Accurate estimation of pasture biomass is essential for determining cattle stocking rates and grazing durations. The objective of this study was to comparatively evaluate five sensor-based systems for estimating aboveground Bermudagrass (Cynodon dactylon) biomass and identify the leading sensing approach for continued development and broader validation. The five systems included Structure-from-Motion (SfM), Ultrasound Sensor and Ski (US-Ski), Inertial Measurement Unit and Ski (IMU-Ski), Inertial Measurement Unit and Roller (IMU-Roller), and Depth Camera (DC). These systems were deployed on unmanned aerial and ground vehicles to measure crop height under identical field conditions. Regression models relating measured crop height to wet biomass yield (WBY) were developed as a common calibration framework for statistically comparing sensor performance. These empirical allometric equations were intended to support comparative benchmarking of the sensing systems and were not developed as final operational biomass prediction models for immediate field deployment. The influence of vegetation coverage on yield predictions generated by the crop height-based equations was also examined. The results indicated that the IMU-Ski system demonstrated the strongest overall comparative performance (R2 = 0.97; SeY = 1112 kg-wet/ha), followed by the DC system (R2 = 0.97; SeY = 1132 kg-wet/ha). Based on its overall benchmarking performance, including calibration accuracy, residual error and simplicity, the IMU-Ski system was identified as the leading sensing approach for continued development and broader validation among the five evaluated methods. The results also indicated that addition of vegetation coverage into the crop height-based regression models did not significantly improve prediction accuracy under the experimental conditions evaluated.
Why it matches plant phenotyping methods複数のセンサーシステムによる牧草バイオマス推定を比較・校正・ベンチマークしており、植物形質の取得法と技術性能の評価が研究の中心です。
abstractcomparatively evaluate five sensor-based systems for estimating aboveground Bermudagrass (Cynodon dactylon) biomass
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Grass seed crops are susceptible to yellow dwarf viruses transmitted by aphids. The Willamette Valley in Oregon, United States, is the leading producer of cool-season grass seed crops globally, and industry reports have attributed seed yield loss and shortened stand longevity to aphid-transmitted yellow dwarf viruses. Genetic resources are needed for effective and sustainable management of this pest, specifically the Rhopalosiphum padi–PAV pathosystem, in grass seed production to reduce foliar insecticide applications and maintain optimum seed yield potential. High-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs. An automated video tracking procedure was optimized to evaluate host plant resistance in cool-season grass seed crops to R. padi–PAV with live plants and viruliferous and nonviruliferous aphid populations. Feeding behavior recorded with automated video tracking was strongly correlated with “ground-truthed” observations by human observers. Partial resistance (antixenosis and antibiosis) and tolerance traits were detected in select perennial ryegrass and tall fescue cultivars evaluated with traditional phenotyping methods in a greenhouse setting and with high-throughput phenotyping using automated video tracking in the laboratory. Across grass cultivars, nonviruliferous aphids had greater fitness and preference for noninfected grass plants compared with viruliferous aphids. Automated video tracking can be used as a high-throughput phenotyping method for continued evaluation of host plant resistance in grasses grown for seed production, as well as to identify resistant genotypes in other grass crops susceptible to aphid–yellow dwarf virus virus–vector systems.
Why it matches plant phenotyping methods自動動画追跡を用いてアブラムシ媒介ウイルスに対する植物抵抗性を高スループットに評価する手法を最適化・検証し、従来観察との相関および抵抗性形質の検出を示しており、表現型取得法が研究の中心である。
abstractHigh-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs.
Biomass is a key trait in pasture plant breeding and agronomy, but measuring Dry Matter Yield or Fresh Weight across large numbers of samples is labour intensive and costly. Efficient biomass assessment systems must balance accuracy, speed, and cost, while ideally enabling non-destructive measurements. We developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution. The system operates under ambient light conditions at a ground speed of 2.7 km per hour. It was evaluated in small-plot perennial ryegrass trials at two field sites in New Zealand, across two seasons at site A and one season at site B. At site A, correlations between LiDAR-derived height and fresh weight ranged from 0.33 to 0.74 across individual measurement cycles, with an overall multilevel R² of 0.72. At site B, the multilevel correlation increased to R² = 0.88. Weekly LiDAR scans at site B were used to estimate plot-level growth rates for 60 plots, demonstrating improved temporal resolution. Statistically significant differences in growth rate within regrowth cycles were detected among plots. The platform reliably differentiates perennial ryegrass plots based on biomass and offers higher temporal resolution than traditional methods.
Why it matches plant phenotyping methodsLiDARプラットフォームを開発し、草高から牧草バイオマスと成長率を非破壊推定・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 rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution.
Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (ηp2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (ηp2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.
Why it matches plant phenotyping methodsRGB画像から芝草キャノピー色を定量化するΔEgなどの指標を導入・比較し、クロロフィルや品質評価との技術的関連性を検証しており、植物表現型取得法が中心である。
abstractWe introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics.
Reproduction assets foundThe paper's Data Availability Statement deposits the phenotype data and the authors' Python image-processing/metric-computation scripts and R statistical analysis scripts in the USDA National Agricultural Library Ag Data Commons, a public repository. The full RGB imagery archive, however, is only available upon requestCode · public2025;23:673–687. doi: 10.1002/lom3.10705.
Associated Data
Data Availability Statement
Data and Python scripts used for image processing and %G, %Gr, %Y, ΔEg, DGCI, HSVi, BA SD , CIELUV v* metric computation, and R scripts used for statistical analysis are be available in the USDA National Agricultural Library Ag Data Commons ( https://agdatacommons.nal.usda.gov/ ), Data for—Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions, accessed on 27 July 2026. The full RGB imagery archive will be made available upon reasonable request.Open asset ↗USDA National Agricultural Library Ag Data Commonslines:691-695Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Accurate seed-yield prediction is essential for optimizing nitrogen (N) management in buffalograss seed production. However, current UAV-based approaches often rely directly on vegetation indices (VIs), which provide limited physiological insight and not transfer well across growing seasons. To address this limitation, we developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC), representing crop structural and physiological status, respectively. Field experiments were conducted from 2022 to 2024 under seven N application rates. Using data from 2022 and 2023, we calibrated a quadratic PH-CNC model and then evaluated its predictive performance with an independent 2024 dataset. We also compared this framework with a conventional direct VI-based model. The trait-based model explained 89% of the variation in seed yield during calibration and showed better cross-year predictive performance than the VI-based model (R 2 = 0.70, NRMSE = 17% versus R 2 = 0.52, NRMSE = 22%). In addition, the model captured the decline in seed yield under excessive N input, indicating that it reflected biologically meaningful crop responses. These results demonstrated that combining structural and physiological traits can provide a more robust and interpretable alternative to conventional VI-based methods for UAV-based yield prediction. This framework has practical potential for improving precise and sustainable N management in buffalograss seed production.
Why it matches plant phenotyping methodsUAV由来の草高と群落窒素濃度を統合した形質ベース予測法を開発し、独立年データで検証・従来法と比較しており、表現型取得と解析手法が中心である。
abstractwe developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC)
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Tiller production is a critical determinant of turfgrass canopy density and plant performance, yet manual tiller counting is too labor-intensive for large breeding programs. Methods: To address this limitation, we evaluated 770 plants from an interspecific bentgrass hybrid population and developed three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8. Using a large annotated image dataset, we assessed each method's accuracy, robustness under occlusion, and computational efficiency. Results: Although two-stage detectors are often expected to provide superior precision for complex plant structures, the one-stage YOLOv8 model achieved the highest accuracy (R² = 0.97) and processed images substantially faster than Faster R-CNN, while both the edge-based method and Faster R-CNN showed reduced performance in dense canopies. Discussion: These findings demonstrate that recall-oriented one-stage detection can outperform more complex two-stage models for phenotyping tasks involving fine, highly occluded structures. The resulting workflow provides a reliable, high-throughput solution for generating biologically meaningful tiller counts and offers a transferable framework for integrating image-derived phenotypes into genetic analyses and breeding pipelines across grass species.
Why it matches plant phenotyping methodsイネ科植物の分げつ数を画像から自動抽出する複数手法を開発・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。
abstractdeveloped three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8.
Efficient phenotyping is essential for accelerating genetic improvement in turfgrass breeding, where manual measurements are labor-intensive. This study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes. HSI data (400-1000 nm) were processed through a user-in-the-loop hybrid segmentation pipeline integrating UMAP dimensionality reduction, DBSCAN clustering, Random Forest classification, and pseudo-RGB refinement. To independently assess vegetation classification performance, 10,000 manually annotated reference points from 50 pseudo-RGB images were compared with the automated module, yielding an overall accuracy of 0.9697, a precision of 0.8830, a recall of 0.9240, a specificity of 0.9779, an F1-score of 0.9030, and Cohen's kappa of 0.8851. A Combined Ranking Score (CRS) integrating five vegetation indices and vegetation pixel count was significantly associated with aerial shoot count ( r = -0.445, p r = -0.207, p < 0.001). The highest-ranked genotype showed a 9370.3-pixel increase in vegetation area between 6 and 16 weeks after transplanting, compared with 1417.7 pixels for the lowest-ranked genotype. Classification performance declined under high-coverage conditions, indicating increased mixed-pixel ambiguity in dense canopies. These results suggest that HSI-based CRS can support rapid, objective, and non-destructive relative ranking of density-related vegetative growth in turfgrass breeding. Because the study was conducted at a single location and season and correlations with manual traits were moderate, the framework is best interpreted as a screening and ranking tool rather than a direct predictive model.
Why it matches plant phenotyping methodsHSI画像と解析パイプラインを用いて、芝草の植生面積・密度関連成長形質を高スループットに推定・検証することが研究の中心であり、育種集団の表現型スクリーニング手法に該当する。
abstractThis study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes.
Key message A new approach is proposed for the use of the genetic markers to manage DUS trials, targeted at individual phenotypic characteristics using genomic prediction, as well for supporting Distinctness decisions. High-performing crop varieties underpin food security. Due to the cost of developing varieties, systems have been established to provide breeders with legal protection for their varieties. In many countries, such protection is afforded by the International Union for the Protection of New Varieties of Plants (UPOV) system. New varieties must be phenotypically Distinct from existing varieties using a set of crop-specific characteristics, as well as Uniform and Stable (DUS). For many crops, DUS assessment is costly as candidates must be compared to many existing varieties in field trials, based on numerous DUS characteristics. The use of genetic markers has long been considered as a potential tool for managing costs of such trials, for example, by identifying existing varieties that need not be compared to candidate varieties. Under UPOV guidance, the use of genetic markers must be reflective of phenotypic differences in DUS characteristics. Within this framework, we propose a new approach for using markers based on the application of genomic prediction, which is used to predict variety differences in individual characteristics. The approach is evaluated with perennial ryegrass and wheat, yielding promising results. Additionally, we propose a novel approach in which genomic prediction is used to refine Distinctness decisions after DUS trials have been run by integrating genetic and trial information. Using perennial ryegrass as an example, we demonstrate that this approach, which respects the primacy of phenotype in DUS testing, could be used to support distinctness decisions, especially for cross-pollinated agricultural crops where Distinctness may be harder to achieve.
Why it matches plant phenotyping methodsゲノム予測を用いてDUS特性の品種差を予測し、DUS試験後のDistinctness判定を支援する手法を提案・評価しており、植物表現型の推定と判定支援が研究の中心である。
abstractWithin this framework, we propose a new approach for using markers based on the application of genomic prediction, which is used to predict variety differences in individual characteristics.
Previous studies have shown that multitemporal data can strengthen the relationship between grassland diversity and spectral reflectance. However, most studies used interpolated Sentinel-2 time series as such. This study investigated whether species richness models can be improved using fitted Sentinel-2 temporal features describing the overall plant growth patterns.We measured plant species richness in 77 quadrats (1m x 1m) from revegetated alpine grasslands located around an open pit mine in Hochfilzen, Austria, characterized by a high range of species richness. Several multi-year Sentinel-2 vegetation index time series were used as inputs for fitting temporal features such as phenology descriptors, harmonic decomposition, frequency decomposition and functional principal components. Those features were compared to interpolated time series of Sentinel-2 bands used in state of the art baseline models (Fauvel et al., 2020; Muro et al., 2022).The feature sets were inserted into a Random Forest regression model pipeline, first selecting the best performing features in a nested cross-validation, then applying the final model over the grassland areas to produce species richness maps. Lastly, SHAP feature analysis was performed to improve model interpretability.Our best model, using fitted CIRE time series features, achieved coefficient of determination R2 = 0.19 in cross-validation and R2 = 0.36 on holdout set (16 quadrats). Features describing events around peak growing season were found especially important. Our model clearly outperformed all baseline models on holdout set across all metrics: R2 (+0.15 to +0.33), absolute Root Mean Squar Error RMSE (-0.35 to -0.91) and relative RMSE (-0.02 to -0.05).All models highlighted similar areas of high or low richness. Differences were observed for most extreme species richness, or less densely vegetated pixels. Results suggest our features are better suited to small datasets. The comparison of models will also be carried out on larger field inventories.Future steps will include extension to species abundance metrics, and a comparison to spectral variation features extracted from multi-scale hyperspectral imagery from Hyspex Mjölnir VS-620 drone and EnMAP satellite.This research is part of the MultiMiner project funded by the European Union’s Horizon Europe research and innovations actions programme under Grant Agreement No. 101091374.
Why it matches plant phenotyping methodsSentinel-2時系列から植物種多様性（種数）を推定する特徴抽出・機械学習パイプラインを開発・比較し、交差検証とホールドアウトで評価しているため、植物形質推定法が中心です。
abstractThis study investigated whether species richness models can be improved using fitted Sentinel-2 temporal features describing the overall plant growth patterns.
Accurate detection of winter damage in turfgrass is essential for proactive management but remains difficult because early-stage injury is faint, irregular, and easily confused with background noise. These characteristics create two major challenges: limited availability of reliable training data and the need for a segmentation model that is highly sensitive to subtle features. To address the data limitation, this study employs a Conditional Deep Convolutional Generative Adversarial Network (cDCGAN) to generate synthetic, high-fidelity vegetation index (VI) maps. Compared with raw spectral bands, VIs are more robust to noise and enhance both dataset diversity and model generalization. To meet the segmentation challenge, we introduce a Transformer-based model with a novel Adaptive Attention Decoder (AAD), which dynamically refines feature representations to improve detection of low-contrast, spatially irregular damage. Field experiments conducted on golf courses in central Oregon, USA, from 2022 to 2023 demonstrate that the proposed pipeline outperforms other advanced deep learning models, achieving an mIoU of 82.47%, an accuracy of 97.85%, a recall of 85.62%, and an F1-score of 88.30%. Overall, this research presents a problem-driven framework that integrates targeted data augmentation with an improved segmentation architecture, offering a robust and accurate solution for early detection of winter damage in precision turfgrass management.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から芝草の冬害をセグメンテーションするデータ拡張・深層学習手法を開発し、圃場で性能検証している。植物の病害状態の取得が研究の中心である。
abstractTo address the data limitation, this study employs a Conditional Deep Convolutional Generative Adversarial Network (cDCGAN) to generate synthetic, high-fidelity vegetation index (VI) maps.
Radiometric calibration of multispectral imagery plays a critical role in the determination of vegetation-related features. This radiometric calibration strongly depends on a proper sensor configuration when acquiring images, the shutter speed being a critical parameter. The objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3) on a homogeneous field of turfgrass ( Cynodon dactylon L. -Poaceae) and on the vegetation index (VI) values calculated from them, under different solar radiation conditions. For this purpose, 10 shutter speed configurations were tested in field campaigns with variable solar radiation values. The main results demonstrated that the reflectance in the green spectral region was more sensitive to shutter speed than that of the red and NIR spectral regions, particularly under high solar radiation conditions. Moreover, VIs calculated using the green band were more sensitive to slow shutter speeds, thus presenting a higher probability of providing meaningless artifact values. In conclusion, this study provides shutter speed recommendations under different illumination conditions to optimize the reflectance and the VI sensitivity within the image, which can be applied as a simple method to optimize image acquisition from unmanned aerial vehicles under varying solar radiation conditions.
Why it matches plant phenotyping methods低コスト multispectral センサーによる植物の反射率・植生指数推定について、シャッター速度と照明条件の影響を評価し、画像取得条件を最適化する方法を示しており、フェノタイピング手法が中心である。
abstractThe objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3)
Abstract The application of small unmanned aircraft systems (sUAS)‐based high‐throughput phenotyping in plant breeding has advanced significantly over the past decade. Hyperspectral images and machine learning approaches offer potential to enhance drought resistance screening in turfgrass. However, large‐scale field applications remain limited, and the transition from controlled environments to real‐world phenotyping is not well understood. This study aimed to develop an sUAS‐based hyperspectral image workflow to monitor changes in turfgrass canopy reflectance during drought, validate previously reported indices from controlled environment studies in a large‐scale field study, and estimate visual turfgrass quality (TQ) from hyperspectral images. Images were collected from a zoysiagrass ( Zoysia spp.) mapping population at three dates under varying soil moisture conditions. Vegetation indices (VIs) related to light use efficiency, leaf pigments, senescence, water status, and green vegetation were computed and compared. Top‐performing genotypes under drought exhibited greater absorption in blue and red wavelengths and higher near‐infrared reflectance than poor‐performing ones. The photochemical reflectance index and plant senescence reflectance index were highly correlated with TQ ( r = 0.84 and −0.76), showed higher coefficient of variation (range 18%–37%), and had higher broad‐sense heritability (0.73–0.74) than normalized difference vegetation index (0.69), warranting their use in large‐scale field study. Machine learning models estimated TQ with a mean absolute error of 0.46. These findings highlight the importance of integrating VIs related to light use efficiency, leaf pigments, senescence, and water status to gain deeper insights into turfgrass drought response and support breeding for stress tolerance.
Why it matches plant phenotyping methodssUASハイパースペクトル画像によるキャノピー形質取得ワークフローを開発し、指標を検証して芝草品質を推定しており、フェノタイピング手法が中心である。
abstractThis study aimed to develop an sUAS‐based hyperspectral image workflow to monitor changes in turfgrass canopy reflectance during drought, validate previously reported indices from controlled environment studies in a large‐scale field study, and estimate visual turfgrass quality (TQ) from hyperspectral images.
Reproduction assets foundThe paper's data availability statement points to a Zenodo-hosted dataset of spectral reflectance measurements from the zoysiagrass mapping population under drought, which directly reproduces this paper's phenotyping measurements. No author analysis code or trained models were identified.Dataset · publicDATA AVA I L A B I L I T Y S TAT E M E N T
The data referenced in this paper are available in a repository
hosted by Zenodo (Zhang, 2025).Open asset ↗Zenodopdf-raw-page:17 lines:1-85Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Advancements in digital three‐dimensional (3D) imaging technology have enabled precise, high‐throughput, and non‐destructive phenotyping of plant morphology. In this study, we developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis. By employing a structure from motion approach, we reconstructed detailed 3D models of zoysiagrass using four industrial cameras and an automated imaging platform. A machine learning algorithm was applied to accurately isolate plant components from non‐plant elements. From these segmented models, we extracted key morphological traits—height, spread area, color, and volume—providing a comprehensive dataset for breeding applications. As a digitally derived trait, volume offers new potential in characterizing plant architecture and assessing yield‐related traits non‐destructively. Additionally, we developed a small‐scale, low‐cost prototype system using Raspberry Pi and LEGO‐based components, demonstrating the scalability and adaptability of 3D phenotyping systems across various experimental settings and budgets. Although 3D phenotyping under controlled conditions using potted plants is not directly transferable to field‐based evaluation, it provides essential, reproducible data that bridge early‐stage screening and later field validation in breeding programs. These digital morphological measurements are expected to enhance the precision, repeatability, and objectivity of turfgrass evaluation. As 3D technologies continue to evolve and integrate with genomic and environmental data, digital phenotyping will play an increasingly important role in accelerating turfgrass improvement and promoting data‐driven plant breeding.
Why it matches plant phenotyping methods植物形態を対象とする3D画像フェノタイピングシステムを開発し、機械学習による分離と形態形質抽出、さらに低コスト試作機まで扱っており、フェノタイピング手法が研究の中心である。
abstractwe developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis.
Abstract Zoysiagrass ( Zoysia genus) is a valuable warm‐season turfgrass species that exhibits significant diversity in growth and morphological traits across different ecotypes. Traditional methods of classifying zoysiagrass ecotypes rely heavily on morphological observations. However, these methods can be labor‐intensive and may not fully capture the phenotypic diversity present within the species. In this study, we evaluated the utility of non‐invasive digital phenotyping to characterize zoysiagrass ecotypes. Using a digital phenotyping system (DPS) allows for precise measurements of plant height, area, 3D volume (representing biomass), and color index. Clustering algorithms were applied to assess diversity and classify zoysiagrass ecotypes. The subsequent results were compared to manual species classification and genetic marker analysis. The cluster results of DPS effectively differentiate between the three Zoysia species and demonstrate a high correspondence between digital phenotyping and traditional morphological methods. The study highlights the advantages of grouping zoysiagrass based on phenotypic traits and growth characteristics rather than solely on morphological observation or genetic markers, particularly in the context of breeding and research, where a broader range of traits provides more opportunities for selection. Future research could integrate this method with genotypic and transcriptomic analyses for a deeper understanding of zoysiagrass diversity.
Why it matches plant phenotyping methodsデジタルフェノタイピングシステムで植物形質を非侵襲的に測定し、クラスタリングによる分類性能を手動分類・遺伝マーカーと比較しており、フェノタイピング手法の適用と技術評価が中心である。
abstractIn this study, we evaluated the utility of non‐invasive digital phenotyping to characterize zoysiagrass ecotypes.
Monitoring the dust retention content in grassland plants around open-pit coal mines is of significant importance for environmental pollution monitoring and the development of dust control strategies. This paper focuses on the HulunBuir grassland in the Inner Mongolia Autonomous Region, China. UAV-borne hyperspectral data and measured dust retention content in plant canopies are used as data sources. The spectral response characteristics of canopy dust retention are analyzed, and four types of optimized spectral indices are constructed, including the difference index (DI), ratio index (RI), normalized difference index (NDI), and inverse difference index (IDI). The spectral index with the highest absolute value of the correlation coefficient with the canopy dust retention is selected as the feature variable for each spectral index. In addition, machine learning methods such as the partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) methods are used to develop models for the inversion of canopy dust retention. The results show that as the dust retention content increases, the canopy reflectance in the visible wavelength initially increases and then decreases, while the reflectance in the near-infrared wavelength gradually decreases. The spectral reflectance values at different dust retention levels exhibit significant differences in the 400–420 nm, 579–698 nm, and 714–1000 nm ranges. The four types of spectral indices constructed exhibit high correlations with the canopy dust retention content, and the spectral index with the highest absolute value of the correlation coefficient is composed of near-infrared bands. The dust retention inversion model established using the RF method is more accurate than those established using the PLSR and SVM methods and yields a higher prediction accuracy. The high canopy dust retention areas are mainly distributed within 900 m of the mining area, and the dust retention gradually decreases with distance. In addition, with increasing dust retention, the fractional vegetation cover (FVC) decreases. The results of this study provide a theoretical basis and technical support for monitoring dust retention in grassland plant canopies and for dust control measures.
Why it matches plant phenotyping methodsUAVハイパースペクトルデータから植物キャノピーのダスト保持量を推定するスペクトル指標と機械学習モデルを開発・比較しており、植物状態の取得・推定手法が中心である。
abstractUAV-borne hyperspectral data and measured dust retention content in plant canopies are used as data sources.
Perennial ryegrass is an important temperate grass used for forage and turf worldwide. It forms symbiotic relationships with endophytic fungi (endophytes), conferring pasture persistence and resistance to herbivory. Endophyte performance can be influenced by the host genotype, as well as environmental factors such as seed storage conditions. It is therefore critical to confirm seed quality and purity before a seed is sown. DNA-based methods are often used for quality control purposes. Recently, near-infrared spectroscopy (NIRS) coupled with hyperspectral imaging was used to discriminate perennial ryegrass cultivars and endophyte presence in individual seeds. Here, a NIRS-based analysis of bulk seeds was used to develop models for discriminating perennial ryegrass cultivars (Alto, Maxsyn, Trojan and Bronsyn), each hosting a suite of eight to eleven different endophyte strains. Sub-sampling, six per bag of seed, was employed to minimize misclassification error. Using a nested PLS-DA approach, cultivars were classified with an overall accuracy of 94.1-98.6% of sub-samples, whilst endophyte presence or absence was discriminated with overall accuracies between 77.8% and 96.3% of sub-samples. Hierarchical classification models were developed to discriminate bulked seed samples quickly and easily with minimal misclassifications of cultivars (<8.9% of sub-samples) or endophyte status within each cultivar (<11.3% of sub-samples). In all cases, greater than four of the six sub-samples were correctly classified, indicating that innate variation within a bag of seeds can be overcome using this strategy. These models could benefit turf- and pasture-based industries by providing a tool that is easy, cost effective, and can quickly discriminate seed bulks based on cultivar and endophyte content.
Why it matches plant phenotyping methods種子のNIRSスペクトルとPLS-DAを用いて、品種およびエンドファイト有無を識別するモデルを開発・評価しており、表現型／種子状態の取得・判別手法が研究の中心である。
abstractHere, a NIRS-based analysis of bulk seeds was used to develop models for discriminating perennial ryegrass cultivars
This study evaluates the effectiveness of fine‐tuning a semantic segmentation model to identify and quantify dollar spot in turfgrasses, the most extensively managed and researched disease of turfgrasses worldwide. Using the DeepLabV3+ model, recognized for its capability to segment complex shapes and integrate multi‐scale contextual information, the research leveraged a diverse dataset comprising various turfgrass species, disease stages, and lighting conditions to ensure robust model training. The trained model is able to identify and segment disease instances accurately and precisely, and the results indicate the potential for model‐based assessment to outperform traditional visual assessment methods in speed, accuracy, and consistency. The development of deep learning models on extensive datasets like ImageNet requires significant computational resources. However, by fine‐tuning a pretrained semantic segmentation model, we adapted it for disease segmentation using only a standard personal computer's graphics processing unit. This approach not only conserves resources but also highlights the practicality of deploying advanced deep learning applications in turfgrass pathology with limited computational capacity. The proposed model provides a new tool for turfgrass researchers and professionals to rapidly and accurately quantify this important disease under real‐world growing conditions. Additionally, the findings suggest the potential to apply deep learning algorithms to other turfgrass diseases to support data‐driven decisions. This could enhance disease management practices and improve decision‐making processes for fungicidal treatments, thereby improving the economic and environmental sustainability of turfgrass management.
Why it matches plant phenotyping methods芝草の病害状態を画像からセグメンテーションし、病斑を定量化する深層学習手法の開発・評価が中心であり、植物病害フェノタイプの取得方法に該当する。
abstractThis study evaluates the effectiveness of fine‐tuning a semantic segmentation model to identify and quantify dollar spot in turfgrasses
Golf courses are increasingly affected by water scarcity and climate change. An understanding of spatial variability of actual evapotranspiration (ETₐ) and turfgrass quality (TQ) site-specific management zones (SSMZ) is important for the implementation of precision turfgrass management. Therefore, the main objectives of this study were to quantify the relationship between remotely sensed TQ and ETₐ estimates and to evaluate the spatial variations of TQ and ETₐ at a golf course in Utah. Ground-based normalized difference vegetation index was collected using a TCM-500 sensor, and aerial multispectral and thermal imagery data were acquired from unpiloted aircraft systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ-random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ETₐ estimates. The TQ and ETₐ SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results demonstrated that TQ-RF model was accurate within a root mean square error of 0.05. The correlation between TQ-RF and ETₐ was stronger for fairways (R² = 0.74), tees (R² = 0.66), and roughs (R² = 0.75) as compared to greens (R² = 0.25) and the driving range (R² = 0.36) on July 20, 2022. Actual evapotranspiration SSMZ, in combination with TQ-RF SSMZ, is useful for irrigation scheduling, addressing the question of how much and where to irrigate. This study demonstrates the ability of TQ-RF and ETₐ SSMZ to identify spatial variation for the purpose of landscape irrigation management in semi-arid areas.
Why it matches plant phenotyping methodsUAS画像とRFモデルにより芝草品質という植物状態を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractA remote sensing TQ-random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm.
Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.
Why it matches plant phenotyping methods安価なカメラ画像から芝草の被覆・色などの形質を抽出し、画像補正の感度、他センサーおよび既存測定との相関を検証しており、植物表現型取得法が中心である。
abstractThis study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations.
Reproduction assets foundThe paper deposits its phenotype measurement datasets (raw data, ANOVA statistics, time series) both in MDPI Supplementary Materials and in a public AgDataCommons dataset. Plant images are only available upon request, and no author analysis code repository with a public URL is stated.Dataset · publicDatasets are supplied in the Supplementary Materials and at https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_Visualizing_Plant_Responses_Novel_Insights_Possible_through_Affordable_Imaging_Techniques_in_the_Greenhouse/26527447 , accessed on 13 August 2024; images are available upon request.Open asset ↗agdatacommons.nal.usda.gov · 26527447lines:97-173Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24206676/s1 . Supplementary S1: F-values. Supplementary S2: p -values for experimental effects ANOVA ( Table 4 ), nine additional individual time series charts. Supplementary S3: of BA, %C, %G, DGCI, HSVi, NDRE, NIR, RED, and RE metrics. Supplementary S4: Additional discussion text. Supplementary S5: Raw data.Open asset ↗lines:97-173Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Global climatic pressures and increased human demands create a modern necessity for efficient and affordable plant phenotyping unencumbered by arduous technical requirements. The analysis and archival of imagery have become easier as modern camera technology and computers are leveraged. This facilitates the detection of vegetation status and changes over time. Using a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results indicate that Red Green Blue-based (RGB) imagery with simple controls is sufficient to measure the effects of plant treatments. Notable correlations were observed for corrected imagery, including between a percent yellow color area classification segment (%Y) with human visual quality ratings (VQ) (R = -0.89), the dark green color index (DGCI) with clipping productivity in mg d-1 (mg) (R = 0.61), and an index combination term (COMB2) with water use in mm d-1 (mm) (R = -0.60). The calculation of green cover area (%G) correlated with Normalized Difference Vegetation Index (NDVI) (R = 0.91) and its RED reflectance spectra (R = -0.87). A CIELAB b*/a* chromatic ratio (BA) correlated with Normalized Difference Red-Edge index (NDRE) (R = 0.90), and its Red-Edge (RE) (R = -0.74) reflectance spectra, while a new calculation termed HSVi correlated strongest to the Near-Infrared (NIR) (R = 0.90) reflectance spectra. Additionally, COMB2 significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analysis of typical image file formats and corrections that included JPEG (JPG), TIFF (TIF), geometric lens correction (LC), and color correction (CC) were conducted. Results underscore the need for further research to support image corrections standardization and better connect image data to biological processes. This study demonstrates the potential of consumer-grade photography to capture plant phenotypic traits.
Why it matches plant phenotyping methods安価なRGB撮像と画像解析を用いて植物形質を取得し、分光測定や視覚評価との相関、画像補正の感度を検証しており、フェノタイピング手法が中心です。
abstractUsing a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period.
The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.
Why it matches plant phenotyping methods植物の葉・個体のハイパースペクトル画像から、ガス漏洩による植物ストレスを植生指標とDNNで検出する手法を開発・評価しており、植物状態の取得が中心です。
abstractHyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs).
Miniaturised hyperspectral cameras are becoming more easily accessible and smaller, enabling efficient monitoring of agricultural crops using unoccupied aerial systems (UAS). This study’s objectives were to develop and assess the performance of UAS-based hyperspectral cameras in the estimation of quantity and quality parameters of grass sward, including the fresh and dry matter yield, the nitrogen concentration (Ncont) in dry matter (DM), the digestibility of organic matter in DM (the D-value), neutral detergent fibre (NDF), and water-soluble carbohydrates (WSC). Next-generation hyperspectral cameras in visible-near-infrared (VNIR, 400–1000 nm; 224 bands) and shortwave-infrared (SWIR; 900–1700 nm; 224 bands) spectral ranges were used, and they were compared with commonly used RGB and VNIR multispectral cameras. The implemented machine-learning framework identified the most informative predictors of various parameters, and estimation models were then built using a random forest (RF) algorithm for each camera and its combinations. The results indicated accurate estimations; the best normalised root-mean-square errors (NRMSE) were 8.40% for the quantity parameters, and the best NRMSEs for the quality parameters were 7.44% for Ncont, 1% for D-value, 1.24% for NDF, and 12.02% for WSC. The hyperspectral datasets provided the best results, whereas the worst accuracies were obtained using the crop height model and RGB data. The integration of the VNIR and SWIR hyperspectral cameras generally provided the highest accuracies. This study showed for the first time the performance of novel SWIR range hyperspectral UAS cameras in agricultural application.
Why it matches plant phenotyping methodsUAS搭載VNIR/SWIRハイパースペクトルカメラと機械学習により、牧草群落の収量・品質形質を推定する手法を開発・性能評価しており、表現型取得・推定が研究の中心である。
abstractThis study’s objectives were to develop and assess the performance of UAS-based hyperspectral cameras in the estimation of quantity and quality parameters of grass sward
The use of imagery collected from small unmanned aerial vehicles (UAVs) in turfgrass breeding has rapidly increased, as has the demand to develop drought‐resistant cultivars. However, prior to adopting UAVs to help guide turfgrass selection under drought stress conditions, a clear understanding of the value and predictive ability of imagery‐based turfgrass characterization is required. In St. Augustinegrass, a major warm‐season turfgrass species grown in the Southeastern United States, limited research has been published about characterizing drought stress using aerial imagery. Specifically, no efforts have compared the various vegetation indices (VIs) commonly used to evaluate vegetative health in other species and sought to identify the most useful index for phenotyping drought stress traits in St. Augustinegrass. In this study, traditional ground‐based approaches for measuring percent green cover (PGC) and normalized difference vegetation index (NDVI) were compared against their UAV‐derived counterparts as well as 13 VIs under drought and non‐drought conditions, and broad‐sense heritability (H²) was calculated. A population of 115 genotypes from a ‘‘Raleigh’’ × ‘‘Seville’’ cross were analyzed at two environmentally distinct field sites in North Carolina. At both sites, a significant relationship between ground‐based and UAV‐derived measurements for PGC and NDVI was observed before and during drought (r = 0.82 to 0.95) and suggests a clear advantage to using UAVs for phenotyping drought traits given the reduced time and labor costs compared to on‐ground efforts. Among all VIs compared, UAV‐derived NDVI (NDVI‐U) showed strong correlation with the PGC taken on the ground (r > 0.85), a similar trend over time, and a higher H² estimate under drought conditions, suggesting that NDVI‐U has the potential to assist in the selection of St. Augustinegrass genotypes with the best phenotypic response to drought. Implementing UAV imagery‐based high‐throughput methods will allow breeders to evaluate germplasm with unbiased quantitative consistency, quickly and thoroughly, and with increased frequency—all without sacrificing the response to selection potential.
Why it matches plant phenotyping methodsUAV画像と植生指数を用いた芝草の乾燥ストレス形質推定を、地上測定との比較・相関・遺伝率評価により技術的に検証しており、フェノタイピング手法が中心である。
abstractno efforts have compared the various vegetation indices (VIs) commonly used to evaluate vegetative health in other species and sought to identify the most useful index for phenotyping drought stress traits in St. Augustinegrass.
From the point of view of the lawns' current existence, use and their existence for the future, but the tasks, goals and challenges of grassland management have changed, and in line with this, we have carried out surveys and developed and modified a method for fast and efficient application. In practice, it is very important to know the quantitative and qualitative characteristics of the forage found in the pasture, as well as the exact economic value of the pasture. The purpose of this study is to present a cheap, fast, reliable and easy-to-apply estimation method. We took the previous methods into account and corrected and further developed them with the help of recent lawn management research results. Based on our tests, the corrected Balázs method can be used well to estimate the yield of grasslands and to determine the fodder value of the grassland. This method is cheap and requires no technical background. It also has the advantage, based on experience, that it takes into account the preference of species by animals. The data from the cenological survey and the height measurement of the plant stock can be used to estimate the yield, fodder value and economic value. Being a non-destructive method involving minimal trampling, it is also particularly suitable for monitoring the grassland habitat of protected plants and animals. The method is also presented on a sample example, which proves its applicability.
Why it matches plant phenotyping methods草地の収量・飼料価値・経済価値を、植生調査と植物群落高から非破壊的に推定する手法の修正・開発が研究の中心であり、植物状態の定量的取得に該当する。
abstractThe purpose of this study is to present a cheap, fast, reliable and easy-to-apply estimation method.
The continuous assessment of grassland biomass during the growth season plays a vital role in making informed, location-specific management choices. The implementation of precision agriculture techniques can facilitate and enhance these decision-making processes. Nonetheless, precision agriculture depends on the availability of prompt and precise data pertaining to plant characteristics, necessitating both high spatial and temporal resolutions. Utilizing structural and spectral attributes extracted from low-cost sensors on unmanned aerial vehicles (UAVs) presents a promising non-invasive method to evaluate plant traits, including above-ground biomass and plant height. Therefore, the main objective was to develop an artificial neural network capable of estimating pasture biomass by using UAV RGB images and the canopy height models (CHM) during the growing season over three common types of paddocks: Rest, bale grazing, and sacrifice. Subsequently, this study first explored the variation of structural and color-related features derived from statistics of CHM and RGB image values under different levels of plant growth. Then, an ANN model was trained for accurate biomass volume estimation based on a rigorous assessment employing statistical criteria and ground observations. The model demonstrated a high level of precision, yielding a coefficient of determination (R2) of 0.94 and a root mean square error (RMSE) of 62 (g/m2). The evaluation underscores the critical role of ultra-high-resolution photogrammetric CHMs and red, green, and blue (RGB) values in capturing meaningful variations and enhancing the model’s accuracy across diverse paddock types, including bale grazing, rest, and sacrifice paddocks. Furthermore, the model’s sensitivity to areas with minimal or virtually absent biomass during the plant growth period is visually demonstrated in the generated maps. Notably, it effectively discerned low-biomass regions in bale grazing paddocks and areas with reduced biomass impact in sacrifice paddocks compared to other types. These findings highlight the model’s versatility in estimating biomass across a range of scenarios, making it well suited for deployment across various paddock types and environmental conditions.
Why it matches plant phenotyping methodsUAV RGB画像とキャノピー高モデルから牧草のバイオマスを推定する画像・計算フェノタイピング手法を開発し、地上観測で検証しているため、方法が研究の中心です。
abstractthe main objective was to develop an artificial neural network capable of estimating pasture biomass by using UAV RGB images and the canopy height models (CHM)
The Forage and Range Lab (FRRL) is implementing a high throughput phenotyping (HTP) project using unmanned aerial vehicles (UAV), multispectral sensors, and programmatic pipelines for computational automation. Here we report on different HTP experiences for forage and turf grasses. Phenotypic traits (plant height, biomass, leaf area index, etc.) have been measured during two field seasons (2022 and 2023), and time series of multispectral imagery have been collected, processed, and used in different regression modeling strategies for sparse and dense forage grass canopies. We also provide examples of automatic classification of turf grasses visual ratings using close-range UAV imagery (infrared and multispectral). Accuracy results from our independent validations have been highly variable for the field-measured traits with excellent results for traits like biomass, and moderately acceptable results for other important features such as grain yield. We describe challenges that impact our ability to model certain traits, and expectations from using hyperspectral and light detection and ranging Lidar sensors in the near future to a) expand the number of phenotypic traits, b) simplify workflows, and c) upscale current models from experimental plots to landscapes. Our HTP work aims at accelerating the process of selecting plant material that scientists at FRRL develop to restore disturbed semiarid landscapes in order to augment their resilience to the impacts of climate change and other global processes.
Why it matches plant phenotyping methodsUAV・マルチスペクトルセンサー・計算パイプラインを用いた高スループット植物表現型計測と、形質推定モデルの検証が研究の中心であるため。
abstractimplementing a high throughput phenotyping (HTP) project using unmanned aerial vehicles (UAV), multispectral sensors, and programmatic pipelines for computational automation
Zoysia grass (Zoysia spp.) is the most widely used warm-season turf grass in Korea due to its durability and resistance to environmental stresses. To develop new longer-period greenness cultivars, it is essential to screen germplasm which maintains the greenness at a lower temperature. Conventional methods are time-consuming, laborious, and subjective. Therefore, in this study, we demonstrate an objective and efficient method to screen maintaining longer greenness germplasm using RGB and multispectral images. From August to December, time-series data were acquired and we calculated green cover percentage (GCP), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Soil-adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) values of germplasm from RGB and multispectral images by applying vegetation indexs. The result showed significant differences in GCP, NDVI, NDRE, SAVI, and EVI among germplasm (p < 0.05). The GCP, which evaluated the quantity of greenness by counting pixels of the green area from RGB images, exhibited maintenance of greenness over 90% for August and September but, sharply decrease from October. The study found significant differences in GCP and NDVI among germplasm. san208 exhibiting over 90% GCP and high NDVI values during 153 days. In addition, we also conducted assessments using various vegetation indexes, namely NDRE, SAVI, and EVI. san208 exhibited NDRE levels exceeding 3% throughout this period. As for SAVI, it initially started at approximately 38% and gradually decreased to around 4% over the course of these days. Furthermore, for the month of August, it recorded approximately 6%, but experienced a decline from about 9% to 1% between September and October. The complementary use of both indicators could be an efficient method for objectively assessing the greenness of turf both quantitatively and qualitatively.
Why it matches plant phenotyping methodsRGB・マルチスペクトル画像から緑被率や植生指数を抽出し、低温下の緑色維持性を客観的・効率的に評価するスクリーニング手法が研究の中心であるため。
abstractwe demonstrate an objective and efficient method to screen maintaining longer greenness germplasm using RGB and multispectral images.
Green urban areas are increasingly affected by water scarcity and climate change. The combination of warmer temperatures and increasing drought poses substantial challenges for water management of urban landscapes in the western U.S. A key component for water management, actual evapotranspiration (ETa) for landscape trees and turfgrass in arid regions is poorly documented as most rigorous evapotranspiration (ET) studies have focused on natural or agricultural areas. ET is a complex and non-linear process, and especially difficult to measure and estimate in urban landscapes due to the large spatial variability in land cover/land use and relatively small areas occupied by turfgrass in urban areas. Therefore, to understand water consumption processes in these landscapes, efforts using standard measurement techniques, such as the eddy covariance (EC) method as well as ET remote sensing-based modeling are necessary. While previous studies have evaluated the performance of the remote sensing-based two-source energy balance (TSEB) in natural and agricultural landscapes, the validation of this model in urban turfgrass remains unknown. In this study, EC flux measurements and hourly flux footprint models were used to validate the energy fluxes from the TSEB model in green urban areas at golf course near Roy, Utah, USA. High-spatial resolution multispectral and thermal imagery data at 5.4 cm were acquired from small Unmanned Aircraft Systems (sUAS) to model hourly ETa. A protocol to measure and estimate leaf area index (LAI) in turfgrass was developed using an empirical relationship between spectral vegetation indices (SVI) and observed LAI, which was used as an input variable within the TSEB model. Additionally, factors such as sUAS flight time, shadows, and thermal band calibration were assessed for the creation of TSEB model inputs. The TSEB model was executed for five datasets collected in 2021 and 2022, and its performance was compared against EC measurements. For actual ET to be useful for irrigation scheduling, an extrapolation technique based on incident solar radiation was used to compute daily ETa from the hourly remotely-sensed UAS ET. A daily flux footprint and measured ETa were used to validate the daily extrapolation technique. Results showed that the average of corrected daily ETa values in summer ranged from about 4.6 mm to 5.9 mm in 2021 and 2022. The Near Infrared (NIR) and Red Edge-based SVI derived from sUAS imagery were strongly related to LAI in turfgrass, with the highest coefficient of determination (R 2 ) (0.76–0.84) and the lowest root mean square error (RMSE) (0.5–0.6). The TSEB’s latent and sensible heat flux retrievals were accurate with an RMSE 50 W m − 2 and 35 W m − 2 respectively compared to EC closed energy balance. The expected RMSE of the upscaled TSEB daily ET estimates across the turfgrass is below 0.6 mm day − 1 , thus yielding an error of 10% of the daily total. This study highlights the ability of the TSEB model using sUAS imagery to estimate the spatial variation of daily actual ET for an urban turfgrass surface, which is useful for landscape irrigation management under drought conditions.
Why it matches plant phenotyping methodssUASの熱・マルチスペクトル画像からLAIと実蒸発散量を推定する手法を開発し、EC測定とTSEBモデルを用いて技術検証しており、植物キャノピーの生理・構造状態の取得が中心である。
abstractA protocol to measure and estimate leaf area index (LAI) in turfgrass was developed using an empirical relationship between spectral vegetation indices (SVI) and observed LAI
Abstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using a 3D model phenotyping system we developed, we reconstructed and analyzed 20 accessions of zoysiagrass (Zoysia spp.), including three species and their hybrid, over a period of 1 year. Artificial neural network with three hidden layers was able to effectively remove nonplant parts while retaining plant parts that were incorrectly removed using the cropping method, offering a robust and flexible approach for post‐processing of 3D models. The system also demonstrated its ability to accurately evaluate a range of traits, including height, area, and color using red green blue (RGB)‐based vegetation indices. The results showed a high correlation between the estimated volume obtained from voxel 3D model and dry weight, enabling its use as a non‐destructive method for measuring plant volume. In addition, we found that the green red normalized difference index from RGB‐based indices was similar to the commonly used normalized difference vegetation index in controlled illumination conditions. These results demonstrate the potential for three‐dimensional model phenotyping to facilitate plant breeding, particularly in the field of turfgrass and feed crops.
Why it matches plant phenotyping methods3Dモデル再構築、ニューラルネットワークによる後処理、RGB形質推定を中核とする植物フェノタイピングシステムの開発・検証研究である。
titleDevelopment of a digital phenotyping system using 3D model reconstruction for zoysiagrass
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code on GitHub (sandysan42/Zoysia3DModel), which reproduces the 3D-model-based phenotyping analysis (height, area, color, volume). No public phenotype dataset or trained model checkpoint is stated; supporting information is generic.Code · publicYAPAIBOON ET AL.
AC K N OW L E D G M E N T S
A part of this study is supported by JST CREST grant number
JPMJCR16O1.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Zoysia3DModel/.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154
RyoAkashi https://orcid.org/0000-0002-5651-8285
R E F E R E N C E S
Bienert, A., Hess, C., Maas, H.-G., & Von Oheimb, G. (2014). A voxel-
based technique to estimate the volume of trees from terrestriOpen asset ↗sandysan42/Zoysia3DModelpdf-raw-page:10 lines:1-78Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
The detection of beneficial microbes living within perennial ryegrass seed causing no apparent defects is challenging, even with the most sensitive and conventional methods, such as DNA genotyping. Using a near-infrared hyperspectral imaging system (NIR-HSI), we were able to discriminate not only the presence of the commercial NEA12 fungal endophyte strain but perennial ryegrass cultivars of diverse seed age and batch. A total of 288 wavebands were extracted for individual seeds from hyperspectral images. The optimal pre-processing methods investigated yielded the best partial least squares discriminant analysis (PLS-DA) classification model to discriminate NEA12 and without endophyte (WE) perennial ryegrass seed with a classification accuracy of 89%. Effective wavelength (EW) selection based on GA-PLS-DA resulted in the selection of 75 wavebands yielding 88.3% discrimination accuracy using PLS-DA. For cultivar identification, the artificial neural network discriminant analysis (ANN-DA) was the best-performing classification model, resulting in >90% classification accuracy for Trojan, Alto, Rohan, Governor and Bronsyn. EW selection using GA-PLS-DA resulted in 87 wavebands, and the PLS-DA model performed the best, with no extensive compromise in performance, resulting in >89.1% accuracy. The study demonstrates the use of NIR-HSI reflectance data to discriminate, for the first time, an associated beneficial fungal endophyte and five cultivars of perennial ryegrass seed, irrespective of seed age and batch. Furthermore, the negligible effects on the classification errors using EW selection improve the capability and deployment of optimized methods for real-time analysis, such as the use of low-cost multispectral sensors for single seed analysis and automated seed sorting devices.
Why it matches plant phenotyping methods単一種子のNIRハイパースペクトル画像と分類モデルを用いて、内生菌の有無および品種を識別する測定・解析手法が研究の中心であり、リアルタイム分析や自動選別への展開も示している。
abstractUsing a near-infrared hyperspectral imaging system (NIR-HSI), we were able to discriminate not only the presence of the commercial NEA12 fungal endophyte strain but perennial ryegrass cultivars of diverse seed age and batch.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
ORCiD: [0000-0001-6507-9985 of Jinyoung Y. Barnaby], [0000-0001-9082-6583 of Scott E. Warnke] Precise assessment of large mapping populations, comprising a few thousand plants including replications (a prerequisite step for breeding) is time-consuming and labor-intensive. Furthermore, phenotyping results tend to be variable and subjective depending on who is doing the scoring. One way to overcome these limitations is by collecting more data in the form of digital images, and precisely evaluating phenotypic variation in stress severity as well as temporal progression of stress symptoms within the population through machine learning methods. 230,400 images representing temporal progression of drought stress symptoms of interspecific turfgrass hybrid mapping population were processed using Python OpenCV and NumPy packages for noise removal, edge-preserving smoothing, color space conversion, contrast enhancement, and identification mapping. Then machine learning-based algorithms and models were developed not only to quantify stress severity but also to monitor temporal progression rate of stress symptoms. Hierarchical clustering was then performed to assess genotypic variation in stress progression. Such machine learning-based high-throughput digital phenotyping platforms can significantly increase the success of quantitative trait locus mapping and candidate gene identification to develop potential molecular markers that will assist in a faster characterization of germplasm to ultimately breed for stress resilient cultivars.
Why it matches plant phenotyping methods画像処理と機械学習により植物の乾燥ストレス症状の重症度と時間的進行を定量化する高スループット表現型解析プラットフォームが研究の中心であるため、収載。
abstractprecisely evaluating phenotypic variation in stress severity as well as temporal progression of stress symptoms within the population through machine learning methods
High throughput phenotyping (HTP) utilizing both remote and proximal sensing technologies has emerged as a vital tool for evaluating the biophysical characteristics of turfgrass. This study was conducted to assess the genetic diversity of hybrid turf bermudagrass using spectral reflectance indices and use of HTP for germplasm enhancement. A total of 50 accessions of the hybrid bermudagrass (Cynodon dactylon × C. transvaalensis) were grown in the greenhouse in three replications. The spectral data were gathered using a height independent active crop canopy sensor, 'RapidScan CS-45', which measures canopy reflectance at the wavelengths of 670 nm, 730 nm, and 780 nm. The reflectance data were used to derive three indices related to canopy photosynthetic area and other three related to chlorophyll content. All vegetation indices showed significant genotype-to-genotype variation. Ten superior genotypes were identified using the multi-trait genotype-ideotype distance index (MGIDI) as a selection differential. On 48 of the genotypes that were established in the field in two replications, establishment rate and winter color data were also gathered. The results of a linear regression analysis demonstrated the importance of spectral vegetation indices (SVI) for the turfgrass quick establishment (percentage area coverage) and winter color retention. This study brings attention to the potential use of the proximal sensing in turfgrass germplasm enhancement for establishment speed, aesthetic value, and mild-winter color retention.
Why it matches plant phenotyping methods芝草遺伝資源評価において、近接型キャノピーセンサーによるスペクトル取得と植生指数算出が主要な表現型評価手法として用いられているため。
abstractHigh throughput phenotyping (HTP) utilizing both remote and proximal sensing technologies has emerged as a vital tool for evaluating the biophysical characteristics of turfgrass.
To optimise grazing livestock nutrition, it is necessary to know both the available dry matter yield and the nutritive characteristics of pasture at the farm-scale in near real time. Previous studies have shown the potential of using field spectrophotometers that measure the reflectance of light across the visible to near infrared spectrums to gather information on pasture dry matter yield (DMY) and nutritive characteristics. This study sought to calibrate and validate new mathematical models for ten parameters including dry matter yield and nine nutritive characteristics of relevance to ruminant nutrition. As a part of the analysis process, two innovative approaches were tested: the use of a hybrid modelling approach where partial least squares regression (PLSR) outputs were used as support vector regression (SVR) inputs; and, the inclusion of covariate data. These approaches were compared with traditional stand-alone PLSR and SVR modelling approaches without covariates. The study was undertaken in six predominantly perennial ryegrass pastures on a single farm in the temperate zone of South-Eastern Australia. A total of 204 pasture samples were scanned with a field spectrophotometer over several spring growth stages in late 2019 and subsequently analysed by wet chemistry to obtain reference nutritive values. The raw reflectance spectra were initially pre-processed using a variety of techniques and then used to test the four kinds of chemometric models. In cross validation, hybrid models showed a superior fit for all variates in comparison to the other model types tested. However, the differential was reduced in independent validation where, out of 10 best-performing models for dry matter yield and key nutrient properties, six were produced by the hybrid modelling, three from SVR and one from PLSR. For every hybrid model that was built, adding covariate(s) consistently improved model performance but the increase was small (a reduction in normalised root mean square error (RMSE) of -0.36 % on average for all properties considered). The best performing models were comparable with other published literature with normalised RMSE of prediction ranging from 1.7 – 23.1 % (a mean of 9.7%). Well-predicted variates included metabolisable energy, digestible energy, DMY, and crude protein. Fibre fractions, ash and dry matter were less well-predicted but still had acceptable normalised RMSE values (< 10 %) while carbohydrate fractions were the poorest predicted variates. It was concluded that hybrid modelling in chemometric analyses can modestly improve accuracy and shows promise as an alternative to more traditional approaches. Using covariates also improved accuracy, but the additional time and effort to gather such information outweighed the minor benefits of inclusion.
Why it matches plant phenotyping methods牧草の乾物収量などの植物形質を、ハイパースペクトル計測とケモメトリックモデルで推定する手法を開発・較正・検証しており、フェノタイピング手法が研究の中心です。
abstractThis study sought to calibrate and validate new mathematical models for ten parameters including dry matter yield and nine nutritive characteristics of relevance to ruminant nutrition.
Lodging is a major limiting factor for the yield, quality and harvesting efficiency of selected crops worldwide. This study presents an efficient, robust and non-destructive assessment of lodging severity for four different grasses for seed production, using images collected by an unoccupied aerial vehicle (UAV) in two field plot experiments across five growing seasons. Canopy texture and height related features were extracted from individual plot images and evaluated for estimating lodging severity. Histograms of oriented gradients (HOG) were used as texture features, and three canopy height distributions features (CHV1, CHV2 and CHV3) were proposed. Each canopy height distribution feature divides the plots into subplots and estimates the average height of each subplot. CHV1 concatenates average height of the subplots into its feature, while CHV2 concatenates the difference in average height between all subplots, and CHV3 concatenates the difference in average height between adjacent subplots. The plots were classified using support vector machines into three categories according to the lodging severity. The results showed that the HOG and height distribution features can be used for grading lodging severity in UAV images with high accuracy (71.9% and 79.1%, respectively). However, the HOG features showed a negative relationship to the ground sample distance (GSD), while the CHV1 had a constant accuracy across the GSDs. Combination of the two features did not significantly improve the classification accuracy. The present results have potential to generate lodging severity maps for application in precision farming and thereby to increase grass seed yield and harvest efficiency at farm scale. It should be noted that results and methods from the current study might not be transferred to other crops due to crop specific lodging characteristics and effect of yields.
Why it matches plant phenotyping methodsUAV画像から作物キャノピーのテクスチャ・高さ特徴を抽出し、倒伏重症度という植物状態を推定する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractThis study presents an efficient, robust and non-destructive assessment of lodging severity for four different grasses for seed production, using images collected by an unoccupied aerial vehicle (UAV) in two field plot experiments across five growing seasons.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: FM~LV∗LV_Den∗NDVI had a determination of coefficient R2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables— FM~LV∗LV_Den . In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.
Why it matches plant phenotyping methodsLiDAR、超音波、マルチスペクトルセンサーを用いて植物バイオマスを非破壊推定するモデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractincluding ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI).
Nitrogen (N) is the most limiting nutrient for turfgrass growth. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the lone turfgrass growth prediction model only takes into account temperature, limiting its accuracy. This study investigated the ability of a machine learning (ML)-based turf growth model using the random forest (RF) algorithm (ML-RF model) to improve creeping bentgrass ( Agrostis stolonifera ) putting green management by estimating short-term clipping yield. This method was compared against three alternative N application strategies including (1) PACE Turf growth potential (GP) model, (2) an experience-based method for applying N fertilizer (experience-based method), and (3) the experience-based method guided by a vegetative index, normalized difference red edge (NDRE)-based method. The ML-RF model was built based on a set of variables including 7-day weather, evapotranspiration (ET), traffic intensity, soil moisture content, N fertilization rate, NDRE, and root zone type. The field experiment was conducted on two sand-based research greens in 2020 and 2021. The cumulative applied N fertilizer was 281 kg ha -1 for the PACE Turf GP model, 190 kg ha -1 for the experience-based method, 140 kg ha -1 for the ML-RF model, and around 75 kg ha -1 NDRE-based method. ML-RF model and NDRE-based method were able to provide customized N fertilization recommendations on different root zones. The methods resulted in different mean turfgrass qualities and NDRE. From highest to lowest, they were PACE Turf GP model, experience-based, ML-RF model, and NDRE-based method, and the first three methods produced turfgrass quality over 7 (on a scale from 1 to 9) and NDRE value over 0.30. N fertilization guided by the ML-RF model resulted in a moderate amount of fertilizer applied and acceptable turfgrass performance characteristics. This application strategy is based on the N cycle and has the potential to assist turfgrass managers in making N fertilization decisions for creeping bentgrass putting greens.
Why it matches plant phenotyping methodsMLモデルで芝草の短期刈り取り収量を推定し、その性能を複数の管理戦略と比較しているため、植物形質推定手法が実質的に中心である。
abstractThis study investigated the ability of a machine learning (ML)-based turf growth model using the random forest (RF) algorithm (ML-RF model) to improve creeping bentgrass ( Agrostis stolonifera ) putting green management by estimating short-term clipping yield.
Forage yield is traditionally measured by manual harvesting, drying, and weighing and has multiple uses, including plant breeding and pasture management. The goal of this paper was to determine the accuracy of unmanned aerial vehicle (UAV)-based prediction of ryegrass percentage cover, vegetation volume, and dry matter (DM) yield in Autumn from 300 rectangular 1.5 m2 plots at a height of 20 m above ground level, compared to the current manual method. The secondary goal was to evaluate the UAV-based method for the determination of dry matter yield from five different ryegrass cultivars. A photogrammetry-based technique combined with a spectral method to determine the soil level was used to determine the percentage cover and vegetation volume of ryegrass plots, which were then used to obtain calibration curves to predict DM yield per plot. Calibration curves were obtained for five different ryegrass cultivars, with concordance between calibration curves for four of the five cultivar populations. The relationship between predicted forage volume (m3) and measured DM (g per 1.5 m2 plot) for ryegrass Populations 1,2,4,5 had an R2 = 0.61. Population 3 was different to Populations 1,2,4,5, with a two-fold difference in DM yield for the same forage volume. This demonstrated that 61% of the variance in DM yield can be explained by forage volume determined by a UAV-based photogrammetry method. We also further tested the methodology from 70 rectangular 2.4 m2 ryegrass plots in a Spring trial. The relationship between the predicted DM yield per 2.4 m2 plot (based on the average predictions from forage volume and forage area models) and measured DM yield per plot had an R2 = 0.66. UAVs can therefore increase the acquisition of field data for research studies and the management of pasture in grazed farm systems.
Why it matches plant phenotyping methodsUAV画像・フォトグラメトリとスペクトル手法を用いて、ライグラスの被覆率、植生体積、乾物収量を推定し、手作業測定と比較検証しているため、植物表現型取得法が中心である。
abstractThe goal of this paper was to determine the accuracy of unmanned aerial vehicle (UAV)-based prediction of ryegrass percentage cover, vegetation volume, and dry matter (DM) yield
Turfgrass is an important urban crop in the United States. Determining the percent green cover (PGC) to assess turfgrass quality/health and the rate of establishment is a crucial parameter for evaluating different species and experimental lines within species. However, evaluating the PGC of individual plots within large breeding nurseries in a conventional way, either visually or through digital image analysis is a time-consuming and laborious process. In the present study, we used the unmanned aerial vehicle (UAV) with multispectral and RGB sensors to estimate PGC during turfgrass establishment. We evaluated thirty approaches with different levels of complexity based on vegetation indices, supervised and unsupervised machine learning classification methods, and image processing methods for high-throughput turfgrass PGC estimation. An HSV (Hue-Saturation-Value) color space-based green pixel identification (GPI) method was introduced for the first time for estimating UAV derived PGC (UAVPGC). The results indicate that the GPI achieved the highest coefficient of determination, 0.86–0.96, with lowest mean absolute error when compared to ground percent green cover (GroundPGC). Overall, UAV-derived RGB image-based support vector machine methods were in agreement with GroundPGC (R² = 0.88–0.95). This suggests that UAV-derived RGB images are adequate in accurately determining percent green cover (green vegetation within an experimental plot); however, multispectral images might offer a solution to determine turfgrass coverage (green and non-green vegetation within an experimental plot) during turfgrass establishment to account for non-green vegetation which is not captured by RGB (visible light spectrum) based estimation of PGC.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から芝草個体群の緑被率を推定する手法を開発・比較・検証しており、植物形質取得が研究の中心です。
abstractwe used the unmanned aerial vehicle (UAV) with multispectral and RGB sensors to estimate PGC during turfgrass establishment.
Turfgrasses are used extensively throughout the world, and there is a steadfast demand to develop turfgrass varieties with improved abiotic and biotic stress tolerances that will perform well with limited management inputs. Modern breeding programs incorporate advanced breeding strategies such as DNA sequencing and high-throughput phenotyping with traditional breeding strategies to identify and select germplasm and genes of interest. Molecular biology methods and DNA sequencing technology have rapidly increased in recent years, and, as a result, plant phenotyping is currently a bottleneck in the process of advancing breeding programs. Recent advances in remote sensing technology have offered improved, non-destructive plant phenotyping approaches such as visible light imaging, spectral imaging, infrared thermal imaging, range sensing, and fluorescence imaging. Integrated mobile and time efficient platforms are being developed, coupling remote sensing with robotics and unmanned aerial systems technology for high-throughput plant phenotyping applications across large field spaces. Modern turfgrass breeding programs will continue to research, develop, and implement remote sensing technologies to assess larger numbers of genotypes and identify elite germplasm. All together, these efforts will improve cultivar development efficiency and aid plant breeders in developing improved turfgrass cultivars to meet current and future demands of the turfgrass industry. This review provides an overview of ground- and aerial-based plant phenotyping platforms, with particular emphasis placed on applications to turfgrass breeding practices. Similarly, imaging technologies that have been used in various plant breeding programs are discussed, with indications as to how those technologies could be applicable to turfgrass breeding programs.
Why it matches plant phenotyping methods植物フェノタイピングの地上・空中プラットフォームと画像・リモートセンシング技術を、芝草育種への応用という観点で体系的にレビューしており、方法論が中心です。
abstractThis review provides an overview of ground- and aerial-based plant phenotyping platforms, with particular emphasis placed on applications to turfgrass breeding practices.
Collection of multispectral imagery from an aerial sensor is a means to obtain plot‐level vegetation index (VI) values; however, postcapture image processing and analysis remains a challenge for small‐plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot‐level VI values (normalized difference VI [NDVI], ratio VI [RVI], and chlorophyll index–red edge [CI‐RE]) from multispectral aerial imagery of small‐plot turfgrass experiments. Users can access and download task items from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small‐plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] grow‐in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine sensitivity (i.e., the ability to detect differences) of the different methodologies. Index values collected by handheld sensor were more sensitive to visual cover than those extracted from aerial imagery. Index values extracted by TurfScout, LLC were generally more sensitive to visual percentage cover than the workflow extraction method, but both detected similar trends of increasing index values as percentage cover increased.
Why it matches plant phenotyping methods小区航空マルチスペクトル画像から植生指数を抽出するArcGIS Proワークフローを開発し、反復性や他手法との感度を評価しており、植物状態の測定・抽出法が中心である。
abstractAn ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot‐level VI values (normalized difference VI [NDVI], ratio VI [RVI], and chlorophyll index–red edge [CI‐RE]) from multispectral aerial imagery of small‐plot turfgrass experiments.
Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 29%, and N fertilization rates of 0 to 17.5 kg ha -1 applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. The highest coefficient of determination (R 2 ) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set upon the evaluation of the model. This represented a large improvement over the existing growth prediction model ( R 2 = 0.01). However, the machine-learning models created were not able to accurately predict the clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.
Why it matches plant phenotyping methods芝草の刈り取り収量という植物形質を推定する機械学習モデルを開発し、実測値との検証と既存モデルとの比較を行っており、表現型取得・推定手法が中心である。
abstractThis study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield.
Turf‐type perennial ryegrass (Lolium perenne L.) success depends on adequate turfgrass quality and economical seed yield. In most breeding programs, spaced plants are the initial unit of selection in which observations of related individuals dictate the selection of superior germplasm for further testing. Therefore, spaced plants must be predictive of seed production and turfgrass growing environments. This study investigated the effectiveness of standard (three plants m⁻²) and competitive (23 plants m⁻²) spaced‐plant nurseries as selection environments with respect to two sward environments as well as applying a novel image analysis technique for several key traits. Seed production, turfgrass, and the two spaced‐plant growing environments were tested at two locations in Minnesota. Turfgrass quality traits were measured in 2017 and 2018 and seed production traits were measured in 2018. Automated image analysis was able to predict the traditional visual scoring values at both locations for crown rust (Puccinia coronata f.sp. lolii) severity [Pearson's correlation (rₚ) > 0.79, P 0.89, P 0.88, P < .001). Increasing the competition among spaced plants altered the plant phenotype and improved accuracy for vegetative biomass, crown rust severity, seed yield, and, at one location, turfgrass quality. There was no benefit of increasing competition for several traits such as genetic color, fertile tillers, and spikelet number. Although the competitive design was not useful for all traits, pragmatically, the competitive design used less space and often made measurements and observations easier for bunch‐type grasses.
Why it matches plant phenotyping methods画像解析による植物形質推定が研究の明示的な技術的要素であり、従来の目視評価との予測性能も検証しているため、単なる農業試験の routine 測定ではない。
abstractas well as applying a novel image analysis technique for several key traits.
Pastures account for more than 56% of the total agricultural area of Ecuador and constitute the main food source for livestock. Hence, the agile, affordable, and reliable quantification of aboveground biomass (AGB) is an essential task in grazing utilization and management. In this paper, a method to estimate the AGB via aerial photogrammetry with a low-cost UAV multirotor is proposed. Digital terrain models and crop surface models were generated from data captured during two flights at different times, and the volume between them was calculated. An empirical relationship between volume and dry biomass was obtained by harvesting and weighing some samples and deriving a density factor (DF). The method was tested over 54 plots with different types of forage under differential fertilization treatments. Fertilized annual ryegrass exhibited the best growth and highest biomass (2632 kg/ha). The estimation and calculation of the crop volume via UAV-based photogrammetry saves time and generates notably precise (R2 = 0.78) information on the dry biomass.
Why it matches plant phenotyping methodsUAV写真測量から牧草区画の地上部バイオマスを推定する手法が研究の中心であり、収穫・計量による検証も実施している。
abstracta method to estimate the AGB via aerial photogrammetry with a low-cost UAV multirotor is proposed.
The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest.
Why it matches plant phenotyping methods個体植物の画像から面積とNDVIを自動抽出する手法を開発し、収量関連形質との相関で評価しており、表現型取得法が研究の中心である。
abstracta new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed.
Permanent wilting point (PWP) is generally used to ascertain plant resistance against abiotic drought stress and designated as the soil water content (θ) corresponding to soil suction (ψ) at 1500 kPa obtained from the soil water retention curve. Determination of PWP based on only pre-assumed ψ may not represent true wilting condition for soils with contrasting water retention abilities. In addition to ψ, there is a need to explore significance of additional plant parameters (i.e., stomatal conductance and photosynthetic status) in determining PWP. This study introduces a new framework for determining PWP by integrating plant leaf response and ψ during drought. Axonopus compressus were grown in two distinct textured soils (clayey loam and silty sand), after which drought was initiated till wilting. Thereafter, ψ and θ within the root zone were measured along with corresponding leaf stomatal conductance and photosynthetic status. It was found that coarse textured silty sand causes wilting at much lower ψ (≈ 300 kPa) than clayey loam (≈ 1600 kPa). Plant response to drought was dependent on the relative porosity and mineralogy of the soil, which governs the ease at which roots can grow, assimilate soil O 2 , and uptake water. For clay loam, the held water within the soil matrix does not facilitate easy root water uptake by relatively coarse root morphology. Contrastingly, fine root hair formation in silty sand facilitated higher plant water uptake and doubled the plant survival time.
Why it matches plant phenotyping methods植物の萎凋点を、土壌水分ポテンシャルだけでなく葉の気孔コンダクタンスと光合成状態を統合して判定する新しい枠組みを提案しており、植物状態の取得・評価法が研究の中心です。
abstractThis study introduces a new framework for determining PWP by integrating plant leaf response and ψ during drought.
Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50-100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.
Why it matches plant phenotyping methodsSfMと携帯型LiDARによる牧草高の推定手法を開発・比較し、点群からDSMへの変換方法と測定精度を評価しており、植物形質取得が研究の中心です。
abstractWe estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems.
Identifying and reporting color differences is important in turfgrass experimentation. Finding ways to improve color differentiation may be desirable. Research was conducted to determine if color‐distance modeling (∆E*ₐb) improves differentiation of color in images of hybrid bermudagrass [Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt‐Davy]. Colors differ perceptibly if ∆E*ₐb >1.0; magnitude of difference increases as ∆E*ₐb increases. Hue angle (θ) and dark green color index (DGCI) of untreated shoots; shoots treated with petroleum hydrocarbons; and turf treated with N, herbicides, or a growth regulator were determined by digital image analysis. Hue and DGCI means were separated using orthogonal contrasts; ∆E*ₐb was computed for each contrast. Delta E*ₐb was a good predictor of color difference magnitude. Association between p‐values for contrasts of θ or DGCI and ∆E*ₐb were strong; smaller p‐values corresponded to larger ∆E*ₐb. When log₁₀ p‐values for θ contrasts for ‘Tifway’ turf treated with N, herbicides, and a growth regulator were regressed on ∆E*ₐb, r² = .9657 (y = − 0.602 − 0.9512x); for DGCI, r² = .8743 (y = − 1.6097 − 0.8743x). Computing ∆E*ₐb helped avoid making false conclusions. Neither θ nor DGCI differed for Tifway treated with mesotrione or glyphosate (p = .2049, .6195) but ∆E*ₐb was 3.8 due to differences in saturation (p = .0137) and brightness (p = .0067). Computing ∆E*ₐb improved differentiation of turfgrass color by enhancing the accuracy of difference detection. Improving the ability to differentiate colors and avoidance of making false conclusions has advanced reporting of turfgrass color data.
Why it matches plant phenotyping methodsデジタル画像から芝草の色という植物形質を抽出し、色差モデルの有効性と測定精度を検証することが研究の中心であるため、植物フェノタイピング手法に該当する。
abstractResearch was conducted to determine if color‐distance modeling (∆E*ₐb) improves differentiation of color in images of hybrid bermudagrass
Recent advances in small unmanned aircraft systems (sUAS) and sensors may improve accuracy and efficiency in turfgrass research and management compared with conventional methods. We evaluated the ability of sUAS combined with ultra‐high spatial resolution remote sensing to detect early drought stress. Results were compared with ground‐based techniques in creeping bentgrass (Agrostis stolonifera L.) irrigated at different levels from well‐watered to severe deficit (100 to 15% evapotranspiration [ET] replacement). Small UAS‐based measurements with a modified digital camera included three reflectance bands (near infrared [NIR, 680–780 nm] and overlapping green [G] and blue [B] bands [400–580 nm]) and eight derived vegetation indices (VIs). Ground‐based measurements included soil volumetric water content (VWC), turfgrass quality (TQ), green cover (GC), soil temperature (Tₛₒᵢₗ), and reflectance with handheld optical sensors. Declines in VWC in deficit‐irrigation treatments were detected with NIR and six of eight VIs from sUAS, and the normalized difference vegetation index (NDVI) and red band reflectance from a handheld sensor, before symptoms appeared in TQ and GC. The most consistently sensitive parameters of sUAS throughout the 3‐yr study were NIR and GreenBlue VI [(G − B)/(G + B)], which detected drought stress >5 d before decreases in TQ. Results indicate that ultra‐high spatial resolution remote sensing with sUAS detected drought stress before it was visible to a human observer and could be valuable for improving irrigation management in turfgrass.
Why it matches plant phenotyping methodssUASと高解像度リモートセンシングによる植物の乾燥ストレス検出性能を評価し、地上測定と比較しているため、植物状態の取得手法が中心である。
abstractWe evaluated the ability of sUAS combined with ultra‐high spatial resolution remote sensing to detect early drought stress.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 9 Sept 2026
Increasing herbage biomass is the predominant objective for pasture plant breeding programs. Three types of field trials are commonly involved during forage plant breeding, i.e. individually spaced plants, row plot, and sward trials. Assessments of biomass production at individual plant, row plot and sward plot levels are through visual scoring and/or cutting of biomass manually or mechanically. Both visual scoring and cutting of plants are laborious, time consuming and costly. The development of sensor technology such as multispectral sensors and unmanned aircraft systems (UAS) provide the opportunity to accelerate the process of biomass evaluation and to increase throughput, improve resolution, reduce time and cost. We tested either the handheld Trimble GreenSeeker® or Parrot Sequoia multispectral sensors attached to a 3DR Solo Quadcopter to assess biomass in perennial ryegrass field trials sown as spaced individual plants, row plots, and simulated sward plots. Significant correlations were observed between visual score and normalized difference vegetation index (NDVI) in a spaced plant field trial and between biomass yield and NDVI in row plot and sward trials. NDVI obtained from multispectral sensors and UAS can replace visual scoring in spaced plant trials. It was also a valuable proxy for yield estimation in row plot and sward trials. The ranking of cultivars by NDVI was correlated with the ranking by biomass, although the ranking order was not in complete agreement. Multispectral sensors and UAS also allow tracking productivity over time for perennial pasture, in particular, the regrowth after grazing or clipping and to explore seasonal changes and responses to environmental factors such as precipitation. These technologies will assist in transition for the forage grass breeding from pen and notepad to digital and data era.
Why it matches plant phenotyping methods多光谱センサーとUASを用いて、育種試験における植物バイオマスをNDVIから推定・評価する方法を開発および検証しており、表現型取得が研究の中心である。
abstractThe development of sensor technology such as multispectral sensors and unmanned aircraft systems (UAS) provide the opportunity to accelerate the process of biomass evaluation and to increase throughput, improve resolution, reduce time and cost.
The majority of the volume in a plant cell is water. Therefore, the growth and metabolism of plants are highly dependent on the changes in plant water content. To optimize plant growth in water limited and drought stress conditions, many mechanisms have been considered. The aim of this study was to design and develop an intelligent system, based on an Artificial Neural Network (ANN) and machine vision that would optimize plant growth in limited water situations. To this end, color, morphological and textural features were extracted from a set of turfgrass plant images under drought stress conditions and were analyzed to determine plant water requirement. To maximize classification accuracy, an optimum set of features [h (hsl color space), L (Lab color space), H (HSV color space) and PDF1 (Probability Density Functions)] were selected using a genetic algorithm. Then a data classification operation was conducted using an ANN. The classifier accuracy for three plant situations (fresh, at the edge of wilting and wilted) as well as its total accuracy were 91.3, 77.8, 97.9 and 90.7%, respectively. The automated irrigation system could measure and determine the plant wilting condition by investigation of four extracted features and then determine and apply the correct amount of water required for optimum plant growth in water limited situations.
Why it matches plant phenotyping methods画像特徴量とANNにより芝草の萎凋状態・必要水量を推定するコンピュータビジョン手法を開発しており、植物状態の取得・判定が中心である。
abstractdesign and develop an intelligent system, based on an Artificial Neural Network (ANN) and machine vision
Image-based modeling, and more precisely, Structure from Motion (SfM) and Multi-View Stereo (MVS), is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimeter-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software-Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset ( n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets ( n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for "High," "Medium," and "Low" quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using "High" and "Medium" quality settings are of small overall magnitude.
Why it matches plant phenotyping methodsSfM/MVSソフトウェアの選択が植生キャノピー高の推定精度と再現性に与える影響を比較検証しており、植物形質取得手法の技術評価が中心である。
abstractWe used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation.
Reproduction assets foundThe paper's own drone image datasets, DGPS ground survey points, and sward height measurements are deposited publicly on Dryad, as stated in the Data Availability Statement. No author analysis code is explicitly deposited.Dataset · publicData available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.q7c400k (Forsmoo et al., 2019 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.q7c400klines:57-85Plant phenotyping relevance match · UnverifiedEurope PMC · checked 10 Sept 2026
Bermudagrass is the most important warm-season pasture in the Southern USA with exceptional forage production potential and abiotic stress tolerance. However, it requires high nitrogen (N) supply to reach its full biomass and quality potential. Our objectives were to: (1) develop a nitrogen use efficiency (NUE) screening protocol for bermudagrass in controlled or semi-controlled conditions, (2) identify contrasting genotypes for NUE from natural variants and, (3) develop a knowledge base of NUE in bermudagrass. A collection consisting 290 Cynodon sp. genotypes was first pre-screened in the greenhouse. Thirty-nine genotypes with high NUE, five with low NUE were selected for further evaluations along with 5 checks in greenhouse and hoop-house under four N rates. Biomass, crude protein and N content were evaluated. N uptake efficiency (NUpE), N utilization efficiency (NUtE) and NUE were calculated based on biomass production. Genotypes showed significant influences (P < 0.0001) in all of the response variables. The genotype × N rate interaction was not significant for NUE in both environments. NUE had strong correlation with biomass production and NUpE, which got stronger as N rates increased. In N limiting conditions, bermudagrass showed a trade-off between biomass maintenance and crude protein content. Lower N applications increased biomass production over crude protein. However, when N is abundant the crop has the ability to improve crude protein. Several genotypes presented high NUE due their high NUtE and NUpE. Genotypes with contrasting NUE were selected and subjected to further field evaluation. Superior genotypes for NUE will be used in the breeding program to enhance NUE in bermudagrass.
Why it matches plant phenotyping methods窒素利用効率を評価するスクリーニングプロトコルの開発が研究目的の中心であり、バイオマスや窒素関連形質に基づく植物表現型取得法を扱っているため。
abstractdevelop a nitrogen use efficiency (NUE) screening protocol for bermudagrass in controlled or semi-controlled conditions
Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often used on a limited number of stolons or rhizomes. For this reason, these traits are limited in their use for morphological characterization of plants. The use of digital image analysis software technology may overcome measurement errors due to human mistakes, which tend to increase as the number and size of samples also increase. The protocol can be used for any kind of crop but is particularly suitable for forage or grasses, where plants are small and numerous. Turf samples consist of aboveground biomass and an upper soil layer to the depth of maximum rhizome development, depending on the species of interest. In studies, samples are washed from the soil, and stolons/rhizomes are cleaned by hand before analysis by digital image analysis software. The samples are further dried in a laboratory heating oven to measure dry weight; therefore, for each sample, the resultant data are total length, total dry weight, and average diameter. Scanned images can be corrected before analysis by excluding visible extraneous parts, such as remaining roots or leaves not removed with the cleaning process. Indeed, these fragments normally have much smaller diameters than stolons or rhizomes, so they can be easily excluded from analysis by fixing the minimum diameter below which objects are not considered. Stolon or rhizome density per unit area can then be calculated based on sample size. The advantage of this method is quick and efficient measurement of the length and average diameter of large sample numbers of stolons or rhizomes.
Why it matches plant phenotyping methodsデジタル画像解析を用いて芝草のストロン・根茎の長さ、径、乾物重、密度を効率的に測定する手法が論文の中心であり、植物形態形質の取得方法を提供している。
abstractThe use of digital image analysis software technology may overcome measurement errors due to human mistakes
A novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed. The combined retrieval of vegetation biochemical and biophysical parameters and Sun-induced chlorophyll fluorescence (F) was investigated exploiting high resolution spectral measurements in the visible and near-infrared spectral regions. First, the retrieval scheme was evaluated against a synthetic dataset. Then, it was applied to very high resolution (sub-nanometer) canopy level spectral measurements collected over a lawn treated with different doses of a herbicide (Chlorotoluron) known to instantaneously inhibit both Photochemical and Non-Photochemical Quenching (PQ and NPQ, respectively). For the first time the full spectrum of canopy F, the fluorescence quantum yield (ΦF), as well as the main vegetation parameters that control light absorption and reabsorption, were retrieved concurrently using canopy-level high resolution apparent reflectance (ρ*) spectra. The effects of pigment content, leaf/canopy structural properties and physiology were effectively discriminated. Their combined observation over time led to the recognition of dynamic patterns of stress adaptation and stress recovery. As a reference, F values obtained with the model inversion were compared to those retrieved with state of the art Spectral Fitting Methods (SFM) and SpecFit retrieval algorithms applied on field data. ΦF retrieved from ρ* was eventually compared with an independent biophysical model of photosynthesis and fluorescence. These results foster the use of repeated hyperspectral remote sensing observations together with radiative transfer and biochemical models for plant status monitoring.
Why it matches plant phenotyping methods放射伝達モデル逆解析により、植物キャノピーの蛍光、量子収率、生化学・生物物理パラメータを推定する手法を開発・評価しており、植物生理状態の取得方法が研究の中心である。
abstractA novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed.
Published30 May 2018The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 11 · OpenAlex ↗
Abstract. Grassland ecology experiments in remote locations requiring quantitative analysis of the biomass in defined plots are becoming increasingly widespread, but are still limited by manual sampling methodologies. To provide a cost-effective automated solution for biomass determination, several photogrammetric techniques are examined to generate 3D point cloud representations of plots as a basis, to estimate aboveground biomass on grassland plots, which is a key ecosystem variable used in many experiments. Methods investigated include Structure from Motion (SfM) techniques for camera pose estimation with posterior dense matching as well as the usage of a Time of Flight (TOF) 3D camera, a laser light sheet triangulation system and a coded light projection system. In this context, plants of small scales (herbage) and medium scales are observed. In the first pilot study presented here, the best results are obtained by applying dense matching after SfM, ideal for integration into distributed experiment networks.
Why it matches plant phenotyping methods草地プロットの地上部バイオマスを定量するため、複数の3D・フォトグラメトリ手法を比較検討しており、植物形質取得法が研究の中心である。
abstractTo provide a cost-effective automated solution for biomass determination, several photogrammetric techniques are examined to generate 3D point cloud representations of plots as a basis, to estimate aboveground biomass on grassland plots
Published30 Apr 2018The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 10 · OpenAlex ↗
Abstract. Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10 % better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40 % compared to approaches where only spectral features were applied. The best estimation RMSE of 239 kg/ha (6.0 %) was obtained with multiview anisotropy corrected data set and the 3D features.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と写真測量3D特徴量の抽出・処理を開発し、牧草群落の生体量という植物形質を推定・比較評価しており、フェノタイピング手法が中心である。
titleOPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use.
Why it matches plant phenotyping methodsUAV搭載アクティブ作物キャノピーセンサーを用いて、トウモロコシの窒素状態・ストレスを測定するプラットフォームを評価し、地上センサーとの比較およびセンサー高度の影響を検証しているため、表現型取得法が中心である。
abstractdetermine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize
Digital image analysis is a frequently used research technique to provide an objective measure of turfgrass color, in addition to the traditional visual rating. A commonly used method relies on commercial software package SigmaScan Pro to quantify mean hue angle, saturation, and brightness values from turf images, and to calculate a dark green color index as the measure of color. To enable turf image analysis to function on an open‐source platform, a method was developed within ImageJ to batch process turf images for color parameters. This Java‐based ImageJ plugin quantifies hue angle, saturation, and brightness values and calculates a dark green color index. In addition, information on the variability of these color parameters can be simultaneously acquired. This new method was used to quantify color parameters of turf images collected from field plots of tall fescue (Schedonorus arundinacea Shreb. Dumort.), Kentucky bluegrass (Poa pratensis L.), ryegrass (Lolium ssp.), hybrid bermudagrass (Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy), and creeping bentgrass (Agrostis stolonifera L.). While color parameter values differed little between ImageJ and SigmaScan, the time saved in processing images using ImageJ was considerable. Aside from software, analysis of color parameters acquired from the five turfgrass species indicated that hue angle alone can adequately measure turf color in digital images. Results also demonstrated that, in addition to light source, camera settings should remain fixed during photo capture to avoid introducing errors. The ImageJ plug‐in developed in this study is made available at www.turffiles.ncsu.edu.
Why it matches plant phenotyping methods芝草画像から色形質を抽出するImageJプラグインの開発、既存ソフトとの比較検証、撮影条件の評価が研究の中心であり、植物表現型計測法に該当する。
abstractTo enable turf image analysis to function on an open‐source platform, a method was developed within ImageJ to batch process turf images for color parameters.
Leaf electrolyte leakage is an important index of the plant cell permeability which plays an important role in the study of turfgrass salt stress. Traditional methods of measuring leaf electrolyte leakage have many disadvantages such as time-consuming, destroying the plants and being unable to monitor salt stress in large area. The aim of this study is to build a hyperspectral inversion model for leaf electrolyte leakage of creeping bentgrass under different salt concentration stresses thus to promote the application of the hyperspectral techniques in turfgrass salt stress monitoring. Creeping bentgrass was used in this study, and it was grown in water for two weeks before salt treatments. Leaves were collected at 7, 14 and 21 d under 0(CK), 100 and 200 mmol·L-1 NaCl respectively. The spectral values were gathered using Unispec-SC Spectral Analysis System (PP SYSTEMS，USA)before collecting grass leaves. Leaf electrolyte leakage was measured with electrical conductivity method. The relation and differences between salt treatments and spectral reflectance values were analyzed with EXCEL. Normalized difference vegetation index (NDVI) and difference vegetation index (DVI) were calculated using the spectral reflectance values. The first-order differential was calculated with difference method. The trilateral parameters of the blue, green and red rays were calculated at the meantime. The correlation analysis of the Leaf electrolyte leakage, spectral reflectance value, DVI and trilateral parameters was achieved by using EXCEL and Matlab software. Electrolyte leakage inversion model of the calibration set consisted of 48 high correlational samples, was built using unary linear regression, multivariate linear regression and partial least-squares regression methods. The prediction set inspection inversion model was established using the other 24 samples. The results showed that there is a positive correlation between salt stresses and 450～700 nm wave band. The leaf electrolyte leakage was positively associated with 450～732 nm band region at 0.01. The green edge amplitude and area of green edge were correlated with the foliar electrolyte leakage positively. Models based on partial least squares regression could inversion the foliar electrolyte leakage optimally. The calibration R2 reached to 0.681, and the validation R2 reached to 0.758. The calibration RMSE was 7.124, and the validation RMSE reached to 7.079. The inversion model made it possible to detect creeping bentgrass leaf electrolyte leakage under salt stress rapidly. This study also provided theoretical reference for monitoring the damage of other creeping bentgrass related plant species resulted by salt stress.
Why it matches plant phenotyping methods塩ストレス下の植物の電解質漏出という生理状態を、ハイパースペクトル計測から推定するモデルを開発・検証しており、表現型取得手法が中心である。
abstractThe aim of this study is to build a hyperspectral inversion model for leaf electrolyte leakage of creeping bentgrass under different salt concentration stresses
Abstract. Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).
Why it matches plant phenotyping methodsUAV画像から作成した作物表面モデルとRGB植生指数により植物高と飼料量を推定し、その指標性能を評価しており、植物表現型の取得・推定手法が中心である。
abstractmulti-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass.
Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).
Why it matches plant phenotyping methodsUAV画像とSfMによる作物表面モデルから植物高を推定し、飼料量との関係を評価する手法が研究の中心であるため、植物形質フェノタイピング手法として採用する。
abstractmulti-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass.
The total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized difference vegetation index (NDVI). In a random plot design, measurements of NDVI and pasture height were combined to estimate biomass with a root mean square error of prediction (RMSEP) equal to ±455.28 kg green dry matter (GDM)/ha, over a range of 286 kg to 3933 kg GDM/ha. The combination of NDVI and height measurements were observed to be more accurate in assessing total biomass than just the NDVI (RMSEP ± 846.51 kg/ha) and height (RMSEP ± 708.13 kg/ha). Based on the results of the study it was concluded the use of combined LiDAR and active optical reflectance sensors can help unlock the complex interrelationship between green fraction and biomass in swards containing both green and senescent material.
Why it matches plant phenotyping methodsLiDARとNDVIセンサーを組み合わせ、牧草キャノピー高・反射情報からバイオマスを推定し、単独測定との精度比較で技術性能を検証しているため、植物表現型取得法が中心である。
abstractThe total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized difference vegetation index (NDVI).