の光合成形質を推定して作物レベルRUEを説明しており、表現型取得・抽出法が研究の中心である。
abstractA novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure
Sahelian Africa must meet the challenge of providing enough food to meet its growing population. Therefore, novel breeding and intensive production methods are needed to mitigate this challenge. The objective of this study was to calibrate and validate sorghum varieties leaf area index (LAI) values estimated from Unmanned Aerial Vehicle (UAV) at different growing seasons in Senegal and Mali. To achieve this objective, four experiments were conducted with 14 sorghum (sorghum bicolor) varieties between 2017 and 2019. At the study sites, LAI was measured and crop reflectance was measured with a multispectral camera mounted on a UAV. The study showed that normalized difference vegetation index (NDVI) and simple ratio (SR) were highly correlated to the area index. The results of validation model revealed a better prediction of measured LAI from NDVI (R² = 0.92) and SR (R² = 0.89) vegetation indices in 2019 dry season in Senegal. In addition, the LAI predictions for Mali from NDVI (p < 0.01) and SR (p < 0.01) were highly correlated. Findings showed that vegetation indices can be used to estimate LAI in Mali and Sahel.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数からソルガムのLAIを推定する手法を較正・検証しており、植物形質取得が研究の中心です。
abstractThe objective of this study was to calibrate and validate sorghum varieties leaf area index (LAI) values estimated from Unmanned Aerial Vehicle (UAV)
SorghumGreenhouseRootClassificationRoot system architecture
The root system architecture (RSA) of sorghum is a major morphological trait, which intensely influences the capacity to access soil moisture and forage nutrients under drought conditions. On this basis, the study is to group a set of potential parents based on the information obtained from multivariate analysis of 214 sorghum genotypes using root system architecture. This experiment was conducted using a high-throughput root system phenotyping custom root chamber method in the greenhouse at the Horticulture and Plant Science Department at Jimma University that was arranged in a randomized complete block design with three replications. The sorghum genotypes in this study were grouped into eight distinct clusters based on their root system architecture. Cluster CL-II had the highest number of genotypes, while clusters CL-III and CL-VI had the lowest number of genotypes. The genetic distance between clusters CL-III and VIII was the highest, indicating that these clusters had the most different root traits. On the other hand, clusters CL-V and CL-VII had the lowest genetic distance, suggesting that they had low variation. CL-III had a combination of a narrowest root angle and the longest root length. The principal component analysis (PCA) shows that Acc#220253(58), Acc#220254(#59), Acc#234102(102), Acc#235791(#108), Acc#235811(#118), and Acc#7125(#193) are the most diverging genotype that belonging to different and distantly located clusters. So that these accessions could have higher probabilities of producing heterotic hybrids or superior progenies during hybridization, they could also be taken into consideration as better parents for an efficient future breeding programe.
Why it matches plant phenotyping methodsソルガムの根系形態を取得する高スループット表現型解析チャンバーを用い、RSA形質を中心に214遺伝子型を評価しているため、表現型取得・解析が研究の主要部分です。
abstractusing a high-throughput root system phenotyping custom root chamber method
Agricultural robotics is an active research area due to global population growth and expectations of food and labor shortages. Robots can potentially help with tasks such as pruning, harvesting, phenotyping, and plant modeling. However, agricultural automation is hampered by the difficulty in creating high resolution 3D semantic maps in the field that would allow for safe manipulation and navigation. In this paper, we build toward solutions for this issue and showcase how the use of semantics and environmental priors can help in constructing accurate 3D maps for the target application of sorghum. Specifically, we 1) use sorghum seeds as semantic landmarks to build a visual Simultaneous Localization and Mapping (SLAM) system that enables us to map 78\\% of a sorghum range on average, compared to 38% with ORB-SLAM2; and 2) use seeds as semantic features to improve 3D reconstruction of a full sorghum panicle from images taken by a robotic in-hand camera.
Why it matches plant phenotyping methods植物の3D構造・器官形状を画像から再構成する手法開発が中心であり、単なるロボット位置推定に留まらず、ソルガム穂全体の3D再構成を扱っている。
abstractshowcase how the use of semantics and environmental priors can help in constructing accurate 3D maps for the target application of sorghum
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to non-invasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water consumption, and water use efficiency every two days on unstressed and osmotically-stressed sorghum cultivars. Our system can be implemented using cheap, basic components and requires no specific technical expertise. It can, in theory, be used for any non-aquatic vascular plant species.
Why it matches plant phenotyping methods低コストで非破壊的に全植物の生理・成長形質を経時測定する水耕チャンバー手法を開発しており、フェノタイピング手法が研究の中心である。
abstractWe developed a low-cost and versatile approach to non-invasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers.
Common beanCowpeaSorghumField / plotThermalLeafPhysiological trait estimationStomatal traitsWater status / transpiration
Stomatal conductance ( g s ) is a critical plant biophysical variable that reflects plant regulation of CO 2 uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g s indirectly through leaf temperature ( T leaf ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T leaf measurements to instead determine parameters of a model for g s that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g s model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T leaf , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g s parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.
Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、ガス交換データで検証しており、植物フェノタイピング手法が中心である。
abstractOur objective was to develop an automated pipeline for g s model parameterization using thermal data
Common beanCowpeaSorghumField / plotThermalLeafPhysiological trait estimationStomatal traitsPlant / canopy temperature
Stomatal conductance ( g ) is a critical plant biophysical variable that reflects plant regulation of CO uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g indirectly through leaf temperature ( T ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T measurements to instead determine parameters of a model for g that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.
Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、葉レベルのガス交換データで検証しており、植物表現型取得法が中心である。
abstractOur objective was to develop an automated pipeline for g model parameterization using thermal data
Near infrared spectroscopy (NIRS) was evaluated as a rapid and non-destructive method for determining the concentration of water-soluble carbohydrates (WSC) in stem fractions for winter canola (Brassica napus L.), maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) crops. For each crop at different growth stages, stem WSC concentration was determined using NIRS, and benchmarked against the anthrone reagent method, chemical lab analysis. Partial least squares regression was implemented to associate the WSC predicted via NIRS relative to those obtained by laboratory analysis. Spectral regions between 1100 and 1480 nm were critical for WSC determination. The predictive models resulted in coefficient of determinations of 0.93, 0.94, and 0.95, and a Root Mean Square Error of prediction of 10, 20, and 17 for winter-canola, maize and sorghum crops, respectively. The NIRS spectroscopy is a reliable method for WSC determination in stem tissues on these major field crops.
Why it matches plant phenotyping methods作物の茎組織における水溶性炭水化物濃度をNIRSで非破壊推定し、化学分析とベンチマークして予測性能を検証しているため、植物形質計測法が中心である。
abstractNear infrared spectroscopy (NIRS) was evaluated as a rapid and non-destructive method for determining the concentration of water-soluble carbohydrates (WSC) in stem fractions
The mechanical and chemical properties of plant cell walls greatly rely on the supramolecular assembly of cellulose fibrils. To study the local orientation of cellulose in secondary plant cell walls, diffraction limited infrared (IR) micro-spectroscopic mapping experiments were conducted at different orientation of transverse leaf section of the grass Sorghum bicolor with respect to the polarization direction of the IR radiation. Two-dimensional maps, based on polarization-sensitive absorption bands of cellulose were obtained for different polarization angles. They reveal a significant degree of anisotropy of the cellulose macromolecules as well as of other biopolymers in sclerenchyma and xylem regions of the cross section. Quantification of the signals assigned to polarization sensitive vibrational modes allowed to determine the preferential orientation of the sub-micron cellulose fibrils in single cell walls. A sample of crystalline nano-cellulose comprising both a single microcrystal as well as unordered layers of nanocrystals was used for validation of the approach. The results demonstrate that diffraction limited IR micro-spectroscopy can be used to study hierarchically structured materials with complex anisotropic behavior.
Why it matches plant phenotyping methods偏光感受性IRマイクロ分光マッピングを開発し、植物細胞壁内のセルロース微細繊維の配向を定量化しており、植物の構造形質取得が研究の中心である。ナノセルロース試料による手法検証も実施している。
abstractQuantification of the signals assigned to polarization sensitive vibrational modes allowed to determine the preferential orientation of the sub-micron cellulose fibrils in single cell walls.
Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R 2 ) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R 2 = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R 2 = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R 2 = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.
Why it matches plant phenotyping methodsソルガム穀粒のタンパク質含量という植物器官形質を対象に、携帯型NIR装置の較正・検証・ベンチトップ装置との性能比較が研究の中心であるため。
abstractparallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W).
Sorghum is a crucial crop in semi-arid regions because of its capacity to resume photosynthesis and physiological growth after being subjected to drought stress. Although it is one of the most resilient crops to drought stress, recurrent drought is affecting its productivity. It is thus of paramount importance to explore genes contributing to drought stress adaptation, thereby increasing the productivity of sorghum. A study was initiated to evaluate and determine the effect of root systems, particularly root angle traits, on drought stress adaptation and grain yield performance. A total of 428 sorghum genotypes from the Ethiopian breeding program were evaluated for their performance in three drought-stress environments. The experimental materials include stay-green, non-stay-green genotypes and released sorghum varieties. A row-column design with three replications was used for the field trials. For root system screenings a high throughput phenotyping platform were used and a row-column design with two replications applied for root trait analysis. The mean grain yield for non-stay green genotypes ranged from 1.63 to 3.1 tons/ha. However, for stay-green genotypes, it ranged from 2.4 to 2.9 tons per hectare. The analysis of the root system architecture showed highly significant variations among the genotypes. The root angle of non-stay-green genotypes ranged from 8.0 to 30.5°, while for stay-green sorghum genotypes it varied from 12.0 to 29.0°. At the same time, for improved varieties, it exhibited between 14.04 and 19.50°. The result of the principal component for stay-green genotypes was computed, and the largest variations were 52.7% and the least were 10.4%. The most contributing traits in dimension one were shoot dry weight and shoot fresh weight, followed by leaf width and shoot length. Positive and significant correlations were observed between leaf areas and shoot dry weight and leaf width and shoot dry weight at phenotypic and genotypic levels. Negative correlations were observed between root angle and leaf area. Root angle and root length traits had a negative phenotypic correlation (r = −0.018). In conclusion, in drought-stressed conditions, narrow root angle genotypes produced the highest grain production. Therefore, narrow root angle genotypes should be taken into account in sorghum breeding to boost sorghum gain yield in drought-stressed areas. Secondly, the association of the narrow root angle trait with grain yield revealed a connection between the two traits to maximize the productivity of sorghum, both for stay-green and non-stay-green sorghum genotypes. However, the productivity of narrow root angle genotypes was higher for stay-green gene introgressed sorghum genotypes. Finally, the negative correlation obtained between the root angle and grain yield traits for stay-green genotypes has justified the possibility of using the stay-green trait to select sorghum genotypes with narrow root angles.
Why it matches plant phenotyping methodsソルガムの根系形態（根角度・根長）を多数の遺伝子型で評価し、高スループット表現型解析プラットフォームを根系スクリーニングに中心的に適用しているため。
abstractFor root system screenings a high throughput phenotyping platform were used and a row-column design with two replications applied for root trait analysis.
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
In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.
Why it matches plant phenotyping methodsUASによる高スループット表現型計測のスケーラビリティ検証、手動計測との比較、CT QTL検出が研究の主要な方法的貢献であるため。
abstractUncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping.
Drought adaptation for water-limited environments relies on traits that optimize plant water budgets. Limited transpiration (LT) reduces water demand under high vapor pressure deficit (VPD) (i.e., dry air condition), conserving water for efficient use during the reproductive stage. Although studies in controlled environments report genetic variation for LT, confirming its replicability in field conditions is critical for developing water-resilient crops. Here we test the existence of genetic variation for LT in sorghum in field trials and whether canopy temperature (TC) is a surrogate method to discriminate this trait. We phenotyped transpiration response to VPD (TR-VPD) via stomatal conductance (gs), canopy temperature (TC) from fixed IRT sensors (TCirt), and unoccupied aerial system thermal imagery (TCimg) in 11 genotypes. Replicability among phenomic approaches for three genotypes revealed genetic variability for TR-VPD. Genotypes BTx2752 and SC979 carry the LT trait, while genotype DKS54-00 has the non-LT trait. TC can determine differences in TR-VPD. However, the broad sense heritability (H2) and correlations suggest that canopy architecture and stand count hampers TCirt and TCimg measurement. Unexpectedly, observations of gs and VPD showed non-linear patterns for genotypes with LT and non-LT traits. Our findings provide further insights into the genetics of plant water dynamics.
Why it matches plant phenotyping methods圃場での蒸散応答という植物生理形質を、固定式赤外線センサーとUAS熱画像で測定し、手法間の再現性と代理指標としての妥当性を評価しており、フェノタイピング手法が中心である。
abstractHere we test the existence of genetic variation for LT in sorghum in field trials and whether canopy temperature (TC) is a surrogate method to discriminate this trait.
This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R 2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.
Why it matches plant phenotyping methodsソルガムの作物含水量という植物生理形質を、ハイパースペクトル測定と機械学習で推定する手法の構築・比較が中心であり、植物フェノタイピング手法に該当する。
abstractthree machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements.
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
Why it matches plant phenotyping methodsRGB・LiDAR・VNIRリモートセンシングによる作物形質抽出について、行選択とプロットトリミングを反復性・収量予測で評価しており、フェノタイピング手法の技術評価が中心である。
abstractThe objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data.
Reproduction assets foundThe paper states that remote sensing data, yield data, and the authors' R analysis code are publicly deposited in the Purdue University Research Repository under DOI 10.4231/PF9S-4G38. This is a paper-specific, publicly actionable asset covering both the phenotyping measurements (RGB/LiDAR/VNIR remote sensing traits, 4Dataset · publicRemote sensing data, yield data, and R code used for this study are available at the Purdue University Research Repository (10.4231/PF9S-4G38).Open asset ↗Purdue University Research Repository · 10.4231/PF9S-4G38lines:404-413Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published19 May 20232023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)Cited by 1 · OpenAlex ↗
Nitrogen (N) is one of the essential nutrients required for healthy crop growth. Field phenotyping for nitrogen stress symptoms is laborious and time-consuming, that way, it is a major bottleneck in nutrition-inclusive agricultural research. Recent advancements in sensors and image processing facilitate color-based quantification of crop greenness from high-resolution RGB images. In this paper, we present unmanned aerial vehicle (UAV)-based digital field phenotyping for the estimation of crop nitrogen content. For this, we conducted a field experiment during the post-rainy season of 2021 at International Crops Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India with long-stature cereal model crop, sorghum (Sorghum bicolor L.) cultivated under three different regimes varying in moisture and soil nitrogen content. A high-resolution RGB sensor (XenmuseX5S) mounted on DJI Matric 210 quadcopter was used for capturing the spatiotemporal imagery. Five different RGB spectrum vegetation indices indicating crop greenness were correlated with ground truth values of crop N content using simple linear regression and stepwise backward regression. With a prediction potential of R2=0.65 and MAE=0.27 for an independent dataset, we present a stepwise backward linear regression model as a promising approach for real-time estimation of the N status of sorghum crop.
Why it matches plant phenotyping methodsUAV搭載RGBセンサーと画像由来植生指数を用いてソルガムの窒素状態を推定する手法を開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractIn this paper, we present unmanned aerial vehicle (UAV)-based digital field phenotyping for the estimation of crop nitrogen content.
The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10\% of original training data.
Why it matches plant phenotyping methodsソルガム穂の検出・計数を植物表現型として取得する画像解析手法を、半教師あり学習で開発・評価しており、表現型取得法が中心である。
abstractDetecting and counting sorghum panicles can provide significant information for plant phenotyping.
Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.
Why it matches plant phenotyping methodsUAVリモートセンシングからソルガムのバイオマスを推定するLSTM・転移学習手法が研究の中心であり、表現型取得データの解析と技術評価を行っている。
abstractIn this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction.
High throughput plant phenotyping is the advanced scientific approach for rapid phenotyping of plant traits, especially high consumable grains or crops, which is designed to process a high volume of data in a short time for plant breeders and cultivars to utilize. Detection and counting of crop traits such as plants, fruits, wheat or rice spikes, sorghum head, and plant diseases is more advanced research in this field, where real-world data are collected using aerial and land-based imaging platforms equipped with a variety of geospatial sensors, and their statistical analysis is conducted using Artificial Intelligence (AI) and Deep Learning-based solutions. In this paper, we contributed to solving such a challenge of phenotyping by detecting and counting wheat spikes from land-based imaging by applying a Region-based Convolutional Neural Network (CNN) model. Our method employs the use of CNN to extract features from the imaging platform and the learning model is trained to detect and count wheat spikes in field images based on these extracted features. Using the publicly available SPIKE dataset to train and test our model, our proposed method achieved 98% average precision and 91% average F1 score on the test set. Our results show a significant improvement of 2.9% and 11.2% in detection accuracy as well as 1% and 3% in average precision metric over state-of-the-art Faster Regionbased Convolutional Neural Network (Faster-RCNN), and RetinaNet, respectively, and have the potential to significantly benefit plant breeders by facilitating the selection of wheat varieties with high yields. DUJASE Vol. 7 (2) 21-30, 2022 (July)
Why it matches plant phenotyping methods小麦穂の画像から検出・計数するCNNベースの表現型抽出法を開発・評価しており、植物表現型測定が研究の中心です。
abstractIn this paper, we contributed to solving such a challenge of phenotyping by detecting and counting wheat spikes from land-based imaging by applying a Region-based Convolutional Neural Network (CNN) model.
Sweet sorghum is typically cultivated for the food and fodder market. Recently, sweet sorghum varieties are being metabolically transitioned to enhance energy density by accumulating oil droplets in their vegetative tissues for bioenergy applications. Owing to the high biomass yield of sorghum, the transgenic lines can compete with oil-seed crops for biodiesel yield per unit area. In the initial phase of transgenic development, a high-throughput phenotyping method can bridge the gap between the production pipeline and analysis to improve the efficiency of the process. To meet the requirement, the present study extends the application of time-domain 1H-NMR spectroscopy for rapid quantification and characterization of the total in-situ lipids of sweet sorghum ‘ramada’ to lay the groundwork for analyzing the upcoming large quantity of transgenic samples. NMR technology has been successfully established for analyzing lipid contents of vegetative tissues of non-transgenic variety. The multiexponential analysis of spin-lattice (T1) relaxation spectra obtained from TD-NMR aided the investigation of the dynamics of the free and bound lipid fraction with plant development. The total lipid concentration of bagasse and leaves of non-transgenic sweet sorghum remained unchanged throughout the plant development. Leaves displayed a higher percentage of bound lipids as compared to bagasse. A significant variation in the lipid concentration of juice was observed at the different growth stages with a maximum lipid accumulation of 1.21 ± 0.04% w/w at the boot stage that decreased with further maturity of the plant.
Why it matches plant phenotyping methods植物組織内脂質を迅速定量するTD-1H-NMR法を高スループット表現型計測法として適用・確立しており、脂質蓄積という植物形質の取得が中心である。
abstracta high-throughput phenotyping method can bridge the gap between the production pipeline and analysis
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
Why it matches plant phenotyping methods植物フェノミクスのマルチモーダル画像・点群から形質を抽出する、スケーラブルで再現可能な処理パイプラインの開発と反復性評価が中心である。
abstractwe developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds.
Reproduction assets foundThe paper's Code and Data Availability statements provide explicit public URLs for the authors' PhytoOracle processing code, ML training-data preparation scripts, trained model training code, and the season-10 lettuce benchmarking dataset (raw RGB/thermal/PSII images and point clouds) hosted on CyVerse.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://datacommons.cyverse.org/browse/iplant/home/shared/phytooracle/season_10_lettuce_yr_2020Open asset ↗iplant/home/shared/phytooracle/season_10_lettuce_yr_2020lines:640-662Code · publicThe automation script and data processing repositories can be accessed at: http://github.com/phytooracleOpen asset ↗github.com/phytooraclelines:640-662Code · publicThe Python scripts used to prepare RGB training data can be accessed here: http://github.com/phytooracle/automation/blob/main/ml/collect_rgb_data.pyOpen asset ↗github.com/phytooracle/automationlines:640-662Code · publicThe Python script used to prepare thermal training data can be accessed here: http://github.com/phytooracle/automation/blob/main/ml/collect_flir_data.pyOpen asset ↗github.com/phytooracle/automationlines:640-662Code · publicThe Python script used to prepare 3D-derived images can be found here: http://github.com/phytooracle/3d_heat_map/blob/main/3d_heat_map.pyOpen asset ↗github.com/phytooracle/3d_heat_maplines:640-662Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.
Why it matches plant phenotyping methods単子葉植物の葉数という形態形質を画像から推定する深層学習手法を開発し、既存手法との比較検証と頑健性評価を行っており、表現型取得が研究の中心です。
abstractwe present a new deep neural network-based method
Non-destructive measurements of internal morphological structures in plant materials such as seeds are of high interest in agricultural research. The estimation of pericarp thickness is important to understand the grain quality and storage stability of seeds and can play a crucial role in improving crop yield. In this study, we demonstrate the applicability of fiber-based Bessel beam Fourier domain (FD) optical coherence microscopy (OCM) with a nearly constant high lateral resolution maintained at over ~400 µm for direct non-invasive measurement of the pericarp thickness of two different sorghum genotypes. Whereas measurements based on axial profiles need additional knowledge of the pericarp refractive index, en-face views allow for direct distance measurements. We directly determine pericarp thickness from lateral sections with a 3 µm resolution by taking the width of the signal corresponding to the pericarp at the 1/e threshold. These measurements enable differentiation of the two genotypes with 100% accuracy. We find that trading image resolution for acquisition speed and view size reduces the classification accuracy. Average pericarp thicknesses of 74 µm (thick phenotype) and 43 µm (thin phenotype) are obtained from high-resolution lateral sections, and are in good agreement with previously reported measurements of the same genotypes. Extracting the morphological features of plant seeds using Bessel beam FD-OCM is expected to provide valuable information to the food processing industry and plant breeding programs.
Why it matches plant phenotyping methodsBesselビームFD-OCMを用いてソルガム種子の果皮厚を非破壊・直接計測する画像計測法を実証し、解像度と分類精度も評価しており、植物形質取得法が研究の中心です。
abstractwe demonstrate the applicability of fiber-based Bessel beam Fourier domain (FD) optical coherence microscopy (OCM) with a nearly constant high lateral resolution maintained at over ~400 µm for direct non-invasive measurement of the pericarp thickness of two different sorghum genotypes.
Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of big data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3D point cloud. This study utilized four 3D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN (DGCNN) for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (R2) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques.
Why it matches plant phenotyping methodsLiDAR点群と深層学習による器官分割・形質抽出を開発し、手測定で妥当性検証しており、植物フェノタイピング手法が研究の中心です。
abstractHowever, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3D point cloud.
BACKGROUND AND OBJECTIVES: Aside from being a staple crop, sorghum is now being used as a gluten‐free food, an animal feed ingredient, and a biofuel source. This growing demand for sorghum has increased interest in grain quality and utilization. This study explored near‐infrared hyperspectral imaging (NIR HSI) as a nondestructive and rapid method to predict the oil content of sorghum grains. FINDINGS: Partial least square (PLS) regression models for oil from NIR HSI spectra achieved 0.19% standard error of calibration (SEC) and 0.21% standard error of prediction (SEP) at 10 PLS factors. The results from the NIR HSI instrument were comparable to those from the single‐kernel near‐infrared reflectance instrument using the same set of samples. CONCLUSION: This study showed the potential of HSI as a quality control method for sorghum grains, specifically for oil content, which could be beneficial for sorghum breeders, growers, and processors. SIGNIFICANCE AND NOVELTY: The increasing interest in sorghum use prompted this study which is one of the first to explore NIR HSI for sorghum oil with the ability to indicate single seed weight.
Why it matches plant phenotyping methodsソルガム種子の油含量という植物器官形質を、NIRハイパースペクトル画像から非破壊推定する手法の開発・精度評価が研究の中心である。
abstractPartial least square (PLS) regression models for oil from NIR HSI spectra achieved 0.19% standard error of calibration (SEC) and 0.21% standard error of prediction (SEP) at 10 PLS factors.
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Background Grasses internodes are made of distinct tissues such as vascular bundles, epidermis, rind and pith. The histology of grasses stem was largely revisited recently taking advantage of the development of microscopy combined with the development of computer-automated image analysis workflows. However, the diversity and complexity of the histological profile complicates quantification. Accurate and automated analysis of histological images thus remains challenging. Results Herein, we present a workflow that automatically segments maize internode cross section images into 40 distinct tissues: two tissues in the epidermis, 19 tissues in the rind, 14 tissues in the pith and 5 tissues in the bundles. This level of segmentation is achieved by combining the Hue, Saturation and Value properties of each pixel and the location of each pixel in FASGA stained cross sectiona. This workflow is likewise able to highlight significant and subtle histological genotypic variations between maize internodes. The grain of precision provided by the workflow also makes it possible to demonstrate different levels of sensitivity to digestion by enzymatic cocktails of the tissues in the pith. The precision and strength of the workflow is all the more impressive because it is preserved on cross section images of other grasses such as miscanthus or sorghum. Conclusions The fidelity of this tool and its capacity to automatically identify variations of a large number of histological profiles among different genotypes pave the way for its use to identify genotypes of interest and to study the underlying genetic bases of variations in histological profiles in maize or other species.
Why it matches plant phenotyping methodsトウモロコシ茎断面の組織プロファイルを自動セグメンテーションし、組織形態を定量化する画像解析ワークフローの開発が中心であるため。
titleA robust and efficient automatic method to segment maize FASGA stained stem cross section images to accurately quantify histological profile.
In this paper, we present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments. This is achieved with a novel reconstruction approach that uses seeds as semantic landmarks in both 2D and 3D. To evaluate the performance, we develop a new metric for assessing the quality of reconstructed point clouds without having a ground-truth point cloud. Finally, a counting method is presented where the density of seed centers in the 3D model allows 2D counts from multiple views to be effectively combined into a whole-panicle count. We demonstrate that using this method to estimate seed count and weight for sorghum outperforms count extrapolation from 2D images, an approach used in most state of the art methods for seeds and grains of comparable size.
Why it matches plant phenotyping methodsソルガム穂の3D再構成と種子計数という植物形質取得手法を開発し、再構成品質評価指標と種子数・重量推定を検証しており、フェノタイピング手法が中心である。
abstractwe present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments
Reproduction assets foundThe paper's authors publicly release their sorghum panicle stereo-image dataset (camera poses, human-labeled seed segmentations, panicle weights, seed counts) via the CMU AIIRA resources page, which is an allowed URL.Dataset · publicection, some unremoved husks were counted as seeds by the counting machine despite manual efforts to separate seeds from husks. We expect the effect on the ground truth to be small. The stereo images, camera poses, human-labeled seed segmentations, panicle weights, and human-counted seed counts can be found in our dataset 3 3
3
https://labs.ri.cmu.edu/aiira/resources/ .
Figure 8: (a) 100 sorghum panicles from 10 different sorghum species. (b) Our data collection system, a stereo camera attached to the UR5 robot arm. (c) Seeds were manually stripped and (d) counted using a seed counting machine.
IV-B 3D Reconstruction Quality
We assess the effectiveness of our approach with ablation tests usiOpen asset ↗lines:141-165Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 8 Sept 2026
Canopy covers can be measured using destructive (visual) and non-destructive methods (spectral indices, photogrammetry, visual assessment, and quantum sensor). The precision of crop cover estimation, however, is dependent on the selection of appropriate methods. Studies were conducted at the Indian Grassland and Fodder Research Institute, Jhansi to compare the forage crops canopy cover estimated using photogrammetry software (Canopeo and SamplePoint) and visual assessments. Assessments were performed in three summer crops (corn, cowpea, and sorghum), two winter crops (Egyptian clover, and oats), and bare ground condition. For each plot, three nadir images (directly above the canopy) were captured using digital cameras from a height of 1.5 m above the soil surface between 10 AM to 2 PM on bright sunny days. The results indicated that the relationships between visual assessment and Canopeo (regression coefficient, (R 2 = 0.96), visual assessment and SamplePoint (0.96), and Canopeo and SamplePoint (0.98) were linear when data were pooled across all the crops. SamplePoint and Canopeo is further, appropriate for cowpea (Pearson coefficient ( R = 0.99 and 0.94), oats (0.92 and 0.97), and sorghum (0.46 and 0.51), respectively. SamplePoint and Canopeo are not suitable for berseem (-0.15) and corn (-0.61), respectively, due to dead residues after the first harvest in berseem and taller corn might have influenced the image quality. Therefore, the stage of the crop, the height of the crop, and dead residues around the plants can greatly influence the estimation of crop cover. In conclusion, the results indicated that this photogrammetry software can be used for non-destructive crop canopy measurement with the above-mentioned precautions in the forage crops tested. •Forage canopy cover is estimated generally by visual scoring, and the outcome varies widely from person to person.•Photogrammetry methods (Canopeo and SamplePoint) were positiviely correlated with visual scoring for cowpea, oats, and sorghum.•However, Canopeo and SamplePoint may not suitable for taller crops like corn and ratoon crops like berseem.
Why it matches plant phenotyping methods飼料作物のキャノピー被覆率という植物形質を対象に、CanopeoとSamplePointの画像解析法を比較・検証しており、フェノタイピング手法が研究の中心です。
abstractThe precision of crop cover estimation, however, is dependent on the selection of appropriate methods.
SorghumField / plotRootPhysiological trait estimation2D/3D reconstructionWater status / transpiration
Most field crop phenotyping research has focused on the above-ground parts of crops, ignoring a “hidden half”: the rooting system and its activity. Here we propose and test a new approach to produce 3D characterizations of crop water use and root activity in large field genotype (G) by environment (E) by management (M) experimentation, using electromagnetic induction (EMI) instrument coupled with a quasi-2D inversion algorithm, and crop canopy sensing technologies. A root activity factor (R) was calculated as a function of crop water use, soil water availability, and an indicator of crop demand. We ask i) can this approach provide accurate 3D characterizations of sorghum water use and root activity?, and (ii) does the approach capture complex GxExM dynamics?. This study was conducted based on an on-farm field experiment consisting of the factorial combination of six commercial sorghum genotypes (G), three times of sowing, two levels of irrigation (E), four plant densities (M), and three replications. Two EMI surveys ten days apart were collected using a DUALEM-21S sensor. An artificial neural network (ANN) model was developed to predict 3D soil moisture (θᵥ) using depth-specific true soil electrical conductivity (σ, mS m⁻¹) estimated by the inversion algorithm. Crop water use between surveys was described as the difference of θᵥ. A multispectral index derived from satellite imagery was used as a proxy for crop demand i.e., size of the crop canopy. Principal components analysis, linear mixed models, and recursive partitioning tree techniques and crop-eco-physiological principles were used to untangle complex GxExM interactions. Results indicate that 3D crop water use could be predicted with high accuracy (LCCC = 0.81) and low prediction error (RMSE = 0.03 cm³ cm⁻³). The calculated water use and the value of R were significantly affected by depth, crop growth stage, irrigation treatment, plant density, and their interactions. At flowering, roots were most active at 0–1.3 m under irrigation, and deeper (0.5–1.5 m) under dryland treatment. The highest water use was for three genotypes (i.e., C, E and F) grown under irrigation and high plant densities (i.e., 9 and 12 pl m⁻²). The smallest water use was observed under dryland treatment, particularly for two genotypes (i.e., B and C) and high plant densities. For the crops at vegetative stages, the values of water use and R were highest in the top 0.5 m of soil depth. Larger water use was observed under dryland treatment and high plant densities, while the effects of genotypes were small (not significant). We conclude that the approach provides a rapid, accurate and cost-efficient option to phenotype crop root activity i.e., water use, in large field experimentation. We also argue that the improved understanding of the crop water use dynamics can help inform optimum combinations of genotypes and management options i.e., crop designs, across contrasting environments, and help untangle complex GxExM interactions.
Why it matches plant phenotyping methodsEMIセンサー、準2次元反転、ANN、キャノピーセンシングを組み合わせ、圃場で作物の3D水利用と根活動を推定・検証する手法が研究の中心である。
abstractHere we propose and test a new approach to produce 3D characterizations of crop water use and root activity in large field genotype (G) by environment (E) by management (M) experimentation, using electromagnetic induction (EMI) instrument coupled with a quasi-2D inversion algorithm, and crop canopy sensing technologies.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Tillers are shoots that arise from the base of a plant. When plants tiller, they place more carbon resources into vegetative growth as opposed to their grains. Understanding the environmental and genetic factors behind tillering is hampered by lack of high-throughput phenotyping for determining plant tiller count and angle. Currently, plant tiller counts are determined through manual inspection, which is laborious and low-throughput. In this study, we introduce a PlantCV (https://plantcv.danforthcenter.org/)-based algorithm for detecting tillers. This method uses OpenCV's line detection algorithm to detect lines that correspond to the tillers of the plant. From this, tiller count and angle of growth can be inferred. We use this method on Sorghum bicolor accessions from the TERRA-REF project that were grown for two weeks, cut back, and then allowed to regrow for two weeks. Of 200 randomly chosen images, this algorithm was able to accurately count within 1 tiller of the true number of tillers for 165 images. Furthermore, we find that these Sorghum bicolor accessions appear to place less resources into their tillers in the regrowth phase.
Why it matches plant phenotyping methodsPlantCVとOpenCVを用いて、画像からソルガムの分げつ数と成長角度を推定するアルゴリズムを開発・精度評価しており、植物表現型取得法が研究の中心です。
abstractwe introduce a PlantCV (https://plantcv.danforthcenter.org/)-based algorithm for detecting tillers.
Perennial grain sorghum [Sorghum bicolor (L.) Moench] has potential to produce grain and forage while improving soil health, ecosystem services, and carbon soil sequestration but requires further genetic improvement. Unoccupied aerial systems (UAS, also known as drones and unmanned aerial systems) provide opportunities to quickly evaluate plant traits on a large scale with precision. Unoccupied aerial system flights were used to evaluate biomass yield and rhizome characteristics of 100 diverse sorghum hybrids, most being from an interspecific hybridization program, in the establishment year and first year of regrowth. Twenty‐one vegetation indices (VIs) with canopy height measurements (CHMs) were processed from seven UAS flights made temporally during each growing season. Regression of the temporal data (VI and CHM) and phenotypic traits, including rhizome characteristics based on plant stand count (PSC), rhizome‐derived shoots (RDS), and fresh and dry biomass yields, showed useful predictions when combining temporal VI with CHM and machine learning. Blue chromatic coordinate index (BCC) best predicted all measured traits. If predictions could be generalized, UAS would reduce field evaluation time for perennial sorghum or breeding perennial grasses in general and allow breeders to evaluate additional genotypes. In this study, we found that optimizing flights to specific dates after planting could minimize resource requirements and costs in prediction of regrowth and biomass yield of perennial sorghum.
Why it matches plant phenotyping methodsUAS画像から植生指数・ canopy height と機械学習を用いてソルガムのバイオマスや根茎関連形質を予測する手法が研究の中心であり、植物表現型の取得・推定を実質的に評価している。
abstractUnoccupied aerial system flights were used to evaluate biomass yield and rhizome characteristics of 100 diverse sorghum hybrids
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
SUMMARY In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index (NDVI) values from the chilling nested association mapping population detected CT QTL that colocialized with manual phenotyping CT QTL. Two of the four first-generation KASP molecular markers, generated using the peak QTL SNPs, failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic F ST analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from two independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13–24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.
Why it matches plant phenotyping methodsUASを用いた高スループット表現型計測のスケーラビリティ検証、手動計測との比較、NDVIによる耐寒性QTL検出が研究の主要な技術的要素であるため。
abstractUncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping.
Sorghum ( Sorghum bicolor ) is an economically important cereal crop that can be used as human food, animal feed, and for industrial use such as bioenergy. In sorghum breeding programs, development of cultivars with desirable seed quality characteristics is important and development of rapid low-cost screening methods for seed nutritional traits are desired, since most standard methods are destructive, slow, and less environmentally friendly. This study investigates the feasibility of single kernel NIR spectroscopy (SKNIRS) for rapid determination of individual sorghum seed components. We developed successful multivariate prediction models based on partial least squares (PLS) regression for protein, oil, and weight in sorghum. The results showed that for sorghum protein content ranging from 8.92% to 18.7%, the model coefficients of determination obtained were R C A L 2 = 0.95 (RMSEC= 0.44) and R P R E D 2 = 0.87 (RMSEP= 0.69). The model coefficients of determination for oil prediction were R C A L 2 = 0.92 (RMSEC= 0.23) and R P R E D 2 = 0.71 (RMSEP= 0.41) for oil content ranging from 1.96% to 5.61%. For weight model coefficients of determination were R C A L 2 = 0.81 (RMSEC= 0.007) and R P R E D 2 = 0.63 (RMSEP= 0.007) for seeds ranging from 4.40 mg to 77.0 mg. In conclusion, mean spectra SKNIRS can be used to rapidly determine protein, oil, and weight in intact single seeds of sorghum seeds and can provide a nondestructive and quick method for screening sorghum samples for these traits for sorghum breeding and industry use.
Why it matches plant phenotyping methods単一種子NIR分光とPLSモデルを開発・検証し、育種利用を想定して種子のタンパク質、油分、重量を非破壊推定する研究であり、表現型取得法が中心である。
abstractThis study investigates the feasibility of single kernel NIR spectroscopy (SKNIRS) for rapid determination of individual sorghum seed components.
Lodging is one of the major constraints in attaining high yield in crop production. Major factors associated with stalk lodging involve morphological traits and anatomical features along with the chemical composition of the stem. However, little relevant research has been carried out in sorghum, particularly on the anatomical aspects. In this study, with a high-throughput procedure newly developed by our research group, the nine parameters related to stem regions and vascular bundles were generated in 58 sorghum germplasm accessions grown in two successive seasons. Correlation analysis and principal component analysis were conducted to investigate the relationship between anatomical aspects and stalk mechanical traits (breaking force, stalk strength and lodging index). It was found that most vascular parameters were positively associated with breaking force and lodging index with the correlation coefficient r varying from −0.46 to 0.64, whereas stalk strength was only associated with rind area with the r = 0.38. The germplasm resources can be divided into two contrasting categories (classes I with 23 accessions and II with 30 accessions). Compared to class II, the class I was characterized by a larger number (+40.7%) and bigger vascular bundle (+30%), thicker stem (+19.6%) and thicker rind (+36.0%) but shorter internode (plant) (−91.0%). This study provides the methodology and information for the studies of the stem anatomical parameters in crops and facilitates the selective breeding of sorghum.
Why it matches plant phenotyping methodsソルガム茎の断面形態・維管束形質を抽出する新規ハイスループット手順が研究の中心であり、機械的特性との関連も評価しているため。
abstractwith a high-throughput procedure newly developed by our research group, the nine parameters related to stem regions and vascular bundles were generated
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe Python codes were provided as
Supplementary Material in PDF format (Figure S1).Open asset ↗pdf-page:4 lines:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Texas A&M University recently completed a set of Automated Precision Phenotyping (APP) Greenhouses that incorporate robotic systems for automated collection of advanced sensor-based plant phenotypes. Transiting the length of a greenhouse is a gantry beam, on which a rolling truck provides a second axis of motion along the gantry. Attached to the truck is a 3.0-m long robotic arm that is controlled to position a sensor head at virtually any position relative to any plant in a greenhouse. The robotic arm can be programmed to operate quickly and safely in complicated scanning patterns to enable data collection on all plants in the greenhouse within a time window of a few hours, ensuring consistent conditions during data collection. The sensor head includes a high-speed multispectral camera and eventually a Raman spectrometer. Relative to phenotyping greenhouses at other institutions, the APP Greenhouses have the advantage of maximum flexibility in configuration of plants in the greenhouses, in positioning of sensors relative to the plants, and in the types of sensors used, making research capabilities in the APP Greenhouses truly unique. Preliminary data have been collected on sorghum and maize plants. Four-band multispectral images have been collected daily, scanning the side of each plant from top to bottom. Preliminary software development is directed at automated image stitching to create a full side-view image of each plant, from which consistent metrics can be automatically calculated, such as plant height, stalk diameter, leaf angle, etc.
Why it matches plant phenotyping methodsロボット搬送型マルチスペクトル撮像、画像 stitching、植物形質の自動算出を中核とするフェノタイピング温室のプラットフォーム報告である。
abstractincorporate robotic systems for automated collection of advanced sensor-based plant phenotypes
Use of a complete dynamic model of NADP-malic enzyme C 4 photosynthesis indicated that, during transitions from dark or shade to high light, induction of the C 4 pathway was more rapid than that of C 3 , resulting in a predicted transient increase in bundle-sheath CO 2 leakiness (ϕ). Previously, ϕ has been measured at steady state; here we developed a new method, coupling a tunable diode laser absorption spectroscope with a gas-exchange system to track ϕ in sorghum and maize through the nonsteady-state condition of photosynthetic induction. In both species, ϕ showed a transient increase to > 0.35 before declining to a steady state of 0.2 by 1500 s after illumination. Average ϕ was 60% higher than at steady state over the first 600 s of induction and 30% higher over the first 1500 s. The transient increase in ϕ, which was consistent with model prediction, indicated that capacity to assimilate CO 2 into the C 3 cycle in the bundle sheath failed to keep pace with the rate of dicarboxylate delivery by the C 4 cycle. Because nonsteady-state light conditions are the norm in field canopies, the results suggest that ϕ in these major crops in the field is significantly higher and energy conversion efficiency lower than previous measured values under steady-state conditions.
Why it matches plant phenotyping methodsソルガムとトウモロコシの光合成中間状態におけるCO₂漏出率を測定する新規手法を開発しており、植物生理形質の取得法が研究の中心である。
abstracthere we developed a new method, coupling a tunable diode laser absorption spectroscope with a gas-exchange system to track ϕ in sorghum and maize through the nonsteady-state condition of photosynthetic induction.
MaizeSorghumMesh / voxelPhotogrammetry / SfM / MVSLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionSkeletonization / topologyVisualization / data management
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional RSA, and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new “3D Root Mesocosms” and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods大型作物の根系を3Dで取得・再構築し、根系形質を抽出するメソッドとメソコスム基盤を開発・検証しており、植物フェノタイピング手法が中心である。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands.In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role.However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming.Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly.The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping.The obtained results were then verified through manually acquired data from the sorghum samples.This study measured the plant height, plant crown diameter and the panicle height and diameter.The R 2 of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively.The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping.The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.
Why it matches plant phenotyping methods3D LiDARによるソルガム形態形質の非接触取得法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractThe aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping.
BACKGROUND AND OBJECTIVES: Sorghum is an alternative crop where poor soil is a limiting factor for the production of corn. Laboratory measurements of chemical compounds of grains are expensive, time‐consuming predominantly done on powdered samples. Therefore, a rapid and reliable method integrating image processing and machine learning was evaluated for the prediction of total phenolic compounds (TPCs), tannin, and protein. FINDINGS: The highest TPC (0.83%) was obtained for genotype KGS36 (the brown pericarp) followed by KGS23 (0.54%). The concentration of tannin ranged from 0.008% to 0.616%. Mean comparison revealed variation for the protein concentration (15.17%–11.53%); the highest content obtained by KGS25 and the lowest by KGS36 (p < .01). Multilayer perceptron (MLP) as one of the common artificial neural networks (ANNs) applied to simulate the chemical of the grains was evaluated through the textural features extracted from grain images. For the MLP network, the minimum number of hidden networks was set to 8 and the maximum number was set to 25. For learning and saving, the number of networks was set to 20 and the number of saved networks was set to 5. The best MLP models were selected based on performance (R) and error values for the train, test, and validation sets. CONCLUSIONS: An ANN for the prediction of the chemical concentration was suggested. The correlations between predicted and observed data were higher than .915. SIGNIFICANCE AND NOVELTY: These models are of great significance for the prediction of chemical concentrations of grain sorghum in plant breeding and food industry.
Why it matches plant phenotyping methods穀粒画像からタンニン、タンパク質、総フェノール含量を機械学習で推定する手法を開発・評価しており、化学的な種子形質の取得が研究の中心である。
abstracta rapid and reliable method integrating image processing and machine learning was evaluated for the prediction of total phenolic compounds (TPCs), tannin, and protein
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Background Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results The models with a single color feature from RGB images predicted chlorophyll content with R 2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R 2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R 2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R 2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル・蛍光画像とセンサーフュージョンを用いた葉緑素量推定手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment.
Sorghum, a genetically diverse C 4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation ( V cmax ), phosphoenolpyruvate (PEP) carboxylation ( V pmax ), and electron transport ( J max ), quantified using a C 4 photosynthesis model, were evaluated in two field-grown training sets ( n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax , V pmax , J max , SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 ( n = 875 plots including 650 genotypes) and 2020 ( n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max . Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se . J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
Why it matches plant phenotyping methodsトラクター搭載ハイパースペクトルセンシングとPLSRにより光合成関連形質を推定し、独立試験でモデル性能を評価している。形質取得法の開発・検証と大規模スクリーニングへの応用が中心である。
abstractPartial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing.
Reproduction assets foundThe authors state that all phenotypic data used to develop the PLSR models (ground truth Vcmax, Vpmax, Jmax, SLN, LMA and associated hyperspectral measurements) is publicly available via a UQ eSpace DOI. Genotypic marker data is only available upon request and is not a phenotyping asset. No author analysis code or URLsDataset · publicAll phenotypic data used to develop the models presented in this manuscript is available here: https://doi.org/10.48610/acbe0df .Open asset ↗10.48610/acbe0dflines:488-522Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Why it matches plant phenotyping methods植物の薬剤抵抗性状態を識別するためのハイパースペクトルセンシングと機械学習分類手法の開発・評価が中心であり、単なる生物学的実験のルーチン測定ではない。
abstractThis paper reports a study on developing a hyperspectral plant sensing approach to explore the spectral features of glyphosate‐resistant (GR) and glyphosate‐sensitive (GS) plants to evaluate this approach using machine learning algorithms to differentiate between GR and GS plants.
ABSTRACT The development of iso-nuclear male sterile (A) and maintainer (B) line along with male fertile (R) lines, referred to as fertility restorers, helped in cost-effective seed production and deployment of hybrids. The pollen viability/fertility for maintenance (B-line) and restoration (R-line) remains a significant phenotyping hurdle in developing new lines. We carried out the present investigation to evaluate the pollen fertility status of Cytoplasmic Genetic Male Sterility (CGMS) based hybrids in sorghum. The experimental material consisted of 238 CGMS based hybrids developed from the crossing of individuals of (296B ×IS 188551) - based Recombinant Inbred Line (RIL) population (F7:F8) with male-sterile line (296A). The experiment was conducted in the rainy and post-rainy seasons during 2016 to evaluate pollen fertility. The authors illustrated the pollen fertility status by counting pollen counts using two methods: viz ., visual counting by the naked eye, and image analysis using ImageJ (NIH, USA) software. We compared results between counts recorded visually and ImageJ reads for randomly selected 50 individuals each during the rainy and post-rainy seasons. The pollen counts from ImageJ were 95% efficient in detecting sterile vs . fertile counts. These results provide a better, efficient, and quick tool for characterizing the pollen behavior and also add value to the genetics studies by accounting for the quantitative variation encoded by multiple loci.
Why it matches plant phenotyping methodsソルガム花粉の稔性を画像解析で定量し、目視計数と比較して手法の効率を検証しているため、植物フェノタイピング手法が中心である。
abstractThe authors illustrated the pollen fertility status by counting pollen counts using two methods: viz ., visual counting by the naked eye, and image analysis using ImageJ (NIH, USA) software.
In the present study, individual and combined effects of drought and heat stress were investigated on key physiological parameters (canopy temperature, membrane stability index, chlorophyll content, relative water content, and chlorophyll fluorescence) in two popular sorghum cultivars (Sorghum bicolor cvs. Phule Revati and Phule Vasudha) during the seedling stage. Estimating canopy temperature through pixel-wise analysis of thermal images of plants differentiated the stress responses of sorghum cultivars more effectively than the conventional way of recording canopy temperature. Cultivar difference in maintaining the canopy temperature was also responsible for much of the variation found in critical plant physiological parameters such as cell membrane stability, chlorophyll content, and chlorophyll fluorescence in plants exposed to stress. Hence, the combined stress of drought and heat was more adverse than their individual impacts. The continued loss of water coupled with high-temperature exposure exacerbated the adverse effect of stresses with a remarkable increase in canopy temperature. However, Phule Vasudha, being a drought-tolerant variety, was relatively less affected by the imposed stress conditions than Phule Revati. Besides, the methodology of measuring and reporting plant canopy temperature, which emerged from this study, can effectively differentiate the sorghum genotypes under the combined stress of drought and heat. It can help select promising genotypes among the breeding lines and integrating the concept in the protocol for precision water management in crops like sorghum.
Why it matches plant phenotyping methods熱画像のピクセル単位解析による植物キャノピー温度の測定・報告法を開発し、ソルガム遺伝子型のストレス応答識別に応用しており、表現型取得法が実質的に扱われている。
abstractEstimating canopy temperature through pixel-wise analysis of thermal images of plants differentiated the stress responses of sorghum cultivars more effectively than the conventional way of recording canopy temperature.
Main conclusion The characteristics of sorghum anthers at 18 classified developmental stages provide an important reference for future studies on sorghum reproductive biology and abiotic stress tolerance of sorghum pollen. Sorghum (Sorghum bicolor L. Moench) is the fifth-most important cereal crop in the world. It has relatively high resilience to drought and high temperature stresses during vegetative growing stages comparing to other major cereal crops. However, like other cereal crops, the sensitivity of male organ to heat and drought can severely depress sorghum yield due to reduced fertility and pollination efficiency if the stress occurs at the reproductive stage. Identification of the most vulnerable stages and the genes and genetic networks that differentially regulate the abiotic stress responses during anther development are two critical prerequisites for targeted molecular trait selection and for enhanced environmentally resilient sorghum in breeding using a variety of genetic modification strategies. However, in sorghum, anther developmental stages have not been determined. The distinctive cellular characteristics associated with anther development have not been well examined. Lack of such critical information is a major obstacle in the studies of anther and pollen development in sorghum. In this study, we examined the morphological changes of sorghum anthers at cellular level during entire male organ development processes using a modified high-throughput imaging variable pressure scanning electron microscopy and traditional light microscopy methods. We divided sorghum anther development into 18 distinctive stages and provided detailed description of the morphological changes in sorghum anthers for each stage. The findings of this study will serve as an important reference for future studies focusing on sorghum physiology, reproductive biology, genetics, and genomics.
Why it matches plant phenotyping methodsソルガム葯の発達段階を分類するため、高スループット画像法と光学顕微鏡を用いた形態計測・解析が研究の中心であり、植物器官の状態を抽出するフェノタイピング手法に該当する。
abstractwe examined the morphological changes of sorghum anthers at cellular level during entire male organ development processes using a modified high-throughput imaging variable pressure scanning electron microscopy and traditional light microscopy methods.
MaizeSorghumMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.
Why it matches plant phenotyping methodsハイスループット hyperspectral imaging と回帰モデルにより、植物の相対含水量・窒素含量という生理形質を非破壊推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractLack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants.
Reproduction assets foundThe paper's hyperspectral reflectance measurements, RWC/NC ground-reference trait data, and metadata are explicitly deposited in the Purdue University Research Repository (PURR) with a public URL stated in the Data Availability Statement. The MDPI supplement contains only stepwise regression tables, model evaluation, VDataset · publicData and meta-data are available at The Purdue University Research Repository (PURR), https://purr.purdue.edu/publications/3958/1 (accessed on 30 January 2022).Open asset ↗The Purdue University Research Repository (PURR)lines:222-238Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.
Why it matches plant phenotyping methods穀粒という植物器官のタンパク質含量をNIRセンサーと機械学習で推定する校正モデルを開発・評価しており、形質取得・抽出法が研究の中心である。
abstractWe explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra.
Reproduction assets foundThe authors explicitly state that the custom CNN analysis code for this paper's NIR protein-prediction models is publicly available on GitHub at the authors' repository URL, which matches an allowed URL. Supplementary tables are only referenced via a placeholder (www.mdpi.com/xxx/s1) and are not actionable; the Video SCode · publicced by the quality
and size of the datasets used for training the model. To minimize the “over-fitting” error,
the large dataset was used and split carefully to include the different multi-cereal species
in both the calibration and validation dataset (as described in section 2.5.1). The code is
available on the GitHub platform (https://github.com/adamavip/nirs-protein-prediction)
and its particular parts can be now utilized to enhance and develop other pipelines and
products.
For our dataset, the algorithms built using ML-based methods (particularly, the
stacked ensemble model via Hone Create and custom-designed CNN; section 3.4)
achieved the better comparative metrics for both spectra typesOpen asset ↗adamavip/nirs-protein-predictionpdf-raw-page:14 lines:1-54Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Sorghum is an important cereal crop grown across the globe for its grain and biomass value. It can also efficiently use resources such as nitrogen, and multiple varieties that are nitrogen-use and light-capture efficient are constantly being developed. This study focuses on using the spectral signature of sorghum varieties to predict flowering days, which could be used as a proxy for plants’ growth/productivity and development trends, thus helping breeders make quick decisions about what varieties to move to the next stage. Multiple sorghum varieties from the sorghum association panel were planted in a replicate-design field experiment with the variable supply of nitrogen. The flowering days were monitored and recorded. The hyperspectral reflectance data were collected and used to build a sorghum flowering days predictive model. Although regression models such as partial least square have been used to predict plants’ phenotypes, the non-parametric ensemble machine learning model turned out to perform better on flowering days with an accurate model up to 5 days.
Why it matches plant phenotyping methodsソルガムのハイパースペクトル反射データから開花日数という植物形質を予測するモデルを構築・評価しており、形質取得と計算推定が研究の中心である。
abstractThis study focuses on using the spectral signature of sorghum varieties to predict flowering days
Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.
Why it matches plant phenotyping methodsUAV時系列画像と3D-CNNを用いて、ソルガムの倒伏検出および倒伏重症度を推定する手法を開発・比較検証しており、植物表現型取得が研究の中心である。
abstractWe compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very high-resolution remote sensing image sets collected repeatedly across a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow improved estimation of crop traits, as well as the effects of genetics and the environment upon them. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time-series imagery with 0.88 accuracy, 0.92 precision, and 0.83 recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 precision, and 0.76 recall. Variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. Success of the improved 3D-CNN analysis approach depended on inclusion of before and after data i.e. images collected on dates before and after the lodging event. Integration of geometric and spectral features with 3D-CNN architecture was also key to improved assessment of lodging severity, which is an important and difficult to assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUAV時系列画像と3D-CNNによるソルガムの倒伏検出・重症度推定手法を開発・比較評価しており、植物表現型取得が中心的である。
abstractWe compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity.
Background The structural characteristics of whole sorghum kernels are known to affect end-use quality, but traditional evaluation of this structure is two-dimensional (i.e., cross section of a kernel). Current technology offers the potential to consider three-dimensional structural characteristics of grain. X-ray computed tomography (CT) presents one such opportunity to nondestructively extract quantitative data from grain caryopses which can then be related to end-use quality. Results Phenotypic measurements were extracted from CT scans of grain sorghum caryopses. Extensive phenotypic variation was found for embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume. CT derived estimates were strongly correlated with ground truth measurements enabling the identification of genotypes with superior structural characteristics. Conclusions Presented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses which increases the throughput efficiency of previously difficult to measure traits. Adaptation of this workflow to other small-seeded crops is possible providing new and unique opportunities for scientists to study grain in a nondestructive manner which will ultimately lead to improvements end-use quality.
Why it matches plant phenotyping methodsX線CTを用いてソルガム穀粒の三次元形質を定量化するフェノタイピングパイプラインを開発・検証しており、形質取得手法が研究の中心である。
abstractPhenotypic measurements were extracted from CT scans of grain sorghum caryopses.
This paper reports the first in-field detection of actual hexanal from the damaged sorghums using the field-deployed nano-gap gas sensor. The previously developed nano-gap gas sensor was fully integrated with electronics and wireless communication units into a portable prototype (10×10× 7 c m3). The field deployed gas sensor prototype successfully detected the 'scream‘ from the mechanically damaged sorghums by detecting a particular gas molecule, hexanal, released after the time point of 3.5 hours since the start of leaf cutting. The sensor prototype was pre-programmed to detect >100 ppm concentrations of hexanal in ambient air. After 1.5 hours since the cutting was stopped, the sensor prototype successfully recovered. The sorghum field testing conditions included a temperature (25~120 °C) and humidity (≤70%RH). The detection demonstration clearly indicated that (1) a prototype successfully captured actual hexanal released from the damaged sorghums real-time and that (2) it was feasible to monitor a mechanically-stressed status of sorghums by gas monitoring.
Why it matches plant phenotyping methods損傷ソルガムの揮発性ガスを検出して機械的ストレス状態をモニタリングするセンサーの試作・フィールド実証が中心であり、植物状態の取得方法に該当する。
abstractThe field deployed gas sensor prototype successfully detected the 'scream‘ from the mechanically damaged sorghums by detecting a particular gas molecule, hexanal, released after the time point of 3.5 hours since the start of leaf cutting.
Abstract Background Stalk lodging (mechanical failure of plant stems during windstorms) leads to global yield losses in cereal crops estimated to range from 5% to 25% annually. The cross-sectional morphology of plant stalks is a key determinant of stalk lodging resistance. However, previously developed techniques for quantifying cross-sectional morphology of plant stalks are relatively low-throughput, expensive and often require specialized equipment and expertise. There is need for a simple and cost-effective technique to quantify plant traits related to stalk lodging resistance in a high-throughput manner. Results A new phenotyping methodology was developed and applied to a range of plant samples including, maize ( Zea mays ), sorghum ( Sorghum bicolor ), wheat ( Triticum aestivum ), poison hemlock ( Conium maculatum ), and Arabidopsis (Arabis thaliana). The major diameter, minor diameter, rind thickness and number of vascular bundles were quantified for each of these plant types. Linear correlation analyses demonstrated strong agreement between the newly developed method and more time-consuming manual techniques (R 2 > 0.9). In addition, the new method was used to generate several specimen-specific finite element models of plant stalks. All the models compiled without issue and were successfully imported into finite element software for analysis. All the models demonstrated reasonable and stable solutions when subjected to realistic applied loads. Conclusions A rapid, low-cost, and user-friendly phenotyping methodology was developed to quantify two-dimensional plant cross-sections. The methodology offers reduced sample preparation time and cost as compared to previously developed techniques. The new methodology employs a stereoscope and a semi-automated image processing algorithm. The algorithm can be used to produce specimen-specific, dimensionally accurate computational models (including finite element models) of plant stalks.
Why it matches plant phenotyping methods植物茎の横断面形態を高スループットに定量する画像ベースの表現型計測法を開発し、手作業法との一致性検証と有限要素モデルへの応用を行っており、方法が研究の中心である。
abstractA new phenotyping methodology was developed and applied to a range of plant samples
Reproduction assets foundThe paper's MATLAB image-processing algorithm (authors' analysis code) and sample cross-sectional images are publicly available as supplementary files (Additional files 2 and 3) attached to this open-access article, along with standard operating protocols (Additional file 1). These directly reproduce the paper's phenotCode · publicThe code for the image-processing algorithm is also provided as Additional file 2 . Sample images and instructions are provided as Additional file 3 .Open asset ↗lines:110-119Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity.
Why it matches plant phenotyping methodsソルガム穀粒の純度を、近赤外ハイパースペクトル画像と深層フォレストで非破壊推定する手法を開発しており、表現型取得・判定法が研究の中心である。
abstractThis study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity.
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods3D画像再構成、骨格化、セグメンテーションによりソルガム個葉角度を自動定量する手法が研究の中心であり、手作業測定との検証と大規模適用も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data Availability statement provides three public, paper-specific assets: the voxel carving/skeletonization reconstruction code on GitHub, the raw RGB phenotyping images on Zenodo, and the phenotypic data, GWAS result files, and figure code on GitHub.Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:351-493Code · publicThe phenotypic data, GWAS result files and code for main figures are available at GitHub: https://github.com/mtross2/Sorghum-3D-Reconstruction .Open asset ↗mtross2/Sorghum-3D-Reconstructionlines:351-493Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Abstract AimsMany governments and companies have committed to moving to net-zero emissions by 2030 or 2050 to tackle climate change, which require the development of new carbon capture and sequestration/storage (CCS) techniques. A proposed method of sequestration is to deposit carbon in soils as plant matter including root mass and root exudates. Adding perennial traits such as rhizomes to crops as part of a sequestration strategy would result in annual crop regrowth from rhizome meristems rather than requiring replanting from seeds which would in turn encourage no-till agricultural practices. Integrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods (i.e., is rapid, inexpensive, reliable, and non-invasive), however none currently exist. MethodsGround penetrating radar (GPR) is a non-invasive subsurface sensing technology that shows potential as a phenotyping technique. In this study, a prototype GPR antenna array was used to scan roots of the perennial sorghum hybrid, PSH09TX15. A-scan level time-domain analyses and B-scan level time/frequency analyses using the continuous wavelet transform were utilized to extract features of interest from the acquired radargrams. ResultsOf six A-scan diagnostic indices examined, the standard deviation of signal amplitude correlated most significantly with belowground biomass. Time frequency analysis using the continuous wavelet transform yielded high correlations of B-scan features with belowground biomass. ConclusionThese results demonstrate that continued refinement of GPR data analysis workflows should yield a highly applicable phenotyping tool for breeding efforts in environments where selection is otherwise impractical on a large scale.
Why it matches plant phenotyping methodsGPRを用いて根系・地下部バイオマスを非破壊推定するセンシングおよび解析ワークフローを開発・評価しており、植物表現型取得法が研究の中心です。
abstractIntegrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods
SorghumThermalStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.
Why it matches plant phenotyping methods高スループット熱画像で気孔コンダクタンスを測定し、ガス交換測定との相関で検証した植物フェノタイピング手法が研究の中心である。
abstractHere, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions.
Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination ( R 2 ) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.
Why it matches plant phenotyping methodsLiDARデータから作物キャノピーのLAIを推定するセンサー・解析手法を、車載およびUAVプラットフォームで評価しており、植物形質取得が研究の中心です。
abstractThis paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize
Sorghum is an important cereal crop grown across the globe for its grain and biomass value. It can also efficiently use resources such as nitrogen, and multiple varieties that are nitrogen-use and light-capture efficient are constantly being developed. This study focuses on using the spectral signature of sorghum varieties to predict flowering days, which could be used as a proxy for plants’ growth/productivity and development trends, thus helping breeders make quick decisions about what varieties to move to the next stage. Multiple sorghum varieties from the sorghum association panel were planted in a replicate-design field experiment with the variable supply of nitrogen. The flowering days were monitored and recorded. The hyperspectral reflectance data were collected and used to build a sorghum flowering days predictive model. Although regression models such as partial least square have been used to predict plants’ phenotypes, the non-parametric ensemble machine learning model turned out to perform better on flowering days with an accurate model up to 5 days.
Why it matches plant phenotyping methodsハイパースペクトル反射データからソルガムの開花日という植物形質を予測するモデルを構築し、精度も評価しており、形質取得・推定手法が中心である。
abstractThis study focuses on using the spectral signature of sorghum varieties to predict flowering days
The high quality of sorghum × sudangrass [Sorghum bicolor (L.) Moench. × S. sudanense (Piper) Stapf.] seed is an important prerequisite for its application in animal husbandry, and germination percentage is one of the most routine indicators used to test seed quality. This study proposes a method for the rapid and nondestructive measurement of sorghum × sudangrass seed germination percentage based on multispectral image technology. We constructed target region in sorghum × sudangrass seed samples, and after white board calibration and ratio conversion, the spectral reflectance of each group of seeds was obtained at five wavebands. A seed germination test was performed in an incubator, and germination percentage was obtained from 100 sorghum × sudangrass seed samples. Using the neural network and the Levenberg–Marquardt method, spectral reflectance and germination percentage data from the 100 seed samples were used to establish a predictive model of seed germination percentage. The input neurons were reflectance in five wavelength bands, and the output neuron was seeds germination percentage. Experimental data from 80 samples were randomly selected for training, and data from the remaining 20 nontraining samples were imported into the predictive model for simulation verification. The fitting correlation coefficient of the model was .73202, representing the relevant closing degree, and the correlation coefficient between the predicted value and the simulation value from 20 nontraining samples was .7533, which referred to the relationship between variables. The model was able to predict the seed germination percentage with acceptable accuracy. Therefore, the nondestructive method described here may be suitable for rapid detection of sorghum × sudangrass seed germination percentage in the context of seed production.
Why it matches plant phenotyping methodsマルチスペクトル画像から種子の発芽率を非破壊推定する手法を開発・検証しており、植物状態の取得が研究の中心である。
abstractThis study proposes a method for the rapid and nondestructive measurement of sorghum × sudangrass seed germination percentage based on multispectral image technology.
ABSTRACT Bacterial isolates that enhance plant growth and suppress plant pathogens growth are essential tools for reducing pesticide applications in plant production systems. The objectives of this study were to develop a reliable fluorescence-based technique for labeling bacterial isolates selected as biological control agents (BCAs) to allow their direct tracking in the host-plant interactions, understand the BCA localization within their host plants, and the route of plant colonization. Objectives were achieved by developing competent BCAs transformed with two plasmids, pBSU101 and pANIC-10A, containing reporter genes eGFP and pporRFP , respectively. Our results revealed that the plasmid-mediated transformation efficiencies of antibiotic-resistant competent BCAs identified as PSL, IMC8, and PS were up 84%. Fluorescent BCA-tagged reporter genes were associated with roots and hypocotyls but not with leaves or stems and were confirmed by fluoresence microscopy and PCR analyses in colonized Arabidopsis and sorghum. This fluorescence-based technique’s high resolution and reproducibility make it a platform-independent system that allows tracking of BCAs spatially within plant tissues, enabling assessment of the movement and niches of BCAs within colonized plants. Steps for producing and transforming competent fluorescent BCAs, as well as the inoculation of plants with transformed BCAs, localization, and confirmation of fluorescent BCAs through fluorescence imaging and PCR, are provided in this manuscript. This study features host-plant interactions and subsequently biological and physiological mechanisms implicated in these interactions. The maximum time to complete all the steps of this protocol is approximately three months. SENTENCE SUMMARY We describe a novel fluorescence localization technique as a powerful tool to directly visualize and determine the route in-situ of BCAs in host-plants interaction. The study features the host-plant interactions, biological and physiological responses implicated.
Why it matches plant phenotyping methods植物体内での細菌の局在・移動経路を蛍光画像で取得する技術の開発とプロトコル化が研究の中心であり、植物の状態を直接評価するため。
titleEfficient Fluorescence-Based Localization Technique for Tracking Endophytes Route in Host-Plants Colonization
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods複数の較正2D画像から3D植物形状を再構成し、葉ごとの葉角度を自動抽出する手法が研究の中心であり、遺伝性・手動測定との相関による検証も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data and Code availability statement provides three paper-specific public assets: the voxel carving/skeletonization code (GitHub cropsinsilico/SorghumVoxelCarving), the raw sorghum images analyzed (Zenodo deposit 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and figure code (GitHub mtCode · publicThe code for reconstruction and skeletonization is hosted on GitHub: https://github.com/cropsinsilico/
SorghumVoxelCarving.Open asset ↗pdf-page:9 lines:1-59Code · publicPhenotypic data, GWAS result files and code for main figures are located on GitHub:
https://github.com/mtross2/Sorghum-3D-ReconstructionOpen asset ↗mtross2/Sorghum-3D-Reconstructionpdf-page:9 lines:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
MaizeSorghumRGB / grayscaleLeafStomata / guard-cell complexClassificationCountingSegmentationStomatal traitsWater status / transpiration
Future food security in the face of climate change requires rapid, efficient, and flexible plant genetic improvement. For this, an integrated understanding of developmental and physiological mechanisms from DNA sequences (genotypes) to terminal traits (phenotypes) under different environmental conditions is indispensable. Stomatal traits influencing photosynthesis, gas exchange, and water use are crucial targets for plant improvement programs involving major crop C4 plants sorghum (Sorghum bicolor) and maize (Zea mays; Leakey et al., 2019). However, the ability to select optimal plant genotypes is still challenged by the pace at which the acquisition and processing of stomatal phenotypic data can be accomplished. Artificial intelligence (AI) is revolutionizing the way in which problems are approached and solved across a wide range of disciplines. Machine learning, an AI subfield, is increasingly being used in agriculture to classify plants, identify pests, predict weather conditions, and track yield, among several other applications (van Dijk et al., 2021). In this issue of Plant Physiology, Bheemanahalli et al. (2021), Ferguson et al. (2021), and Xie et al. (2021) introduce the use of AI-enabled high-throughput stomatal phenotyping platforms in combination with screening methods to identify specific genes and variations controlling stomatal-related traits in sorghum and maize. While considerable attempts have been made to address the bottlenecks associated with the phenotyping of stomatal traits through computer-aided image acquisition, previously developed methods suffered from issues of being time- and labor-intensive and of inaccurate stomata identification, classification, and quantification in C4 grass species (Furbank and Tester, 2011). By addressing those issues, the studies by the three groups present end-to-end pipelines that use a deep learning algorithm to automatically identify, classify, and quantify stomatal traits associated with plant water use efficiency (WUE) and drought tolerance (Figure 1A). By integrating this pipeline with genomic studies, the authors further report the underlying genetic architecture of stomatal traits (Figure 1C). Developing and integrating deep learning-based high-throughput-phenotyping with genomic studies identifies genetic regions for stomatal traits in C4 grasses. A, A phenotypic pipeline showing rapid image acquisition by optical topometry and image analysis by a deep learning algorithm (Mask R-CNN) provides a powerful tool for identifying optimal stomatal traits. B, Correlation between manually measured and computationally predicted stomatal complex area in sorghum. C, Association of phenotypic data with genetic variants. Adapted from Figure 1B inXie et al. (2021), Figure 3A inBheemanahalli et al. (2021), and Figure 5D inFerguson et al. (2021). Developing and integrating deep learning-based high-throughput-phenotyping with genomic studies identifies genetic regions for stomatal traits in C4 grasses. A, A phenotypic pipeline showing rapid image acquisition by optical topometry and image analysis by a deep learning algorithm (Mask R-CNN) provides a powerful tool for identifying optimal stomatal traits. B, Correlation between manually measured and computationally predicted stomatal complex area in sorghum. C, Association of phenotypic data with genetic variants. Adapted from Figure 1B inXie et al. (2021), Figure 3A inBheemanahalli et al. (2021), and Figure 5D inFerguson et al. (2021). Traditional stomatal phenotyping involves plant tissue collection and preparation for imaging, image data acquisition under microscope, and manual phenotyping of traits of interest. To relieve the phenotyping bottleneck, Ferguson et al. (2021) and Xie et al. (2021) used optical topometry, a rapid and nondestructive method for measuring surface characteristics at the nanometer scale, and acquired images of leaves to extract morphology-related stomata traits. The three-dimensional topographic layer of the raw images was first filtered to capture the points of interest and then flattened to two dimensions in grayscale with luminosity optimization and contrast enhancement. After preprocessing, the authors trained a convolutional neural network model (Mask R-CNN) for automatic detection and counting of stomatal traits, such as number, density, and area (He et al., 2017). A typical deep learning framework for phenotyping starts with feeding and preprocessing of raw images, followed by several layers of automatic feature extraction during training, and ends up with the trained and validated model that can identify, classify, quantify, and predict the phenotypic traits of interest (Singh et al., 2018). Mask R-CNN detects and localizes objects of interest, such as stomata, using bounding boxes and generates precise segmentation masks (Figure 1A). The algorithm then uses several convolutional layers to identify and classify object region and then to predict object type. To train the Mask R-CNN model, the authors first labeled the input images as stomata and/or pavement cells and randomly split the entire image set into a training set for model training and validation set for model validation. Stomatal traits predicted by the deep learning-based methods exhibited strong correlations with manual measurements across the three studies while dramatically reducing the required time and labor burden. For example, manual and computer-predicted measurements of stomatal complex area in sorghum leaves were significantly and positively correlated with each other (R2 > 0.96, Figure 1B). In maize, the computer-predicted means of stomatal complex density and pavement cell density showed high significant correlations with those of the manually obtained values (R2SCD = 0.974 and R2PD = 0.961, respectively). Bheemanahalli et al. (2021) showed a significant (P < 0.001) strong relationship between predicted and manual observations of abaxial and adaxial stomata density, suggesting the reliability and accuracy of automated deep learning-based methods. Next, the three sets of authors applied the deep learning-based pipelines to sets of maize and sorghum accessions and identified candidate genes with known and putative roles for key WUE traits. Xie et al. (2021) utilized QTL mapping in a biparental mapping population of maize and identified high-confidence QTLs that were putatively pleiotropic and correlated with stomatal patterning and leaf gas exchange traits consistently in 2 years. They found these QTLs overlap with genetic position harboring not only known stomatal developmental and patterning genes, such as putative maize orthologs of Arabidopsis thaliana (Arabidopsis) EPIDERMAL PATTERNING FACTOR 2, PANGLOSS1, and CYCLINA2;1, but also genes previously not linked to stomatal traits. A genome-wide association study (GWAS) on diverse grain sorghum accessions by Bheemanahalli et al. (2021) provided evidence for more than 71 genetic loci having significant association with stomatal traits, such as abaxial and adaxial stomatal density and stomatal complex area, and almost half as many overlapped with previously reported genomic regions. Further clarification of these regions revealed candidate putative genes including ATP-binding cassette transporter, BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1, homeodomain-START transcription factor, and basic helix-loop-helix family transcription factor, putative orthologs of which are known to regulate leaf development, stomatal morphology, and stomatal lineage, respectively, in dicot and monocot models. To improve the efficiency of GWAS and boost confidence in the identification of candidate genes, Ferguson et al. (2021) determined the transcriptomes of sorghum leaf tissues using a transcriptome-wide association study (TWAS). They used Fisher’s combined test to integrate information from TWAS and GWAS and identified 394 unique candidate genes with high confidence of being associated with key stomatal and/or photosynthetic traits (Figure 1C). Included among these were two-thirds of genes that contained deleterious nonsynonymous/missense variants and had been previously identified as regulators of stomatal patterning and leaf development and anatomy in Arabidopsis. Examples include BETA-KETOSYL-CoA SYNTHASE 1, a cell wall EXPANSIN-TYPE PROTEIN 2, an ABA-sensitive MAP KINASE, PURPLE ACID PHOSPHATASE 10, and EPIDERMAL PATTERNING FACTOR 2. Overall, the work by Bheemanahalli et al. (2021), Ferguson et al. (2021), and Xie et al. (2021) extends our understanding of stomatal biology and opens up the potential to engineer stomatal traits to enhance WUE and drought resistance without compromising yield in crops. Although the deep convolutional neural network platform showed potential for rapid and efficient WUE phenotyping, many questions arise, such as whether this platform is generalizable to diverse physiological traits across different crops and environments. In the coming years, it will be interesting to see how high-throughput phenotyping tools accelerate the rate at which traits of interest are quantified and characterized, revealing gene-environment interaction modules of complex traits.
Why it matches plant phenotyping methods深層学習と光学トポメトリーによる気孔形態の高スループット取得・自動定量、および手動測定との技術検証を中心に扱う方法論的レビューである。
abstractintroduce the use of AI-enabled high-throughput stomatal phenotyping platforms
We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. The pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on. We demonstrate the capacity of deep CNNs at performing this classification task, and show the utility of these visualizations on RGB imagery of biomass sorghum captured by the TERRA-REF gantry. We focus on several different genetic markers with known phenotypic expression, and discuss the possibilities of using this approach to uncover genotype x phenotype relationships.
Why it matches plant phenotyping methods植物画像からSNPに対応する表現型をCNNで分類・可視化する解析パイプラインが研究の中心であり、画像に基づく表現型抽出手法として適格です。
abstractThe pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on.
Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
Why it matches plant phenotyping methods光学トモグラフィーと機械学習ツールによる気孔密度測定を中心的な方法として開発・適用し、ガス交換等の表現型を大規模集団で評価しているため。
abstractThis study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD).
Reproduction assets foundThe paper's optical tomography leaf images (the sensor inputs used for machine-learning stomatal density phenotyping) are publicly deposited in the Illinois Data Bank. Phenotypic trait data (Supplemental Table S12) are public but only via the article's supplemental material without a listed URL; RNA-seq (PRJNA522466) GDataset · publichttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA522466/ .
Genotyping-by-sequencing data are available at: https://doi.org/10.5281/zenodo.5019227 . Phenotypic data are available as
part of the supplemental
material ( Supplemental Table
S12 ). Optical tomography images from this article can be found in the Illinois
Data Bank under: https://doi.org/10.13012/B2IDB-1411926_V1 .
Supplemental data
The following materials are available in the online version of this article.Open asset ↗10.13012/B2IDB-1411926_V1lines:985-1051Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
The continuous growth of the world’s population requires immediate action to ensure food security. Sorghum is among the five most-produced cereals and is a dietary staple in many developing countries. Therefore, it is of great importance to obtain precise information for improving cereal productivity. An indicator for estimating sorghum yields is the number of crop heads in different branching arrangements. Approaches based on image processing and artificial intelligence have proved useful for automatically and efficiently obtaining this type of information for different crops. However, their application to sorghum crops presents some additional challenges owing to differences in the shape and color of sorghum heads. In this study, a methodology to detect sorghum heads in unmanned aerial vehicle imagery was investigated, and its performance was evaluated using a standard quality index in object detection problems (mean average precision). Specifically, test-time-augmentation (TTA) techniques have been implemented using a set of geometrical and color transformations selected according to the sorghum plant imagery requiring analysis, as well as four different ensemble learning methods. Because these methods are weighted, two different approaches for calculating these weights to improve sorghum head detection have been proposed. The results show that in sorghum head detection, TTA strategies outperform detection based only on individual transformed testing sets. Moreover, these results were improved by the use of different weights during the ensemble of TTA results.
Why it matches plant phenotyping methodsUAV画像からソルガムの穂を検出し、TTAとアンサンブル手法を開発・評価している。穂数という植物器官形質・収量指標の抽出が中心であり、単なる生物学的実験のルーチン測定ではない。
abstracta methodology to detect sorghum heads in unmanned aerial vehicle imagery was investigated, and its performance was evaluated using a standard quality index in object detection problems (mean average precision).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background Stalk lodging (mechanical failure of plant stems during windstorms) leads to global yield losses in cereal crops estimated to range from 5% - 25% annually. The cross-sectional morphology of plant stalks is a key determinant of stalk lodging resistance. However, previously developed techniques for quantifying cross-sectional morphology of plant stalks are relatively low-throughput, expensive and often require specialized equipment and expertise. There is need for a simple and cost-effective technique to quantify plant traits related to stalk lodging resistance in a high-throughput manner.Results A new phenotyping methodology was developed and applied to a range of plant samples including, maize ( Zea mays ), sorghum ( Sorghum bicolor ), wheat ( Triticum aestivum ), poison hemlock ( Conium maculatum ), and Arabidopsis (Arabis thaliana). The major diameter, minor diameter, rind thickness and number of vascular bundles were quantified for each of these plant types. Linear correlation analyses demonstrated strong agreement between the newly developed method and more time-consuming manual techniques (R 2 >0.9). In addition, the new method was used to generate several specimen-specific finite element models of plant stalks. All the models compiled without issue and were successfully imported into finite element software for analysis. All the models demonstrated reasonable and stable solutions when subjected to realistic applied loads.Conclusions A rapid, low-cost, and user-friendly phenotyping methodology was developed to quantify two-dimensional plant cross-sections. The methodology offers reduced sample preparation time and cost as compared to previously developed techniques. The new methodology employs a stereoscope and a semi-automated image processing algorithm. The algorithm can be used to produce specimen-specific, dimensionally accurate computational models (including finite element models) of plant stalks.
Why it matches plant phenotyping methods茎の断面形態を高スループットに定量する画像ベースの表現型解析法を開発し、手作業法との一致を検証しているため、方法が研究の中心である。
abstractA new phenotyping methodology was developed and applied to a range of plant samples
There is an urgent need to accelerate energy crop development for the production of renewable transportation fuels from biomass. Greater knowledge of factors that influence crop growth and development is required to improve the breeding development pipeline for energy crops. Genomics tools have advanced and the pace of genotyping has accelerated exponentially while the cost of sequencing has dramatically decreased. The bottleneck has thus shifted emphasis from understanding the genotype to understanding the phenotype. Recent technological advancements in remote sensing have made it possible to extract massive volumes of morphological, physiological, and agronomic data from bioenergy crops, but complexities in data processing, feature extraction, and data analytics make predictions of crop performance from remote sensing data a challenge. Plant phenotyping pipelines for measuring and predicting plant productivity and performance from remotely sensed data are needed. These may include both ground-based and airborne sensor platforms for high-throughput phenotyping. High-throughput phenotyping data can be matched with genetic data through complex analytics to enable optimization of sorghum for biomass and energy yield for transportation fuel. The purpose of this project was to develop a disruptive technology system based on airborne and ground-based mobile sensor systems, confirmed by handheld sensors and agronomic performance data that enables phenotyping of sorghum for biomass and energy yield for transportation fuel. We achieved the goals of the project with key successes in (1) optimizing high-throughput remote-sensing technologies to acquire relevant data on sorghum plant phenotypes, (2) implementation of data analytics algorithms for segmentation and feature extraction, (3) developing predictive models for plant growth and performance, and (4) designing and implementing genetic analysis pipelines to identify genes controlling sorghum performance; and (5) designing a user-friendly system platform to enable breeders and other end users to interact with the needed data and analytics. This data set is a sampling of the data collected for the Purdue TERRA project and is presented for use by others for research purposes. This publication includes: RGB image data at 1 cm spatial resolution for 3 dates in 2018 (6/4, 7/10, & 8/1) Hyperspectral data at 4 cm spatial resolution for 3 dates in 2018 (6/4, 7/11, & 8/2) Lidar digital surface model (dsm) data for 3 dates in 2018 (6/4, 7/10, 8/1) in las formatted files Documentation txt and csv files documenting the biomass, plant height, leaf appearance, and leaf area for each of the plots Esri shape and geojson vector files describing spatial outline of the plots within the experiment Esri shape and geojson vector files describing spatial outline of each of the rows with the plots Incidence radiation graphs for the image data sets dates summarizing the illumination variations during the day The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000593. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Why it matches plant phenotyping methodsソルガムの形態・生理・農業形質をリモートセンシングで取得・抽出する高スループット表現型解析基盤とデータセットが中心であり、RGB、ハイパースペクトル、LiDARおよび解析手法を明示している。
abstractThe purpose of this project was to develop a disruptive technology system based on airborne and ground-based mobile sensor systems, confirmed by handheld sensors and agronomic performance data that enables phenotyping of sorghum for biomass and energy yield for transportation fuel.
The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.
Why it matches plant phenotyping methods画像ベース高スループット表現型測定の自動抽出値を手動測定（ground truth）と比較し、測定誤差と精度を遺伝的に評価しているため、表現型取得法の技術的検証が中心です。
abstractDuring automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes used for automated tassel segmentation, skeleton construction and trait extraction are available at https://github.com/schnablelab/Tassel-Image-Trait-Extraction-Tool .Open asset ↗schnablelab/Tassel-Image-Trait-Extraction-Toollines:170-188Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential to maintenance of water-use efficiency (WUE). However, little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. High-throughput thermal imaging was used to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum accessions. Results from thermal imaging significantly correlated with photosynthetic gas-exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study (GWAS/TWAS) identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with greatest confidence, 77 were orthologs of Arabidopsis genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production. One sentence summaryRapid phenotyping of 659 accessions of Sorghum bicolor revealed heritable stomatal responses to a decrease in light. GWAS/TWAS was used to identify candidate genes influencing traits important to WUE.
Why it matches plant phenotyping methods熱画像を用いた高スループットな気孔コンダクタンス測定を開発・適用し、ガス交換測定との相関で検証しているため、植物表現型取得法が研究の中心である。
abstractHigh-throughput thermal imaging was used to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum accessions.
Abstract The bioenergy crops such as energycane, miscanthus, and sorghum are being genetically modified using state of the art synthetic biotechnology techniques to accumulate energy‐rich molecules such as triacylglycerides (TAGs) in their vegetative cells to enhance their utility for biofuel production. During the initial genetic developmental phase, many hundreds of transgenic phenotypes are produced. The efficiency of the production pipeline requires early and minimally destructive determination of oil content in individuals. Current screening methods require time‐intensive sample preparation and extraction with chemical solvents for each plant tissue. A rapid screen will also be needed for developing industrial extraction as these crops become available. In the present study, we have devised a proton relaxation nuclear magnetic resonance (1H‐NMR) method for single‐step, non‐invasive, and chemical‐free characterization of in‐situ lipids in untreated and pretreated lignocellulosic biomass. The systematic evaluation of NMR relaxation time distribution provided insight into the proton environment associated with the lipids in the biomass. It resolved two distinct lipid‐associated subpopulations of proton nuclei that characterize total in‐situ lipids into bound and free oil based on their “molecular tumbling” rate. The T1T2 correlation spectra also facilitated the resolution of the influence of various pretreatment procedures on the chemical composition of molecular and local 1H population in each sample. Furthermore, we show that hydrothermally pretreated biomass is suitable for direct NMR analysis unlike dilute acid and alkaline pretreated biomass which needs an additional step for neutralization.
Why it matches plant phenotyping methods植物バイオマス中の脂質含量を非破壊・高スループットに測定する1H-NMR法を開発・評価しており、表現型取得法が研究の中心である。
titleDevelopment and validation of time‐domain 1H‐NMR relaxometry correlation for high‐throughput phenotyping method for lipid contents of lignocellulosic feedstocks
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
In the U.S. since 2013, the sugarcane aphid is a perennial pest to all types of sorghum. Rating sugarcane aphid population density, plant damage, and other traits in sorghum requires a large amount of labor and ratings, especially damage ratings, may vary by evaluator. Thus Unmanned Aerial Systems (UAS)–based imagery may be exceedingly useful to more accurately quantify the effects on sorghum caused by sugarcane aphids. This study quantified the dynamic nature of sugarcane aphid infestations on silage sorghum varieties using UAS-based imagery data, and demonstrated the UAS-based measurements correlated to ground measurements. Two UAS platforms equipped with RGB (red, green, and blue) and multispectral cameras respectively were used to evaluate the silage sorghum variety trials during the growing seasons of 2019 and 2020. For the purpose of high throughput phenotyping in sorghum breeding, a new workflow scheme was developed including UAS image processing, raster calculation, DTM (digital terrain model) and CHM (canopy height model) generation, image extraction of sorghum plants, and tabular dataset generation from zonal statistics for further statistical analyses. Ground-based measurements included aphid sampling, aphid damage ratings, plant height, and biomass yields. The normalized difference red edge index (NDRE) and canopy cover collected by the UAS showed negative linear relationship with aphid damage ratings in both trials (R² = 0.55–0.64). In addition to assessing spatial differences among the varieties in 2019, temporal change in both NDRE and canopy cover from the baseline sampling date in 2020 better estimated aphid damage, R² of 0.68 and 0.79 respectively, than using the spatial difference of NDRE (R² = 0.55) and canopy cover (R² = 0.57) before harvest. Plant height (R² = 0.84, Root-Mean-Square Error (RMSE) = 0.16 m) can be estimated with efficiency and precision using UAS-derived measurements during high throughput phenotyping of sorghum. Fresh yield estimates for the primary harvests were consistent in both years, but green yield estimates differed among harvests and need to be improved. Future development of UAS-based high throughput phenotyping would benefit from increased temporal resolutions of growth parameters and vegetation indices throughout a growing season.
Why it matches plant phenotyping methodsUAS画像からNDRE、キャノピー被覆、草丈、収量などの植物形質を抽出するワークフローを開発し、地上測定との相関で検証しており、表現型取得法が研究の中心である。
abstractFor the purpose of high throughput phenotyping in sorghum breeding, a new workflow scheme was developed including UAS image processing, raster calculation, DTM (digital terrain model) and CHM (canopy height model) generation, image extraction of sorghum plants, and tabular dataset generation from zonal statistics for further statistical analyses.
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Why it matches plant phenotyping methodsUAV時系列画像から作物形質を抽出し、成長動態と収穫期バイオマスを予測するHTP手法を、サンプリング頻度や予測性能とともに技術的に評価しており、フェノタイピング手法が中心である。
abstractUnmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops
Reproduction assets foundThe paper's UAV-derived sorghum phenotyping datasets (imagery features, AGB measurements) are deposited in the Illinois Databank with a public DOI listed in the Data Availability Statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability Statement: The datasets used and analyzed during the current study are available
from the corresponding author via Illinois Databank at https://doi.org/10.13012/B2IDB-5649852_V2.Open asset ↗Illinois Databank · B2IDB-5649852_V2pdf-page:14 lines:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · checked 8 Sept 2026
ABSTRACT Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create micro-treatments throughout the field. To make matters worse, the variation within different soil properties is often non-randomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a tool with which sources of environmental variation in field data can be identified and removed, improving our ability to resolve effects of interest and to quantify subtle phenotypes. IMPORTANCE Data from field experiments are notoriously noisy. Proper field designs with high replication aid in mitigating this challenge, yet true biological correlations are still often masked by environmental variability. This work identifies soil property composition as a spatially distributed source of variance to three types of characteristics: plant phenotype, microbiome composition, and leaf traits. We show that once identified, spatial principal component regression was able to account for these effects so that more precise estimates of experimental factors were obtained. This generalizable method is applicable to diverse field experiments.
Why it matches plant phenotyping methods空間的な土壌変動を補正して植物形質を高精度に定量する一般化可能な統計手法を開発・適用しており、フェノタイピング手法が研究の中心である。
abstractWe describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom.
Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からソルガムのバイオマス、LAI、草丈を推定する高スループット表現型解析法を開発し、別年のデータで検証しており、手法が研究の中心である。
abstractThe objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract BackgroundLeaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput phenotyping. High throughput imaging techniques are now widely used for nondestructive analysis of plant phenotypic traits. In this study three imaging modules, namely, RGB, hyperspectral, and fluorescence imaging, were used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and regression models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. ResultsModels that included two additional variables, DAS (day after sowing) and SLW (specific leaf weight), were also investigated to improve the prediction of chlorophyll. R 2 for chlorophyll concentration for multiple linear models at various color components were 0.77 for R, 0.79 for G, 0.70 for B. To obtain additional spectral information, color component H, S, and I were calculated after color spaces being transformed. The result of HSI space showed that R 2 for chlorophyll concentration for multiple linear models were 0.67 for H, 0.88 for S, 0.77 for I. The R 2 values for different hyperspectral index like the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), modified chlorophyll absorption ratio index (MCARI) between 0.77 and 0.78. R 2 =0.79 was obtained with fluorescence image. Partial least squares regression (PLSR) was employed to using the selected vegetation indices computed from different imaging data to estimate the chlorophyll concentration for sorghum plants. Among all the imaging data, chlorophyll content was predicted with high accuracy (R 2 from 0.84 to 2.92, RPD from 2.49 to 3.58). ConclusionAccording to the Akaike's Information Criterion (AIC) error function, the model was better fitted based on images, DAS and SLW than that based on images and DAS. This study indicated that the accuracy for chlorophyll estimation was increased by the image traits combined with DAS and SLW. High throughput imaging provides a simple, rapid, and nondestructive method to estimate the leaf chlorophyll concentration.
Why it matches plant phenotyping methodsソルガム葉のクロロフィル濃度をRGB・ハイパースペクトル・蛍光画像から推定する画像計測および回帰モデルが研究の中心であり、植物フェノタイピング手法に該当する。
abstractIn this study three imaging modules, namely, RGB, hyperspectral, and fluorescence imaging, were used to estimate chlorophyll content of sorghum plants in a greenhouse environment.
Abstract Stomatal density (SD) and stomatal complex area (SCA) are important traits that regulate gas exchange and abiotic stress response in plants. Despite sorghum (Sorghum bicolor) adaptation to arid conditions, the genetic potential of stomata-related traits remains unexplored due to challenges in available phenotyping methods. Hence, identifying loci that control stomatal traits is fundamental to designing strategies to breed sorghum with optimized stomatal regulation. We implemented both classical and deep learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments. Nearly 12,000 images collected from abaxial (Ab) and adaxial (Ad) leaf surfaces revealed substantial variation in stomatal traits. Our study demonstrated significant accuracy between manual and deep learning methods in predicting SD and SCA. In sorghum, SD was 32%–39% greater on the Ab versus the Ad surface, while SCA on the Ab surface was 2%–5% smaller than on the Ad surface. Genome-Wide Association Study identified 71 genetic loci (38 were environment-specific) with significant genotype to phenotype associations for stomatal traits. Putative causal genes underlying the phenotypic variation were identified. Accessions with similar SCA but carrying contrasting haplotypes for SD were tested for stomatal conductance and carbon assimilation under field conditions. Our findings provide a foundation for further studies on the genetic and molecular mechanisms controlling stomata patterning and regulation in sorghum. An integrated physiological, deep learning, and genomic approach allowed us to unravel the genetic control of natural variation in stomata traits in sorghum, which can be applied to other plants.
Why it matches plant phenotyping methodsソルガム気孔形質の遺伝解析が主目的だが、古典的手法と深層学習による画像フェノタイピングを実装し、手動法との精度比較・検証を行っており、形質抽出法が実質的に中心的な役割を持つ。
abstractWe implemented both classical and deep learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments.
Leaf stomata regulate the process of gas exchange between the plant and the atmosphere, therefore play an important role in plant growth and water use. Thermal infrared sensing of leaf surface temperature is proved to be an indirect but effective approach to estimate leaf stomatal conductance, and shows the potential to rapidly differentiate genotypes for water-use related traits. The objective of this study was to estimate leaf stomatal conductance from thermal IR images of crops and relevant environmental parameters. The experiment was conducted in the NU-Spidercam field phenotyping facility near Mead, NE. Leaf stomatal conductance was measured from soybean, sorghum, maize, and sunflower using a leaf porometer. Thermal IR images of the crop canopies were captured by a thermal IR camera and then processed to extract crop canopy temperature (Tc). In addition, weather variables including solar radiation, air temperature, relative humidity, and wind speed were extracted from a nearby weather station. Correlation analysis was implemented to explore the relationships between these variables. Multiple linear regression (MLR), random forest (RF), gradient boosting machine (GBM) were applied to model stomatal conductance from Tc and weather variables. The Pearson correlation coefficients between predicted and measured stomatal conductance were 0.495 for MLR, 0.591 for RF, and 0.878 for GBM when Tc was not used as an input variable. After adding Tc as input, Pearson correlation coefficients were improved to 0.584 for MLR, 0.593 for RF, and 0.896 for GBM. The mean absolute errors for the three models were 225, 237, and 129 mmol/(m2·s) when Tc was included as a model input. This research would lead to rapid assessment of leaf stomatal conductance and crop water status using thermal IR imaging.
Why it matches plant phenotyping methods熱赤外画像と環境変数から葉の気孔コンダクタンスを推定する手法を開発・評価しており、植物生理形質の取得が研究の中心です。
abstractThe objective of this study was to estimate leaf stomatal conductance from thermal IR images of crops and relevant environmental parameters.
Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of absorbed photosynthetically active radiation (fAPAR), chlorophyll concentration (CI) and normalized difference vegetation (NDVI) indices were used in crop monitoring, but their comparative performances in sorghum monitoring is lacking. This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance. Bayesian ridge regression showed good cross-validated performance, and high reliability (R2 = 35%) and low bias (mean absolute prediction error, MAPE = 0.4%) during the validation step. Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to Sentinel-2's fAPAR. The best times to sample the vegetation indices were the months of June (second half) and July. The results obtained in this work will serve several purposes including improvements in plant breeding, farming management and sorghum biomass yield forecasting at extension services and policy making levels.
Why it matches plant phenotyping methodsソルガムのバイオマス収量という植物形質を、リモート・近接センシングと機械学習で推定し、指標間の性能比較と検証を行っており、フェノタイピング手法が中心である。
abstractThis work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance.
In order to ensure the availability of food in the future, plant stress identification is one of the crucial tasks used in plant phenotyping to develop better crops. In this research, we use a convolution neural network (CNN) combined with LSTM to identify the early state of plant stress caused by a deficiency of nutrients. We use a treatment study dataset of sorghum (S. bicolor) which consists of more than 40,000 images of growing sorghum images captured in the phenotyping facility in 3 views. The experiment studies plant growing under 3 treatment conditions: 100/100 (100% ammonium/100% nitrate), 50/10, and 10/10. The network is divided into two parts: the features extraction and classification network. VGG16 with pre-trained weights from the ImageNet dataset is used as the feature extractor. LSTM cell with multi-layer perceptron (MLP) is used to classify extracted features to determine the stress of the plants after subjected to the stressor. The result revealed that the network can detect the stress at the accuracy of more than 85% at 2 days after plants subjected to the stressor treatment.
Why it matches plant phenotyping methods植物画像から栄養ストレスを早期推定するCNN-LSTM手法が研究の中心であり、画像データセットと精度評価も含むため。
abstractplant stress identification is one of the crucial tasks used in plant phenotyping
Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.
Why it matches plant phenotyping methodsUAV・地上リモートセンシング画像から植物器官（トウモロコシの雄穂・ソルガムの穂）の検出に適したオルソフォト生成法を開発し、画質と位置精度を定量評価しており、フェノタイピング用画像取得手法が中心である。
abstractthis paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles
Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight.
Why it matches plant phenotyping methodsUAV画像由来の3D点群からソルガム個体穂を検出・特徴付け、長さ・幅・体積・重量関連形質を推定する手法の開発と検証が中心である。
abstractAn accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed
MaizeSorghumSoybeanField / plotLiDAR / point cloudStereoLeafSeed / grainStem / branchWhole plant / canopy / plot / field
Highlights A custom-built camera module named PhenoStereo was developed for high-throughput field-based plant phenotyping. Novel integration of strobe lights facilitated application of PhenoStereo in various environmental conditions. Image-derived stem diameters were found to have high correlations with ground truth, which outperformed any previously reported sensing approach. PhenoStereo showed promising potential to characterize a broad spectrum of plant phenotypes. Abstract. The stem diameter of sorghum plants is an important trait for evaluation of stalk strength and biomass potential, but it is a challenging sensing task to automate in the field due to the complexity of the imaging object and the environment. In recent years, stereo vision has offered a viable three-dimensional (3D) solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping is adversely affected by textureless regions, occlusion of plants, variable outdoor lighting, and wind conditions. In this study, a portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo features a self-contained embedded design, which makes it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights is integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blur. PhenoStereo was used to acquire a set of sorghum plant images, and an automated point cloud data processing pipeline was developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a mask region convolutional neural network (Mask R-CNN) for detecting stalk contours and a semi-global block matching (SGBM) stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approach. These results demonstrate that, with proper customization, stereo vision can be an effective sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. Based on the results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad range of plant phenotypes, such as the leaf angle and tassel shape of maize plants and the seed pods and stem nodes of soybean plants. Keywords: Field-based high-throughput phenotyping, Point cloud, Stem diameter, Stereo vision.
Why it matches plant phenotyping methodsソルガム茎径という植物形質を対象に、ステレオ撮像モジュールと自動点群処理パイプラインを開発し、地上真値との精度検証まで行っており、表現型取得手法が研究の中心である。
abstracta portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping
Abstract High‐throughput genotyping coupled with molecular breeding approaches have dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into developing large‐scale downstream processing of the imaging datasets derived from high‐throughput phenotyping (HTP) platforms. However, most available tools require some programming skills. We developed PhenoImage , an open‐source graphical user interface (GUI) based cross‐platform solution for HTP image processing intending to make image analysis accessible to users with either little or no programming skills. The open‐source nature provides the possibility to extend its usability to meet user‐specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high‐performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived red, green, and blue (RGB) color intensity and plant pigmentation‐based fluorescence shoot images from two plant species: sorghum [ Sorghum bicolor (L.) Moench] and wheat ( Triticum aestivum L.) differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage .
Why it matches plant phenotyping methods植物画像から表現型情報を抽出するオープンソースGUIの開発・実装・有効性検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractWe developed PhenoImage , an open‐source graphical user interface (GUI) based cross‐platform solution for HTP image processing
In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from "stitched" mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から反射率を抽出し、圃場区画内の植物数・穂数・穂色を推定するパイプラインの開発と精度評価が中心であり、植物表現型取得手法に該当する。
abstractIn this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicTrial details, sample imagery, and essential codes used in this article can be accessed through https://github.com/YanZhao15/AltumApplication.git .Open asset ↗YanZhao15/AltumApplicationlines:150-152Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published1 Jan 2021Advances in Artificial Intelligence and Machine LearningCited by 1 · OpenAlex ↗
One of the most promising and difficult challenges for field phenotyping is accurate and reliable counting of sorghum panicles using drone imagery both from RGB and multispectral cameras.In this paper, we present a hybrid Machine Learning method for sorghum panicle identification and counting.The methodology first consists in building a Machine Learning classifier following the two most used methods in the literature for drone and agriculture applications: Support Vector Machine Learning (SVM) and, Artificial Neural Networks (ANN).The present dataset includes 5300 images, and 60% of the dataset were used for training and 20% for testing and validation.Following the results obtained from these models, image segmentation using super-pixel affinity propagation and k-means clustering was used based on simple linear iterative clustering.With an accuracy of 99%, SVM gave a superior performance also in terms of precision and kappa when compared to the ANN model whose accuracy was 98%.Concerning the SVM, a radial basis kernel was used, and the sigma parameter was kept constant at a value of 5.6 determined analytically.
Why it matches plant phenotyping methodsドローン画像からソルガム穂数を識別・計数する機械学習および画像分割手法の開発と性能比較が中心であり、植物形質の抽出方法を扱う。
abstractaccurate and reliable counting of sorghum panicles using drone imagery both from RGB and multispectral cameras
Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.
Why it matches plant phenotyping methodsリモートセンシングと作物成長モデルを統合し、フェノロジーおよび収量関連形質を推定する手法をパラメータ化・検証しており、フェノタイプ取得・推定が研究の中心である。
abstractThe goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids.
MaizeSorghumLeafRootSeed / grainCountingObject detectionTrackingGrowth / development / phenologyLeaf traits
ABSTRACT Leaf number and leaf emergence rate are phenotypes of interest to plant breeders, plant geneticists, and crop modelers. Counting the extant leaves of an individual plant is straightforward even for an untrained individual, but manually tracking changes in leaf numbers for hundreds of individuals across multiple time points is logistically challenging. This study generated a dataset including over 150,000 maize and sorghum images for leaf counting projects. A subset of 17,783 images also includes annotations of the positions of individual leaf tips. With these annotated images, we evaluate two deep learning-based approaches for automated leaf counting: the first based on counting-by-regression from whole image analysis and a second based on counting-by-detection. Both approaches can achieve RMSE (root of mean square error) smaller than one leaf, only moderately inferior to the RMSE between human annotators of between 0.57 and 0.73 leaves. The counting-by-regression approach based on CNNs (convolutional neural networks) exhibited lower accuracy and increased bias for plants with extreme leaf numbers which are underrepresented in this dataset. The counting-by-detection approach based on Faster R-CNN object detection models achieve near human performance for plants where all leaf tips are visible. The annotated image data and model performance metrics generated as part of this study provide large scale resources for the comparison and improvement of algorithms for leaf counting from image data in grain crops.
Why it matches plant phenotyping methods画像から作物の葉数を自動推定する手法の開発・比較、性能評価、データセット構築が研究の中心であり、植物表現型計測法に該当する。
abstractThis study generated a dataset including over 150,000 maize and sorghum images for leaf counting projects.
Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.
Why it matches plant phenotyping methods高速・高スループットな植物カウント手法を開発し、複数作物で検証した植物フェノタイピング手法の中心的研究。
abstractThe goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery.
Reproduction assets foundThe paper's authors publicly released their TasselNetV2+ PyTorch implementation (the paper's plant counting/phenotyping analysis code) online. The three plant counting datasets used are cited prior datasets, not paper-specific deposits.Code · publicTo encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plusOpen asset ↗TasselNetV2pluslines:225-304Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
SorghumLaboratory / benchtopX-ray / CTRoot2D/3D reconstructionSegmentationRoot system architecture
Plants establish their root system as a three-dimensional structure, which is then used to explore the soil to absorb resources and provide mechanical anchorage. Simplified two-dimensional growth systems, such as agar plates, have been used to study various aspects of plant root biology. However, it remains challenging to study the more realistic three-dimensional structure and function of roots hidden in opaque soil. Here, we optimized X-ray computer tomography (CT)-based visualization of an intact root system by using Toyoura sand, a standard silica sand used in geotechnology research, as a growth substrate. Distinct X-ray attenuation densities of root tissue and Toyoura sand enabled clear image segmentation of the CT data. Sorghum grew especially vigorously in Toyoura sand and it could be used as a model for analyzing root structure optimization in response to mechanical obstacles. The use of Toyoura sand has the potential to link plant root biology and geotechnology applications.
Why it matches plant phenotyping methods不透明な砂中の植物根系をX線CTで可視化・分割する手法を最適化しており、根系構造の取得が研究の中心であるため。
abstractwe optimized X-ray computer tomography (CT)-based visualization of an intact root system by using Toyoura sand
The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.
Why it matches plant phenotyping methods植物HTPデータから形質を抽出・補正する解析パイプラインを開発し、実データとシミュレーションで検証しているため、方法が中心的です。
abstractwe developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment.
Reproduction assets foundThe paper explicitly provides two public GitHub repositories: the SpaTemHTP R pipeline package and a validation repository containing all data, scripts, and functions needed to reproduce the paper's phenotyping analyses. Raw phenotypic data itself is only available on request.Code · publicThe R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP .Open asset ↗ICRISAT-GEMS/SpaTemHTPlines:316-319Code · publicAll data, scripts, and functions required to reproduce the results can be found at: https://github.com/ICRISAT-GEMS/SpaTemHTP_Validation .Open asset ↗ICRISAT-GEMS/SpaTemHTP_Validationlines:457-479Plant phenotyping relevance match · UnverifiedbioRxiv · checked 13 Sept 2026
Sorghum is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is also studied as a feedstock for biofuel and forage. Mechanistic modelling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping for discovery of genotype to phenotype associations remain bottlenecks in efforts to understand the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were then the subject of genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) across 869 field-grown biomass sorghum accessions. SD was correlated with plant height and biomass production. Plasticity in SD and SLA were interrelated with each other, and productivity, across wet versus dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population supported identification of associations between DNA sequence variation, or RNA transcript abundance, and trait variation. 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose orthologs in Arabidopsis have functions related to stomatal or leaf development and leaf gas exchange. These advances in methodology and knowledge will aid efforts to improve the WUE of C4 crops.
Why it matches plant phenotyping methods光学トモグラフィーと新規機械学習ツールによる気孔密度測定が研究の中心的な方法的貢献であり、大規模集団への実質的な適用も行っている。
abstractThis study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD).
High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.
Why it matches plant phenotyping methodsUAV搭載ハイパースペクトルおよびLiDARからソルガムのバイオマスを推定する予測モデルを開発・比較し、データ源や取得時期、回帰法の性能を検証しており、表現型取得・推定手法が研究の中心である。
abstractIn this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated.
Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions' accuracy.
Why it matches plant phenotyping methodsソルガムのRGB画像から3D植物体を自動再構成し、光 interception 特性を推定する高スループット手法の開発が中心であるため。
abstractWe develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos.
Reproduction assets foundThe paper's voxel carving analysis code is explicitly stated as publicly available on GitHub. The raw images, 3D reconstructions, and trait values were only promised for future DataDryad deposit with no URL, so they are not actionable. The FigShare deposit contains genetic marker data (molecular omics), not phenotypingCode · publicThe code is available at https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗https://github.com/cropsinsilico/SorghumVoxelCarvinglines:289-474Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.
Why it matches plant phenotyping methodsUAS画像、植生指数、機械学習分類を用いてソルガムの除草剤傷害を評価する手法を比較・検証しており、植物状態の取得・推定が研究の中心です。
abstractevaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction
Drought is a recurring phenomenon that puts crop yields at risk and threatens the livelihoods of many people around the globe. Stay-green is a drought adaption phenotype found in sorghum and other cereals. Plants expressing this phenotype show less drought-induced senescence and maintain functional green leaves for longer when water limitation occurs during grain fill, conferring benefits in both yield per se and harvestability. The physiological causes of the phenotype are postulated to be water saving through mechanisms such as reduced canopy size or access to extra water through mechanisms such as deeper roots. In sorghum breeding programs, stay-green has traditionally been assessed by comparing visual scores of leaf senescence either by identifying final leaf senescence or by estimating rate of leaf senescence. In this study, we compared measurements of canopy dynamics obtained from remote sensing on two sorghum breeding trials to stay-green values (breeding values) obtained from visual leaf senescence ratings in multienvironment breeding trials to determine which components of canopy development were most closely linked to the stay-green phenotype. Surprisingly, canopy size as estimated using preflowering canopy parameters was weakly correlated with stay-green values for leaf senescence while postflowering canopy parameters showed a much stronger association with leaf senescence. Our study suggests that factors other than canopy size have an important role in the expression of a stay-green phenotype in grain sorghum and further that the use of UAVs with multispectral sensors provides an excellent way of measuring canopy traits of hundreds of plots grown in large field trials.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによるソルガム群落動態・キャノピー形質の高スループット取得と、stay-green表現型との比較が研究の中心であるため。
titleHigh-Throughput Phenotyping of Dynamic Canopy Traits Associated with Stay-Green in Grain Sorghum
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
Machine learning and computer vision technologies based on high-resolution imagery acquired using unmanned aerial systems (UAS) provide a potential for accurate and efficient high-throughput plant phenotyping. In this study, we developed a sorghum panicle detection and counting pipeline using UAS images based on an integration of image segmentation and a convolutional neural networks (CNN) model. A UAS with an RGB camera was used to acquire images (2.7 mm resolution) at 10-m height in a research field with 120 small plots. A set of 1,000 images were randomly selected, and a mask was developed for each by manually delineating sorghum panicles. These images and their corresponding masks were randomly divided into 10 training datasets, each with a different number of images and masks, ranging from 100 to 1,000 with an interval of 100. A U-Net CNN model was built using these training datasets. The sorghum panicles were detected and counted by a predicted mask through the algorithm. The algorithm was implemented using Python with the Tensorflow library for the deep learning procedure and the OpenCV library for the process of sorghum panicle counting. Results showed the accuracy had a general increasing trend with the number of training images. The algorithm performed the best with 1,000 training images, with an accuracy of 95.5% and a root mean square error (RMSE) of 2.5. The results indicate that the integration of image segmentation and the U-Net CNN model is an accurate and robust method for sorghum panicle counting and offers an opportunity for enhanced sorghum breeding efficiency and accurate yield estimation.
Why it matches plant phenotyping methodsUAS画像からソルガム穂を検出・計数する画像解析パイプラインを開発し、学習画像数による性能と精度を評価しており、植物形質取得手法が研究の中心です。
abstractwe developed a sorghum panicle detection and counting pipeline using UAS images based on an integration of image segmentation and a convolutional neural networks (CNN) model.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract High-throughput genotyping coupled with molecular breeding approaches has dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into the development of large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programing skills. We developed PhenoImage – an open-source GUI based cross-platform solution for HTP image processing with the aim to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived RGB and fluorescence shoot images from two plant species: sorghum and wheat differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage . Highlight PhenoImage is an open-source application designed for analyzing images derived from high-throughput phenotyping.
Why it matches plant phenotyping methods植物画像を解析するオープンソースGUIの開発・実装・評価が中心であり、HTP画像から表現型を抽出するソフトウェア研究に該当する。
abstractWe developed PhenoImage – an open-source GUI based cross-platform solution for HTP image processing
BACKGROUND AND OBJECTIVES: Sorghum grains may be subjected to weathering damage in the field as naked grains on the panicle are exposed to adverse environmental conditions during grain development or dry down before harvest. A near infrared (NIR) protein calibration model did not work as expected for weathered grains. Extended multiplicative signal correction was tested as a preprocessing method for spectra to improve the prediction accuracy of the model when weathered grain samples are evaluated. FINDINGS: NIR spectra of weathered sorghum grain samples showed marked distortions with a higher apparent absorbance at lower wavelengths in the 950–1650 nm spectral range. A partial least squares calibration developed with sound grain samples using spectra preprocessed with multiplicative signal correction (MSC) predicted protein content of validation sample set of sound grains with a root mean square error of prediction (RMSEP) = 0.67% and coefficient of determination (r²) = 0.83. However, performance of this calibration model dropped with RMSEP = 13.14% and r² = 0.04 when used to predict proteins in weathered grain samples. Introduction of weathered grain spectra to the calibration improved predictive performance with RMSEP = 0.92% and r² = 0.69. Application of extended multiplicative scatter correction (EMSC) to preprocess the spectra of the calibration with weathered grain samples enhanced the prediction performance with RMSEP = 0.61% and r² = 0.85. CONCLUSIONS: Inclusion of weathered grain spectra in the calibration and preprocessing spectra with EMSC markedly improved the robustness of the sorghum protein calibration. SIGNIFICANCE AND NOVELTY: Sorghum breeders using NIR spectroscopy for evaluation of protein levels in sorghum breeding lines can get the weathered grain samples also analyzed for protein levels by using this method.
Why it matches plant phenotyping methodsソルガム穀粒のタンパク質含量をNIRで推定する校正法について、weathering対応の前処理（EMSC）と予測性能を検証しており、植物形質取得法が研究の中心である。
abstractExtended multiplicative signal correction was tested as a preprocessing method for spectra to improve the prediction accuracy of the model when weathered grain samples are evaluated.
Background Photosynthesis is one of the most important biological reactions and forms the basis of crop productivity and yield on which a growing global population relies. However, to develop improved plant cultivars that are capable of increased productivity, methods that can accurately and quickly quantify photosynthetic efficiency in large numbers of genotypes under field conditions are needed. Chlorophyll fluorescence imaging is a rapid, non-destructive measurement that can provide insight into the efficiency of the light-dependent reactions of photosynthesis. Results To test and validate a field-deployed fluorescence imaging system on the TERRA-REF field scanalyzer, leaves of potted sorghum plants were treated with a photosystem II inhibitor, DCMU, to reduce photochemical efficiency (F V /F M ). The ability of the fluorescence imaging system to detect changes in fluorescence was determined by comparing the image-derived values with a handheld fluorometer. This study demonstrated that the imaging system was able to accurately measure photochemical efficiency (F V /F M ) and was highly correlated ( r = 0.92) with the handheld fluorometer values. Additionally, the fluorescence imaging system was able to track the decrease in photochemical efficiency due to treatment of DCMU over a 7 day period. Conclusions The system's ability to capture the temporal dynamics of the plants' response to this induced stress, which has comparable dynamics to abiotic and biotic stressors found in field environments, indicates the system is operating correctly. With the validation of the fluorescence imaging system, physiological and genetic studies can be undertaken that leverage the fluorescence imaging capabilities and throughput of the field scanalyzer.
Why it matches plant phenotyping methods圃場用クロロフィル蛍光イメージングシステムを開発・検証し、光化学効率という植物生理形質を画像から測定する方法が研究の中心である。
abstractmethods that can accurately and quickly quantify photosynthetic efficiency in large numbers of genotypes under field conditions are needed.
Abstract. Canopy height (CH) and leaf area index (LAI) provide key information about crop growth and productivity. A rapid and accurate retrieval of CH and LAI is critical for a variety of agricultural applications. LiDAR and RGB photogrammetry have been increasingly used in plant phenotyping in recent years thanks to the developments in Unmanned Aerial Vehicle (UAV) and sensor technology. The goal of this study is to investigate the potential of UAV LiDAR and RGB photogrammetry in estimating crop CH and LAI. To this end, a high resolution 32 channel LiDAR and RGB cameras mounted on DJI Matrice 600 Pro UAV were employed to collect data at sorghum fields near Maricopa, Arizona, USA. A series of canopy structure metrics were extracted using LiDAR and RGB photogrammetry-based point clouds. Random Forest Regression (RFR) models were established based on the UAV-LiDAR and photogrammetry-derived metrics and field-measured LAI. The results show that both UAV-LiDAR and RGB photogrammetry demonstrated promising accuracies in CH extraction and LAI estimation. Overall, UAV-LiDAR yielded superior performance than RGB photogrammetry in both low and high canopy density sorghum fields. In addition, Pearson’s correlation coefficient, as well as RFR-based variable importance analysis demonstrated that height-based metrics from both LiDAR and photogrammetric point clouds were more useful than density-based metrics in LAI estimation. This study proved that UAV-based LiDAR and photogrammetry are important tool in sustainable field management and high-throughput phenotyping, but LiDAR is more accurate than RGB photogrammetry due to its greater canopy penetration capability.
Why it matches plant phenotyping methodsUAV LiDARとRGBフォトグラメトリによるソルガムの草冠高・LAI推定を比較検証しており、形質取得手法の技術評価が研究の中心である。
abstractThe goal of this study is to investigate the potential of UAV LiDAR and RGB photogrammetry in estimating crop CH and LAI.
Premise Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment. Methods High-throughput time series image data from water-deprived maize ( Zea mays subsp. mays ) and sorghum ( Sorghum bicolor ) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions. Results Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period. Discussion Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.
Why it matches plant phenotyping methodsLAXは画像から葉角度を抽出・定量するために開発された高スループット画像処理フレームワークであり、植物表現型取得手法が研究の中心です。
abstractA MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions.
Reproduction assets foundThe paper's authors explicitly state that the LAX source code and GUI are publicly available on GitHub, and the paper's time-lapse image data (Video S1) is publicly hosted on Vimeo. Both are paper-specific, public, and actionable.Code · publicnowledgments
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Data Availability
The source code and GUI interface are available at https://github.com/Kenchanmane‐Raju/Leaf‐Angle‐eXtractor .
LITERATURE CITED
Araus , J. L.
,
S. C.
Kefauver
,
M.
Zaman‐Allah
,
M. S.
Olsen
, and
J. E.
Cairns
. 2018
Translating high‐throughput phenotyping into genetic gain
. Trends in Plant Science
23 ( 5 ): 451 – 466 .
29555431
10.1016/j.tplants.2018.02.001
PMC5931794
Awada , L.
,
P. W. B.
Phillips
, and
S. J.
Smyth
.Open asset ↗Kenchanmane‐Raju/Leaf‐Angle‐eXtractorlines:182-386Dataset · publicgle boxes and leaf number. Clicking the ‘Export Data’ icon at the bottom outputs leaf angle measurements for the selected leaves as a .csv file .
Click here for additional data file.
VIDEO S1. Time‐lapse video showing the drop of maize leaves in response to water deficit stress over a single day. This video is also available at https://vimeo.com/256137800 .
Click here for additional data file.
Acknowledgments
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Data Availability
The sourOpen asset ↗lines:182-386Plant phenotyping relevance match · UnverifiedOpenAlex · checked 9 Sept 2026
Frequent measurements of the plant phenotypes make it possible to monitor plant status during the growing season. Stem diameter is an important proxy for overall plant biomass and health. However, the manual measurement of stem diameter in plants is time consuming, error prone, and laborious. The use of agricultural robots to automatically collect plant phenotypic data for trait measurements can overcome many of the drawbacks of manual phenotyping. The objective of this research was to develop a robotic system that can automatically detect and grasp the stem, and measure its diameter of maize and sorghum plants. The robotic system comprises of a four degree of freedom robotic manipulator, a time-of-flight camera for vision system, and a linear potentiometer sensor to measure the stem diameter. Deep learning and conventional image processing were used to detect stem in images and find grasping point of stem, respectively. An experiment was conducted in a greenhouse using maize and sorghum plants to evaluate the performance of the robotic system. The system demonstrated successful grasping of stem and a high correlation between manual and robotic measurements of diameter depicting its ability to be used as a prototype to integrate other sensors to measure different physiological and chemical attributes of the stem.
Why it matches plant phenotyping methodsロボットによる茎径という植物形質の自動取得システムを開発し、手動測定との性能比較で評価しており、フェノタイピング手法が研究の中心です。
abstractThe objective of this research was to develop a robotic system that can automatically detect and grasp the stem, and measure its diameter of maize and sorghum plants.
The development of a robust method to non-invasively visualize root morphology in natural soils has been hampered by the opaque, physical, and structural properties of soils. In this work we describe a novel technology, low field magnetic resonance imaging (LF-MRI), for imaging energy sorghum ( Sorghum bicolor (L.) Moench) root morphology and architecture in intact soils. The use of magnetic fields much weaker than those used with traditional MRI experiments reduces the distortion due to magnetic material naturally present in agricultural soils. A laboratory based LF-MRI operating at 47 mT magnetic field strength was evaluated using two sets of soil cores: 1) soil/root cores of Weswood silt loam (Udifluventic Haplustept) and a Belk clay (Entic Hapluderts) from a conventionally tilled field, and 2) soil/root cores from rhizotrons filled with either a Houston Black (Udic Haplusterts) clay or a sandy loam purchased from a turf company. The maximum soil water nuclear magnetic resonance (NMR) relaxation time T 2 (4 ms) and the typical root water relaxation time T 2 (100 ms) are far enough apart to provide a unique contrast mechanism such that the soil water signal has decayed to the point of no longer being detectable during the data collection time period. 2-D MRI projection images were produced of roots with a diameter range of 1.5-2.0 mm using an image acquisition time of 15 min with a pixel resolution of 1.74 mm in four soil types. Additionally, we demonstrate the use of a data-driven machine learning reconstruction approach, Automated Transform by Manifold Approximation (AUTOMAP) to reconstruct raw data and improve the quality of the final images. The application of AUTOMAP showed a SNR (Signal to Noise Ratio) improvement of two fold on average. The use of low field MRI presented here demonstrates the possibility of applying low field MRI through intact soils to root phenotyping and agronomy to aid in understanding of root morphology and the spatial arrangement of roots in situ .
Why it matches plant phenotyping methods根の形態・構造を非侵襲的に取得する低磁場MRI法を開発・評価し、機械学習再構成による画像品質改善も検証しているため、植物フェノタイピング手法が中心である。
abstractThe development of a robust method to non-invasively visualize root morphology in natural soils
Unmanned aircraft systems are increasingly used in data-gathering operations for precision agriculture, with compounding benefits. Analytical processing of image data remains a limitation for applications. We implement an unsupervised machine learning technique to efficiently analyze aerial image data, resulting in a robust method for estimating plant phenotypes. We test this implementation in three settings: rice fields, a plant nursery, and row crops of grain sorghum and soybeans. We find that unsupervised subpopulation description facilitates accurate plant phenotype estimation without requiring supervised classification approaches such as construction of reference data subsets using geographic positioning systems. Specifically, we apply finite mixture modeling to discern component probability distributions within mixtures, where components correspond to spatial references (for example, the ground) and measurement targets (plants). Major benefits of this approach are its robustness against ground elevational variations at either large or small scale and its proficiency in efficiently returning estimates without requiring in-field operations other than the vehicle overflight. Applications in plant pathosystems where metrics of interest are spectral instead of spatial are a promising future direction.
Why it matches plant phenotyping methods無人航空機画像から有限混合モデルで植物表現型を推定する解析手法を実装・評価しており、表現型取得・抽出法が研究の中心である。
abstractWe implement an unsupervised machine learning technique to efficiently analyze aerial image data, resulting in a robust method for estimating plant phenotypes.
Summary Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity. Here, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor , which vary extensively in numbers, lengths, and angles of primary branches, as well as the three‐dimensional shape, size, and distribution of the seed. We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative. However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.
Why it matches plant phenotyping methodsX線CTと詳細な形態計測を組み合わせ、ソルガム穂の3次元形態を取得・解析する画像ベース表現型手法が研究の中心であるため。
abstractHere, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture.
Reproduction assets foundThe paper explicitly states that the full 3D X-ray imaging dataset of sorghum panicles is publicly downloadable from the Topp lab resources page, and that all image processing, feature extraction, and statistical analysis code is available in a public GitHub repository (Topp-Roots-Lab/3D-Sorghum-Inflorescence). Both URDataset · publicThe full 3D imaging dataset for this work can be downloaded from: https://www.danforthcenter.org/scientists‐research/principal‐investigators/chris‐topp/resourcesOpen asset ↗lines:51-62Code · publicAll code used for image processing, digital feature extraction, and statistical analysis from this study can be found at the following GitHub repository: https://github.com/Topp‐Roots‐Lab/3D‐Sorghum‐InflorescenceOpen asset ↗Topp‐Roots‐Lab/3D‐Sorghum‐Inflorescencelines:51-62Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and radiation use efficiency of agricultural crop canopies will be essential to meeting growing needs for food. Canopy architecture is inherently 3D, but many approaches to measuring canopy architecture component traits treat the canopy as a two dimensional structure in order to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct three dimensional representations of maize and sorghum from a small number of RGB photos. This approach was employed to generate three dimensional reconstructions of a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of both known and previously unreported loci controlling light interception efficiency in sorghum. The approach described here is generalizable and scalable and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. For future datasets we propose a set of best practices to increase the accuracy of three dimensional reconstructions.
Why it matches plant phenotyping methodsRGB画像から作物の3D構造を再構成し、光 interception 指標を推定する高スループット表現型計測法の開発が中心であるため。
abstractWe develop a high throughput voxel carving strategy to reconstruct three dimensional representations of maize and sorghum from a small number of RGB photos.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
BACKGROUND: Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. RESULTS: We propose a point supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets-Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time. CONCLUSION: We proposed a cost effective method to train reliable panicle detectors for cereal crops. A low cost panicle detection method for cereal crops is highly beneficial to both breeders and agronomists. Plant breeders can obtain quick crop yield estimates to make important crop management decisions. Similarly, obtaining real time visual crop analysis is valuable for researchers to analyze the crop's response to various experimental conditions.
Why it matches plant phenotyping methods穀類の穂密度を推定する画像ベースの検出手法を開発し、複数データセットで性能とラベリングコストを評価しており、植物表現型取得法が中心である。
abstractTo phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques.
The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track phenotypic change over time. Identifying genetic loci regulating differences in the pattern of phenotypic change remains challenging. In this study we used functional principal component analysis (FPCA) to achieve this aim. Time-series phenotype data was collected from a sorghum diversity panel using a number of technologies including RGB and hyperspectral imaging. Imaging lasted for thirty-seven days centered on reproductive transition. A new higher density SNP set was generated for the same population. Several genes known to controlling trait variation in sorghum have been cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotype. Partitioning was consistent with the known molecular function of the individual cloned genes. FPCA-based genome-wide association studies can enable robust time-series mapping analyses in a wide range of contexts. Time-series analysis can increase the accuracy and power of quantitative genetic analyses.
Why it matches plant phenotyping methods時系列画像から得た植物表現型をFPCAで解析し、表現型変化のパターンを定量化して遺伝子座同定に用いる計算手法が研究の中心である。
abstractIn this study we used functional principal component analysis (FPCA) to achieve this aim.
This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.
Why it matches plant phenotyping methodsイネ科植物のハイパースペクトル画像から器官をセグメンテーションし、形態形質を抽出する手法を開発・評価しており、表現型取得法が研究の中心です。
abstractThis study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle).
Reproduction assets foundThe paper deposits its authors' analysis code, extracted phenotypes, and manually annotated sorghum/maize pixel data in a public GitHub repository, and its Zooniverse crowdsourcing project (used to generate the pixel annotations) is publicly accessible. Both are paper-specific, public, and actionable.Code · publicAll the R and python code implemented in this study, phenotypes extracted from segmented sorghum images, and the manually annotated sorghum and maize pixels have been deposited on GitHub at https://github.com/freemao/Sorghum_Semantic_Segmentation .Open asset ↗https://github.com/freemao/Sorghum_Semantic_Segmentationlines:85-113Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Automatic tools for plant phenotyping have received increased interest in recent years due to the need to understand the relationship between plant genotype and phenotype. Building upon our previous work, we present a robust, deep learning method to accurately estimate the height of biomass sorghum throughout the entirety of its growing season. We mount a vertically oriented LiDAR sensor onboard an agricultural robot to obtain 3D point clouds of the crop fields. From each of these 3D point clouds, we generate a height contour and density map corresponding to a single row of plants in the field. We then train a multiview neural network in order to estimate plant height. Our method is capable of accurately estimating height from emergence through canopy closure. We extensively validate our algorithm by performing several ground truthing campaigns on biomass sorghum. We have shown our proposed approach to achieve an absolute height estimation error of 7.47% using ground truth data obtained via conventional breeder methods on 2715 plots of sorghum with varying genetic strains and treatments.
Why it matches plant phenotyping methodsLiDARによる作物画像取得と深層学習による草丈推定を中心に開発・大規模検証した、明確な植物フェノタイピング手法研究。
abstractwe present a robust, deep learning method to accurately estimate the height of biomass sorghum throughout the entirety of its growing season.
Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C 4 grass Setaria , a diversity panel of sorghum ( S. bicolor ), and the founder panel for a nested association mapping population of canola ( Brassica napus L. ). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.
Why it matches plant phenotyping methods画像から処置応答を自動検出・定量する新規フェノタイピング手法を開発し、実データと合成データで検証しており、手法が研究の中心である。
abstractIn this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images.
Sorghum’s natural adaptation to a wide range of abiotic stresses provides diverse genetic reserves for potential improvement in crop stress tolerance. Growing interest in sorghum research has led to the expansion of genetic resources though establishment of the sorghum association panel (SAP), generation of mutagenized populations, and recombinant inbred line (RIL) populations, etc. Despite rapid improvement in biotechnological tools, lack of efficient phenotyping platforms remains one of the major obstacles in utilizing these genetic resources. Scarcity of efforts in root system phenotyping hinders identification and integration of the superior root traits advantageous to stress tolerance. Here, we explored multiple approaches in root phenotyping of an ethyl methanesulfonate (EMS)-mutagenized sorghum population. Paper-based growth pouches (PGP) and hydroponics were employed to analyze root system architecture (RSA) variations induced by mutations and to test root development flexibility in response to phosphorus deficiency in early growing stages. PGP method had improved capabilities compared to hydroponics providing inexpensive, space-saving, and high-throughput phenotyping of sorghum roots. Preliminary observation revealed distinct phenotypic variations which were qualitatively and quantitatively systemized for association analysis. Phenotypes/ideotypes with root architecture variations potentially correlated with Pi acquisition were selected to evaluate their contribution to P-efficiency (PE). Sand mixed with P-loaded activated alumina substrate (SAS) provided closely to natural but still controlled single-variable conditions with regulated Pi availability. Due to higher labor and cost input we propose SAS to be used for evaluating selected sorghum candidates for PE. The ability of rapidly screening root phenotypes holds great potential for discovering genes responsible for relevant root traits and utilizing mutations to improve nutrient efficiency and crop productivity.
Why it matches plant phenotyping methodsソルガム根系構造を高スループットに測定する複数の根部フェノタイピング手法とプラットフォームを開発・比較しており、表現型取得が研究の中心である。
titleDevelopment of Root Phenotyping Platforms for Identification of Root Architecture Mutations in EMS-Induced and Low-Path-Sequenced Sorghum Mutant Population
Background \nPanicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. \n \nResults \nWe propose a point supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets—Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time. \n \nConclusion \nWe proposed a cost effective method to train reliable panicle detectors for cereal crops. A low cost panicle detection method for cereal crops is highly beneficial to both breeders and agronomists. Plant breeders can obtain quick crop yield estimates to make important crop management decisions. Similarly, obtaining real time visual crop analysis is valuable for researchers to analyze the crop’s response to various experimental conditions.
Why it matches plant phenotyping methods穀類の穂密度を推定する画像ベースのパニクル検出法を、点ラベルを用いた能動学習として開発・評価しており、低コストな表現型取得が研究の中心である。
abstractTo phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques.
Abstract High‐throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high‐throughput traits could lead to significant optimization of HTP‐assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [ Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS‐based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75; and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health; 104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field‐based breeding program and suggest additional optimization options.
Why it matches plant phenotyping methodsUASマルチスペクトル画像から植物健全性と穀粒収量を推定するため、飛行時期・植生指数・予測アルゴリズムを比較最適化しており、表現型取得・推定法が研究の中心である。
titleOptimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum
Plant architectural traits are important factors in determining grain and biomass productivity of sorghum (Sorghum bicolor (L) Moench). However, collecting data on these architectural traits is labor-intensive and time-consuming, especially when using numerous lines in quantitative genetic studies or breeding programs. Therefore, we used the high-throughput field-based robotic platform PhenoBot 1.0 to collect whole canopy stereo images from a large association mapping panel under field conditions. These images were used to create a plot-based 3D reconstruction of the canopy from which phenotypic features were automatically extracted. These features included: plot-based plant height (PPH), plot-based plant width (PPW), plant surface area (PSA), and convex-hull volume (CHV). A small sub-set of sorghum lines were used to obtain ground-truth measurements to validate the image-derived descriptors, and determine their biological significance. PPH was highly correlated with manually measured plant height; PPW correlated with SinAL, defined as leaf length multiplied by the sine of its angle; PSA was associated with manually measured total plant surface area; and CHV was a function of both flag-leaf height and SinAL. Association mapping of PPH identified chromosomal regions containing known plant height genes, confirming the accuracy of the automatic feature extraction process. For the other phenotypic features, significant markers were identified within genomic regions that have been previously reported to control plant architectural characteristics in sorghum such as tiller number, shoot compactness, leaf length, surface area, and angle. The image processing method used in this study contributes new knowledge to the development of high-throughput phenotyping techniques and represents a novel tool for plant breeders.
Why it matches plant phenotyping methods圃場ロボットで取得したステレオ画像から3D植物形態特徴を自動抽出し、地上真値で検証した高スループット表現型計測研究であり、方法が中心的です。
abstractwe used the high-throughput field-based robotic platform PhenoBot 1.0 to collect whole canopy stereo images from a large association mapping panel under field conditions.
Low soil moisture is a major abiotic stress wide spread in arid and semi-arid regions, limiting both seed germination and seedling establishment. Deep-seeding technique allows seeds to use the moisture in deep soil, however, most sorghum seeds cannot penetrate soil layer of 10 cm or deeper. To develop sorghum varieties tolerant to deep-seeding, it is necessary to identify promising accessions with good agronomic traits and tolerance to deep-seeding. A high throughput hydroponic method was developed to screen sorghum accessions with long mesocotyl under dark conditions. This method revealed large genetic variations for mesocotyl length in the panel of 105 sorghum accessions, which were validated by soil culture and deep-seeding experiments. The lines with long mesocotyl under hydroponic culture had good seedling establishments in deep-seeding experiments, while the lines with short mesocotyl length could not emerge. Compared to conventional sand or soil cultural methods, the hydroponic culture was highly efficient that requires less time, space, and labor. The mesocotyl lengths of screened sorghum accessions under the newly developed hydroponic method were highly correlated with soil culture and germination rate under deep-seeding in soil. As an efficient screening tool for the mesocotyl lengths under soil culture and the seedling emergence rates from deep soil layer, the hydroponic culture method can be easily integrated in breeding programs targeting deep-seeding tolerance. Breeding for long mesocotyl sorghum varieties may lead to good seedling establishments under arid and semi-arid regions.
Why it matches plant phenotyping methodsソルガムのメソコチル長と深播き時の出芽を評価する高スループット水耕スクリーニング法を開発し、土壌栽培・深播き試験で検証しているため、表現型取得法が中心である。
abstractA high throughput hydroponic method was developed to screen sorghum accessions with long mesocotyl under dark conditions.
The sugary juice from sweet sorghum [ Sorghum bicolor (L.) Moench] stalks can be used to produce edible syrup, biofuels, or bio-based chemical feedstock. The current cultivars are highly susceptible to damage from sugarcane aphids [ Melanaphis sacchari (Zehntner)], but development of new cultivars is hindered by a lack of rapid analytical methods to screen for juice quality traits. The mechanism of aphid resistance/tolerance is also largely unknown, though the importance of defense phytochemicals has been suggested. The purpose of this study was to develop low-cost methods sensitive to fluorescent fingerprints in sweet sorghum juice, which is a complex mixture of saccharides, carboxylates, polyphenols, and metal ions. Of primary juice components, tryptophan and trans -aconitic acid were the highest intensity contributors to the overall fluorescence and UV/visible absorbance, respectively, while tyrosine and polyphenols contributed to a less extent. In a test of 24 sweet sorghum cultivars, tryptophan and tyrosine contents were the highest in the aphid-susceptible hybrid N109A x Chinese, while sucrose, trans -aconitic acid, and polyphenols were the highest in the resistant line No. 5 Gambela. This suggests that the accumulation of carboxylate ( trans -aconitic acid) and polyphenolic secondary products in No. 5 Gambela may contribute to its aphid resistance, thus allowing it to maintain sucrose production. Rapid detection of these chemical signatures could be used to prescreen the breeding material for potential resistance and juice quality traits, without analytical separation required for metabolomics.
Why it matches plant phenotyping methodsスイートソルガム果汁の蛍光・吸光シグネチャーから品質およびアブラムシ抵抗性関連形質を迅速評価する低コスト手法の開発が中心であり、育種材料のスクリーニングに再利用可能な植物フェノタイピング手法に該当する。
abstractThe purpose of this study was to develop low-cost methods sensitive to fluorescent fingerprints in sweet sorghum juice
Seed shape is an important agronomic trait with continuous variation among genotypes. Therefore, the quantitative evaluation of this variation is highly important. Among geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations. Elliptic Fourier analysis is an approximation method to treat contours as a waveform. Semi-landmark analysis is a method of superimposed points in which the differences of multiple contour positions are minimized. However, no detailed comparison of these methods has been undertaken. Moreover, these shape descriptors vary when the scale and direction of the contour and the starting point of the contour trace change. Thus, these methods should be compared with respect to the standardization of the scale and direction of the contour and the starting point of the contour trace. In the present study, we evaluated seed shape variations in a sorghum (Sorghum bicolor Moench) germplasm collection to analyze the association between shape variations and genome-wide single-nucleotide polymorphisms by genomic prediction (GP) and genome-wide association studies (GWAS). In our analysis, we used all possible combinations of three shape description methods and eight standardization procedures for the scale and direction of the contour as well as the starting point of the contour trace; these combinations were compared in terms of GP accuracy and the GWAS results. We compared the shape description methods (elliptic Fourier descriptors and the coordinates of superposed pseudo-landmark points) and found that principal component analysis of their quantitative descriptors yielded similar results. Different scaling and direction standardization procedures caused differences in the principal component scores, average shape, and the results of GP and GWAS.
Why it matches plant phenotyping methodsソルガム種子形状という植物形態形質の定量化手法を比較・標準化し、GP精度とGWAS結果で評価しており、形質取得・抽出法が研究の中心である。
abstractAmong geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations.
Reproduction assets foundThe paper's seed contour shape data (the phenotyping measurements used for GP/GWAS) are publicly deposited in the authors' GitHub repository, explicitly stated in the Data Availability statement. Supporting tables (S1–S3) also contain accession lists and GWAS results but the GitHub repository is the primary paper-qualiDataset · publicData Availability: All seed counter shape data are available from the https://github.com/risasakamoto/Comparison-of-shape-quantification-methods .Open asset ↗risasakamoto/Comparison-of-shape-quantification-methodslines:177-188Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeSorghumField / plotRootMorphology / geometry measurementRoot system architecture
Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize ( Zea mays ) and sorghum ( Sorghum bicolor ), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.
Why it matches plant phenotyping methods根系形態の画像取得・特徴抽出パイプライン（CREAMD-COFE）の開発が研究の中心であり、RSA形質を大規模に抽出しているため。
abstractWe developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images.
Reproduction assets foundThe paper's COFE root-image analysis software is explicitly stated to be publicly available on Bitbucket, and the paper's RSA phenotype measurements (maize BLUP trait values and sorghum trait values) are released as supplemental tables accessible with the article.Code · publiche Maize273 and SAM273
panels is a subset of the data used for the root-GWAS of the SAM Diversity
Panel. GWAS was conducted with the same protocol as in comparative GWAS
between maize and sorghum (see above section), except an arbitrarily relaxed
window of 100 kb, centered on the TAS was used here.
COFE Software is available at https://bitbucket.org/baskargroup/cofe/src/master/.Accession Numbers
The maize sequence data from this article can be found in the GenBank/
EMBL data libraries under accession numbers SRP055871. The sorghum SNP
data were downloaded from https://www.morrislab.org/data.Supplemental Data
The following supplemental materials are available.
Supplemental Text S1. CREAMD-COOpen asset ↗baskargroup/cofepdf-raw-page:12 lines:1-84Plant phenotyping relevance match · UnverifiedarXiv · checked 9 Sept 2026
Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. We propose a point supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets - Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time.
Why it matches plant phenotyping methods穀粒穂の密度を対象とする画像ベースの検出・アクティブラーニング手法を開発し、複数作物データセットで性能とラベリングコストを評価しており、表現型取得手法が中心である。
abstractTo phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques.
Background and aims Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction. Methods Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre). Key results The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes. Conclusions By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.
Why it matches plant phenotyping methodsEcomeristemの複数モデルを比較・検証し、分げつ成長・枯死・バイオマス生産などの植物形質を遺伝型・環境差と関連づけて予測する計算手法が研究の中心である。
abstractThis study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction.
ABSTRACT Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy because of its potential for high biomass yields. However, in its early growth phases, sorghum is sensitive to cold stress, preventing early planting in temperate environments. Cold temperature adaptability is vital for the successful cultivation of both bioenergy and grain sorghum at higher latitudes and elevations, and for early season planting or to extend the growing season. Identification of genes and alleles that enhance biomass accumulation of sorghum grown under early cold stress would enable the development of improved bioenergy sorghum through breeding or genetic engineering. We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP is a collection of densely genotyped and racially, geographically, and phenotypically diverse accessions. The plants were weighed, watered, and imaged daily to measure growth dynamics and water use efficiency (WUE). Daily, non-destructive imaging allowed for a temporal analysis of growth-related traits in response to cold stress. We performed a genome-wide association study (GWAS) to identify candidate genomic intervals and genes controlling response to early cold stress. GWAS identified transient quantitative trait loci (QTL) strongly associated with each growth-related trait, permitting an investigation into the genetic basis of cold stress response at different stages of development. The analysis identified a priori and novel candidate genes associated with growth-related traits and the temporal response to cold stress. SIGNIFICANCE STATEMENT Genome-wide association study of bioenergy sorghum accessions phenotyped under early season cold stress revealed transient QTLs for highly heritable biomass and growth-related traits that appeared as the temperature increased and plants developed. Sorghum accessions clustered into multiple groups for each heritable trait with distinct growth profiles. GWAS identified candidate genes associated with growth traits and cold stress responses. The top-performing accessions with the highest growth-related trait values over time and temperature shifts will be useful for further genetic analysis and breeding or engineering efforts directed at biomass yield enhancements.
Why it matches plant phenotyping methods画像ベースで成長動態と水利用効率を日次・非破壊測定し、低温応答の形質を時系列評価しており、表現型取得ワークフローが研究の主要部分を占める。
abstractWe conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment.
In plant phenotyping, leaf-level physiological and chemical trait measurements are needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, enabling automated monitoring with minimal human intervention. In this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties. The robotic system comprised of a four Degree of Freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. A robotic gripper was developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, and a thermistor for leaf temperature measurement. A MATLAB program along with a Graphical User Interface (GUI) was developed to control the robotic system and its components, and for acquiring and recording data obtained from the sensors. The system was tested in a greenhouse using maize and sorghum plants. The results showed that leaf temperature measurements by the phenotyping robot were significantly correlated with those measured manually by a human researcher (R2 = 0.58 for maize and 0.63 for sorghum). The leaf spectral measurements by the phenotyping robot predicted leaf chlorophyll, water content and potassium with moderate success (R2 ranged from 0.52 to 0.61), whereas the prediction for leaf nitrogen and phosphorus were poor. The total execution time to grasp and take measurements from one leaf was 35.5 ± 4.4 s for maize and 38.5 ± 5.7 s for sorghum. Furthermore, the test showed that the grasping success rate was 78% for maize and 48% for sorghum. The phenotyping robot can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo leaf-level physiological and biochemical trait measurements.
Why it matches plant phenotyping methods植物葉の生理・化学形質を自動取得するロボット型フェノタイピングシステムを開発し、手動測定との相関、予測性能、把持成功率、実行時間を評価しており、方法が研究の中心である。
abstractIn this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties.
This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.
Why it matches plant phenotyping methods個体の草丈・茎幅を取得する高スループット表現型計測ロボットの設計と圃場評価が中心であり、方法開発・技術評価に該当する。
abstractThis article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production.
The ability to correlate morphological traits of plants with their genotypes plays an important role in plant phenomics research. However, measuring phenotypes manually is time-consuming, labor intensive, and prone to human errors. The 3D surface model of a plant can potentially provide an efficient and accurate way to digitize plant architecture. This study focused on the extraction of morphological traits at multiple developmental timepoints from sorghum plants grown under controlled conditions. A non-destructive 3D scanning system using a commodity depth camera was implemented to capture sequential images of a plant at different heights. To overcome the challenges of overlapping tillers, an algorithm was developed to first search for the stem in the merged point cloud data, and then the associated leaves. A 3D skeletonization algorithm was created by slicing the point cloud along the vertical direction, and then linking the connected Euclidean clusters between adjacent layers. Based on the structural clues of the sorghum plant, heuristic rules were implemented to separate overlapping tillers. Finally, each individual leaf was automatically segmented, and multiple parameters were obtained from the skeleton and the reconstructed point cloud including: plant height, stem diameter, leaf angle, and leaf surface area. The results showed high correlations between the manual measurements and the estimated values generated by the system. Statistical analyses between biomass and extracted traits revealed that stem volume was a promising predictor of shoot fresh weight and shoot dry weight, and the total leaf area was strongly correlated to shoot biomass at early stages.
Why it matches plant phenotyping methodsソルガムの3D点群から形態形質を自動抽出する手法を開発し、手動測定との相関で検証しており、フェノタイピング手法が中心である。
abstractA non-destructive 3D scanning system using a commodity depth camera was implemented to capture sequential images of a plant at different heights.
BACKGROUND AND OBJECTIVES: A near‐infrared (NIR) spectroscopy method was developed for rapid and nondestructive evaluation of protein content of intact sorghum grains. Effect of grain sample moisture variation on the robustness of protein calibration was investigated. FINDINGS: An initial NIR protein calibration model with sorghum grains in 7.28%–11.57% moisture content range with coefficient of determination (R²) of 0.94 and standard error of cross‐validation (SECV) of 0.41%, predicted protein content of an external validation set of different varieties with R² = 0.90, root‐mean‐square error of prediction (RMSEP) = 0.42% and bias = −0.02%. However, when grains with a wider range of moisture content (7.50%–17.75%) were used for validation, prediction errors increased with R² = 0.72 RMSEP = 0.87% and bias = −0.32%. Inclusion of grains with a wider moisture range to the calibration set improved the performance of the calibration model with a R² = 0.83, RMSEP = 0.67% with a bias of −0.04%. CONCLUSIONS: Variation of moisture content in grains affected the performance of the NIR protein calibration model. Likewise, inclusion of moisture variation in the calibration sample set improved the robustness of the model. SIGNIFICANCE AND NOVELTY: In addition to the traits of interest, variation of other physical or chemical traits should also be considered for inclusion into the calibration sample set to improve the robustness of NIR calibration models. This is especially important when NIR spectroscopy methods are developed for evaluation of breeding populations as the future grain samples from numerous crosses may be substantially diverse.
Why it matches plant phenotyping methodsソルガム穀粒のタンパク質含量という植物形質をNIRで非破壊測定する校正法を開発し、水分変動に対する頑健性を検証・改善しており、フェノタイピング手法が中心である。
abstractEffect of grain sample moisture variation on the robustness of protein calibration was investigated.
The yield of cereal crops such as sorghum ( Sorghum bicolor L. Moench) depends on the distribution of crop-heads in varying branching arrangements. Therefore, counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field. However, measuring such phenotypic traits manually is an extremely labor-intensive process and suffers from low efficiency and human errors. Moreover, the process is almost infeasible for large-scale breeding plantations or experiments. Machine learning-based approaches like deep convolutional neural network (CNN) based object detectors are promising tools for efficient object detection and counting. However, a significant limitation of such deep learning-based approaches is that they typically require a massive amount of hand-labeled images for training, which is still a tedious process. Here, we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images. We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance ( R 2 between human count and machine count is 0.88) by using a semitrained CNN model (i.e., trained with limited labeled data) to perform synthetic annotation. In addition, we also visualize key features that the network learns. This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.
Why it matches plant phenotyping methodsUAV画像からソルガム穂数を検出・計数する弱教師あり深層学習手法を開発し、人的計数との性能も検証しており、植物表現型取得が研究の中心です。
abstractHere, we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes used for generating results and reproducing the results presented in this work are available at DeepSorghumHead ( https://github.com/oceam/DeepSorghumHead ).Open asset ↗DeepSorghumHeadlines:66-80Plant phenotyping relevance match · UnverifiedarXiv · OpenAlex · checked 15 Sept 2026
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in the crop breeding pipeline. In this paper, we propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our counting approach uses the image density map obtained from dot or region annotation as the target with a novel deep convolutional neural network architecture. We also propose a novel instance segmentation algorithm using the estimated density map, to identify the individual panicles in the presence of occlusion. With real Sorghum aerial images, we obtain a mean absolute error (MAE) of 1.06 for counting which is better than using well-known crowd counting approaches such as CCNN, MCNN and CSRNet models. The instance segmentation model also produces respectable results which will be ultimately useful in reducing the manual annotation workload for future data.
Why it matches plant phenotyping methodsソルガム穂（パンicles）の計数・個体セグメンテーションという植物形質を航空画像から自動抽出する手法の開発・評価が中心であり、明確なフェノタイピング方法論である。
abstractwe propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Summary Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high‐throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field‐based plant phenomics can enable next‐generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from ‘Green Revolution’ traits such as harvest index become exhausted. Application of Li DAR , thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high‐throughput phenotyping could enable the next generation of crop research and breeding is presented.
Why it matches plant phenotyping methods穀類の圃場フェノミクスを中心に、LiDAR、熱画像、スペクトル反射、クロロフィル蛍光、機械学習などの表現型計測手法をレビューしているため。
abstractThis review focuses on how field‐based plant phenomics can enable next‐generation physiological breeding in cereal crops
This project will support innovation in the mathematical and algorithmic methodologies required to develop spectral/lidar based proxies of phenotypic traits up to now not accessible based on standard imaging (rgb); it will thus generate innovation in the area of field crop phenotyping and accordingly improve the capacity to study the genetic and physiological architecture of complex traits and to predict GxE.
Why it matches plant phenotyping methods2D・3D・スペクトル画像とセンサーフュージョンを用いて、従来取得困難だった作物形質の表現型プロキシを開発する研究であり、手法開発が中心である。
titleMachine learning and sensor fusion approaches to set up phenotyping proxies from 2D, 3D and spectral images
ABSTRACT Background Stalk lodging (breakage of plant stems prior to harvest) is a major problem for both farmers and plant breeders. A limiting factor in addressing this problem is the lack of a reliable method for phenotyping stalk strength. Previous methods of phenotyping stalk strength induce failure patterns different from those observed in natural lodging events. This paper describes a new device for field-based phenotyping of stalk strength called “DARLING” (Device for Assessing Resistance to Lodging IN Grains). The DARLING apparatus consists of a vertical arm which is connected to a horizontal footplate by a hinge. The operator places the device next to a stalk, aligns the stalk with a force sensor, steps on the footplate, and then pushes the vertical arm forward until the stalk breaks. Force and rotation are continuously recorded during the test and these quantities are used to calculate two quantities: stalk flexural stiffness and stalk bending strength. Results Field testing of DARLING was performed at multiple sites. Validation was based upon three factors. First, the device induces the characteristic “crease” or Brazier buckling failure patterns observed in naturally lodged stalks. Second, in agreement with prior research, flexural stiffness values attained using the DARLING apparatus are strongly correlated with bending strength measurements. Finally, a paired specimen experimental design was used to determine that the flexural data obtained with DARLING is in agreement with laboratory-based flexural testing results of the same specimens. DARLING was also deployed in the field to assess phenotyping throughput (# of stalks phenotyped per hour). Over approximately 5000 tests, the average testing rate was found to be 210 stalks/hour. Conclusions The DARLING apparatus provides a quantitative assessment of stalk strength in a field setting. It induces the same failure patterns observed in natural lodging events. DARLING can also be used to perform non-destructive flexural tests. This new technology has many applications, including breeding, genetic studies on stalk strength, longitudinal studies of stalk flexural stiffness, and risk assessment of lodging propensity.
Why it matches plant phenotyping methodsトウモロコシとソルガムの茎の強度を定量化する携帯型フィールド表現型測定装置を開発し、実地試験、既存測定との比較、スループット評価まで行っており、方法が研究の中心である。
abstractThis paper describes a new device for field-based phenotyping of stalk strength called “DARLING” (Device for Assessing Resistance to Lodging IN Grains).
A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenotyping (HTP) uses robotic technologies to analyze crops in order to determine species with favorable traits, however, the current practices rely on exhaustive coverage and data collection from the entire crop field being monitored under the breeding experiment. This works well in relatively small agricultural fields but can not be scaled to the larger ones, thus limiting the progress of genetics research. In this work, we propose an active learning algorithm to enable an autonomous system to collect the most informative samples in order to accurately learn the distribution of phenotypes in the field with the help of a Gaussian Process model. We demonstrate the superior performance of our proposed algorithm compared to the current practices on sorghum phenotype data collection.
Why it matches plant phenotyping methods植物表現型データ収集を効率化する能動学習・ガウス過程アルゴリズムを開発し、ソルガムの表現型データで既存手法と比較評価しており、表現型取得ワークフローが中心である。
abstractIn this work, we propose an active learning algorithm to enable an autonomous system to collect the most informative samples in order to accurately learn the distribution of phenotypes in the field with the help of a Gaussian Process model.
Reproduction assets foundThe authors explicitly open-sourced their code repository, simulation environment, and the sorghum phenotype dataset used in this paper, with a public GitHub URL.Code · publicWe have open-sourced our code repository, simulation environment and the sorghum dataset 1 1
1
Our github repository can be found at https://github.com/sumitsk/algp.git for the research community to carry out further work in this direction.Open asset ↗sumitsk/algp · sumitsk/algplines:55-75Dataset · publicOur simulation environment, code repository and the sorghum dataset are open-sourced and can be found at https://github.com/sumitsk/algp.git .Open asset ↗sumitsk/algp · sumitsk/algplines:180-200Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Efficient plant phenotyping methods are necessary to accelerate the development of high yield biofuel crops. Manual measurement of plant phenotypes, such as height is inefficient, labor intensive and error prone. We present a robust, LiDAR based approach to estimate the height of biomass sorghum plants. A vertically oriented laser rangefinder onboard an agricultural robot captures LiDAR scans of the environment as the robot traverses between crop rows. These LiDAR scans are used to generate height contours for a single row of plants corresponding to a given genetic strain. We apply ground segmentation, iterative peak detection and peak filtering to estimate the average height of each row. Our LiDAR based approach is capable of estimating height at all stages of the growing period, from emergence e.g. 10 cm through canopy closure e.g. 4 m. Our algorithm has been extensively validated by several ground truthing campaigns on biomass sorghum. These measurements encompass typical methods employed by breeders as well as higher accuracy methods of measurement. We are able to achieve an absolute height estimation error of 8.46% ground truthed via ?by-eye? method over 2842 plots, an absolute height estimation error of 5.65% ground truthed at high granularity by agronomists over 12 plots, and an absolute height estimation error of 7.2% when ground truthed by multiple agronomists over 12 plots.
Why it matches plant phenotyping methodsLiDARによるソルガム草丈推定法を開発し、広範な実測値との比較検証を行っており、植物形質取得手法が研究の中心である。
abstractWe present a robust, LiDAR based approach to estimate the height of biomass sorghum plants.
Efficient plant phenotyping methods are necessary in order to accelerate the development of high yield biofuel crops. Manual measurement of plant phenotypes, such as width, is slow and error-prone. We propose a novel approach to estimating the width of corn and sorghum stems from color and depth images obtained by mounting a camera on a robot which traverses through plots of plants. We use deep learning to detect individual stems and employ an image processing pipeline to model the boundary of each stem and estimate the pixel and metric width of each stem. This approach results in 13.5% absolute error in the pixel domain on corn averaged over 153 estimates and 13.2% metric absolute error on phantom sorghum averaged over 149 estimates.
Why it matches plant phenotyping methods植物茎径という形態形質を、ロボット搭載カメラのカラー・深度画像と画像処理で推定する手法を開発・評価しており、フェノタイピング手法が中心である。
abstractWe propose a novel approach to estimating the width of corn and sorghum stems from color and depth images obtained by mounting a camera on a robot which traverses through plots of plants.
A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number of plants grown and imaged under a controlled environment in a high-throughput plant phenotyping (HTPP) system are increasingly becoming accessible to research communities. They can be useful to compute novel phenotypes for early stress detection. In early stages of stress induction, plants manifest responses in terms of physiological changes rather than morphological, making it difficult to detect using visible spectrum cameras which use only three wide spectral bands in the 380nm - 740 nm range. In contrast, hyperspectral imaging can capture a broad range of wavelengths (350nm - 2500nm) with narrow spectral bands (5nm). Hyperspectral imagery (HSI), therefore, provides rich spectral information which can help identify and track even small changes in plant physiology in response to stress. In this research, a data-driven approach has been developed to identify regions in plants that manifest abnormal reflectance patterns after stress induction. Reflectance patterns of age-matched unstressed plants are first characterized. The normal and stressed reflectance patterns are used to train a classifier that can predict if a point in the plant is stressed or not. Stress maps of a plant can be generated from its hyperspectral image and can be used to track the temporal propagation of stress. These stress maps are used to compute novel phenotypes that represent the level of stress in a plant and the stress trajectory over time. The data-driven approach is validated using a dataset of sorghum plants exposed to drought stress in a LemnaTec Scanalyzer 3D HTPP system. Advisers: Ashok Samal and Sruti Das Choudhury
Why it matches plant phenotyping methodsハイパースペクトル画像から植物のストレス状態を推定し、ストレスマップと新規表現型を抽出するデータ駆動手法を開発・検証しており、植物表現型取得が中心である。
abstractIn this research, a data-driven approach has been developed to identify regions in plants that manifest abnormal reflectance patterns after stress induction.
Maize and sorghum are important cereal crops in the world. To increase the maize grain yield, two approaches are used: exploring hybrid maize in plant breeding and improving the crop management system. Plant population is a parameter for calculating the germination rate, which is an important phenotypic trait of seeds. An automated way to obtain the plant population at early growth stages can help breeders to save measuring time in the field and increase the efficiency of their breeding programs. Similar to what has been taking place in production agriculture, plant scientists and plant breeders have been looking for and adopting precision technologies into their research programs; and analyzing plant performance plot-by-plot and even plant-by-plant is becoming the norm and vitally important plant phenomics research and seed industry. Accurate plant location information is needed for determining plant distribution and generating plant stand maps. Two automated plant population detection and location estimation systems using different sensors were developed in this research.\nA 2D machine vision technique was applied to develop a real-time automatic plant population estimation and plant stand map generation system for maize and sorghum in early growth stages. Laser sensors were chosen as they are not affected by outdoor lighting conditions. Plant detection algorithms were developed based on the unique plant stem structure. Since maize and sorghum look similar at early growth stages, the system was tested over both plants in greenhouse condition. The detection rate of over 93.1% and 83.0% were achieved for maize and sorghum plants from V2 to V6 growth stage, respectively. The mean absolute error and root-mean-error of plant location were 3.1 cm and 3.2 cm m for maize and 2.8 cm and 2.9 cm for grain sorghum plants, respectively.\nApart from using laser sensors, a 3D Time-of-Flight camera-based automatic system was also developed for maize and sorghum plant detection at their early growth stages. The images were captured by using a Swift camera from a side-view of the crop row without any shade during the daytime in a greenhouse. A serious of image processing algorithms including point cloud filtering, plant candidate extraction, invalid plant removal, and plant registration were developed for this system. By comparing with the manual measurement, for the maize plant, the average true positive detection rate was 89% with 0.06 standard deviation. For grain sorghum plants, the average true positive detection rate was 85% with 0.08 standard deviation.
Why it matches plant phenotyping methodsレーザーおよび3D ToFセンサーを用いて、作物個体の自動検出・個体数推定・位置推定を行うシステムと画像処理アルゴリズムを開発・検証しており、植物表現型取得が研究の中心である。
abstractTwo automated plant population detection and location estimation systems using different sensors were developed in this research.
The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short time and by less labor. There are currently many options for customized UAV system on market; however, data analysis efficiency is still one limitation for the fully implementation of HTPP. To this end, the focus of this program was data analysis of UAV acquired data. The specific objectives were two-fold, one was to investigate statistical correlations between UAV derived phenotypic traits and manually measured sorghum biomass, nitrogen and chlorophyll content. Another was to conduct variable selection on the phenotypic parameters calculated from UAV derived vegetation index (VI) and plant height maps, aiming to find out the principal parameters that contribute most in explaining winter wheat grain yield. Corresponding, two studies were carried out. Good correlations between UAV-derived VI/plant height and sorghum biomass/nitrogen/chlorophyll in the first study suggested that UAV-based HTPP has great potential in facilitating genetic improvement. For the second study, variable selection results from the single-year data showed that plant height related parameters, especially from later season, contributed more in explaining grain yield. Advisor: Yeyin Shi
Why it matches plant phenotyping methodsUAVを用いた植物表現型データの取得・解析が研究の中心であり、植生指数や草丈から形質を推定し、手動測定値および収量との関連を検証している。
abstractthe focus of this program was data analysis of UAV acquired data
The sugarcane aphid, Melanaphis sacchari (Zehntner), is a destructive insect pest of sorghum, Sorghum bicolor (L.) Moench. Outbreaks of sugarcane aphids were reported in commercial sorghum fields in Kansas, Louisiana, Mississippi, Oklahoma, and Texas. Infestations of sugarcane aphids in sorghum fields are initiated when winged aphids land on plants in the field and produce nymphs. Infestations in a field are not uniform and can increase rapidly in size and intensity. The study used airborne multispectral remote sensing to assess change over time in infestations by sugarcane aphids in sorghum fields. Differencing of bi-temporal normalized differenced vegetation index images followed by analysis of change depicted numerically in the image was effective for assessing the extent of temporal change in infestation by sugarcane aphids in a commercial sorghum field. Classification of normalized differenced vegetation index imagery from two dates into land-cover categories including one for sorghum infested with sugarcane aphid, followed by comparison of change in area and distribution of categories was also a useful method for assessing temporal change in infestations by sugarcane aphids in sorghum. Results indicated it was possible to detect and assess change in sorghum fields infested by sugarcane aphids, with 65% increase in area in a field severely infested by sugarcane aphids in only 1 week.
Why it matches plant phenotyping methodsソルガム個体群のアブラムシ被害状態を、航空マルチスペクトル画像とNDVI差分・分類で時系列評価する手法が研究の中心であり、植物病害・害虫状態のフェノタイピングに該当する。
abstractThe study used airborne multispectral remote sensing to assess change over time in infestations by sugarcane aphids in sorghum fields.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R2 > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R2 = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R2 = 0.99) existed between wind speed and image blurriness.
Why it matches plant phenotyping methodsUAV画像とSfMによる作物の草丈推定を開発・校正・検証しており、植物形質の取得手法が研究の中心です。
abstractThis paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field.
Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time consuming for the large populations needed to conduct genome-wide association studies (GWAS) and is difficult for multidimensional traits such as volume. A semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract unidimensional and multidimensional features from images of 1,064 sorghum ( Sorghum bicolor ) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel. GWAS detected 35 unique single-nucleotide polymorphisms associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated single-nucleotide polymorphisms (TASs) are located within chromosomal regions associated with similar traits in previously published quantitative trait locus and GWAS analyses of sorghum. Additionally, sorghum homologs of maize ( Zea mays ) and rice ( Oryza sativa ) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding.
Why it matches plant phenotyping methodsソルガム穂のRGB画像から多次元形質を抽出する半自動フェノタイピングパイプラインを開発し、大規模GWASに適用しており、形質取得手法が研究の中心である。
abstractA semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract unidimensional and multidimensional features from images of 1,064 sorghum ( Sorghum bicolor ) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel.
Abstract Sorghum ( Sorghum bicolor ) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.
Why it matches plant phenotyping methodsステレオ画像を用いた圃場ロボット表現型解析システムと、植物形態形質を抽出する処理パイプラインの開発・検証が研究の中心であるため。
abstractThis paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season.
Sorghum ( Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination ( R 2 ) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed.
Why it matches plant phenotyping methodsUAV画像からソルガムの穂の外観と数を抽出する画像処理法を開発し、手動ラベルとの精度・再現性を検証しており、植物形質取得法が研究の中心である。
abstractWe propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial.
Reproduction assets foundThe paper's sorghum head detection/counting training data and manually labeled image datasets (Datasets 1 and 2) are explicitly stated to be available in the article's Supplementary Materials, hosted at the Frontiers supplementary-material URL. This is a paper-specific, publicly accessible asset containing the phenotypDataset · publicTo aid in this growth, datasets 1 and 2 along with the manual labeling used in this study are available in the Supplementary Materials .Open asset ↗lines:410-460Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.
Why it matches plant phenotyping methods個体の草高・茎幅を取得するロボット型ハイスループット表現型解析プラットフォームの設計と圃場評価が研究の中心である。
abstractThis article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production.
Sorghum mutants with altered protein body structure have improved protein nutritional quality; however, practical methods to accurately track heritability of the trait are lacking. We evaluated suitability of the in vitro pepsin assay, and a new high-resolution field emission electron microscopy (FE-SEM) method to detect the mutation (HD) in hard-endosperm sorghum; and compared the physicochemical properties of experimental HD sorghums to wild type (LD) lines. FE-SEM reliably resolved sorghum protein body structure, allowing for qualitative classification of sorghum as HD or LD. The pepsin assay was less reliable, with significant variations across environments. Nevertheless, HD lines averaged higher protein digestibility (69.4% raw, 57.6% cooked) than LD lines (61.7% raw, 45.6% cooked). The HD lines also had better water solubility and starch pasting profiles than LD lines. FE-SEM, but not pepsin assay, reliably detects HD nutation in sorghum. The HD trait may improve food-use functionality of sorghum.
Why it matches plant phenotyping methodsソルガム種子のタンパク質体構造という植物形質を検出するFE-SEM法を開発・比較検証しており、表現型取得法が研究の中心である。
abstractWe evaluated suitability of the in vitro pepsin assay, and a new high-resolution field emission electron microscopy (FE-SEM) method to detect the mutation (HD) in hard-endosperm sorghum
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 10 Sept 2026
BACKGROUND: In bioenergy/forage sorghum, morpho-anatomical stem properties are major components affecting standability and juice yield. However, phenotyping these traits is low-throughput, and has been restricted by the lack of a high-throughput phenotyping platforms that can collect both morphological and anatomical stem properties. X-ray computed tomography (CT) offers a potential solution, but studies using this technology in plants have evaluated limited numbers of genotypes with limited throughput. Here we suggest that using a medical CT might overcome sample size limitations when higher resolution is not needed. Thus, the aim of this study was to develop a practical high-throughput phenotyping and image data processing pipeline that extracts stem morpho-anatomical traits faster, more efficiently and on a larger number of samples. RESULTS: A medical CT was used to image morpho-anatomical stem properties in sorghum. The platform and image analysis pipeline revealed extensive phenotypic variation for important morpho-anatomical traits in well-characterized sorghum genotypes at suitable repeatability rates. CT estimates were highly predictive of morphological traits and moderately predictive of anatomical traits. The image analysis pipeline also identified genotypes with superior morpho-anatomical traits that were consistent with ground-truth based classification in previous studies. In addition, stem cross section intensity measured by the CT was highly correlated with stem dry-weight density, and can potentially serve as a high-throughput approach to measure stem density in grass stems. CONCLUSIONS: The use of CT on a diverse set of sorghum genotypes with a defined platform and image analysis pipeline was effective at predicting traits such as stem length, diameter, and pithiness ratio at the internode level. High-throughput phenotyping of stem traits using CT appears to be useful and feasible for use in an applied breeding program.
Why it matches plant phenotyping methodsCT撮像と画像解析パイプラインを開発し、ソルガム茎の形態・解剖学的形質を高スループットに抽出・検証しているため、フェノタイピング手法が中心である。
abstractthe aim of this study was to develop a practical high-throughput phenotyping and image data processing pipeline that extracts stem morpho-anatomical traits faster, more efficiently and on a larger number of samples.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 10 Sept 2026
Plant height is an important morphological and developmental phenotype that directly indicates overall plant growth and is widely predictive of final grain yield and biomass. Currently, manually measuring plant height is laborious and has become a bottleneck for genetics and breeding programs. The goal of this research was to evaluate the performance of five different sensing technologies for field-based high throughput plant phenotyping (HTPP) of sorghum [ Sorghum bicolor (L.) Moench] height. With this purpose, (1) an ultrasonic sensor, (2) a LIDAR-Lite v2 sensor, (3) a Kinect v2 camera, (4) an imaging array of four high-resolution cameras were evaluated on a ground vehicle platform, and (5) a digital camera was evaluated on an unmanned aerial vehicle platform to obtain the performance baselines to measure the plant height in the field. Plot-level height was extracted by averaging different percentiles of elevation observations within each plot. Measurements were taken on 80 single-row plots of a US × Chinese sorghum recombinant inbred line population. The performance of each sensing technology was also qualitatively evaluated through comparison of device cost, measurement resolution, and ease and efficiency of data analysis. We found the heights measured by the ultrasonic sensor, the LIDAR-Lite v2 sensor, the Kinect v2 camera, and the imaging array had high correlation with the manual measurements ( r ≥ 0.90), while the heights measured by remote imaging had good, but relatively lower correlation to the manual measurements ( r = 0.73). These results confirmed the ability of the proposed methodologies for accurate and efficient HTPP of plant height and can be extended to a range of crops. The evaluation approach discussed here can guide the field-based HTPP research in general.
Why it matches plant phenotyping methods複数のセンシング技術を用いたソルガム草丈のフィールド高スループット計測を比較・検証しており、植物表現型取得法が研究の中心である。
abstractThe goal of this research was to evaluate the performance of five different sensing technologies for field-based high throughput plant phenotyping (HTPP) of sorghum [ Sorghum bicolor (L.) Moench] height.
Global population growth drives increasing food demand, which is anticipated to increase by at least 20% over the next 15 years. Rapid detection and identification of plant pathogens allows for up to a 50% increase in the total agricultural yield worldwide. Current molecular methods for pathogen diagnostics, such as polymerase chain reaction (PCR), are costly, time-consuming, and destructive. These limitations recently catalyzed a push toward developing minimally invasive and substrate general techniques that can be used in the field for confirmatory detection and identification of plant pathogens. Raman spectroscopy (RS) is a noninvasive, nondestructive, and label-free technique that can be used to determine chemical structure of analyzed specimens. In this study, we demonstrate that by using a hand-held Raman spectrometer, we can identify whether wheat or sorghum grains are healthy or not and identify present plant pathogens. We show that RS enables diagnosis of simple diseases, such as ergot, that are caused by one pathogen, as well as complex diseases, such as black tip or mold, which are induced by several different pathogens. The combination of chemometric analysis and RS allows for distinguishing between healthy and infected grains with high accuracy. We also show that RS can be used to determine states of disease development on grain. These results demonstrate that Raman-based approach for disease detection on plants is sample agnostic.
Why it matches plant phenotyping methods手持ちRaman分光とケモメトリクスを用いて穀粒の感染状態・病害進行を直接推定する手法が研究の中心であり、植物病害表現型の取得・判定方法に該当する。
abstractIn this study, we demonstrate that by using a hand-held Raman spectrometer, we can identify whether wheat or sorghum grains are healthy or not and identify present plant pathogens.
Main conclusions A high-throughput method combining liquid handling system and 96-well microplate pipetting format was developed for total sugar determination. With this new method, we characterized diverse sugar accumulation in sorghum varieties. Sweet sorghum accumulates large amounts of sucrose in its stalk and, therefore, has emerged as one important bioenergy crop. The commonly used sugar measurement, Brix, limits the characterization of internode variation of the sugar concentrations due to its low throughput. Here we developed a low-cost, high-throughput method to determine profiles of total sugars in sorghum internodes with a liquid handling system-based sample preparation and a phenol-sulfuric acid assay in 96-well microplate format. The present method generates results highly correlated with commonly used Brix measurements (r = 0.922). The inter-assay coefficient of variation ranged from 4.8 to 7.6%. The present method can reliably estimate mixed sugars composed of 80% sucrose. We characterized the profiles of 35 sorghum accessions and identified 21 accessions with significantly different sugar concentrations between internodes either due to dried-up internodes or concentration differences. As a high-throughput alternative to Brix measurements, the new method makes it possible to phenotype total sugars from large numbers of internode samples and, therefore, will be useful for genetic and breeding purposes.
Why it matches plant phenotyping methodsソルガム節間の糖含量を大規模に測定する高スループット手法の開発・検証が研究の中心であり、植物形質の取得法として明示されています。
abstractA high-throughput method combining liquid handling system and 96-well microplate pipetting format was developed for total sugar determination.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Automatic leaf area index (LAI) measurements are important for obtaining sufficient amounts of field data over an extended period of time. A seasonal field campaign was carried out to obtain continuous LAI measurements over maize, soybean, and sorghum fields in northeast China in 2016. Field LAI measurements were acquired with the automatic PASTIS-57 (PAI Autonomous System from Transmittance Instantaneous Sensed from 57°) instrument and two smartphone applications, PocketLAI and LAISmart. These measurements were compared with data obtained using the LAI-2200 Plant Canopy Analyzer, digital hemispherical photography (DHP), and destructive sampling measurements.The effective plant area index (PAIₑff) estimates from LAI-2200 and DHP are consistent over the season, with the overall relative errors (RE) of less than 5%. The PASTIS-57 data exhibit a small underestimation of the LAI-2200 and DHP values (RE 40%) and saturates at around PAIₑff = 3.5. The canopy clumping index (CI) exhibits an S-shaped seasonal variation that decreases with the increase of PAIₑff during the vegetative growth stage but increases after this stage. PASTIS-57 shows great potential for obtaining continuous LAI measurements in agricultural crop fields, but the smartphone applications should be further examined before they can be used for research purposes. The data collected in this study are valuable for the validation of remote sensing products.
Why it matches plant phenotyping methods作物キャノピーのLAIとクラumping indexを取得するセンサーおよびスマートフォン手法を比較・検証しており、植物形質測定法が研究の中心です。
abstractAutomatic leaf area index (LAI) measurements are important for obtaining sufficient amounts of field data over an extended period of time.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 10 Sept 2026
Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R² > 0.91 for individual leaf area; R² > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.
Why it matches plant phenotyping methodsLiDARによる3D植物形態形質の高スループット取得装置と解析パイプラインを開発し、基準法との検証も行っており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants.
The sugarcane aphid, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae), is a major pest of sorghum (Sorghum bicolor (L.) Moench). Outbreaks of sugarcane aphid occurred in sorghum fields in Mexico and the Gulf Coast region of the United States in 2013 and subsequently spread throughout most of the sorghum-growing region. The aphid induces stress to sorghum by damaging foliage. Multispectral remote sensing can be used to detect plant stress in agricultural crops. The study investigated the utility of multispectral imagery to delineate spatially variable infestations of sugarcane aphid in commercial grain sorghum fields. Multispectral images were acquired from fields by using a Duncan Tech MS3100-CIR, a 3-band (NIR, R, G) digital camera, mounted nadir in an aircraft fuselage. ERDAS Image was used for unsupervised classification of multispectral images of five fields. The study indicated it is feasible to use multispectral imagery to detect and spatially delineate patches of plants infested by sugarcane aphids in sorghum fields. The overall classification accuracy ranged from 89 to 96% for differentiating areas damaged by sugarcane aphid from areas where plants were not damaged. Results indicated good potential for mapping spatially variable sugarcane aphid infestations in grain sorghum fields.
Why it matches plant phenotyping methodsソルガムのアブラムシ被害という植物状態をマルチスペクトル画像から検出・空間 delineation する手法を中心に、分類精度も評価しているため。
abstractMultispectral remote sensing can be used to detect plant stress in agricultural crops.
Stem biomechanical properties dictate the mechanical stability of crop plants and ultimately their lodging resistance. This study evaluated stem mechanical, morphological, anatomical, and composition traits to assess their association with historical lodging ratings and developed new approaches to predict stem strength. Significant genotypic variation for stem strength, rigidity, and stiffness existed among 15 bioenergy Sorghum bicolor (L.) Moench genotypes that were selected to represent a range of stem lodging tendencies. Repeatabilities for the mechanical traits ranged from moderate to high across environments (0.58–0.92), high within environments (0.81–0.89), and low to high for maturity groups (0.31–0.89). Lodging rating was moderately correlated with internode density (r = 0.60, P < 0.01), length (r = 0.61, P < 0.01), and rigidity (r = 0.60, P < 0.05). Mechanical traits were highly correlated with morphological traits; correlations with anatomical or composition traits were lower. Using this information, two predictive models were developed. In Model 1, stiffness explained 69% of the total variation for stem strength. In Model 2, a combination of internode density, volume, and stiffness explained 75% of the total variation. Both prediction models were robust and were not confounded by internode number, cultivar type, maturity group, or environment. The results indicate that indirect selection of lodging‐related traits may be possible with the specific use of stiffness as a selective breeding tool to improve lodging resistance.
Why it matches plant phenotyping methods茎の強度・剛性という植物形質を対象に、予測モデルを開発し、その頑健性を評価しているため、表現型取得・推定手法が中心的です。
abstractdeveloped new approaches to predict stem strength
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant height is an essential trait to evaluate in grain sorghum, being positively associated with potential grain yield. Standard manual measures of plant height for large breeding trials are labour-intensive and time-consuming. Due to potential field access issue and the remote nature of breeding trials, Unmanned Aerial vehicles (UAVs) are well-suited to measure plant height if the ground surface can be referenced. In this study, we compared existing algorithms with a new method for estimating plant height for a sorghum breeding trial. Images were captured by a RGB camera mounted on an UAV before emergence and near maturity to generate digital surface models (DSMs). Two existing methods (‘point cloud’ and ‘reference ground’) and a new method (‘self-calibration’) were used to estimate ground level and plant height at the plot level. The self-calibration method required manual measurements of the actual plant height in a sample of plots (fewer than 30), which could be completed during the 30-min flight time. UAV-derived plant heights from each method were compared to manual measurements. The self-calibration method had the best performance (R2 = 0.63; RMSE = 0.07 m; repeatability = 0.74), with similar repeatability to manual measurement (0.78). The point cloud and reference ground methods had lower repeatabilities (0.34 and 0.38, respectively). For the self-calibration method, we tested different sampling strategies to balance accuracy and the workload of manual measurements, finding that a sample of 30–40 plots from the1440 total could obtain precision similar to manual measurement of the entire trial. The self-calibration method offers a pragmatic, robust and universal approach to high throughput phenotyping of plot plant height with UAV surveys.
Why it matches plant phenotyping methodsUAV画像とDSMを用いたソルガム草丈推定法を開発し、既存法との比較・精度検証・サンプリング戦略評価を行っており、植物表現型取得手法が研究の中心である。
abstractwe compared existing algorithms with a new method for estimating plant height for a sorghum breeding trial.
Recently, imaged-based high-throughput phenotyping methods have gained popularity in plant phenotyping. Imaging projects the 3D space into a 2D grid causing the loss of depth information and thus causes the retrieval of plant morphological traits challenging. In this study, LiDAR was used along with a turntable to generate a 360-degree point cloud of single plants. A LABVIEW program was developed to control and synchronize both the devices. A data processing pipeline was built to recover the digital surface models of the plants. The system was tested with maize and sorghum plants to derive the morphological properties including leaf area, leaf angle and leaf angular distribution. The results showed a high correlation between the manual measurement and the LiDAR measurements of the leaf area (R2>0.91). Also, Structure from Motion (SFM) was used to generate 3D spectral point clouds of single plants at different narrow spectral bands using 2D images acquired by moving the camera completely around the plants. Seven narrow band (band width of 10 nm) optical filters, with center wavelengths at 530 nm, 570 nm, 660 nm, 680 nm, 720 nm, 770 nm and 970 nm were used to obtain the images for generating a spectral point cloud. The possibility of deriving the biochemical properties of the plants: nitrogen, phosphorous, potassium and moisture content using the multispectral information from the 3D point cloud was tested through statistical modeling techniques. The results were optimistic and thus indicated the possibility of generating a 3D spectral point cloud for deriving both the morphological and biochemical properties of the plants in the future. Advisor: Yufeng Ge
Why it matches plant phenotyping methodsLiDAR・回転台・SfMを用いた植物の3D形態・スペクトル形質取得システムと処理パイプラインを開発し、手動測定との検証も行っており、フェノタイピング手法が研究の中心である。
abstractLiDAR was used along with a turntable to generate a 360-degree point cloud of single plants.
Spontaneous Raman scattering microspectroscopy, second harmonic generation (SHG) and 2‐photon excited fluorescence (2PF) were used in combination to characterize the morphology together with the chemical composition of the cell wall in native plant tissues. As the data obtained with unstained sections of Sorghum bicolor root and leaf tissues illustrate, nonresonant as well as pre‐resonant Raman microscopy in combination with hyperspectral analysis reveals details about the distribution and composition of the major cell wall constituents. Multivariate analysis of the Raman data allows separation of different tissue regions, specifically the endodermis, xylem and lumen. The orientation of cellulose microfibrils is obtained from polarization‐resolved SHG signals. Furthermore, 2‐photon autofluorescence images can be used to image lignification. The combined compositional, morphological and orientational information in the proposed coupling of SHG, Raman imaging and 2PF presents an extension of existing vibrational microspectroscopic imaging and multiphoton microscopic approaches not only for plant tissues.
Why it matches plant phenotyping methods植物組織の形態、細胞壁組成、セルロース配向、リグニン化を取得するためのRaman、SHG、2PFおよび解析手法の組合せが研究の中心であり、植物表現型の画像計測法として実質的です。
abstractSpontaneous Raman scattering microspectroscopy, second harmonic generation (SHG) and 2‐photon excited fluorescence (2PF) were used in combination to characterize the morphology together with the chemical composition of the cell wall in native plant tissues.
Silica cells are specialized leaf epidermal cells in grasses with almost the whole cell volume filled with solid silica. In sorghum, silica deposition in silica cells takes place in young, elongating leaves around the mid-length of the leaf. We developed a protocol for estimating the level of silica cell silicification in Sorghum bicolor leaves using in situ charring method ( Kumar et al. , 2017a ). Here, we provide greater details on our protocol and method of image analysis. Although we based our protocol on sorghum, this protocol can be extended for estimating silica cell silicification level in any grass species.
Why it matches plant phenotyping methodsイネ科葉の珪化レベルという植物形質を推定するプロトコルと画像解析法を詳細化しており、形質取得法が中心である。
abstractWe developed a protocol for estimating the level of silica cell silicification in Sorghum bicolor leaves using in situ charring method
Background The compressional modulus of elasticity is an important mechanical property for understanding stalk lodging, but this property is rarely available for thin-walled plant stems such as maize and sorghum because excised tissue samples from these plants are highly susceptible to buckling. The purpose of this study was to develop a testing protocol that provides accurate and reliable measurements of the compressive modulus of elasticity of the rind of pith-filled plant stems. The general approach was to relying upon standard methods and practices as much as possible, while developing new techniques as necessary. Results Two methods were developed for measuring the compressional modulus of elasticity of pith-filled node-node specimens. Both methods had an average repeatability of ± 4%. The use of natural plant morphology and architecture was used to avoid buckling failure. Both methods relied up on spherical compression platens to accommodate inaccuracies in sample preparation. The effect of sample position within the test fixture was quantified to ensure that sample placement did not introduce systematic errors. Conclusions Reliable measurements of the compressive modulus of elasticity of pith-filled plant stems can be performed using the testing protocols presented in this study. Recommendations for future studies were also provided.
Why it matches plant phenotyping methods植物茎の圧縮弾性率という機械的形質を測定する試験プロトコルを開発・検証しており、表現型取得法が研究の中心である。
abstractThe purpose of this study was to develop a testing protocol that provides accurate and reliable measurements of the compressive modulus of elasticity of the rind of pith-filled plant stems.
Sorghum ( Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G x E) interactions remains a progress bottleneck. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity), and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, in terms of both biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis, we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low-cost, and high-throughput option for characterizing G x E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open-access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.
Why it matches plant phenotyping methods植物の形状・色・バイオマス変化を高スループットに定量化し、時間経過に伴うストレス応答解析ツールと公開データセットを提示しており、表現型取得・解析が研究の中心である。
abstractmeasured responses using high-throughput phenotyping technology
Reproduction assets foundThe authors explicitly state that the raw phenotyping data (approximately 90,000 images' worth of shape/color measurements) and the analysis scripts used to generate the manuscript figures are publicly available through the PlantCV Danforth Center sorghum abiotic stress dataset page. This is a paper-specific, publicly,Dataset · publicScripts used to make the figures within this manuscript, along with the raw data, are available here: http://plantcv.danforthcenter.org/pages/data-sets/sorghum_abiotic_stress.htmlOpen asset ↗plantcv.danforthcenter.orglines:130-134Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
Crop height is a very important attribute to assess overall crop condition, irrigation, and estimation of terminal yield. In this study, a novel method to monitor crop height of Sorghum (Sorghum bicolor) using an Unmanned Aerial System (UAS) is proposed. UAS data were acquired seven times over the growing season and each aerial acquisition included over 200 images with significant image overlap at an altitude of 50m above ground. Ortho-mosaic image and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the images. Ground control points (GCPs) were installed around the study area and they were surveyed using a real time kinematic (RTK) GPS unit for accurate geo-referencing of the geospatial data products. A Digital Terrain Model (DTM) and Digital Surface Model (DSM) were generated from the 3D point cloud data, and a Crop Height Model (CHM) was then created by subtracting DTM from DSM. Uniform crop grids along the center line of each variety were defined for further processing. The maximum CHM value within each individual grid was taken to represent crop height of the grid, and average of all grid heights over the whole area of each variety was calculated as crop height of individual variety. These measurements were compared with manual crop height measurements. Root Mean Square Error (RMSE) between field measurements and the proposed approach was 0.33m. In addition, the height estimates from both field measurement and the proposed approach could be used to derive a growth curve by fitting a sigmoidal curve. The residual RMSEs between the observed and predicted value of the curve established from UAS and field measurements were calculated as 0.05m and 0.1m, respectively. The growth curve results showed that the proposed approach indicated less RMSE and generated more reliable growth curves for monitoring sorghum height.
Why it matches plant phenotyping methodsUAS画像、SfM、3Dモデルから作物高を抽出する手法を開発し、手動測定と比較検証しており、植物形質取得が研究の中心です。
abstracta novel method to monitor crop height of Sorghum (Sorghum bicolor) using an Unmanned Aerial System (UAS) is proposed.
Remote estimation of leaf nitrogen (N) or pigments through hyperspectral reflectance offers an opportunity to non-destructively diagnose plant N status. Two sweet sorghum (Sorghum bicolor [L.] Moench) cultivars (Top 76-6 and Dale) were grown with 0, 56, 112, 168, and 224kgNha−1 in 2009 and 2010. Reflectance measurements were coupled with plant height, main-stem node number, leaf N concentration, and total chlorophyll content to establish the relationship of these traits with canopy reflectance. Canopy reflectance was most sensitive to N status in the visible region, specifically near green (595nm) and red (701nm) wavebands. Simple-ratio spectral models comprised of visible wavebands or wavebands from the visible and near infrared region outperformed models developed using only the most sensitive single-waveband. Based on the cross-validation of spectral models between data from two years and two cultivars, the simple-ratio models comprising the reflectance (R) ratios of 595nm vs. 1676nm and 595nm vs. 508nm predicted leaf N concentration and chlorophyll content with the greatest accuracy (highest r2 and lowest relative error, RE). These simple-ratio models were used to develop general-purpose spectral models to derive coefficients to estimate leaf N concentration (-66.63×R595/R1676+34.14; r2 0.52; RE 16.8%) and chlorophyll content (-49.12×R595/R508+107.47; R2 0.64; RE 17%). The identified spectral models can be used to assess growth, diagnose sweet sorghum N status and may be useful to make N management decisions for site-specific fertilizer applications.
Why it matches plant phenotyping methodsキャノピー反射スペクトルから葉N濃度とクロロフィル含量を推定する手法を開発し、品種・年次間の交差検証で精度評価しており、植物表現型取得が研究の中心です。
abstractRemote estimation of leaf nitrogen (N) or pigments through hyperspectral reflectance offers an opportunity to non-destructively diagnose plant N status.
SorghumGreenhouseRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
Background In sorghum, the growth angle of nodal roots is a major component of root system architecture. It strongly influences the spatial distribution of roots of mature plants in the soil profile, which can impact drought adaptation. However, selection for nodal root angle in sorghum breeding programs has been restricted by the absence of a suitable high throughput phenotyping platform. The aim of this study was to develop a phenotyping platform for the rapid, non-destructive and digital measurement of nodal root angle of sorghum at the seedling stage. Results The phenotyping platform comprises of 500 soil filled root chambers (50 × 45 × 0.3 cm in size), made of transparent perspex sheets that were placed in metal tubs and covered with polycarbonate sheets. Around 3 weeks after sowing, once the first flush of nodal roots was visible, roots were imaged in situ using an imaging box that included two digital cameras that were remotely controlled by two android tablets. Free software ( openGelPhoto.tcl ) allowed precise measurement of nodal root angle from the digital images. The reliability and efficiency of the platform was evaluated by screening a large nested association mapping population of sorghum and a set of hybrids in six independent experimental runs that included up to 500 plants each. The platform revealed extensive genetic variation and high heritability (repeatability) for nodal root angle. High genetic correlations and consistent ranking of genotypes across experimental runs confirmed the reproducibility of the platform. Conclusion This low cost, high throughput root phenotyping platform requires no sophisticated equipment, is adaptable to most glasshouse environments and is well suited to dissect the genetic control of nodal root angle of sorghum. The platform is suitable for use in sorghum breeding programs aiming to improve drought adaptation through root system architecture manipulation.
Why it matches plant phenotyping methodsソルガムの節根角度をデジタル測定する高スループット表現型解析プラットフォームを開発し、信頼性・再現性を検証した研究であり、表現型取得手法が中心である。
abstractThe aim of this study was to develop a phenotyping platform for the rapid, non-destructive and digital measurement of nodal root angle of sorghum at the seedling stage.
Transport networks serve critical functions in biological and engineered systems, and yet their design requires trade-offs between competing objectives. Due to their sessile lifestyle, plants need to optimize their architecture to efficiently acquire and distribute resources while also minimizing costs in building infrastructure. To understand how plants resolve this design trade-off, we used high-precision three-dimensional laser scanning to map the architectures of tomato, tobacco, or sorghum plants grown in several environmental conditions and through multiple developmental time points, scanning in total 505 architectures from 37 plants. Using a graph-theoretic algorithm that we developed to evaluate design strategies, we find that plant architectures lie along the Pareto front between two simple length-based objectives-minimizing total branch length and minimizing nutrient transport distance-thereby conferring a selective fitness advantage for plant transport processes. The location along the Pareto front can distinguish among species and conditions, suggesting that during evolution, natural selection may employ common network design principles despite different optimization trade-offs.
Why it matches plant phenotyping methods高精度3Dレーザースキャンによる植物構造の取得と、構造を評価する新規グラフ理論アルゴリズムが研究の中心であり、植物アーキテクチャという形態形質を定量化している。
abstractwe used high-precision three-dimensional laser scanning to map the architectures of tomato, tobacco, or sorghum plants
A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.
Why it matches plant phenotyping methodsUAS画像からSfMで作物高とNDVIを抽出し、圃場区画レベルでストレス・収量との関連および他センサーとの比較を評価しており、フェノタイピング手法の取得・処理・検証が中心です。
abstractThe raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds.
Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective, and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short-stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum ( Sorghum bicolor ). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed, and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included the following: (1) User-interactive Individual Plant Height Extraction (UsIn-PHe) based on dense stereo three-dimensional reconstruction; (2) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; (3) User-interactive Dense Stereo Matching Stem Diameter Extraction; and (4) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height, while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species.
Why it matches plant phenotyping methodsソルガムの草丈・茎径を取得する野外型高スループット画像計測プラットフォームを開発し、アルゴリズムの精度検証と地上実測比較を行っており、表現型取得法が研究の中心である。
abstractTherefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops
ABSTRACT Sorghum ( Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G × E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G × E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.
Why it matches plant phenotyping methods高スループット画像計測による形状・色・バイオマス応答の定量化と、経時的なストレス指標の解析手法が研究の中心であり、データセットと解析スクリプトも提供している。
abstractmeasured responses using high-throughput phenotyping technology followed by ionomic profiling
Reproduction assets foundThe authors publicly release the raw phenotyping data and figure-generating analysis scripts for this sorghum nitrogen-stress study via the PlantCV Danforth Center data-sets page, which is an allowed URL.Dataset · public29 concentrations in the two lower nitrogen treatment groups significantly affects shape but
130 not color. To further explore the effect that our experimental treatments had on the
131 measured shape characteristics and color for each individual genotype, an interactive
132 version of the generated data is available here:
133 (http://plantcv.danforthcenter.org/pages/data-sets/sorghum_abiotic_stress.html).
134 Many factors contribute to the ability of plants to utilize nutrients and presumably,
135 much of this is genetically explained. Correspondingly, genotype was a highly significant
136 variable (p-value = 0.003 when measuring area) within this dataset. To investigate how
137 much nitrogOpen asset ↗pdf-layout-page:5 lines:1-40Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · OpenAlex · checked 10 Sept 2026
Genomics-assisted breeding methods have been rapidly developed with novel technologies such as next-generation sequencing, genomic selection and genome-wide association study. However, phenotyping is still time consuming and is a serious bottleneck in genomics-assisted breeding. In this study, we established a high-throughput phenotyping system for sorghum plant height and its response to nitrogen availability; this system relies on the use of unmanned aerial vehicle (UAV) remote sensing with either an RGB or near-infrared, green and blue (NIR-GB) camera. We evaluated the potential of remote sensing to provide phenotype training data in a genomic prediction model. UAV remote sensing with the NIR-GB camera and the 50th percentile of digital surface model, which is an indicator of height, performed well. The correlation coefficient between plant height measured by UAV remote sensing (PH UAV ) and plant height measured with a ruler (PH R ) was 0.523. Because PH UAV was overestimated (probably because of the presence of taller plants on adjacent plots), the correlation coefficient between PH UAV and PH R was increased to 0.678 by using one of the two replications (that with the lower PH UAV value). Genomic prediction modeling performed well under the low-fertilization condition, probably because PH UAV overestimation was smaller under this condition due to a lower plant height. The predicted values of PH UAV and PH R were highly correlated with each other ( r = 0.842). This result suggests that the genomic prediction models generated with PH UAV were almost identical and that the performance of UAV remote sensing was similar to that of traditional measurements in genomic prediction modeling. UAV remote sensing has a high potential to increase the throughput of phenotyping and decrease its cost. UAV remote sensing will be an important and indispensable tool for high-throughput genomics-assisted plant breeding.
Why it matches plant phenotyping methodsUAV画像・リモートセンシングによるソルガム草丈の高スループット推定システムを確立し、定規測定およびゲノム予測で性能検証しており、表現型取得手法が研究の中心です。
abstractwe established a high-throughput phenotyping system for sorghum plant height and its response to nitrogen availability
Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing structures make plants difficult for three-dimensional (3D) scanning and reconstruction -- two critical steps in automated visual phenotyping. Many current solutions such as laser scanning, structured light, and multiview stereo can struggle to acquire usable 3D models because of limitations in scanning resolution and calibration accuracy. In response, we have developed a fast, low-cost, 3D scanning platform to image plants on a rotating stage with two tilting DSLR cameras centred on the plant. This uses new methods of camera calibration and background removal to achieve high-accuracy 3D reconstruction. We assessed the system's accuracy using a 3D visual hull reconstruction algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum plants and 2 wheat plants across different sets of tilt angles. Scan times ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes (to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas and perimeters of the plastic models were measured manually and compared to measurements from the scanning system: results were within 3-4% of each other. The 3D reconstructions obtained with the scanning system show excellent geometric agreement with all six plant specimens, even plants with thin leaves and fine stems.
Why it matches plant phenotyping methods植物の3D形状・葉形質を取得するスキャン基盤を開発し、実植物および模型で精度検証しており、フェノタイピング手法が研究の中心です。
abstractwe have developed a fast, low-cost, 3D scanning platform to image plants on a rotating stage with two tilting DSLR cameras centred on the plant.
Plant phenotyping, or the measurement of plant traits such as stem width and plant height, is a critical step in the development and evaluation of higher yield biofuel crops. Phenotyping allows biologists to quantitatively estimate the biomass of plant varieties and therefore their potential for biofuel production. Manual phenotyping is costly, time-consuming, and errorprone, requiring a person to walk through the fields measuring individual plants with a tape measure and notebook. In this work we describe an alternative system consisting of an autonomous robot equipped with two infrared cameras that travels through fields, collecting 2.5D image data of sorghum plants. We develop novel image processing based algorithms to estimate plant height and stem width from the image data. Our proposed method has the advantage of working in situ using images of plants from only one side. This allows phenotypic data to be collected nondestructively throughout the growing cycle, providing biologists with valuable information on crop growth patterns. Our approach first estimates plant heights and stem widths from individual frames. It then uses tracking algorithms to refine these estimates across frames and avoid double counting the same plant in multiple frames. The result is a histogram of stem widths and plant heights for each plot of a particular genetically engineered sorghum variety. In-field testing and comparison with human collected ground truth data demonstrates that our system achieves 13% average absolute error for stem width estimation and 15% average absolute error for plant height estimation.
Why it matches plant phenotyping methods赤外線画像と自律ロボットを用いてソルガムの草丈・茎幅を推定する画像処理手法を開発し、地上真値との比較検証も行っており、表現型取得法が研究の中心である。
abstractIn this work we describe an alternative system consisting of an autonomous robot equipped with two infrared cameras that travels through fields, collecting 2.5D image data of sorghum plants.
Plant phenotyping using digital images has increased the throughput of the trait measurement process, and it is considered to be a potential solution to the problem of the phenotyping bottleneck. In this study, RGB images were used to study relative growth rate (RGR) and water use efficiency (WUE) of a diverse panel of 300 sorghum plants of 30 genotypes, and hyperspectral images were used for chemical analysis of macronutrients and cell wall composition. Half of the plants from each genotype were subjected to drought stress, while the other half were left unstressed. Quadratic models were used to estimate the shoot fresh and dry weights from plant projected area. RGR values for the drought-stressed plants were found to gradually lag behind the values for the unstressed plants. WUE values were highly variable with time. Significant effects of drought stress and genotype were observed for both RGR and WUE. Hyperspectral image data (546 nm to 1700 nm) were used for chemical analysis of macronutrients (N, P, and K), neutral detergent fiber (NDF), and acid detergent fiber (ADF) for plant samples separated into leaf and three longitudinal sections of the stem. The accuracy of the models built using the spectrometer data (350 nm to 2500 nm) of dried and ground biomass was found to be higher than the accuracy of models built using the image data. For the image data, the models for N(R2 = 0.66, RPD = 1.72), and P(R2=0.52, RPD = 1.46) were found to be satisfactory for quantitative analysis whereas the models for K, NDF, and ADF were not suitable for quantitative prediction. Models built after the separation of leaf and stem samples showed variation in the accuracy between the two groups. This study indicates that image-based non-destructive analysis of plant growth rate and water use efficiency can be used for studying and comparing the effects of drought across multiple genotypes. It also indicates that two dimensional hyperspectral imaging can be a useful tool for non-destructive analysis of chemical content at the tissue level, and potentially at the pixel level. Advisor: Yufeng Ge
Why it matches plant phenotyping methodsRGB画像とハイパースペクトル画像を用いた成長率・水利用効率・組織化学特性の非破壊推定を中心に、モデル精度も評価しており、植物表現型取得法の実質的な適用研究である。
abstractPlant phenotyping using digital images has increased the throughput of the trait measurement process
Forage sorghum has potential as alternative to corn silage in rotation with winter cereals. Crop sensing is a promising approach for predicting end‐of‐season yields. Yield prediction is the first step in development of algorithms for sensor‐based N management. To develop reliable algorithms for fertility management of forage sorghum in double crop rotations that account for timing, height of scanning and sensor orientation. To evaluate which method of reporting of sensor measurements (NDVI, INSEYGDD, or INSEYDAP) gives the better prediction of yield. Increasing home‐grown forage production is important for the dairy industry. Double cropping of forage crops like corn (Zea mays L.) silage with cereal rye (Secale cereale L.) or triticale (× Triticosecale spp.) can increase full‐season yield but could impact the length of the growing season for corn silage. Brown midrib (BMR) brachytic dwarf forage sorghum (Sorghum bicolor L.) has great potential as an alternative to corn silage in double crop rotations. Both winter cereals and forage sorghum require N management. Crop sensing is a promising approach for predicting end‐of‐season yields, the first step in development of algorithms for sensor‐based N management. Here we evaluated the impact of timing, sensor orientation and height of scanning, and the use of normalized difference vegetation index (NDVI) data vs. in‐season estimated yield (INSEY) on the ability of sensor data to predict yield of forage sorghum. Four trials with N rates ranging from 0 to 224 or 280 kg of N ha⁻¹ at planting (site‐specific) were implemented in four replications in 2014–2015. Scanning took place from 19 to 69 d after planting (DAP). Yield was measured at soft dough (111–124 DAP). Sensor height and orientation impacted the NDVI prior to 45 DAP but not once the canopy was fully developed. Most accurate yield predictions were obtained 49 DAP when the sorghum was 0.76 m tall. The INSEY expressed as plant growth per day (INSEYDAP) best correlated with yield. We conclude that crop sensors can be used to accurately predict forage sorghum yields.
Why it matches plant phenotyping methods飼料ソルガムの収量という植物形質を、近接センサーの測定条件とNDVI/INSEY指標から推定する方法を比較・評価しており、センシング手法が研究の中心である。
abstractWe conclude that crop sensors can be used to accurately predict forage sorghum yields.
Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動計測を行う半自動パイプラインを開発し、ソルガムの草型形質を取得する方法が研究の中心である。
abstractA semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe authors explicitly deposit their image acquisition/processing and QTL mapping code (C++, Bash, Python, R) plus genotype/phenotype data on GitHub, and per-plant depth images, RGB images, and segmented meshes on Dryad. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for QTL mapping, the genotype and phenotype data, and the full multiple- QTL models for each phenotype-by-time point combination can be found on GitHub at https://github.com/MulletLab/SorghumReconstructionAndPhenotyping .Open asset ↗MulletLab/SorghumReconstructionAndPhenotypinglines:240-292Dataset · publicFor each imaged plant, its depth images, a single RGB image, and the segmented mesh can be found at the Dryad Digital Repository ( http://dx.doi.org/10.5061/dryad.9vs26 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.9vs26lines:240-292Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and non-destructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of Sorghum bicolor, an important grain, forage, and bioenergy crop, at multiple developmental timepoints from a greenhouse-grown recombinant inbred line population. A semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci (QTL) for standard measures of shoot architecture such as shoot height, leaf angle and leaf length, and for novel composite traits such as shoot compactness. The phenotypic variability associated with some of the QTL displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動抽出を行う半自動パイプラインを開発し、ソルガムのシュート構造形質を取得・評価しており、表現型取得法が研究の中心です。
abstractA semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe paper explicitly deposits its authors' image acquisition/processing and QTL mapping code on GitHub, and its per-plant depth images, RGB images, and segmented meshes on the Dryad repository. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for
QTL mapping, the genotype and phenotype data, and the full multiple-QTL models for each phenotype
by timepoint combination can be found on GitHub at
https://github.com/MulletLab/SorghumReconstructionAndPhenotyping.Open asset ↗MulletLab/SorghumReconstructionAndPhenotypingpdf-page:8 lines:1-43Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r² = 0.30 and RMSE = 1.6 Mg ha⁻¹ in 2012 and r² = 0.37 and RMSE = 4.0 Mg ha⁻¹, but was strongly correlated for the high biomass sorghum in 2013 (r² = 0.72 and RMSE = 4.6 Mg ha⁻¹). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.
Why it matches plant phenotyping methodsキャノピー分光反射とPLSR・植生指数を比較し、植物のN濃度とバイオマス収量を推定する手法の性能評価が研究の中心である。
abstractcompare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield
The thickness of grain pericarp, the outer layer of the kernel, is an important breeding criterion for sorghum. This cereal is mainly used through traditional processing in family-based food systems in many regions of the world. We investigated in this study how pericarp thickness could be predicted by Near Infrared Reflectance Spectroscopy (NIRS), a fast and non-destructive measurement method that is commonly used to measure physico-chemical parameters of sorghum grains, and how this trait also influences the prediction of those parameters. We showed that, using a classification approach, it was possible to discriminate thick from thin pericarp whole grain samples with a good accuracy and that the proportion of thin and thick grains in mixed samples could also be predicted. In addition, pericarp thickness had a significant effect on the calibration performance for other grain parameters indicating that the pericarp can distort spectral information of whole grain samples. As a practical consequence, we suggest to develop separate whole grain calibration models for thin and thick pericarp samples, combined with a two-steps prediction approach to improve the accuracy of whole grain NIRS calibrations for grain quality parameters in sorghum.
Why it matches plant phenotyping methodsソルガム穀粒の形態形質（果皮厚）をNIRSで非破壊推定する方法を開発・評価しており、植物形質の取得が研究の中心である。
abstractWe investigated in this study how pericarp thickness could be predicted by Near Infrared Reflectance Spectroscopy (NIRS), a fast and non-destructive measurement method
Ground cover is an important physiological trait affecting crop radiation capture, water-use efficiency and grain yield. It is challenging to efficiently measure ground cover with reasonable precision for large numbers of plots, especially in tall crop species. Here we combined two image-based methods to estimate plot-level ground cover for three species, from either an ortho-mosaic or undistorted (i.e. corrected for lens and camera effects) images captured by cameras using a low-altitude unmanned aerial vehicle (UAV). Reconstructed point clouds and ortho-mosaics for the whole field were created and a customised image processing workflow was developed to (1) segment the 'whole-field' datasets into individual plots, and (2) 'reverse-calculate' each plot from each undistorted image. Ground cover for individual plots was calculated by an efficient vegetation segmentation algorithm. For 79% of plots, estimated ground cover was greater from the ortho-mosaic than from images, particularly when plants were small, or when older/taller in large plots. While there was a good agreement between the ground cover estimates from ortho-mosaic and images when the target plot was positioned at a near-nadir view near the centre of image (cotton: R2=0.97, sorghum: R2=0.98, sugarcane: R2=0.84), ortho-mosaic estimates were 5% greater than estimates from these near-nadir images. Because each plot appeared in multiple images, there were multiple estimates of the ground cover, some of which should be excluded, e.g. when the plot is near edge within an image. Considering only the images with near-nadir view, the reverse calculation provides a more precise estimate of ground cover compared with the ortho-mosaic. The methodology is suitable for high throughput phenotyping for applications in agronomy, physiology and breeding for different crop species and can be extended to provide pixel-level data from other types of cameras including thermal and multi-spectral models.
Why it matches plant phenotyping methodsUAV画像・オルソモザイクから作物プロットの地表被覆率を抽出する画像処理ワークフローを開発・比較検証しており、植物形質取得法が中心である。
abstracta customised image processing workflow was developed to (1) segment the 'whole-field' datasets into individual plots, and (2) 'reverse-calculate' each plot from each undistorted image.
The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers.
Why it matches plant phenotyping methodsRGB画像の分割・分類と深度カメラを用いて、ソルガムの茎径という植物形質を測定するHTP手法を評価・開発しており、方法が研究の中心である。
abstractThe aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness.
Sorghum is known as a major potential feedstock for biofuel production. Being able to efficiently discover genetic control of many traits over a large number of genotypes, genome-wide association study (GWAS) has become a powerful tool for studying sorghum biomass yield components. However, automated high-throughput field-based plant phenotyping is now the bottleneck for scaling up such experiments. This paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle. Three levels of stereo camera heads were instrumented to capture images of plants up to 3 meters tall. The stereo images were processed offline to reconstruct 3D point clouds using Semi-Global Block Matching. A semi-automated software interface was developed to measure stem diameter due to the strict sampling strategy and the complexity of high-density crop canopy. An automated hedge-based feature extraction pipeline was proposed to quantify other variations in plant architecture traits such as plant height, leaf area index (LAI) and vegetation volume index (VVI). The stem diameter measured using the semiautomatic method showed high correlation (0.958) to hand measurement.
Why it matches plant phenotyping methodsステレオ画像取得、3D再構成、半自動・自動特徴抽出を開発し、茎径・草丈・LAI・VVIなどの植物形質を測定・検証することが中心であるため。
abstractThis paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle.