Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.
Why it matches plant phenotyping methodsサトウダイコンのリモートセンシングによるキャノピー形質、ストレス、根収量などの推定手法を体系的にレビューしており、センシング基盤とモデル化・検証課題が中心的に扱われている。
abstractThis review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management.
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Sugar beet is a major sugar crop in temperate regions and rapid, high-throughput, and accurate estimation of field phenotypes is essential for variety selection and production optimization. In this paper, ten commercial sugar beet varieties adapted to high latitudes are investigated using unmanned aerial vehicle (UAV) based red-green-blue (RGB), multispectral, and thermal infrared imaging across multiple growth stages. Canopy structural, texture, spectral, and temperature features are extracted, and three machine learning algorithms, random forest (RF), partial least squares (PLS), and support vector machine (SVM), are used to predict sugar content, root fresh weight, and yield. The results show that all three methods estimate sugar content well, with relative root mean square error (rRMSE) values below 11.0%, while RF and PLS outperform SVM. Multispectral features provide higher accuracy than RGB features, and multi-sensor feature combinations generally improve sugar content prediction compared with single-sensor inputs. For root fresh weight, SVM slightly outperforms RF and PLS, and RGB features are more informative than multispectral features. The integration of thermal infrared features does not notably improve RF or PLS models, but the combination of multispectral and thermal infrared features achieves the best SVM performance ( R2=0.58, RMSE = 75.3 g, and rRMSE = 23.7%). For yield estimation, RF achieves the highest accuracy, with rRMSE values ranging from 15.4% to 18.8%. Yield prediction accuracy increases as the time of image acquisition approaches harvest, and combining multi-temporal data from periods close to harvest further improves model performance. Overall, multi-sensor UAV data can effectively estimate sugar content, root fresh weight, and yield in sugar beet, providing a useful approach for phenotypic analysis, precision management, and variety selection.
Why it matches plant phenotyping methodsUAVマルチセンサー画像から糖含量、根 fresh weight、収量という植物形質を抽出・推定し、センサー特徴量と機械学習モデルの性能を比較しているため、表現型取得・推定手法が中心である。
abstractrapid, high-throughput, and accurate estimation of field phenotypes is essential for variety selection and production optimization.
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Improving sugar beet yield under variable environmental conditions requires a detailed understanding of the physiological mechanisms that drive yield formation. In sugar beet, canopy development determines resource capture, while radiation use efficiency (RUE) regulates the transformation efficiency of primary resources, and assimilate partitioning regulates the allocation of dry matter to the storage root. High-throughput phenotyping offers opportunities to quantify these physiological processes across diverse environments and genetic backgrounds, thereby identifying key traits for yield improvement. A scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (LAI), radiation interception efficiency (RIE), RUE, and harvest index (HI). Unmanned Aerial Vehicle (UAV)-derived multispectral imagery, combined with environmental records and harvest measurements, was used across more than 1300 field plots in Germany and Italy (2023–2024), covering three contrasting environments, two irrigation managements, and up to 171 genotypes. LAI estimation was calibrated and validated under different water regimes in northern Germany (mean absolute error, MAE = 0.30 m² m⁻²). Dynamic UAV-based LAI enabled continuous estimation of radiation interception and biomass accumulation. Total dry matter correlated strongly with cumulative effective (temperature-dependent) radiation interception (R² = 0.81), indicating a comparatively stable RUE across diverse conditions. Genotypic variation in yield formation was mainly driven by canopy-level processes: RIE accounted for 65 % of variation under water-limited conditions, while RUE accounted for 46 % under irrigation. Partitioning traits (HI and Sugar HI) contributed minimally in both irrigation managements. The results highlight the dominant role of canopy development and radiation use in sugar beet yield formation under contrasting environmental conditions. The proposed UAV-based framework provides a transferable, high-throughput approach to quantify physiological yield drivers in field settings. This enables targeted trait selection for breeding and facilitates integration of functional yield components into crop improvement strategies.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるLAI等の生理的形質推定パイプラインを構築・検証しており、フェノタイピング手法が研究の中心である。
abstractA scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (LAI), radiation interception efficiency (RIE), RUE, and harvest index (HI).
Cuscuta spp. are stem holoparasitic plants that use haustoria to draw water, photosynthates, and nutrients from host plant vascular systems. Cuscuta has served as a model plant for understanding plant-plant interactions and haustoria development of stem parasitic plants; however, studies of the three-dimensional (3D) internal host-parasite interface and interconnections are limited due to their unique structures developed inside host stems. This study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images. LATscan imaging of Cuscuta invading Arabidopsis (Arabidopsis thaliana) and beet (Beta vulgaris) stems yielded 3D renderings and detailed images of the anatomy of Cuscuta-host tissue interactions, including Cuscuta searching hyphae penetrating the host vasculature. Laser-tissue interactions generated color contrast and facilitated easy differentiation between Cuscuta and host tissues in 3D renderings and 2D images, demonstrating that LATscan technology can be an efficient tool to investigate the development and function of host-parasitic plant interactions.
Why it matches plant phenotyping methodsレーザーアブレーション断層撮影による植物組織の3D画像化・再構成が研究の中心であり、宿主—寄生植物組織の形態・構造状態を取得する手法を実証している。
abstractThis study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images.
Aboveground biomass (AGB) is a critical indicator for assessing crop growth status and productivity, yet accurately linking fine-scale ground measurements with coarse-resolution satellite imagery remains challenging. Here, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model. Multi-variety sugar beet cultivated across heterogeneous habitats was used as a case study. Results indicate that a power-law model effectively captures the allometric relationships between AGB, plant height, and the Dreg vegetation index in sugar beet, achieving high accuracy and strong transferability. Incorporating phenological information from Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) codes and a thermal index further enhanced model robustness across independent habitat trials, yielding coefficients of determination ( R 2 ) of 0.80 and 0.83. The SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage, thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps. These samples were used to develop satellite-based AGB estimation models for PlanetScope and Sentinel-2A imagery, achieving R 2 values of 0.83 and 0.73, respectively. This study provides a practical and scalable framework for field-to-satellite AGB upscaling, offering new insights for the scale conversion of multi-source data in agricultural remote sensing.
Why it matches plant phenotyping methodsUAV・衛星観測とSS-GMIサンプリング、モデル化を組み合わせ、サトウダイコンの地上部バイオマスという植物形質を推定する統合手法が研究の中心である。
abstractHere, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model.
LettuceSugar beetGreenhouseLeafPhysiological trait estimationBiomass / plant weightWater status / transpiration
Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of R2=0.697 and R2=0.794 for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops.
Why it matches plant phenotyping methods葉の電気応答を用いて葉面水分量と生体重量を推定する非侵襲センシング手法を開発・評価しており、植物表現型の取得が研究の中心である。
abstractthis work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Climate change is driving urgent demand for resilient crop varieties capable of withstanding extreme and changing conditions. Identifying resilient varieties requires systematic plant phenotyping research under controlled conditions, where dynamic environmental impacts can be studied. Current growth cabinets (GC) provide this capability but remain limited by high costs, static environments, and scalability. These limitations pose a challenge for climate change-based phenotyping research which requires large-scale trials under a variety of dynamic climate conditions. Presented is a microclimate-controlled smart growth cabinet (MCSGC) platform, addressing these limitations through four innovations. The first is dynamic microclimate simulation through programmable environmental ‘recipes’ reproducing real climactic variability. The second is interconnected scalable multi-cabinet for parallel experiments. The third is modular hardware able to reconfigure for different plant species, remaining cost-effective at <$10,000 AUD. The fourth is automated data collection and synchronisation of environmental and phenotypic measurements for Artificial Intelligence (AI) applications. Experimental validation confirmed precise climate control, broad crop compatibility, and high-throughput data generation. Environmental control stayed within ±2 °C for 97.42% while dynamically simulating Hobart, Australia, weather. The MCSGC provides an environment suitable for diverse crops (temperature 14.6–31.04 °C, and Photosynthetically Active Radiation (PAR) 0–1241 µmol·m−2·s−1). Multi-species cultivation validated the adaptability of the MCSGC across Cannabis sativa (544.1 mm growth over 34 days), Beta vulgaris (123.6 mm growth over 36 days), and Lactuca sativa (19-day cultivation). Without manual intervention the system generated 456 images and 164,160 sensor readings, creating datasets optimised for AI and digital twin applications. The MCSGC addresses critical limitations of existing systems, supporting advancements in plant phenotyping, crop improvement, and climate resilience research.
Why it matches plant phenotyping methods植物フェノタイピング用のスマート成長キャビネットを開発し、環境制御、拡張性、自動データ収集、作物適応性を実験的に検証しており、フェノタイプ取得基盤が研究の中心である。
abstractPresented is a microclimate-controlled smart growth cabinet (MCSGC) platform, addressing these limitations through four innovations.
Background Protoplasts, which are plant cells devoid of cell walls, are valuable tools in plant biotechnology. However, they are highly sensitive to mechanical and osmotic stress during isolation and early culture, often leading to significant loss of viability. Reliable and efficient methods for monitoring protoplast quality are essential for downstream applications. Results We applied impedance flow cytometry to assess the viability, cell size, and early division of freshly isolated protoplasts from Arabidopsis thaliana, Brassica napus, and Beta vulgaris. This label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations. Importantly, IFC-derived viability metrics strongly correlated with microcallus formation, demonstrating their predictive value for culture competence. Conclusions Impedance flow cytometry provides a robust, efficient and reproducible method for characterizing protoplast cultures. It enables rapid assessment of viability and growth potential, supporting quality control and optimization in plant cell culture workflows.
Why it matches plant phenotyping methodsインピーダンスフローサイトメトリーを用いて、植物プロトプラストの生存性・細胞サイズ・初期分裂を高速かつ高スループットに測定し、培養能力との相関で妥当性を検証しているため、植物表現型取得法が中心です。
abstractThis label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations.
This study analyzes the evolution of phenological (start-of-season, end-of-season, length-of-season, day of maximum-of-season) and productivity (small and large seasonal integrals) parameters for six major crop types in Czechia (winter cereals, spring cereals, winter rapeseed, fodder crops, sugar beetroot, and corn), using a 35-year Landsat time series (1986–2020). The leaf area index (LAI) was retrieved using an artificial neural network regression model trained on PROSAIL radiative transfer simulations and validated with extensive in situ measurements collected in 2017 and 2018 in the lowlands of Central Bohemia. The supervised classification of Landsat quarterly composites enabled the identification of crop spatial patterns for each growing season. Phenological and productivity indicators were then derived from LAI time series aggregated at the level of ten agro-climatic regions using the threshold approach. Changes in phenological and productivity parameters over the examined period were assessed through the linear least squares regression analysis and the significance of trends was tested. Results revealed significant negative trends in the end-ofseason and day of maximum-of-season for winter and spring cereals, winter rapeseed (up to –0.7 days/year), and fodder crops (up to –1.6 days/year), indicating an earlier maturation and harvest. Significant differences in trends in phenological and productivity parameters were observed between agro-climatic regions in more than 40% of cases, and the response was observed to be highly crop-specific. While the shift in harvest dates and the shortening of the season for corn and fodder crops were more pronounced in warmer regions, the shift in winter rapeseed phenology occurred more rapidly in colder regions. The findings underscore the relevance of crop type and regional climate in shaping phenological responses, offering a basis for future research and planning of agricultural adaptation strategies.
Why it matches plant phenotyping methodsLandsatからLAIを推定し、作物のフェノロジー・生産性形質を抽出するリモートセンシング手法を、PROSAIL/ANNモデルと現地測定で検証しており、形質取得ワークフローが主要な役割を担う。
abstractThe leaf area index (LAI) was retrieved using an artificial neural network regression model trained on PROSAIL radiative transfer simulations and validated with extensive in situ measurements collected in 2017 and 2018 in the lowlands of Central Bohemia.
Fractional vegetation cover (FVC) is an important indicator of crop growth and a key parameter in vegetation modeling. Unmanned aerial vehicles (UAVs) equipped with RGB cameras offer a practical and cost-effective alternative to labor-intensive field surveys and multispectral imaging for FVC estimation. In this study, 18 segmentation methods, derived from the combination of six vegetation indices and three thresholding algorithms, were applied to UAV imagery of sugar beet fields during the 2022 growing season. The methods were validated using ground truth data collected from 30 plots across four growth stages. Results indicated that the Excess Green (ExG), Green Leaf Index (GLI), and Red-Green-Blue Vegetation Index (RGBVI), when combined with Otsu and Ridler-Calvard (RC) thresholding, generally provided the most accurate segmentation of vegetation cover. In particular, ExG with Otsu and RC achieved the highest accuracy (NRMSE = 5.1%, R² = 0.96), whereas ExGB with the Two-Peaks method showed the weakest performance (NRMSE = 42.3%, R² = 0.34). Statistical analyses confirmed that ExG-based approaches demonstrated stronger correlations with field measurements compared to other methods. These findings suggest that ExG in combination with Otsu or RC can be considered a promising option for UAV-based estimation of sugar beet vegetation cover, although further validation under different environmental and crop conditions is recommended.
Why it matches plant phenotyping methodsUAV画像の植物領域分割手法を開発・比較し、地上真値で検証してサトウダイコンの植生被覆率を推定しており、表現型取得手法が研究の中心です。
abstract18 segmentation methods, derived from the combination of six vegetation indices and three thresholding algorithms, were applied to UAV imagery of sugar beet fields
Abstract Background and Aims: Portable X-ray fluorescence spectrometry (pXRF) has emerged as a robust analytical approach for elemental determination in plant tissues, enabling rapid, non-destructive, and reagent-free measurements. This study developed and validated an empirical calibration of pXRF for quantifying silicon (Si) in plants, using autoclave-induced digestion (AID) as the reference method. Methods A total of 374 samples from seven plant species (rice, maize, soybean, cowpea, sorghum, lettuce, and beet) were analyzed. Silicon concentrations obtained via AID ranged from 1.07 to 19.23 g kg − ¹ (mean = 4.48 g kg − ¹; coefficient of variation = 67%), reflecting substantial interspecific variability. Each sample was also analyzed by pXRF under optimized instrumental conditions, and a calibration model was constructed using 75% of the dataset to predict Si concentrations relative to AID values. Results The pXRF calibration exhibited a strong linear relationship with AID results (R² = 0.94; R = 0.97; p
Why it matches plant phenotyping methods植物組織中のケイ素濃度を測定するpXRF法の開発と、基準法との校正・検証が研究の中心であり、植物形質の測定法に該当する。
abstractThis study developed and validated an empirical calibration of pXRF for quantifying silicon (Si) in plants, using autoclave-induced digestion (AID) as the reference method.
Efficient acquisition of 3D plant structures is crucial for investigating growth mechanisms and phenotype analysis. Traditional 3D reconstruction methods exhibited significant limitations when faced with complex background interference, leading to low reconstruction efficiency and compromised result integrity. To address these challenges, a cross-scene 3D plant reconstruction framework P3DFusion was proposed with two key technological modules: (1) GSAM2 multi-view image processing method with Vision Foundation Models, which combines Grounding DINO and Segment Anything Model 2 (SAM2) to achieve high-precision plant segmentation under zero-shot conditions; (2) High-fidelity modeling based on 3D Gaussian splatting (3DGS) to generate high-quality, measurable meshes optimized for plant structural analysis. We evaluated P3DFusion using two datasets: Dataset1 (greenhouse-potted plants) and Dataset2 (open-field sugar beets). The P3DFusion exhibited significant improvements in reconstruction efficiency (SfM-Time reductions of 8.5 %/47.9 %, Total processing time reductions of 60.9 %/65.2 %) and quality metrics (SSIM increases of 12.9 %/19.8 %, PSNR increases of 11.8 %/13 % reaching 24.26 dB/24.75 dB, and LPIPS reductions of 70 %/85 %) for Dataset 1 and Dataset 2, respectively, compared to the original 3DGS. The P3DFusion outperforms InstantNGP (PSNR: 22.3 %/32.9 % increase) and COLMAP (PSNR: 231.4 %/266.1 % increase). Phenotype trait extraction from reconstructed models shows strong consistency with ground truth measurements (R² > 0.93). The proposed method not only provides an effective solution for cross-scene 3D plant reconstruction but also establishes a robust technical foundation for advanced plant phenotype research.
Why it matches plant phenotyping methods植物の3D再構成・セグメンテーションと形質抽出を中核とする手法開発および比較検証であり、植物フェノタイピング手法として明確に該当する。
abstractEfficient acquisition of 3D plant structures is crucial for investigating growth mechanisms and phenotype analysis.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. This study aims to disclose the benefit of incorporating dynamic spatio-temporal development of 3D parameters for automated crop genotype differentiation. A greenhouse experiment was conducted covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed over time, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and the noticeable higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial genotypic variations in the dynamic development of 3D morphological parameters could be demonstrated. The higher and more stable clustering performance using time series analysis underlines the importance of 4D data for plant genotype differentiation. Future work should focus on identifying important growth stages for data collection.
Why it matches plant phenotyping methods3Dモデルを用いた時系列植物形態計測、形態パラメータ抽出、クラスタリングによる遺伝型識別が研究の中心であり、4Dフェノタイピング手法の実質的な応用・評価に該当する。
abstractHigh-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted.
Abstract. Field-scale estimation of evapotranspiration (ET) using high-resolution data supports water conservation and yield optimization by enabling localized water use monitoring and early detection of crop stress. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023). Key inputs included thermal infrared (TIR) for land surface temperature (LST), multispectral (MS) and LiDAR data for canopy characterization, and a fusion of MS derived green area index (GAI) and LiDAR derived plant area index (PAI) to derive the fraction of green LAI (fg). Model outputs were validated against eddy covariance (EC) flux data using footprint modeling. Results showed high sensitivity to LST, emphasizing the importance of accurate thermal calibration. While both GAI and PAI provided comparable LAI inputs during peak growth, GAI better captured functional canopy decline during stress and senescence, especially in winter wheat, where dense structure led to cooling effects unrelated to transpiration. Dynamic fg improved ET accuracy across all crops, particularly under declining canopy function. Overall, TSEB-PT showed strong agreement with EC measurements (RMSE = 0.14 mm/h, R² = 0.49; R² = 0.81 excluding senescence). UAV TIR based ET maps also revealed early stress signals prior to changes in MS or LiDAR based metrics. This study demonstrates the value of integrating very-high-resolution UAV data with the TSEB-PT model for multi-crop and season-long ET monitoring and early stress detection.
Why it matches plant phenotyping methodsUAVの熱・マルチスペクトル・LiDARデータとTSEB-PTモデルにより、作物の蒸発散と水ストレスを推定する手法を中心に扱い、渦相関データで技術検証しているため、植物フェノタイピング手法研究に該当する。
abstractThis study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023).
Results: proved that synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as input to a Multi-Layer Perceptron, achieving 100% accuracy for overall stress detection, and 95.6% and 86.7% for water and nitrogen stress identification, respectively. A Stochastic Gradient Descent model, using only the green band, achieved 97.78% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using RGB bands and canopy cover achieved 86.7% for water stress, while RF with the excess green index reached 75.6% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency.
Why it matches plant phenotyping methodsRGB画像・画像処理・機械学習を用いてテンサイの水・窒素ストレスを識別する画像ベースの表現型推定手法を開発・比較しており、ストレス状態の取得・抽出が研究の中心です。
abstractsynchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress
Reproduction assets foundThe paper's Data Availability Statement explicitly states the supporting data (the sugar beet RGB image dataset and derived inputs used for stress-detection ML) are openly available in a HydroShare repository, matching an allowed URL. No code or model deposit is stated.Dataset · publicualization, SRH, MH, RCP.; supervision, SR MH, RCP,
MS.; project administration, SRH, MH, RCP, MS. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding
Data Availability Statement: The data that support the findings of this study are openly available in
http://www.hydroshare.org/resource/02b0a248417c4dd6b1b2d7a3c24bc5b6
Acknowledgments: We acknowledge the Writing Centre at Utah State University, USA, for assisting us in
improving the English in this paper, Imam Khomeini International University, Iran, for providing the supporting
resources, and Tehran Municipality, Iran, for their collaboration and support during thiOpen asset ↗hydroshare.org · 02b0a248417c4dd6b1b2d7a3c24bc5b6pdf-raw-page:15 lines:1-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods4Dの3Dモデルから植物形態形質を時系列抽出し、遺伝型識別のためのクラスタリング手法を評価することが研究の中心である。
titleSpatio-Temporal 4D Phenotyping for Automated Morphological Genotype Differentiation of Sugar Beet
Background Agricultural production is crucial for nutrition, but it frequently faces challenges such as decreased yield, quality, and overall output due to the adverse effects of diseases and pests. Remote sensing technologies have emerged as valuable tools for diagnosing and monitoring these issues. They offer significant advantages over traditional methods, which are often time-consuming and limited in sampling. High-resolution images from drones and satellites provide fast and accurate solutions for detecting and diagnosing crops' health and identifying pests and diseases affecting them. Methods The research focused on the early detection of Cercospora leaf spot ( Cercospora beticola Sacc .) and powdery mildew ( Erysiphe betae (Vaňha) Weltzien ), which cause significant economic losses in sugar beet before visible symptoms emerge. The study was accomplished by capturing images of uncrewed aerial vehicle (UAV) in field conditions. To effectively evaluate different detection methods in agricultural contexts, the study targeted two key areas: (1) monitoring Cercospora in fields without pesticide application, utilizing the Metos climate station early warning system alongside UAV-based image analysis, and (2) monitoring powdery mildew, which involved visual disease detection and targeted spraying based on UAV image processing. Trial plots were established for this purpose, with six replications for each method. Results UAV-based images show that Normalized Difference Vegetation Index values in leaves decreased before disease onset. This change is an important warning sign for the emergence of the disease. Additionally, the study demonstrated that early detection of diseases is possible using K-nearest neighbors and logistic regression algorithms, exhibiting high discrimination and predictive accuracy.
Why it matches plant phenotyping methodsUAV画像と機械学習により、砂糖大根の病害状態を発症前に推定する方法が研究の中心であり、植物の病害表現型を直接評価している。
titleEarly detection of Cercospora beticola and powdery mildew diseases in sugar beet using uncrewed aerial vehicle-based remote sensing and machine learning.
Reproduction assets foundThe article's Data Availability section explicitly deposits the authors' classification code on GitHub and the paper-specific Sugar Beet Dataset (UAV/phenotyping measurements) on Zenodo, both with public URLs.Code · publicThe data and code are available at GitHub and Zenodo:Open asset ↗lines:960-1125Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Plant phenotyping, which involves measuring and analysing plant traits, has seen significant advances in recent years by integrating autonomous platforms and sophisticated sensor systems. In contrast to traditional methods, modern unmanned ground vehicles (UGVs) provide robust and accurate phenotyping capabilities by enabling close, detailed and continuous monitoring of crops under different environmental conditions. This study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques. The platform incorporates red, green, and blue channel (RGB), hyperspectral from visible light (VIS) and near-infrared light (NIR) spectrum, thermal sensors, and a three-dimensional (3D) light detection and ranging (LiDAR), all subjected to extensive calibration to ensure precise temporal and spatial alignment. Intrinsic calibration was applied, including correcting the spectral signatures of VIS and NIR. Additionally, timestamps were synchronised using the VIS sensor as the primary reference due to its central position and higher data acquisition frequency. Homography matrices were computed using checkerboard patterns for geometric alignment across sensors, and motion corrections accounted for UGV movement and ground sample distance. LiDAR point clouds were transformed into depth-maps (DMs) using radial basis function interpolation, enriching the spatial data for further analysis. The co-registered and synchronised MSP was tested for detecting Cercospora leaf spot (CLS) in sugar beet plants during a field experiment. Two models were implemented: (1) a soil and plant segmentation model based on the DeepLabV3+ architecture, achieving an F1-score of 0.85 and an accuracy of 0.95, and (2) a CLS severity scoring model using a custom convolutional neural network (CNN). The severity model, leveraging NIR and DM channels, achieved an F1-score of 0.7066, accuracy of 0.7104, and recall of 0.7167, with NIR wavelengths between 814 and 851 contributing significantly to performance. These results highlight the importance of accurate data fusion and synchronisation in multi-sensor systems for plant phenotyping. Moreover, the study demonstrates the potential of integrating multiple sensors on a UGV for precision agriculture, thereby enhancing MSP effectiveness in crop monitoring and disease detection. • Multi-sensor platform supports detailed plant phenotyping using data fusion. • Effective synchronization ensured accurate temporal alignment across sensors. • RGB, hyperspectral, thermal, and LiDAR sensors calibrated for accurate alignment. • Soil-plant and segmentation Cercospora leaf spot disease severity estimated using neural network. • NIR and depth map sensor fusion enhance plant phenotyping accuracy for Cercospora leaf spot disease severity.
Why it matches plant phenotyping methodsマルチセンサーUGVプラットフォームの構成、校正、同期、データ融合を開発・検証し、植物病害の重症度という表現型を推定しているため、方法が研究の中心である。
abstractThis study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques.
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case of disease severity scoring for CLS in sugar beet. With concepts of DLDL, special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems, as we show by a pretraining on environmental metadata. Furthermore, we perform several comparison experiments with state-of-the-art methods and models to constitute our modeling and preprocessing choices.
Why it matches plant phenotyping methods植物病害重症度をUAV画像から自動推定するVision Transformerベースの手法を開発・比較評価しており、植物表現型取得が中心である。
abstractThis work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case of disease severity scoring for CLS in sugar beet.
Reproduction assets foundThe paper's Data Availability statement explicitly says the data and code supporting the findings are publicly available on GitHub at the authors' repository URL, which is an allowed URL. This qualifies as a paper-specific public asset containing the authors' analysis code and the UAV multispectral plant image dataset.Code · publicData Availability: The data and code supporting the findings in this paper are available at GitHub ( https://github.com/mrcgndr/disease_severity_prediction/ ).Open asset ↗https://github.com/mrcgndr/disease_severity_prediction/lines:154-190Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Continuous information on plant traits such as plant height, leaf area index (LAI), and above-ground biomass (AGB) is important in the study of plant growth and such information can help farmers achieve better yields while reducing agricultural inputs, e.g. through more efficient water use. Knowledge on plant traits is also key to further test and develop crop and land surface models. Cosmic-ray neutron sensors (CRNS) have primarily been used to determine soil moisture. Recently, Jakobi et al. (2022) found that thermal neutrons can be used to monitor aboveground biomass (ABG) and that the variations in measured thermal neutron intensity may also depend on the vegetation biomass and structure. However, different soil properties of the test sites may have influenced the results (e.g. related to differences in soil chemistry). In this follow-up study, a single agricultural field was investigated over a long measurement period (2015-2023) to avoid site-specific effects on the CRNS measurements. This new dataset contains different crop rotations with repetitions of the same crop and continuous measurements of plant height instead of sporadic biomass measurements. Based on this data, we developed regression models that take into account plant structure to predict traits (i.e. plant height and LAI) from observed thermal neutron intensity.The annual regression models for plant height provided generally high R²-values (0.86 on average), with the highest values found for potato and winter wheat. An aggregation by crop type of the different seasons resulted in a slight reduction of the R² to 0.84 for winter wheat (3 seasons), 0.68 for sugar beet (2 seasons), and 0.75 for potato (2 seasons). The slope values of these regressions were distinctly different, thus supporting the assumption that the relationship between plant traits and thermal neutron intensity depends on vegetation structure. The root mean square error (RMSE) of the plant height predicted with thermal neutrons were 12 cm for winter wheat and 14 cm for both sugar beet and potato. In addition, we tested a prediction of LAI based on thermal neutrons. For this, we used a regression model that predicts LAI based on plant height (R²: 0.78). Using this model, we were able to predict the LAI for a period of 5 years with LAI observation data with an RMSE of 1.23 m/m, which is still within the uncertainty range of radiation-based LAI methods (Fang et al., 2019). Independent validation was performed also against spatio-temporal LiDAR-based plant height and multispectral-based LAI measurements, each averaged for the CRNS footprint area. Our results demonstrate the potential of cosmic-ray neutron sensing for continuous monitoring of plant traits at the field scale. LiteratureFang, H., F. Baret, S. Plummer and G. Schaepman‐Strub (2019): An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics 57(3): 739-799. DOI: 10.1002/hyp.11274Jakobi, J., J.A. Huisman, H. Fuchs, H. Vereecken and H. Bogena (2022): Potential of Thermal Neutrons to Correct Cosmic-Ray Neutron Soil Moisture Content Measurements for Dynamic Biomass Effects. Water Resour. Res. 58(8): e2022WR031972. DOI: 10.1029/2022WR031972
Why it matches plant phenotyping methods熱中性子センサーを用いて草丈・LAI・地上部バイオマスを連続推定する手法を開発し、回帰モデルとLiDAR・マルチスペクトル測定で検証しており、植物形質の取得手法が中心である。
abstractBased on this data, we developed regression models that take into account plant structure to predict traits (i.e. plant height and LAI) from observed thermal neutron intensity.
Despite their vital role for agricultural management practices and plant breeding experiments, it is still challenging to characterize plant roots non-invasively in their natural environment. A promising new method for plant root characterization is the spectral electrical impedance tomography (sEIT) method, which is able to image the conductive and polarizable subsurface properties with high spatio-temporal resolution. Electrical polarization signatures have been shown to be sensitive to root structure and activity, although superimposed soil signatures complicate the interpretation. Recent studies have demonstrated that impedance measurements can be used to estimate root traits under laboratory conditions, especially in hydroponic experiments. However, field studies using sEIT on plant-root systems are still scarce.In this study we present a field dataset of multi-frequency sEIT measurements on sugar beet and maize. Three different growth stages were measured during a whole growing season. We performed complex resistivity inversions for each measurement frequency, and subsequently analyzed the spatially resolved spectral response using a Debye decomposition analysis. Characteristic relaxation times, extracted from the spectral analysis, serve as proxies indicating the length scales of the observed polarization processes. We find that the physiologically different plant root systems cause distinct polarization responses in the low-frequency range. While both root systems exhibit an increasing polarization response towards higher frequencies, sugar beet develops an additional low-frequency polarization peak near 10 Hz later in the season, corrseponding with increasing size of the sugar beets. We attribute this peak to the polarization of root structures associated with the macroscopic dimensions of the beet roots, and demonstrate this link through the correlation of the retrieved mean relaxation time at the sugar beet positions with the square of the respective maximum beet diameter. Additionally, we evaluate the intrinsic spectral form of the polarization signatures extracted from the maize root area, and find a moderate correlation with the fresh biomass.In conclusion, our results highlight that sEIT can be used in the field for plant root trait estimations, but structurally differing plants require different analysis procedures to extract root information. Additionally, environmental factors, like a varying soil composition or soil water content, have a strong influence on the measured impedance signal, and can make precise root trait estimation difficult.
Why it matches plant phenotyping methods植物根系の形態・バイオマス形質を非破壊推定するsEIT測定・解析を中心に、圃場データセットと環境要因の技術評価を行っているため。
abstractA promising new method for plant root characterization is the spectral electrical impedance tomography (sEIT) method
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As a use case, time series HSI measurements of sugar beet plants infested with Cercospora leaf spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis. RESULTS: The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r>0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination. CONCLUSION: The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI.
Why it matches plant phenotyping methods植物フェノタイピング用ハイパースペクトル撮像システムの品質保証パイプラインを開発・検証し、病害進展の表現型推定にも適用しており、取得・解析手法が中心的である。
abstractThis research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping.
BarleySugar beetWheatLaboratory / benchtopMicroscopyLiDAR / point cloudCell / cellular structureLeafSegmentationVisualization / data management
The ability of laser scanning confocal microscopy to generate high-contrast 2D and 3D images has become essential in studying plant-fungal interactions. Techniques such as visualization of native fluorescence, fluorescent protein tagging of microbes, green fluorescent protein (GFP)/red fluorescent protein (RFP)-fusion proteins, and fluorescent labeling of plant and fungal proteins have been widely used to aid in these investigations. Use of fluorescent proteins has several pitfalls, including variability of expression in planta and the requirement of gene transformation. Here, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline that uses propidium iodide (PI), which stains RNA and DNA, and wheat germ agglutinin labeled with fluorescein isothiocyanate (WGA-FITC), which stains chitin, to visualize fungal colonization of plants. This pipeline relies on the use of KOH to remove the cutin layer of the leaf, increasing its permeability, allowing the different stains to penetrate and effectively bind to their targets, resulting in a consistent visualization of cellular structures. To expand the utility of this pipeline, we used the staining techniques in conjunction with machine learning to analyze fungal biomass through volume analysis, as well as quantifying nuclear breakdown, an early indicator of programmed cell death (PCD). This pipeline is simple to use, robust, consistent across host and fungal species, and can be applied to most plant-fungal interactions. Therefore, this pipeline can be used to characterize model systems as well as nonmodel interactions where transformation is not routine. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.
Why it matches plant phenotyping methods植物-真菌相互作用を可視化し、真菌バイオマスと核崩壊を画像から定量する染色・共焦点顕微鏡・機械学習パイプラインの開発と評価が中心である。
abstractHere, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline
LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture.
Why it matches plant phenotyping methodsLiDARによる植物形質（草丈、樹冠面積、株間など）の取得・評価方法、試験手順、精度ベンチマークを中心にレビューしており、植物フェノタイピング手法が主要テーマである。
abstractThe objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments.
This study investigates the potential of high‐resolution (<0.5 cm/pixel) aerial imagery and convolutional neural networks (CNNs) for disease incidence scoring in sugar beet, focusing on two important aphid‐transmitted viruses, beet mild yellowing virus (BMYV) and beet chlorosis virus (BChV). The development of tolerant sugar beet cultivars is imperative in the context of increased disease management concerns due to the ban on neonicotinoids in the European Union. However, traditional methods of disease phenotyping, which rely on visual assessment by human experts, are both time‐consuming and subjective. Therefore, this study assessed whether aerial multispectral and RGB images could be harnessed to perform automated disease ratings comparable to those performed by trained experts. To this end, two variety trials were conducted in 2021 and 2022. The 2021 dataset was used to train and validate a CNN model on five cultivars, while the 2022 dataset was used to test the model on two cultivars different from those used in 2021. Additionally, this study tests the use of transformed features instead of raw spectral bands to improve the generalization of CNN models. The results showed that the best CNN model was the one trained for BMYV on RGB images using transformed features instead of conventional raw bands. This model achieved a root mean square error score of 11.45% between the model and expert scores. These results indicate that while high‐resolution aerial imagery and CNNs hold great promise, a complete replacement of human expertise is not yet possible. This research contributes to an innovative approach to disease phenotyping, driving advances in sustainable agriculture and crop breeding.
Why it matches plant phenotyping methods航空画像とCNNを用いてサトウダイコンのウイルス病発生率を自動推定し、専門家評価との検証・汎化性能評価を行うことが中心の研究であるため。
abstracttraditional methods of disease phenotyping, which rely on visual assessment by human experts, are both time‐consuming and subjective.
Members of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield and extractable sugar. Improving host plant resistance against Fusarium-induced diseases, like Fusarium yellows, represents an important long-term breeding target in sugar beet breeding programmes. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Version 1 and the improved version 2 SAD showed no statistical differences in Lin's concordance correlation coefficient (LCC) values, which was used to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, pc = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine-learning models that used pixel classification to determine disease score were tested for congruency to human estimates in SAD version 2. Root mean square error was lowest compared to the 'true' values for the unweighted model and a model where necrotic tissue was given a 2× weight (12.4 and 12.6, respectively). The creation of this SAD enables breeding programmes to make consistent, accurate disease ratings regardless of personnel's previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.
Why it matches plant phenotyping methodsフザリウム萎黄病の植物症状の重症度を画像ベースで定量評価する標準面積図と画素分類モデルを開発・検証しており、表現型取得手法が研究の中心です。
abstractIn this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD).
The type of crops plays a critical role in determining the canopy light interception and is a decisive factor for yield. Thus, it is of significant importance to have a comprehensive understanding of the similarities and differences in plant type for crop improvement. In this study, the Structure-from-Motion in conjunction with multi-view stereo (SfM-MVS) method was employed to capture multi-angle images of 132 sugar beet varieties at two growth stages, from which three-dimensional(3D) point clouds were reconstructed for all individual sugar beets. Nine plant phenotypic traits were extracted based on the point clouds, and their correlations and heritability were calculated. An unsupervised machine learning approach was utilized to classify all varieties based on their plant type, and the characteristics of different types were statistically analyzed. Subsequently, a variety of different canopies were simulated, and a ray-tracing software was used to simulate light interception of the day. The results revealed that sugar beet plants could be roughly classified into five distinct types with significant differences of the structure. The coefficient of variation of phenotypic parameters for all varieties was 33.2 % in July and decreased to 26.7 % in August. The heritability similarly declined from 0.82 to 0.50, indicating that the structure of the sugar beet plants was exacerbated by environmental influences as the growing season progressed. The light interception results showed that intercropping with different plant types had different effects on light interception, with differences in light interception of up to 1000 W/h across the canopy in July, but this effect was not always favorable, and a decrease in the total amount of light interception also occurred in intercropping with different plant types compared to monocropping.
Why it matches plant phenotyping methodsSfM-MVSによる3D再構築から9つの植物表現型形質を抽出する手法が研究の中心であり、多数品種への実質的な適用と形質解析を行っている。
abstractthe Structure-from-Motion in conjunction with multi-view stereo (SfM-MVS) method was employed to capture multi-angle images of 132 sugar beet varieties at two growth stages, from which three-dimensional(3D) point clouds were reconstructed for all individual sugar beets.
Field robots are an important tool when improving the efficiency and decreasing the climatic impact of food production. Although several commercial field robots are available, the advantages, limitations, and optimal utilization methods of this technology are still not well understood due to its novelty. This study aims to evaluate the performance of a commercial field robot for seeding and weeding tasks. The evaluation was carried out in a 2-hectare sugar beet field. The robot’s performance was assessed by counting plants and weeds using image processing. The YOLOv8 model was trained to detect sugar beets and weeds. The plant and weed densities were compared on a robotically weeded area of the field, a chemically weeded control area, and an untreated control area. The average weed density on the robotically treated area was about two times lower than that on the untreated area and about three times higher than on the chemically treated area. The testing robot in the specific testing environment and mode showed intermediate results, weeding a majority of the weeds between the rows; however, it left the most harmful weeds close to the plants. Software for robot performance assessment can be used for monitoring robot performance and plant conditions several times during plant growth according to the weeding frequency.
Why it matches plant phenotyping methodsYOLOv8画像処理で作物・雑草を検出し、植物密度を定量化する手法とロボット性能評価ソフトが研究の中心であり、植物状態の反復モニタリングに用いるため。
abstractThe robot’s performance was assessed by counting plants and weeds using image processing.
Reproduction assets foundThe paper's field image dataset (2272 sugar beet/weed images) is openly available on Zenodo, and the authors' Matlab robot-performance analysis software is publicly hosted on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe dataset consisting of 2272 images collected in this study is available in open
access (https://zenodo.org/records/10716274, accessed 18 September 2024).Open asset ↗zenodo · 10716274pdf-page:3 lines:1-146Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 7 Sept 2026
Magnetic resonance imaging (MRI) is a versatile technique in the biomedical field, but its application to the study of plant metabolism in vivo remains challenging because of magnetic susceptibility problems. In this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI. This method enables noninvasive access to the metabolism of sugars and amino acids in complex sink organs (seeds, fruits, taproots, and tubers) of major crops (maize, barley, pea, potato, sugar beet, and sugarcane). Because of its high signal detection sensitivity and low susceptibility to magnetic field inhomogeneities, CEST analyzes heterogeneous botanical samples inaccessible to conventional magnetic resonance spectroscopy. The approach provides unprecedented insight into the dynamics and distribution of sugars and amino acids in intact, living plant tissue. The method is validated by chemical shift imaging, infrared microscopy, chromatography, and mass spectrometry. CEST is a versatile and promising tool for studying plant metabolism in vivo, with many applications in plant science and crop improvement.
Why it matches plant phenotyping methods植物の生体内代謝を非侵襲的に測定するCEST-MRI法を確立し、複数手法で検証しており、植物表現型取得法が研究の中心である。
abstractIn this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI.
Cercospora leaf spot (CLS) disease, triggered by the fungus Cercospora beticola, represents the most severe foliar disease affecting sugar beets globally. The significant vertical heterogeneity of the plant canopy makes traditional 2D spectral imaging insufficient to accurately determining the CLS disease ratio. Integrating 3D and spectral imaging from dual sensors to form a plant spectral point cloud faces challenges due to alignment issues and high costs. An approach combining multi-view spectral images with the Structure from Motion (SfM) algorithm was introduced to generate a detailed multispectral 3D point cloud of plant structure. This technique was employed to assess the CLS disease ratio and its spatial heterogeneity. Specifically, a discriminant-based model was developed to differentiate healthy and diseased leaves using various ratio-based or normalized vegetation indices at the leaf scale. This model was then utilized to extract 3D CLS point clouds from the multispectral point clouds reconstructed by the new method at both plant and plot levels. Three-dimensional spatial heterogeneity analysis explored the vertical and horizontal distribution patterns of CLS in sugar beets. The findings revealed that disease levels determined by the 3D CLS model surpassed those of expert visual assessments (75 % vs. 58.3 %). The estimated disease ratio closely matched the measured values (RMSE = 8.4 %). Additionally, plot-scale CLS distribution maps aligned well with RGB image distributions. The analysis indicated that CLS initially spread from lower leaves upwards and displayed a pattern moving from the periphery to the interior. The introduced method offers a cost-effective, convenient alternative for generating detailed multispectral 3D point clouds. This study emphasizes the potential of spectral point clouds in monitoring plant canopy health and physiological activities.
Why it matches plant phenotyping methodsマルチビュー分光画像とSfMを融合して3D植物点群を構築し、葉の病害状態と病害率を推定する方法が研究の中心であり、技術精度も評価している。
abstractAn approach combining multi-view spectral images with the Structure from Motion (SfM) algorithm was introduced to generate a detailed multispectral 3D point cloud of plant structure.
Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality NDVI time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality NDVI time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original NDVI time series and producing high-quality NDVI profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an RMSE of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality NDVI time series data could produce timely and spatially explicit phenological information at the field and crop levels.
Why it matches plant phenotyping methods衛星時系列から作物のフェノロジー（SOS・EOS等）を抽出するNDVI再構成法と閾値最適化法を開発し、独立した圃場観測で検証しており、植物状態の取得手法が研究の中心である。
abstractwe introduce a new method, termed UE-WS, to reconstruct high-quality NDVI time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter.
An open-source software for field-based plant phenotyping, Precision Plots Analyzer (PREPs), was developed using Window.NET. The software runs on 64-bit Windows computers. This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model (DSM) images generated by Structure-from-Motion/Multi-View-Stereo (SfM-MVS) tools. Moreover, there is no need to acquire skills in geographical information system (GIS) or programming languages for image analysis. Three use cases illustrated the software's functionality. The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle (UAV), where differences among varieties were detected through estimates of crop height, coverage, and volume index. Second, mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits. A strong correlation was observed between the manually measured crop height and UAV-estimated crop height. Finally, using a multicamera array attached to a tractor, the height, coverage, and volume index of the 3 potato varieties were precisely estimated. PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.
Why it matches plant phenotyping methods植物形質抽出ソフトウェアの開発と複数センサーによる検証が中心であり、圃場画像から草丈・被覆率・体積指標を推定する再利用可能な表現型解析手法である。
abstractAn open-source software for field-based plant phenotyping, Precision Plots Analyzer (PREPs), was developed using Window.NET.
Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.
Why it matches plant phenotyping methodsSentinel-1レーダー特徴量とInSARコヒーレンスを用いて作物のフェノロジーを追跡し、複数年・軌道・特徴量間で性能を比較しており、植物状態の取得・推定方法が中心である。
abstractThis study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits.
Data-driven techniques could be used to enhance decision-making capacity of breeders and farmers. We used an RGB camera on an unmanned aerial vehicle (UAV) to collect time series data on sugar beet canopy coverage (CC) and canopy height (CH) from small-plot breeding fields including 20 genotypes per season over 3 seasons. Digital orthomosaic and digital surface models were created from each flight and were converted to individual plot-level data. Plot-level data including CC and CH were calculated on a per-plot basis. A multiple regression model was fitted, which predicts root weight (RW) ( r = 0.89, 0.89, and 0.92 in the 3 seasons, respectively) and sugar content (SC) ( r = 0.79, 0.83, and 0.77 in the 3 seasons, respectively) using individual time point CC and CH data. Individual CC and CH values in late June tended to be strong predictors of RW and SC, suggesting that early season growth is critical for obtaining high RW and SC. Coefficient of parentage was not a strong factor influencing SC. Integrals of CC and CH time series data were calculated for genetic analysis purposes since they are more stable over multiple growing seasons. Calculations of general combining ability and specific combining ability in F1 offspring demonstrate how growth curve quantification can be used in diallel cross analysis and yield prediction. Our simple yet robust solution demonstrates how state-of-the-art remote sensing tools and basic analysis methods can be applied to small-plot breeder fields for selection purpose.
Why it matches plant phenotyping methodsUAV画像から圃場プロット単位の樹冠被覆率・高さを抽出し、時系列成長量を用いて収量を予測するフェノタイピング手法の実質的な適用である。
abstractWe used an RGB camera on an unmanned aerial vehicle (UAV) to collect time series data on sugar beet canopy coverage (CC) and canopy height (CH)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Background: This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As an usecase, time series HSI measurements of sugar beet plants infested with Cercospora Leaf Spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis. Results: The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r \(>\) 0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can causes a distortion of the spectral response at 677 \(nm\) and 752$nm$. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination. Conclusion: The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI.
Why it matches plant phenotyping methods植物フェノタイピング用ハイパースペクトル撮像システムの品質保証パイプラインを開発・評価し、植物病害重症度推定への応用も検証しているため、方法が中心的である。
abstractThis research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、サトウダイコンの葉緑素関連形質であるキャノピーSPADを推定・検証する手法が研究の中心である。
abstractThese images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets.
Cercospora leaf spot (CLS) disease, triggered by the fungus Cercospora beticola, represents the most severe foliar disease affecting sugar beets globally. The significant vertical heterogeneity of the plant canopy makes traditional 2D spectral imaging insufficient to accurately determining the CLS disease ratio. Integrating 3D and spectral imaging from dual sensors to form a plant spectral point cloud faces challenges due to alignment issues and high costs. An approach combining multi-view spectral images with the Structure from Motion (SfM) algorithm was introduced to generate a detailed multispectral 3D point cloud of plant structure. This technique was employed to assess the CLS disease ratio and its spatial heterogeneity. Specifically, a discriminant-based model was developed to differentiate healthy and diseased leaves using various ratio-based or normalized vegetation indices at the leaf scale. This model was then utilized to extract 3D CLS point clouds from the multispectral point clouds reconstructed by the new method at both plant and plot levels. Three-dimensional spatial heterogeneity analysis explored the vertical and horizontal distribution patterns of CLS in sugar beets. The findings revealed that disease levels determined by the 3D CLS model surpassed those of expert visual assessments (75 % vs. 58.3 %). The estimated disease ratio closely matched the measured values (RMSE = 8.4 %). Additionally, plot-scale CLS distribution maps aligned well with RGB image distributions. The analysis indicated that CLS initially spread from lower leaves upwards and displayed a pattern moving from the periphery to the interior. The introduced method offers a cost-effective, convenient alternative for generating detailed multispectral 3D point clouds. This study emphasizes the potential of spectral point clouds in monitoring plant canopy health and physiological activities.
Why it matches plant phenotyping methodsマルチビュー分光画像とSfMを融合して植物の3Dスペクトル点群を生成し、テンサイ葉の病害比率を抽出・検証する手法が研究の中心であるため。
abstractAn approach combining multi-view spectral images with the Structure from Motion (SfM) algorithm was introduced to generate a detailed multispectral 3D point cloud of plant structure.
Abstract The study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting. In this approach, sub-nanosecond laser pulses with a repetition rate of 10 MHz are applied to excite the sample, followed by the analysis of arrival times of the emitted fluorescence photons. Photon statistics are generated by iteratively fitting the sum of two exponential functions. The tool was tested on both plastid and in vivo leaf samples of Savoy cabbage ( Brassica oleracea var. sabauda) with 3–4 subsequent leaves giving a complete sample coverage starting from the outermost. The Chl-a fluorescence lifetime exhibited a gradual increase in both the isolated plastid suspensions and the in vivo leaf samples towards the innermost leaf layers explained by an increase of natural absence of light (etiolation syndrome). Furthermore, cadmium stress and iron deficiency were investigated on treated sugar beet ( Beta vulgaris ) samples in vivo using TCSPS measurements. The reduced fluorescence quenching resulted in an increased fluorescence lifetime. Finally, a long-term (10 week) testing of the setup was carried out on Chl-retaining resurrection Haberlea rhodopensis plants protecting themselves by an elevated non-photochemical quenching yielding a decrease of fluorescence lifetime during their desiccation.
Why it matches plant phenotyping methods植物の生体クロロフィル蛍光寿命を測定する装置を開発し、複数の植物試料・ストレス条件・長期試験で検証しており、植物生理状態の取得法が研究の中心である。
abstractThe study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting.
Background The detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article. Results 2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating. Conclusion This work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.
Why it matches plant phenotyping methods種子内部欠陥という植物器官の状態を2D X線画像から自動抽出する深層学習パイプラインを開発・検証しており、表現型取得手法が中心である。
abstractThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging.
ABSTRACT Members of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield, and extractable sugar. Improving host plant resistance against Fusarium -induced diseases, like Fusarium yellows, represents an important long-term breeding target in sugar beet breeding programs. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Comparing both the pilot and improved version showed no statistical differences in Lin’s Concordance Correlation Coefficient (LCC) values to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, ρ c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine learning models which used pixel classification to determine disease score, were tested for congruency to human estimates in version 2. Root mean square error was lowest compared to the “true” values for the unweighted model and a model where necrotic tissue was given a 2x weight (12.4 and 12.6, respectively). The creation of this standard area diagram enables breeding programs to make consistent, accurate disease ratings regardless of personnel’s’ previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.
Why it matches plant phenotyping methodsフザリウム萎黄病の植物症状を対象に、標準面積図と画像ピクセル分類による病害重症度評価法を開発・検証しており、植物フェノタイピング手法が中心である。
abstractIn this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD).
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' scripts, plant images, and excel sheets (including the RGB classifier training data) on a public GitHub repository, which is paper-specific and actionable.Code · publiceen 0-20%.
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The authors would like to acknowledge the raters’ participation in this study. Funding
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provided by USDA-ARS CRIS projects 3012-21220-011-000-D and 5050-21220-017-000-D.
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Scripts, images and excel sheets are available on the following Github page:
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https://github.com/oetodd/Fusarium_standard_area_diagram_2024
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was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105
The copyright holder for this preprint (which
this version posted April 28, 2024.
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https://doi.org/10Open asset ↗https://github.com/oetodd/Fusarium_standard_area_diagram_2024pdf-raw-page:13 lines:1-50Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani , can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.
Why it matches plant phenotyping methodsUAV画像と機械学習を用いて、テンサイ個体・区画の病害症状を自動スコア化する再利用可能な表現型取得・推定ワークフローが研究の中心であるため。
abstractA workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 7 Sept 2026
Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.
Why it matches plant phenotyping methodsRGB-Dデータセットと植物検出モデルを開発・評価し、密集環境での植物個体の画像ベース検出を中心的に扱っているため、植物フェノタイピング手法として収録する。
abstractTherefore, we collected an RGB-D dataset using a stereo vision camera.
Abstract The importance of a safe food supply has increased due to climate change and its consequences. The number and severity of floods, droughts and plant diseases are rising which causes massive crop failures. Early and precise detection of plant diseases can lower crop failures as it enables early containment. Moreover, it promotes the targeted use of pesticides to protect the biodiversity. Satellite sensors improve the detection of plant diseases by enabling frequent and extensive vegetation observation. Hence, we present the design of a camera system for Earth observation on small satellites with a focus on the detection of plant diseases. The disease detection of sugar beets was chosen as the primary objective due to their importance for the German agriculture. This work is divided into two parts. First, the spectra of sugar beets are analyzed to determine spectral channels for a camera system. Second, a camera system is defined to capture spectral information within the previously defined wavelength ranges. The spectra of healthy and diseased sugar beets were measured in fields for agricultural research in Central Germany using a portable spectroradiometer. The investigated diseases are Cercospora leaf spot and virus yellows, which are among the most important pathogens worldwide. Based on the measured data, the so-called Normalized Difference Sugar Beet Index (NDSBI) is defined, which is a custom index to detect these diseases. We can prove that this index enables more precise detection of these sugar beet diseases than existing indices like the Normalized Difference Vegetation Index (NDVI). The camera system is designed as a payload for the small satellite Research and Observation in Medium Earth Orbit (ROMEO), which is being developed at the University of Stuttgart’s (US) Institute of Space Systems (IRS). The proposed design fulfills the high radiation requirements as well as the system constraints of mass and volume. Three different options for the camera system are developed: two designs for the development at the US, one with lens optics and one with mirror optics, and an adjusted commercial camera system. All defined camera systems permit the measurement of the NDSBI to precisely detect sugar beet diseases.
Why it matches plant phenotyping methods植物病害をスペクトル計測で検出する衛星搭載カメラと専用指標を設計しており、植物状態の取得・抽出法が研究の中心であるため。
abstractwe present the design of a camera system for Earth observation on small satellites with a focus on the detection of plant diseases.
MaizeSoybeanSugar beetSunflowerAerial / UAVField / plotLiDAR / point cloudRootWhole plant / canopy / plot / fieldAnnotation / quality control
Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.
Why it matches plant phenotyping methodsUAS搭載LiDARの運用パラメータ最適化と多時期点群からの作物キャノピー高さ推定を検証する、植物フェノタイピング手法の開発・ベンチマーク研究である。
abstractThis article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis.
Use of canopy temperature for deficit irrigation (DI) scheduling of sugar beet was evaluated in a 3-year plot study in a semi-arid climate. Four irrigation treatments were evaluated; full irrigation and three deficit irrigation treatments where an irrigation application of 25 mm was applied when an average daily crop water stress index (CWSI) threshold of 0.2, 0.35, and 0.55 was exceeded. There were significant irrigation treatment differences in seasonal evapotranspiration, soil water extraction, seasonal average CWSI, root yield, estimated recoverable sugar (ERS) yield, and water use efficiencies. Seasonal soil water extraction of the full irrigation treatment was significantly less than for the DI treatments in all study years. In 2021 and 2022, soil water balanced based evapotranspiration was significantly less for the DI treatments compared to the full irrigation treatment. However, there was no significant difference in root yield, ERS yield, and water use efficiencies between the full irrigation treatment and irrigating when daily CWSI exceeded 0.2 in any study year. The results indicate that irrigating when average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. Calculation of sugar beet CWSI was an integral part of this study. A neural network model was used to estimate non-water stressed canopy temperature. Documentation of the neural network model and its use to estimate non-water stressed canopy temperature are demonstrated in a Microsoft Excel spreadsheet. Calculation of non-transpiring canopy temperature is also demonstrated in the spreadsheet. The spreadsheet computes CWSI based on five inputs: solar radiation, air temperature, relative humidity, wind speed, and measured canopy temperature.
Why it matches plant phenotyping methodsキャノピー温度からCWSIを算出し、ニューラルネットワークで非水ストレス時のキャノピー温度を推定する再利用可能なExcelワークフローを提示しており、植物の水ストレス状態の取得・推定が方法論的に中心である。
abstractCalculation of sugar beet CWSI was an integral part of this study.
Use of canopy temperature for deficit irrigation (DI) scheduling of sugar beet was evaluated in a 3-year plot study in a semi-arid climate. Four irrigation treatments were evaluated; full irrigation and three deficit irrigation treatments where an irrigation application of 25 mm was applied when an average daily crop water stress index (CWSI) threshold of 0.2, 0.35, and 0.55 was exceeded. There were significant irrigation treatment differences in seasonal evapotranspiration, soil water extraction, seasonal average CWSI, root yield, estimated recoverable sugar (ERS) yield, and water use efficiencies. Seasonal soil water extraction of the full irrigation treatment was significantly less than for the DI treatments in all study years. In 2021 and 2022, soil water balanced based evapotranspiration was significantly less for the DI treatments compared to the full irrigation treatment. However, there was no significant difference in root yield, ERS yield, and water use efficiencies between the full irrigation treatment and irrigating when daily CWSI exceeded 0.2 in any study year. The results indicate that irrigating when average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. Calculation of sugar beet CWSI was an integral part of this study. A neural network model was used to estimate non-water stressed canopy temperature. Documentation of the neural network model and its use to estimate non-water stressed canopy temperature are demonstrated in a Microsoft Excel spreadsheet. Calculation of non-transpiring canopy temperature is also demonstrated in the spreadsheet. The spreadsheet computes CWSI based on five inputs: solar radiation, air temperature, relative humidity, wind speed, and measured canopy temperature.
Why it matches plant phenotyping methodsキャノピー温度からCWSI（植物の水ストレス状態）を推定する計算手法と、非水ストレス温度を推定するニューラルネットワークおよびExcel実装を具体的に文書化しており、再利用可能な表現型取得・推定ワークフローが中心的に含まれる。
abstractA neural network model was used to estimate non-water stressed canopy temperature. Documentation of the neural network model and its use to estimate non-water stressed canopy temperature are demonstrated in a Microsoft Excel spreadsheet.
Digital cameras are widely used tools for plant monitoring in plant science today. Used to track plant growth or even visible symptoms, they are important tools for breeding and plant protection field trials. Nevertheless, its extension to measure the near infrared (NIR) region (700–1000 nm) includes great potential as plants show a higher light reflectance within this spectrum. Various applications have shown its use for disease detection, quantification, virus content estimation, and stress monitoring. As the next step is a comprehensive integration into agricultural routines, this study will show two use-cases with a high technological readiness level. One use-case shows a handheld multispectral sensor, which is used for manual measurements to detect and discriminate different virus types in sugar beet. In contrast, the second use-case shows a transfer to an UAV based disease quantification routine based on spectral imaging for Cercospora leaf spot. In addition, two prototypical workflows are shown for processing non-imaging and imaging spectral data in an agricultural setting. This study shows the state of the art in spectral sensing in the field for the two major sugar beet diseases – virus yellows and Cercospora leaf spot. Furthermore a future perspective for coming technological challenges regarding the integration of AI in sensors or robotic workflows is provided.
Why it matches plant phenotyping methods植物病害の検出・定量を目的としたマルチスペクトル／UAVセンシングとデータ処理ワークフローが研究の中心であり、植物状態の取得・抽出法を扱っている。
abstractOne use-case shows a handheld multispectral sensor, which is used for manual measurements to detect and discriminate different virus types in sugar beet.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. RESULTS: Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from -10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. CONCLUSION: Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.
Why it matches plant phenotyping methods3Dプリント植物モデルを用いた3D植物フェノタイピングの基準物開発・検証であり、センサー比較や形態形質抽出アルゴリズムの精度評価を可能にする方法が中心。
abstractThis work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool.
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment. Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) and breeding of novel and more resilient crop varieties are helpful to address these challenges. The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor. With this paper, we address the problem of automatic fine-grained organ-level geometric analysis needed for precision phenotyping. As the availability of real-world data in this domain is relatively scarce, we propose a novel dataset that was acquired using UAVs capturing high-resolution images of a real breeding trial containing 48 plant varieties and therefore covering great morphological and appearance diversity. This enables the development of approaches for autonomous phenotyping that generalize well to different varieties. Based on overlapping high-resolution images from multiple viewing angles, we compute photogrammetric dense point clouds and provide detailed and accurate point-wise labels for plants, leaves, and salient points as the tip and the base. Additionally, we include measurements of phenotypic traits performed by experts from the German Federal Plant Variety Office on the real plants, allowing the evaluation of new approaches not only on segmentation and keypoint detection but also directly on the downstream tasks. The provided labeled point clouds enable fine-grained plant analysis and support further progress in the development of automatic phenotyping approaches, but also enable further research in surface reconstruction, point cloud completion, and semantic interpretation of point clouds.
Why it matches plant phenotyping methods植物の器官レベル表現型解析を目的とするUAV画像由来の点群データセットで、植物・葉のラベルと専門家による形質測定を提供し、自動フェノタイピング手法の評価を可能にするため、方法論が中心である。
abstractwe propose a novel dataset that was acquired using UAVs capturing high-resolution images of a real breeding trial
Abstract. Active sensing with LiDAR, and terrestrial laser scanners (TLS) in particular, are increasingly being used in plant phenotyping for assessing structural or 3D geometrical plant traits. Although these technologies provide the unprecedented possibility for remote, non-destructive, automatable, and efficient estimation of plant geometry, their deployment does not come without challenges. In this publication, we present a systematic overview of all challenges impacting TLS-based 3D plant phenotyping. We provide actionable recommendations for the end users of the technology, as well as the research questions and possible directions that can contribute the most to resolving these challenges. We specifically focus on TLSs, as we detected a lack in the existing literature dedicated to this sensing system providing a unique compromise between data quality and resolution vs. measurement efficiency and covered volume. The presented discussions are based on the literature review and our own experience in estimating the structural traits of sugar beet and wheat in plant phenotyping experiments.
Why it matches plant phenotyping methodsTLSを用いた3D植物フェノタイピングの課題を体系的にレビューし、構造・幾何学的形質の推定に関する実務的提言を行う、方法中心のレビューである。
abstractIn this publication, we present a systematic overview of all challenges impacting TLS-based 3D plant phenotyping.
With the need to feed a growing world population, the efficiency of crop production is of paramount importance. To support breeding and field management, various characteristics of the plant phenotype need to be measured, a time-consuming process when performed manually. We present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning. We create digital twins of the plants through 3D reconstruction. This allows the estimation of phenotypic traits, such as leaf area, leaf angle, and plant height. We validate our system on a real field, where we reconstruct accurate point clouds and meshes of sugar beet, soybean, and maize.
Why it matches plant phenotyping methodsレーザー・カメラ搭載のフィールドロボットによる3D植物表現型取得と形質推定を開発・実地検証しており、方法が研究の中心である。
abstractWe present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning.
This study introduces two methods for crop identification and growth stage determination, focused primarily on enabling mobile robot navigation. These methods include a two-phase approach involving separate models for crop and growth stage identification and a one-phase method employing a single model capable of handling all crops and growth stages. The methods were validated with maize and sugar beet field images, demonstrating the effectiveness of both approaches. The one-phase approach proved to be advantageous for scenarios with a limited variety of crops, allowing, with a single model, to recognize both the type and growth state of the crop and showed an overall Mean Average Precision (mAP) of about 67.50%. Moreover, the two-phase method recognized the crop type first, achieving an overall mAP of about 74.2%, with maize detection performing exceptionally well at 77.6%. However, when it came to identifying the specific maize growth state, the mAP was only able to reach 61.3% due to some difficulties arising when accurately categorizing maize growth stages with six and eight leaves. On the other hand, the two-phase approach has been proven to be more flexible and scalable, making it a better choice for systems accommodating a wide range of crops.
Why it matches plant phenotyping methods作物の生育段階という植物状態を画像から推定するモデルを開発・検証しており、単なる位置検出を超えた表現型推定が中心。ただし自律走行支援が主目的のため、適用範囲は限定的。
abstractThis study introduces two methods for crop identification and growth stage determination
Abstract In crop protection, disease quantification parameters such as disease incidence (DI) and disease severity (DS) are the principal indicators for decision making, aimed at ensuring the safety and productivity of crop yield. The quantification is standardized with leaf organs, defined as individual scoring units. This study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN). Five strategies for achieving network robustness with limited labeled images are tested and compared, employing simple and copy-paste image augmentation techniques. The study also evaluates the impact of environmental conditions on network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a performance drop. Focusing on the practical application, we employ Mask R-CNN models in an image-processing pipeline to calculate leaf-based parameters including DS and DI. The pipeline was applied in time-series in an experimental trial with five varieties and two fungicide strategies to illustrate epidemiological development. Disease severity calculated with the model with highest Average Precision (AP) shows the strongest correlation with the same parameter assessed by experts. The time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery in contrasting varieties for resistance, as well as the limits for disease control measurements. This study identifies key components for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. It also provides a tool for delivering leaf-based parameters relevant to optimize crop production through automated disease quantification by imaging tools.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とMask R-CNNによる個葉セグメンテーションを開発・比較し、病害の重症度・発生率という植物状態を自動推定して専門家評価と検証しているため、フェノタイピング手法が中心である。
abstractThis study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN).
With the need to feed a growing world population, the efficiency of crop production is of paramount importance. To support breeding and field management, various characteristics of the plant phenotype need to be measured -- a time-consuming process when performed manually. We present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning. We create digital twins of the plants through 3D reconstruction. This allows the estimation of phenotypic traits such as leaf area, leaf angle, and plant height. We validate our system on a real field, where we reconstruct accurate point clouds and meshes of sugar beet, soybean, and maize.
Why it matches plant phenotyping methodsレーザー・カメラ搭載ロボットによる3D植物スキャンと再構成を開発し、葉面積・葉角度・草丈を推定、圃場で検証しているため、表現型取得手法が中心です。
abstractWe present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning.
Sugar beetField / plotRootClassificationCountingRoot system architecture
Purpose Beetroot is a model crop for studying root competition in intercropping systems because its red-coloured roots facilitate non-destructive visual discrimination with other root systems of intercropped plants. However, beetroot also has white roots, which could alter how root competition is interpreted. Here we investigated the quantity of white versus red roots in beetroot to quantify the effect of this phenomenon. Methods Beetroot was mono-cropped or inter-cropped with white cabbage in a field trial. The distribution of beetroot roots was recorded to 2.5 m soil depth on three dates following the minirhizotron method. Roots in each 0.5 m soil layer were counted and categorised into groups based on colour (white roots, coloured roots, and white roots traced back to be coloured) to investigate the influence of white roots on accuracy of root registration. A pot experiment was conducted with three cultivars to verify if white roots are a general characteristic of beetroot. Results White roots in mono-cropped beetroot represented 2.5-4.8% of total roots, on average, across the rooted soil profile. However, white roots represented 6.9% and 11.6% of total roots in the deepest soil layer during August and October, respectively. White roots caused mono-cropped beetroot roots to be underestimated by 1-22% based on root colour discrimination. However, tracing white roots backwards and forwards to coloured parts of roots reduced underestimates to 0.5-15%. Intercropping did not influence the traceability of white roots compared to monocropping. The highest occurrence of white roots appeared during the early growth period and in the deepest soil layers, indicating a linkage to younger roots or higher root proliferation rates. Conclusion Beetroot represents a model crop for visual studies linking eco-physiology and root proliferation. The white roots of beetroot must be incorporated by studies of root competition in intercropping systems that use colour as a criterion.
Why it matches plant phenotyping methodsミニリゾトロンと根色による根の識別・計数精度を検証し、白色根による根量推定の過小評価と補正法を評価しており、根形質取得法が研究の中心です。
abstractThe distribution of beetroot roots was recorded to 2.5 m soil depth on three dates following the minirhizotron method.
Regular scouting for plant diseases and insect pests by growers, crop consultants, extension educators, and researchers (herein defined as stakeholders) is the cornerstone of integrated pest management practices. Sequential sampling plans have the potential to save time and labor in field scouting and reduce the frequency of errors surrounding decision-making. The incorporation of the algorithms behind sequential sampling plans into mobile devices can make scouting for diseases and insect pests more straightforward, practical, and enjoyable. Here, we introduce an iOS application called Sampling . The application was designed for stakeholders to use on a mobile device for assessing disease and insect pest incidence in the field using sequential sampling plans. The application allows users to select a disease or insect pest from a prepopulated list and specify the objective of sampling: Estimation or classification. Conducting sequential sampling depends upon different precision levels and action thresholds within each objective. Detailed instructions for each sequential sampling plan are available as a guide. When sampling begins, users enter the number of diseased individuals at each sampling unit. The specific algorithm developed for the disease or insect pest will inform the user when to stop sampling for the desired goal and return the final incidence and precision or threshold achieved. Results are automatically saved in the application, and the user can inspect and share results by exporting them to a range of compatible programs. The initial version of Sampling (1.1) was released with the sequential sampling plans for Cercospora leaf spot of table beet. Sequential sampling plans for additional diseases or pests will be added to Sampling in subsequent versions. Sampling is available as a free download from the Apple Store (https://apple.co/3pUiYKy) and is compatible with iOS 14.0 or greater on the iPhone or iPad.
Why it matches plant phenotyping methods植物病害の発生率を逐次サンプリングで推定・分類するiOSアプリを開発し、圃場での病害状態の取得と精度・閾値判定を支援することが中心であるため、植物フェノタイピング手法として採用。
abstractHere, we introduce an iOS application called Sampling . The application was designed for stakeholders to use on a mobile device for assessing disease and insect pest incidence in the field using sequential sampling plans.
Early diagnosis of nutrient deficiencies can play a major role in avoiding significant agricultural losses and increasing the final yield while preserving the environment through efficient fertilizer usage. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images by using deep neural networks and transfer learning. Two different datasets, presenting real-world conditions, were used for this purpose. The first one was the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets presenting nitrogen (N), phosphorous (P), and potassium (K) deficiencies, the omission of liming (Ca) and full fertilization. The second one, collected on the field for this research and currently publicly available, was a dataset combining different orange tree images with iron (Fe), potasssium (K), magnesium (Mg), and manganese (Mn) deficiencies. Image classification via fine-tuning with EfficientNetB4, whose original weights came from a noisy student training on ImageNet, obtained the best performances on both datasets with 98.65% and 98.52% Top-1 accuracies. Additionally, the Grad-CAM++ analysis showed that the models were performing an accurate analysis of the most relevant part inside the images. Finally, the use of agricultural transfer learning did not report improvement in the performances.
Why it matches plant phenotyping methods植物の栄養欠乏症状をRGB画像から認識する深層学習手法を開発・比較評価し、複数データセットで性能検証しているため、表現型取得・判定手法が中心である。
abstractwe study how well nutrient deficiency symptoms can be recognized in RGB images by using deep neural networks and transfer learning.
Background Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet (Beta vulgaris). Sugar beet storage roots are characterized by heterogeneous tissue with several cambium rings surrounded by small-celled vascular tissue and big-celled sugar-storing parenchyma between the rings. This study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging. Results Ten Beta genotypes (nine sugar beet and one fodder beet) were included to establish a pipeline for the automated histologic evaluation of cell characteristics and tissue arrangement using digital image processing written in the programming language R. The identification of cells has been validated by comparison with manual cell identification. Cells are reliably discriminated from intercellular spaces, and cells with similar morphological features are assigned to biological tissue types. Conclusions Genotypic differences in cell diameter and cell arrangement can straightforwardly be phenotyped by the presented workflow. The presented routine can further identify genotypic differences in cell diameter and cell arrangement during early growth stages and between sugar storage capabilities.
Why it matches plant phenotyping methodsデジタル画像処理による根の細胞形態・組織配置の自動フェノタイピング手法を開発し、手動同定との比較で検証しているため。
abstractThis study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習による、植物病害の発生率・重症度および病斑関連形質の自動抽出パイプラインを開発・評価しており、表現型取得手法が研究の中心です。
abstractTo develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period.
Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola , which causes Cercospora leaf spot disease (CLS), and Uromyces betae , which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.
Why it matches plant phenotyping methodsUV域ハイパースペクトル画像と機械学習により、植物病害に伴う症状・生理状態を非破壊的に識別する手法が研究の中心である。
abstractNondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
AO_SCPLOWBSTRACTC_SCPLOWIn crop production plant diseases cause significant yield losses. Therefore, the detection and scoring of disease occurrence is of high importance. The quantification of plant diseases requires the identification of leaves as individual scoring units. Diseased leaves are very dynamic and complex biological object which constantly change in form and color after interaction with plant pathogens. To address the task of identifying and segmenting individual leaves in agricultural fields, this work uses unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields and deep instance segmentation networks (Mask R-CNN). Based on standard and copy-paste image augmentation techniques, we tested and compare five strategies for achieving robustness of the network while keeping the number of labeled images within reasonable bounds. Additionally, we quantified the influence of environmental conditions on the network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a drop of up to 7% of average precision (AP) in comparison with images under cloudy, diffuse illumination conditions. The lowest performance in leaf detection was found on images with severe disease damage and sunny weather conditions. Subsequently, we used Mask R-CNN models in an image-processing pipeline for the calculation of leaf-based parameters such as leaf area, leaf slope, disease incidence, disease severity, number of clusters, and mean cluster area. To describe epidemiological development, we applied this pipeline in time-series in an experimental trial with five varieties and two fungicide strategies. Disease severity of the model with the highest AP results shows the highest correlation with the same parameter assessed by experts. Time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery for contrasting varieties for resistance, and the limits for disease control measurements. With this work we highlight key components to consider for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. Moreover, we offer a tool for delivering leaf-based parameters relevant to optimize crop production thought automated disease quantification imaging tools.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とMask R-CNNによる葉の個体別セグメンテーションを開発・比較し、葉面積や病害重症度などの形質を自動推定・検証しており、表現型取得手法が中心である。
abstractTo address the task of identifying and segmenting individual leaves in agricultural fields, this work uses unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields and deep instance segmentation networks (Mask R-CNN).
Sugar beet (Beta vulgaris L.) is the second largest sugar source crop in the world. Sugar content in its beetroot is an important quality index of sugar beet. Rapid and accurate estimation of sugar content in beetroot is helpful to high-throughput phenotype and genotype breeding. The purpose of the current study is to explore the potential of UAV-based multimodal remote sensing data in sugar content estimation. Multimodal data were collected by different UAV-platform carried with RGB, multi-spectral, thermal infrared, hyperspectral and LiDAR. Spectral, structure and thermal features of canopy were extracted from different sensor combinations to estimate sugar content during sugar accumulation period of beetroot by using Partial Least Squares Regression (PLSR), Bayesian Ridge Regression (BRR) and Support Vector Regression (SVR). The main results are as follows: (i) In estimating sugar content, published vegetation indices (PBI, NDVI, TCARI, etc.) calculated from hyperspectral as spectral features outperformed these of multispectral. Structure features calculated from LiDAR outperformed those from RGB. (ii) Eight newly developed vegetation indices presented better performance than published vegetation indices. Among them (R730-R734)/(R730 + R734) index using two bands and (R694-R714)/(R710-R714) using three bands presented the best performance. (iii) Multimodal data combination from different sensors improved estimation accuracy of sugar content than data from individual sensors. PLSR based multimodal data from LiDAR, hyperspectral, thermal infrared sensor presented the best accuracy with R² = 0.64 and relative root mean square error (rRMSE) = 7.2 %. These results showed that UAV-based canopy features from multimodal data can be used to quickly and accurately obtain sugar content of beetroot. This study provides a basic reference for estimating bioactive substances in roots of root tuber crops based on multimodal canopy remote sensing data.
Why it matches plant phenotyping methodsUAVマルチセンサーによるキャノピー特徴量からビート根の糖含量という植物形質を推定し、センサー融合・特徴量・回帰手法の性能を比較評価しており、表現型取得手法が中心である。
abstractThe purpose of the current study is to explore the potential of UAV-based multimodal remote sensing data in sugar content estimation.
Water fulfils key roles in maintaining a plant's biological activity. Water shortage induces stomatal closure, causing a reduction in photosynthesis and transpiration rates. Sun-induced chlorophyll fluorescence (SIF) emission is sensitive to subtle, stress-induced variations in non-photochemical quenching and in photosynthetic electron transport, caused by e.g., a fluctuation in the water availability. Based on this sensitivity, a framework for calibrating a water stress function in a crop growth model using ground-based SIF observations is proposed. SIF time series are simulated by coupling the AgroC crop growth model to the Soil Canopy Observations Photosynthesis Energy (SCOPE) model. This allowed parametrizing the water stress function in the AgroC crop growth model, resulting in improved estimates of actual evapotranspiration and net ecosystem exchange over a sugar beet stand during stressed periods. The improvement in the estimation of the water and carbon fluxes by AgroC during the summer months highlights the ability of canopyscale SIF observations to serve as a remote sensing metric to indicate the intensity of a stress condition. We argue that our framework, linking SIF emission to stress functions, can be used to extract information concerning drought stress from the Fluorescence Explorer (FLEX) satellite, scheduled for launch in 2024.
Why it matches plant phenotyping methods植物キャノピーの乾燥ストレス状態をSIF観測から推定し、作物モデルのストレス関数を較正する方法論が中心であり、単なる生理測定ではない。
abstracta framework for calibrating a water stress function in a crop growth model using ground-based SIF observations is proposed.
Abstract Purpose The accurate and frequent estimation of the leaf plant potassium concentration enabled by hyperspectral imaging techniques has allowed growers to optimize fertilizer applications and reduce the negative impact on the environment. In this study, we examined the feasibility of using leaf spectral data to accurately estimate the potassium content in sugar beet and celery plants. Methods Leaf images in the visible and near infrared region (VNIR: 400–1000 nm) and short-wavelength infrared region (SWIR: 1000-2500 nm) were captured by a hyperspectral camera. The potassium content was measured by ordinary destructive laboratory methods. The correlation-based feature selection (CFS) algorithm was implemented to select important wavelengths that carried the most useful information for predicting the potassium content in plant leaves. Four multivariate regression methods were tested to find a model with strong predictive performance. Results The experimental results showed that the Random Forest (RF) model using 12 bands (425, 443, 479, 599, 631, 662, 798, 863, 897, 921, 1978 and 2053 nm) had the highest accuracy for predicting potassium content in sugar beet, celery and both plant datasets (Rp2 = 0.85, Rp2 = 0.79, and Rp2 = 0.81, respectively). Conclusion The results confirm the universality of the described method. Although further validation studies involving other plant species are needed, it appears that the spectral reflectance technique could be a promising tool for the rapid, noninvasive and cost-effective estimation of K content in plant leaves, contributing to a significant step forward in precision fertilization management.
Why it matches plant phenotyping methodsハイパースペクトル画像と波長選択・回帰モデルにより、植物葉のカリウム含量を非破壊推定する手法が研究の中心であり、植物生理形質の取得・推定を技術的に評価している。
abstractThe accurate and frequent estimation of the leaf plant potassium concentration enabled by hyperspectral imaging techniques
Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U 2 -Net; and (4) leaf segmentation with U 2 -Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.
Why it matches plant phenotyping methods植物画像の物体検出・インスタンスセグメンテーションを行うソフトウェア自体が中心で、植物科学での検証例も含むため、植物フェノタイピング手法として含める。
abstractHere, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning.
Reproduction assets foundThe paper's authors publicly deposited their analysis software JustDeepIt (the tool used for all four plant phenotyping case studies) on GitHub under an MIT License, and the data availability statement confirms the original contributions are available there.Code · publicThe source code is deposited in GitHub at https://github.com/biunit/JustDeepIt under an MIT License.Open asset ↗biunit/JustDeepItlines:277-285Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Background: Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet ( Beta vulgaris ). Sugar beet storage roots are characterized by heterogeneous tissue with several cambium rings surrounded by small-celled vascular tissue and big-celled sugar-storing parenchyma between the rings. This study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging. Results Ten Beta genotypes (nine sugar beet and one fodder beet) were included to establish a workflow for the automated histologic evaluation of cell characteristics and tissue arrangement using digital image processing written in the programming language R. The identification of cells has been validated by comparison with manual cell identification. Cells are reliably discriminated from intercellular spaces, and cells with similar morphological features are assigned to biological tissue types. Conclusions Genotypic differences in cell diameter and cell arrangement can straightforwardly be phenotyped by the presented workflow. The presented routine can further identify genotypic differences in cell diameter and cell arrangement during early growth stages and between sugar storage capabilities.
Why it matches plant phenotyping methodsデジタル画像処理とRによる根組織の細胞形態・配置の自動フェノタイピング手法を開発し、手動同定との比較で検証しているため、方法が研究の中心である。
abstractThis study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging.
Sugar beet (Beta vulgaris L. ssp. vulgaris) accounts for roughly 20% of the global sugar production, with the remainder derived from sugar cane (Saccharum officinarum L.). To maximize sugar yield, high performing sugar beet varieties are needed, in combination with good agronomical practices. Delivering vigorous seeds to the market and meeting the highest quality standards is, therefore, essential. Seed vigor is highly determined by fruit morphology, with the main characteristics of interest being fruit and true seed size, pericarp morphology and fruit filling. Current methods for evaluating fruit morphology mostly rely on labor-intensive and destructive analyses. Here we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning. A 3D convolutional neural network was trained for the semantic segmentation of the pericarp, true seed and air in X-ray micro-CT scans. High average Dice scores of 0.996, 0.971 and 0.930 were found for the pericarp, true seed and air, respectively. Additionally, since farmers target single plants after emergence in the field, we present a method to identify whether sugar beet fruit are monogerm or contain more than one seed (bigerm). An excellent overall classification accuracy, false positive and false negative rate of respectively 98.6, 1.0and 1.8% were achieved. The presented methods have a high potential for integration into tools for breeding programs and the sample-wise monitoring and adjustment of production processes.
Why it matches plant phenotyping methodsX線マイクロCTと深層学習により、サトウダイコン果実・種子の形態を非破壊かつ高スループットに定量評価する手法を開発しており、表現型取得が研究の中心である。
abstractHere we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning.
Background Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Results In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. Conclusion The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
Why it matches plant phenotyping methodsUAV画像から個体を時空間的に同定・個別化し、植物画像データセットを抽出するコンピュータビジョン手法が研究の中心であり、精度検証も行っている。
abstractwe present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs
Reproduction assets foundThe paper's authors publicly released their plant cataloging workflow code on GitHub and deposited a supporting subset of the sugar beet UAV image data with code snapshots in GigaDB (10.5524/102225). The GitHub repository URL is in the allowed list; the GigaDB DOI is not, so only the code asset is listed with an exact-Code · publicponding data. By automatizing the plant cataloging and providing a data framework, our work helps to exploit the full potential of UAV imaging in agricultural contexts.
Availability of Source Code
The source code of our workflow is available in the following repository:
Project name: Plant Cataloging Workflow
GitHub repository: https://github.com/mrcgndr/plant_cataloging_workflow
RRID: SCR_022276
Operating system(s): Platform independent (with conda), Linux (with Docker)
Programming language: Python (3.9 or higher)
License: Apache License 2.0
Data Availability
A subset of the sugar beet data is available in order to run the workflow and reproduce our results. The data have been uploaded to tOpen asset ↗https://github.com/mrcgndr/plant_cataloging_workflowlines:172-190Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published26 Apr 2022HAL (Le Centre pour la Communication Scientifique Directe)Cited by 0 · OpenAlex ↗
The Phenaufol project consisted in the development of a robotic phenotyping process for sugar beet trial plots, and of algorithms for automated quantification of symptoms. A preliminary go/no-go approach allowed us to focus on the most suitable sensors for leaf diseases phenotyping. In order to measure the impact of each main foliar disease, several image analysis methods (thresholding, texture, machine learning) were compared. At the same time, a mathematical modeling of the chosen robotic rig was done for a precise motion execution. Several in-field phenotyping campaigns were conducted to validate the system improvements and to collect disease dynamics data. Geographic visualization was done as a proof of concept. Finally, a vision / robotics pairing was implemented with a tracking algorithm, to move the camera at the closest to the symptoms. Some blockages still need to be removed before running a wide-scale phenotyping campaign (many commercial varieties, national network of experimental sites).
Why it matches plant phenotyping methods甜菜葉病の症状を自動定量するロボット型植物フェノタイピング工程、画像解析、センサー選定、動作モデル、現地検証を中心に開発しており、方法論が明確に主題である。
abstractThe Phenaufol project consisted in the development of a robotic phenotyping process for sugar beet trial plots, and of algorithms for automated quantification of symptoms.
Phenotyping is essential in the process of varietal selection. In the case of sugar beets, richness (g/100g), that is, sugar content, is the key information. The need to acquire this information in a rapid, non-destructive and cheap manner leads the sugar industry to look for portable solutions that enable the suitable field measurements. In this work, a low-cost handheld and narrow visible-NIR spectral range microspectrometer is assessed for its ability to provide such information. During a two-year campaign from 2017 to 2018, a total of 649 samples of sugar beet were measured. The resulting data, along with the reference values for richness, were used to build a predictive model with partial least squares (PLS) regression. Acceptable performance in the estimation of richness from both 2017 data (SEP = 0.84 g/100 g) and 2018 data (SEP = 0.90 g/100 g) is achieved. This study also shows that updating the spectral database is possible by calibration transfer models. From the different tested transfer strategies, the combination of model update and slope-bias correction achieves the best performance, demonstrating that the use of 2017 model on different years is possible and only 75 new sugar beets are necessary to guarantee a richness error lower than 1.05 g/100 g. This work suggests that the molecular sensor could offer a useful tool for a rapid, low cost and non-destructive prediction of richness in sugar beets.
Why it matches plant phenotyping methods携帯型可視近赤外分光計を用いてテンサイの糖含量を非破壊推定する手法を評価・検証しており、センサー性能、予測モデル、年次校正移転が研究の中心である。
titleAssessing the potential of a handheld visible-near infrared microspectrometer for sugar beet phenotyping
Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.
Why it matches plant phenotyping methods個葉の3D点群セグメンテーション手法を開発し、手動セグメンテーションを基準に評価、葉長・葉面積の推定精度も検証しており、表現型取得が研究の中心である。
abstractThis paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves.
Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field.
Why it matches plant phenotyping methodsNIRSを用いてテンサイ根部のRhizoctonia病を検出・定量する測定モデルを開発し、独立検証しており、植物病害状態の取得手法が中心である。
abstractWe aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process.
Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provide relevant agronomic information. UAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study. Five locations were monitored at different growth stages, and the crop number per plot was automatically predicted by using a fully convolutional network (FCN) pipeline. Our FCN-based approach is a single model for jointly determining both the exact stem location of crop and weed plants and a pixel-wise plant classification considering crop, weed, and soil. To determinate the approach performance, predicted crop counting was compared to visually assessed ground truth data. Results show that UAV-based counting of sugar-beet plants delivers forecast errors lower than 4.6%, and the main factors for performance are related to the intra-row distance and the growth stage. The pipeline’s extension to other crops is possible; the errors of the predictions are lower than 4% under practical field conditions for maize and strawberry fields. This work highlight the feasibility of automatic crop counting, which can reduce manual effort to the farmers.
Why it matches plant phenotyping methodsUAV画像と深層学習パイプラインによって作物個体数を自動推定する手法が研究の中心であり、複数作物・圃場で地上真値と比較検証しているため、植物フェノタイピング手法に該当する。
abstractUAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study.
Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.
Why it matches plant phenotyping methods収穫中のテンサイの機械的損傷という植物状態を、画像とCNNで自動検出する手法を開発・比較し、性能検証しているため、植物フェノタイピング手法が中心である。
abstractthe main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine.
Selecting and breeding crop varieties with high economic benefits is of great significance for social stability and development. The economic benefit of crops is usually reflected by the purchase price. Traditional estimation of economic benefits using purchase price formula based on manual measured traits is time-consuming. Structure-from-Motion in conjunction with multi-view stereo (SFM-MVS) method could extract plant phenotypic traits and has the potential for the efficient and timely estimation of economic benefits for sugar beet. In this study, a framework was developed to obtain phenotypic traits in order to estimate the economic benefits of sugar beet with 207 genotypes based on the calculation of a non-linear formula and the partial least square regression (PLSR) model. The first part of the framework was the designing of a low-cost portable equipment that can be used to obtain multi-view images of taproot in order to facilitate its three-dimensional (3D) reconstruction based on SFM-MVS method. The following part was the development of an automated pipeline for estimating ten traits from the reconstructed 3D taproot. Good agreement was found between measured and estimated traits with R² >0.97. The PLSR model constructed using the data in 2018 was used to predict the data in 2019 with moderate performance (R² = 0.5). A new PLSR model built using 70 % of the data collected in 2018 and 2019 could predict the remaining 30 % of the data with a higher R² of 0.61. The model built with multi-years data had a higher accuracy in estimating phenotypic traits, which suggests that PLSR model can estimate beet economic benefit by using the SFM-MVS method with multi-year data. The current method is more efficient than the manual measurement and may provide a basis for selecting and cultivating sugar beet with high economic benefit.
Why it matches plant phenotyping methodsSFM-MVSによるビート根の3D画像取得と、自動的な10形質推定パイプラインを開発・検証しており、植物フェノタイピング手法が研究の中心です。
abstractStructure-from-Motion in conjunction with multi-view stereo (SFM-MVS) method could extract plant phenotypic traits
Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF₇₆₀ from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF₇₆₀ to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.
Why it matches plant phenotyping methods航空機イメージング分光計による作物のSIFを葉レベルへダウンスケールし、光合成効率に関連する生理形質を推定する手法を技術的に解析・適用しており、フェノタイピング手法が中心である。
abstractThis unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf).
Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction ( f esc ) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine f esc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy ( SIF 760 canopy ) to leaf level ( SIF 760 leaf ). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine f esc once a day to reliably scale SIF 760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating f esc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF 760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency ( ε SIF ) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.
Why it matches plant phenotyping methods航空イメージング分光計と補正手法を用いて、作物キャノピーのSIFを葉レベルへダウンスケーリングし、光合成効率を推定する方法が研究の中心である。
abstractThis unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy ( SIF 760 canopy ) to leaf level ( SIF 760 leaf ).
Abstract The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=−0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.
Why it matches plant phenotyping methods衛星スペクトル指標から作物のクロロフィル蛍光という生理形質を推定する手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractThe aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Controlled plant growth facilities provide the possibility to alter climate conditions affecting plant growth, such as humidity, temperature, and light, allowing a better understanding of plant responses to abiotic and biotic stresses. A bottleneck, however, is measuring various aspects of plant growth regularly and non-destructively. Although several high-throughput phenotyping facilities have been built worldwide, further development is required for smaller custom-made affordable systems for specific needs. Hence, the main objective of this study was to develop an affordable, standalone and automated phenotyping system called "Phenocave" for controlled growth facilities. The system can be equipped with consumer-grade digital cameras and multispectral cameras for imaging from the top view. The cameras are mounted on a gantry with two linear actuators enabling XY motion, thereby enabling imaging of the entire area of Phenocave. A blueprint for constructing such a system is presented and is evaluated with two case studies using wheat and sugar beet as model plants. The wheat plants were treated with different irrigation regimes or high nitrogen application at different developmental stages affecting their biomass accumulation and growth rate. A significant correlation was observed between conventional measurements and digital biomass at different time points. Post-harvest analysis of grain protein content and composition corresponded well with those of previous studies. The results from the sugar beet study revealed that seed treatment(s) before germination influences germination rates. Phenocave enables automated phenotyping of plants under controlled conditions, and the protocols and results from this study will allow others to build similar systems with dimensions suitable for their custom needs.
Why it matches plant phenotyping methods低コストで自動化された植物表現型計測システムの開発、構築仕様の提示、ケーススタディによる評価が研究の中心である。
abstractthe main objective of this study was to develop an affordable, standalone and automated phenotyping system called "Phenocave" for controlled growth facilities.
Fast and non-destructive estimation of canopy chlorophyll content from hyperspectral sensing is essential to monitor the physiological status of vegetation or to estimate crop above ground nitrogen status. The objective of this study is to propose an optimal method for the relative chlorophyll content (SPAD) estimation of sugar beet canopy using ground-based hyperspectral imagery. Field experiments were conducted over three years at three different growth stages, across three different sites, using different cultivars and nitrogen (N) application rates. Quantitative correlations between SPAD value and canopy hyperspectral reflectance of sugar beet canopy after different pretreatment algorithms were established. Ten classical spectral indexes selected from the literature for estimating SPAD value in sugar beet canopy were evaluated and compared to a novel modified chlorophyll index (MCI) produced in this study by introducing a parameter to chlorophyll index (CI) to improve the estimation accuracy. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) were optimized by using all possible combinations of spectral bands from the range of 390 nm to 990 nm. The prediction performance of partial least squares (PLS) regression models for optimized indexes (e.g., NDVI, CI and MCI), compared to the corresponding classical spectral indexes (e.g., ND₅₅₀, ND₇₀₅, CIgᵣₑₑₙ and CIᵣₑd ₑdgₑ) was examined. Results showed that standard normal variate transformation (SNV) was the best pretreatment method for the hyperspectral data of this study. Models resulted after bands combinations optimization were found to be more accurate than models developed using the classical spectral indexes. The performance of proposed spectral index, MCI (R₇₄₇, R₈₃₉), MCI (R₈₆₁, R₈₈₄) and MCI (R₉₃₁, R₇₇₀), were best for the prediction accuracy of SPAD value in sugar beet for the validation set with the coefficient of determination (R²) of 0.83, 0.70 and 0.75, the root mean square error (RMSE) of 2.37, 3.11 and 2.78, and the relative root mean square error (RRMSE) of 4.95%, 6.05% and 5.75%, for the rapid growth stage of leaf cluster, sugar growth stage and sugar accumulation stage, respectively. It can be concluded that the index proposed can be implemented for the prediction of SPAD value of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.
Why it matches plant phenotyping methodsサトウダイコンの葉緑素含量という植物形質を、近接ハイパースペクトルセンシングで推定する新規スペクトル指数を開発・検証しており、フェノタイピング手法が中心的である。
abstractThe objective of this study is to propose an optimal method for the relative chlorophyll content (SPAD) estimation of sugar beet canopy using ground-based hyperspectral imagery.
Progresses in agronomy rely on accurate measurement of the experimentations conducted to improve the yield component. Measurement of the plant density is required for a number of applications since it drives part of the crop fate. The standard manual measurements in the field could be efficiently replaced by high-throughput techniques based on high-spatial resolution images taken from UAVs. This study compares several automated detection of individual plants in the images from which the plant density can be estimated. It is based on a large dataset of high resolution Red/Green/Blue (RGB) images acquired from Unmanned Aerial Vehicules (UAVs) during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively on the images. Performances of handcrafted method (HC) were compared to those of deep learning (DL). The HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. The DL method is based on the Faster Region with Convolutional Neural Network (Faster RCNN) model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop. Results show that simple DL methods generally outperforms simple HC, particularly for maize and sunflower crops. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. The image quality determines part of the performances for HC methods which makes the segmentation step more difficult. Performances of DL methods are limited mainly by the presence of weeds. A hybrid method (HY) was proposed to eliminate weeds between the rows using the rules developed for the HC method. HY improves slightly DL performances in the case of high weed infestation. When few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL, a drastic increase of performances for all the crops is observed, with relative RMSE below 5% for the estimation of the plant density.
Why it matches plant phenotyping methodsUAV画像から個体を検出・計数し、作物密度を推定する画像解析手法を比較・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。
abstractThis study compares several automated detection of individual plants in the images from which the plant density can be estimated.
Reproduction assets foundThe paper's authors explicitly state that the deep-learning model architecture and data augmentation details are given in their public code repository on GitHub, which is an authors' public URL implementing the paper's plant detection/counting analysis.Code · public258 architectural details are given in the code (https://github.com/EtienneDavid/plants-counting-detection)Open asset ↗EtienneDavid/plants-counting-detectionpdf-page:9 lines:1-52Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Sugar beetThermalRootClassificationMorphology / geometry measurementRoot system architecture
A plant’s root system absorbs water and necessary nutrients, and synthesizes organic matter, which is essential for plant growth and regeneration. Therefore, investigating root system architecture (RSA) can potentially provide deep understanding and useful information about plant growth. Current approaches involve soil-coring and use of mini-rhizotrons, which can damage the root or be time consuming. Groundpenetrating radar has been employed but is not suitable for small plants because of the resolution needed. Nuclear magnetic resonance could provide valuable information of tiny roots, but the equipment is costly. In this study, infrared imaging—a-non-destructive method—was used to reveal the shape and position of small root systems, such as sugar beet roots. The finite element analysis (FEA) methodology was implemented toA plant’s root system absorbs water and necessary nutrients, and synthesizes organic matter, which is essential for plant growth and regeneration. Therefore, investigating root system architecture (RSA) can potentially provide deep understanding and useful information about plant growth. Current approaches involve soil-coring and use of mini-rhizotrons, which can damage the root or be time consuming. Ground-penetrating radar has been employed but is not suitable for small plants because of the resolution needed. Nuclear magnetic resonance could provide valuable information of tiny roots, but the equipment is costly. In this study, infrared imaging, a-non-destructive method, was used to reveal the shape and position of small root systems, such as sugar beet roots. The finite element analysis (FEA) methodology was implemented to validate the practicality of applying infrared imaging to detect roots. Artificial neural network (ANN) methods were used to determine the existence of a root system. Support vector machine (SVM) and ANN were employed to predict root depth and statistical tests were used to compare the results. The results of these experiments suggest that infrared imaging can be used to predict the presence and depth of roots. validate the practicality of applying infrared imaging to detect roots. Artificial neural network (ANN) methods were used to determine the existence of a root system. Support vector machine (SVM) and ANN were employed to predict root depth and statistical tests were used to compare the results. The results of these experiments suggest that infrared imaging can be used to predict the presence and depth of roots.
Why it matches plant phenotyping methods赤外線画像と機械学習を用いて植物根系の存在、形状、位置、深さを推定する手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractIn this study, infrared imaging, a-non-destructive method, was used to reveal the shape and position of small root systems, such as sugar beet roots.
Rhizomania is a grave disease affecting sugar beet ( Beta vulgaris L.). It is caused by the Beet Necrotic Yellow Vein Virus (BNYVV), an RNA virus transmitted by the plasmodiophorid vector Polymyxa betae . Genetic resistance to the virus has been accomplished mostly using phenotype-genotype association studies. As yet, the most convenient method to ascertain plant resistance has been the quantification of viral titer in roots through the ELISA test. This method is particularly time-consuming and clashes with the necessities of modern plant breeding. Here, we propose an alternative and successful phenotyping method based on the automatic extraction of the viral RNA from sugar beet roots and its relative and absolute quantification by quantitative real-time PCR (qRT-PCR) and digital PCR (dPCR), respectively. Such a method enables an improved standardization of the study, as well as an accurate quantification of the virus also in those samples presenting low virus titer, with respect to the ELISA test. Supplementary information The online version contains supplementary material available at 10.1007/s13337-021-00674-7.
Why it matches plant phenotyping methodsサトウダイコン根のウイルス量を用いて抵抗性を評価する植物表現型取得法を開発・標準化し、ELISAと比較して検証しているため、方法が研究の中心である。
abstractHere, we propose an alternative and successful phenotyping method based on the automatic extraction of the viral RNA from sugar beet roots and its relative and absolute quantification by quantitative real-time PCR (qRT-PCR) and digital PCR (dPCR), respectively.
With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe at L-band (ROSE-L) and its combination with existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. To investigate the potential for estimating soil and plant parameters, the SARSense campaign was conducted between June and August 2019 at the agricultural test site Selhausen in Germany. In this regard, we introduce a new publicly available, extensive SAR dataset and present a first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. The analysis includes C- and L-band airborne recordings as well as Senitnel-1 and ALOS-2 acquisitions, accompanied by in-situ soil moisture measurements and plant samplings. In addition, soil moisture was measured using cosmic-ray neutron sensing as well as unmanned aerial system (UAS) based multispectral and temperature measurements were taken during the campaign period. First analysis of the dataset revealed, that due to misalignments of corner reflectors during the SAR acquisition, temporal consistency of airborne SAR data is not given. In this regard, a scene-based, spatial analysis of backscatter behaviour from airborne SAR data was conducted, while the spaceborne SAR data enabled the analysis of temporal changes in backscatter behaviour. Focusing on root crops with radial canopy structure (sugar beet and potato) and cereal crops with elongated canopy structure (wheat, barley), the lowest correlations can be observed between backscattering signal and soil moisture, with R² values ranging below 0.35 at C-band and below 0.36 at L-band. Higher correlations can be observed focusing on vegetation water content, with R² values ranging between 0.12 and 0.64 at C-band and 0.06 and 0.64 at L-band. Regarding plant height, at C-band higher correlations with R² up to 0.55 can be seen compared to R² up to 0.36 at L-band. Looking at the individual agricultural corps in more detail, in almost all cases, the backscatter signals of C- and L-band contain a different amount of information about the soil and plant parameters, indicating that a multi-frequency approach is envisaged to disentangle soil and plant contributions to the signal and to identify specific scattering mechanisms related to the crop type, especially related to the different characteristics of root crops and cereals.
Why it matches plant phenotyping methodsSARによる植物パラメータ推定を目的とした公開データセットと解析を中心に扱い、植生含水量や草丈との後方散乱の関係を評価しているため、植物フェノタイピング手法・データセットとして収録する。
abstractwe introduce a new publicly available, extensive SAR dataset and present a first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters.
Precision management of agricultural fields as well as plant breeding are central factors for keeping yields high and to provide food, feed, and fiber for our society. A key element in breeding trials but also for targeted management actions is to analyze the growth state of individual plants objectively and at a large scale. In this letter, we address the problem of analyzing crops in real agricultural fields based on camera data recorded with mobile robots and to derive information about the plant development, e.g., to monitor phenotypic traits such as growth stage. We propose a novel single-stage object detection approach that localizes crops and weeds in the field. At the same time, it detects plant-specific leaf keypoints intending to estimate leaf count at a plant level, which is a key trait for classifying the growth stage. We implemented and thoroughly tested our approach on real sugar beet fields. As our experiments show, it performs the required detections and shows superior performance with respect to a state-of-the-art two-stage approach based on Mask R-CNN.
Why it matches plant phenotyping methods圃場ロボット画像から個体ごとの葉数を推定し、生育段階という植物形質を抽出する検出手法を開発・比較評価しており、フェノタイピング手法が中心である。
titleJoint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.
Why it matches plant phenotyping methods植物のリン栄養状態を hyperspectral imaging と機械学習で非侵襲推定する手法が研究の中心であり、分類器の開発と評価も行っている。
abstractThe measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment.
SoybeanSugar beetLiDAR / point cloudLeafMorphology / geometry measurement2D/3D reconstructionLeaf traits
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Why it matches plant phenotyping methods3D点群から植物葉面を再構成し、ノイズ耐性と葉面積推定の安定性を従来法と比較検証する手法開発研究であり、植物フェノタイピング手法が中心です。
abstractwe developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their proposed leaf surface reconstruction method is publicly available on GitHub. No public deposit of the 3D point cloud phenotype data (soybean/sugar beet scans) is mentioned, so only the code qualifies as a paper-specific public asset.Code · publicWe implemented the algorithm for the proposed method in Python ( http://www.python.org/ ). The source code is at https://github.com/oceam/LeafSurfaceReconstruction .Open asset ↗oceam/LeafSurfaceReconstructionlines:46-55Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Abstract Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.
Why it matches plant phenotyping methods植物葉のリン栄養状態を非侵襲的に推定するため、ハイパースペクトル画像と機械学習分類器を中心的に開発・適用しており、植物フェノタイピング手法に該当する。
abstractThe measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment.
Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.
Why it matches plant phenotyping methodsUGV・UAV画像から病徴の重症度を抽出し、専門家スコアと比較検証する高スループット植物表現型計測手法が中心である。
abstractwe compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination.
Abstract Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.
Why it matches plant phenotyping methods植物のリン栄養状態を非侵襲的に推定するため、ハイパースペクトル画像と機械学習による分類手法を中心的に適用・評価している。
abstractThe measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment.
Sugar beetLiDAR / point cloudRootClassificationMorphology / geometry measurementRoot system architecture
Background The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination. Results The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results. Conclusion The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.
Why it matches plant phenotyping methodsレーザースキャンによる植物器官の3D形状取得と、形状PCAによる根形状の自動定量・分類が研究の中心であるため。
abstractThe computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.
Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental conditions. More generally, these traits are also measured in the field of ecology as simple traits that can be correlated to other adaptative traits more difficult to measure on adult plants, as for example traits of the rooting system. Methods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth. The present dataset comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first sugar beet crop ontology. This dataset corresponds to the first automated phenotyping of a population of 198 genotypes and 4 commercial control varieties and is hosted on INRAE public depository under the reference number doi.org/10.15,454/AKNF4Q. The equipment and methods presented here are available on a phenotyping platform opened to collaborative research and adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping values can also be linked to genomic information to study the genetic determinism of the trait values.
Why it matches plant phenotyping methods種子から幼苗までの形質を対象とした自動・高スループット表現型解析手法、データセット、公開プラットフォームが研究の中心であるため。
abstractMethods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth.
Reproduction assets foundThe paper is a data descriptor whose sugar beet seed/seedling phenotyping dataset (28 traits for 202 genotypes) is publicly deposited in the URGI Plant and Fungi Dataverse with DOI 10.15454/AKNF4Q. No author analysis code is publicly released (scripts in Avizo/TCL/MATLAB and Fiji are described but no deposit URL is给定).Dataset · publiculgaris L.) grown area and an exotic accession of Beta vulgaris maritima from Denmark. Institution: Florimond Desprez; City/Town/Region: Cappelle-en Pévèle; Country: France. Latitude and longitude for collected samples 50.5167; 3.1667
Data accessibility
Repository name: URGI Plant and Fungi Dataverse
Data identification number: https://doi.org/10.15454/AKNF4Q
Direct URL to data: https://doi.org/10.15454/AKNF4Q
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Seed and seedling traits are increasingly measured in the field of ecology as simple traits that can be used to describe species diversity. A deeper phenotyping of genetic diversity in crops is also necessary to better understand their tolOpen asset ↗URGI Plant and Fungi Dataverse · 10.15454/AKNF4Qlines:129-170Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 9 Sept 2026
Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.
Why it matches plant phenotyping methods圃場のSfM画像から3D植物構造を再構成し、個体分割と複数形質の自動抽出を行うパイプラインを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractIn this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field.
Sugar beetLiDAR / point cloudRootClassificationMorphology / geometry measurementRoot system architecture
Abstract Background: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laser scanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination. Results: The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results. % (classification), based on characteristic tap root shapes. Conclusion: The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laser scanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.
Why it matches plant phenotyping methodsレーザースキャンによる植物器官の3D形状取得と、形状PCAによる根形状の定量化・品種分類が研究の中心であり、再利用可能な植物表現型解析手法を提示している。
abstractLaser scanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces.
Two glasshouse experiments with sugar beet cvs Penta and Macarena inoculated, respectively, with 0 or 1500 and 0, 500, 1000 or 1500 juveniles of Heterodera schachtii, were conducted to estimate the capability of laser-induced chlorophyll fluorescence (LIF) and pulse amplitude modulated (PAM) chlorophyll fluorescence techniques to detect H. schachtii infestation and to differentiate between infestation levels. Fluorescence and gas exchange parameters, nitrogen and chlorophyll content of sugar beet leaves were measured weekly after nematode inoculation. Sugar beet plants responded to H. schachtii infestation initially with a decrease in photosynthesis rate and later with a reduction in nitrogen uptake and chlorophyll concentration. At the early stages of nematode infestation, before visual symptoms were evident, infested sugar beet plants displayed increased fluorescence (F680, F740). Later stages of infection were accompanied by an increase in the F686/F740 ratio, ground fluorescence (Fo) and a decrease in photochemical efficiency (Fv/Fm) induced by degradation of leaf chlorophyll. Sugar beet plants infested with 500, 1000 or 1500 juveniles per 100 cm3 of soil did not differ either in their nitrogen and chlorophyll content or in photosynthesis and transpiration rate. The linear discrimination analysis based on the combination of PAM and LIF parameters resulted in 100% correct classification of control plants and high classification rates (60-100%) of the infested treatments on all the sampling dates. Whether the fluorescence technique will differentiate nematode densities under field conditions needs further study.
Why it matches plant phenotyping methodsLIFおよびPAM蛍光を用いて、植物の線虫感染状態を非視覚的に検出・分類する手法の性能を評価しており、植物フェノタイピング手法が研究の中心である。
abstractestimate the capability of laser-induced chlorophyll fluorescence (LIF) and pulse amplitude modulated (PAM) chlorophyll fluorescence techniques to detect H. schachtii infestation and to differentiate between infestation levels
Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.
Why it matches plant phenotyping methodsUAV-LiDARとアルゴリズムにより作物バイオマスと草高を推定し、複数作物で精度評価・較正条件の影響を検証しているため、表現型取得法が研究の中心である。
abstractThe potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Due to its high damaging potential, Cercospora leaf spot (CLS) caused by Cercospora beticola is a continuous threat to sugar beet production worldwide. Breeding for disease resistance is hampered by the quantitative nature of resistance which may result from differences in penetration, colonization, and sporulation of the pathogen on sugar beet genotypes. In particular, problems in the quantitative assessment of C. beticola sporulation have resulted in the common practice to assess field resistance late in the growth period as quantitative resistance parameter. Recently, hyperspectral sensors have shown potential to assess differences in CLS severity. Hyperspectral microscopy was used for the quantification of C. beticola sporulation on sugar beet leaves in order to characterize the host plant suitability / resistance of genotypes for decision-making in breeding for CLS resistance. Results Assays with attached and detached leaves demonstrated that vital plant tissue is essential for the full potential of genotypic mechanisms of disease resistance and susceptibility. Spectral information (400 to 900 nm, 160 wavebands) of CLSs recorded before and after induction of C. beticola sporulation allowed the identification of sporulating leaf spot sub-areas. A supervised classification and quantification of sporulation structures was possible, but the necessity of genotype-specific reference spectra restricts the general applicability of this approach. Fungal sporulation could be quantified independent of the host plant genotype by calculating the area under the difference reflection spectrum from hyperspectral imaging before and with sporulation. The overall relationship between sensor-based and visual quantification of C. beticola sporulation on five genotypes differing in CLS resistance was R 2 = 0.81; count-based differences among genotypes could be reproduced spectrally. Conclusions For the first time, hyperspectral imaging was successfully tested for the quantification of sporulation as a fungal activity depending on host plant suitability. The potential of this non-invasive and non-destructive approach for the quantification of fungal sporulation in other host–pathogen systems and for the phenotyping of crop traits complex as sporulation resistance is discussed.
Why it matches plant phenotyping methodsサトウダイコン葉の病害関連形質である胞子形成を、ハイパースペクトル画像で定量する手法を開発・検証しており、植物フェノタイピング手法が中心です。
abstractHyperspectral microscopy was used for the quantification of C. beticola sporulation on sugar beet leaves in order to characterize the host plant suitability / resistance of genotypes for decision-making in breeding for CLS resistance.
Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.
Why it matches plant phenotyping methods3Dセンシングによる植物形質取得、精度比較、点群処理ワークフローを中心に扱う植物フェノタイピング手法レビューである。
abstractThis review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications.
Successful adoption of drone‐based remote sensing depends on changes in sensitivity over vegetation indices (VIs) and growth stage(s). During 2017–2018, experiments were conducted to relate between vegetation indices and corn (Zea mays L.) and sugarbeet (Beta vulgaris L.) yields in western Minnesota. Aerial images were collected using an unmanned aerial vehicle (UAV) equipped with a passive light optical sensor (Micasense RedEdge). Using Pix4D software, spectral reflectance data were derived from flights at V6 and VT growth stages of corn, and V10 and V15 growth stages of sugarbeet in 2017. In 2018, images were collected every week from the V4 to R2 growth stages in corn, and from the V4 to V15 stages in sugarbeet. In addition to red normalized vegetation index (RNDVI) and red edge normalized vegetation index (RENDVI), crop height was determined from UAV based digital terrain and digital surface models. Yield prediction (YP) model was derived from the linear regression between crop yield and vegetation indices. For corn‐YP model, R² value increased over the growing period and optimized at the R1 growth stage. Considering 3 site‐years, RENDVI was the best predictor for corn YP than other VIs based on the maximum R² value. For sugarbeet YP, model R² value declined over the growing season and optimized at V7 or V10 growth stages. Considering 4 site‐years, RNDVI was best related to root yield and recoverable sugar yield. Drone‐based remote sensing can be successfully used for corn and sugarbeet YP. Drone‐based remote sensing has potential in corn and sugarbeet YP, but it varied over growing seasons. Core Ideas Drone‐based passive optical sensor can be used to predict crop’s yield. Red‐ and red edge‐normalized vegetation index and crop height are potential indices. Red edge normalized vegetation index best correlated with corn yield. Red normalized vegetation index best correlated with root yield prediction. Over growing season predictability improved for corn but declined for sugarbeet.
Why it matches plant phenotyping methodsUAV画像・光学センサー・DSMから植生指数と作物高を抽出し、収量を予測・比較する手法の実質的な適用と評価が中心である。
abstractAerial images were collected using an unmanned aerial vehicle (UAV) equipped with a passive light optical sensor (Micasense RedEdge).
Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. One of the main limitations for its competitiveness is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots, especially in breeding programs. In this context, a dedicated near infrared (NIR) fiber-optic probe based approach is proposed. NIR technology is widely used for the estimation of sugar content in vegetable products, while optic fibers allow a wide choice of technical properties and configurations. The objective of this research was to study the best architecture through different technical choices for the estimation of sugar content in intact sugar beet roots. NIR spectral measurements were taken on unprocessed sugar beet samples using two types of geometries, single and multiple fiber-probes. Sugar content estimates were more accurate when using multiple fiber-probes (up to R 2 = 0.93) due to a lesser disruption of light specular reflection. In turn, on this configuration, the best estimations were observed for the smallest distances between emitting and collecting fibers, reducing the proportion of multiply scattered light in the spectra. Error of prediction (RPD) values of 3.95, 3.27 and 3.09 were obtained for distances between emitting and collecting fibers of 0.6, 1.2 and 1.8 µm respectively. These high RPD values highlight the good predictions capacities of the multi-fiber probes. Finally, this study contributes to a better understanding of the effects of the technical properties of optical fiber-probes on the quality of spectral models. In addition, and beyond this specificity related to sugar beet, these findings could be extended to other turbid media for quantitative optical spectroscopy and eventually to validate considered fiber-optic probe design obtained in this experimental study.
Why it matches plant phenotyping methodsサトウダイコン根の糖含量という植物形質を対象に、NIR光ファイバープローブの構成を開発・比較し、予測精度を検証している。形質取得法が研究の中心である。
abstracta dedicated near infrared (NIR) fiber-optic probe based approach is proposed
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.
Why it matches plant phenotyping methods低コストSfMおよびRGB-Dによる植物3D再構成手法を開発・比較し、草丈、葉面積指数、乾物バイオマスを実測値で検証しており、表現型取得手法が研究の中心である。
abstractThis study examined the suitability of two low-cost systems for plant reconstruction.
The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.
Why it matches plant phenotyping methodsサトウダイコン種子の機械的損傷という植物器官の状態を、多波長画像と統計モデルで分類する方法を評価しており、種子品質評価のための画像ベース表現型計測が中心である。
abstractThis study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds.
Agricultural monitoring is required to enhance crop production, control plant stress, and predict pests and crop infection. Apart from monitoring the external influences, the state of the plant itself must be tracked. However, the modern methods for plant analysis are expensive and require plants processing often in a destructive way. Optical spectroscopy can be used for the non-invasive monitoring requiring no consumables, and little to none sample preparation. In this context, we found that the red beet growth can be monitored by Raman spectroscopy. Our analysis shows that, as plants age, the rate of betalain content increases. This increase makes betalain dominate the whole Raman spectra over other plant components. The dominance of betalain facilitates its use as a molecular marker for plant growth. This finding has implications in the understanding of plant physiology, particularly important for greenhouse growth and the optimization of external conditions such as artificial illumination.
Why it matches plant phenotyping methodsラマン分光法を用いて植物の発育状態を非侵襲的に推定する手法が研究の中心であり、betalainを成長の分子マーカーとして検証している。
abstractOptical spectroscopy can be used for the non-invasive monitoring requiring no consumables, and little to none sample preparation.
Three experiments were conducted to develop a bioassay method for assessing the bioavailability of prosulfocarb, pyroxasulfone and trifluralin in both crop residue and soil. In preliminary experiments, Italian ryegrass (Lolium multiflorum Lam.), cucumber (Cucumis sativus L.) and beetroot (Beta vulgaris L.) were tested as bioassay plant species for the three pre-emergent herbicides. Four growth parameters (shoot length, root length, fresh weight and dry weight) were measured for all plant species. Shoot-length inhibition was identified as the most responsive to the herbicide application rates. Italian ryegrass was the most sensitive species to all tested herbicides, whereas beetroot and cucumber had lower and similar sensitivity to shoot inhibition for the three herbicides. The bioassay species performed similarly in wheat and canola residues collected a few days after harvest. In bioassay calibration experiments, dose–response curves were developed for prosulfocarb, pyroxasulfone and trifluralin in a sandy loam soil typical of the grain belt of Western Australia and with wheat residue. The developed bioassay uses ryegrass shoot inhibition for relatively low suspected concentrations of herbicide, and cucumber shoot inhibition for higher rates. The bioassay was validated by spraying the three herbicides separately onto wheat residue and soil and comparing the concentrations derived from chemical analysis with those from the bioassay. All of the linear correlations between concentrations derived from chemical analyses and the bioassays were highly significant. These results indicate that the bioassay calibration curves are suitable for estimating herbicide concentrations in crop residue collected soon after harvest and a sandy-loam soil, low in organic matter.
Why it matches plant phenotyping methods除草剤濃度を推定するため、植物の生育阻害（特にシュート長）を測定するバイオアッセイを開発・較正・検証しており、表現型取得が研究の中心である。
abstractThree experiments were conducted to develop a bioassay method for assessing the bioavailability of prosulfocarb, pyroxasulfone and trifluralin in both crop residue and soil.
The rapid development of image-based phenotyping methods based on ground-operating devices or unmanned aerial vehicles (UAV) has increased our ability to evaluate traits of interest for crop breeding in the field. A field site infested with beet cyst nematode (BCN) and planted with four nematode susceptible cultivars and five tolerant cultivars was investigated at different times during the growing season. We compared the ability of spectral, hyperspectral, canopy height- and temperature information derived from handheld and UAV-borne sensors to discriminate susceptible and tolerant cultivars and to predict the final sugar beet yield. Spectral indices (SIs) related to chlorophyll, nitrogen or water allowed differentiating nematode susceptible and tolerant cultivars (cultivar type) from the same genetic background (breeder). Discrimination between the cultivar types was easier at advanced stages when the nematode pressure was stronger and the plants and canopies further developed. The canopy height (CH) allowed differentiating cultivar type as well but was much more efficient from the UAV compared to manual field assessment. Canopy temperatures also allowed ranking cultivars according to their nematode tolerance level. Combinations of SIs in multivariate analysis and decision trees improved differentiation of cultivar type and classification of genetic background. Thereby, SIs and canopy temperature proved to be suitable proxies for sugar yield prediction. The spectral information derived from handheld and the UAV-borne sensor did not match perfectly, but both analysis procedures allowed for discrimination between susceptible and tolerant cultivars. This was possible due to successful detection of traits related to BCN tolerance like chlorophyll, nitrogen and water content, which were reduced in cultivars with a low tolerance to BCN. The high correlation between SIs and final sugar beet yield makes the UAV hyperspectral imaging approach very suitable to improve farming practice via maps of yield potential or diseases. Moreover, the study shows the high potential of multi- sensor and parameter combinations for plant phenotyping purposes, in particular for data from UAV-borne sensors that allow for standardized and automated high-throughput data extraction procedures.
Why it matches plant phenotyping methodsUAV・地上センサーによるスペクトル、ハイパースペクトル、草冠高・温度から、線虫耐性や収量関連形質を抽出・識別する手法を比較評価しており、植物フェノタイピングが中心である。
abstractWe compared the ability of spectral, hyperspectral, canopy height- and temperature information derived from handheld and UAV-borne sensors to discriminate susceptible and tolerant cultivars and to predict the final sugar beet yield.
Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R 2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R 2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.
Why it matches plant phenotyping methods作物キャノピー水分含量という植物生理状態を、ハイパースペクトルデータから推定する新規指標を開発・検証しており、フェノタイピング手法が中心である。
abstractWe propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale.
Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods複数作物の植物パラメータ、表現型、ハイパースペクトル反射を体系的に取得した地上基準データベースであり、取得設計と各パラメータの測定法を詳細に記述して、リモートセンシングモデルの校正・検証に用いる点が中心的です。
abstractwe describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations
Reproduction assets foundThis is a data descriptor paper whose plant-phenotyping measurements (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM, soil moisture, photos, survey data) are explicitly deposited as public PANGAEA datasets with DOIs listed in the text. Multiple paper-specific public assets are直接Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.
Why it matches plant phenotyping methodsスマートフォン画像から葉の病徴を自動抽出・分類するアルゴリズムとツールが研究の中心であり、植物病害状態の画像ベース表現型測定に該当する。
abstractThis paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases.
Assessment of disease severity is a foundational component of plant pathology and essential for robust disease management. Researchers often estimate disease severity using standard area diagrams (SADs) that are reference images representing disease severity in percentage increments. SADs provide assessments of disease severity that are more accurate, precise, and reliable than other methods. Although specific SADs have been constructed for many plant diseases, they often depict severity in unrealistic black-and-white or grayscale illustrations. SADs are also usually printed, static references that can burden data collection in the field and require data to be transferred manually to a computer spreadsheet for manipulation. This data entry process and verification are prone to errors and require additional inputs of time and labor. We developed a new iPad application (app) called Estimate for researchers and crop managers for their use on a mobile device at the field-level for assessing plant disease severity in order to collect data or aid in treatment decisions. The app is a repository for digital, photographic SADs and offers savings in time for data collection and processing. Estimate allows users to select a disease from a prepopulated list and specify the reference disease images in either logarithmic or linear intervals. Data may be collected as the midpoint of an interval (ordinal) or as 1% increments (continuous). Users then select among photographic images by touching those that best match the observed disease severity on successive samples. Estimate allows data entry at the plant and leaf hierarchical levels within plots and subplots. Alternatively, data may be collected on single sampling units with an undefined experimental design (i.e., 1 to x leaves). The user may inspect and e-mail the final data in comma-separated values format for analysis using conventional spreadsheet software. Estimate was released with SADs for assessing the severity of Cercospora leaf spot in red and yellow table beet cultivars. A list of collaborators and up-to-date list of SADs included in Estimate is available at http://evade.pppmb.cals.cornell.edu/estimate/ . SADs for other diseases will be added to Estimate as they become available. Estimate is available for free download from iTunes ( https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=1193605571&mt=8 ) and is compatible with an iPad Air 2 or equivalent using iOS 9.0 or greater.
Why it matches plant phenotyping methods植物病害の重症度という植物状態を、写真標準面積図とiPadアプリで取得・記録する手法を開発した研究であり、表現型測定が中心です。
abstractWe developed a new iPad application (app) called Estimate for researchers and crop managers for their use on a mobile device at the field-level for assessing plant disease severity
Background Phenotyping technologies are expected to provide predictive power for a range of applications in plant and crop sciences. Here, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods. Strong links between the above and belowground parts of sugar beet plants have made BCN suitable targets for use of non-destructive phenotyping methods. We compared the ability of visible light imaging, thermography and spectrometry to evaluate the effect of BCN on the growth of sugar beet plants. Results Two microplot experiments were sown with the nematode susceptible cultivar Aimanta and the nematode tolerant cultivar BlueFox under semi-field conditions. Visible imaging, thermal imaging and spectrometry were carried out on BCN infested and non-infested plants at different times during the plant development. Effects of a chemical nematicide were also evaluated using the three phenotyping methods. Leaf and beet biomass were measured at harvest. For both susceptible and tolerant cultivar, canopy area extracted from visible images was the earliest nematode stress indicator. Using such canopy area parameter, delay in leaf growth as well as benefit from a chemical nematicide could be detected already 15 days after sowing. Spectrometry was suitable to identify the stress even when the canopy reached full coverage. Thermography could only detect stress on the susceptible cultivar. Spectral Vegetation Indices related to canopy cover (NDVI and MCARI2) and chlorophyll content (CHLG) were correlated with the final yield (R = 0.69 on average for the susceptible cultivar) and the final nematode population in the soil (R = 0.78 on average for the susceptible cultivar). Conclusion In this paper we compare the use of visible imaging, thermography and spectrometry over two cultivars and 2 years under outdoor conditions. The three different techniques have their specific strengths in identifying BCN symptoms according to the type of cultivars and the growth stages of the sugar beet plants. Early detection of nematicide benefit and high yield predictability using visible imaging and spectrometry suggests promising applications for agricultural research and precision agriculture.
Why it matches plant phenotyping methods可視画像、熱画像、分光法を用いた植物ストレス・生育表現型の取得能力を比較評価しており、非破壊フェノタイピング手法の技術比較が研究の中心です。
abstractHere, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods.
Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (VIs) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these VIs may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400–1000nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1mm to 35cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various VIs computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected VIs included some classical VIs from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated.For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red-edge and near-infrared domains. Overall, mNDblue[728,850] defined as (R440−R728)/(R440+R850) and computed over the brightest green pixels obtains the best correlations with Cab for spatial resolutions finer than 8.8cm with a root mean square error of prediction better than 2.6μg/cm². Conversely, mNDblue[728,850] poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between Cab and these canopy structure variables. As mNDblue[728,850] can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve Cab from millimeter- to centimeter-scale reflectance imagery.
Why it matches plant phenotyping methods高解像度ハイパースペクトル画像からサトウダイコンの葉クロロフィル含量を推定する方法を開発・評価しており、植物形質の取得が研究の中心である。
abstractThe objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content (RMSE≤10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE=10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE=17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
Why it matches plant phenotyping methods圃場リモートセンシングによる複数の植物形質推定法（植生指数とPROSAIL逆解析）を比較・評価し、フェノタイピング用途での精度と頑健性を検証しているため、方法論が中心である。
titleRetrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping
Sensor technologies are expedient tools for precision agriculture, aiming for yield protection while reducing operating costs. A portable sensor based on chlorophyll fluorescence imaging was used in greenhouse experiments to investigate the response of sugar beet and soybean cultivars to the application of herbicides. The sensor measured the maximum quantum efficacy yield in photosystem II (PS-II) ( F v /F m ). In sugar beet, the average F v /F m of 9 different cultivars 1 d after treatment of desmedipham plus phenmedipham plus ethofumesate plus lenacil was reduced by 56% compared to the nontreated control. In soybean, the application of metribuzin plus clomazone reduced F v /F m by 35% 9 d after application in 7 different cultivars. Sugar beets recovered within few days from herbicide stress while maximum quantum efficacy yield in PS-II of soybean cultivars was reduced up to 28 d. At the end of the experiment, approximately 30 d after treatment, biomass was reduced up to 77% in sugar beet and 92% in soybean. Chlorophyll fluorescence imaging is a useful diagnostic tool to quantify phytotoxicity of herbicides on crop cultivars directly after herbicide application, but does not correlate with biomass reduction.
Why it matches plant phenotyping methodsクロロフィル蛍光イメージングを用いて除草剤による植物傷害・光合成生理状態を定量化し、その診断性能と有用性を評価しており、表現型取得法が中心である。
abstractA portable sensor based on chlorophyll fluorescence imaging was used in greenhouse experiments to investigate the response of sugar beet and soybean cultivars to the application of herbicides.
Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky–Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations.
Why it matches plant phenotyping methods衛星NDVIから作物の生育フェノロジーを抽出する手順を開発・最適化し、地上観測で校正・検証しており、植物形質取得手法が研究の中心です。
abstractwe developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds.
CORE IDEAS: Satellite imagery could be used to predict yield the study crops. Satellite imagery could be used to screen fields for in‐season N application. Obtaining satellite imagery early enough in the season to screen fields for in‐season N is a problem. Algorithms using active‐optical (AO) sensors have been developed to direct in‐season N application to crops. Many farmers in the United States have a large number of farm fields to manage. Farmers using AO technology must visit each field and operate the sensor across the entire field in order to conduct in‐season N application. A field might be driven over with an on‐the‐go N fertilizer applicator, but the application might not be required. The objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunflower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fields that would benefit from in‐season N application. Two N‐rate studies in sugar beet, spring wheat, corn and sunflower, were conducted with experimental unit size of 9 by 9 m large enough to fit a satellite pixel of 5 by 5 m size within each unit. The AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunflower in some site‐years. The problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. These results indicate that even though satellite imagery could be used as a field screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).
Why it matches plant phenotyping methods衛星画像と地上AOセンサーによる作物収量推定を比較・検証しており、収量という植物形質の取得・予測手法が中心である。窒素施用判断への応用も扱うが、センサー手法の性能比較が明示されている。
abstractThe objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunflower similar to the yield prediction possible using AO sensors.
We present the Fluspect-B model (generally referred to as Fluspect), which simulates leaf chlorophyll fluorescence (ChlF), reflectance and transmittance spectra. The existing PROSPECT model and its concept of a compact leaf are used as a starting point, and the differential equations for radiative transfer within the leaf are solved by an efficient doubling algorithm. Due to the simplicity of these equations, Fluspect offers a high computational speed. With incident light provided as the main input parameter, Fluspect calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II. We investigated the model performance using measurements of leaf reflectance, transmittance and ChlF spectra, collected for barley and sugar beet leaves in both a laboratory and outdoors setting. The plants had been grown under various illumination conditions to increase between-leaf variability of leaf biochemical and structural properties. We retrieved the model parameters, compared them to corresponding destructive measurements and finally, used them to simulate ChlF on either side of the leaf at several light intensities. The results show that the model reproduces observed SIF accurately, especially for leaves measured under natural illumination. Most of the observed between-leaf variability of ChlF could be explained from differences in leaf biochemical and structural properties, with potential additional information held by ChlF emission efficiency parameters.
Why it matches plant phenotyping methods葉の蛍光・反射・透過スペクトルから生理・構造特性を推定するFluspectモデルを開発し、実測スペクトルとの比較で性能検証しており、植物表現型取得・推定手法が研究の中心です。
abstractWe present the Fluspect-B model (generally referred to as Fluspect), which simulates leaf chlorophyll fluorescence (ChlF), reflectance and transmittance spectra.
Cercospora beticola is an economically significant fungal pathogen of sugar beet, and is the causative pathogen of Cercospora leaf spot. Selected host genotypes with contrasting degree of susceptibility to the disease have been exploited to characterize the patterns of metabolite responses to fungal infection, and to devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen. Sugar beet genotypes were analyzed for metabolite profiles and hyperspectral signatures. Correlation of data matrices from both approaches facilitated identification of candidates for metabolic markers. Hyperspectral imaging was highly predictive with a classification accuracy of 98.5-99.9% in detecting C. beticola . Metabolite analysis revealed metabolites altered by the host as part of a successful defense response: these were L-DOPA, 12-hydroxyjasmonic acid 12- O -β-D-glucoside, pantothenic acid, and 5- O -feruloylquinic acid. The accumulation of glucosylvitexin in the resistant cultivar suggests it acts as a constitutively produced protectant. The study establishes a proof-of-concept for an unbiased, presymptomatic and non-invasive detection system for the presence of C. beticola . The test needs to be validated with a larger set of genotypes, to be scalable to the level of a crop improvement program, aiming to speed up the selection for resistant cultivars of sugar beet. Untargeted metabolic profiling is a valuable tool to identify metabolites which correlate with hyperspectral data.
Why it matches plant phenotyping methodsサトウダイコン感染の早期植物状態を、ハイパースペクトル画像で非侵襲的に検出する方法が研究の中心であり、分類精度も評価されているため。
abstractto devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen
Abstract. We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.
Why it matches plant phenotyping methodsサトウダイコンの葉の病徴を、ハイパースペクトル画像と3D形状から検出する手法を開発・比較しており、植物病害状態の表現型取得が中心である。
abstractWe analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants.
We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.
Why it matches plant phenotyping methodsサトウダイコンの病害症状を、ハイパースペクトル画像と3D形状から検出する手法の比較・開発が研究の中心であり、植物状態の推定に直接関与する。
abstractWe analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants.
Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).
Why it matches plant phenotyping methodsTLSとRTK-GPSを用いて作物高・生長を抽出する測定手法を開発・適用し、画像および手動測定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。
abstractterrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach
The quantitative resistance of sugar beet (Beta vulgaris L.) against Cercospora leaf spot (CLS) caused by Cercospora beticola (Sacc.) was characterised by hyperspectral imaging. Two closely related inbred lines, differing in two quantitative trait loci (QTL), which made a difference in disease severity of 1.1-1.7 on the standard scoring scale (1-9), were investigated under controlled conditions. The temporal and spatial development of CLS lesions on the two genotypes were monitored using a hyperspectral microscope. The lesion development on the QTL-carrying, resistant genotype was characterised by a fast and abrupt change in spectral reflectance, whereas it was slower and ultimately more severe on the genotype lacking the QTL. An efficient approach for clustering of hyperspectral signatures was adapted in order to reveal resistance characteristics automatically. The presented method allowed a fast and reliable differentiation of CLS dynamics and lesion composition providing a promising tool to improve resistance breeding by objective and precise plant phenotyping.
Why it matches plant phenotyping methods病斑の時間・空間的発達をハイパースペクトル画像で測定し、クラスタリングにより病害抵抗性を自動・客観的に評価する手法が中心であるため。
abstractThe temporal and spatial development of CLS lesions on the two genotypes were monitored using a hyperspectral microscope.
In this special issue of Functional Plant Biology, we present a perspective of the current state of the art in plant phenotyping. The applications of automated and detailed recording of plant characteristics using a range of mostly non-invasive techniques are described. Papers range from tissue scale analysis through to aerial surveying of field trials and include model plant species such as Arabidopsis as well as commercial crops such as sugar beet and cereals. The common denominators are high throughput measurements, data rich analyses often utilising image based data capture, requirements for validation when proxy measurement are employed and in many instances a need to fuse datasets. The outputs are detailed descriptions of plant form and function. The papers represent technological advances and important contributions to basic plant biology, and these studies are commonly multidisciplinary, involving engineers, software specialists and plant physiologists. This is a fast moving area producing large datasets and analytical requirements are often common between very diverse platforms.
Why it matches plant phenotyping methods植物フェノタイピングの現状を、非侵襲的・自動・高スループットな計測技術、画像データ取得、検証、データ融合の観点から概説するレビューであり、方法論が中心です。
abstractwe present a perspective of the current state of the art in plant phenotyping.
Cercospora leaf spot (CLS) caused by Cercospora beticola is the most destructive leaf disease of sugar beet and may cause high losses in yield and quality. Breeding and cultivation of disease-resistant varieties is an important strategy to control this economically relevant plant disease. Reliable and robust resistance parameters are required to promote breeding progress. CLS lesions on five different sugar beet genotypes incubated under controlled conditions were analyzed for phenotypic differences related to field resistance to C. beticola. Lesions of CLS were rated by classical quantitative and qualitative methods in combination with noninvasive hyperspectral imaging. Calculating the ratio of lesion center to lesion margin, four CLS phenotypes were identified that vary in size and spatial composition. Lesions could be differentiated into subareas based on their spectral characteristics in the range of 400 to 900 nm. Sugar beet genotypes with lower disease severity typically had lesions with smaller centers compared with highly susceptible genotypes. Accordingly, the number of conidia per diseased leaf area on resistant plants was lower. The assessment of lesion phenotypes by hyperspectral imaging with regard to sporulation may be an appropriate method to identify subtle differences in disease resistance. The spectral and spatial analysis of the lesions has the potential to improve the screening process in breeding for CLS resistance.
Why it matches plant phenotyping methodsサトウダイコン葉病斑の表現型を、ハイパースペクトル画像で非侵襲的に抽出・分類し、抵抗性評価や育種スクリーニングへの有用性を検討しており、病害表現型取得法が中心的です。
titleImprovement of Lesion Phenotyping in Cercospora beticola –Sugar Beet Interaction by Hyperspectral Imaging
Crop growth is an important quality assessment in plant breeding, especially in open field crops which grow in fluctuating and unfavorable outdoor conditions. To evaluate the growth potential of different plant varieties, researchers conduct leaf area measurements of emerged plants to evaluate its growth potential. This is a time consuming and labor intensive activity and therefore often only conducted on random spots on the field. An automatic computer vision system was built to automate and to speed up this plant phenotyping process. The system consist of three color cameras mounted on an implement facing straight downwards, lamps for illumination, an encoder wheel and a computer system. Natural light was blocked by a surrounding cover to limit the effect of variable outdoor light conditions on the image quality. The computer vision software makes use of an excessive green algorithm (2G - R - B) to segment the plant material from the soil. As the crop plants are sown by a precision sowing device in a regular pattern a method based on the fast-fourier transform (FFT) is used to distinguish crop plants from weed plants. A rectangular based clustering algorithm, based on 8-pixel nearest-neighbor connectivity, is used to cluster separated plant-parts together as one individual plant object used to measure the leaf area. The system was validated in an open-field sugar beet crop at the growing stage off our leaves. Fifty-five sugar beet plants were manually measured by experienced plant scouts(“ground truth”). The same plants were measured with the computer vision system. An ANOVA F-test(P<0.05) was used to discriminate the two measurement methods. The F-probability was 0.055 an djust above the significance level. So the H0 hypothesis that there is not a difference between human measurement and machine vision measurement was no trejected. Possible causes of difference was the inability of the system to detect and measure plants damaged by animals and very small plants which were occluded by clods or bigger plants. Nevertheless,with improvements on the vision software and camera/lamp configuration, the system is profitable for a fast and accurate leaf area measurement and corresponding plant phenotyping.
Why it matches plant phenotyping methods圃場でのビート個体の葉面積を自動取得するコンピュータビジョンシステムを開発し、手動測定と比較検証しており、植物表現型取得法が研究の中心である。
abstractAn automatic computer vision system was built to automate and to speed up this plant phenotyping process.
Yield prediction in sugar beet (Beta vulgaris L.) is important as a basis for in-season N application. Active optical sensors have been researched in sugar beet for yield estimation. A common field method for using active-optical sensors is to establish an N non-limiting area, and compare the yield predicted from sensor readings with readings from the rest of the field. Yield difference is the basis for calculation of N rate. Sugar beet gains root mass and sugar content with time. The objectives of these experiments were to utilize two active-optical sensors at two timings with canopy height measurements and relate readings to root yield and recoverable sugar yield at consecutive harvest dates. A 2-yr study in the Red River Valley of North Dakota and Minnesota was conducted on four sites to compare two active-optical sensors, GreenSeeker and Holland Crop Circle, red normalized differential vegetative index (NDVI), red edge NDVI, with and without canopy height for use in sugar beet yield prediction. The red NDVI and red edge NDVI, used at V 6–8 and V 12–14 were similar in their relationship to sugar beet yield over several harvest dates. The r² of sensor measurement and yield relationships at V 6–8 improved when canopy height was considered but not at V 12–14. Active-optical sensors when canopy height is considered could be used to predict sugar beet root yield and recoverable sugar yield over a range of harvest dates, which would be useful in developing algorithms for in-season N fertilization.
Why it matches plant phenotyping methods複数のアクティブ光学センサーとキャノピー高からビートの根収量・回収糖収量を推定し、センサー間比較と予測性能を評価しており、植物形質取得法が研究の中心である。
abstractThe objectives of these experiments were to utilize two active-optical sensors at two timings with canopy height measurements and relate readings to root yield and recoverable sugar yield at consecutive harvest dates.