Callus induction is a complex procedure in plant organ, cell, and tissue culture that underpins processes such as metabolite production, regeneration, and genetic transformation. It is important to monitor callus formation alongside subjective evaluations, which require labor-intensive care. In this research, the first curated lentil (Lens culinaris) callus dataset for instance segmentation was experimentally generated using three genotypes as one data set: Firat-87, Cagil, and Tigris. Leaf explants were cultured on MS medium fortified with different concentrations of gross regulators of BA and NAA to induce callus formation. Three biologically relevant stages, the leaf stage, the green callus, and the necrosis callus, were produced. During this process, 122 high-resolution images were obtained, resulting in 1185 total annotations across them. The dataset was evaluated across four successive generations (v5/7/8/11) of YOLO deep learning models under identical conditions using mAP, Dice coefficient, Precision, Recall, and IoU, together with efficiency metrics including parameter counts, FLOPs, and inference speed. The results show that anchor-based variants (YOLOv5/7) relied on predefined priors and showed limited boundary precision, whereas anchor-free designs (YOLOv8/11) used decoupled heads and direct center/boundary regression that provided clear advantages for callus structures. YOLOv8 reached the highest instance segmentation precision with mAP50@0.855, while it matched the accuracy with greater efficiency and achieved real-time inference with 166 FPS.
Why it matches plant phenotyping methods植物組織培養におけるカルスの形成段階・壊死状態を画像からインスタンスセグメンテーションする手法、データセット、モデル比較を中心に扱っており、植物状態の取得・定量化が本研究の主要な技術貢献である。
titleReal-Time Callus Instance Segmentation in Plant Tissue Culture Using Successive Generations of YOLO Architectures
Reproduction assets foundThe paper's lentil callus image dataset with annotations (122 images, 1185 annotations) is publicly available on Roboflow Universe per the Data Availability Statement. The YOLOv5 GitHub link and Ultralytics docs are generic third-party libraries, not authors' analysis code, and the FAO link is a cited reference, so allDataset · publicThe dataset used in this study, including annotated images for callus detection, is publicly available and can be accessed at Roboflow Universe: https://universe.roboflow.com/yunus-7v2b5/callus-hug7d , accessed on 13 September 2025. This repository contains all images and annotations generated and analyzed during the current study.Open asset ↗Roboflow Universe · callus-hug7dlines:314-345Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
LentilRootClassificationMorphology / geometry measurementRoot system architecture
Root System Architecture (RSA) plays a central role in plant performance by regulating water and nutrient uptake. As agriculture faces increasing challenges from environmental variability, nutrient limitation and water scarcity, identifying adaptive root traits in wild relatives is critical for developing resilient crop varieties. We screened a diverse panel of cultivated and wild lentil (Lens spp.) accessions using the Rhizoscope, a high-throughput root phenotyping system developed by CIRAD. In total, 42 wild accessions and eight advanced breeding lines were evaluated for RSA traits and quantified at 30 days after sowing using a rhizobox-based phenotyping platform. Our objectives were to assess RSA variation within wild species and compare RSA traits between cultivated and wild genotypes. Cultivated lentil showed higher values for traits such as root mass, diameter, root volume, root angle (RA) and maximum root depth (MRD), suggesting greater resource acquisition efficiency. In contrast, wild accessions exhibited higher root:shoot ratios and Collar-First Ramification length (CRL), consistent with adaptation to resource-limited environments. To understand the drivers of RSA variation, we incorporated environmental variables from the center of origin of each accession, including Aridity Index, soil type and bedrock depth, into multivariate analyses using Linear Discriminant Analysis and Classification and Regression Trees. Results showed that variation in traits such as MRD, RA and CRL was more strongly linked to environmental conditions than species classification. Deeper roots were associated with arid regions and deep bedrock, while wider RAs and shorter CRL lengths were typical of genotypes from compacted or shallow soils. These findings suggest that RSA traits in wild lentil species are shaped primarily by local environmental selection rather than taxonomic identity. This highlights the importance of integrating ecological provenance with phenotypic assessments when evaluating wild germplasm. Relying solely on species classification may overlook key adaptive traits. Incorporating environmental data can improve the identification of genotypes with root traits conferring tolerance to drought and edaphic stress, thereby supporting the development of more resilient lentil cultivars.
Why it matches plant phenotyping methodsRhizoscopeを用いた高スループット根系表現型解析プラットフォームによるRSA形質の取得が研究の中心であり、単なる生物学的評価ではない。
abstractWe screened a diverse panel of cultivated and wild lentil (Lens spp.) accessions using the Rhizoscope, a high-throughput root phenotyping system developed by CIRAD.
LentilSeed / grainPhysiological trait estimationGrowth / development / phenology
Objective We propose the application of laser biospeckles-a non-destructive, non-contact, real-time technique-to investigate the effect of sound pressure levels and different frequency sounds on lentil (Lens esculenta puyensis) seeds. Lentil seeds were illuminated by a laser diode source with a wavelength of 630 nm, and biospeckle patterns resulting from the interference of scattered laser light were captured as time-sequenced image frames. Biospeckles were recorded under white noise as well as under different frequencies of 100 Hz, 1 kHz, and 10 kHz at 80 dB. The time-sequenced frames were analyzed for the effects of sound by calculating the correlation between the frames and were characterized by a parameter called BA (Biospeckle Activity), which reflects the level of internal activity within the seeds. Results We found that the BA value was lower under white noise, indicating reduced activity within the seeds. Furthermore, a clear dependence of BA on sound frequency was observed, with this trend becoming apparent after six hours-well before visible germination began. It was found that, depending on the frequency applied, lentil seed germination could either be accelerated (e.g., at 1 kHz) or decelerated (e.g., at 100 Hz or 10 kHz). These results suggest that the laser speckle method may enable faster characterization of the characteristic frequency response of different plant seed species.
Why it matches plant phenotyping methodsレーザーバイオスペックルによる非破壊画像計測とBA指標で種子内部活動・発芽応答を抽出する手法が研究の中心であり、植物フェノタイピング手法の応用・開発に該当する。
abstractWe propose the application of laser biospeckles-a non-destructive, non-contact, real-time technique-to investigate the effect of sound pressure levels and different frequency sounds on lentil (Lens esculenta puyensis) seeds.
Abstract Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.
Why it matches plant phenotyping methodsUAV画像から作物の形態・成長形質を抽出し、時系列成長モデルと収量予測を検証するフレームワークが研究の中心であるため。
abstractUAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points.
Reproduction assets foundThe paper's data availability statement points to a public KnowPulse experiment page hosting the study's UAV-derived phenotyping and yield data; no author analysis code repository is stated.Dataset · publicof S).
We thank Dr. Ana Vargas at the Crop Development Center, U
of S for generously providing yield data from the independent
field trial.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data supporting this study are available at:
https://knowpulse.usask.ca/experiment/AGILE-NAM-UAV-growth-modelling or from the authors upon request.
O RC I D
SandeshNeupane https://orcid.org/0000-0003-3679-1046
KirstinE. Bett https://orcid.org/0000-0001-7959-6959
SteveJ. Shirtliffe https://orcid.org/0000-0002-3603-7417
R E F E R E N C E S
Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J.Open asset ↗KnowPulse · AGILE-NAM-UAV-growth-modellingpdf-raw-page:15 lines:1-90Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract The development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits. We used imagery from unoccupied aerial vehicles (UAVs) with red/green/blue (RGB) and multispectral cameras flown over multiple site‐years in Saskatchewan, Canada, and Metaponto, Italy, to gather data for crop height, area, and volume in a lentil diversity panel (324 genotypes). The temporal nature of the UAV image‐derived data enabled the modeling of growth curves for volume, height, and area, something that would be impractical under traditional phenotyping procedures in such a large population grown in multiple environments. A principal component analysis and hierarchical clustering revealed differential growth patterns across contrasting environments, with large variations in temperature and photoperiod, within our lentil diversity panel. Combining this analysis with genome‐wide genotyping data, we identified markers, from an exome capture array (267,845 single nucleotide polymorphisms), associated with crop growth that could be used for marker‐assisted selection. Our study demonstrates the potential for UAV‐based imaging to obtain large‐scale time‐series data across multiple environments to model growth curves and investigate genotype‐by‐environment interactions. In addition, we can now use phenotypic traits that were once impractical to collect and derive novel phenotypes to improve our understanding of crop growth and the genetics underlying adaptation in lentil, approaches that will be useful for both researchers and breeders.
Why it matches plant phenotyping methodsUAV画像からレンティルの高さ・面積・体積を時系列推定し、大規模集団で成長曲線をモデル化するフェノタイピング手法の実質的な適用・評価が中心である。
abstractThe development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits.
Reproduction assets foundThe paper's UAV-derived lentil growth phenotypes are publicly available on KnowPulse, and the authors' full analysis code/workflow is public on GitHub with a rendered vignette. Both are explicitly stated in the data availability statement and methods.Dataset · publiciluppo e di Innovazione
in Agricoltura) in Metaponto, Italy. Special thanks to Laura
Jardine for help with editing.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are
available online at https://knowpulse.usask.ca/research-experiment/AGILE-UAV and https://github.com/derekmichaelwright/AGILE_LDP_UAV or from the authors
upon request.
O RC I D
DerekM. Wright https://orcid.org/0000-0002-9639-7596
SandeshNeupane https://orcid.org/0000-0003-3679-1046
Tania Gioia https://orcid.org/0000-0001-8980-3034
Giuseppina Logozzo https://orcid.org/0000-0002-7951-2425
SOpen asset ↗knowpulse.usask.ca · AGILE-UAVpdf-raw-page:11 lines:1-84Code · publical user-
calculated traits as described in Figure 2. G × E analysis was
done with “lme4” using linear mixed models (Bates et al.,
2015). Principal component analysis (PCA) and hierarchical
k-means clustering were performed using the “FactoMineR”
R package (Lê et al., 2008). The source code for all data
analyses is available at: https://derekmichaelwright.github.io/AGILE_LDP_UAV/LDP_UAV_Vignette.html.25782703,
2025,
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WileyOpen asset ↗derekmichaelwright.github.io/AGILE_LDP_UAV · LDP_UAV_Vignettepdf-raw-page:3 lines:1-106Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Significant concerns regarding the impact of copper (Cu) and copper oxide (CuO) nanoparticles (NPs) and microparticles (MPs) on plant systems have been brought to light through the growing use of these materials in industry and agriculture. The properties of NPs are critical in determining their uptake by plant cells and the ensuing effects on plant physiology. This emphasizes the need for accurate monitoring techniques to determine the impact caused by NPs on seed development and plant growth. This study uses foliar exposure at 0 and 100 mg/L, as well as seed exposure at 0, 25, and 100 mg/L, to explore the effects of Cu ( Lens culinaris ). Biospeckle optical coherence tomography (bOCT) was employed to monitor internal physiological activity in real time, non-invasively-capabilities that static imaging methods, such as OCT, are unable to provide. Results showed that exposure to Cu and CuO NPs led to significant reductions in biospeckle contrast, indicating heightened physiological stress, while MPs generally produced minimal or even positive effects. These early changes detected by bOCT within just 6 h of exposure were consistent with traditional morphological and biochemical assessments-such as germination rate, growth, biomass, and catalase activity-that typically require several days to detect. The study demonstrates that bOCT enables the rapid, functional assessment of nanomaterial effects, including those resulting from foliar exposure, thereby offering a powerful tool for early and non-destructive evaluation of plant responses to engineered particles in agricultural contexts.
Why it matches plant phenotyping methodsbOCTによる植物内部の生理活性をリアルタイム・非侵襲的に測定し、ナノ粒子ストレスを早期評価する手法が研究の中心であるため。
abstractBiospeckle optical coherence tomography (bOCT) was employed to monitor internal physiological activity in real time, non-invasively
Abstract This study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes. The standard analytical approach for TDF analysis is based on the Association of Official Analytical Collaboration method 985.29, which requires extensive sample preparation with extended analysis times of up to 30 h. The FT‐MIR approach was developed to enhance rapid and non‐destructive analysis and minimize the traditional workload associated with phenotyping TDF in pulse crops by accomplishing the same task in a shorter time and at minimal cost. Partial least squares regression (PLSR) was applied with chemometric modeling in MIR regions (650–1480 and 2771–3700 cm −1 ), encompassing spectral bands associated with undigested polysaccharides and partially or undigested protein and fatty acid methyl ester fractions that fingerprint TDF. K‐fold cross‐validation was used for PLSR modeling to enhance computational speeds with large‐scale data processing. These PLSR models for chickpea, dry pea, and lentil have coefficients of determination ( R 2 ) as 0.91, 0.96, and 0.94 with root mean square errors of prediction in the range of 0.05–0.5 g/100 g. This technique supports rapid phenotyping of TDF from raw flour in <1 min. The FT‐MIR technique can relieve the phenotyping bottleneck in pulse breeding and pulse‐based food and feed industries, targeting the measurements of TDF and ensuring a rapid and high‐throughput pipeline for plant breeding and cultivar development.
Why it matches plant phenotyping methodsFT-MIR分光法とPLSRモデルを開発・検証し、パルス作物のTDFという植物形質を高速・非破壊測定する手法が研究の中心である。
abstractThis study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes.
Lentils in Australia are primarily grown in temperate and Mediterranean climates, especially in the southern and western regions of the country. As in other parts of the world, lentil yields in these areas are significantly influenced by factors such as frost, heat, and drought, contributing to variable production. Therefore, selecting appropriate lentil varieties and determining optimal sowing times that align with favourable growing conditions is crucial. Accurate predictions of crop development are essential in this context. Current models mainly rely on photoperiod and temperature to predict lentil phenology; however, they often neglect the impact of soil water on flowering and pod set. This study investigated whether incorporating soil water as an additional factor could improve predictions for these critical growth stages. The modified model was tested using 281 data points from various lentil experiments that examined the timing of flowering (61–147 days) and pod set (77–163 days) across different combinations of location, variety, sowing time, and season. The results indicated that including soil water in the prediction model achieved an R² value of 0.84 for flowering and 0.83 for pod set. The normalised root mean square error (NRMSE) was 0.07, and Lin's concordance correlation coefficient (LinCCC) was 0.91. The model produced an R² of 0.88, an NRMSE of 0.05, and a LinCCC of 0.93 flowering compared to the default model, which yielded an R² of 0.24, an NRMSE of 0.17, and a LinCCC of 0.36 for flowering. A limited sensitivity analysis of the modified model showed that variations in initial soil water and in-season rainfall significantly affected the timing of flowering and pod set. Additionally, we employed a probability framework to assess the crop's vulnerability to the last frost day and early heat stress events during the reproductive stage. This approach provided valuable insights for decision-making to mitigate risks associated with frost and heat stress. Our study suggests that integrating soil water dynamics into lentil phenology models improves the accuracy and precision of predictions regarding the timing of flowering and pod set. These improvements lead to better forecasts, ultimately helping to minimise damage from frost and heat stress during lentil cultivation and can better explain the effect of climate variability.
Why it matches plant phenotyping methods土壌水分を組み込んだレンズマメの開花・莢形成時期予測モデルを開発し、複数実験データで性能検証している。予測対象は明示的な植物フェノロジー形質であり、モデル手法が研究の中心である。
abstractThis study investigated whether incorporating soil water as an additional factor could improve predictions for these critical growth stages.
This study aimed to investigate the biochemical basis of seed morphological traits in red lentils that are important for lentil producers in relation to quality, consumers’ preferences and commercial value. To achieve this objective, proton Nuclear Magnetic Resonance (¹H NMR) spectroscopy combined with multivariate statistical analyses was employed. A collection of 64 red lentil varieties exhibiting diversity in seed colour, size, weight, and cotyledon pigmentation was analysed. Aqueous extracts of the seeds were profiled using ¹H NMR, and spectra were processed into bucketed variables. Partial Least Squares Regression and Multiple Linear Regression were applied to assess relationships between spectral data and continuous morphological traits: lightness (L*), chromatic indexes (a*, b*), Hundred Kernel Weight, and seed size. For categorical traits like cotyledon colour, Partial Least Squares Discriminant Analysis (PLS-DA) and binomial logistic regression were used. Variable Importance in Projection scores helped to identify key metabolite buckets significantly contributing to trait prediction. Metabolites such as leucine, fructose, and phenolic compounds were positively associated with seed size and weight, while NAD⁺ and short-chain fatty acids showed negative associations. Cotyledon colour classification achieved high accuracy (up to 100%) using both PLS-DA and logistic models, with amino acids like leucine and alanine linked to yellow pigmentation and tryptophan and citrate linked to orange. Overall, the study demonstrates that ¹H NMR fingerprinting, combined with rigorous statistical modelling, effectively elucidates the multivariate relationships between metabolomic profiles and key agronomic traits, providing a valuable tool for phenotypic prediction and lentil breeding.
Why it matches plant phenotyping methods¹H NMRフィンガープリンティングと統計モデルを用いて、レンズマメ種子の形態・色形質を予測する手法を明示的に構築・評価しており、メタボローム測定が単なる生物学的結果ではなく表現型推定の中心である。
abstractproton Nuclear Magnetic Resonance (¹H NMR) spectroscopy combined with multivariate statistical analyses was employed
The Lentil, a vital legume globally cultivated, faces significant challenges from diseases like ascochyta blight, lentil rust, and powdery mildew. Ensuring optimal harvest timing and effectively discerning healthy and diseased lentil plants are crucial for maintaining crop quality and economic viability, particularly in regions such as Bangladesh. This paper introduces a comprehensive dataset comprising high-resolution images of lentil plants gathered meticulously over four months from diverse locations across Bangladesh, under expert supervision. The dataset aims to support the development of machine-learning models for precise disease detection and quality assessment in lentil cultivation. Potential applications include enhancing the accuracy of quality evaluation, and improving packaging processes, thereby enhancing overall lentil production efficiency. Agricultural researchers can utilize this dataset to advance applications of computer vision and deep learning in managing crop diseases and enhancing yield outcomes. The dataset's creation involved collaboration with domain experts to ensure its relevance and reliability for agricultural research. By leveraging this dataset, researchers can explore innovative approaches to tackle challenges in lentil farming, contributing to sustainable agricultural practices and food security. Moreover, the dataset serves as a valuable resource for training and testing machine learning algorithms tailored to agricultural settings, facilitating advancements in automated agricultural technologies. Ultimately, this initiative aims to empower stakeholders in the lentil industry with tools to mitigate disease impact and optimize production practices, paving the way for more resilient and efficient agricultural systems globally .
Why it matches plant phenotyping methodsレンティル植物の病害状態を画像から評価する高解像度データセットを構築し、機械学習による病害検出を支援することが中心であり、植物フェノタイピング用データセットに該当する。
abstractThis paper introduces a comprehensive dataset comprising high-resolution images of lentil plants
Reproduction assets foundThe paper is a Data in Brief article describing a lentil plant disease image dataset (1,898 original and 4,550 augmented images across four classes) deposited publicly on Mendeley Data with DOI 10.17632/7vb77bz2st.1. This is a paper-specific, publicly accessible image dataset directly reproducing the paper's phenotypicDataset · publicnd research stations in Barisal, Bangladesh
Coordinates:
1. Farm A: 22.7083° N, 90.3653° E
2. Farm B: 22.6715° N, 90.3252° E
3. Research Station C: 22.7032° N, 90.3863° E
Zone: Barisal]
Country: [Bangladesh]
Data accessibility
Repository name: [Mendeley Data]
Data identification number: 10.17632/7vb77bz2st.1
Direct URL to data: https://data.mendeley.com/datasets/7vb77bz2st/1
Related research article
[None]
How the dataset helps in packaging
1. [ Quality Assessment : Automates the assessment of lentil quality, ensuring only high-quality products are packaged.
2. Sorting and Grading : Aids in developing algorithms for sorting lentils based on size, color, and disease presence, enhancing efficiOpen asset ↗Mendeley Data · 10.17632/7vb77bz2st.1lines:1-55Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Abiotic factors, including heat stress, significantly impact the growth and development of lentil across the globe. Although these stresses impact the plant's phenotypic, genotypic, metabolic, and yield development, predicting those traits in lentil is challenging. This study aimed to construct a machine learning-based yield prediction model for lentil using various yield attributes under two different sowing conditions. Twelve genotypes were planted in open-field conditions, and images were captured 45 days after sowing (DAS) and 60 DAS to make predictions for agro-morphological traits with the assessment for the influence of high-temperature stress on lentil growth. Greening techniques like Excess Green, Modified Excess Green (ME × G), and Color Index of Plant Extraction (CIVE) were used to extract 35 vegetative indices from the crop image. Random forest (RF) regression and artificial neural network (ANN) models were developed for both the normal-sown and late-sown lentils. The ME × G-CIVE method with Otsu's thresholding provided superior performance in image segmentation, while the RF model showed the highest level of model generalization. This study demonstrated that yield per plant and number of pods per plant were the most significant attributes for early prediction of lentil production in both conditions using the RF models. After harvesting, various yield parameters of the selected genotypes were measured, showing significant reductions in most traits for the late-sown plants. Heat-tolerant genotypes like RLG-05, Kota Masoor-1, and Kota Masoor-2 depicted decreased yield and harvest index (HI) reduction than the heat-sensitive HUL-57. These findings warrant further study to correlate the data with more stress-modulating attributes. Supplementary information The online version contains supplementary material available at 10.1007/s13205-024-04031-5.
Why it matches plant phenotyping methods画像から植生指標を抽出し、画像分割と機械学習によってレンティルの形態・収量形質を予測する手法の構築と評価が研究の中心である。
abstractimages were captured 45 days after sowing (DAS) and 60 DAS to make predictions for agro-morphological traits
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract The standard method of estimating in vitro protein digestibility, the protein digestibility corrected amino acid score (PDCAAS) assay, does not support the expected workflow of a pulse breeding program. This is mainly due to its low‐throughput design and long processing time (∼16–24 h) per sample. Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses. The mid‐infrared region representing the amide I band (1756.81–1586.27 cm −1 ) was utilized to perform chemometric modeling with partial least squares regression (PLSR) to estimate in vitro protein digestibility in dry pea ( Pisum sativum L.), lentil ( Lens culinaris Medik.), and chickpea ( Cicer arietinum L.) flours. The root mean square error of predictions of the developed PLSR models for dry pea, lentil, and chickpea were 0.00039, 0.00024, and 0.00017, respectively. Phenotyping with the FT‐MIR approach is more rapid than with the PDCAAS assay and estimates protein digestibility from the flour of a single seed in a shorter time (∼1–2 min). The FT‐MIR approach is robust as the spectroscopic data are consistent and chemically fingerprint this nutritional trait. Accordingly, FT‐MIR can resolve the phenotypic bottleneck of pulse breeding related to in vitro protein digestibility measurements by enabling a high‐throughput phenotyping workflow.
Why it matches plant phenotyping methodsFT-MIR分光とPLSRモデルを用いて、豆類のタンパク質消化性という植物形質を高速推定する方法を開発・評価しており、表現型取得手法が研究の中心である。
abstractFourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses.
Background Lentil (Lens culinaris Medik.) is a globally-significant agricultural crop used to feed millions of people. Lentils have been cultivated in the Australian states of Victoria and South Australia for several decades, but efforts are now being made to expand their cultivation into Western Australia and New South Wales. Plant architecture plays a pivotal role in adaptation, leading to improved and stable yields especially in new expansion regions. Image-based high-throughput phenomics technologies provide opportunities for an improved understanding of plant development, architecture, and trait genetics. This paper describes a novel method for mapping and quantifying individual branch structures on immature glasshouse-grown lentil plants grown using a LemnaTec Scanalyser 3D high-throughput phenomics platform, which collected side-view RGB images at regular intervals under controlled photographic conditions throughout the experiment. A queue and distance-based algorithm that analysed morphological skeletons generated from images of lentil plants was developed in Python. This code was incorporated into an image analysis pipeline using open-source software (PlantCV) to measure the number, angle, and length of individual branches on lentil plants. Results Branching structures could be accurately identified and quantified in immature plants, which is sufficient for calculating early vigour traits, however the accuracy declined as the plants matured. Absolute accuracy for branch counts was 77.9% for plants at 22 days after sowing (DAS), 57.9% at 29 DAS and 51.9% at 36 DAS. Allowing for an error of ± 1 branch, the associated accuracies for the same time periods were 97.6%, 90.8% and 79.2% respectively. Occlusion in more mature plants made the mapping of branches less accurate, but the information collected could still be useful for trait estimation. For branch length calculations, the amount of variance explained by linear mixed-effects models was 82% for geodesic length and 87% for Euclidean branch lengths. Within these models, both the mean geodesic and Euclidean distance measurements of branches were found to be significantly affected by genotype, DAS and their interaction. Two informative metrices were derived from the calculations of branch angle; 'splay' is a measure of how far a branch angle deviates from being fully upright whilst 'angle-difference' is the difference between the smallest and largest recorded branch angle on each plant. The amount of variance explained by linear mixed-effects models was 38% for splay and 50% for angle difference. These lower R 2 values are likely due to the inherent difficulties in measuring these parameters, nevertheless both splay and angle difference were found to be significantly affected by cultivar, DAS and their interaction. When 276 diverse lentil genotypes with varying degrees of salt tolerance were grown in a glasshouse-based experiment where a portion were subjected to a salt treatment, the branching algorithm was able to distinguish between salt-treated and untreated lentil lines based on differences in branch counts. Likewise, the mean geodesic and Euclidean distance measurements of branches were both found to be significantly affected by cultivar, DAS and salt treatment. The amount of variance explained by the linear mixed-effects models was 57.8% for geodesic branch length and 46.5% for Euclidean branch length. Conclusion The methodology enabled the accurate quantification of the number, angle, and length of individual branches on glasshouse-grown lentil plants. This methodology could be applied to other dicotyledonous species.
Why it matches plant phenotyping methods画像からレンティルの分枝数・角度・長さを抽出する手法を開発し、精度検証とプラットフォーム上での適用を行っており、植物表現型取得が研究の中心である。
abstractThis paper describes a novel method for mapping and quantifying individual branch structures on immature glasshouse-grown lentil plants grown using a LemnaTec Scanalyser 3D high-throughput phenomics platform
Today, image classification methods are widely utilized on agricultural products or in agricultural applications. However, many of these methods based on traditional approaches remain unsatisfactory in terms of obtaining effective results. Within this context, this study aimed to classify lentil images by machine learning algorithms, a current and effective method. In line with this purpose, first of all, a camera system was prepared primarily and a dataset was created by recording lentil grains at 225 × 225 resolution via this system. The dataset contains a total of 33,938 data obtained from 3 lentil species as green, yellow, and red. SqueezeNet, InceptionV3, DeepLoc, and VGG16 architectures, among the CNN methods, were used in order to extract features from the recorded images. Lastly, Artificial Neural Network (ANN), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AB), and Decision Tree (DT) algorithms were utilized with the aim of creating models for lentil images’ classification. The classification success of the created machine learning models was calculated and the results were analyzed. The highest classification success with the deep features obtained from the SqueezeNet model, 99.80%, was achieved in the ANN algorithm. The results also revealed that grain size and shape features in image classification can yield much more detailed and precise data than can be obtained practically with manual quality assessment.
Why it matches plant phenotyping methodsレンズマメ粒の画像取得系、データセット作成、深層特徴抽出、機械学習分類を中心的に開発・評価しており、粒の形状・サイズという植物器官形質の画像ベース推定に該当する。
abstracta camera system was prepared primarily and a dataset was created by recording lentil grains at 225 × 225 resolution via this system
Background Stemphylium blight incited by Stemphylium botryosum poses a significant threat to lentil crops worldwide, inducing severe defoliation and causing substantial yield losses in susceptible varieties under favorable conditions. While some moderate levels of resistance have been identified within lentil germplasm, a low number of resistant cultivars are available to farmers. Adding to the common constraints of resistance breeding, a notable challenge is generating a sufficient number of spores for large-scale screenings, which are essential for pinpointing additional sources of resistance for integration into breeding programs. Therefore, there is a pressing need to improve existing screening methods and tailor them for large-scale material selection. In pursuit of this objective, a protocol for the efficient production of fungal material has been adapted. Results Optimization of fungal material production was successfully achieved by comparing the use of fungal mycelia and spores. Spore production was found to be optimal when produced on solid V8-PDA(hi) medium, while liquid Richard's medium was identified as superior for mycelium yield. Furthermore, a refined screening method was developed by evaluating the resistance of six lentil accessions to stemphylium blight. This assessment included the use of either fungal mycelia (at densities ranging from 1 to 5 g L - 1 ) or spores (with densities ranging from 5 × 10 4 to 2 × 10 5 conidia mL - 1 ) under three different relative humidity levels (from 50 to 100%). Both humidity levels and inoculum dose significantly influenced the final disease rating (DR) and the relative Area Under the Disease Progress Curve (rAUDPC). Differences among genotypes in final symptom severity (DR) became more pronounced after inoculation with inoculum densities of 5 g L - 1 of mycelium or of 10 5 and 2 × 10 5 conidia mL - 1 of spore under 100% relative humidity. Given the challenges associated with the large-scale production of S. botryosum spores, inoculations with 5 g L - 1 of mycelium is highly recommended as a practical alternative for conducting mass-scale screenings. Conclusions The findings from this study underscore the critical importance of maintaining high level of humidity during inoculation and disease progression development for accurately assessing resistance to stemphylium blight. The optimization of mycelial production for suspension inoculation emerges as a more reliable and efficient approach for conducting large-scale screening to assess germplasm resistance against stemphylium blight in lentil crops.
Why it matches plant phenotyping methodsレンティルの病害抵抗性（症状重症度・病害進展）を大規模評価するため、接種材料生産とスクリーニング条件を最適化した方法開発研究であり、植物表現型取得が中心です。
abstractTherefore, there is a pressing need to improve existing screening methods and tailor them for large-scale material selection.
For broad‐acre crops grown in Mediterranean‐type environments, variation in lentil (Lens culinaris) yield and quality occurs due to seasonal abiotic and biotic stresses. Because grain quality affects the price paid to growers, in‐season assessment of likely final quality using remote sensing technologies could limit economic losses by informing spatial management at harvest. For a survey of lentil crops grown in southern Australia, in 2019 and 2020, Moran's I analysis identified significant field spatial autocorrelation for the grain quality traits of grain protein concentration (GPC), grain size, and grain brightness (CIE L*, where CIE is International Commission on Illumination), indicating an opportunity for zoning at harvest. Partial least squares calibration models of observed grain quality and proximal reflectance spectra were successfully derived for grain weight (R² = 0.80), GPC (R² = 0.80), and CIE L* (R² = 0.86). For late senescence, Sentinel‐2 satellite canopy reflectance, grain size was best predicted (R² = 0.79) and GPC was poorer (R² = 0.42). Spatial maps of fields for grain size, informed by models, could be derived and determined that for the market critical threshold (38 mg), field area that exceeded this threshold ranged between 30% and 94%. Overall, we determined that sensing technologies had utility for mapping lentil grain quality across fields, providing a potential tool for growers to selectively harvest to achieve best aggregate price based on grain quality targets. Further calibration and validation with multiple years and locations is also needed to test model stability and application to varying environments.
Why it matches plant phenotyping methodsリモートセンシングとPLSモデルを用いてレンティルの穀粒品質形質を推定・マッピングし、モデル性能も評価しているため、植物フェノタイピング手法が中心である。
abstractPartial least squares calibration models of observed grain quality and proximal reflectance spectra were successfully derived for grain weight (R² = 0.80), GPC (R² = 0.80), and CIE L* (R² = 0.86).
Lentil ( Lens culinaris L. subsp. culinaris ) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 10 6 spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot ( r = 0.92 and β 1 = 0.96) than root rot ( r = 0.62 and β 1 = 0.67).
Why it matches plant phenotyping methodsレンズマメの根腐病の病徴・重症度を対象に、視覚評価尺度とRGB画像による自動スコアリング手法を開発・比較検証しており、植物表現型取得が中心である。
abstractThis work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments.
Lentil (Lens culinaris L. subsp. culinaris) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 10⁶ spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot (r = 0.92 and β₁ = 0.96) than root rot (r = 0.62 and β₁ = 0.67).
Why it matches plant phenotyping methodsレンズマメのFusarium根腐病について、視覚評価スケールとRGB画像による自動病害重症度推定法を開発・比較検証しており、植物病害状態の表現型取得が研究の中心である。
abstractThis work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments.
The lentil ( Lens culinaris Medik.) is one of the major pulse crops cultivated worldwide. However, in the last decades, lentil cultivation has decreased in many areas surrounding Mediterranean countries due to low yields, new lifestyles, and changed eating habits. Thus, many landraces and local varieties have disappeared, while local farmers are the only custodians of the treasure of lentil genetic resources. Recently, the lentil has been rediscovered to meet the needs of more sustainable agriculture and food systems. Here, we proposed an image analysis approach that, besides being a rapid and non-destructive method, can characterize seed size grading and seed coat morphology. The results indicated that image analysis can give much more detailed and precise descriptions of grain size and shape characteristics than can be practically achieved by manual quality assessment. Lentil size measurements combined with seed coat descriptors and the color attributes of the grains allowed us to develop an algorithm that was able to identify 64 red lentil genotypes collected at ICARDA with an accuracy approaching 98% for seed size grading and close to 93% for the classification of seed coat morphology.
Why it matches plant phenotyping methods画像解析を用いてレンズマメ種子のサイズ、形状、種皮形態、色を非破壊測定・分類する手法を開発し、精度も評価しているため、植物表現型取得が中心です。
abstractwe proposed an image analysis approach that, besides being a rapid and non-destructive method, can characterize seed size grading and seed coat morphology.
Reproduction assets foundThe paper's lentil seed image dataset (64 images of ICARDA genotypes used for the computer vision phenotyping pipeline) is explicitly stated to be publicly available on the authors' GitHub repository. The Data Availability Statement only offers other data upon request, but the image dataset itself has a public URL.Dataset · publicpaigns, and we found that its repositioning contained a non-negligible error for the purposes of the evaluation process here described. This means that the images in some cases showed a different scaling factor that was handled by the algorithms. In Figure 1 , the acquisition setup is shown. The dataset is publicly available at https://github.com/beppe2hd/unconstrainedLentils (accessed on 10 November 2022).
2.1. Plant Materials
In the present study, we analyzed the grains of 64 lentil genotypes received by ICARDA in Lebanon, including 48 varieties released in 19 different countries between 1984 and 2018, 9 germplasm accessions, and 7 elite breeding lines developed at ICARDA in Lebanon ( TablOpen asset ↗https://github.com/beppe2hd/unconstrainedLentilslines:31-42Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae . Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions.
Why it matches plant phenotyping methods機械学習により、取得が困難なレンチルの病害重症度（DSr）という植物の病害状態を定量予測する手法が研究の中心であり、現況・将来条件での適用も評価している。
abstractwe used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's datasets (lentil CWR distribution/calibration data and DSr-related analysis data). Supplementary material is also available via the Frontiers article page, but the Zenodo deposit is the explicit, actionable paper-specific资产Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://zenodo.org/record/6883274 .Open asset ↗Zenodo · 6883274lines:494-522Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 8 Sept 2026
Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m 2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity.
Why it matches plant phenotyping methodsUAV画像から植生面積・作物体積を抽出し、レンズマメのバイオマスと成長率を推定・検証する手法が研究の中心である。
abstractThe aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
In recent decades, the field of phenomics has lagged behind the advances in genomics, which have become increasingly high-throughput and low-cost. In comparison, manually collected phenotypes are often time-consuming, labor intensive, and more costly to obtain. The development of high-throughput phenotyping platforms (HTPP) are bridging these gaps and enabling improved spatial and temporal resolution for researchers. We used imagery from unoccupied aerial vehicles (UAV) flown over multiple site years in Saskatchewan and Italy to gather data for crop height, area and volume in a lentil diversity panel. We found high correlations for our UAV-derived traits (height & volume) with our manually collected phenotypes (height & biomass). In addition, the high-throughput nature of the UAV allowed for the collection of time-series data which enabled the modelling of growth curves for volume, height and area, which would be impractical under traditional phenotyping procedures given the large population grown in multiple environments. Principal component analysis and hierarchical clustering revealed differential growth strategies amongst our diverse lentil population across contrasting environments. Our study demonstrates the potential for HTPP to obtain data that traditionally require destructive sampling, e.g., volume as a proxy for vegetative biomass, and improve the temporal quality of phenotype data enabling researchers to take their analysis beyond single time points, e.g., model growth curves. In addition, performing our analysis on data from contrasting environments, i.e., Saskatchewan and Italy, has helped elucidate optimal adaptation with regard to growth strategies in lentils.
Why it matches plant phenotyping methodsUAV画像を用いたレンティルの草丈・面積・体積の高スループット表現型取得と、手測定との技術比較・時系列解析が研究の中心であるため。
abstractWe used imagery from unoccupied aerial vehicles (UAV) flown over multiple site years in Saskatchewan and Italy to gather data for crop height, area and volume in a lentil diversity panel.
Abstract Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy is a high‐throughput, cost‐effective method to quantify nutritional traits, such as total protein and sulfur‐containing amino acid (SAA) concentrations, in plant matter. This study used the spectroscopic technique FT‐MIR coupled with attenuated total internal reflectance sampling interface to develop multivariate models for total protein concentration in chickpea (Cicer arietinum L.), dry pea (Pisum sativum L.), and lentil (Lens culinaris Medik.), in addition to SAA concentration in lentil. Total nitrogen data from combustion analysis and SAA data from high‐performance liquid chromatography analysis following acid hydrolysis were used for model calibration and validation. Models for the total protein concentration of chickpea (calibration root mean square error [RMSE] = 0.093, R2 = 0.948, prediction RMSE = 0.10), dry pea (calibration RMSE = 0.096, R2 = 0.845, prediction RMSE = 0.093), and lentil (calibration RMSE = 0.13, R2 = 0.845, prediction RMSE = 0.11) utilized infrared regions associated with protein structures, namely amide bands A, I, and II. In sulfur‐related models for lentil total SAA (calibration RMSE = 0.014, R2 = 0.827, prediction RMSE = 0.022) and methionine (calibration RMSE = 0.0075, R2 = 0.815, prediction RMSE = 0.014) models utilized the C‐S and S‐CH3 stretching and bending bands. Study findings support the conclusion that FT‐MIR spectroscopy is a promising high‐throughput and cost‐effective phenotyping technique that will allow quantifying protein traits quickly and easily in pulse crops.
Why it matches plant phenotyping methodsFT-MIR分光法を用いてマメ科作物のタンパク質・アミノ酸形質を定量するモデルを開発・校正・検証しており、表現型取得法が研究の中心である。
titleFourier‐transform infrared spectroscopy (FTIR) as a high‐throughput phenotyping tool for quantifying protein quality in pulse crops
Lentil and field pea are each commonly marketed as split and dehulled product. For plant-breeding programmes, the genetic improvement in split-yield is a targeted trait. However, the standard laboratory method for assessment of split-yield requires milled grain to be manually sorted into split and dehulled fractions. This process is time-consuming and impacts the number of germplasm lines that can be evaluated.A machine vision approach, based on artificial neural networks, was proposed to classify split and dehulled fractions from multispectral images of grains. Three neural networks were trained on different inputs derived from the images. The networks were: (1) a convolutional network trained on the full images, (2) a convolutional network trained on distributions of image-features, and (3) a fully connected network trained on mean and standard deviation values of image-features. The accuracy and training times were compared to determine the trade-offs between training networks with smaller inputs for computational efficiency and full-image inputs for accuracy.The networks with reduced input-data dimensionality completed network training and predictions in half the time of the image-based network. The convolutional network based on the distributions of image-features achieved a validation accuracy of 88.1%. On average, this was 1.6% greater than the image-based convolutional network and 4.6% greater than the fully connected network based on simple (mean and standard deviation) features. Feature-distributions extracted from the multispectral images captured the diversity of image data required to differentiate milling categories, leading to gains in computational efficiency over the image-based network without loss of network generality.
Why it matches plant phenotyping methods穀粒の分別・split-yieldという育種対象形質を、マルチスペクトル画像とニューラルネットワークで自動分類する手法を開発・比較しており、表現型取得・抽出が中心です。
abstractA machine vision approach, based on artificial neural networks, was proposed to classify split and dehulled fractions from multispectral images of grains.
Abstract Background : Seed coat thickness is a parameter of interest in lentil breeding and processing. Methods described in the literature are destructive and time consuming, limiting the usefulness of this seed characteristic in breeding or grading. In this study, a low-cost optical coherence tomography (OCT) system (OQ Labscope 2.0 from Lumedica, Inc., Durham, NC USA) was used to non-destructively measure lentil seed coat thicknesses. These measurements were compared to destructive measurements obtained via optical microscopy of cross-sectioned seeds. Results : Measurements from the two methods were comparable with consistent trends in thickness and population separability among the five lentil accessions used. The average microscope-based measurements for each accession were higher than those from the OCT, with a linear relationship between the two sets of measurements. The discrepancy in absolute thicknesses is attributed to differences in instrument calibration and in the definition of seed coat boundaries in the two methods. Based on data collected from a larger population, OCT measurements took a mean time of 30 seconds/seed, and a median time of 16 seconds/seed, with no sample preparation required. Conclusions : Optical coherence tomography is a viable method for acquiring comparative data on seed coat thickness of lentil seeds, and presumably other species with similar seed characteristics. The method is non-destructive, and fast enough to be practical for studying large breeding populations. Opportunities for greater efficiencies as part of an automated imaging system are being explored.
Why it matches plant phenotyping methodsOCTによる種皮厚の非破壊・高速測定法を開発し、光学顕微鏡測定と比較検証しており、植物形質取得が研究の中心である。
abstractThese measurements were compared to destructive measurements obtained via optical microscopy of cross-sectioned seeds.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Abstract The combination of poorly drained soils and high rainfall can cause transient waterlogging and reduce yield of lentil. We screened 111 lentil lines for response to waterlogging in 2019 and 2020 using a pot assay outdoors. At 484 °Cd after emergence (38 d) in 2019 and 452 °Cd after emergence (42 d) in 2020, plants were waterlogged for 184 °Cd (11 d, 2019) and 167 °Cd (14 d, 2020) and allowed to recover for 323 °Cd (20 d, 2019) and 307 °Cd (26 d, 2020). We combined 2‐D digital images of canopy cover and plant height to derive a 3‐D trait that correlates with biomass to derive plant growth rate. Actual biomass at the end of recovery in the waterlogged plants varied 2.6‐fold with genotype, and genotypic and phenotypic correlations showed associations with plant growth rate both during (rg = .92; rp = .74) and after waterlogging (rg = .75; rp = .72); there was no trade‐off between maintenance of growth during waterlogging and growth during recovery. The ratio of biomass between waterlogged and control plants at the end of recovery was associated with growth rate during recovery (rg = .88, rp = .65) and biomass at the end of waterlogging (rg = .61, rp = .67). Broad‐sense heritability was 0.27 for growth rate during waterlogging, 0.37 for growth rate during recovery, 0.51 for biomass at the end of waterlogging and 0.47 for biomass at the end of recovery. High biomass at the end of recovery correlated with cooler canopies but correlations varied with season and measurement date, and heritability of canopy temperature was low. We identified genotypes with consistently higher tolerance to waterlogging and provide an improved understanding of the physiological response of lentil to hypoxia highlighting the importance of growth rate not only during waterlogging but also during recovery.
Why it matches plant phenotyping methods2-D画像から3-D形質と成長速度を推定する高スループット表現型解析を、水logging耐性スクリーニングに中心的に適用しているため。
titleHigh‐throughput phenotyping of plant growth rate to screen for waterlogging tolerance in lentil
Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.
Why it matches plant phenotyping methods植物フェノタイピング用UAV画像の超解像モデルと前処理ワークフローを開発・比較検証しており、植物形質測定への適用性が中心的である。
titleSpatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping
LentilRootStress / disease detectionRoot system architectureStress response / tolerance
Aluminium (Al) toxicity in acid soils inhibits root elongation and development causing reduced water and nutrient uptake by the root system, which ultimately reduces the crop yield. This study established a high throughput hydroponics screening method and identified Al toxicity tolerant accessions from a set of putative acid tolerant lentil accessions. Four-day old lentil seedlings were screened at 5 µM Al (pH 4.5) for three days in hydroponics. Measured pre and post treatment root length was used to calculate the change in root length (ΔRL) and relative root growth (RRG%). A subset of 15 selected accessions were used for acid soil Al screening, and histochemical and biochemical analyses. Al treatment significantly reduced the ΔRL with an average of 32.3% reduction observed compared to the control. Approximately 1/4 of the focused identification of germplasm strategy accessions showed higher RRG% than the known tolerant line ILL6002 which has the RRG% of 37.9. Very tolerant accessions with RRG% of > 52% were observed in 5.4% of the total accessions. A selection index calculated based on all root traits in acid soil screening was highest in AGG70137 (636.7) whereas it was lowest in Precoz (76.3). All histochemical and biochemical analyses supported the hydroponic results as Northfield, AGG70137, AGG70561 and AGG70281 showed consistent good performance. The identified new sources of Al tolerant lentil germplasm can be used to breed new Al toxicity tolerant lentil varieties. The established high throughput hydroponic method can be routinely used for screening lentil breeding populations for Al toxicity tolerance. Future recommendations could include evaluation of the yield potential of the selected subset of accessions under acid soil field conditions, and the screening of a wider range of landrace accessions originating from areas with Al toxic acid soils.
Why it matches plant phenotyping methodsアルミニウム耐性 germplasm の同定が主目的だが、高スループット水耕スクリーニング法の確立と、酸性土壌・組織化学・生化学による検証が明示され、根の成長を定量する再利用可能な表現型取得法が中心的に扱われている。
abstractThis study established a high throughput hydroponics screening method and identified Al toxicity tolerant accessions
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations ( P r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering ( r up to 0.76 and 0.85, respectively), and days to physiological maturity ( r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と半自動画像処理により、作物形質を抽出・推定し、複数環境で検証するフェノタイピング手法の実質的応用である。
abstractAn unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019.
Soil salinity is a major abiotic stress in Australian lentil-producing areas. It is therefore imperative to identify genetic variation for salt tolerance in order to develop lentil varieties suitable for saline soils. Conventional screening methods include the manual assessment of stress symptoms, which can be very laborious, time-consuming, and error-prone. Recent advances in image-based high-throughput phenotyping (HTP) technologies have provided unparalleled opportunities to screen plants for a range of stresses, such as salt toxicity. The current study describes the development and application of an HTP method for salt toxicity screening in lentils. In a pilot study, six lentil genotypes were evaluated to determine the optimal salt level and the growth stage for distinguishing lentil genotypes using red–green–blue (RGB) images on a LemnaTec Scanalyzer 3D phenomics platform. The optimized protocol was then applied to screen 276 accessions that were also assessed earlier in a conventional phenotypic screen. Detailed phenotypic trait assessments, including plant growth and green/non-green color pixels, were made and correlated to the conventional screen (r = 0.55; p < 0.0001). These findings demonstrated the improved efficacy of an image-based phenotyping approach that is high-throughput, efficient, and better suited to modern breeding programs.
Why it matches plant phenotyping methodsレンティルの塩害スクリーニング向け画像ベースHTP法を開発し、最適化・従来法との相関検証・大規模適用まで行っており、表現型取得法が研究の中心です。
abstractThe current study describes the development and application of an HTP method for salt toxicity screening in lentils.
ChickpeaLentilLaboratory / benchtopRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
A much better understanding of root system development and Measuring fine root growth is significant to the understanding of ecosystem structure and function and in predicting how ecosystems react to climate changeability. Their study, however, is hampered by their underground development, and characterizing complex root system architecture, hence, remains a challenge. Nowadays, a number of methods have been utilized to estimate root growth. Specialised, are costly for reproduced pot experiments, and aren’t promptly available for a nondestructive sampling of roots and soil. Here, Employing a Novel Cheap Rhizotron for Root Growth System Analyses on Plants. The root image securing using cheap technology Due to the inaccessibility of root systems, special methods are required to explore the dissemination and dynamics of roots. Utilizing this Rhizotron development, growth of the response the chickpea (Cicer arietinum L.) and Lentil (Lens culinaris Medik) under the hydroponic systems were explored. Significant differences in both architectural and morphological characteristics were observed among tested genotypes, especially for add up to root length, branch number, and particular root length and department thickness. It comes results illustrated that the framework was effective in screening root characteristics, permitting for rapid measurement of dimensional root architecture over time with negligible unsettling influence on plant growth and without destructive root sampling. The setup allows us to simultaneously characterize the root system and shoot development seedling stages. All components of the system are made from commodity components, locally available worldwide to facilitate the adoption of this affordable technology in low-income countries and detailed methods of construction allow researchers to construct and use similar Rhizotrons for experimental research.
Why it matches plant phenotyping methods安価なRhizotronと根画像取得系を開発し、根系形態・構造を非破壊かつ経時的に測定する方法を提示・評価しており、植物フェノタイピングが中心である。
abstractThe root image securing using cheap technology
Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches. The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets. However, to harness the power of phenomics technologies, more sophisticated data analysis methods are required. In this study, Aphanomyces root rot (ARR) resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue (RGB) images of roots. We created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease severity. Two approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible. The results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0.91 ± 0.004 (0.96 ± 0.005 resistant, 0.82 ± 0.009 partially resistant, and 0.92 ± 0.007 susceptible) compared to CNN with an accuracy of about 0.84 ± 0.009 (0.96 ± 0.008 resistant, 0.68 ± 0.026 partially resistant, and 0.83 ± 0.015 susceptible). The resistant class was accurately detected using both classification methods. However, partially resistant class was challenging to detect as the features (data) of the partially resistant class often overlapped with those of resistant and susceptible classes. Collectively, the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.
Why it matches plant phenotyping methodsレンティル根のRGB画像から根腐病の重症度・抵抗性を推定する画像解析および機械学習手法を開発・比較しており、植物表現型取得が中心的です。
abstractTwo approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible.
Reproduction assets foundThe paper's Data Availability statement points to a Zenodo deposit (DOI 10.5281/zenodo.4018168) containing the paper-specific lentil root rot image dataset (6,460 annotated RGB root images) used for the EN and CNN phenotyping analyses. No separate author analysis code URL is given; the R project URL is a generic tool, Dataset · publicData available at: https://doi.org/10.5281/zenodo.4018168 .Open asset ↗zenodo · 10.5281/zenodo.4018168lines:121-134Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users’ experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crops, including alfalfa, canola, lentil, wheat, chickpea, and soybean. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website ( https://zzlab.net/GridFree ).
Why it matches plant phenotyping methods穀粒画像からの分割・計数・形質測定を目的とするソフトウェアを開発し、既存ソフトウェアとの性能比較も行っており、植物フェノタイピング手法が研究の中心である。
abstractIn this study, we developed a software package, named GridFree, to overcome these challenges.
El objetivo de este trabajo fue evaluar 81 cultivares de lenteja usando caracteres morfológicos y características de semilla utilizando fenotipado digital. El Calibre (C) y los caracteres Luminosidad (L), las coordenadas de color a y b, y el índice de color (IC) fueron medidos y analizados con un software apropiado; también fueron medidos el rendimiento (Y), altura de planta (PH) y los días a floración (DF). Se encontraron diferencias altamente significativas entre cultivares para todos los caracteres y se obtuvieron elevados valores de heredabilidad en sentido amplio (H2 B) para las variables C (97%), IC (94%), a (93%) y L y b (83%) indicando la presencia de alta variabilidad genética. El fenotipado digital mostró ser una poderosa herramienta para la caracterización de germoplasma junto con la evaluación a campo de caracteres agronómicos. El Análisis de Componentes Principales y el análisis de agrupamiento permitieron la identificación de diferentes grupos de cultivares con características similares lo que conduce a un uso más eficiente del germoplasma disponible como cultivares comerciales o como parentales en un programa de mejoramiento genético. Entre estos grupos, el grupo 1 tuvo 32 cultivares con mayor C y el grupo 2 tuvo 21 cultivares con mayor Y.
Why it matches plant phenotyping methodsデジタルフェノタイピングを用いたレンズマメ遺伝資源の形態・種子形質評価が研究目的の中心であり、ソフトウェアによる形質測定と手法の有用性を明示している。
abstractEl objetivo de este trabajo fue evaluar 81 cultivares de lenteja usando caracteres morfológicos y características de semilla utilizando fenotipado digital.
Abstract Background Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を画像から自動取得・定量化する撮像システムと解析ソフトウェアを開発しており、植物フェノタイピング手法が研究の中心である。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper explicitly states that the phenoSEED analysis script is publicly available on GitLab and that the BELT-captured seed image datasets are available on the first author's Figshare page. Both are paper-specific, public, and actionable.Code · publicAt the time of publication, a version of the processing script is available at https://gitlab.com/usask-speclab/phenoseed .Open asset ↗usask-speclab/phenoseedlines:145-153Dataset · publicThe image datasets captured by BELT analysed for the sample study are available from https://figshare.com/authors/Keith_Halcro/8363580 . The phenoSEED script is available from https://gitlab.com/usask-speclab/phenoseed .Open asset ↗lines:173-192Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Extreme temperatures at critical developmental phases reduce grain yield. Combinations of sowing date and cultivar that favour faster development reduce the likelihood of heat stress but increase the risk of frost at critical phases. Current models are unable to predict pulse yield in response to frost and heat, hence our focus on phenology. Our aim was to model phenological variation with sowing date and cultivar for lentil and faba bean against the climatic patterns of frost and heat in 45 Australian locations that spanned 29 °S-41 °S, 11−340 m.a.s.l., and 1−423 km to the coast.For both crops, modelled mean and standard deviation of time to flowering were close to actuals and mean prediction error was below 5%. Comparison of actual and modelled time to flowering returned: r = 0.89 (n = 121, P 34 °C) probabilities between 1957 and 2018 were used to estimate the date of 10 % frost probability and the date of 30 % heat probability as the boundaries of a frost-heat risk window for the critical period. Out of the 45 locations, 12 were frost-free but with risk of heat, 7 were heat-free but with risk of frost, 3 were frost- and heat-free, and 23 featured a window defined by both frost and heat boundaries. Frost variables discriminated locations more strongly than heat variables. Geographical patterns in thermal regimes emerged that were associated with latitude, altitude and continentality.Realised warming between 1957 and 2018 advanced the time to 200 °Cd after flowering and shortened the critical period in most locations, particularly in early-sown crops. Comparisons of the probability curves of frost and heat between 1957–1985 and 1986–2018 showed, with few exceptions, an asymmetry between delayed late frost (up to 44 d) and earlier heat onset (up to 11 d), with a narrowing of the frost-heat risk window from 46 to 90 d for the period 1957–1985 to 34–64 d for 1986–2018.We identified a dominant role of frost as (i) the main discriminating factor among geographically distinct locations, (ii) the main source of variation of the frost-heat window, and (iii) a putatively increased risk factor with climate change. Adaptation to frost in the critical period for yield is important for pulses despite warming trends. Increased frost tolerance can directly improve yield and indirectly contribute to reduce risk of heat and drought later in the season.
Why it matches plant phenotyping methods開花期という植物形質の予測モデルを開発・検証し、実測値との比較で予測性能を評価しているため、単なる生物学的測定ではなく計算による形質推定手法が中心です。
abstractOur aim was to model phenological variation with sowing date and cultivar for lentil and faba bean against the climatic patterns of frost and heat in 45 Australian locations
Background Seed analysis is currently a bottleneck in phenotypic analysis of seeds. Measurements are slow and imprecise with potential for bias to be introduced when gathered manually. New acquisition tools were requested to improve phenotyping efficacy with an emphasis on obtaining colour information. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically and individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help breeders to develop more productive cultivars. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を自動取得・抽出する撮像システムと解析ソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper's phenoSEED image-analysis script (used for seed shape, size, colour, and clustering phenotyping) is explicitly stated to be publicly available on the authors' GitLab repository. The BELT image datasets themselves are only available on request, so they do not qualify as public assets.Code · publicde and hard-
ware plans available for academic and non-commercial
use. It is hoped that this will support the collection
of more easily cross-comparable data and encourage
other research groups to contribute to further de-
velopment of the project. At the time of publica-
tion, a version of the processing script is available at
https://gitlab.com/usask-speclab/phenoseed. Further
information on a comprehensive hardware and soft-
ware bundle will be made available as it is packaged
for distribution.
Methods
BELT System Design and Description
BELT (Figure 1) was designed around a 150 mm wide
conveyor with at white, low-gloss belt (Mini-Mover
Conveyors, Volcano CA) mounted on an audio-visual
carOpen asset ↗usask-speclab/phenoseedpdf-raw-page:8 lines:1-99Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 10 Sept 2026
Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets ( R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ | r | ≤ 0.24, P r | ≤ 0.50, P R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤ r ≤ 0.50, P r | ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.
Why it matches plant phenotyping methods画像・ハイパースペクトル・マルチスペクトル画像からレンズマメの根腐病重症度を定量推定し、視覚評価との相関、予測モデル、スペクトル指標を検証しており、植物表現型取得法が中心です。
abstractThis study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse
Lentil (Lens culinaris, Medik.) is an important legume crop, which often experience drought stress especially at the flowering and grain filling phenological stages. The availability of efficient and robust screening tools based on relevant non-destructive quantifiable traits would facilitate research on crop improvement for drought tolerance. The objective of this study was to evaluate the drought tolerance of 37 lentil genotypes using infrared thermal imaging (IRTI), drought tolerance parameters and multivariate data analysis. Potted plants were kept in a completely randomized design in a growth chamber with five replicates. Plants were subjected to three different drought treatments: 100, 50 and 20% of field capacity at the onset of reproductive period. The relative drought stress tolerance was determined based on a set of morpho-physiological parameters including non-destructive measures based on IRTI, such as: canopy temperature (Tc), canopy temperature depression (CTD) and crop water stress index (CWSI) during the growing period and destructive measures at harvest, such as: dry root-shoot ratio (RS ratio), relative water content (RWC) and harvest index (HI). The drought tolerance indices used were drought susceptibility index (DSI) and drought tolerance efficiency (DTE). Results showed that drought stress treatments significantly reduced the RWC, HI, CTD and DSI, whereas, the values of Tc, CWSI, RS ratio and DTE significantly increased for all the genotypes. The cluster analysis from morpho-physiological parameters clustered genotypes in three distinctive groups as per the level of drought stress tolerance. The genotypes with higher values of RS ratio, RWC, HI, DTE and CTD and lower values of DSI, Tc and CWSI were identified as drought-tolerant genotypes. Based on this preliminary screening, the genotypes Digger, Cumra, Indianhead, ILL 5588, ILL 6002 and ILL 5582 were identified as promising drought-tolerant genotypes. It can be concluded that the IRTI analysis is a high-throughput constructive screening tool along with RS ratio, RWC, HI and other drought tolerance indices to define the drought stress tolerance variability within lentil plants. These results provide a foundation for future research directed at identifying powerful drought assessment traits using rapid and non-destructive techniques, such as IRTI along with the yield traits, and understanding the biochemical and molecular mechanisms underlying lentil tolerance to drought stress.
Why it matches plant phenotyping methods赤外線熱画像を用いた非破壊的な植物形質（冠温度、CTD、CWSI）の取得と、干ばつ耐性スクリーニングへの適用が研究の中心である。
titleThe use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes.
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 · UnverifiedEurope PMC · checked 15 Sept 2026
The standard methodology for assessing seed size distribution of pulses is to sieve seeds into size classes, weighing each class and calculating a weighted mean of the sieve sizes (seed size index). A single unit measure of uniformity of size within a sample via sieving is not available, despite being an important trait in terms of appearance, ease of milling, and consistency in processing. This study investigated several different models for estimating the size variability within seed samples by using a variety of desi and kabuli chickpea, faba bean, lupin, lentil, and mungbean samples. Fitting a normal distribution to the frequency distribution and using the estimated variance parameter as a measure for seed size variability was found to be the most suitable method for attaining seed size and a single value of uniformity. Mean seed size (SSₙₒᵣₘ) and within‐sample size variability (SVₙₒᵣₘ) were unrelated variables, thus allowing selection of more uniform samples of any desired size in breeding programs. Sieve selection is discussed and needs to be appropriate for the samples under investigation. Examples of using the R software to calculate these measures and the R functions needed are available as supplementary files to facilitate the use of this proposed method. This method will be useful to pulse breeders and researchers and in the development of image analysis methods characterizing seed sizes of pulse samples and other grains.
Why it matches plant phenotyping methods種子サイズのばらつきと均一性を定量化する統計モデルおよびR実装を提案しており、植物形質の取得・抽出法が研究の中心である。
abstractA single unit measure of uniformity of size within a sample via sieving is not available