We present a protocol to evaluate greening capacity in etiolated Arabidopsis seedlings during the critical dark-to-light transition. We describe steps for sample preparation and sowing and then detail procedures for quantifying protochlorophyllide accumulation in darkness, the greening rate upon illumination, and reactive oxygen species levels as an indicator of photo-oxidative stress. This protocol can be used for screening and phenotypic quantification across genetic backgrounds. For complete details of this protocol, please refer to Zhong et al. 1 and Zhong et al. 2 .
Why it matches plant phenotyping methods暗所から光への移行における植物の緑化能を定量するプロトコルで、表現型スクリーニングと遺伝背景間の定量が中心であるため。
abstractWe present a protocol to evaluate greening capacity in etiolated Arabidopsis seedlings during the critical dark-to-light transition.
OliveMicroscopyCell / cellular structureVisualization / data management
The pollen tube is widely recognized as a suitable model for investigating the structure and spatial organization of cell wall components during polarized growth. This chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins, using specific monoclonal antibodies from the JIM series. JIM5 and JIM7 were employed to detect de-esterified and esterified homogalacturonan regions of pectin, respectively, while JIM8 and JIM13 were used to label distinct epitopes of arabinogalactan proteins. The protocol includes pollen germination, paraformaldehyde fixation, enzymatic digestion with cellulysin (for arabinogalactan protein detection only), and sequential antibody incubation, followed by confocal microscopy imaging using FITC filter settings. This approach enables precise visualization of the distribution patterns of pectins and arabinogalactan proteins in the pollen tube wall and provides a reliable framework for further studies on cell wall architecture in plant reproductive tissues.
Why it matches plant phenotyping methods植物花粉管細胞壁の成分分布を共焦点免疫蛍光で可視化するプロトコルが研究の中心であり、植物組織の空間的状態を測定する方法として扱える。
abstractThis chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins
LiDAR / point cloudThermalFruitGrowth / time-series analysisGrowth / development / phenologyFruit / seed / panicle traitsPlant / canopy temperature
Temperature plays a vital role in plant metabolism, and effective crop temperature appears to be influenced by variables related to climate change. While extreme weather events are widely discussed, the effects of moderate temperature changes pose consistent yet underexplored challenges for farmers. The “growing degree days” (GDD) also termed “heat unit”, is the most widely used approach in agricultural and ecological studies to quantify the relationship between temperature and plant development. This review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest. It is the first integrated synthesis of the conceptual evolution, methodological refinement, and broad application of GDD, thereby highlighting the need to optimize GDD approaches in light of emerging technological tools. While the GDD model is valuable for predicting crop development based on heat accumulation, it has limitations in capturing the effects of other environmental factors. Additionally, air temperature may not provide precise data on each plant organ. Recent advances in remote sensing, such as the integration of thermal imaging, RGB cameras, and lidar have enabled the measurement of spatially resolved temperature distribution within crop canopies, including fruit surface temperature. Recent advances, highlighted in the literature, suggest that integrating sensor innovations with machine learning approaches holds high potential for improving the precision of modeling temperature-dependent growth responses and their interactions with other environmental variables. By addressing these challenges and expanding its applications, GDD can continue to serve as an essential tool in promoting sustainable horticultural practices and adapting to global warming.
Why it matches plant phenotyping methodsGDDを用いて温度から作物の発育段階・成熟を推定する方法論を中心にレビューしており、植物状態の計算的な表現型推定に該当する。
abstractThis review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest.
Machine learning use in plant phenotyping has grown exponentially. These algorithms empowered the use of image data to measure plant traits rapidly and to predict the effect of genetic and environmental conditions on plant phenotype. However, the lack of interpretability in machine learning models has limited their usefulness in gaining insights into the underlying biological processes that drive plant phenotypes. Explainable AI (XAI) emerges to help understand the 'why' behind machine learning model predictions and allow researchers to investigate the most influential features that lead to prediction, classification or segmentation results. Understanding the mechanisms behind model prediction is also central to sanity-checking models, increasing model reliability and identifying dataset biases that may limit the model's applicability across different conditions. This review introduces the concept of XAI and presents current algorithms, emphasizing their suitability for different data types or machine learning algorithms. The use of XAI to leverage trait information is highlighted, showcasing how recent studies employed model explanations to recognize the features that impact plant phenotype. Overall, this review presents a framework for using XAI to gain insights into intricate biological processes driving plant phenotypes, underscoring the significance of transparency and interpretability in machine learning.
Why it matches plant phenotyping methods植物フェノタイピングにおける説明可能AIを主題とするレビューであり、画像データからの形質測定、予測・分類・セグメンテーションの解釈と検証を方法論的に扱っている。
abstractThis review introduces the concept of XAI and presents current algorithms, emphasizing their suitability for different data types or machine learning algorithms.
Fluorescence imaging has become a central tool in plant cell biology, enabling detailed analysis of cellular structures and dynamics. However, challenges such as phototoxicity, photobleaching, and the invasiveness of fluorescent labeling have driven the development of artificial intelligence (AI)-based alternatives. Among these, deep learning-based segmentation and virtual staining have shown significant promise for advancing plant cell microscopy. Compared with traditional methods reliant on manual operations or simple thresholding algorithms, segmentation powered by AI-based image transformation offers enhanced accuracy and reproducibility in quantifying cellular features. Moreover, virtual staining transforms bright-field images into synthetic fluorescence images, enabling non-invasive, high-resolution analyses while bypassing the need for physical labeling. Together, these techniques expand the analytical capabilities of plant cell microscopy, facilitating efficient and precise imaging workflows. Despite their potential, these approaches face technical challenges. Virtual staining relies heavily on high-quality bright-field images and is currently constrained when applied to three-dimensional analyses of complex plant tissues. Future efforts must focus on developing diverse training datasets and advancing AI technologies to overcome these limitations. By offering automated segmentation and virtual staining, AI is transforming plant cell microscopy into a more versatile and powerful tool, paving the way for groundbreaking discoveries and broader applications in plant cell biology.
Why it matches plant phenotyping methods植物細胞画像におけるAIセグメンテーションと仮想染色を扱うレビューで、細胞特徴の定量化と再現性向上を目的とした画像解析手法が中心である。
abstractdeep learning-based segmentation and virtual staining have shown significant promise for advancing plant cell microscopy.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Non-invasive Micro-test Technology (NMT) represents a pioneering approach in the study of physiological functions within living organisms. This technology possesses the remarkable capability to monitor the flow rates and three-dimensional movement directions of ions or molecules as they traverse the boundaries of living organisms without sample destruction. The advantages of NMT are multifaceted, encompassing real-time, non-invasive assessment, a wide array of detection indicators, and compatibility with diverse sample types. Consequently, it stands as one of the foremost tools in contemporary plant physiological research. This comprehensive review delves into the applications and research advancements of NMT within the field of plant abiotic stress physiology, including drought, salinity, extreme temperature, nutrient deficiency, ammonium toxicity, acid stress, and heavy metal toxicity. Furthermore, it offers a forward-looking perspective on the potential applications of NMT in plant physiology research, underscoring its unique capacity to monitor the flux dynamics of ions/molecules (e.g., Ca2+, H+, K+, and IAA) in real time, reveal early stress response signatures through micrometer-scale spatial resolution measurements, and elucidate stress adaptation mechanisms by quantifying bidirectional nutrient transport across root–soil interfaces. NMT enhances our understanding of the spatiotemporal patterns governing plant–environment interactions, providing deeper insights into the molecular mechanism of abiotic stress resilience.
Why it matches plant phenotyping methods植物のイオン・分子フラックスを非侵襲的に測定するNMTの応用と技術的特性を扱うレビューであり、植物の生理状態・ストレス応答を定量する方法が中心です。
abstractNon-invasive Micro-test Technology (NMT) represents a pioneering approach in the study of physiological functions within living organisms.
Abstract—Diseased images can severely reduce agricultural productivity and harm a nation's food supply. Typically, farmers and specialists closely monitor the diseases. It can be labor-intensive, costly, and ineffective. Detecting plant diseases can be achieved by affected leaves. The approach for detecting plant diseases by building a classification model that analyzes leaf images. To identify plant diseases, we utilize image processing alongside a (CNN). CNNs are a class of models capable of takes features from images, make an ideal for recognizing disease patterns in plant leaves. Keywords—CNN, image processing, training set, test set
Why it matches plant phenotyping methods植物葉の画像から病徴・病害状態をCNNで分類する手法の構築が中心であり、植物の疾病状態を観測する画像ベース表現型解析に該当します。
abstractThe approach for detecting plant diseases by building a classification model that analyzes leaf images.
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data managementGrowth / development / phenology
Plant growth and development rely on a delicate balance between cell proliferation and cell differentiation. The root apical meristem (RAM) of Arabidopsis thaliana is an excellent model to study the cell cycle due to the coordinated relationship between nucleus shape and cell size at each stage, allowing for precise estimation of the cell cycle duration. In this study, we present a method for high-resolution visualization of RAM cells. This is the first protocol that allows for simultaneous high-resolution imaging of cellular and nuclear stains, being compatible with DNA replication markers such as EdU, including fluorescent proteins (H2B::YFP), SYTOX DNA stains, and the cell wall stain SR2200. This protocol includes a clarification procedure that enables the acquisition of high-resolution 3D images, suitable for detailed subsequent analysis.
Why it matches plant phenotyping methodsArabidopsis根端分裂組織の細胞・核形態と増殖状態を取得する高解像度3Dイメージング手法の開発が中心であり、植物状態の定量的解析に再利用可能な方法を提示している。
abstractIn this study, we present a method for high-resolution visualization of RAM cells.
The gaseous hormone ethylene regulates different processes in plant life. Ethylene is generally considered as a stress hormone that is stimulated by biotic and abiotic stress. To understand its role in different arrays of plant life, in-vivo quantification of ethylene is essential to understand the physiological aspects of plant metabolism. Several techniques are employed for its accurate estimation; one such popular technique is gas chromatography. Gas chromatography is commonly employed for the estimation of different types of volatile compounds. The quantification of ethylene by gas chromatography is reliable and efficient as a large number of samples can be estimated simultaneously. The measurement of ethylene is accomplished by utilizing a standard curve that is prepared from certified ethylene gas used as the standard. Here, we describe a gas chromatography-flame ionization detection (GC-FID)-based method for the quantification of ethylene from live plants.
Why it matches plant phenotyping methods生体植物のエチレン量という生理形質をGC-FIDで定量する具体的な測定法を提示しており、植物フェノタイピング手法が中心である。
abstractHere, we describe a gas chromatography-flame ionization detection (GC-FID)-based method for the quantification of ethylene from live plants.
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Understanding the root system architecture (RSA) is necessary for elucidation of plant growth patterns in response to environmental stimuli and hormonal signals. Ethylene, a gaseous phytohormone, modulates root developmental plasticity, including primary root elongation, lateral root formation, and root hair growth. We present a protocol for mapping ethylene-specific RSA traits in Arabidopsis thaliana using a hydroponic growth system. Arabidopsis seedlings grow on a polypropylene mesh supported by polycarbonate wedges in a magenta box-based setup. We treat seedlings with ethylene or its precursor, then spread root system on agar plates with an art brush. High-resolution images are recorded and analyzed with free ImageJ software. This protocol allows detailed RSA analysis under controlled ethylene treatments and can be adapted for other plant species.
Why it matches plant phenotyping methodsエチレン処理下の根系構造を高解像度画像とImageJで取得・解析するRSA表現型プロトコルが研究の中心であり、植物フェノタイピング手法に該当する。
abstractWe present a protocol for mapping ethylene-specific RSA traits in Arabidopsis thaliana using a hydroponic growth system.
Aerial / UAVPhotogrammetry / SfM / MVSLiDAR / point cloudSegmentation
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAV LiDARと多視点画像から個体樹冠を分割する新規手法の開発が主題で、樹冠という植物形態の抽出に直接関わるため。
titleA novel self-similarity cluster grouping approach for individual tree crown segmentation using multi-features from UAV-based LiDAR and multi-angle photogrammetry data
Trunk diameter is related to the overall health and level of carbon sequestration in a tree. Trunk diameter measurement, therefore, is a key task in both forest plot and urban settings. Unlike the traditional approach of manual measurement with a measuring tape or calipers, several recent approaches rely on sophisticated technologies such as Terrestrial Laser Scanning (TLS), LiDAR, and time-of-flight sensors that provide fine-grain depth maps, which are used for depth-assisted image segmentation in downstream processing. These technologies are supported only on specialized devices or high-end smartphones. We present a mobile application that uses coarse-grain depth maps derived from an optical sensor, and so can be run on most common Android devices. Moreover, we use a state-of-the-art deep neural network to estimate trunk diameter from an image and its corresponding coarse depth map (RGB-D). We tested our app using a dataset collected from four countries and under challenging conditions including occlusion, leaning trees, and irregular shapes and found that our algorithm has a MAE of 2.58 cm and an RMSE of 3.57 cm, which is comparable to accuracy from fine-grain depth maps. Moreover, diameter measurement using our app is more than 5 times faster than traditional manual surveying.
Why it matches plant phenotyping methodsRGB-D画像と粗い深度マップから樹幹直径を推定するモバイル手法を開発し、複数国のデータセットと困難条件で精度検証しており、植物形質取得が中心である。
abstractWe present a mobile application that uses coarse-grain depth maps derived from an optical sensor
Reproduction assets foundThe paper's DBH estimation evaluation dataset (154 RGB + depth tree images with metadata and ground-truth DBH) is publicly deposited on Zenodo, and the app/algorithm source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicing the quality of the DBH estimate, includ-
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ing non-cylindrical trunks, burl presence, degrees of leaning and occlusion, and poor lighting, as illustrated in Fig. 4. Sample images
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from the dataset are available in Supplement 10.9, and the complete set of RGB and depth images, along with metadata, is acces-
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sible at https://zenodo.org/records/10199711.200
In Thailand, we collected data in Bangkok’s Lumphini Park and Chulalongkorn Centenary Park. As a tropical location, Bangkok is
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home to many tropical trees, such as rain trees (Samanea saman), banyan trees, palm trees, and coconut trees52. At Lumphini Park,
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where most of our data came from, small forests grow next to watOpen asset ↗zenodo.org · 10199711pdf-raw-page:8 lines:1-31Code · public; Z.F. analyzed the data and led the writing of the manuscript.
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A.H. and S.K. reviewed the manuscript and provided constructive suggestions. All authors contributed critically to the drafts and
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gave final approval for publication.
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8 DATA AVAILABILITY
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The algorithm and app code are publicly available on GitHub at https://github.com/MingyueX/GreenLens, with APK available from
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APKPure at https://apkpure.com/p/com.cleeg.greenlens. All the data for the app evaluation can be accessed at https://zenodo.org/357
records/10199711.
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9 AUTHOR COMPETING INTERESTS
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The authors declare no conflict of interest.
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REFERENCES
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[1] Kenneth G MacDicken. Global forest resourOpen asset ↗github.com/MingyueX/GreenLenspdf-raw-page:15 lines:1-92Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Reactive oxygen species (ROS) production is a key early defense mechanism in plants when exposed to biotic stress. Upon recognition of conserved microbe-associated molecular patterns (MAMPs) from pathogens by plant receptors, nicotinamide adenine dinucleotide phosphate (NADPH) oxidases in the plasma membrane are activated to produce hydrogen peroxide (H 2 O 2 ). This, in turn, regulates multiple signaling pathways to trigger immunity and suppress pathogen infection. Monitoring the ROS burst in plant leaves can be done within minutes of MAMPs treatment. However, there is limited research on the quantification of ROS production in plant root tissues during the activation of plant immunity. In this study, we introduce a rapid, accessible, and straightforward technique for measuring MAMPs-triggered ROS bursts in the roots of the model legume Medicago truncatula. This method will facilitate the investigation of plant root responses to biotic and abiotic stresses.
Why it matches plant phenotyping methods植物根におけるMAMP誘導ROSバーストの定量法を導入する研究であり、根の生理状態を測定する方法開発が中心です。
abstractwe introduce a rapid, accessible, and straightforward technique for measuring MAMPs-triggered ROS bursts in the roots of the model legume Medicago truncatula.
Plants generate reactive oxygen species (ROS) during different metabolic processes, which play an essential role in coordinating growth and response. ROS levels are sensitive to environmental stresses and are often used as a marker for stress in plants. While various methods can detect ROS changes, histochemical staining with nitroblue tetrazolium (NBT) and 3,3'-diaminobenzidine (DAB) is a popular method, though it has faced criticism. This staining method is advantageous as it enables both the quantification and localization of ROS and the identification of the enzymatic origin of ROS in plants, cellular compartments, or gels. In this protocol, we describe the use of NBT and DAP staining to detect ROS generation under different stresses such as nitrogen starvation, wounding, or UV-C. Additionally, we describe the use of NBT staining for detecting enzymatic generation of ROS in native and native SDS PAGE gels. Our protocol also outlines the separation and comparison of the origin of ROS generated by xanthine dehydrogenase1 (XDH1) using different substrates.
Why it matches plant phenotyping methods植物のROS生成という生理状態をNBT/DAB染色で検出・定量・局在化するプロトコルが論文の中心であり、植物フェノタイピング手法に該当する。
abstracthistochemical staining with nitroblue tetrazolium (NBT) and 3,3'-diaminobenzidine (DAB) is a popular method
Plant leaf disease automated classification and recognition can play a significant part in reducing these threats. Agricultural Plant Leaf Disease Detection (APLDD) model is Computer Vision (CV) based process developed for automatic classification and recognition of abnormalities or ailments in plant leaves. This approach can play a critical part in precision farming by allowing initial recognition and appropriate interference in preventing the disease's spread and reducing the loss of crops. By the influence of CV and Deep Learning (DL), the APLDD technique can remarkably assist scientists and agriculturalists in employing proactive measures in safeguarding crops, promote sustainable farming routines, and enhance yields. Therefore, this article presents an Automated Agricultural Plant Leaf Disease Detection using Butterfly Optimization Algorithm with Deep Learning (APLDD -BOADL) technique. The presented model integrates VGG16 for feature extraction, Butterfly Optimization Algorithm (BOA) for hyperparameter tuning, and Long Short -Term Memory (LSTM) for disease classification. The VGG16 architecture is exploited for the generation of high -level features from these images. For optimizing the results of the VGG16 model, Butterfly Optimization Algorithm (BOA) is applied for hyperparameter tuning process. Finally, the LSTM classification enables accurate disease identification and differentiation between healthy and diseased leaves. The results demonstrate the superior accuracy and robustness of our approach compared to traditional methods.
Why it matches plant phenotyping methods植物葉の画像から病徴・病害状態を分類するコンピュータビジョン手法を開発しており、植物フェノタイプ取得が中心的な研究です。
abstractPlant leaf disease automated classification and recognition can play a significant part in reducing these threats.
Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R 2 = 0.99 and a mean error of 1.9 × 10 -5 . This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.
Why it matches plant phenotyping methodsトマト果皮画像の色からリコペン含量を非破壊推定する低コストセンサーと予測モデルの開発が中心であり、植物器官の形質測定法に該当する。
abstractthis work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp.
Reproduction assets foundThe paper's Data Availability Statement links to a public Google Drive folder containing the data supporting the reported lycopene measurement results (tomato RGB/L*a*b* image-derived measurements and HPLC-calibrated model data). No separate code deposit is described; the models were built in MATLAB toolboxes without aDataset · publicl analysis, M.-G.B.-S.; investigation, J.-A.P.-M.; writing—original draft preparation, J.P.-O. and M.-J.V.-A.; writing—review and editing, A.-I.B.-G.; supervision, A.-I.B.-G. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data supporting reported results can be found at: https://drive.google.com/drive/folders/1d1Q_RtEWmo2lbpipMCNG4x53s09-pB-C?usp=sharing .
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Listed below are the 18 inference rules and weights for each of the two fuzzy systems red, green, and blue:
If (L is Low_L) and (a is Low_a) and (b is Low_b) then (Lycopene is Lycopenemf1)
If (L is Low_L) Open asset ↗lines:75-128Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods植物葉の画像を対象に、適応的セグメンテーションとResNet/LSTM-DNNによる多病害分類モデルを開発する研究であり、葉の病害状態を画像から推定する手法が中心と判断できる。
titleAdaptive Segmentation with Intelligent ResNet and LSTM–DNN for Plant Leaf Multi-disease Classification Model
Reproduction assets foundThe paper's leaf-disease classification experiments use the publicly available PlantifyDR Kaggle dataset (~87k RGB leaf images, 37 classes) as its input image data. No author code, models, or supplementary deposits are mentioned.Dataset · publicbability of
beggars and producers in terms of searching for food. The pseudo-code of the sug-
gested HBM-BSO is given here, Algorithm 1.
4.2 Description of Datasets
The developed multi-disease plant leaf classification model gathered the images
from standard online sources. The selected input images are obtained from the
link “https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset: access date:
2022-05-02”. Here, sample images are collected from the dataset kaggle, whereas
the original dataset is collected from the GitHub repo. This dataset holds nearly 87 k
rgb healthy and non-healthy images of plant leaves, and it is classified into 37 varie-
ties of classes. The whole dataset is sOpen asset ↗kaggle · lavaman151/plantifydr-datasetpdf-raw-page:21 lines:1-29Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multicellular organisms result from complex developmental processes largely orchestrated through the quantitative spatiotemporal regulation of gene expression. Yet, obtaining absolute counts of messenger RNAs at a three-dimensional resolution remains challenging, especially in plants, owing to high levels of tissue autofluorescence that prevent the detection of diffraction-limited fluorescent spots. In situ hybridization methods based on amplification cycles have recently emerged, but they are laborious and often lead to quantification biases. In this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues. In addition, with the use of fluorescent protein reporters, our method also enables simultaneous detection of mRNA and protein quantity, as well as subcellular distribution, in single cells. With this method, research in plants can now fully explore the benefits of the quantitative analysis of transcription and protein levels at cellular and subcellular resolution in plant tissues.
Why it matches plant phenotyping methods植物組織内のmRNA・タンパク質量を細胞および細胞内解像度で可視化・定量するsmFISH法の開発であり、植物の状態を測定する方法が中心的です。
abstractIn this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues.
Reproduction assets foundThe authors openly deposited all raw microscopy images (WM-smFISH mRNA/protein imaging of Arabidopsis and barley tissues) used for their quantification pipeline on Figshare. No separate author analysis code repository with explicit availability language is stated in the supplied text.Dataset · publicAll the raw microscopy images used in this manuscript are openly available in Figshare at https://doi.org/10.6084/m9.figshare.22699132 .Open asset ↗Figshare · 10.6084/m9.figshare.22699132lines:110-216Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published17 May 2023Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for PhotobiologyCited by 7 · OpenAlex ↗
Oxygenic photosynthesis involves light and dark phases. In the light phase, photosynthetic electron transport provides reducing power and energy to support the carbon assimilation process. It also contributes signals to defensive, repair, and metabolic pathways critical for plant growth and survival. The redox state of components of the photosynthetic machinery and associated routes determines the extent and direction of plant responses to environmental and developmental stimuli, and therefore, their space- and time-resolved detection in planta becomes critical to understand and engineer plant metabolism. Until recently, studies in living systems have been hampered by the inadequacy of disruptive analytical methods. Genetically encoded indicators based on fluorescent proteins provide new opportunities to illuminate these important issues. We summarize here information about available biosensors designed to monitor the levels and redox state of various components of the light reactions, including NADP(H), glutathione, thioredoxin, and reactive oxygen species. Comparatively few probes have been used in plants, and their application to chloroplasts poses still additional challenges. We discuss advantages and limitations of biosensors based on different principles and propose rationales for the design of novel probes to estimate the NADP(H) and ferredoxin/flavodoxin redox poise, as examples of the exciting questions that could be addressed by further development of these tools. Genetically encoded fluorescent biosensors are remarkable tools to monitor the levels and/or redox state of components of the photosynthetic light reactions and accessory pathways. Reducing equivalents generated at the photosynthetic electron transport chain in the form of NADPH and reduced ferredoxin (FD) are used in central metabolism, regulation, and detoxification of reactive oxygen species (ROS). Redox components of these pathways whose levels and/or redox status have been imaged in plants using biosensors are highlighted in green (NADPH, glutathione, H 2 O 2 , thioredoxins). Analytes with available biosensors not tried in plants are shown in pink (NADP + ). Finally, redox shuttles with no existing biosensors are circled in light blue. APX, ASC peroxidase; ASC, ascorbate; DHA, dehydroascorbate; DHAR, DHA reductase; FNR, FD-NADP+ reductase; FTR, FD-TRX reductase; GPX, glutathione peroxidase; GR, glutathione reductase; GSH, reduced glutathione; GSSG, oxidized glutathione; MDA, monodehydroascorbate; MDAR, MDA reductase; NTRC, NADPH-TRX reductase C; OAA, oxaloacetate; PRX, peroxiredoxin; PSI, photosystem I; PSII: photosystem II; SOD, superoxide dismutase; TRX, thioredoxin.
Why it matches plant phenotyping methods植物体内の光合成関連成分のレベル・酸化還元状態を測定する遺伝子 encoded 蛍光バイオセンサーを中心に扱う方法論レビューであり、植物の生理状態の表現型取得に直接関係する。
abstractWe summarize here information about available biosensors designed to monitor the levels and redox state of various components of the light reactions, including NADP(H), glutathione, thioredoxin, and reactive oxygen species.
Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.
Why it matches plant phenotyping methods植物の葉の病害や生育の検出・分類に用いるANN研究を体系的に分析するレビューであり、植物状態の推定手法が中心です。
abstractThe purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsチェリートマトの器官を対象に、フォトグラメトリ、インスタンスセグメンテーション、空間制約探索を組み合わせた三次元表現・形質推定法の開発が題名から明確であり、方法が中心である。
titleThree-Dimensional Plant Pivotal Organs Photogrammetry on Cherry Tomatoes Using an Instance Segmentation Method and a Spatial Constraint Search Strategy
Chlorophyll fluorescenceCell / cellular structureVisualization / data management
The plant cell wall comprises various types of macromolecules whose abundance and spatial distribution change dynamically and are crucial for plant architecture. High-resolution live cell imaging of plant cell wall components is, therefore, a powerful tool for plant cell biology and plant developmental biology. To acquire suitable data, the experimental setup for staining and imaging of non-fixed samples must be straightforward and avoid creating stress-induced artifacts. We present a detailed sample preparation and live image acquisition protocol for fluorescence visualization of cell wall components using commercially available probes and stains.
Why it matches plant phenotyping methods植物細胞壁成分の空間分布を取得するためのライブ蛍光染色・画像取得プロトコルが研究の中心であり、単なる生物学的測定ではない。
abstractWe present a detailed sample preparation and live image acquisition protocol for fluorescence visualization of cell wall components using commercially available probes and stains.
ArabidopsisMicroscopyCell / cellular structureVisualization / data management
Labeling of the nucleolus in Arabidopsis thaliana can be achieved by incorporation of 5'-ethynyl uridine (EU) into bulk RNA. Although EU does not selectively label the nucleolus, the abundance of ribosomal transcripts results in the predominant accumulation of the signal in the nucleolus. Ethynyl uridine has the advantage of being detected via Click-iT chemistry providing a specific signal and low background. While the protocol presented here employs fluorescent dye and allows visualization of the nucleolus by microscopy, this method can also be used for other downstream applications. Though we tested nucleolar labeling only in A. thaliana, in principle it can be applied to other plant species.
Why it matches plant phenotyping methodsArabidopsisの核小体を蛍光顕微鏡で可視化するためのEU標識プロトコルが中心であり、植物細胞状態の画像取得法を提示している。
abstractLabeling of the nucleolus in Arabidopsis thaliana can be achieved by incorporation of 5'-ethynyl uridine (EU) into bulk RNA.
Non-invasive techniques are needed to enable an integrated understanding of plant metabolic responses to environmental stresses. Raman spectroscopy is one such technique, allowing non-destructive chemical characterisation of samples in situ and in vivo and resolving the chemical composition of plant material at scales from microns to metres. Here, we review Raman band assignments of pigments, structural and non-structural carbohydrates, lipids, proteins and secondary metabolites in plant material and consider opportunities this technology raises for studies in vascular plant physiology.
Why it matches plant phenotyping methods植物試料の化学組成を非破壊・in situで測定するラマン分光法を中心に、植物成分のバンド帰属と生理研究への応用機会をレビューしており、測定手法自体が主題である。
abstractRaman spectroscopy is one such technique, allowing non-destructive chemical characterisation of samples in situ and in vivo and resolving the chemical composition of plant material at scales from microns to metres.
Major advances have been made in our understanding of anther developmental processes in flowering plants through a combination of genetic studies, cell biological technologies, biochemical analyses, microarray and high-throughput sequencing-based approaches. In this chapter, we summarize widely used protocols for pollen viability staining, investigation of anther morphogenesis by scanning electron microscopy (SEM), light microscopy of semi-thin sections, ultrathin section-based transmission electron microscopy (TEM), TUNEL (terminal deoxynucleotidyl transferase-mediated 2'-deoxyuridine 5'-triphosphate (dUTP) nick end labeling) assay for tapetum programmed cell death, and laser microdissection procedures to obtain specific cells or cell layers for transcriptome analysis.
Why it matches plant phenotyping methods花粉生存性や葯の形態を観察・評価する複数の植物表現型取得プロトコルを体系的に扱う方法論的章であり、測定手法が中心である。
abstractIn this chapter, we summarize widely used protocols for pollen viability staining, investigation of anther morphogenesis by scanning electron microscopy (SEM), light microscopy of semi-thin sections, ultrathin section-based transmission electron microscopy (TEM)
In natural conditions, plants are affected by various adverse environmental factors that can disrupt the photosynthetic apparatus, which can reduce the productivity of plants and ultimately reduce their yield. Measurement of the chlorophyll fluorescence induction (CFI) curve is a simple, non-destructive, inexpensive, and fast tool that can be used to analyze photosynthetic reactions and plant conditions. Mathematical modeling of the chlorophyll fluorescence induction curve is important not only for understanding the complex processes of photosynthesis but also can have practical applications in predicting ways to increase plant productivity. Currently, there are a sufficient number of models of varying complexity and detail that describe the processes of photosynthesis, however, no final agreement has been reached. A new model of reactions occurring in the process of the fast phase of the CFI curve, i.e. in the process of electron transport in the electron transport chain (ETC), is presented here. In the ETC model, it is considered as a system of elements in which electrons are sequentially transferred from one element of the system to another, according to the properties of the elements themselves and the connections between them. In addition, the mathematical model is based on the idea of dividing the entire flow of electrons, which moves through the ETC, into a sequence of individual flows. The proposed mathematical model differs in that each stage of electron transfer along the ETC is described separately and sequentially with the help of connection functions. This makes it possible to write the equations for the real OJIP curve and, as a result of their solution, to obtain the parameters of the entire electron transfer process.
Why it matches plant phenotyping methods植物のクロロフィル蛍光誘導曲線を対象に、電子伝達過程とそのパラメータを推定する数学モデルを開発しており、植物生理状態の取得・解析手法が研究の中心である。
titleMathematical model of the fast phase of the chlorophyll fluorescence induction curve.
Bernd Zechmann, Günther Zellnig; 3D Reconstruction of Plant Leaf Cells Using TEM and FIB-SEM, Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022,
Why it matches plant phenotyping methods植物葉細胞の3D形態を画像から再構成する手法が題名上の中心であり、植物の構造的形質の取得に該当する。
title3D Reconstruction of Plant Leaf Cells Using TEM and FIB-SEM
Soybean is an important oil crop and plant protein source, and phenotypic traits' detection for soybean diseases, which seriously restrict yield and quality, is of great significance for soybean breeding, cultivation, and fine management. The recognition accuracy of traditional deep learning models is not high, and the chemical analysis operation process of soybean diseases is time-consuming. In addition, artificial observation and experience judgment are easily affected by subjective factors and difficult to guarantee the accuracy of the objective. Thus, a rapid identification method of soybean diseases was proposed based on a new residual attention network (RANet) model. First, soybean brown leaf spot, soybean frogeye leaf spot, and soybean phyllosticta leaf spot were used as research objects, the OTSU algorithm was adopted to remove the background from the original image. Then, the sample dataset of soybean disease images was expanded by image enhancement technology based on a single leaf image of soybean disease. In addition, a residual attention layer (RAL) was constructed using attention mechanisms and shortcut connections, which further embedded into the residual neural network 18 (ResNet18) model. Finally, a new model of RANet for recognition of soybean diseases was established based on attention mechanism and idea of residuals. The result showed that the average recognition accuracy of soybean leaf diseases was 98.49%, and the F1-value was 98.52 with recognition time of 0.0514 s, which realized an accurate, fast, and efficient recognition model for soybean leaf diseases.
Why it matches plant phenotyping methods大豆葉の病害状態を画像から認識する深層学習手法の開発が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstracta new model of RANet for recognition of soybean diseases was established based on attention mechanism and idea of residuals.
Laboratory / benchtopChlorophyll fluorescenceMicroscopyCell / cellular structureTissueVisualization / data management
The accumulation of the cell wall component callose at plasmodesmata (PD) is crucial for the regulation of symplastic intercellular transport in plants. Here we describe protocols to fluorescently image callose in sectioned plant tissue using monoclonal antibodies. This protocol achieves high-resolution images by the fixation, embedding, and sectioning of plant material to expose internal cell walls. By using this protocol in combination with high-resolution confocal microscopy, we can detect PD callose in a variety of plant tissues and species.
Why it matches plant phenotyping methods植物組織内のPD calloseという細胞状態を蛍光・共焦点画像で取得するプロトコル自体が中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere we describe protocols to fluorescently image callose in sectioned plant tissue using monoclonal antibodies.
When dealing with plant roots, a multiscale description of the functional root structure is needed. Since the beginning of 21st century, new devices such as laser confocal microscopes have been accessible for coarse root structure measurements, including three-dimensional (3D) reconstruction. Most researchers are familiar with using simple 2D geometry visualization that does not allow quantitative determination of key morphological features from an organ-like perspective. We provide here a detailed description of the quantitative methods available for 3D analysis of root features at single-cell resolution, including root asymmetry, lateral root analysis, cell size and nuclear organization, cell-cycle kinetics, and chromatin structure analysis. Quantitative maps of the root apical meristem (RAM) are shown for different species, including Arabidopsis thaliana (L.), Heynh, Nicotiana tabacum L., Medicago sativa L., and Setaria italica (L.) P. Beauv. The 3D analysis of the RAM in these species showed divergence in chromatin organization and cell volume distribution that might be used to study root zonation for each root tissue. Detailed protocols and possible pitfalls in the usage of the marker lines are discussed. Therefore, researchers who need to improve their quantitative root biology portfolio can use them as a reference.
Why it matches plant phenotyping methods根の3D形態・細胞特性を定量化する方法とプロトコルを中心に扱う方法論的レビューであり、植物フェノタイピング手法が主題である。
abstractWe provide here a detailed description of the quantitative methods available for 3D analysis of root features at single-cell resolution
Focusing on non-destructive and automated acquisition of plant phenotypic parameters,this extended abstract proposed an end-to-end deep RNN based network structure for single perspective sparse raw point cloud regression task called DRN. It has been proven to achieve accuracy improvements in PointNet++ and PonitCNN when it comes to regression of lettuce plant height. We believe DRN structure is suitable for feature extraction from plant point cloud data and regression of spatial distance related plant phenotypes like plant height.
Why it matches plant phenotyping methods単一視点の疎な点群からレタスの草丈を非破壊・自動推定する深層RNN手法を開発しており、植物表現型の取得・推定が中心である。
abstractproposed an end-to-end deep RNN based network structure for single perspective sparse raw point cloud regression task called DRN
Modern light microscopy imaging techniques have substantially advanced our knowledge about the ultrastructure of plant cells and their organelles. Laser-scanning microscopy and digital light microscopy imaging techniques, in general—in addition to their high sensitivity, fast data acquisition, and great versatility of 2D–4D image analyses—also opened the technical possibilities to combine microscopy imaging with spectroscopic measurements. In this review, we focus our attention on differential polarization (DP) imaging techniques and on their applications on plant cell walls and chloroplasts, and show how these techniques provided unique and quantitative information on the anisotropic molecular organization of plant cell constituents: (i) We briefly describe how laser-scanning microscopes (LSMs) and the enhanced-resolution Re-scan Confocal Microscope (RCM of Confocal.nl Ltd. Amsterdam, Netherlands) can be equipped with DP attachments—making them capable of measuring different polarization spectroscopy parameters, parallel with the ‘conventional’ intensity imaging. (ii) We show examples of different faces of the strong anisotropic molecular organization of chloroplast thylakoid membranes. (iii) We illustrate the use of DP imaging of cell walls from a variety of wood samples and demonstrate the use of quantitative analysis. (iv) Finally, we outline the perspectives of further technical developments of micro-spectropolarimetry imaging and its use in plant cell studies.
Why it matches plant phenotyping methods植物細胞壁・葉緑体の異方性を定量化する差分偏光イメージング技術を中心に扱うレビューであり、植物状態の画像計測手法が主題。
abstractIn this review, we focus our attention on differential polarization (DP) imaging techniques and on their applications on plant cell walls and chloroplasts
Plants continually synthesize and degrade proteins, for example, to adjust protein content during development or during adaptation to new environments. In order to estimate global protein synthesis and degradation rates in plants, we developed a relatively simple and inexpensive method using a combination of 13 CO 2 labeling and mass spectrometry-based analyses. Arabidopsis thaliana plants are subjected to a 24-hr 13 CO 2 pulse followed by a 4-day 12 CO 2 chase. Soluble alanine and serine from total protein and glucose from cell wall material are analyzed by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) and their 13 C enrichment (%) is estimated. The rate of protein synthesis during the 13 CO 2 pulse experiment is defined as the rate of incorporation of labeled amino acids into proteins normalized by a correction factor for incomplete enrichment in free amino acid pools. The rate of protein degradation is estimated as the difference between the rate of protein synthesis and the relative growth rate calculated using the 13 C enrichment of glucose from cell wall material. Degradation rates are also estimated from the 12 CO 2 pulse experiment. The following method description includes setting up and performing labeling experiments, preparation and measurement of samples, and calculation steps. In addition, an R script is provided for the calculations. 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Setting up the 13 CO 2 labeling system and stable isotope labeling of Arabidopsis thaliana rosette leaves Basic Protocol 2: Extraction of soluble amino acids for GC-TOF-MS analysis Basic Protocol 3: Preparation of amino acids from total protein for GC-TOF-MS analysis Basic Protocol 4: Preparation of sugars from cell wall material for GC-TOF-MS analysis Basis Protocol 5: GC-TOF-MS analysis of 13 C-labeled samples and estimation of 13 C enrichment (%) Basis Protocol 6: Estimation of protein synthesis and degradation rates.
Why it matches plant phenotyping methods植物のタンパク質合成・分解速度という生理状態を定量する標識・質量分析法を開発し、試料調製と計算手順まで体系的に記載した方法論研究である。
abstractwe developed a relatively simple and inexpensive method using a combination of 13 CO 2 labeling and mass spectrometry-based analyses.
TeaLiDAR / point cloudLeaf2D/3D reconstructionArchitecture / morphology / geometry
Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.
Why it matches plant phenotyping methods茶葉点群から葉の3D構造を推定するNURBS表面モデリング手法の開発が主題であり、植物形態のフェノタイピング手法に該当する。
abstracta parametric surface modelling method was proposed for accurately fitting tea leaf point cloud.
MicroscopyCell / cellular structureRootVisualization / data management
Histological stains are useful tools for characterizing cell shape, arrangement and the material they are made from. Stains can be used individually or simultaneously to mark different cell structures or polymers within the same cells, and to visualize them in different colors. Histological stains can be combined with genetically-encoded fluorescent proteins, which are useful for understanding of plant development. To visualize suberin lamellae by fluorescent microscopy, we improved a histological staining procedure with the dyes Fluorol Yellow 088 and aniline blue. In the complex plant organs such as roots, suberin lamellae are deposited deep within the root on the endodermal cell wall. Our procedure yields reliable and detailed images that can be used to determine the suberin pattern in root cells. The main advantage of this protocol is its efficiency, the detailed visualization of suberin localization it generates in the root, and the possibility of returning to the confocal images to analyze and re-evaluate data if necessary.
Why it matches plant phenotyping methods根のスベリン局在を可視化・再解析する染色プロトコルを改良しており、植物組織の状態を取得する方法自体が中心である。
abstractwe improved a histological staining procedure with the dyes Fluorol Yellow 088 and aniline blue.
Gene expression is tightly linked to the position of genes in the nucleus. Genomic regions associated with the nuclear envelope are usually repressed, including the heterochromatin carrying chromocenters. The shape and size of nuclei varies within tissues in plants and is dependent on proteins associated with the nuclear envelope. Here, we describe a protocol to isolate Arabidopsis thaliana nuclei and measure their size and morphology. Using this method, novel components regulating the nuclear envelope and chromatin association can be identified and analyzed.
Why it matches plant phenotyping methods植物核のサイズと形態を取得・測定するプロトコルが研究の中心であり、植物の形態的表現型を測定する方法論として収載対象です。
abstractHere, we describe a protocol to isolate Arabidopsis thaliana nuclei and measure their size and morphology.
The eukaryotic cell nucleus is a central organelle whose architecture determines genome function at multiple levels. Deciphering nuclear organizing principles influencing cellular responses and identity is a timely challenge. Despite many similarities between plant and animal nuclei, plant nuclei present intriguing specificities. Complementary to molecular and biochemical approaches, 3D microscopy is indispensable for resolving nuclear architecture. However, novel solutions are required for capturing cell-specific, sub-nuclear and dynamic processes. We provide a pointer for utilising high-to-super-resolution microscopy and image processing to probe plant nuclear architecture in 3D at the best possible spatial and temporal resolution and at quantitative and cell-specific levels. High-end imaging and image-processing solutions allow the community now to transcend conventional practices and benefit from continuously improving approaches. These promise to deliver a comprehensive, 3D view of plant nuclear architecture and to capture spatial dynamics of the nuclear compartment in relation to cellular states and responses. Abbreviations: 3D and 4D: Three and Four dimensional; AI: Artificial Intelligence; ant: antipodal nuclei (ant); CLSM: Confocal Laser Scanning Microscopy; CTs: Chromosome Territories; DL: Deep Learning; DLIm: Dynamic Live Imaging; ecn: egg nucleus; FACS: Fluorescence-Activated Cell Sorting; FISH: Fluorescent In Situ Hybridization; FP: Fluorescent Proteins (GFP, RFP, CFP, YFP, mCherry); FRAP: Fluorescence Recovery After Photobleaching; GPU: Graphics Processing Unit; KEEs: KNOT Engaged Elements; INTACT: Isolation of Nuclei TAgged in specific Cell Types; LADs: Lamin-Associated Domains; ML: Machine Learning; NA: Numerical Aperture; NADs: Nucleolar Associated Domains; PALM: Photo-Activated Localization Microscopy; Pixel: Picture element; pn: polar nuclei; PSF: Point Spread Function; RHF: Relative Heterochromatin Fraction; SIM: Structured Illumination Microscopy; SLIm: Static Live Imaging; SMC: Spore Mother Cell; SNR: Signal to Noise Ratio; SRM: Super-Resolution Microscopy; STED: STimulated Emission Depletion; STORM: STochastic Optical Reconstruction Microscopy; syn: synergid nuclei; TADs: Topologically Associating Domains; Voxel: Volumetric pixel.
Why it matches plant phenotyping methods植物核の3D構造を定量・細胞特異的に取得する顕微鏡および画像処理手法を中心に扱う方法論レビューであり、植物の細胞状態・核構造という観測可能な状態の表現型計測に該当する。
abstract3D microscopy is indispensable for resolving nuclear architecture.
As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.
Why it matches plant phenotyping methods種子フェノタイピングにおけるマルチスペクトル画像の取得・処理システムを中心に扱うレビューであり、種子の品質・生理状態・欠陥・健全性などの形質評価手法を整理しているため。
abstractThis paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues.
This chapter gives examples of basic procedures of quantification of plant structures with use of image analysis, which are commonly employed to describe differences among experimental treatments or phenotypes of plant material. Tasks are demonstrated with the use of ImageJ, a widely used public domain Java image processing program. Principles of sampling design based on systematic uniform random sampling for quantitative studies of anatomical parameters are given to obtain their unbiased estimations and simplified "rules of thumb" are presented. The basic procedures mentioned in the text are: (1) sampling, (2) calibration, (3) manual length measurement, (4) leaf surface area measurement, (5) estimation of particle density demonstrated on an example of stomatal density, and (6) analysis of epidermal cell shape.
Why it matches plant phenotyping methods植物構造の画像解析による定量手順を体系的に説明する方法論的章であり、葉面積、気孔密度、細胞形状などの表現型取得が中心です。
abstractThis chapter gives examples of basic procedures of quantification of plant structures with use of image analysis
Many assays focus on determining NO content within plant tissues to assess the actual concentration that impacts on cellular processes. Diaminofluorescein fluorescent dyes (DAFs) have been very widely used by plant scientists to reveal likely sites of NO production inside and outside cells. In general, DAFs dyes react with N 2 O 3 , a byproduct of NO oxidation, resulting in fluorescence. It is initially available in the form of diacetate (DAF-2DA), which allowed the ready absorption by the cells. The diacetate group is removed by cell esterases leaving the membrane impermeable to DAF-2 and available for N 2 O 3 nitration to generate the highly fluorescent triazole (DAF-2T). Here, we describe two methods for detection of NO by fluorescence, one for NO extracellular detection by DAF-2 and the other one for NO intracellular detection, in this case using DAF-2DA.
Why it matches plant phenotyping methods植物細胞内外のNOという生理状態を蛍光で検出する具体的手法を中心に記述したプロトコルであり、植物フェノタイピング用の生理計測法として該当する。
abstractHere, we describe two methods for detection of NO by fluorescence, one for NO extracellular detection by DAF-2 and the other one for NO intracellular detection, in this case using DAF-2DA.
Understanding the distribution of elements within plant tissues is important across a range of fields in plant science. In this review, we examine synchrotron-based x-ray fluorescence microscopy (XFM) as an elemental imaging technique in plant sciences, considering both its historical and current uses as well as discussing emerging approaches. XFM offers several unique capabilities of interest to plant scientists, including in vivo analyses at room temperature and pressure, good detection limits (approximately 1-100 mg kg -1 ), and excellent resolution (down to 50 nm). This has permitted its use in a range of studies, including for functional characterization in molecular biology, examining the distribution of nutrients in food products, understanding the movement of foliar fertilizers, investigating the behavior of engineered nanoparticles, elucidating the toxic effects of metal(loid)s in agronomic plant species, and studying the unique properties of hyperaccumulating plants. We anticipate that continuing technological advances at XFM beamlines also will provide new opportunities moving into the future, such as for high-throughput screening in molecular biology, the use of exotic metal tags for protein localization, and enabling time-resolved, in vivo analyses of living plants. By examining current and potential future applications, we hope to encourage further XFM studies in plant sciences by highlighting the versatility of this approach.
Why it matches plant phenotyping methods植物組織内元素の分布を可視化・測定するX線蛍光顕微鏡法を植物科学向けの技術としてレビューしており、植物状態の取得手法が中心である。
abstractIn this review, we examine synchrotron-based x-ray fluorescence microscopy (XFM) as an elemental imaging technique in plant sciences
Field / plotGreenhouseSeed / grainWhole plant / canopy / plot / fieldGrowth / development / phenology
Abstract Flow cytometry (FCM) has been used for plant DNA content estimation since the 1980s; however, presently, the number of laboratories equipped with flow cytometers has significantly increased and these are used extensively not only for research but also in plant breeding (especially polyploid and hybrid breeding) and seed production and technology to establish seed maturity, quality and advancement of germination. A broad spectrum of horticultural and medicinal species has been analyzed using this technique, and various FCM applications are presented in the present review. The most common application is genome size and ploidy estimation, but FCM is also very convenient for establishing cell cycle activity and endoreduplication intensity in different plant organs and tissues. It can be used to analyze plant material grown in a greenhouse/field as well as in vitro . Due to somaclonal variation, plant material grown in tissue culture is especially unstable in its DNA content and, therefore, FCM analysis is strongly recommended. Horticultural species are often used as internal standards in genome size estimation and as models for cytometrically studied cytotoxic/anticancer/allelopathic effects of different compounds. With the growing interest in genome modification, increased application of FCM is foreseen.
Why it matches plant phenotyping methods植物のゲノムサイズ、倍数性、細胞周期などをフローサイトメトリーで測定する方法の応用を扱うレビューであり、植物表現型取得法が中心です。
abstractFlow cytometry (FCM) has been used for plant DNA content estimation since the 1980s
ABSTRACT Small GTPases act as molecular switches that regulate various plant responses such as disease resistance, pollen tube growth, root hair development, cell wall patterning and hormone responses. Thus, to monitor their activation status within plant cells is believed to be the key step in understanding their roles. We have established a plant version of a Förster resonance energy transfer (FRET) probe called Ras and interacting protein chimeric unit (Raichu) that can successfully monitor activation of the rice small GTPase OsRac1 during various defence responses in rice cells. Here, we describe the protocol for visualizing spatiotemporal activity of plant Rac/ROP GTPase in living plant cells, transfection of rice protoplasts with Raichu-OsRac1 and acquisition of FRET images. Our protocol should be widely adaptable for monitoring activation for other plant small GTPases and for other FRET sensors in various plant cells.
Why it matches plant phenotyping methods植物細胞内のGTPase活性という生理状態を、FRETバイオセンサーで可視化・測定するプロトコルを中心的に開発・記述しているため、植物フェノタイピング手法に該当する。
abstractWe have established a plant version of a Förster resonance energy transfer (FRET) probe called Ras and interacting protein chimeric unit (Raichu) that can successfully monitor activation of the rice small GTPase OsRac1 during various defence responses in rice cells.
Reactive oxygen and nitrogen species (ROS/RNS) are signaling molecules involved in a plethora of physiological processes in plants. Especially, ROS and nitric oxide (NO) are key players that are required for programmed cell death (PCD). The PCD associated with the hypersensitive response (HR) has been well characterized and the role of H 2 O 2 and NO as key signaling molecules inducing HR has been established. Localization of ROS and NO production in plant tissues in response to pathogens can be imaged by confocal laser microscopy by using specific fluorescent probes. Deciphering the time and spatial regulation of ROS and NO is very important to establish the cellular response of plants to adverse conditions. This chapter is mainly focused on the imaging of ROS and RNS accumulation in vivo in plant tissues undergoing PCD.
Why it matches plant phenotyping methods植物組織におけるROS/RNS蓄積を蛍光プローブと共焦点顕微鏡で可視化する手法を中心に扱う方法論章であり、植物の生理状態の取得・評価が主題である。
abstractLocalization of ROS and NO production in plant tissues in response to pathogens can be imaged by confocal laser microscopy by using specific fluorescent probes.
Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets.
Why it matches plant phenotyping methods植物形質とメタボロームデータを結び付ける回帰モデリング・ワークフロー自体を提案しており、植物形質の推定・解析手法が中心である。
titleRegression-Based Modeling of Complex Plant Traits Based on Metabolomics Data.
Eukaryotic cells depend on a dynamic actin cytoskeleton to regulate many conserved intracellular events such as endocytosis, morphogenesis, polarized cell growth, and cytokinesis (Engqvist-Goldstein and Drubin, 2003; Salbreux et al., 2012; Pruyne et al., 2004; Pollard, 2010). These activities depend on a precise and well-organized spatiotemporal actin assembly that involves many conserved processes found in eukaryotic cells ranging from a unicellular organism, such as yeast, to multicellular organisms, such as plants and human. In particular, both budding yeast Saccharomyces cerevisiae and plant Arabidopsis thaliana have been proven to be the powerful and great model organisms to study the molecular mechanisms of the polymerization of the actin cytoskeleton and the actin-driven processes in walled-cells. Here we describe the methods in imaging and image processing to analyze dynamic actin filament assembly in budding yeast and Arabidopsis using a wide-field fluorescent microscope.
Why it matches plant phenotyping methods植物の動的アクチン構造を蛍光顕微鏡と画像処理で定量解析する手法を中心に記述しており、植物細胞状態の画像ベース計測に該当する。
abstractHere we describe the methods in imaging and image processing to analyze dynamic actin filament assembly in budding yeast and Arabidopsis using a wide-field fluorescent microscope.
Induction and secretion of acid phosphatase (APase) is a universal adaptive response of higher plants to low-phosphate stress ( Tran et al. , 2010 ). The intracellular APases are likely involved in the remobilization and recycling of phosphate (Pi) from intracellular Pi reserves, whereas the extracellular or secreted APases are believed to release Pi from organophosphate compounds in the rhizosphere. The phosphate starvation-induced secreted APases can be released into the rhizosphere or retained on root surfaces (root-associated APases). In this article, we describe the protocols for analyzing root-secreted APase activity in the model plant Arabidopsis thaliana (Arabidopsis ). In Arabidopsis , the activity of both root-associated APases and APases that are released into the rhizosphere can be quantified based on their ability to cleave a synthesized substrate, para-nitrophenyl-phosphate (pNPP), which releases a yellow product, para-nitrophenol (pNP) ( Wang et al. , 2011 and 2104). The root-associated APase activity can also be directly visualized by applying a chromogenic substrate, 5-bromo-4-chloro-3-indolyl-phosphate (BCIP), to the root surface ( Lloyd et al. , 2001 ; Tomscha et al. , 2004 ; Wang et al. , 2011 and 2014) whereas the isozymes of APases that are released into rhizosphere can be profiled using an in-gel assay (Trull and Deikman, 1998; Tomscha et al. , 2004 ; Wang et al. , 2011 and 2014). The protocol for analysis of intracellular APase activity in Arabidopsis has been previously described (Vicki and William, 2013).
Why it matches plant phenotyping methods植物のリン欠乏応答に関連する根分泌酸性ホスファターゼ活性を、定量・可視化・アイソザイム解析するプロトコルが論文の中心であり、植物の生理状態を測定する方法として該当する。
abstractIn this article, we describe the protocols for analyzing root-secreted APase activity in the model plant Arabidopsis thaliana (Arabidopsis ).
In the agriculture environment, the detection and classification of the plant disease system plays very important role.In this first leaf image is captured and uploaded to the system where this image is compared with another image which is stored in the database.Comparison is take place with the help of algorithm which is named as content based histogram algorithm.For detecting the leaf disease image processing is used.The Image processing consist color extraction and then affected area is compare.The system helps to initial precautionary measures.If proper care is not taken then it will affected on quality, quantity and finally on productivity.This paper presents survey on different detection and classification techniques for plant diseases and also image processing technique which is used for automatic, fast and accurate detection as well as classification of plant leaf diseases.
Why it matches plant phenotyping methods植物葉の画像から病変領域を抽出し、画像処理・分類によって病害状態を推定する手法を中心に扱うレビューであり、植物表現型（病害症状）の取得・推定方法が主題である。
abstractThis paper presents survey on different detection and classification techniques for plant diseases and also image processing technique which is used for automatic, fast and accurate detection as well as classification of plant leaf diseases.
Plants tightly regulate the biosynthesis of ethylene to control growth and development and respond to a wide range of biotic and abiotic stresses. To understand the molecular mechanism by which plants regulate ethylene biosynthesis as well as to identify stimuli triggering the alteration of ethylene production in plants, it is essential to have a reliable tool with which one can directly measure in vivo ethylene concentration. Gas chromatography is a routine detection technique for separation and analysis of volatile compounds with relatively high sensitivity. Gas chromatography has been widely used to measure the ethylene produced by plants, and has in turn become a valuable tool for ethylene research. Here, we describe a protocol for measuring the ethylene produced by dark-grown Arabidopsis seedlings using a gas chromatograph.
Why it matches plant phenotyping methodsアラビドプシス幼植物のエチレン産生量をガスクロマトグラフィーで直接測定するプロトコルが中心であり、植物の生理状態を定量する測定法に該当する。
abstractHere, we describe a protocol for measuring the ethylene produced by dark-grown Arabidopsis seedlings using a gas chromatograph.
Cell theory has officially reached 350 years of age as the first use of the word 'cell' in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication 'Micrographia: or some physiological definitions of minute bodies'. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the subcellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more interdisciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking.
Why it matches plant phenotyping methods植物の生細胞画像からオブジェクトや特徴を抽出して定量化するバイオイメージング手法を中心に扱うレビューであり、植物表現型の取得・解析手法に該当する。
abstractIn this review we show how this quiet revolution has provided tools available to any personal computer user.