Calibrating the mutagenic dose is the first practical step of any radiation mutation-breeding programme, and it is usually summarised by the median lethal dose (LD50) or the median growth-reduction dose (GR50). We asked whether an accessible, image-based phenotyping pipeline can quantify the early radiation response of cowpea (Vigna unguiculata L. Walp.) seedlings finely enough to estimate GR50 and to rank organ- and pigment-level sensitivities. Seeds of the traditional Paraguayan landrace kumandá pyta’i were exposed to Cobalt-60 gamma rays at 0, 100, 200, 300, 400, 500, 600, and 700 Gy, grown in a greenhouse, and photographed at the early seedling stage. A single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits (total, root, and shoot length, root:shoot ratio, tortuosity, and a two-dimensional biomass proxy) and colorimetric traits (CIE L*a*b*, a normalised greenness index, and colour-class pixel fractions). Because the data departed from normality, dose effects were tested with Kruskal–Wallis, Spearman rank correlation, and Dunn post hoc tests, and GR50 was estimated by regression of each trait expressed as a percentage of the control. Total length, shoot length, and the biomass proxy declined significantly with dose (Spearman ρ = −0.40, −0.51, and −0.47; all p < 0.001), preceded by a low-dose stimulation at 100 Gy. Estimated GR50 values were ≈390 Gy for shoot length, ≈510 Gy for total length, and ≈550 Gy for the biomass proxy, within the range reported for other cowpea genotypes. Shoot elongation was more radiosensitive than root elongation, so the root:shoot ratio did not decline; tortuosity showed no dose response. Among pigment traits, the loss of greenness was the most robust signal (a* increased, ρ = +0.62, p = 5 × 10−10; green pixel fraction fell from 0.32 to near zero by 500 Gy). These results show that single-photograph phenotyping resolves a coherent, statistically supported dose response and yields a GR50 estimate usable for dose calibration. For kumandá pyta’i, doses of roughly 300–400 Gy (below GR50) are the most defensible starting window for mutation induction. The framework is reproducible and low-cost, but it is based on one greenhouse experiment and a single genotype, and should be validated across independent trials and cultivars.
Why it matches plant phenotyping methods画像取得・セグメンテーション・解析による形態および色彩形質の抽出を中心に、放射線応答とGR50を推定する低コスト画像ベース表現型解析法を提示しているため。
abstractA single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits
Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.
Why it matches plant phenotyping methods圃場光合成データから時間分解特徴量や遺伝的シグナルを抽出する計算手法を中心に扱っており、植物生理形質の実質的なフェノタイピング手法応用に該当する。
abstractmechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield.
ABSTRACT The present study evaluated the applicability of Portable X‐ray Fluorescence (pXRF) for rapid determination of seed mineral concentrations in cowpea [ Vigna unguiculata (L.) Walp.] by comparing pXRF measurements with those obtained using Atomic Absorption Spectroscopy (AAS). Fifty‐seven cowpea genotypes, including two check varieties, were analysed for iron (Fe), zinc (Zn), manganese (Mn), copper (Cu), potassium (K), and calcium (Ca). Simple linear regression was used to assess the relationship between pXRF‐ and AAS‐derived mineral concentrations using training ( n = 47) and independent validation ( n = 10) datasets. The pXRF measurements showed good agreement with the corresponding AAS values for both macro‐ and micronutrients, with comparatively stronger relationships observed for Fe, Zn, Mn, and Cu. Residual and normal Q–Q plot analyses supported the suitability of the regression models. The findings demonstrate that pXRF enables rapid, simultaneous multielement analysis with minimal sample preparation and provides an efficient approach for high‐throughput mineral phenotyping and biofortification‐oriented cowpea breeding programmes.
Why it matches plant phenotyping methodspXRFによる種子ミネラル形質測定をAASと比較し、独立検証データで妥当性を評価しており、鉱物フェノタイピング手法が中心である。
abstractThe present study evaluated the applicability of Portable X‐ray Fluorescence (pXRF) for rapid determination of seed mineral concentrations in cowpea
High-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons, yet such models often lose accuracy under new conditions. Annotating real imagery for every genotype-by-environment (G x E) combination a breeding program encounters is prohibitively expensive. We quantify how G x E shifts affect AI-based detection of cowpea flowers and pods across two California locations and two growing seasons. Flower detection mAP@50 fell from 76.3% to as low as 50.6% under unseen shifts, and pod detection was more sensitive. Feature-space and image-quality diagnostics confirmed these losses track measurable distributional shifts. Because closing this gap with real data alone is not practical, we test whether synthetic imagery, rendered from a procedural 3D cowpea model, can substitute for that annotation burden. Synthetic supervision alone improved over pretraining but remained limited by a domain gap driven by camera image formation, not scene content. A domain-gap-aware camera-realism augmentation strategy, optimized against measured real-image statistics via Wasserstein distance, narrowed this gap, and a linear HDR representation converted a smaller measured gap into a larger detection gain than an 8-bit representation. Optimized HDR synthetic data combined with as few as five real images matched or exceeded the real-data baseline for spatial generalization, and pod detection benefited most at the lowest shot counts, with more modest gains under temporal shift. These results show that synthetic data can overcome the generalization limits of AI-based flower and pod detection, but only when the domain gap is measured and optimized rather than assumed away.
Why it matches plant phenotyping methods花・莢という植物器官の画像検出を対象に、異なる遺伝型・環境への一般化、合成画像、カメラリアリズム拡張、HDR表現を技術的に評価しており、植物表現型取得手法が研究の中心である。
abstractHigh-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons
Pod shattering is a major domestication-related trait and one of the principal causes of pre-harvest yield losses in cowpea (Vigna unguiculata (L.) Walp.), particularly under hot and dry conditions. Rapid and reliable identification of shattering-resistant genotypes is essential for improving breeding efficiency. The present study evaluated a national core collection of 245 diverse cowpea genotypes over three consecutive years (2023–2025) to identify a simple phenotypic marker associated with pod-shattering resistance. Genotypes were screened using a modified Random Impact Method (RIM), and pod physical traits, including pod length, pod breadth, pod thickness, pod wall weight, seed-to-pod ratio, and dorsal suture morphology, were examined for their association with shattering response. A distinct and consistent morphological marker was identified in the dorsal suture of mature pods. Shattering-resistant genotypes exhibited a single, prominent dorsal ridge positioned above the dehiscence zone, whereas susceptible genotypes consistently displayed a two-ridged dorsal suture separated by a central depression that appeared to reduce tissue integrity and facilitate pod rupture under mechanical impact. The observed marker remained stable across years and environmental conditions, indicating its reliability as a rapid visual indicator of shattering resistance. The modified RIM provided a standardised, reproducible, and cost-effective approach for evaluating pod shattering while minimising environmental variation associated with field phenotyping. The identified dorsal ridge morphology offers a simple, non-destructive, and efficient phenotypic marker for large-scale germplasm screening and the selection of resistant genotypes. This marker can accelerate breeding for pod-shattering resistance, improve yield stability, and facilitate the development of climate-resilient cowpea cultivars adapted to drought-prone environments.
Why it matches plant phenotyping methods鞘の裂莢抵抗性を評価する標準化手法と、再現性のある形態マーカーを開発・検証しており、植物フェノタイピングが研究の中心である。
abstractGenotypes were screened using a modified Random Impact Method (RIM)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Purpose The variability in tolerance to water stress among cowpea genotypes requires fast and accurate phenotyping methods. The integration of infrared thermography with artificial intelligence is emerging as a robust solution for large-scale, non-invasive monitoring. Thus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images. Methods Ten genotypes (five varieties: Corujinha – G1, Paulistinha – G2, Sempre Verde – G3, Pintado – G4, and Rabo de Tatu – G5) and the cultivars BRS Novaera – G6, BRS Pajeú – G7, IPA 206 – G8, BRS Tapaihum – G9, and BRS Miranda – G10) were subjected to four water regimes (25%, 50%, 75%, and 100% of ETc). Thermographic images were collected at the V3 and R2 stages and processed using Deep Learning architectures (InceptionV3, SqueezeNet, VGG16, and VGG19) to extract features (vectorization). The k-NN, Decision Tree, Random Forest, SVM, Neural Network, and AdaBoost algorithms were trained to classify stress levels and genotypes. Results The vegetative stage (V3) proved more effective for diagnosis than the reproductive stage (R2), exhibiting more stable thermal signatures. The SVM algorithm, combined with the VGG16 vectorizer, achieved the best performance, achieving an accuracy greater than 0.910 in classifying water regimes. The landrace varieties exhibited thermal patterns distinct from those of the improved cultivars, enabling high-precision genotypic identification. Conclusions The proposed approach demonstrates that infrared thermography, combined with machine learning models, is an effective tool for high-throughput digital phenotyping, optimizing the selection of drought-tolerant materials and irrigation management in precision agriculture.
Why it matches plant phenotyping methods赤外線サーモグラフィ画像から水ストレス状態と遺伝型を抽出する機械学習手法を開発・評価しており、植物フェノタイピングが研究の中心である。
abstractThus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images.
Abstract Virus-associated diseases are among the biological stresses that affect common bean yield, such as Carlavirus vignae ( Cowpea mild mottle virus , CPMMV), transmitted by the whitefly Bemisia tabaci . CPMMV is present in different continents, and recent outbreaks have concerned Brazilian farmers and researchers. Integrated Pest Management routines for field monitoring of viral spread are laborious and may be limited to visible symptoms. We hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance. To test this hypothesis, we used a hyperspectral sensor mounted on a drone to capture images of CPMMV-inoculated and non-inoculated field plots in 2022 and 2023, across a tolerant common bean cultivar (BRS FC420 RMD) and a susceptible one (BRS FC401 RMD). Results showed that CPMMV was detected in common bean plants by hyperspectral imaging at early infection stages (~ 6 DAI) before symptom onset and at an advanced infection stage (~ 22 DAI). Reflectance within the visible light spectrum was affected by soil cover on all flights, and in most of these, also in the near-infrared region. The main differences between CPMMV-inoculated and control plants were consistent across two years of experiments, regardless of the common bean phenological stage and genotype. Fit statistics using the sum of squared errors, R 2 and AIC indicated that reflectance from 401 to 425 nm, especially near 415 nm, differed significantly between infected and healthy plants. Such changes are associated with chlorophyll degradation and disruption of the photosynthetic apparatus, and are detectable even before symptom onset. Progress in the disease severity index also differentiated the tolerant cultivar from the susceptible one. CPMMV infection significantly reduced common bean yield by ~ 21% compared with healthy plants. CPMMV detection by hyperspectral imaging enables early scouting to optimize disease management.
Why it matches plant phenotyping methodsハイパースペクトル画像を用いて、症状発現前の感染植物の反射特性と病害状態を検出し、複数年・品種で技術性能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。
abstractWe hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Cowpea is a crop of great importance worldwide, which is why many heirloom varieties and improved cultivars are explored. Consuming pods and green beans provides vitamins, minerals, and functional components for people with limited access to vegetables. The pods and green beans of these materials have intrinsic characteristics that distinguish them. Therefore, the objective was to adjust machine learning models to identify cowpea from digital images of pods and green beans using artificial intelligence techniques. Digital images of four heirloom Creole of the cowpea genotypes (Sempre Verde, Rabú de tatu, Corujinha, and Paulistinha) and nine cultivars (BRS No-vaera, BRS Olhonegro, BRS Verdejante, BRS Exuberante, BRS Pajeú, BRS Miranda, IPA 206, BRS Tapaihum, and BRS Pingo de Ouro) were processed using four deep learning architectures for feature extraction (vectorization): InceptionV3, SqueezeNet, VGG16, and VGG19. Six machine learning algorithms were evaluated: K-Nearest Neighbors (KNN), Decision Tree, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The MLP (Artificial Neural Network) and SVM models, particularly when integrated with the InceptionV3 embedder, demonstrated superior performance. For pod classification, these models achieved near-perfect performance, with Area Under the Curve (AUC) and Classification Accuracy (CA) of 1.000. For green beans, the MLP maintained high accuracy (CA = 0.977) and better probabilistic calibration (lower Log-Loss) than the SVM. Digital image-based identification associated with machine learning is an efficient, non-destructive approach for the morphological characterization and discrimination of cowpea genotypes, supporting high-throughput phenotyping (HTP) applications.
Why it matches plant phenotyping methodsデジタル画像と機械学習による莢・サヤインゲンの形態的特徴抽出と遺伝子型識別が研究の中心であり、ハイスループット植物表現型解析への応用を明示している。
abstractthe objective was to adjust machine learning models to identify cowpea from digital images of pods and green beans using artificial intelligence techniques.
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
As the demand for plant-based nutrition increases, improving the protein profile of legumes like cowpea has become a breeding priority. Cowpea, a multiuse legume and staple in many low-income regions, provides important dietary protein that can help meet the demand in our growing population. Our research used genome-wide association studies (GWAS) and phenomic tools to investigate the genetic architecture of seed protein content in cowpea and integrated 4 cohorts of undergraduate researchers through a USDA-AFRI REEU program. Using wet chemistry and near-infrared spectroscopy (NIRS), we assessed crude protein (CP) within the University of California Riverside Minicore collection, developed and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86), and performed GWAS with ∼41k single-nucleotide polymorphisms (SNPs). Significant SNPs associated with protein content were identified on chromosomes 1, 3, 7, 10, and 11, and candidate genes were linked to functions including nutrient transport, stress response, and seed storage protein regulation. These results provide a foundation for future marker validation and functional studies, and demonstrate the value of pairing trait discovery with undergraduate training.
Why it matches plant phenotyping methods種子タンパク質含量という植物形質の取得に用いるNIRS校正式を開発・検証しており、表現型測定法が研究の主要な技術的要素である。
abstractdeveloped and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86)
Reproduction assets foundThe paper's Data Availability statement deposits the phenotypic data (wet chemistry CP, NIRS-derived CP phenotypes used for calibration and GWAS) in Dryad. No author analysis code or trained NIRS model files are explicitly deposited; other URLs are generic tools or citations.Dataset · publicThe phenotypic data collected and used in this research are available in the Dryad Digital Repository under DOI: https://doi.org/10.5061/dryad.8cz8w9h72 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.8cz8w9h72lines:305-345Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters for these models at the field and population scales remains prohibitively labor-intensive. We present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters, providing a more comprehensive representation of the plant’s architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we propose a method that generates token sequences containing a procedural definition of the plant architecture. This work uses only synthetic images for training and testing, where “exact” architectural parameters were known, which allowed for testing of the hypothesis that organ-level architectural parameters could be extracted from imagery data using a vision language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. Then, a VLM was trained to predict plant architecture token sequences from images. Our results demonstrate that the model can predict plant architecture tokens with an F1 score of 0.73 in a teacher-forcing method. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. Our model achieves lower MAPE than feature regression-based methods in estimating bulk plant-level traits that require understanding of the occluded 3D structure of the plant, such as leaf count and leaf area. We conclude that generating plant architecture and parameter extraction from synthetic imagery are feasible using a VLM approach, supporting future extension to real imagery.
Why it matches plant phenotyping methods画像から器官レベルの植物構造と形態形質を抽出するVLM手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractWe present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters
Reproduction assets foundThe paper's footnotes explicitly state that the authors' code is available on GitHub and the synthetic cowpea image/XML dataset is available on Hugging Face, both paper-specific and publicly actionable. The Helios URL is a generic third-party simulator library, not a paper-specific asset.Code · public1. ^ Code is available at: https://github.com/GEMINI-Breeding/Image2PlantArchitecture .Open asset ↗GEMINI-Breeding/Image2PlantArchitecturelines:600-676Dataset · public2. ^ Dataset is available at: https://huggingface.co/datasets/heesup/Cowpea-Architecture-XML .Open asset ↗heesup/Cowpea-Architecture-XMLlines:600-676Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
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.
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Water deficit during the early development of cowpea (Vigna unguiculata (L.) Walp.) can compromise seedling establishment and reduce crop uniformity. This study aimed to evaluate morphological responses and biomass allocation in eight cowpea genotypes, including four commercial cultivars and four landraces, under two water conditions (control and deficit). A randomized block design was applied in a 2 × 8 factorial scheme. Morphological traits of roots and shoots, including length, surface area, volume, and diameter, were measured using image-based analysis. Dry biomass and root-to-shoot ratio were determined through gravimetric methods. Significant genotype-by-environment interactions were observed. Commercial cultivars tended to maintain structural attributes such as stem and root diameter, while landraces, particularly “Marronzinha” and “Verdinha”, exhibited greater plasticity in root morphology and biomass accumulation under water restriction. Although the methodology allowed efficient early phenotyping, limitations such as the short stress duration and use of two-dimensional imaging may restrict broader inferences. Future studies should incorporate extended drought periods, field validation, and physiological assessments to enhance the identification of drought-resilient genotypes.
Why it matches plant phenotyping methods画像解析による根・シュート形態形質の抽出を中心に、乾燥耐性フェノタイピングへ適用した研究であり、単なる生物学的測定にとどまらない。
titleImage-based assessment of morphological responses and biomass allocation in cowpea seedlings: A methodological approach to drought resilience phenotyping
Accurate estimation of plant disease severity is pivotal for effective management and decision-making. Field experiments were conducted to understand the correlation and predict the yellow mosaic disease severity in yard-long beans using visible image indices. A total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models. Out of 143 genotypes screened based on final disease severity 3, 18, 18, 17, 34 and 53 genotypes were grouped in immune, resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible categories, respectively. Model performances was evaluated using R 2 , d-index, mean bias error, and normalized Root Mean Square Error (n-RMSE) metrics. Results revealed that 34 indices exhibited significant correlations (p 2 and d-index values exceeding 0.92 and 0.98, respectively, in calibration, and 0.88 and 0.96 in validation, underscoring their effectiveness in predicting YMD severity using RGB images only. Random Forest (RF), Cubist, XGBoost (XGB), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM) emerged as the five top-performing models for predicting YMD severity using visible indices in yard-long beans. These findings hold practical implications for timely disease management strategies, expediting breeding programs, and aiding policy planners and farmers in making well-informed decisions.
Why it matches plant phenotyping methodsRGB画像から可視画像指標を抽出し、機械学習で植物病害の重症度を推定・検証する手法が研究の中心であり、植物状態の定量的フェノタイピングに該当する。
abstractA total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models.
CowpeaRootMorphology / geometry measurementRoot system architecture
Improving crop production in changing environments can be achieved through selective breeding; however, limited advanced root phenotyping and genotyping in early growth stages hinder assessing root architecture variation and diversity, despite its importance. Therefore, this study utilized advanced image phenotyping on a diverse set of 222 cowpea accessions, revealing significant variations in key phenotypic traits and the genomic regions influencing them. Our study revealed a total of 55 genes linked to major root traits. Among eight root traits-total root length (TRL), surface area (SA), average diameter (AD), root volume (RV), tip number (TN), fork number (FN), primary root length (PRL), and lateral root length (LRL), analyzed, seven significant single nucleotide polymorphisms (SNPs) demonstrated particularly strong associations with three key traits, including surface area (SA), tip number (TN), and fork number (FN). SA emerged as a significant trait, exhibiting considerable variation across the studied accessions. The mean SA was 59.59 cm 2 , with some genotypes surpassing 140.72 cm 2 . Further analysis identified two SNPs that showed significant association with SA, located on two distinct chromosomes: 3 and 11. Similarly, two significant SNPs associated with TN were found on chromosome 3, while three SNPs associated with FN were identified on chromosomes 2, 3, and 8. These findings significantly advance our understanding of the genetic foundations underlying important phenotypic traits in cowpeas, offering a robust framework for future genetic improvement initiatives. The results strongly suggest that implementing breeding programs focused on selecting root phenotypes could significantly enhance cowpea productivity across various environments.
Why it matches plant phenotyping methods根系形態を対象とした高度な画像フェノタイピングを多数アクセッションに適用し、複数の根形質を抽出・解析しているため、フェノタイピング手法の実質的な応用研究と判断する。
titleAdvanced High-Throughput Root Phenotyping and GWAS Identifies Key Genomic Regions in Cowpea During Vegetative Growth Stage.
Long beans (Vigna unguiculata subsp. sesquipedalis), have high nutritional value, besides long beans also have a significant role in the economy of farmers in Indonesia. However, the productivity of this plant is often hampered by various diseases that attack the leaves, which can result in a decrease in the quantity and quality of the harvest. This study has succeeded in developing a Convolutional Neural Network (CNN) model with the ResNet-50 architecture to identify six types of diseases in long bean leaves. The dataset used consists of 2,316 images, divided into training data (80%), validation (15%), and testing (5%). The ResNet-50 model, which consists of 50 layers, applies the transfer learning technique by not training the first 35 layers using a specific dataset, but utilizing weights from ImageNet. Training for 100 epochs produces high accuracy, namely 98.3% for training data, 98.4% for validation data, and 98.7% for testing data. Evaluation using Confusion Matrix, Precision, Recal and F1 Score shows very good performance without prediction errors. The final result of this research is a mobile-based software system that can diagnose diseases quickly and accurately, which can help farmers take appropriate action, and support sustainable agriculture in Indonesia.
Why it matches plant phenotyping methods長豆葉の画像から病害状態をCNNで識別する手法を開発・評価しており、植物病害表現型の取得が研究の中心です。
abstractThis study has succeeded in developing a Convolutional Neural Network (CNN) model with the ResNet-50 architecture to identify six types of diseases in long bean leaves.
CowpeaSoybeanRootMorphology / geometry measurementSegmentationRoot system architecture
A simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes (adzuki bean, mung bean, cowpea, and soybean) based on two-dimensional images. Four different thresholding methods; Otsu, Gaussian adaptive, mean adaptive and triangle threshold were used to know the effect of thresholding in root trait estimation and to optimize the accuracy of root trait estimation. The results generated by the algorithm applied to 400 legume root images were compared with those generated by two separate software (WinRHIZO and RhizoVision), and the algorithm was validated using ground truth data. Distance transform method was used for estimating SA, AD, and RV and ConnectedComponentsWithStat function for TRL estimation. Among the thresholding methods, Otsu thresholding worked well for distance transform, while triangle threshold was effective for TRL. All the traits showed a high correlation with an R² ≥0.98 (p < 0.001) with the ground truth data. The root mean square error (RMSE) and mean bias error (MBE) were also minimal when comparing the algorithm-derived values to the ground truth values, with RMSE and MBE both < 10 for TRL, < 6 for SA, and < 0.5 for AD and RV. This lower value of error metrics indicates smaller differences between the algorithm-derived values and software-derived values. Although the observed error metrics were minimal for both software, the algorithm-derived root traits were closely aligned with those derived from WinRHIZO. We provided a simple Python algorithm for easy estimation of legume root traits where the images can be analyzed without any incurring expenses, and being open source; it can be modified by an expert based on their requirements.
Why it matches plant phenotyping methods根の二次元画像から主要形質を抽出するPythonアルゴリズムを開発し、既存ソフトウェアおよびグラウンドトゥルースで検証しており、植物フェノタイピング手法が研究の中心です。
abstractA simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes
Reproduction assets foundThe authors publicly release their Python root-trait analysis source code together with the 400 legume root images and validation images on GitHub, as stated in the article text and Data availability statement. The Zenodo DOI cited for ground-truth images is a third-party dataset from Rose and Lobet (2018), i.e., citedCode · publicThe source code along with the root images and the validation images can be downloaded from ( https://github.com/AG9843/Legume-Root-Analysis.git ).Open asset ↗AG9843/Legume-Root-Analysislines:65-75Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
CowpeaRootMorphology / geometry measurementRoot system architecture
Improving crop production in changing environments can be achieved through selective breeding; however, limited advanced root phenotyping and genotyping in early growth stages hinder assessing root architecture variation and diversity, despite its importance. Therefore, this study utilized advanced image phenotyping on a diverse set of 222 cowpea accessions, revealing significant variations in key phenotypic traits and the genomic regions influencing them. Our study revealed a total of 55 genes linked to major root traits. Among eight root traits—total root length (TRL), surface area (SA), average diameter (AD), root volume (RV), tip number (TN), fork number (FN), primary root length (PRL), and lateral root length (LRL), analyzed, seven significant single nucleotide polymorphisms (SNPs) demonstrated particularly strong associations with three key traits, including surface area (SA), tip number (TN), and fork number (FN). SA emerged as a significant trait, exhibiting considerable variation across the studied accessions. The mean SA was 59.59 cm², with some genotypes surpassing 140.72 cm². Further analysis identified two SNPs that showed significant association with SA, located on two distinct chromosomes: 3 and 11. Similarly, two significant SNPs associated with TN were found on chromosome 3, while three SNPs associated with FN were identified on chromosomes 2, 3, and 8. These findings significantly advance our understanding of the genetic foundations underlying important phenotypic traits in cowpeas, offering a robust framework for future genetic improvement initiatives. The results strongly suggest that implementing breeding programs focused on selecting root phenotypes could significantly enhance cowpea productivity across various environments.
Why it matches plant phenotyping methods根系形態を対象とした高度な画像フェノタイピングを多数アクセッションに適用し、複数の根形質を定量化しているため、フェノタイピング手法の実質的な適用研究である。
abstractTherefore, this study utilized advanced image phenotyping on a diverse set of 222 cowpea accessions, revealing significant variations in key phenotypic traits and the genomic regions influencing them.
Agriculture greatly impacts Bangladesh's economy, and vegetable cultivation plays a significant role in Agriculture by providing nourishment, and food security as well as improving the economy. The necessity of food production is growing similarly to the population growth. The farmers of Bangladesh are working hard to meet this need for food production and to gain yields. However, every year the farmers face a significant amount of loss in production due to the attack of different diseases and viruses due to the lack to technological development. The reason behind most of these losses is the lack of knowledge about diseases and being unable to detect the diseases early. Therefore, the early detection of plant disease is significant in balancing the country's economy and preventing undesirable losses. To bring a solution to this problem our dataset provides a total of 4467 images of Beans and Cowpeas leaf images which include different disease classes and fresh leaves. The dataset comprises 2,273 images of Bean and 2,194 images of Cowpea plants where each plant provides 4 classes of different disease along with the healthy leaves. This dataset will assist researchers in identifying plant diseases and farmers as well as contribute to the economy of the country.
Why it matches plant phenotyping methods豆類葉の画像から病害状態を推定する画像データセットが研究の中心であり、植物病害表現型のデータ資源として収録対象です。
titleComprehensive smartphone image dataset for bean and cowpea plant leaf disease detection and freshness assessment from Bangladesh vegetable fields
Reproduction assets foundThe paper is a Data in Brief article describing a smartphone image dataset of bean and cowpea leaf disease/freshness. The authors' own dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, making it a paper-specific, publicly actionable asset. The Kaggle bean disease dataset is cited priorDataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/ykvcrjffzd.1
Direct URL to data: https://data.mendeley.com/datasets/ykvcrjffzd/1Open asset ↗Mendeley Data · 10.17632/ykvcrjffzd.1lines:1-51Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
New and simple crop yield prediction methods are expected to be developed owing to the increasing environmental stress caused by climate change. Algorithms of machine learning could be a powerful tool for predicting crop yield; however, the required feature variables and differences in their prediction accuracy are poorly addressed. The objectives of this study were to identify the best combination of feature variables to predict the yield of cowpea (Vigna unguiculata), which is widely grown in central Sudan Savanna under environmentally restricted conditions, and clarify the differences in the accuracy of major machine learning algorithms. The study also explored the environmental and plant factors affecting the prediction errors. Sample data were obtained from cowpea field experiments in central Sudan Savanna. The prediction was performed using 28 models, encompassing four machine learning algorithms and seven combinations of feature variables. Support Vector Regression and Neural Network algorithms effectively predicted cowpea yields using continuous leaf coverage rates as feature variables; however, some differences were observed in their prediction accuracy depending on the soil types and growth habits. The use of feature variables that are related to shoot growth and plant physiological status could minimize prediction errors.
Why it matches plant phenotyping methodsササゲ収量という植物形質を、葉面被覆率などの植物特徴量から機械学習で予測するモデル開発とアルゴリズム比較が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractNew and simple crop yield prediction methods are expected to be developed
Nondestructive plant phenotyping forms a key technique for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-throughput phenotyping facilities can further our understanding of plant development and stress responses, their high costs greatly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration. We paired these devices to a suite of computational pipelines for integrated and straightforward data analysis. The developed tools were validated for their suitability for large genetic screens by evaluating a cowpea (Vigna unguiculata) diversity panel for responses to drought stress. The observed natural variation was used as an input for a genome-wide association study, from which we identified nine genetic loci that might contribute to cowpea drought resilience during early vegetative development. The homologs of the candidate genes were identified in Arabidopsis (Arabidopsis thaliana) and subsequently evaluated for their involvement in drought stress by using available T-DNA insertion mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating the identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of plant species.
Why it matches plant phenotyping methods低コストの画像・重量ベース装置と解析パイプラインを開発し、遺伝資源パネルで適用性を検証しており、植物表現型取得法が研究の中心である。
abstractwe developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration.
Scientists rely heavily on quantitative measurements to develop and test scientific hypotheses. However, certain processes of biology can be truly complex, making it challenging to quantify. Plant phenotyping is the science of characterizing plants' physiological, anatomical, or biochemical properties (Walter et al. 2015). While some phenotypes arise purely from the genetic makeup of the plant, others may develop as a result of an organism's interactions with its environment, which is determined by various genetic and environmental factors (Pieruschka and Schurr 2019). Therefore, certain plant phenotypes, like plant growth, are dynamic by nature and constantly build on feedback mechanisms between the environment and the organisms' genetic makeup. The “living” nature of these plant phenotypes, dependent on time and space, makes it particularly difficult for quantification. Despite the challenges, plant phenotyping has been crucial to scientists since the beginning of plant science. What started as a simple visual observation by farmers, often referred to as the breeder's eye, has now evolved into high-throughput phenotyping, where plant physiological status and growth are analyzed in a multidimensional manner (Xiao et al. 2022). High-throughput phenotyping techniques focusing on plant growth mostly use continuous, usually short-interval, measurements on plants to monitor dynamic changes in plants over time. As a result, high-throughput phenotyping thrives on expensive equipment such as cameras, drones, and infrared spectroscopy techniques (Fahlgren et al. 2015). Furthermore, data collected from high-throughput phenotyping are usually analyzed with complex computer algorithms and require advanced knowledge programming, standing as another challenge in front of accessibility of these techniques to a broader group of plant scientists. In this issue of Plant Physiology, Yu et al. (2024) report the development of a new high-throughput phenotyping system that is particularly optimized for drought sensitivity screening. This new phenotyping system's low-cost design and open-source nature are compelling to users looking for alternatives to expensive, high-throughput phenotyping platforms. This system consists of 3 low-cost hardware setups, namely, PhenoRig, PhenoCage, and AWWESmo, a computational pipeline for image analysis. PhenoRig system consists of a wooden frame to hold pots and 2 Raspberry Pi cameras and computers that collect plant images every 30 min (Fig. 1). PhenoCage is another low-cost framing system with a rotating platform within which the plants are placed. While the PhenoRig system was used to collect top-view images of plants, PhenoCage is built and used for side-view images, ensuring adequate information about the 3D architecture of the plant. The third component of this system, AWWESmo, was developed to monitor plant evapotranspiration in a more automatized way. Using an Arduino-Watering and Weighing unit, pots are automatically weighted and watered to their target weight. The authors also developed computational tools, RasPiPheno Pipe, and shiny app, RaspiPheno App, for performing downstream data analysis and statistics. Data collected from PhenoRig and PhenCage are then analyzed to generate a digital plant biomass using RasPiPheno Pipe, which benefits from a previous platform, PlantCV (Gehan et al. 2017). Differences between different genotypes were analyzed using the RaspiPheno App throughout time and treatments. This app uses R studio to analyze the digital biomass data statistically, as well as leaf area using t test, Wilcox, ANOVA, or 2-way ANOVA, and can generate graphs using ggplot2 and ggpubr packages. A summary for the new high-throughput, low-cost phenotyping system developed by Yu et al. (2024). The phenotyping system relies on 2 house-built, low-cost pieces of equipment: PhenoCage and PhenoRig. The PhenoRig system is used to collect top-view images of plants, and the PhenoCage is used to collect side-view images. Images are collected every 30 min and then analyzed using the RaspiPheno pipeline to extract digital biomass information. The RaspiPheno App can take the output of the RaspiPheno pipeline and analyze the data statistically using and create images R software (adapted from Yu et al. 2024). To show the potential of their new system, the authors performed high-throughput phenotyping using 3 different plant systems, model plant Arabidopsis thaliana, cowpea, and tepary beans, under drought stress. The authors also conducted a genome-wide association study on a natural diversity panel of cowpea consisting of 368 genotypes. Using this new phenotyping platform, they identified multiple new drought-responsive loci in cowpeas. These loci were screened for annotated genes within the linkage disequilibrium (30 kbp) of the identified SNP, and their homologs were further studied in model plant A. thaliana. To test the functional role of the newly identified drought-responsive genes, the authors took advantage of the Arabidopsis T-DNA insertional mutagenesis collection. They grew 13 Arabidopsis mutant lines under control and drought conditions. They discovered that mutations in 1,8-cineole synthase (AtTPS27, EVT2-2), CAAX amino terminal protease (EVT8), Alpha carbonic anhydrase 7 (AtACA7, EVT3-1, EVT3-2) showed significantly higher rosette size under drought conditions, suggesting that they play a role in plant performance under drought conditions. Increasing the accessibility of high-throughput plant phenotyping is important, considering the challenges agriculture faces due to changing climate. Yu et al. (2024) offer an exciting alternative to the costly, high-throughput phenotyping techniques. Equipment needed for building the system is low cost, and the authors provided detailed instructions (in manuscript and as videos) to build the system in-house. Data analysis followed by phenotyping is made simple with open source data analysis pipeline, RasPiPheno Pipe, and the shiny app, RasPiPheno App. Phenotyping software of this kind will provide an opportunity for plant scientists hoping to perform high-throughput phenotyping but who do not have access to expensive resources. B.A. wishes to thank Plant Physiology for the opportunity to act as an Assistant Features Editor. No new data were generated or analyzed in support of this research.
Why it matches plant phenotyping methods低コストの画像取得・蒸散計測装置と解析パイプライン／アプリから成る植物表現型解析プラットフォームの開発を中心に扱っているため。
abstractreport the development of a new high-throughput phenotyping system that is particularly optimized for drought sensitivity screening
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Plant 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.
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
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.
Why it matches plant phenotyping methods植物の栄養状態をハイパースペクトル画像から抽出するCNN手法を開発し、前処理・複数モデルとの比較・異なる生育段階での性能評価を行っており、表現型取得が中心である。
abstractThis study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 is the description of the selected growth stages based on the BBCH system for coding the phenological growth stages of plants ( Meier et al.Open asset ↗lines:339-346Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
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
Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.
Why it matches plant phenotyping methodsNIRSとPLS回帰による飼料品質形質の推定式を開発・検証しており、植物試料の品質形質取得法が研究の中心です。
abstractThe main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS)
Abstract Nondestructive plant phenotyping is fundamental for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-through phenotyping facilities can further our understanding of plant development and stress responses, their high costs significantly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration. We paired these devices with a suite of computational pipelines for integrated and straightforward data analysis. We validated the suitability of our system for large screens by evaluating a cowpea diversity panel for responses to drought stress. The observed natural variation was subsequently used for Genome-Wide Association Study, where we identified nine genetic loci that putatively contribute to cowpea drought resilience during early vegetative development. We validated the homologs of the identified candidate genes in Arabidopsis using available mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of species.
Why it matches plant phenotyping methods低コストの画像・重量ベース装置と計算パイプラインを開発し、植物成長・蒸発散を測定するフェノタイピングシステムとして検証しており、手法が研究の中心である。
abstractwe developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration.
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 · public142 how to build and program the device can be found at https://github.com/ok84-star/AAWSMO. DetailsOpen asset ↗ok84-star/AAWSMOpdf-page:6 lines:1-42Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Published29 May 2023Brazilian journal of biology = Revista brasleira de biologiaCited by 3 · OpenAlex ↗
The extracts of medicinal plants are used for the treatment of seeds in order to reduce the action of phytopathogens and increase the vigor of the seeds. Currently, computerized image analysis has been used to assess the physiological quality of seed lots. The objective was to evaluate the efficiency of the Vigor-S® software in the evaluation of the physiological quality of cowpea seeds treated with essential oils, comparing with a traditional test and the principal component analysis. Two cowpea cultivars were analyzed, BRS Tumucumaque and BRS Guariba, treated with doses of natural extracts of Alfavaca, garlic, horsetail, citronella and pyroligneous acid. The traditional method consisted of evaluations for germination, first germination count, seedling emergence, emergence speed index, accelerated aging, fresh matter and dry matter of seedling and the image analysis for: seedling length, growth index, uniformity index, vigor index, and germination. A Principal component analysis was applied to reduce the number of variables. Horsetail, Alfavaca and citronella extracts were efficient in increasing the physiological quality of the seeds of at least one cultivar. The Vigor-S® software proved to be efficient compared to traditional tests to assess the physiological quality of seeds. Principal Component Analysis is an ally to identify the best extracts and doses to be used. The image analysis method proved to be effective when compared to the traditional method and can therefore be used.
Why it matches plant phenotyping methodsVigor-S®画像解析による種子・幼植物の生理的品質評価を従来法と比較検証しており、画像ベースの表現型取得・解析が中心的に扱われている。
abstractThe objective was to evaluate the efficiency of the Vigor-S® software in the evaluation of the physiological quality of cowpea seeds treated with essential oils, comparing with a traditional test and the principal component analysis.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.
Why it matches plant phenotyping methods植物画像の自動セグメンテーション手法を開発し、複数手法との性能比較とSPAD推定で検証しており、フェノタイピング手法が研究の中心です。
abstractThis study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner.
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.
Cowpea ( Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQ external values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p -value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world.
Why it matches plant phenotyping methodsNIRSによる豆類遺伝資源の複数生化学形質推定モデルを開発・評価しており、高スループットな植物表現型取得法が研究の中心である。
abstractNIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression.
Abstract The success of a breeding program largely depends on the presence of sufficient genetic diversity in crops to provide an avenue for selection of desirable genotypes for utilization in crop improvement. However, the primary gene pools of many crop plants are so depleted in genetic variability which is a consequence of continuous selections imposed by plant breeders. This necessitates exploring the potentials of landraces for sources of resistance to biotic and abiotic stresses. Thus, the “Wooden box techniques” was adopted to screen cowpea genotypes for their response to seedling drought stress, owing to its rapid and high throughput nature. Here, 420 cowpea genotypes were evaluated for their tolerance to seedling drought. Time course analysis of growth and agronomic traits revealed gradual cessation of growth as drought stress intensified as evidenced by reduction in trifoliate number, increase in leaf senescence and stem wilting. Multivariate analysis using principal component (PC) analysis and k-mean clustering identified 3 major clusters where PC1 and PC2 explained 46.7% of the variability in response to drought stress. The biplot analysis showed that plant height, stem greenness and trifoliate number contributed positively to PC1 while leaf senescence score was negatively related to the clustering on this axis. The comprehensive data analysis pipeline allows us to identify the relationship between the agronomic and stay-green parameters, which provides us with the understanding of traits that could be useful during the selection of lines under drought stress at the seedling stage. Our method provides an aid-to-selection for rapid screening of a large collection of cowpea lines for their response to seedling drought stress. Additionally, our results identified potential tolerant genotypes for use as parents for genetic analysis of drought tolerant traits and incorporation into breeding programs targeting the development and deployment of drought tolerant varieties.
Why it matches plant phenotyping methods木箱法と時系列の形態・生理形質評価、データ解析パイプラインを用いた大規模な幼苗干ばつ表現型スクリーニングが研究の中心であり、再利用可能な高速表現型評価法として記述されている。
abstractThus, the “Wooden box techniques” was adopted to screen cowpea genotypes for their response to seedling drought stress, owing to its rapid and high throughput nature.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Drought is one of the most devasting and frequent abiotic stresses in agriculture. While many morphological, biochemical and physiological indicators are being used to quantify plant drought responses, stomatal control, and hence the transpiration and photosynthesis regulation through it, is of particular importance in marking the plant capacity of balancing stress response and yield. Due to the difficulties in simultaneous, large-scale measurement of stomatal traits such as sensitivity and speed of stomatal closure under progressive soil drought, forward genetic mapping of these important behaviors has long been unavailable. The recent emerging phenomic technologies offer solutions to identify the water relations of whole plant and assay the stomatal regulation in a dynamic process at the population level. Here, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress, which, in combination of genome-wide association study (GWAS), enables genetic mapping of the complex, stomata-related drought responsive traits “critical soil water content” (θ cri ) and “slope of transpiration rate declining” (K Tr ). The 106 accessions showed large variations in θ cri and K Tr , indicating that they had broad spectrum of stomatal control in response to soil water deficit, which may confer them different levels of drought tolerance. Univariate GWAS identified six and fourteen significant SNPs associated with θ cri and K Tr , respectively. The detected SNPs distributed in nine chromosomes and accounted for 8.7–21% of the phenotypic variation, suggesting that both stomatal sensitivity to soil drought and the speed of stomatal closure to completion were controlled by multiple genes with moderate effects. Multivariate GWAS detected ten more significant SNPs in addition to confirming eight of the twenty SNPs as detected by univariate GWAS. Integrated, a final set of 30 significant SNPs associated with stomatal closure were reported. Taken together, our work, by combining phenomics and genetics, enables forward genetic mapping of the genetic architecture of stomatal traits related to drought tolerance, which not only provides a basis for molecular breeding of drought resistant cultivars of cowpea, but offers a new methodology to explore the genetic determinants of water budgeting in crops under stressful conditions in the phenomics era.
Why it matches plant phenotyping methods高スループット生理フェノタイピングにより、乾燥下の気孔関連形質を大規模・動的に測定し、再利用可能な測定ワークフローとしてGWASに適用しているため、フェノタイピング手法が中心的です。
abstractHere, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress
Plant growth is the product of gene by environment (GxE) interactions during plant development. Effective characterization of plant growth under various conditions provides insight into genetic components of plant development and mechanisms of stress resilience. While the emergence of high-through phenotyping facilities provides new avenues to further understand plant development and stress responses, the large costs of such facilities are hindering the study of dynamic growth processes. To democratize high-throughput plant phenotyping, we developed three sets of image-based phenotyping devices utilizing Raspberry Pi computers and low-weight/low-cost materials to continuously monitor shoot and root growth. The process is further automated by our workflows including data collection and statistical analysis. Our devices and workflows are customizable to image a wide variety of plants and tissues. To validate our system, we measured growth of Arabidopsis rosettes, tomato roots, and characterized the relationship between cowpea growth and evapotranspiration. These results demonstrate the variety of applications for Raspberry Pi based phenotyping. Importantly, this low-cost system is ideal for studying the genetics of plant growth and identifying new components of abiotic stress tolerance in a wide variety of species.
Why it matches plant phenotyping methods低コストの画像ベース植物フェノタイピング装置と解析ワークフローを開発し、複数植物で成長測定により検証しており、フェノタイプ取得法が研究の中心である。
abstractwe developed three sets of image-based phenotyping devices utilizing Raspberry Pi computers and low-weight/low-cost materials to continuously monitor shoot and root growth
This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were non-destructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis (LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction Rp2 of 0.93, 0.80 and 0.78 and root mean square errors in prediction (RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with Rp2 of 0.87 and 0.87 and RMSEP of 0.071% and 0.485%, respectively; but for the prediction of sucrose and raffinose the accuracy was relatively limited with Rp2 of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of 96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy, the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.
Why it matches plant phenotyping methods個別種子の化学成分と発芽能力を推定するマルチチャンネルスペクトル画像システムを開発し、回帰・分類モデル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ある。
abstractset a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Assessment of the severity of Fusarium wilt disease in cowpea and other crops relies mainly on visual rating scales which are prone to errors, which can compromise the reproducibility of the data. Furthermore, the rating scales require considerable practical training and routine experience for reliable assessment. Two objective metrics, stem vascular discoloration length (%VDL) and number of Fusarium necrotic vessels (NFNV), for quantitative measurement of vascular damage incited by Fusarium oxysporum f. sp. tracheiphilum race 4 (Fot4) of cowpea, were compared and their utility as a measure of disease severity and potential usefulness in other crop pathosystems is proposed. The metrics were tested in seven F 2 populations and one F 2:3 population, segregating for wilt response, and inoculated with race Fot4 at the seedling stage. %VDL and NFNV were highly correlated with plant wilting for all populations ( r = 0.51 – 0.93 and 0.52 – 0.94, respectively). Furthermore, the relationships between the variables were linear in all populations ( R 2 = 0.81 to 0.87 and 0.71 to 0.91), indicating that they can provide accurate and reliable measurement of severity of Fusarium wilt disease. Also, %VDL and NFNV were strongly correlated ( r = 0.88 - 0.97) and demonstrated a linear relationship ( R 2 = 0.69 – 0.94). Analysis of goodness-of-fit in two F 2 populations revealed that errors in measurement of vascular discoloration length can result in higher segregation distortion when compared to enumeration of necrotic vessels. However, both metrics were highly effective in accounting for the severity of vascular damage caused by Fusarium wilt disease.
Why it matches plant phenotyping methodsFusarium萎凋病による植物の血管障害・病徴重症度を定量化する2つの指標を開発・比較検証しており、表現型取得法が研究の中心である。
abstractTwo objective metrics, stem vascular discoloration length (%VDL) and number of Fusarium necrotic vessels (NFNV), for quantitative measurement of vascular damage incited by Fusarium oxysporum f. sp. tracheiphilum race 4 (Fot4) of cowpea, were compared
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Cowpea is a warm-season legume, often characterized as an orphan or underutilized crop, with great future potential, particularly under the global change. A traditional cowpea landrace in Cyprus is highly valued for fresh pod consumption in the local cuisine. In order to improve the yield potential of the landrace, the long-term response to direct selection for fresh pod yield and the associated changes in fodder and root biomass were investigated in a variety of fertility regimes under real field conditions. The non-stop selection process employed comprehensive pod, fodder, and root phenotyping at the level of the individual plant and resulted in the creation of a range of highly improved sibling lines with differential adaptation to micro-environments and with an improved ratio of pod to shoot and root biomass. The average rate of increase per year for fresh pod yield is at the level of 180 g per plant despite the relatively narrow genetic base of a single landrace and it is seemingly inexhaustible testifying to the great plasticity of the cowpea genome and the potential of the methodology to capture it. The corresponding high genetic gain was also confirmed under dense stands where the difference in pod yield between the best selection and the control amounted to 31.37%. Thus, the new focus apart from the simple variety maintenance should also include the continuous improvement and exploitation of micro-adaptation processes specific for individual fields that allow quick responses to environmental and climatic changes. This work presents also a novel approach to the multiple challenges encountered in root phenotyping and a method to meaningfully associate it with whole-plant performance in field conditions.
Why it matches plant phenotyping methods個体レベルのポッド・飼料・根の包括的フェノタイピングを選抜に中核的に用い、圃場での根フェノタイピング手法も提示しているため、単なる生物学的測定ではなく実質的なフェノタイピング応用研究である。
abstractThe non-stop selection process employed comprehensive pod, fodder, and root phenotyping at the level of the individual plant
Background The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea ( Vigna unguiculata ) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination. Results The results revealed that the LDA models had good accuracy in distinguishing 'Aged' and 'Non-aged' seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, 'Germinated' and 'Non-germinated' seeds with OCC of 81.80, 79.05 and 81.0%, 'Early germinated', 'Medium germinated' and 'Dead' seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give 'Normal' and 'Abnormal' seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner. Conclusion The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality.
Why it matches plant phenotyping methodsコンピュータビジョンとマルチスペクトル画像、画像処理、判別モデルを用いて種子の生存性・発芽・幼植物状態などの表現型を高スループットに分類する方法が研究の中心である。
abstractthe objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea ( Vigna unguiculata ) seeds.
Legumes are protein sources for billions of humans and livestock. These traits are enabled by symbiotic nitrogen fixation (SNF), whereby root nodule-inhabiting rhizobia bacteria convert atmospheric nitrogen (N) into usable N. Unfortunately, SNF rates in legume crops suffer from undiagnosed incompatible/suboptimal interactions between crop varieties and rhizobia strains. There are opportunities to test much large numbers of rhizobia strains if cost/labor-effective diagnostic tests become available which may especially benefit researchers in developing countries. Inside root nodules, fixed N from rhizobia is assimilated into amino acids including glutamine (Gln) for export to shoots as the major fraction (amide-exporting legumes) or as the minor fraction (ureide-exporting legumes). Here, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity following inoculation of both amide exporting and ureide exporting legumes. The assay is based on measuring Gln output using the GlnLux biosensor, which consists of Escherichia coli cells auxotrophic for Gln and expressing a constitutive lux operon. Subsistence farmer varieties of an amide exporter (lentil) and two ureide exporters (cowpea and soybean) were inoculated with different strains of rhizobia under controlled conditions, then extracts of single leaf punches were incubated with GlnLux cells, and light-output was measured using a 96-well luminometer. In the absence of external N and under controlled conditions, the results from the leaf punch assay correlated with 15 N-based measurements, shoot N percentage, and shoot total fixed N in all three crops. The technology is rapid, inexpensive, high-throughput, requires minimum technical expertise and very little tissue, and hence is relatively non-destructive. We compared and contrasted the benefits and limitations of this novel diagnostic assay to methods.
Why it matches plant phenotyping methods葉片穿孔とGlnLuxバイオセンサーを用いて窒素固定関連状態を迅速・高スループットに測定する新規アッセイを開発し、15N測定等と比較検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity
Reproduction assets foundThe article's Supplementary Material (Figures S1–S4) contains the paper's own phenotyping measurements (GlnLux luminometer outputs, Gln standard curves, nodulation counts, root/shoot morphometric data, and root images) and is publicly available at the Frontiers supplementary-material URL stated in the text. No author分析Supplement · publicewan) and Alice Chang (University of British Columbia) for 15 N analysis.
Footnotes
Funding. This research was supported by CIFSRF grant 107791 to MR from the International Food Development Centre (IDRC, Ottawa) and Global Affairs Canada.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2017.01714/full#supplementary-material
FIGURE S1
Luminescence measurement of Gln standards using the GlnLux 96-well luminometer bioassay to demonstrate the linearity of the assay. Luminescence was measured using a concentration gradient of pure Gln standards (0, 125 × 10 -8 , 25 × 10 -7 , 5 × 10 -6 , and 1 × 10 -5 M) uOpen asset ↗lines:155-178Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Common beanCowpeaField / plotLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Low phosphorus (P) availability and drought are primary constraints to common bean and cowpea production in developing countries. Genetic variation of particular root architectural phenes of common bean is associated with improved acquisition of water and phosphorus. Quantitative evaluation of root architectural phenotypes of mature plants in the field is challenging Nonetheless, in situ phenotyping captures responses to environmental variation and is critical to improving crop performance in the target environment. The objective of this study was to develop flexible high-throughput root architectural phenotyping platforms for bean and cowpea, which have distinct but comparable root architectures. The bean phenotyping platform was specifically designed to scale from the lab to the field. Initial laboratory studies revealed cowpea does not have basal root whorls so the cowpea phenotypic platform was taken directly to field evaluation. Protocol development passed through several stages including comparisons of lab to field quantification systems and comparing manual and image-based phenotyping tools of field grown roots. Comparing lab-grown bean seedlings and field measurements at pod elongation stage resulted in a R2 of 0.66 for basal root whorl number (BRWN) and 0.92 for basal root number (BRN) between lab and field observations. Visual ratings were found to agree well with manual measurements for 12 root parameters of common bean. Heritability for 51 traits ranged from zero to eighty-three, with greatest heritability for BRWN and least for disease and secondary branching traits. Heritability for cowpea traits ranged from 0.01 to 0.80 to with number of large hypocotyl roots (1.5A) being most heritable, nodule score (NS) and tap root diameter at 5cm (TD5) being moderately heritable and tap root diameter 15cm below the soil level (TD15) being least heritable. Two minutes per root crown were required to evaluate 12 root phene descriptors manually and image analysis required 1h to analyze 5000 images for 39 phenes. Manual and image-based platforms can differentiate field-grown genotypes on the basis of these traits. We suggest an integrated protocol combining visual scoring, manual measurements, and image analysis. The integrated phenotyping platform presented here has utility for identifying and selecting useful root architectural phenotypes for bean and cowpea and potentially extends to other annual legume or dicotyledonous crops.
Why it matches plant phenotyping methodsマメ科作物の根系形態を対象に、圃場対応のハイスループット表現型解析プラットフォームを開発し、手動・画像ベース手法、実験室と圃場測定を比較検証しているため、表現型取得法が中心である。
abstractThe objective of this study was to develop flexible high-throughput root architectural phenotyping platforms for bean and cowpea