Plant phenotyping methods.

In Situ Monitoring of Stress-Induced Hydrogen Peroxide in Plants Using NIR-II Fluorescent Microneedles. ↗

In situ monitoring of plant responses to stress is one of the most challenging aspects of precision agriculture, and the dynamic control of crop growth according to fluctuating environmental factors. Although fluorescence imaging provides a nondestructive approach for monitoring stress-related biomarkers, its performance is often hindered by the low abundance of endogenous signaling molecules and strong tissue autofluorescence. Here, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants. The platform incorporates a second near-infrared fluorescent nanoprobe composed of Er 3+ -doped lanthanide nanoparticles emitting at 1550 nm and Mo-doped polymetallic oxomolybdates serving as the H 2 O 2 -responsive unit. Embedding the nanoprobe into custom-fabricated microneedles allows precise positioning on plant midribs for continuous monitoring of H 2 O 2 dynamics. Under stress conditions, the system successfully visualized spatiotemporal fluctuations of H 2 O 2 in living tomato, spinach, and tobacco plants. This work establishes a strategy for early stress diagnosis and developing universal plant health monitoring technologies.

Why it matches plant phenotyping methods植物ストレス状態を生体内H2O2として連続取得・可視化するマイクロニードル統合センシング基盤の開発が中心であり、単なる生物学的測定ではない。

abstractHere, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants.

RGB-Based Estimation of Chlorophyll-Fluorescence-Derived Photochemical Status Across Garden Plant Species Under Progressive Drought ↗

Chlorophyll fluorescence provides sensitive information on plant photochemical responses, but its measurement requirements can limit high-throughput application. This study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying. A Photochemical Status Index (PSI) was constructed by principal component analysis from five highly correlated JIP-test energy-flux variables (RC/CS, ABS/CS, TRo/CS, ET2o/CS, and RE1o/CS). The dataset comprised 50 aggregated species-by-soil-moisture-stage observations representing ten species and five sequential soil-moisture stages. Eleven RGB-derived variables were evaluated, and a partial least-squares regression model was assessed using nested leave-one-species-out validation, with all data-dependent procedures repeated within each outer training fold. PC1 explained 96.5% of the shared variation among the fluorescence-derived fluxes. The predictors g, GLI, ExG, ExGR, and CIVE were retained in all ten outer folds. The final model yielded a pooled out-of-fold R2 of 0.469, an RMSE of 1.585, and an MAE of 1.183. However, species-specific R2 ranged from −0.179 to 0.959, and a calibration slope of 0.509 indicated prediction-range compression. These findings provide proof-of-concept evidence of moderate RGB-based approximation of fluorescence-derived photochemical status, but inconsistent species transferability and the common soil-moisture/time gradient require external validation before practical deployment.

Why it matches plant phenotyping methodsRGB画像から蛍光由来の植物光化学状態を推定する手法を構築し、種間交差検証で性能評価しており、植物フェノタイピング手法が中心である。

abstractThis study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying.

Video-rate label-free molecular mapping in living plant tissue with a deployable optical encoder ↗

Abstract Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering LDPE layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.

Why it matches plant phenotyping methods生体植物組織の生化学的状態を動画取得・推定する光学センシング手法を開発し、校正、比較評価、不確実性推定まで行っており、表現型取得法が中心である。

abstractHere we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue.

Development and Verification of an Automatic Tower-Based SIF Observation System Based on Narrow Field-of-View Scanning and DOAS Atmospheric Correction ↗

Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H2O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions.

Why it matches plant phenotyping methods植物の光合成状態を示すSIFを取得するタワー型分光観測システムとDOAS補正アルゴリズムを開発し、シミュレーションおよび圃場データで検証しているため、植物フェノタイピング手法が中心である。

abstractWe present a DOAS-based SIF retrieval algorithm

Optimizing cell segmentation and downstream processing for plant probe-based spatial transcriptomics ↗

Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.

Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。

abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.

From Field Photosynthesis to Genetic Architecture: Insights from the First Dedicated Photosynthesis Hackathon ↗

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

Why it matches plant phenotyping methods圃場光合成データから時間分解特徴量や遺伝的シグナルを抽出する計算手法を中心に扱っており、植物生理形質の実質的なフェノタイピング手法応用に該当する。

abstractmechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield.

Phenomics and High-Throughput Phenotyping of Photosynthetic Traits for Improving Abiotic Stress Resilience in Wheat and Rice ↗

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production.

Why it matches plant phenotyping methods植物の光合成形質を対象に、HTP技術やセンサー手法を体系的にレビューしており、フェノタイピング手法が中心である。

abstractThis review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding

RADIX: a deep learning framework that maps root barriers across species and reveals genetic and environmental contributions ↗

Root anatomical barriers, including the suberized and lignified walls of the endodermis and exodermis, and cortical aerenchyma, regulate water and nutrient transport, gas exchange, and rhizosphere interaction. Their adaptive function places them as an important target for breeding environmentally resilient plant species. Quantifying these structures at high resolution is a manual bottleneck that limits experimental scale. We present RADIX (Root Anatomy Deep- learning Image segmentation across species and platforms), a framework that adapts a large self-supervised vision-transformer foundation encoder (DINOv3), pre-trained on billions of natural images, to root anatomy by fine-tuning its encoder with a dense-prediction-transformer decoder. Transferring these general-purpose vision encoders to a specialized biological domain with a high-quality annotated dataset is what allows RADIX to generalize across species and imaging platforms. We train and evaluate it on the first expert-annotated benchmark of root anatomical structures at scale, comprising 1,695 high-quality fluorescence images spanning 17 monocot and dicot species, six anatomical structures, and three imaging platforms. RADIX segments all six structures at inter-annotator-level accuracy and generalizes to unseen species, genotypes, growth conditions, and an imaging platform from an independent laboratory. A single unified model surpasses monocot- and dicot-specialist models without sacrificing in-group accuracy. Predicted masks yield aerenchyma and suberin/lignin measurements matching expert annotation at ∼1.2 s per image with a single GPU, reducing weeks of manual analysis to minutes. Applying RADIX across genotypes, microbial treatments, and growth systems, we show that these cell type features form a coordinated, multidimensional, and context-dependent system shaped by genetic and environmental factors.

Why it matches plant phenotyping methods根の解剖学的構造を画像から自動抽出・定量する深層学習フレームワークを開発し、注釈付きベンチマークで検証しているため、植物フェノタイピング手法が中心である。

abstractQuantifying these structures at high resolution is a manual bottleneck that limits experimental scale.

Ozone suppresses rice photosynthesis and yield in China's middle-lower yangtze plain: satellite evidence from SIF and panel regression. ↗

Ground-level ozone (O 3 ) adversely affects rice physiology and is associated with yield reductions. This study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production in China's primary rice-growing region-the Middle and Lower Reaches of the Yangtze River (MLYR)-from 2019 to 2023. We fused Sentinel-5P TROPOMI total ozone column (TOC) data, a harmonized multi-satellite solar-induced chlorophyll fluorescence (SIF) product (LHSIF), high-precision rice distribution maps, and ERA5 meteorological reanalysis data. In addition to SIF, we examined multiple vegetation indicators (chlorophyll content, leaf area index, and vegetation indices) to capture broad physiological responses. A bidirectional fixed-effects panel model was employed to control for spatiotemporal confounders, revealing a significant inhibitory effect of O 3 on photosynthesis (β = -1.334 × 10 -5 , p 3 concentrations would increase regional SIF by 36.36%, while a commensurate 10% reduction in annual exposure could elevate rice yields by approximately 8.4%. This spaceborne remote sensing approach provides a robust and transferable methodology for the precise regional monitoring of ozone stress and for informing targeted mitigation strategies to safeguard crop productivity.

Why it matches plant phenotyping methods衛星リモートセンシングによるSIF等の植物生理指標を用いてイネのオゾンストレスを地域スケールで推定する評価フレームワークが研究の中心であり、単なる生物学的実験の routine 測定ではない。

abstractThis study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production

Early identification of glufosinate-resistant soybeans using plant image analysis ↗

The use of glufosinate-resistant GM soybean has expanded, raising concerns about resistant weed development and unintended transgene flow. To support monitoring for timely management, we propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment. We evaluated the potential of spectral imaging, using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors, for early detection of glufosinate resistance in soybean. In the dose-response test, the key spectral indices including NDI, temperature difference, F v /F m , and NPQ distinguished between resistant and susceptible soybeans within 4 to 24 hours after treatment (HAT). IR thermal and CF imaging showed higher sensitivity in identifying resistance than RGB imaging by detecting spectral responses associated with physiological changes before visual symptoms appeared. Validation test with a single dose treatment of glufosinate reconfirmed that image analysis by both the naked eye and machine learning (ML) can discriminate between resistant and susceptible soybeans in a single day after glufosinate treatment. ML-based classification using IR thermal index achieved 100% accuracy as early as 6 HAT and the classification by the naked eye using IR thermal images showed 96.6% accuracy at 24 HAT. These results suggest that plant imaging enables early and non-destructive identification of herbicide-resistant individuals by detecting early spectral changes to herbicide treatment. These findings support its use as a potential alternative to conventional diagnostic methods for detecting individuals containing transgenes in herbicide-resistant GM soybean cultivation for future applications in herbicide-resistant weed monitoring.

Why it matches plant phenotyping methodsスペクトル画像（RGB、熱赤外、クロロフィル蛍光）と機械学習を用いて、薬剤処理後の植物の生理応答から耐性を早期識別する方法を開発・検証しており、植物表現型の取得が中心である。

abstractwe propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment.

Detecting Callose Deposition in Soybean Lateral Roots During Fungal Infections. ↗

Callose is a β-1,3-glucan polysaccharide deposited at the plant cell wall interface. It is involved in numerous plant physiological processes and responses to both biotic and abiotic stresses. Callose deposition under biotic stress conditions is typically associated with pattern-triggered immunity, which is activated upon recognition of pathogen-associated molecular patterns or damage-associated molecular patterns at the cell wall interface. These depositions reinforce compromised and damaged cell walls caused by pathogen invasion. The standard method for visualizing callose deposition in various plant tissues involves aniline blue staining. Aniline blue fluorochrome preferentially binds to β-1,3-glucans, which enables this staining technique to specifically locate callose deposition. Although multiple protocols for callose detection using aniline blue are available in various model plants, such as Arabidopsis, there is no optimized method for lignified lateral root tissues after fungal infection. Lignification of roots can hinder the clear visualization of callose depositions; therefore, it is essential to remove them for improved callose detection. Here, we have optimized a robust and reliable method for detecting callose deposition in soybean lateral roots during Macrophomina phaseolina infections. M. phaseolina is a filamentous, soil-borne, necrotrophic fungus that causes charcoal rot disease in soybean and other crop plants. Here, we also provide a detailed methodology for soybean root infection with M. phaseolina using the root-dip method of inoculation, followed by aniline blue staining. Furthermore, we provide a detailed workflow for employing open-source Fiji software together with the Trainable Weka Segmentation (TWS) plugin to detect and count callose structures in fungal-infected root tissues. This protocol may be applicable for detecting callose deposition in other crop plants during fungal infections. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Infection assay with M. phaseolina using root dip method of inoculation Basic Protocol 2: Staining of M. phaseolina-infected soybean roots with aniline blue and imaging of stained soybean roots using fluorescence microscopy Support Protocol: Callose quantification using ImageJ software combined with the TWS plugin.

Why it matches plant phenotyping methods感染根のカルロース沈着という植物の病態・生理状態を、染色・蛍光画像・Fiji/TWSで検出および定量する方法を最適化した手法論文であり、表現型取得が中心です。

abstractHere, we have optimized a robust and reliable method for detecting callose deposition in soybean lateral roots during Macrophomina phaseolina infections.

Early-stage diagnosis of nutrient deficiency by chlorophyll fluorescence in maize. ↗

Rapid, non-destructive phenotyping is vital for early nutrient deficiency detection in plant research and agriculture. Nitrogen (N) and magnesium (Mg) deficiency are hard to distinguish with instruments measuring only chlorophyll content since both deficiencies lead to loss of chlorophyll. This study evaluated the suitability of a commercially available dual-excitation fluorescence sensor (measuring chlorophyll (Chl) and epidermal UV absorbing compounds) for identifying nutrient deficiencies in maize seedlings. Both N and Mg deficiencies in maize seedlings caused a decrease in the Chl index and an increase in the flavonol (Flav) index, although the response of the Flav index was much weaker under Mg deficiency. This happened in spite of a much larger increase in sugar concentrations under Mg deficiency. Furthermore, the nitrogen balance index (NBI) detected N deficiency earlier in leaves developing under nutrient deficiency than in those present before treatment application. Spatiotemporal analysis revealed distinct patterns of Flav index increase: N deficiency caused a marked increase in upper (younger) leaves, whereas Mg deficiency initiated Flav index increases at the tips of lower (older) leaves. Utilizing green- and red-light excitation of chlorophyll to infer epidermal anthocyanins turned out to be not straightforward in nutrient-deficient maize leaves. Taken together, these findings reveal advantages and limitations of Chl fluorescence in diagnosing nutrient stress and underscore the importance of understanding spatial-temporal nutrient dynamics for accurate early detection. Leaf age should be considered for fertilization decisions based on the NBI. A strategy is suggested how anthocyanins can be detected without problems.

Why it matches plant phenotyping methodsトウモロコシの栄養欠乏を診断する蛍光センサーの適用性と性能・限界を評価しており、植物状態の取得手法が研究の中心である。

abstractThis study evaluated the suitability of a commercially available dual-excitation fluorescence sensor (measuring chlorophyll (Chl) and epidermal UV absorbing compounds) for identifying nutrient deficiencies in maize seedlings.

Blue-Green Fluorescence for Downy Mildew Phenotyping in Lettuce: Linking Transcriptional, Metabolic, and Optical Changes. ↗

Abstract Effective plant disease phenotyping is crucial for resistance breeding, but traditional visual assessment is often inaccurate and inefficient. This is particularly challenging when breeding lettuce (Lactuca sativa) for resistance to downy mildew, given the obligate biotrophic lifestyle of the causal pathogen Bremia lactucae. We discovered that B. lactucae-infected lettuce exhibits patches of increased blue-green fluorescence (BGF) under UV-A excitation from 6 d post-inoculation, preceding visible symptoms. Co-localization of BGF with hyphae, visualized with trypan blue, indicates that BGF is induced by downy mildew colonization. We therefore investigated its potential for non-invasive disease detection and quantification, as well as the underlying physiological changes. Using a custom imaging system, we demonstrate that BGF leaf area correlates with downy mildew severity and can be automatically quantified via a U-Net-based convolutional neural network, enabling early, objective disease assessment. Exploring transcriptomic and metabolomic changes associated with BGF, we found that induction of the phenylpropanoid pathway led to accumulation of caffeoylquinic acids, whose fluorescence spectra overlap with that of BGF tissue, supporting the hypothesis that these compounds contribute to the fluorescence signal. BGF imaging offers a powerful tool for phenotyping in lettuce breeding and for identifying quantitative resistance traits that support durable downy mildew resistance.

Why it matches plant phenotyping methods青緑蛍光のカスタム撮像とU-Netによる自動定量を開発し、レタスべと病の重症度を非侵襲・早期に評価する方法が研究の中心である。

abstractUsing a custom imaging system, we demonstrate that BGF leaf area correlates with downy mildew severity and can be automatically quantified via a U-Net-based convolutional neural network, enabling early, objective disease assessment.

Bridging affordable phenomics with high-efficiency controlled environment agriculture for data-driven agriculture ↗

Controlled environment agriculture (CEA) is essential for resilient crop production but faces high energy demands and operational costs. While high-throughput phenotyping (HTP) provides critical biological feedback to optimize these systems, conventional HTP platforms remain prohibitively expensive, infrastructure-heavy, and technically complex for widespread adoption. This review examines the emerging shift toward “affordable phenomics”, an approach integrating low-cost, open-source microcontrollers and Internet-of-Things (IoT) devices to continuously capture dynamic plant physiological data. By utilizing customizable tools such as modular chlorophyll fluorometers and wearable sensors, researchers and commercial growers can non-destructively monitor key traits like photosynthetic efficiency and water status in real time. Coupling these accessible sensing networks with artificial intelligence (AI)-driven analytics allows static environmental controls to transition into dynamic, plant-centered feedback systems. We synthesize recent advancements in affordable sensor technologies and review how temporal AI modeling extracts biologically meaningful features from longitudinal datasets to direct adaptive lighting and irrigation strategies. Furthermore, we critically assess current technological limitations, including sensor calibration, signal noise, cross-platform data standardization, and edge-versus-cloud computation tradeoffs. Finally, we highlight essential future research directions, particularly the development of robust edge-computing frameworks and predictive crop digital twins, demonstrating how affordable phenomics offers a scalable, data-driven pathway to improve resource-use efficiency in modern agriculture.

Why it matches plant phenotyping methods植物フェノタイピングの低コストセンサー、IoT、AI解析、校正・標準化などを中心に扱うレビューであり、単なる農業応用ではなく手法・プラットフォームの評価が主題である。

abstractThis review examines the emerging shift toward “affordable phenomics”, an approach integrating low-cost, open-source microcontrollers and Internet-of-Things (IoT) devices to continuously capture dynamic plant physiological data.

From plant oxidative stress to food safety: a versatile fluorescent probe for H 2 O 2 imaging in plant roots, living cells, and residual detection in milk. ↗

Hydrogen peroxide (H 2 O 2 ) is an important signaling molecule in plants under stress, and its level can be stimulated by abiotic stress and oxidative stress, which will seriously affect plant growth and development. Additionally, the presence of excessive residual H 2 O 2 in food can pose significant health risks to humans, because intake of H 2 O 2 can lead to serious pathological conditions. Therefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food. In this paper, we designed a fluorescence probe NBP, which has the advantages of high selectivity, low detection limit (80 nM) and long emission wavelength (648 nm). The imaging effect of exogenous H 2 O 2 was realized in the roots of Platycodon grandiflorum . By exploring the interplay between H 2 O 2 , plant metals, and drought stress, we can observe the up-regulation of H 2 O 2 in the roots of Platycodon grandiflorum under adverse conditions, and the root 3D imaging study could be realized. Then we combined the fluorescence probe with a smartphone, which enables on-site detection of residual H 2 O 2 in various milk samples. In addition, we investigated the fluorescence imaging of endogenous and exogenous H 2 O 2 in living cells using NBP. Therefore, this study provides a new way to assess the oxidative stress risk of Platycodon grandiflorum roots under abiotic stress, which is expected to improve plant production and has broad application prospects in food sample detection.

Why it matches plant phenotyping methods植物根におけるH2O2の蛍光イメージング手法を開発し、乾燥ストレス下の酸化ストレス状態を評価しているため、植物フェノタイピング手法が中心です。

abstractTherefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food.

Chlorophyll fluorescence-based control of greenhouse supplemental lighting improves energy use efficiency in lettuce. ↗

Plant-driven lighting control has been proposed as a strategy to regulate supplemental light-emitting diode (LED) intensity according to real-time plant physiological status. This study developed a multiple linear regression (MLR) model to predict quantum yield of photosystem II (Φ PSII ) from environmental variables and evaluated its integration into a chlorophyll fluorescence-based biofeedback light control. The model incorporated light intensity, CO 2 concentration, air temperature, vapor pressure deficit, short-term light history, and diurnal effects. In a greenhouse validation experiment, supplemental lighting was regulated using either direct chlorophyll fluorometer measurements of Φ PSII (sensor-based control) or Φ PSII values predicted by the machine learning model (ML-based control), and compared with a constant photosynthetic photon flux density (PPFD) treatment. Both sensor- and ML-based control stabilized photochemical activity across the photoperiod relative to constant PPFD. Although plant growth did not differ among treatments, sensor-based ETR control achieved the highest energy use efficiency for LED lighting in this study. These findings demonstrate the feasibility of integrating predictive ML models into plant-based lighting control systems and indicate that sensor-based biofeedback control improved the energy-use efficiency of greenhouse supplemental lighting without compromising crop growth.

Why it matches plant phenotyping methods植物の光合成生理状態（ΦPSII）を予測・計測するモデルを開発し、蛍光センサーによるフィードバック照明制御へ統合して検証しており、植物フェノタイピング手法が中心です。

abstractThis study developed a multiple linear regression (MLR) model to predict quantum yield of photosystem II (Φ PSII ) from environmental variables and evaluated its integration into a chlorophyll fluorescence-based biofeedback light control.

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network. ↗

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (R p 2 =0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Why it matches plant phenotyping methods油糠菜葉の鉛含量を蛍光ハイパースペクトル画像とニューラルネットワークで非破壊推定する手法の開発・比較検証が研究の中心であり、植物の化学的ストレス状態を定量するため。

titleNon-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Multisource data fusion for estimating cotton leaf nitrogen: A small-sample modeling perspective with neural network and interpretable deep forest architectures ↗

Accurate monitoring of nitrogen nutrition is critical for optimizing cotton production. Traditional machine learning-based inversion models have limited effectiveness for precision monitoring. Multisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity. Cotton plants subjected to different nitrogen treatments were investigated. A two-year pot experiment was conducted to collect main-stem leaf images to construct an image pretraining dataset for model transfer. In a field experiment conducted over one year, main-stem leaf data were collected using hyperspectral, chlorophyll fluorescence, and digital camera sources, thereby providing a multisource dataset for training monitoring models. Two architectures—a neural network (NN) and an interpretable deep forest (DF), which are suitable for small-sample spectral, fluorescence, image color, and texture-sequence features—were constructed to improve the accuracy of nitrogen content inversion. Additionally, a two-dimensional sliding-window processing method was introduced into the DF multigranularity scanning module, and a transfer-learning-based two-dimensional convolutional NN was employed to directly model small-sample two-dimensional images. Building upon the outcome, multilayer fusion models were constructed, with corresponding fusion strategies designed for homogeneous sequence inputs and heterogeneous image–sequence inputs. The results showed that NN and DF can effectively handle limited sample sizes and outperform traditional machine learning models. Among the fusion models, the optimal secondary decision-level fusion model achieved an R² of 0.926 on the independent test set, indicating good performance under small-sample conditions. This study provides a methodological reference for the precise monitoring of crop phenotypic parameters under small-sample conditions.

Why it matches plant phenotyping methods綿花葉の窒素含量という植物形質を、画像・ハイパースペクトル・蛍光データの融合と深層学習で推定する手法を開発・評価しており、形質取得・推定法が研究の中心である。

abstractMultisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity.

Canopy chlorophyll fluorescence escape fraction under polychromatic irradiation. ↗

Accurately retrieving Sun-Induced Fluorescence (SIF) is critical for monitoring plant physiological status, yet the signal is significantly distorted by light reabsorption and scattering within the canopy. While empirical models exist for the far-red region of the spectrum, accurately accounting for the photon escape fraction in the complete Chlorophyll Fluorescence (ChlF) emission range remains challenging. Based on our previous work under monochromatic conditions, in this work we present a photophysical framework to estimate the chlorophyll fluorescence escape fraction (f esc ) across the full chlorophyll emission spectrum (600-800 nm) under polychromatic excitation. The methodology integrates experimental radiance measurements of Bistorta amplexicaulis with an algorithm to decouple reflectance from emission. To evaluate the model's robustness in the field, we conducted a global sensitivity analysis using a synthetic dataset generated by coupling the SMARTS atmospheric radiative transfer model with the PROSAIL canopy model. Our results demonstrate that failing to account for canopy light reabsorption and scattering can underestimate fluorescence yields by approximately 25%. We identified distinct drivers for f esc in SIF-relevant bands: f esc in the red region (687 nm) is primarily governed by chlorophyll content and Leaf Area Index (LAI) due to intense fluorescence reabsorption, while f esc in the far-red region (760 nm) is dominated by canopy structure and leaf inclination (LIDFa). This study provides a practical and robust estimation method for f esc at the canopy level, offering a key tool for improving the accuracy of SIF-based photosynthetic efficiency assessments in both environmental and agronomic remote sensing applications.

Why it matches plant phenotyping methods植冠クロロフィル蛍光の脱出率を推定する物理モデルとアルゴリズムを開発し、実測放射輝度および合成データで頑健性を評価しており、植物生理状態の計測手法が中心である。

abstractwe present a photophysical framework to estimate the chlorophyll fluorescence escape fraction (f esc ) across the full chlorophyll emission spectrum (600-800 nm) under polychromatic excitation.

Field Validation of Chlorophyll Fluorescence Imaging for Early Detection of Wheat Powdery Mildew: Identifying QYmax as a Key Physiological Indicator ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsコムギうどんこ病の早期検出に向け、クロロフィル蛍光画像法を圃場で検証し、QYmaxという植物生理形質を指標化する研究であり、フェノタイピング手法が中心です。

titleField Validation of Chlorophyll Fluorescence Imaging for Early Detection of Wheat Powdery Mildew: Identifying QYmax as a Key Physiological Indicator

Improving Nitrogen Use Efficiency in Wheat: Integrating Agronomic, Genomics, and Remote Sensing for Sustainable Production. ↗

Improving nitrogen use efficiency (NUE) in wheat is critical for addressing the dual challenges of global food security and environmental sustainability. Globally, only 42%-47% of applied nitrogen (N) fertilisers taken up by crops, with remainder lost to the environment, driving soil and water pollution, greenhouse gas emissions, and ecological imbalances. This review provides a comprehensive synthesis and integrative framework- integrating agronomic practices, advanced remote sensing and genomic approaches to enhance wheat NUE. We first examine the physiological basis of NUE, emphasising the synergy between photosynthetic carbon assimilation and N metabolism, the critical role of Rubisco in carbon-nitrogen coupling, and the temporal dynamics of N uptake, transport, and remobilisation throughout the wheat growth cycle. The temporal mismatch between source-sink N partitioning during grain filling emerges as a major physiological constraint limiting NUE in modern high-yielding varieties. We then explore transformative advances in remote sensing technologies, highlighting the paradigm shift from traditional vegetation indices to physiological sensing approaches. Through integration of multispectral imaging, LiDAR, thermal infra-red sensing, and solar-induced chlorophyll fluorescence, coupled with three-dimensional radiative transfer models and machine learning algorithms, these technologies enable non-destructive, real-time monitoring of crop N status while overcoming spectral-structural ambiguity and saturation limitations. From a genomic perspective, we synthesise recent progress in quantitative trait loci mapping and genome-wide association studies (GWAS), identifying key genetic loci controlling root architecture, N uptake transporters (NRT/AMT families), and grain filling efficiency. Multi-omics integration-spanning genomics, transcriptomics, and metabolomics-reveals temporal genetic networks distinguishing short-term nitrogen signalling responses from long-term adaptive remodelling, with genes such as TaNAC2-5A, TaNPF6.2, and QMrl-7B emerging as promising targets for molecular breeding. High-throughput phenotyping platforms enable time-series GWAS analysis, capturing developmental dynamics and genotype × environment interactions that traditional approaches miss. Finally, we discuss sustainable N management strategies, including enhanced efficiency fertilisers, precision application technologies, and soil health optimisation. By integrating these multidisciplinary approaches within a Genotype × Environment × Management framework, this review provides a roadmap for developing climate-smart, N-efficient wheat varieties and precision N management systems that simultaneously enhance productivity, reduce environmental footprints, and ensure sustainable agricultural intensification.

Why it matches plant phenotyping methods小麦の窒素状態を非破壊・時系列に測定するリモートセンシングと高スループット表現型解析を、技術的課題や統合手法とともにレビューしており、表現型取得法が実質的に扱われている。

abstractWe then explore transformative advances in remote sensing technologies, highlighting the paradigm shift from traditional vegetation indices to physiological sensing approaches.

Fluorescence Lifetime Imaging in Plants: Practical guidelines for multiplexing, label-free imaging and data analysis ↗

ABSTRACT Fluorescence Lifetime Imaging Microscopy (FLIM) is becoming a key technique for live-cell multiplexing and label-free detection of endogenous fluorescence in animal systems. Its potential in plant biology, however remains largely unexploited, despite its integration into a number of commercial microscopy setups. Here, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores. Lifetime imaging of different fluorescent reporters targeted to distinct organelles (nucleus, plasma membrane, endoplasmic reticulum, etc.) and subsequent analysis of the decay curves using different modes allowed us to simultaneously discriminate up to four spectrally overlapping fluorophores solely by lifetime differences in specific subcellular compartments. Remarkably, fluorophores with lifetimes differing by as little as 0.1 ns can be reliably discriminated using one of these modes, namely Phasor-based analysis. Moreover, we show that the same fluorophores exhibit compartment-specific lifetime shifts, enabling Phasor separation of identical tags residing in different organelles. Finally, we extended the Phasor approach to label-free imaging of endogenous plant fluorescence. Together, these results establish FLIM-Phasor as a versatile, multiplex-capable tool for plant cell biology, opening new avenues for imaging strategies that yield higher content information at both cellular and tissue-level resolution.

Why it matches plant phenotyping methods植物細胞・組織の蛍光状態を取得・解析するFLIM-Phasor法を体系的に構築・検証し、マルチプレックスおよびラベルフリー植物蛍光イメージングへの応用を示した、方法中心の研究である。

abstractHere, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores.

Software and reproducibility package ↗

First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.

Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性用データを提供しており、フェノタイピング手法が中心である。

abstractFirst stable release of the RGB and NPQ pixel-wise phenotyping pipeline

Software and reproducibility package ↗

First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.

Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性資料が中心であり、植物フェノタイピング手法に該当する。

abstractPython-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data.

An early detection model of wheat stripe rust utilizing two-dimensional correlation spectroscopy for eliminate growth-related interference. ↗

Wheat stripe rust, caused by Puccinia striiformis f. sp. Tritici (Pst), represents a significant threat to global wheat production. Early detection, particularly during the asymptomatic phase, is critical for effective disease management. Hyperspectral sensing can detect subtle physiological alterations associated with initial infection; However, its effectiveness is frequently limited by substantial background interference from normal plant growth. In this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR). To mitigate background the interference, generalized two-dimensional correlation spectroscopy (2D-COS) was employed, utilizing infection time as the perturbation variable. This approach surpasses conventional dimensionality reduction techniques such as principal component analysis (PCA) and the chemometric feature selection algorithm known as competitive adaptive reweighted sampling (CARS). Through this methodology, six feature bands exhibiting distinct absorption changes were identified. Analysis of synchronous and asynchronous 2D-COS correlation features from 1 to 6 days post-inoculation (dpi), enabled effective discrimination between spectral variations attributable to growth and those specific to disease responses. The biological significance of these spectral dynamics was empirically validated using steady-state chlorophyll fluorescence imaging and destructive biomass measurements. This combined evidence confirmed that Pst-induced chloroplast functional impairment strictly precedes macroscopic tissue structural collapse. This process effectively suppressed background noise while preserving critical infection-related signals. Subsequently, three classifiers-support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)-were evaluated using the extracted 2D-COS features. Asynchronous features generally produced superior classification performance, with XGBoost achieving the highest accuracy (86.79%) and area under the receiver operating characteristic curve (AUC) (94.12%). Compared to conventional methods like PCA and CARS, 2D-COS more effectively attenuated growth-related interference and accentuated early disease signatures. These results demonstrate that the integrated framework of "multiplicative scatter correction (MSC) + Asynchronous Correlation Features + XGBoost" offers substantial potential for accurate, non-destructive, and early diagnosis of wheat stripe rust.

Why it matches plant phenotyping methods小麦赤さび病の無症状期を対象に、ハイパースペクトル計測と2D-COS・機械学習による植物病害状態の抽出手法を開発・評価しており、表現型取得が中心である。

abstractIn this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR).

Review and perspective of key generic technologies in crop phenomics ↗

传感技术、人工智能算法及高性能计算能力的飞速发展正推动作物表型检测从单一性状描述向多尺度、智能化感知体系的转变。在此背景下，作物表型智能检测应运而生，其核心原理在于解析电磁波谱与植物组织相互作用的物理机制，即不同波段的光子与植物组织发生电子跃迁、分子振动及热辐射等能量交换，从而携带形态结构、生理生化及环境互作等多维信息。基于此，本文系统阐述了可见光成像、三维成像、光谱成像、叶绿素荧光成像、热红外成像等传感器的技术特性与适用场景，并梳理了从细胞/组织到器官、单株及群体水平的表型检测平台，比较了不同平台的应用场景及其优势与局限。针对当前多源数据异构性高、标准化体系缺失及模型泛化能力不足等核心瓶颈，本文提出发展作物表型智能检测的关键路径：建立面向多平台的数据标准化采集与校准体系，从源头保证数据质量；发展鲁棒性强、可迁移的智能解析算法，推动表型解析从传统特征工程向视觉基础模型及多模态大模型的演进；构建符合FAIR原则的共享数据库，打破“数据孤岛”现象；明确模型在不同物种、环境及生育阶段的适用边界，提升其在真实农业场景下的可靠性与泛化能力。利用智能检测技术实现作物表型的高通量、精准化解析，是未来作物表型组学发展的必然趋势，也为智慧育种与精准农业提供核心技术与理论支撑。

Why it matches plant phenotyping methods作物表型检测技术综述，系统评述多类成像传感器、表型检测平台及智能解析算法，方法学内容是核心。

abstract本文系统阐述了可见光成像、三维成像、光谱成像、叶绿素荧光成像、热红外成像等传感器的技术特性与适用场景，并梳理了从细胞/组织到器官、单株及群体水平的表型检测平台

Hyperspectral imaging reveals early drought stress and associated molecular responses in lettuce for space agriculture. ↗

In NASA's controlled-environment plant growth systems, early and autonomous detection of crop stress is critical for sustaining food production during long-duration space missions. Hyperspectral imaging (HSI) has proven effective for early stress detection, yet the molecular processes underlying diagnostically informative spectral signals remain poorly defined. Here, we present a two-stage phenomics-to-molecular framework to evaluate whether hyperspectral signatures associated with early drought detection correspond to coordinated molecular stress responses in lettuce. In the first stage, reflectance and fluorescence HSI were used to identify early drought detection windows in 'Dragoon' lettuce subjected to controlled water limitation over a 15-day treatment period with daily imaging. Classification models integrating reflectance and fluorescence outperformed single-modality models and achieved high accuracy as early as day after treatment (DAT) 4, reaching up to 97% at DAT 5. Partial least squares discriminant analysis (PLS-DA) identified predictive wavelengths concentrated in blue-green, red, and red-edge regions associated with chlorophyll absorption and photosystem II activity. In the second stage, independent transcriptomic and untargeted metabolomic profiles were integrated with hyperspectral signatures using MOFA2 to establish biological context. This analysis revealed a dominant drought axis characterized by early activation of ABA signaling, osmotic adjustment, phenylpropanoid metabolism, and lipid and membrane remodeling, with maximal molecular divergence at DAT 5, coinciding with peak hyperspectral classification performance. Notably, wavelengths optimized for early stress discrimination were systematically shifted toward shorter, optically efficient regions relative to those most strongly associated with downstream metabolic abundance, indicating that HSI primarily captures early structural and energetic consequences of molecular stress responses rather than direct biochemical composition. Together, these results demonstrate that hyperspectral imaging can function as a non-destructive, biologically interpretable molecular proxy for drought stress, providing a foundation for compact, hands-free sensing systems capable of distinguishing stress-specific plant states in space agriculture.

Why it matches plant phenotyping methodsレタスの乾燥ストレス状態を hyperspectral imaging で早期推定し、分類性能と分子応答との対応を評価することが研究の中心であり、植物フェノタイピング手法の開発・検証に該当する。

abstractwe present a two-stage phenomics-to-molecular framework to evaluate whether hyperspectral signatures associated with early drought detection correspond to coordinated molecular stress responses in lettuce.

High-throughput phenotyping for climate-resilient forests: integrating multi-sensor fusion and root-shoot dynamics. ↗

Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.

Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、マルチセンサー融合、深層学習、検証課題、標準化・ベンチマークをレビューしているため。

abstractIn this review, we argue that developing climate-resilient forests requires looking below the canopy.

Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe ↗

Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.

Why it matches plant phenotyping methods植物の光合成・機能を推定するリモートセンシング手法（SIF）を中心に、その機構とスケール間利用をレビューしており、植物フェノタイピング手法のレビューに該当する。

abstractThe emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales

Chemical Probes for Functional Plant Imaging ↗

The advent of spatial and quantitative biology has led to immense advances in understanding the complex inner workings of plants, down to the molecular scale. Functional imaging of live plants, which enables the spatial and quantitative mapping of biochemical cues, physicochemical properties of cellular structures, and the dynamics of physical and chemical signals with unprecedented resolution, has become a key technology for advancing the mechanistic understanding of plant cell biology. In this review, we highlight progress in live functional imaging in plants through the use and development of chemical fluorescent probes, which enable plant functional imaging without requiring genetic manipulation of the study object. We explain how probes sense, target, and report on functional features within the plant cell; discuss their limitations, including toxicity; and provide case studies to exemplify how these tools can complement biological studies to unravel the complex machinery that makes plants work. We conclude by outlining the expected future development of this field and identifying key challenges that lie ahead.

Why it matches plant phenotyping methods植物の生体機能を空間的・定量的に可視化する化学蛍光プローブの開発と利用を扱うレビューであり、植物イメージング手法が中心です。

abstractIn this review, we highlight progress in live functional imaging in plants through the use and development of chemical fluorescent probes

An ESIPT-AIE nanosensor for ONOO - imaging: decoding heavy metal stress and ferroptosis in living plants. ↗

Peroxynitrite (ONOO - ) serves as a critical redox signaling molecule in plant stress responses and ferroptosis, yet real time monitoring within complex plant matrices remains challenging. To address this, we synthesized a ratiometric nanosensor (DA) through molecular self-assembly. The DA particles (258 nm in diameter) exhibited enhanced sensitivity driven by an excited-state intramolecular proton transfer (ESIPT)-triggered restricted intramolecular motion mechanism, resulting in distinctive aggregation-induced emission (AIE) behavior. This nanoscale configuration improved tissue penetration and eliminated the aggregation-caused quenching (ACQ) effect commonly observed in traditional rhodamine derivatives. Meanwhile, the DA probe featured a large Stokes shift of 167 nm and an ultra-low limit of detection (LOD) of 6.4 nM. Leveraging these optical advantages, the nanosensor enabled real-time visualization of ONOO - dynamics in plant tissues and quantitative assessment of ONOO - accumulation under cadmium (Cd 2+ ), sodium chloride (NaCl), and erastin induced stress. This work represents the first application of an ESIPT-AIE hybrid probe for ONOO - detection in plants, providing a powerful analytical platform for elucidating oxidative stress mechanisms and advancing strategies to enhance crop resilience.

Why it matches plant phenotyping methods植物組織内の酸化ストレス状態を可視化・定量する新規ナノセンサーを開発し、植物ストレス下で検証しており、表現型状態の取得法が中心である。

abstractwe synthesized a ratiometric nanosensor (DA) through molecular self-assembly.

Leveraging fluorescent sensor with prominent-response viscosity for evaluating metal-ion stress in plants. ↗

Effectively imaging the variation of heavy metal induce stress (HMIS) in plant is significantly important for stress resistance research in the fields of environmental and plant biology. However, due to the absence of distinctive parameter to reveal the relationship between HMIS and plant homeostasis, the reported fluorescence sensors fail to assess HMIS. Herein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants. Spectral experiments indicate that QVP exhibited selectivity, sensitive, photochemical stability, and pH adaptability for viscosity detection. Motivated by the robust detection capacities, QVP was further applied for clear fluorescence imaging of viscosity changes of plant cell (onion epidermis and scallion bulb) induced by HMIS (Cu 2+ , Au 3+ and Ag + ). Notably, the cellular viscosity was positively correlated with Cu 2+ concentration. More importantly, the sensor QVP had good penetration within plant tissues and enabled viscosity imaging of root hairs, leaves and other tissues. This work not only provides a novel molecular tool for understanding HMIS resistance of the plant by investigating the dynamic change of intracellular viscosity, but also provides an additional dimension for evaluating crop stress resistance.

Why it matches plant phenotyping methods植物細胞・組織の細胞内粘度を蛍光イメージングで測定し、金属イオンストレスを評価する新規センサーを開発・適用しており、植物状態の取得方法が研究の中心である。

abstractHerein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants.

LOCOPOTS: a low-cost high-throughput screening platform for in vitro potato phenotyping under abiotic stress ↗

Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Ch ol , cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, β-aminobutyric acid, γ-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.

Why it matches plant phenotyping methods低コストRGB撮像とU-Netによる自動セグメンテーションを中核とする、ジャガイモ表現型取得プラットフォームの開発・検証であり、形態・色・植生指数形質を自動抽出している。

abstractWe present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture.

Multi-scale thermal homeostasis: Plants achieve temperature control through hierarchical regulation ↗

Abstract Temperature fundamentally impacts plants growth and physiology. However, the mechanisms by which plants sense and response to environmental changes remain unclear due to the lack of effective methods for measuring internal plant temperatures. Here, by combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature in response to environmental temperature variations. We discovered a multilevel temperature regulation mechanism during the process by which plants establish thermal homeostasis. In Nicotiana benthamiana leaves, when environment temperature changes from approximately 24°C to 45°C, the maximum of internal plant temperature change is only approximately 10°C near cell wall, and less than 7°C in cytoplasm, while remaining nearly constant in chloroplasts (ΔTchl ≈ 1°C). Similar compartment-specific thermal regulation was observed in Arabidopsis thaliana and tomato, indicating that hierarchical regulation represents a conserved strategy for maintaining internal temperature stability in plants. Together, these findings provide direct evidence for multiscale thermal homeostasis in plants and establish a framework for understanding how cellular and subcellular organization contributes to temperature regulation.

Why it matches plant phenotyping methodsナノ温度計プローブと時間ゲート imaging により植物内部温度を測定する手法が研究の中心であり、植物の生理状態を直接定量している。

abstractby combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature

Frequency-domain identification of photosynthetic regulation under fluctuating light ↗

Plant photosynthesis operates under naturally fluctuating light, yet its dynamic responses across timescales remain incompletely understood. Here, we apply sinusoidal light modulation as a controlled periodic input and analyze the response in the frequency domain, enabling quantitative system identification of photosynthetic dynamics. Using a minimal biochemical model of photosynthetic electron transport and regulation, we show that photosynthetic performance under fluctuating light differs systematically from that under constant illumination, even when the mean photon flux density is identical. Large-amplitude oscillations generate higher harmonics and alter time-averaged chlorophyll fluorescence, oxygen evolution, and non-photochemical quenching (NPQ), demonstrating that fluctuating light acts not merely as a perturbation but as a distinct physiological regime. For sufficiently small perturbations, the system behaves approximately linearly and can be characterized by transfer functions and Bode plots. We identify two dynamic regimes separated by a characteristic timescale of approximately 10 s. In the high-frequency domain, the response is governed by constitutive photochemical processes and reflects local steady-state properties, including the redox state of the plastoquinone pool. In the low-frequency domain, adaptive regulatory feedback dominates, particularly NPQ, which reshapes both the amplitude and phase of the photosynthetic response. Characteristic frequency-response features, including gain transitions and phase extrema, provide direct information about physiologically relevant quantities such as effective relaxation times and regulatory coupling strengths. We further introduce the concept of regulation fingerprints, defined as ratios of transfer functions between regulated and unregulated systems. These fingerprints reveal distinct spectral signatures of fast PsbS-dependent and slower zeaxanthin-dependent NPQ, enabling their quantitative separation and providing experimentally testable predictions for regulatory dynamics. Together, these results establish frequency-domain analysis as a general framework for probing, identifying, and testing the dynamic regulation of photosynthesis under fluctuating light. More broadly, they suggest that fluctuating illumination, often regarded as experimental noise, can instead serve as a structured probe of photosynthetic function in both laboratory and field environments.

Why it matches plant phenotyping methods植物の光合成動態を定量化する周波数領域解析を中心的に提案し、蛍光・酸素発生・NPQなどの生理状態を抽出する方法論研究であるため。

abstractTogether, these results establish frequency-domain analysis as a general framework for probing, identifying, and testing the dynamic regulation of photosynthesis under fluctuating light.

A xanthene-based red-emitting fluorescent probe for the detection and monitoring of carbon monoxide in bacterial pneumonia models and in plant samples. ↗

Carbon monoxide (CO) functions as a critical signaling molecule in both mammalian inflammation and plant stress responses. However, existing techniques face challenges in real-time monitoring of CO dynamics across biological kingdoms. Here we developed Z2CO, a xanthene-based red-emitting fluorescent probe constructed on a Pd(0)-triggered Tsuji-Trost allylic cleavage mechanism. Upon CO recognition, Z2CO generates a distinct turn-on fluorescence signal at 625 nm within 10 min. The probe exhibits favorable properties including an 80 nm Stokes shift, low detection limit (0.193 μM, 3σ/k criterion), excellent water solubility, and minimal cytotoxicity, making it suitable for complex biological applications. Using Z2CO, we successfully visualized endogenous CO generation in pulmonary tissues of lipopolysaccharide-induced bacterial pneumonia mice and quantitatively evaluated anti-inflammatory drug efficacy. Furthermore, we extended Z2CO to plant systems, achieving real-time monitoring of CO dynamics in cadmium-stressed edible sprouts and brassica rapa. These investigations provide direct evidence for CO involvement in heavy metal-triggered signal transduction networks. Collectively, Z2CO constitutes a versatile tool for elucidating CO-mediated physiological and pathological processes across animal and plant systems.

Why it matches plant phenotyping methods植物内在CO動態をリアルタイム可視化・定量する蛍光プローブを開発し、植物のストレス応答という生理状態の測定に実質的に適用しているため、測定法が中心である。

abstractHere we developed Z2CO, a xanthene-based red-emitting fluorescent probe

LipoTag: A minimal motif for live and functional imaging of plant cell membranes. ↗

Abstract The plant plasma membrane is a highly dynamic structure that is crucial for cell compartmentalization, the maintenance of (bio)chemical gradients, signaling and cell growth and responses to stress. In plants, plasma membranes are tightly connected to the cell walls that encase them. These cell walls can act as diffusion barriers and prevent the use of a wide range of synthetic fluorescent probes that have been developed to study animal cell membranes, which lack a cell wall, with live functional imaging. Here, we introduce LipoTag, a minimal chemical motif that, upon chemical conjugation, transforms hydrophobic fluorophores into water-soluble, membrane-targeted probes that can permeate plant cell walls to reach their intended location. LipoTag uses a localized positive charge in combination with a short aliphatic spacer to direct cargo to the plasma membrane. We used LipoTag to develop a suite of membrane-specific fluorescent probes that work in walled organisms beyond the plant kingdom. In addition, we used LipoTag to develop functional reporters for the quantitative imaging of membrane density, lipid order and membrane oxidation in living plant tissues. LipoTag forms a modular platform for exploring the plant plasma membrane with a suite of contemporary imaging modalities.

Why it matches plant phenotyping methods植物組織で膜密度・脂質秩序・膜酸化を定量イメージングする蛍光プローブと基盤を開発しており、植物状態の取得法が中心的です。

abstractwe used LipoTag to develop functional reporters for the quantitative imaging of membrane density, lipid order and membrane oxidation in living plant tissues.

Chlorophyll Fluorescence-Based High-Throughput Phenotyping Reveals Mechanisms and Enables Rapid Screening of Desiccation-Tolerant Wild Tomato Species. ↗

Desiccation tolerance is a critical adaptive trait that enables plants to survive extreme water loss, yet its physiological basis in tomato and its wild relatives remains poorly understood. In this study, chlorophyll a fluorescence imaging was used as a reliable tool to evaluate photosystem II (PSII) response to progressive desiccation. The analysis was conducted in cultivated tomato (Solanum lycopersicum) and five wild relatives (Solanum chilense, Solanum habrochaites, Solanum peruvianum, Solanum pimpinellifolium, and Solanum pennellii). Detached leaves were subjected to controlled desiccation for up to 50 h. During this period, tissue moisture content (TMC), relative water content (RWC), PSII photochemical efficiency [Fv/Fm; maximum quantum yield (QY_max)], minimal fluorescence (F0), maximal fluorescence (Fm), and variable fluorescence (Fv) were monitored to assess changes in photosynthetic performance. Desiccation caused a significant, moisture-dependent decline in PSII efficiency across all species, with QY_max showing a strong linear relationship with RWC (R2 = 0.80–0.90). Interspecific variation was evident as S. chilense, S. habrochaites, S. peruvianum, and S. pimpinellifolium exhibited rapid PSII impairment, while S. lycopersicum showed moderate tolerance. In contrast, S. pennellii maintained higher PSII stability, with 50% loss of efficiency occurring only at lower RWC (30–35%). Overall, chlorophyll fluorescence imaging effectively captured functional diversity in desiccation tolerance, highlighting S. pennellii as a valuable genetic resource for improving drought resilience in tomato.

Why it matches plant phenotyping methodsクロロフィル蛍光イメージングを用いた高スループット表現型解析と乾燥耐性スクリーニングが研究の中心であり、PSII効率などの植物生理形質を定量化している。

titleChlorophyll Fluorescence-Based High-Throughput Phenotyping Reveals Mechanisms and Enables Rapid Screening of Desiccation-Tolerant Wild Tomato Species.

Dual-Target Fluorescent Imaging of Hg 2+ and Cell Membrane Stress in Live Plants. ↗

Monitoring mercury ion (Hg 2+ ) accumulation and its phytotoxicity in plants requires analytical methods that provide both spatial and functional information beyond simple destructive quantification. We report HBTD-Hg , a dual-target fluorescent probe engineered to anchor to the cell membrane and exhibit a selective fluorescence turn-on response to Hg 2+ . This design enables simultaneous in situ imaging of Hg 2+ distribution and real-time assessment of membrane integrity in live plant tissues. The probe quantifies Hg 2+ with a detection limit of 49.7 nM. Importantly, it allows for the nondestructive tracking of Hg 2+ uptake dynamics directly through leaf imaging. Furthermore, it directly visualizes the ensuing Hg 2+ induced loss of cellular membrane integrity, including distinct vesiculation. This work provides a versatile tool for the real-time assessment of heavy metal stress, effectively linking environmental ion detection to observable cytological damage in plants.

Why it matches plant phenotyping methods植物組織の膜完全性・細胞障害を生体蛍光イメージングで可視化・追跡するプローブを開発しており、植物ストレス状態の取得法が中心的である。

abstractWe report HBTD-Hg , a dual-target fluorescent probe engineered to anchor to the cell membrane and exhibit a selective fluorescence turn-on response to Hg 2+ .

Rhizobacterial Biosensors Spatially Map Natural and Engineered Sucrose Exudation ↗

Root exudation mediates the delivery of plant primary and secondary metabolites into soil, where they regulate plant–microbe interactions and terrestrial carbon cycling. Conventional exudate analyses quantify total root-released carbon yet obscure the spatial origin and rhizosphere influence of individual compounds. Here, we develop a rhizobacterial biosensor platform, named Suc-MAPP, to map local exudate profiles along the surface of colonized root tissues. Focusing on sucrose, we engineered sfGFP-based, sucrose-responsive gene circuits in Pseudomonas putida KT2440 for live imaging of exudate concentrations in the micromolar range. These biosensors reveal spatially structured sucrose exudation patterns across eudicots and monocots and implicate photoassimilated source–sink dynamics as a major determinant. We further apply this platform to phenotype exudation modulated by synthetic gene circuitry in Arabidopsis thaliana , identifying genetic design rules for graded sucrose release and quantifying how engineered export sculpts rhizosphere assembly of a defined bacterial community. Together, these results establish programmable rhizobacterial biosensors as tools to spatially resolve plant–environment carbon exchange in situ and provide a framework for extending this approach to diverse exudate targets.

Why it matches plant phenotyping methods植物根からのスクロース滲出を空間的・定量的に測定する生体センサープラットフォームを開発し、植物表現型として適用しているため、方法が中心的である。

abstractHere, we develop a rhizobacterial biosensor platform, named Suc-MAPP, to map local exudate profiles along the surface of colonized root tissues.

Towards a standard approach to investigating the Thermal Load Sensitivity of photosystem II via chlorophyll fluorescence ↗

Evaluating the drivers of variation in plant thermal tolerance limits requires a clearer understanding of how methodological matters can lead to different tolerance estimates. Chlorophyll fluorometry – to measure the temperature-dependent change in F V / F M – is a well-established approach to derive tolerance thresholds of photosystem II (PSII) in plants, but one-off, time-specific thermal exposures do not consider the fundamental dose-dependent effect of heat. The resurgent thermal death time (TDT) approach integrates both the temperature intensity and the exposure duration to derive time-based critical temperature thresholds and sensitivity parameters. We build upon this foundation to develop a protocol for evaluating thermal load sensitivity (TLS; non-lethal heat stress) of PSII in plants. Through five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery. There were dramatic changes in tolerance threshold estimates based on thermal load (i.e. dose-dependent) effects on F V / F M , and strong effects of light intensity during heat and the presence of light post-heat. We offer recommendations pertaining to method implementation and discuss future empirical avenues. Appraising cumulative heat stress will enhance the utility of thermal tolerance estimates – the TLS approach outlined here moves us toward a new standard.

Why it matches plant phenotyping methods植物のPSII熱耐性をクロロフィル蛍光で定量する方法を開発・検証し、実装上の条件を評価した研究であり、方法が中心的です。

abstractThrough five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery.

Exploring the potential role of multi-source remote sensing data during different growth stages in crop yield prediction. ↗

Accurate prediction of grain yield is essential for enhancing food security, particularly in the context of climate change. Although remote sensing indices have been extensively utilized to monitor vegetation growth and estimate crop yields, there has been limited research comparing their effectiveness for predicting grain yield, especially across different growth stages. This study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages at Shangshan Rice Research Station in Zhejiang Province, China. The results indicated that SIF exhibited the strongest and most consistent correlation with grain yield ( R 2 = 0.34 to 0.75), followed by NIR V ( R 2 = 0.34 to 0.71). SIF also demonstrated advantages in capturing the dynamic changes of GPP during the reproductive period. During both the vegetative and reproductive stages, leaf area index (LAI) showed significant correlations with NDVI, NIR V , and SIF, whereas leaf chlorophyll concentration exhibited comparatively weaker associations with these indicators. These findings provide valuable insights for improving crop yield forecasts using remote sensing, thereby contributing to enhanced agricultural management and food security strategies under climate change.

Why it matches plant phenotyping methods作物収量を推定するマルチソースリモートセンシング指標の成長段階別性能比較・検証が研究の中心であり、植物の収量やLAIなどの形質推定手法を評価している。

abstractThis study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages

Design and characterization of a compact and lightweight dual-wavelength chlorophyll fluorescence light detection and ranging sensor (ChloroFLiDAR) for remote plant stress assessment ↗

Chlorophyll fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength chlorophyll fluorescence light detection and ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech ( Fagus sylvatica ) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.

Why it matches plant phenotyping methods植物ストレス状態を遠隔測定するクロロフィル蛍光LiDARセンサーを開発し、測定性能とストレス検出能力を実証しており、植物フェノタイピング手法が中心である。

abstractwe present a novel, lightweight, and portable dual-wavelength chlorophyll fluorescence light detection and ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research.

Design and Characterization of a Compact and Lightweight Dual-Wavelength Chlorophyll Fluorescence Light Detection and Ranging Sensor (ChloroFLiDAR) for Remote Plant Stress Assessment ↗

Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.

Why it matches plant phenotyping methods植物ストレス状態を測定する新規ChlF LiDARセンサーの設計・性能評価が研究の中心であり、植物での検証も行っているため。

abstractwe present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research.

Design and Characterization of a Compact and Lightweight Dual-Wavelength Chlorophyll Fluorescence Light Detection and Ranging Sensor (ChloroFLiDAR) for Remote Plant Stress Assessment ↗

Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.

Why it matches plant phenotyping methods植物ストレス状態を測定する遠隔クロロフィル蛍光センサーを設計・実証しており、表現型取得手法が研究の中心である。

abstractwe present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research.

Comparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development ↗

Abstract Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Désirée) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers β-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth–defence trade-offs across platforms. Highlight High-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g

Why it matches plant phenotyping methods二つの独立施設で高スループット光学フェノタイピングを比較・検証し、画像由来形質と蛍光指標の再現性を評価しており、フェノタイピング手法が研究の中心である。

titleComparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development

Mm-VitnNet: a gated image-text interaction network for soybean salt tolerance recognition using chlorophyll fluorescence phenotypes. ↗

Traditional methods for identifying salt tolerance levels in soybean varieties are often cumbersome, time-consuming, and labor-intensive. These challenges are further exacerbated by the limited utility of chlorophyll fluorescence imaging phenotype data, which are insufficiently diverse and difficult to analyze. Additionally, the corresponding parameter text data have not been fully explored and utilized. In this study, salt stress experiments were conducted on 178 soybean varieties, and a multimodal dataset comprising chlorophyll fluorescence images and corresponding textual data was constructed using a chlorophyll fluorescence imaging instrument. A novel gated mechanism network for learnable image-text interaction (Mm-VitnNet) is proposed, which enables global cross-modal interaction between image and text data. The model introduces a gated mechanism to dynamically regulate the fusion intensity of cross-modal information and incorporates two learnable tokens that focus on feature learning for each individual modality. This approach effectively mitigates interference between modalities while preserving modality-specific features, thereby enhancing model performance. The proposed model demonstrates an accuracy rate of 98.97%, significantly outperforming typical models: it improves by 1.09 and 2.33 percentage points compared to CNN-based models such as EfficientNetV2-s (97.88%) and MobileNetV2 (96.64%), respectively, and by 3.21 and 2.60 percentage points compared to Transformer-based Swin Transformer_tiny (95.76%) and hybrid models like MobileViT_S (96.37%), respectively. The model has 10.22M parameters and a computational cost (FLOPs) of 1.84G, which is significantly lower than models like VGG and ResNet50, and only slightly higher than some lightweight CNNs, achieving an effective balance between accuracy and efficiency. The improved model demonstrates notable performance in identifying samples with varying salt tolerance levels, even under limited computational resources, ensuring reliable classification performance. Moreover, this multimodal non-destructive identification method based on chlorophyll fluorescence technology offers an efficient and feasible approach for assessing the salt tolerance levels of soybeans, while also advancing agricultural phenotyping towards greater precision and intelligence.

Why it matches plant phenotyping methodsダイズの塩耐性という植物状態をクロロフィル蛍光画像から推定するマルチモーダル画像解析手法を開発・評価しており、表現型取得と分類モデルが研究の中心である。

abstracta multimodal dataset comprising chlorophyll fluorescence images and corresponding textual data was constructed using a chlorophyll fluorescence imaging instrument.

Multispectral Fluorescence Imaging for Fast Identification of Cold Stress in Pepper Plants. ↗

This paper investigated the feasibility of snapshot multispectral fluorescence imaging for nondestructive identification of cold stress in pepper plants. Fluorescence spectra were obtained by exciting the plant with a 405 nm ultraviolet LED. The plants were grown under three temperature conditions: 17 °C (control), 10 °C (moderate cold stress), and 5 °C (severe cold stress). Raw fluorescence spectra extracted from the demosaiced snapshot images were used as inputs for a deep-learning pipeline consisting of feature extraction, an encoder-decoder GRU, and a multilayer perceptron (MLP), and the results were compared with conventional machine learning classifiers, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and a Gaussian support vector machine (G-SVM). Tukey's HSD test indicated that the proposed deep-learning model achieved the highest cross-validation accuracy and consistently produced superior classification metrics (accuracy of 85.7%, precision of 85.3%, recall of 85.3%, F1-score of 85.2). The trained model was further applied to hyperspectral cubes to generate classification maps; however, moderate misclassification was observed, consistent with the overall prediction performance.

Why it matches plant phenotyping methodsスナップショット多波長蛍光画像からピーマンの低温ストレス状態を推定する画像取得・深層学習手法が研究の中心であり、性能比較と検証も実施している。

abstractThis paper investigated the feasibility of snapshot multispectral fluorescence imaging for nondestructive identification of cold stress in pepper plants.

Imaging and genetic toolbox to study Arabidopsis embryogenesis ↗

Embryogenesis in the model plant Arabidopsis thaliana provides a framework for understanding how cell polarity and patterning coordinate with hormonal signalling to establish the plant body plan. Following fertilisation, the zygote divides asymmetrically to generate apical and basal lineages, establishing the apical-basal axis that defines future shoot and root poles. Genetic and molecular analyses of classical mutants including gnom, monopteros (mp), bodenlos (bdl) and topless revealed that localised auxin biosynthesis, directional transport and downstream transcriptional responses are central to apical-basal axis establishment and organ initiation. The main components of this regulation are polarly localised PIN auxin transporters and downstream modules involving MONOPTEROS and WUSCHEL-RELATED HOMEOBOX transcription factors. Advances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics. Live ovule imaging setups with confocal laser scanning and multiphoton microscopes enable real-time observation of embryo development, while laser-assisted cell ablation can be used to probe cell-to-cell communication and fate plasticity. Together, these methodological breakthroughs position Arabidopsis embryos as a prime model for dissecting the chemical and biophysical cues that shape plant development.

Why it matches plant phenotyping methods胚発生を解析するための蛍光イメージング、三次元再構成、ライブ撮像などの方法論と定量的な細胞形態・分裂方向測定が中心であり、植物フェノタイピング手法に該当する。

abstractAdvances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics.

CryoFluorSEM – A new approach for fluorescence and EM imaging of cryofractured plant samples ↗

Abstract Cryo‐scanning electron microscopy (CryoSEM) permits the preparation and detailed imaging of bulky samples while keeping them in a hydrated state. For plant biology, cryofractures give information on cell ultrastructure and tissue organisation within a much larger context that is the whole organ or organism. To date, a method to locate fluorescence reporters on the cryofracture has not been reported. Our approach uses a stereofluorescence microscope with an 80 mm working distance and a high‐zoom ratio to image the fracture through a viewing port of the cryopreparation chamber while the sample is still frozen and under vacuum. We have applied this method to look at fluorescent reporters of auxin transport and signalling in plant shoot apices and seedlings, the expression of a poorly characterised gene in the young floral pedicel and nitrogen‐fixing rhizobial bacteria, expressing GFP, in root nodules. This method is applicable to any cryopreserved bulky sample that has a fluorescent output and paves the way for correlative light‐electron microscopy for cryoSEM‐based imaging.

Why it matches plant phenotyping methods植物試料の蛍光レポーターを凍結破断面上で位置特定・画像化する新規CryoFluorSEM法の開発であり、植物の構造・組織状態を取得する方法が中心である。

abstractTo date, a method to locate fluorescence reporters on the cryofracture has not been reported.

A non-destructive plant screening method for improving sample uniformity in horticultural crops based on a hydrogen peroxide fluorescent probe ↗

Introduction Hydrogen peroxide (H 2 O 2 ) functions as a key signaling molecule in plants responding to stress. Although numerous detection methods have been developed, simple and non-destructive techniques for the semi-quantitative monitoring of H 2 O 2 in plant tissues remain scarce. Methods In this study, we developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues, and conducted spectroscopic and in vivo toxicity tests. Furthermore, under experimentally controlled stress conditions, we utilized this probe to detect H 2 O 2 levels in four distinct plant types exposed to salt, waterlogging, cadmium, and drought stresses. Additionally, H 2 O 2 was detected in a grafting model under non-experimentally controlled stress conditions. Results The results showed that the probe demonstrated excellent selectivity, a strong linear correlation (R2 = 0.9849), and a low detection limit of 0.6450 μmol/L. Importantly, it exhibits good biocompatibility with plant tissues and effectively minimizes detection errors caused by transient H 2 O 2 fluctuations induced by environmental changes. Consequently, it provides more accurate and stress-reflective H 2 O 2 measurements. Under experimentally controlled stress conditions, the changes in relative fluorescence intensity conformed to the typical response patterns observed when plants experience graded levels of stress. Notably, even under complex grafting conditions without imposed stress gradients, applying the probe to bottle gourd (Lagenaria siceraria) rootstocks with different graft compatibility produced fluorescence dynamics consistent with the typical H 2 O 2 responses of compatible and incompatible rootstocks, and the distribution of relative fluorescence intensity within the population underscored the importance of prescreening plants for biological studies. Pearson correlation and Bland-Altman analyses confirmed good agreement between our method and the commercial assay kit. Discussion These results demonstrate that the LWS probe enables H 2 O 2 detection and, in combination with the IVIS in vivo imaging system, can screen individual plants differing in stress responses more effectively than other sensors. This non-destructive approach preserves the structural integrity of plant samples, enabling follow-up physiological, biochemical, and genomic analyses on the same specimens. This method provides a reliable prescreening platform for investigating plant stress responses at the biological level.

Why it matches plant phenotyping methods植物組織中のH2O2を非破壊的・半定量的に検出する蛍光プローブとIVIS画像化を開発し、性能検証およびストレス応答個体のスクリーニングに応用しており、植物状態の取得手法が中心である。

abstractwe developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues

Loss of qE Does Not Necessarily Lead to Photoinhibition: Sustained Non-Photochemical Quenching in the Absence of PsbS and Zeaxanthin. ↗

Photosynthetic light-harvesting complexes mediate light absorption and energy dissipation. By modulating the photosystems' absorption cross-section, they affect both photosynthetic activity and non-photochemical quenching (NPQ). These processes are often studied by spectrally integrated chlorophyll fluorescence, masking their associated spectral information. We explore in Aspen and Arabidopsis npq mutants how qE affects the development of NPQ spectra under two contrasting conditions: in the absence and the presence of photoinhibition. We introduce a new parameter, the development of new emitting species (NESD), during time- and spectrally resolved NPQ inductions, and develop a pipeline to resolve PSII energy-partitioning heterogeneity. LHCII, PsbS, and zeaxanthin are required for NESD. Combining gas exchange, P700 oxidation, and spectrally resolved kinetics, we show that under photoinhibitory conditions, NES can develop even without PsbS or zeaxanthin, producing sustained quenching independent of photoinhibition of PSII or PSI. Furthermore, the absence of LHCII and CURVATURE THYLAKOID 1 leads to increased photoinhibition, indicating that long-term photoprotection relies on LHCII and thylakoid plasticity, whereas PsbS and zeaxanthin mainly facilitate LHCII-dependent quenching. Finally, we show the limitations of traditional parameters in discriminating between photoinhibition and photoprotective sustained quenching and propose time-resolved monitoring of CO₂ assimilation and Y(II) for their accurate assessment.

Why it matches plant phenotyping methods時間・スペクトル分解蛍光による光合成状態・光防御の評価法を開発し、新規パラメータと解析パイプラインを提示しているため、植物フェノタイピング手法が中心である。

abstractWe introduce a new parameter, the development of new emitting species (NESD), during time- and spectrally resolved NPQ inductions, and develop a pipeline to resolve PSII energy-partitioning heterogeneity.

Comparative Analysis of High‐Throughput Phenotyping Techniques for Early Detection of Herbicide Stress ↗

ABSTRACT Early detection of herbicide‐induced stress is essential for optimising weed control, improving crop safety and advancing precision agriculture practices. While non‐destructive imaging technologies offer great potential for rapid stress diagnosis, direct comparisons of their performance across species, herbicides and application rates remain scarce. This study systematically compared three high‐throughput imaging methods: chlorophyll fluorescence imaging, multispectral imaging and 3D multispectral scanning, for their ability to detect and classify early physiological and morphological responses to bentazone (HRAC 6) and glyphosate (HRAC 9) in a weed (velvetleaf, Abutilon theophrasti ) and a crop species (common bean, Phaseolus vulgaris ). Plants were imaged daily, immediately before treatment and for five consecutive days after herbicide application. Chlorophyll fluorescence parameters, particularly NPQ, q P and F s ′, emerged as the earliest and most sensitive indicators, detecting stress within 24 h of treatment. Stepwise discriminant analysis revealed that intermediate time points (48–72 h after application) achieved the highest classification accuracies, reaching up to 100%, with chlorophyll fluorescence as the dominant trait for selection. Multispectral traits, such as reflection in red and far red, and saturation, together with morphological traits like total leaf area and leaf inclination, provided complementary information and enhanced discrimination accuracy among herbicides. These findings highlight the superiority of chlorophyll fluorescence imaging for detecting early herbicide stress, while demonstrating the added value of integrating spectral and morphological traits. The results support the development of multi‐sensor phenotyping pipelines for rapid, accurate and field‐adaptable diagnostics in both weed and crop management.

Why it matches plant phenotyping methods複数の高スループット画像計測法を比較・評価し、植物の生理・形態応答を用いた早期ストレス検出性能を検証しているため、フェノタイピング手法が中心です。

abstractThese findings highlight the superiority of chlorophyll fluorescence imaging for detecting early herbicide stress, while demonstrating the added value of integrating spectral and morphological traits.

Integrating canopy structure into remote sensing inversion strategies: Optimizing plant functional trait retrieval ↗

Accurate retrieval of plant functional traits is critical for monitoring crop growth and improving agronomic management. Canopy structural parameters, such as leaf area index (LAI) and leaf inclination distribution function (LIDFa), strongly influence inversion accuracy. Quantifying canopy structural uncertainties and developing strategies to improve retrieval accuracy are crucial. In this study, we developed an inversion framework based on the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, integrating reflectance and solar-induced fluorescence (SIF) data. Using both simulation modelling and field measurements in NEON STER crop field, we introduced multi-level prior noise and evaluated how uncertainties in LAI and LIDFa propagate into the retrieval of chlorophyll content (Cab), maximum carboxylation rate (Vcmax), and fluorescence quantum efficiency (fqe). To assess the influence of canopy structure and improve retrieval accuracy, three inversion strategies—Prior-Matched (PM), Regularized (RI), and No-Prior (NP)—were designed and tested for their accuracy and robustness. The results showed that second-order Sobol’ indices (S2) captured interactions among canopy structural parameters and functional traits, particularly between Cab-LAI, Cab-LIDFa and fqe-LAI, with sensitive spectral ranges at 680–740 nm (fluorescence) and 600–720 nm (reflectance). Error amplification analysis under six noise levels showed that structural uncertainties significant amplified reflectance and fluorescence variations, with red-edge shifts (ΔRE) and 740 nm fluorescence changes (ΔF740) being most sensitive. Incorporating prior canopy structure information improved inversion accuracy by up to 7.93 % in R² and reduced RMSE by 21.25 %, although this advantage diminished under high noise levels. LAI uncertainty had a greater impact than LIDFa, and additive noise introduced more uncertainty than multiplicative noise. Comparison of the inversion strategies revealed that the RI strategy achieved higher accuracy (simulated data: R²=0.954; measured data: R²=0.799) and greater robustness to noise than the PM strategy. These findings demonstrate the value of integrating canopy structure into computational inversion models to enhance the reliability of remote sensing trait retrieval, supporting precision agriculture and sustainable crop production.

Why it matches plant phenotyping methods植物機能形質のリモートセンシング推定を対象に、SCOPEモデルに基づく反演フレームワークと複数の反演戦略を開発・比較し、ノイズへの頑健性と精度を検証している。形質取得・推定手法が研究の中心である。

abstractwe developed an inversion framework based on the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, integrating reflectance and solar-induced fluorescence (SIF) data.

Signaling Pathway of Serotonin in Plants: Monitoring With an Emergent Nanosensor. ↗

Serotonin, widely recognized as a mammalian pineal hormone, is also present in plants, yet its in vivo dynamics and physiological roles remain poorly understood due to the absence of real-time sensing tools. Herein, we report nitrogen-doped carbon quantum dot (N-CQD) nanosensors (∼5 nm; quantum yield 36%) for the selective detection and visualization of serotonin in plant systems. The sensing mechanism involves static-dominated mixed fluorescence quenching accompanied by a blue shift, corroborated by UV-Vis spectral changes, Stern-Volmer analysis, and fluorescence lifetime decay. The nanosensor exhibits a low detection limit of 0.391 µM and a linear response range of 4.74-75 µM. Using Arachis hypogaea seedlings as a model, stronger and more consistent serotonin-dependent fluorescence responses were observed compared with those in other plant species, enabling reliable in vivo monitoring. Real-time sensing revealed a condition-dependent regulatory role for serotonin, including growth inhibition under non-stress conditions and growth enhancement under stress, indicating a dual function in stress adaptation. Fluorescence microscopy further confirmed the intracellular association of serotonin with N-CQDs, providing direct visual evidence of its localization. This work establishes a nanosensor-based platform for real-time detection of serotonin in plants and advances understanding of serotonin-mediated signalling in plant growth and stress responses.

Why it matches plant phenotyping methods植物内セロトニンをリアルタイム検出・可視化するナノセンサーを開発し、植物の生理状態やストレス応答の測定に実質的に用いているため、方法中心の研究として含める。

abstractwe report nitrogen-doped carbon quantum dot (N-CQD) nanosensors (∼5 nm; quantum yield 36%) for the selective detection and visualization of serotonin in plant systems.

Near-Infrared Fluorescent Probe for High-Fidelity and On-Site Quantitative Detection of Lipid Droplets in Food Crops. ↗

Lipid droplets (LDs) play pivotal roles in crop physiology, stress adaptation, and product quality by serving as dynamic reservoirs of energy and essential nutrients. However, the lack of rapid and accurate methods for on-site quantitative detection of LDs has hindered their comprehensive analysis in agricultural systems. Herein, we report the rational design and synthesis of three near-infrared (NIR) fluorescent probes, YD-1 , YD-2 , and YD-3 , for the sensitive and specific detection of LDs in crops. These probes feature a hydrophobic donor-π-acceptor (D-π-A) framework comprising a 7-diethylaminoquinoline electron donor and distinct electron-accepting groups, including (5,5-dimethylcyclohex-2-en-1-ylidene)malononitrile (DCM), 4-fluorophenyl, and phenyl moieties. Among them, YD-1 exhibited the most pronounced fluorescence response to LDs, displaying intense NIR emission within LDs and remarkable quenching in non-LD regions, enabling high-fidelity LD imaging. TD-DFT calculations revealed that the superior sensitivity of YD-1 originates from its efficient intramolecular charge transfer (ICT) process, which is highly responsive to the polarity differences between LDs and their surroundings. YD-1 was successfully applied to monitor LDs dynamics in living cells, zebrafish under a high-fat diet, and soybeans at different growth stages. Furthermore, a custom-built mobile fluorescence analysis device was developed and coupled to probe YD-1 to achieve rapid, on-site quantitative detection of LDs in crop samples. This work provides a powerful analytical platform for on-site monitoring of LDs dynamics, offering insights into crop lipid metabolism and quality control.

Why it matches plant phenotyping methods作物の脂質滴を対象に、蛍光プローブと携帯型蛍光解析装置を開発し、生体内画像化および現場での定量検出を実現しているため、植物表現型取得法が研究の中心である。

abstractwe report the rational design and synthesis of three near-infrared (NIR) fluorescent probes, YD-1 , YD-2 , and YD-3 , for the sensitive and specific detection of LDs in crops.

Investigating soil-plant-atmosphere interactions by combining spectral electrical impedance tomography with environmental and physiological timeseries ↗

The soil-plant-atmosphere continuum ( SPAC ) plays a critical role in the distribution of water and nutrients in terrestrial ecosystems. To understand the complex and rapid dynamics within the SPAC , it is necessary to observe its components with sub-daily resolution. While measurements of above-ground processes are frequently employed, monitoring of the below-ground part remains scarce due to its inaccessibility. In this study, we monitored water and nutrient transport processes in a maize field over several months. The rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity. Stem water transport and photosynthetic activity were measured with sapflow sensors and a fluorescence sensor, respectively, while atmospheric conditions were measured with a weather station. Timeseries were analyzed using cross and coherence wavelet analysis. Electrical imaging results revealed spatially and temporally resolved daily variations in subsurface conductivity and polarization properties, suggesting a sensitivity to water and ion uptake processes. Conductivity development was strongly correlated with SWC dynamics controlled by evaporation and water uptake of plants. Wavelet power showed that belowground polarization diurnality was consistent with a typical growth pattern of maize, and disappeared shortly after harvest. Cross wavelet analysis of sun-induced fluorescence, sapflow density, photosynthetically active radiation, and vapor pressure deficit revealed lags caused by environmental conditions, highlighting the coupling of plant activity to the atmosphere. Our results show that sEIT is a valuable tool to study rhizosphere processes and may aid in the holistic modeling of the SPAC .

Why it matches plant phenotyping methodssEITを用いて根圏の水分動態・根構造・根の活動を時空間的に取得し、他センサーとの時系列解析で植物の水輸送・生理状態を評価しており、植物状態のセンシング手法の実質的な適用が中心です。

abstractThe rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity.

Reverse temporal spectral signal extraction and deep spectral signal decoupling fusion for detection of SCLB infection process ↗

Close-range spectral imaging provides technical support for leaf detection during the early infection process of SCLB (southern corn leaf blight). However, due to the randomness of pathogen infection and the low visibility of early lesions, the temporal spectral signals obtained using this technique have poor continuity and low sensitivity. To improve the ability of temporal spectral signals to detect early-stage infection, this study proposes a signal extraction method based on reverse temporal spectral image matching, and a signal decoupling method based on DSO-CWT (decomposition of scale-optimized continuous wavelet transform) is also proposed to improve signal sensitivity. First, the spectral images were preprocessed. Image entropy was used to quantify the changing patterns of symptoms in early SCLB infection. Second, a temporal spectral signal extraction method based on ASpanFormer (adaptive span transformer) reverse temporal spectral image matching is proposed. The calculation results of LPIPS (learned perceptual image patch similarity) indicates that the average matching error between adjacent periods is less than 0.2, which suggests that this method can enhance the extraction accuracy of weak temporal spectral signals in early infection. Third, the T-test was used to evaluate the detection sensitivity of temporal spectral signals at different early stages of infection. The results showed that the temporal spectral signal still had low sensitivity for detecting different stages of infection. Therefore, a temporal signal decoupling method based on DSO-CWT is proposed, which enhances the detection sensitivity of temporal spectral signals by performing time–frequency domain conversion. Finally, a diagnostic model for the early SCLB infection was established by fusing fluorescence and reflectance spectral signals. After DSO-CWT processing, the accuracy of the modelling set improved from 47.62% to 94.22%, and the accuracy of the validation set was improved from 47.62% to 91.27%. This study improves the extraction accuracy and detection sensitivity of temporal spectral signals for early SCLB infection by using signal extraction based on reverse temporal spectral image matching and deep signal decoupling based on DSO-CWT, providing new insights for early detection of SCLB infection.

Why it matches plant phenotyping methods植物病害の感染状態を対象に、時系列スペクトル信号の抽出・分離手法を開発し、検出感度と診断精度を検証しているため、病徴状態のフェノタイピング手法が中心です。

abstractthis study proposes a signal extraction method based on reverse temporal spectral image matching, and a signal decoupling method based on DSO-CWT

Spectral overlap of far-red light with chlorophyll fluorescence causes artifacts in PAM-based Φ PSII determination. ↗

Far-red light (FR, 700-800 nm) can enhance photosynthesis by stimulating photosystem I (PSI). However, during chlorophyll fluorescence (CF) measurements using pulse-amplitude modulation (PAM) fluorometry, unusually high quantum yields of photosystem II ( Φ PSII ) have been observed under high FR light intensities, raising concerns about measurement artifacts. To test this, we constructed light response curves for sweet basil ( Ocimum basilicum L.) grown under light-emitting diode (LED) light (R:G:B = 44%:18%:38%) with varying photosynthetic photon flux densities (PPFD, 0-1,000 μmol m -2 s -1 ) and FR fractions (0, 0.26, 0.45, and 0.63). FR treatments consistently increased Φ PSII , but when total photon flux density (TPFD, 400-800 nm) exceeded 1,000 μmol m -2 s -1 , Φ PSII rose abruptly. Nonfluorescent reference tests using white and black paper confirmed that FR induced spurious fluorescence signals, likely due to spectral overlap between FR photons and the PAM detection range (680-760 nm). Tilting the LED panel to reduce reflected FR eliminated the abrupt Φ PSII peak but introduced unexpectedly increased Φ PSII across treatments, likely due to probe-induced shading. These findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology. Accurate and reliable assessment of photosynthetic performance under extended spectral lighting conditions requires careful management of lighting geometry and FR intensity.

Why it matches plant phenotyping methodsPAMによる光合成量子収率測定のアーティファクトを検証し、照明条件と測定 geometry が植物生理形質の取得精度に与える影響を評価しているため、測定法の技術的検証が中心である。

abstractThese findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology.

Host-specific fluorescence dynamics in legume-rhizobium symbiosis during nodulation. ↗

The legume-rhizobium symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (P nifH ) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) systems. We show that P nifH -driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and P nifH -driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify P nifH -driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobium symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. Importance The legume-rhizobium symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (P nifH ) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.

Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量法を複数宿主で評価し、深層学習画像解析パイプラインを標準法と比較検証しており、植物形質（根粒形成・占有）の取得手法が中心的です。

abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot. ↗

Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential for further genetic gains in productivity of major crops from improved light interception and harvest index has largely been exhausted, naturally occurring or induced genetic variation in photosynthetic traits still offers considerable potential for further yield improvement. However, since photosynthesis is highly dynamic under fluctuating field conditions, it is difficult to conduct a targeted selection for photosynthetic performance unless high spatial and temporal resolution data are available. To bridge this gap, we installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm'), which has been shown to be well correlated with overall photosynthetic performance. The LIFT method uses sub-saturating flashes at a fast repetition rate to induce maximum fluorescence, enabling measurements in less than 1 ms from a distance of up to 1 m. The robot moves at a speed of 0.5 m s -1 , autonomously navigating the entire field based on global navigation satellite system (GNSS) coordinates. Spectral measurements and stereo red, green, and blue (RGB) cameras provide additional information about three-dimensional (3D) plant architecture-related traits, such as leaf angle and light intensity on the target leaf. The resulting high spatiotemporal resolution maps of photosynthetic efficiency provide detailed information about the growth performance of plants in agronomic field trials or plant breeding nurseries.

Why it matches plant phenotyping methods自律走行ロボットに搭載したLIFTセンサーで光合成効率を高スループット測定し、作物の生理形質と構造形質を圃場でマッピングする方法・プラットフォームが研究の中心である。

abstractwe installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm')

Real-Time Subcellular Imaging of Plant Signaling Molecules and Bio-Coronas by Near-Infrared Nanosensors. ↗

Chemical imaging at high spatiotemporal resolution is crucial for advancing plant sciences and biotechnology. We demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues at high spatial ( 2 O 2 waves (100 μM) from plant mesophyll to stomata and pavement cells. Ca 2+ induced higher endogenous H 2 O 2 in mesophyll cells, whereas organelle electron transport chain disruptors and salt stress generated similar H 2 O 2 across all leaf cell types. Furthermore, the nanosensor quenching kinetics in photosynthetic mesophyll (0.018 s -1 ) and epidermal (0.004 s -1 ) cells enabled the detection of plant lipid corona formation. Optical nanosensors elucidate spatiotemporal dynamics of plant signaling molecules and advance our understanding of biocorona formation.

Why it matches plant phenotyping methods植物組織内のシグナル分子を高時空間分解能で可視化する光学ナノセンサーの開発・実証が研究の中心であり、植物の生理状態を測定する方法論に該当する。

abstractWe demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues

Plant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications. ↗

Plant leaf spectrophotometry has been used successfully as a means to detect stress, and it has been complemented by fluorescence analysis. This identification can be achieved in the ultraviolet (UV), visible (red, green, blue; RGB), near-infrared (NIR), and infrared (IR) spectral regions. Hyperspectral (measuring continuous wavelength bands) and multispectral (measuring discrete wavelength bands) imaging modalities can provide detailed information concerning the physiological well-being of plants, often diagnosing them at an earlier stage than visual or other more traditional biochemical assays. Because hyperspectral methods are highly sensitive and accurate, they cost a lot and produce vast quantities of data, which demand sophisticated computing software, and compared to multimedia, multispectral, and RGB cameras, they are less expensive and easier to carry but have reduced spectral resolution. Such methods are justified by thermal and fluorescence images revealing variations in the temperature and efficiency of photosynthesis of the leaves in response to stress. New digital imaging, thermal imaging, and optical filter technologies, and advancements in smartphone cameras have rendered low-cost, field-deployable platforms to monitor plant stress in real time feasible. Machine learning also supports these techniques by automating feature extraction, classification, and prediction to reduce the use of expensive instrumentation and human skill. But also problems like sensor calibration in a changing field, low model generalization across species and environments, and large, annotated datasets are needed. Beyond highlighting the relative strengths of the conventional and contemporary sensing approaches, the paper also examines the possibility of applying machine learning to multimodal images, as well as the growing impact of smartphone- based solutions in supplying inexpensive agricultural diagnostics. It concludes by overviewing the current limitations and limits to future research into scalable, cost-effective, and generalizable plant stress models.

Why it matches plant phenotyping methods植物ストレスを対象としたマルチモーダル画像・分光・熱・蛍光センシングと機械学習による表現型抽出を中心に扱う方法レビューであり、植物フェノタイピング手法の範囲に明確に該当する。

titlePlant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.

Measuring Phloem Transport Velocity on a Tissue-Level Using a Phloem-Mobile Dye. ↗

Here, we describe an in vivo dye-tracking method for measuring phloem transport velocity in seedlings, leaves, and petioles and potentially other translucent plant tissues. The method requires measurement of the fluorescent signal of a phloem-mobile dye using sensitive photo-sensors placed externally to the plant. Following dye application, velocity is determined by either following a dye pulse or using laser fluorescence bleaching. Velocity is estimated by dividing the distance traveled by the dye by the time it takes to travel. This method can be used to measure phloem transport velocity on intact plants with minimal disturbance and has the potential to be used under a variety of growth conditions. Because there are large differences among species in their anatomy, this method should be optimized for individual plants and tissue types.

Why it matches plant phenotyping methods植物体内の師部輸送速度という生理形質を、色素追跡と外部光センサーで測定する方法を開発・記述しており、表現型取得法が研究の中心である。

abstractHere, we describe an in vivo dye-tracking method for measuring phloem transport velocity in seedlings, leaves, and petioles and potentially other translucent plant tissues.

Dynamic Adjustment of LED Lighting Through Real‐Time Chlorophyll Fluorescence Monitoring Across an Entire Lettuce Growth Cycle ↗

Biofeedback control of light‐emitting diode (LED) lighting based on real‐time photosynthetic performance offers a promising framework for plant‐responsive light management in controlled environment agriculture (CEA). While the short‐term feasibility of electron transport rate (ETR)‐based light regulation has been demonstrated, its long‐term performance remains untested. This study evaluated the ETR‐based biofeedback lighting control system over an entire crop cycle of lettuce under three target ETR levels (55, 90, and 125 μmol m⁻² s⁻¹) in a climate‐controlled growth chamber. The system continuously monitored the quantum yield of photosystem II (ΦPSII) and adjusted photosynthetic photon flux density (PPFD) every 15 min to maintain the target ETR, used as an indirect proxy for carbon assimilation. Target ETRs were maintained within ±2.5% with minimal variability among replicates, and the corresponding average PPFDs (means ± standard deviations) were 183.5 ± 5.4, 316.1 ± 14.3, and 457.3 ± 23.5 μmol m⁻² s⁻¹, respectively. Despite stable environmental conditions, the system dynamically responded to both diurnal and long‐term acclimation in terms of photosynthetic efficiency. PPFD was reduced during the early photoperiod, when ΦPSII was high, and increased in the late photoperiod to compensate for the decline in ΦPSII. Under the target ETR of 125 μmol m⁻² s⁻¹, ΦPSII increased over time, enabling a 14% reduction in PPFD while maintaining a stable ETR, highlighting the potential for reduced light input as plants acclimated. These results demonstrate the long‐term feasibility and stability of plant‐responsive, CF‐based biofeedback lighting control for precise and replicable regulation of photochemical energy input in CEA crop production.

Why it matches plant phenotyping methods植物の光合成性能（ΦPSII、ETR）をリアルタイム測定し、その値に基づく照明制御システムを作製・長期検証しており、植物表現型取得と制御手法が研究の中心である。

abstractBiofeedback control of light‐emitting diode (LED) lighting based on real‐time photosynthetic performance offers a promising framework for plant‐responsive light management in controlled environment agriculture (CEA).

Histochemical Staining of Arbuscular Mycorrhizal Roots for Quantification of Fungal Colonization, High-Resolution Imaging, and Localization of Symbiotic Gene Expression. ↗

Histochemical staining and microscopy-based techniques have been widely used to detect, quantify, and analyze the morphology of arbuscular mycorrhizal fungi (AMF) in roots. Here, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents. We also summarize common approaches for quantifying arbuscular mycorrhizal colonization. In addition, we present a simple fluorescent staining protocol, using wheat germ agglutinin-Alexa Fluor conjugates, for high-resolution imaging of fungal colonization patterns and arbuscule morphology in roots. Finally, we describe a GUS staining method for localizing the promoter activity of plant genes potentially involved in mycorrhization, using transformed mycorrhizal hairy roots carrying promoter-GUS fusions.

Why it matches plant phenotyping methodsAMF感染根の菌根菌コロニー形成量・形態を染色と顕微鏡で取得・定量する標準化プロトコルおよび高解像度画像法を中心に扱っており、植物状態の表現型計測法として中心的です。

abstractHere, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents.

Dynamic Adjustment of LED Lighting Through Real-Time Chlorophyll Fluorescence Monitoring Across an Entire Lettuce Growth Cycle. ↗

Biofeedback control of light-emitting diode (LED) lighting based on real-time photosynthetic performance offers a promising framework for plant-responsive light management in controlled environment agriculture (CEA). While the short-term feasibility of electron transport rate (ETR)-based light regulation has been demonstrated, its long-term performance remains untested. This study evaluated the ETR-based biofeedback lighting control system over an entire crop cycle of lettuce under three target ETR levels (55, 90, and 125 μmol m -2 s -1 ) in a climate-controlled growth chamber. The system continuously monitored the quantum yield of photosystem II (Φ PSII ) and adjusted photosynthetic photon flux density (PPFD) every 15 min to maintain the target ETR, used as an indirect proxy for carbon assimilation. Target ETRs were maintained within ±2.5% with minimal variability among replicates, and the corresponding average PPFDs (means ± standard deviations) were 183.5 ± 5.4, 316.1 ± 14.3, and 457.3 ± 23.5 μmol m -2 s -1 , respectively. Despite stable environmental conditions, the system dynamically responded to both diurnal and long-term acclimation in terms of photosynthetic efficiency. PPFD was reduced during the early photoperiod, when Φ PSII was high, and increased in the late photoperiod to compensate for the decline in Φ PSII . Under the target ETR of 125 μmol m -2 s -1 , Φ PSII increased over time, enabling a 14% reduction in PPFD while maintaining a stable ETR, highlighting the potential for reduced light input as plants acclimated. These results demonstrate the long-term feasibility and stability of plant-responsive, CF-based biofeedback lighting control for precise and replicable regulation of photochemical energy input in CEA crop production.

Why it matches plant phenotyping methods植物のクロロフィル蛍光・光合成性能をリアルタイムに測定し、その値に基づく照明制御システムを作成・長期検証しており、植物生理状態の取得とフィードバック手法が中心である。

abstractBiofeedback control of light-emitting diode (LED) lighting based on real-time photosynthetic performance offers a promising framework for plant-responsive light management in controlled environment agriculture (CEA).

Expression Analysis of ROS-Related Genes During the Germination of Chickpea (Cicer Arietinum L.) Seeds. ↗

Seed germination is a critical physiological process that transforms a quiescent seed into a metabolically active seedling and is also a crucial factor in determining maximum crop production. This transition is influenced by various intrinsic and extrinsic factors. Interestingly, reactive oxygen species (ROS) plays an important role in breaking seed dormancy by oxidation of biomolecules, weakening of the testa and degradation of endosperm. Similarly, molecular internal oxygen is also considered vital for the transition of dormancy to seed germination. However, it is essential to establish a correlation between the internal oxygen and the generation of ROS during seed germination. This chapter details protocols for imaging internal oxygen concentrations using VisiSens and fluorescent detection of ROS using H 2 DCFDA in chickpea seeds, complemented by qPCR analysis of key ROS-related genes (RBOH, AOX 1, UCP 1, and NADH dehydrogenase). These findings from these methods help advance our understanding of the inverse relationship between molecular oxygen and ROS dynamics during seed germination.

Why it matches plant phenotyping methods発芽研究を背景とするが、種子内部酸素濃度とROSを画像・蛍光で測定するプロトコル自体が章の中心であり、植物の生理状態を取得する方法として適格。

abstractThis chapter details protocols for imaging internal oxygen concentrations using VisiSens and fluorescent detection of ROS using H 2 DCFDA in chickpea seeds

Recent advances in plant imaging technology: a concise review ↗

Imaging technologies have become indispensable tools in modern plant phenotyping, transforming visual information into measurable traits essential for analyzing morphology, physiology, biochemistry, and micro- to nanoscale structures. This concise review summarizes recent advances by dividing plant imaging into two major categories: (1) physiological and biochemical, which includes hyperspectral, multispectral, and fluorescence hyperspectral imaging, as well as terahertz imaging, surface-enhanced Raman scattering, and carbon dot-based techniques; and (2) structural and morphological, encompassing RGB, thermal, light detection and ranging (LiDAR), confocal microscopy, and optical coherence tomography. Together, these modalities deliver insights from the canopy to the molecular level, enabling precise monitoring of plant stress, disease, and developmental traits. By integrating these multimodal imaging techniques with artificial intelligence, the review highlights key developments, current challenges, and future perspectives in plant measurement and analysis.

Why it matches plant phenotyping methods植物フェノタイピングに用いる画像技術を体系的にレビューし、植物形質の測定・解析手法と課題を扱うことが中心である。

abstractThis concise review summarizes recent advances by dividing plant imaging into two major categories

Next-Generation Strategies for Developing and Commercializing Rust-Resistant Wheat through High-Throughput Phenotyping and Genomic Innovations ↗

Wheat rusts are the most important diseases leading to substantial yield losses. Early and precise detection of wheat rusts for early mitigation and disease control is imperative. This review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection. Additionally, various genomic interventions leading to the development of rust resistance in wheat are discussed in detail. High-throughput phenotyping (HTP) approaches enable early, non-destructive, and repeatable detection of wheat diseases. However, they need initial investment, expertise, and computational resources. RGB imaging achieves ~80% accuracy by capturing infected leaf coloration, while hyperspectral and fluorescence imaging can predict rust with over 90% accuracy, 3–8 days before visible symptoms. LiDAR, UAVs, and robotic platforms automate large-scale field phenotyping, and spectral indices (NDVI, PRI), thermal, and chlorophyll sensors detect early physiological changes. AI and machine learning models, including CNNs and SVMs, enhance diagnostic precision and reduce bias, while mobile apps, lateral flow devices, and IoT-based systems facilitate affordable, real-time rust detection and forecasting. Genomic interventions complement phenotyping, with marker-assisted selection (MAS) enabling precise tracing of rust resistance genes, and genomic selection (GS) allowing early multi-trait prediction. QTL mapping and GWAS identify major and minor resistance loci, while introgression from wild relatives and MAS reduce linkage drag and introduce novel alleles. Transgenic approaches, RNA interference (RNAi), and CRISPR/Cas9 gene editing enhance resistance through targeted gene modification, and gene pyramiding combines multiple loci for durable protection. Wheat pan-genome resources further support precise trait targeting, and speed breeding integrated with MAS, GS, or gene editing accelerates rust-resistant line development. Efficient seed system pathways ensure rapid dissemination, adoption, and resilience. The development and commercialization of rust-resistant wheat varieties under harsh climatic conditions are crucial for mitigating yield losses, reducing fungicide use, safeguarding farmer livelihoods, and ensuring sustainable food security.

Why it matches plant phenotyping methodsコムギさび病の高スループット表現型解析手法を中心にレビューしており、画像・分光・熱・LiDAR・AIなどによる植物病徴の検出方法を扱うため。

abstractThis review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection.

Multimodal Optical Biosensing and 3D-CNN Fusion for Phenotyping Physiological Responses of Basil Under Water Deficit Stress ↗

Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture.

Why it matches plant phenotyping methodsバジルの水ストレス応答を、RGB・深度・クロロフィル蛍光画像と3D-CNNで非破壊推定するフェノタイピング手法が研究の中心である。

abstractThis study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress.

Spectral image classification of asymptomatic peanut leaf diseases based on deep learning algorithms. ↗

Peanut leaf diseases have a major impact on peanut yield and quality. Timely, rapid, and accurate early diagnosis and control of peanut leaf diseases are key to ensuring high quality and yield of peanuts. This work focuses on the early diagnosis of peanut diseases and pests and conducts systematic research on the hardware system for imaging and spectral sensing of peanut plant leaves, as well as the software for deep learning classification algorithms. First, we designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases, including scab, scorch spot, and anthracnose. Second, we constructed a convolutional neural network to extract the basic features of spectral images. Third, an adaptive channel attention mechanism is introduced to update the weights of different channels. Fourth, a sparse second-order attention mechanism driving network is constructed to enhance the discriminative ability of deep feature information. Finally, the classification is completed utilizing the Softmax classifier. The experimental results demonstrate that the spectral image information improves the robustness of deep learning models to data transformation and achieves a high-precision classification score of 98.45% for asymptomatic peanut leaf diseases. Compared to traditional optical devices and software algorithms, the proposed multispectral imaging system and deep learning algorithm significantly improve detection ability and classification accuracy, which can assist botanists in making more accurate diagnoses of peanut leaf diseases.

Why it matches plant phenotyping methodsピーナッツ葉の病害状態を対象に、マルチスペクトル・蛍光画像取得システムと深層学習分類手法を開発・評価しており、植物病害表現型の取得・判定が中心的な技術貢献である。

abstractwe designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases

Tracking Myrosinase Regulation across Multiscale Interactions with Fluorescent Glucosinolates. ↗

The conversion of glucosinolates (GSLs) into chemopreventive isothiocyanates (ITCs) primarily relies on plant myrosinase (MYR) or specific bacteria. MYR dynamics are deeply involved in plant defense systems, gut microbiota metabolism, and complex interactions and regulation across species. A set of activity-based probes was developed to track MYR in vivo by biomimicking natural GSL with robust sensitivity and selectivity. The dynamics and heterogeneous distribution of MYR in distinct sections and species were captured via fluorescence imaging of live plants. Specifically, under herbivore challenge to leaves, a systemic, long-distance upregulation of MYR activity in root tissues has confirmed cross-species MYR regulation in plant defense. Furthermore, for the first time, quantitative visualization of the dynamic metabolic competition of GSL and sugar has confirmed the metabolic priority of sugar in gut microbiota and colonized zebrafish in vivo. The competitive metabolism is involved in the crosstalk during cross-species microbes and host-microbe interactions. Tracking MYR regulation across species by the designed probes has offered rich insights into the dynamic interplay among diet, microbiota, and host health.

Why it matches plant phenotyping methods植物体内のミロシナーゼ活性を蛍光プローブとライブ植物イメージングで可視化・追跡する手法開発が中心であり、植物防御応答という生理状態を定量的に測定しているため。

abstractA set of activity-based probes was developed to track MYR in vivo by biomimicking natural GSL with robust sensitivity and selectivity.

Differential response of plasma membrane versus chloroplast functions to freeze-thaw stress by Antarctic species, Deschampsia antarctica and Colobanthus quitensis, as explored through freeze-injury and post-thaw recovery. ↗

Main conclusion Antarctic plants employ distinct cold acclimation strategies: Deschampsia antarctica uses general membrane-chloroplast stabilization while Colobanthus quitensis relies on chloroplast-focused tolerance mechanisms. The two native vascular plants of Antarctica, Deschampsia antarctica and Colobanthus quitensis, persist in one of the most extreme terrestrial environments on Earth, where episodic freeze-thaw cycles are frequent even during the growing season. Survival under such conditions necessitates not only tolerance to freezing alone but also effective recovery from freeze-induced injuries-a composite trait referred to as freeze-thaw stress tolerance (FTST). Yet, estimates of FTST of Antarctic plants have remained inconsistent across studies, largely due to methodological differences in freezing regimes and injury assessment metrics. Here, we employed a standardized, ice-nucleation-controlled freeze-thaw protocol and assessed FTST using two independent physiological indicators: electrolyte leakage (membrane integrity) and chlorophyll fluorescence (Fv/Fm; PSII function). We further validated the LT 50 values-the temperature causing 50% injury-through post-thaw recovery (PTR) assays, and examined total soluble sugar dynamics as a metabolic indicator of recovery capacity. D. antarctica exhibited coordinated enhancements in both membrane and chloroplast resilience following cold acclimation, with LT 50 values from both metrics closely aligned. In contrast, C. quitensis demonstrated a chloroplast-centered acclimation strategy, characterized by pronounced improvement in Fv/Fm-based LT 50 , while electrolyte-leakage based estimates remained largely unchanged. PTR results and sugar profiling supported the biological relevance of Fv/Fm as a more reliable FTST marker in C. quitensis. Together, these findings reveal distinct, species-specific acclimation frameworks to freeze-thaw stress; a global stabilization strategy in D. antarctica and a chloroplast-focused tolerance mechanism in C. quitensis, underscoring divergent evolutionary pathways for polar plant survival.

Why it matches plant phenotyping methods凍結融解耐性の評価プロトコルと複数の生理指標を比較・検証し、LT50測定の妥当性を評価しているため、表現型取得法が中心的です。

abstractYet, estimates of FTST of Antarctic plants have remained inconsistent across studies, largely due to methodological differences in freezing regimes and injury assessment metrics.

Rapid and nondestructive identification of low-temperature stress severity in Juncao seedlings: Application of chlorophyll a fluorescence combined with visible-near infrared spectroscopy and machine learning. ↗

Low-temperature stress severely restricts the geographical distribution and growth of Juncao (Cenchrus fungigraminus), causing growth retardation and yield reduction. Thus, rapid nondestructive monitoring of low-temperature stress is crucial for accurate severity assessment and timely agronomic intervention. The traditional temperature threshold method often misjudges due to individual plant differences. To address this, this study developed a nondestructive method integrating chlorophyll a fluorescence (ChlF), visible-near infrared (Vis-NIR) spectroscopy, and machine learning (ML) algorithms for precise identification of stress levels. Firstly, under gradient temperature treatments, ChlF parameters and Vis-NIR spectral data were synchronously collected from Juncao leaves. Then, a stress classification criterion was established via ChlF parameters and an unsupervised learning algorithm to calibrate the Vis-NIR dataset. Finally, identification models were constructed based on Vis-NIR data and ML algorithms. Results showed that most ChlF parameters were closely correlated with Juncao's physiological and biochemical indicators. All samples were classified into three categories-no stress, mild stress, and severe stress-using ChlF parameters combined with the K-means clustering algorithm. SHapley Additive exPlanations (SHAP) analysis revealed the maximum photochemical efficiency of PSII as the top contributing classification indicator. Clustering reliability was validated by significant intergroup differences (P < 0.05) in ChlF transients, antioxidant enzyme activities, malondialdehyde (MDA) content, photosynthetic pigments, and SPAD values. Specifically, with increasing stress intensity, ChlF induction kinetic curves, Vis-NIR reflectance curves, chlorophyll a, chlorophyll b, total chlorophyll, and SPAD values decreased, while superoxide dismutase (SOD), peroxidase (POD), and MDA content generally increased. Among all combinations, Savitzky-Golay (SG) smoothing of Vis-NIR data combined with a one-dimensional convolutional neural network (1D-CNN) exhibited the optimal and robust performance, with a test set accuracy of 90.00 ± 0.73 %. This study confirms that integrating ChlF, Vis-NIR spectroscopy, and ML enables rapid nondestructive identification of low-temperature stress severity in Juncao seedlings, providing an efficient technical tool for monitoring physiological status and chilling injury early warning of Juncao.

Why it matches plant phenotyping methodsChlF・Vis-NIR・機械学習を統合し、植物の低温ストレス重症度を非破壊推定する手法の開発と検証が中心である。

abstractthis study developed a nondestructive method integrating chlorophyll a fluorescence (ChlF), visible-near infrared (Vis-NIR) spectroscopy, and machine learning (ML) algorithms for precise identification of stress levels.

Integration of High-Throughput Water-Sensitive Phenotyping for Crop Water Demand Diagnosis: Technical Pathways, Research Progress, and Challenges ↗

Accurate diagnosis of crop water demand is a core challenge in alleviating agricultural water scarcity. Traditional diagnostic methods, which rely mainly on soil moisture sensor monitoring or empirical models based on meteorological data, suffer from limitations such as insufficient spatiotemporal representativeness and an inability to reflect crop physiological status in real time, leading to an annual water waste of 10–30%. Therefore, developing technologies that enable real-time, non-destructive, and precise monitoring of crop water status is crucial. In recent years, the rapid advancement of high-throughput phenotyping technology has provided revolutionary tools to address this challenge. By integrating multi-source sensors (e.g., thermal infrared and hyperspectral imaging), multi-dimensional response characteristics of crops under water stress can be rapidly acquired. This paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models. It focuses on: (1) the connotation and acquisition techniques of key water-sensitive phenotypic indicators, such as canopy temperature, spectral indices, and chlorophyll fluorescence; (2) the advantages, limitations, and fusion strategies of multi-platform data acquisition systems, including unmanned aerial vehicles (UAVs), ground mobile platforms, and satellite remote sensing; and (3) the construction methods, performance evaluation, and practical application cases of diagnostic models based on machine learning (e.g., Random Forest, XGBoost), deep learning (e.g., CNN, LSTM), and mechanism-coupled models. The innovation of this review lies in its systematic integration of the entire technological chain—"phenotyping acquisition → model construction → decision-making"—while identifying current research challenges, including field environmental complexity, model generalization capability, data barriers, and interpretability. Future development pathways are proposed, focusing on low-cost sensing, explainable AI, multi-source data fusion, and cloud-edge collaborative decision systems. This review aims to provide a systematic theoretical and practical reference for water management in precision irrigation and smart agriculture.

Why it matches plant phenotyping methods作物の水状態に関する表現型形質の取得技術と診断モデルを体系的にレビューしており、植物フェノタイピング手法が中心である。

abstractThis paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models.

Non-destructive detection of microplastics stress in rice seedling: an interpretable deep learning approach using excitation emission matrix fluorescence spectra of root exudates. ↗

Introduction Microplastics (MPs), ubiquitous and insidious pollutants pervading agricultural systems, pose an escalating threat to global food security. This makes the development of nondestructive methods for the early detection of MPs stress in rice seedling an urgent scientific imperative. Method Rice seedlings were cultivated under exposure to polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) MPs at concentrations of 0 (control), 10, and 100 mg/L. Based on the stress-induced alterations in root exudates composition, a novel detection method for MPs stress in rice seedlings was developed using excitation-emission matrix fluorescence (EEMF) spectra combined with deep learning. Results Analysis of the original EEMF spectra revealed discernible differences. Feature extraction was performed using both the peak method and the PARAFAC method. Spectral changes in seedlings exposed to the low MP concentration (10 mg/L) were relatively minor compared to the control group. In contrast, exposure to the high concentration (100 mg/L) induced significant alterations in humic acid-like and amino acid-like substances. Subsequently, enhanced Vision Transformer (VIT) models were developed utilizing three distinct data representations: full EEMF spectra, emission spectra at specific excitation wavelengths, and extracted characteristic fluorescence values. The optimal model achieved 100% classification accuracy. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to evaluate feature importance, identifying both humic acid-like and marine humic acid-like components as major contributors to the model's predictions. Conclusion In summary, this study establishes a novel, non-destructive, and interpretable framework for the early detection of MPs stress in rice seedlings based on EEMF spectra of root exudates combined with deep learning.

Why it matches plant phenotyping methodsイネ幼苗のマイクロプラスチックストレス状態を、根圏滲出物の蛍光スペクトルと深層学習から非破壊推定する手法の開発が中心であり、単なる生物学的測定ではない。

abstractthe development of nondestructive methods for the early detection of MPs stress in rice seedling

An interpretable nondestructive detection model for maize seed viability: Based on grouped hyperspectral image fusion and key biochemical indicators ↗

Seed viability is crucial for ensuring crop quality and yield. However, existing nondestructive detection methods, which primarily rely on spectroscopic techniques and simple data fusion strategies, often suffer from limited accuracy and reliability. To address these limitations, this study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability. With regard to enhancing the prediction accuracy of seed viability, a grouped hyperspectral image fusion (GHIF) strategy was proposed to more effectively integrate complementary information from visible-near-infrared hyperspectral imaging (VisNIR-HSI) and fluorescence hyperspectral imaging (Fluo-HSI) datasets. With respect to improving model interpretability, eight biochemical components in the embryo of maize seeds were measured, and two key biochemical indicators—catalase (CAT) activity and malondialdehyde (MDA) content—were identified and validated as highly correlated with seed viability and predictable from spectral data. Building on these findings, a two-stage detection model was constructed. In the first stage, the two key biochemical indicators were predicted from the fused data using regression models. In the second stage, seed viability was determined using a dual-threshold strategy based on the predicted biochemical values. Experimental results showed that the proposed method achieved 90 % classification accuracy, comparable to direct spectral models while offering greater interpretability. This approach provides a reliable and explainable solution for nondestructive seed viability evaluation.

Why it matches plant phenotyping methodsトウモロコシ種子の生存性という植物状態を、可視近赤外・蛍光ハイパースペクトル画像の融合と解釈可能な予測モデルで非破壊推定する手法が研究の中心である。

abstractthis study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability

CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China ↗

Satellite-derived solar-induced chlorophyll fluorescence (SIF) provides critical insights into large-scale ecosystem functions. However, inherent trade-offs between satellite scan range and spatial resolution, coupled with incomplete coverage and irregular temporal sampling, constrain its utility for fine-scale ecological studies. In this study, we present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data. CNSIF accurately captures spatial patterns of vegetation photosynthetic activity and reveals a significant annual growth trend (0.054 mW m⁻² sr⁻¹ nm⁻¹ year⁻¹). Validation against tower-based SIF demonstrates its ability to track monthly photosynthetic dynamics across diverse ecosystems, with R² ranging from 0.324 (p < 0.01) to 0.947 (p < 0.001). A strong correlation with tower-based GPP (R² = 0.55, p < 0.001) further highlights its utility for carbon flux estimation. Comparative analyses show CNSIF’s superiority over existing high-resolution SIF products in resolving fragmented landscapes, reducing spatial artifacts, and improving delineation of fine-scale features (e.g., winter wheat fields, urban boundaries) in heterogeneous ecosystems. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics and assessing fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. CNSIF is publicly available at https://doi.org/10.6084/m9.figshare.27075145.

Why it matches plant phenotyping methods高解像度SIFの再構成手法と公開データセットを開発し、タワー観測およびGPPで検証しており、植生の光合成活動という生理状態の推定が中心である。

abstractwe present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data.

Shining light on olive trees: Hydroxytyrosol screening in leaves by fluorescence spectroscopy ↗

An innovative, non-destructive, and rapid method was introduced to quantify hydroxytyrosol content in olive leaves by means of advanced photonic technologies and chemometrics. At the heart of this approach is a custom-made, pocket-sized fluorometer with LED excitation at 375 nm and a miniaturized spectrometer for emission detection in the visible range, designed to measure intact olive leaves with ease and precision. Four Italian olive cultivars, namely Frantoio, Leccino, Leccio del Corno, and Moraiolo, were studied, with nine leaf samplings conducted over the 2022–2024 period. Simultaneously, hydroxytyrosol concentration was measured using HPLC-DAD-MS on the same samples to establish the reference data. Chemometric analysis was applied to the spectroscopic and HPLC-DAD-MS results. The Orthogonal Partial Least Squares (OPLS) regression method was successfully used to build a predictive model to quantify hydroxytyrosol concentrations ranging from 200 to 7000 mg/kg. The model achieved very good regression coefficients of 0.9 for calibration and 0.83 for cross-validation, with root mean square errors of 600 mg/kg and 720 mg/kg, respectively. This non-destructive method, combined with the portability of the pocket-sized fluorometer, is an innovation for high-throughput screening of olive leaves. It offers significant potential for evaluating olive leaves before their use in tea production or as raw material for dietary supplements, making it highly appealing for both the agriculture and health industries.

Why it matches plant phenotyping methodsオリーブ葉の化合物含量という植物器官形質を、携帯型蛍光計とケモメトリクスで非破壊推定する手法を開発し、HPLC-DAD-MSで検証しており、測定法が研究の中心である。

abstractAn innovative, non-destructive, and rapid method was introduced to quantify hydroxytyrosol content in olive leaves by means of advanced photonic technologies and chemometrics.

BODIPY-Rhodacycle Fluorescent Probe for In Situ Imaging of Ethylene Dynamics in Live Systems. ↗

Endogenous ethylene production occurs across biological kingdoms, yet its pathophysiological roles remain incompletely defined. In situ detection of ethylene is impeded by its inherent volatility and chemical inertness. Here, we report BORh, a new turn-on fluorescent probe that operates via ethylene-triggered displacement of a rhodium quencher from a BODIPY-rhodacycle scaffold, liberating the intensely fluorescent BOET. BORh exhibits high sensitivity and selectivity, broad pH tolerance, and negligible cytotoxicity. In mammalian PC12 cells, it permits real-time visualization of both exogenously supplied and in situ generated ethylene. Within photosynthetic systems, BORh overcomes cell wall barriers and chlorophyll autofluorescence, enabling in situ monitoring of ethylene dynamics in algae ( Chlamydomonas reinhardtii ) and higher plant ( Arabidopsis thaliana and Allium cepa ) tissues. Remarkably, BORh-enabled fluorescence imaging revealed synchronous upregulation of ethylene and reactive oxygen species (ROS) under plant abiotic stress. H 2 O 2 exhibited concentration-dependent biphasic regulation of ethylene biosynthesis, whereas ethylene exerted no reciprocal effect on ROS generation. These findings establish ROS as upstream regulators of ethylene biosynthesis within plant stress signaling cascades. BORh emerges as a robust chemical tool for spatiotemporal dissection of ethylene biochemistry, offering new insights into ROS-ethylene crosstalk during plant stress responses and paving the way for future investigations of ethylene function in mammalian pathophysiology.

Why it matches plant phenotyping methods植物組織内のエチレン動態を可視化・測定する蛍光プローブを開発し、植物での性能を実証しているため、植物生理状態の取得法が中心である。

abstractHere, we report BORh, a new turn-on fluorescent probe

A user-friendly machine-learning program to quantify stomatal features from fluorescence images ↗

In nearly all plants, pores on the leaf surface called stomata are essential for photosynthesis and gas exchange. The shape and distribution of stomata on the leaf varies widely between plants and is directly connected to photosynthetic efficiency. However, our understanding of the factors, both genetic and environmental, that exert subtle but significant effects on stomatal morphology is limited by the time required to manually annotate stomata in large imaging datasets. Here, we present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images. First, we establish QuickSpotters ability to automatically and accurately annotate mature stomata across developmental time. We also introduce an optional, speedy proofreading utility, StomEdit, that allows the researcher to quickly validate and correct machine-generated annotations. We use QuickSpotter and StomEdit to quantify how stomatal morphology evolves at the population level during cotyledon development and demonstrate how the programs can be used to extract subtle differences in stomatal development following pharmacological treatments. Finally, we describe PairCaller, a pair-calling classifier that accompanies QuickSpotter and can be used to identify stomatal clusters, a physiologically relevant and widely studied developmental phenotype. Taken together, our suite of programs facilitates quantitative analyses of stomatal development at scale, enabling high-throughput analyses of leaf phenotypes under varied conditions.

Why it matches plant phenotyping methods蛍光画像から気孔形態・分布を半自動抽出するソフトウェア群を開発し、精度検証と植物表現型への適用を行っており、フェノタイピング手法が研究の中心である。

abstractwe present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images.

PlantCV v4: Image analysis software for high-throughput plant phenotyping ↗

ABSTRACT PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEAS PlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. PlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. The software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science.

Why it matches plant phenotyping methodsPlantCVは画像から植物形質を自動抽出するソフトウェアであり、形態形質測定、蛍光・熱・ハイパースペクトル画像対応、手動測定との比較が中心的な方法論的貢献です。

abstractPlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions.

Design and Characterization of a Compact and Lightweight Dual-Wavelength Chlorophyll Fluorescence Light Detection and Ranging Sensor (ChloroFLiDAR) for Remote Plant Stress Assessment ↗

Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress and can be measured using simple, non-invasive methods. Therefore, it can be a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we introduce a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for photosynthesis research and remote plant stress assessment. The sensor utilizes modulated laser light to induce ChlF and employs an I/Q lock-in amplification method to isolate the signal from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions, along with measurement of the distance to the leaves. Our results demonstrate that the sensor can detect ChlF at a distance of 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water stress and high light intensity demonstrated the sensor's ability to detect changes in ChlF indices due to plant stress.

Why it matches plant phenotyping methods植物のストレス状態を遠隔で測定するクロロフィル蛍光LiDARセンサーの開発・性能実証が研究の中心であり、植物状態の計測手法に該当する。

abstractwe introduce a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for photosynthesis research and remote plant stress assessment.

Construction of a biocompatible supramolecular sensor for fluorescence imaging of Nitric oxide in plant tissues. ↗

Nitric oxide (NO) serves as a crucial signaling molecule regulating plant growth and stress responses, and its dynamic monitoring is crucial. This work presents a red-emitting aggregation-induced emission (AIE) supramolecular fluorescent sensor (β-CD/AIENAP) constructed through β-cyclodextrin encapsulation of organic AIE-active molecules (AIENAP). The extension of the material's conjugated structure red shifts the emission wavelength and improves the tissue penetration ability. Meanwhile, β-CD encapsulation restricts intramolecular motion, thereby enhancing fluorescence while improving biocompatibility and cell permeability. Density functional theory calculations verify both the luminescence mechanism and NO-responsive characteristics. The developed probe demonstrates rapid NO response (2 min) via specific triazole formation, exhibiting a large Stokes shift (180 nm), exceptional selectivity, and ultrahigh sensitivity (LOD = 77 nM). Through confocal imaging technology, the sensing system successfully realized the dynamic tracking of the dynamic spatial and temporal distribution of endogenous NO in plants, and systematically studied the NO response characteristics under different abiotic stresses in plants. Exogenous NO application experiments further validate stress resistance regulation. This study provides not only a novel nanosensor for plant NO detection but also an essential tool for analyzing NO signaling transduction and crop stress resistance mechanisms.

Why it matches plant phenotyping methods植物組織内の内生NOという生理状態を可視化・動態追跡する蛍光センサーを開発し、植物ストレス下で実証しており、表現型取得法が中心である。

abstractThis work presents a red-emitting aggregation-induced emission (AIE) supramolecular fluorescent sensor (β-CD/AIENAP) constructed through β-cyclodextrin encapsulation of organic AIE-active molecules (AIENAP).

Development and application of two-photon fluorescent probe for visual monitoring of isoprene in plants. ↗

As the main volatile organic compounds (VOCs), isoprene plays a dual role in plant stress protection and air pollution. However, its spatiotemporal dynamic monitoring in plants is insufficient, which limits environmental risk assessment. In this study, by systematically investigating the effects of the introduction of alkyne groups, maleimide groups and conjugated structures on the performance of probes, a probe (TPCM-π-M), with excellent two-photon properties was prepared. It showed good linear response in 1-240 ppm range with a detection limit of 0.2 ppm, enabling accurate detection of isoprene in various plant samples. In addition, the spatial distribution of endogenous isoprene in deep plant tissues and dynamic visual monitoring of isoprene under abiotic stress were achieved through two-photon imaging, overcoming the shortcomings of traditional single-photon imaging such as insufficient penetration depth. In particular, the dynamic regulation mechanism of plant isoprene metabolism under abiotic stress was revealed through the carotenoid/photorespiration inhibition model, and the correlation between the isoprene content in different flowers and their stress response ability was confirmed. This study provides technical support for analyzing the role of isoprene in plant metabolism and environmental adaptation, and has theoretical and applied value in plant physiology, pollution monitoring and biomedical imaging.

Why it matches plant phenotyping methods植物内イソプレンの定量・空間分布・ストレス応答を可視化する二光子蛍光プローブとイメージング法を開発し、植物試料で検証・応用しているため、表現型取得法が中心である。

abstracta probe (TPCM-π-M), with excellent two-photon properties was prepared

Host-Specific Fluorescence Dynamics in Legume-Rhizobia Symbiosis During Nodulation ↗

The legume-rhizobia symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (PnifH) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) systems. We show that PnifH-driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and PnifH-driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify PnifH-driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobia symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. IMPORTANCEThe legume-rhizobia symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (PnifH) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.

Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量手法と、深層学習による結節形質の自動画像解析パイプラインを開発・検証しており、植物フェノタイピング手法が中心です。

abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis.

Integrating data assimilation with chlorophyll fluorescence signatures: An innovative framework for real-time monitoring of crop nitrogen dynamics ↗

Crop organ-level nitrogen (N) dynamics (accumulation and transport) are strongly associated with final quality and yield. Conventional crop N monitoring methods either have high uncertainty (crop model) or limited capacity to diagnose N status in stems and grains (remote sensing tools). Data assimilation overcomes the shortcomings of crop model and remote sensing tools, but whether it can accurately simulate nitrogen dynamics at the field scale remains unknown. We aimed to develop a novel dual assimilation framework coupling a crop model and unmanned aerial vehicle (UAV) remote sensing, incorporating fluorescence information to enhance the monitoring of crop N dynamics. Firstly, the selection of WOFOST parameters was based on the sensitivity analysis results, and the calibration was conducted through optimization algorithm. Next, machine learning and multi-task neural network (MDNN) were employed to construct the inversion models of four state variables (leaf area index, LAI; leaf dry matter, LDM; leaf N accumulation, LNA; soil moisture content, SMC) based on UAV multispectral data. Meanwhile, a fluorescence operator was constructed using machine learning to capture the complex relationship between fluorescence parameters (actual photochemical efficiency, ΦPSⅡ) and state variables. Finally, the remote sensing inversion results and ΦPSⅡ were incorporated into the dual assimilation framework to update WOFOST. The results showed that MDNN outperformed traditional machine learning in the remote sensing inversion tasks for four state variables. The joint assimilation of LAI, LDM, and LNA improved the simulation accuracy of organ N accumulation. The dual assimilation strategy significantly enhanced the monitoring performance for N accumulation in leaves, stems, and grains (R²: 0.76–0.84, 0.68–0.80, and 0.70–0.75; NRMSE: 15.04–18.74 %, 16.00–25.34 %; 20.40–23.60 %). The treatment of 30 mm irrigation combination with 200 kg ha⁻¹ N fertilizer exhibited the highest N transport (71.22 %) and contribution (60.12 %) to grain. Overall, the dual assimilation framework demonstrated robust performance in monitoring organ-level N dynamics for wheat, providing a promising approach for acquiring spatially variable information about N accumulation and transport.

Why it matches plant phenotyping methodsUAVリモートセンシング、蛍光情報、機械学習、作物モデルを統合した器官レベルの窒素動態推定フレームワークを開発・評価しており、植物状態の取得・推定方法が研究の中心である。

abstractWe aimed to develop a novel dual assimilation framework coupling a crop model and unmanned aerial vehicle (UAV) remote sensing, incorporating fluorescence information to enhance the monitoring of crop N dynamics.

Studies on the Differentiation of Transient Chlorophyll a Fluorescence Signals in Papaya Plants Showing Symptoms and Without Symptoms in the Presence of PRSV-P and PMeV Viruses. ↗

Viral infections represent a critical threat to cultivated plant species. In papaya cultivation, two viral diseases-papaya mosaic (caused by papaya ringspot virus type P-PRSV-P) and papaya sticky disease (caused by a virus complex of papaya meleira virus-PMeV, and papaya meleira virus-PMeV2)-are prevalent and capable of devastating entire plantations, incurring substantial economic losses. Current diagnostic practices rely on visual identification of symptoms and elimination of infected plants (roguing). Monitoring photosynthetic efficiency in orchards prone to PRSV-P and PMeV2 coinfection may allow early intervention, mitigating productivity losses and reducing fruit quality. This study aimed to evaluate chlorophyll a fluorescence as a biomarker for photosynthetic impairment and symptom severity in papaya infected with PRSV-P and/or PMeV2 and to explore the feasibility of early detection of the infection by these dual pathogens, as an exploratory study under field conditions. Chlorophyll a fluorescence revealed details about the physiology of plants coinfected with the complex of PMeV2 and PRSV-P: the electron motive force within PSII decreases in infected plants and in those without visual symptoms of infection, being proportional to the age and developmental stage of the plants. A slowdown in the multiple electron transfer turnover of PSII and a decrease in the efficiency of the redox reactions of photosystem I were observed in plants with or without visual detection of infection. The evidence generated suggests that the chlorophyll a fluorescence technique can be used to monitor the pathophysiological state of plants under biotic stress.

Why it matches plant phenotyping methods植物の光合成機能と感染症状の早期検出を目的に、クロロフィルa蛍光を植物状態の測定・評価手法として中心的に検討しているため。

abstractThis study aimed to evaluate chlorophyll a fluorescence as a biomarker for photosynthetic impairment and symptom severity in papaya infected with PRSV-P and/or PMeV2 and to explore the feasibility of early detection of the infection by these dual pathogens, as an exploratory study under field conditions.

Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects ↗

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.

Why it matches plant phenotyping methods植物病害の画像・センサ計測、機械学習による病害分類・重症度推定を中心に扱うレビューであり、植物状態の取得・評価手法が主題。

abstractThis review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies.

Spatiotemporal analysis of herbicide-induced stress in chicory leaves using chlorophyll-a fluorescence imaging. ↗

The widespread use of herbicides such as atrazine and paraquat, although essential for weed control, can unintentionally impact non-target plant species, compromising agricultural productivity and environmental health. Understanding the physiological responses induced by these compounds is critical for developing more sustainable agricultural practices. Despite the extensive use of chlorophyll fluorescence techniques, most studies typically average signals across tissues, overlooking localized and heterogeneous stress patterns. To address this gap, the present study evaluated the use of time-resolved chlorophyll-a fluorescence imaging as a non-invasive, spatially resolved technique for the assessment of herbicide-induced stress in chicory (Cichorium intybus) leaves. The results revealed that chlorophyll fluorescence parameters, particularly F v /F m , F 0 , Fₘ, and ϕNPQ, detected heterogeneous stress patterns depending on the herbicide across the leaf. Atrazine exposure increased F 0 , indicating a blockage in the electron transport chain, while paraquat decreased F 0 , suggesting chlorophyll degradation. Both herbicides reduced F m and altered ETR, yet through distinct physiological mechanisms. These findings demonstrate that time-resolved fluorescence imaging can distinguish specific modes of herbicide action with high sensitivity. Notably, the technique enabled detection of physiological alterations before the appearance of visible symptoms, highlighting its potential for early stress diagnosis. This work establishes time-resolved fluorescence imaging as a powerful diagnostic tool, capable of discriminating specific herbicide actions and for monitoring plant stress at a fine spatial scale. Furthermore, its compatibility with automated and AI-based analysis platforms highlights its potential for advancing precision agriculture, optimizing herbicide application, and promoting sustainable farming practices.

Why it matches plant phenotyping methods除草剤処理を題材にしているが、時間分解クロロフィル蛍光イメージングによる植物ストレス状態の空間的・早期評価が研究の中心であり、植物フェノタイピング手法の実質的な適用に該当する。

abstractthe present study evaluated the use of time-resolved chlorophyll-a fluorescence imaging as a non-invasive, spatially resolved technique for the assessment of herbicide-induced stress in chicory (Cichorium intybus) leaves.

Denoised portable fluorescence lifetime imaging system for on-site analysis: applications in photosynthesis ↗

Fluorescence lifetime measurements offer crucial insights into molecular interactions and fluorophore microenvironments, with significant potential to advance research in areas like photosynthesis and field phenotyping. Currently, these measurements are largely confined to controlled laboratory settings. To overcome this limitation, we developed a portable fluorescence lifetime imaging system utilizing a dual-tap CMOS sensor. Acknowledging the unique noise characteristics of CMOS sensors and the temperature-dependent noise challenges inherent in on-site measurements, we propose a dedicated denoising method for CMOS-based fluorescence lifetime images. This procedure significantly improves the accuracy of fluorescence lifetime data and enhances image clarity, enabling on-site measurements of intact plants.

Why it matches plant phenotyping methods携帯型蛍光寿命イメージングシステムとCMOS画像の専用デノイジング法を開発し、植物の光合成関連状態をオンサイトで測定可能にすることが中心的な方法論的貢献である。

abstractwe developed a portable fluorescence lifetime imaging system utilizing a dual-tap CMOS sensor.

Fluorescence-based sensing for leaf nicotine prediction, nitrogen estimation and variable rate fertilization of tobacco ↗

Sustainable and low-nicotine production of tobacco requires rapid and accurate on-site assessment of the leaf nitrogen (N) status. This issue can be supported by fluorescence-based sensors, which are promising tools for precision N management. We then aimed to 1) evaluate the suitability of the Multiplex® fluorescence sensor (Mx) to predict, at an early stage, the final nicotine content of tobacco leaves; 2) develop a model for in-season tobacco foliar N estimation using the Partial Least Square (PLS) multivariate regression technique; and finally, 3) test the effectiveness of a Mx map-based Variable Rate Nitrogen Fertilization (VRNF) in reducing the spatial variability in leaf Nitrogen Balance Index (NBI), that is the N status, of a commercial field of Virginia Bright tobacco. The NBI measured by the Mx about two months after transplanting was found to linearly relate to the nicotine content measured after curing (R² = 0.72, P < 0.001) over a nicotine range of 0.25 – 4.12 %. NBI, defined as the ratio between the leaf chlorophyll (SFRR) and Flavonoids (FLAV) indices better related to nicotine than the single SFRR and FLAV indices (R² = 0.47, P < 0.001 and R² = 0.52, P < 0.001, respectively. Furthermore, the NBI estimated the actual leaf N content before flowering better (R² = 0.33) than single SFRR and FLAV indices (R² = 0.28), over a range of 21 – 37.6 mgg⁻¹. Leaf fluorescence sensor indices were thus combined with growth stages and weather variables across diverse varieties and sites. The resulting PLS model successfully predicted leaf N (R² = 0.72, RMSEP = 2.73 mgg⁻¹ and relMAE = 7.75 %) over a range of 20.6–28.0 mgg⁻¹. The most significant variables, primarily related to solar radiation, were identified for a robust general model development. Finally, the spatial pattern of the NBI was mapped over a 2.04 ha commercial plot of the ITB 6118 variety, and used to produce a three-zone prescription map. Two weeks after the intervention of VRNF based on the defined prescription map, the overall NBI variability had dropped from 23.5 % coefficient of variation (CV) to 7.9 % CV. Our results show the feasibility of using the Mx sensor for precision fertilization of Virginia Bright tobacco and highlight its potential to support future developments aimed at more sustainable production of plants with reduced nicotine content.

Why it matches plant phenotyping methods蛍光センサーによる葉のニコチン含量・窒素状態の推定モデルを開発・検証し、NBIマッピングと可変施肥へ応用しており、植物形質の取得・推定手法が中心である。

abstractevaluate the suitability of the Multiplex® fluorescence sensor (Mx) to predict, at an early stage, the final nicotine content of tobacco leaves

Real-time fluorescence imaging platform for high-throughput screening of arbuscular mycorrhizal fungi enhancing plant nutrient uptake ↗

Abstract Background Arbuscular mycorrhizal fungi (AMF) are ancient soil symbionts that form mutualistic associations with approximately 80% of terrestrial plant species. They enhance host nutrient and water acquisition in exchange for photosynthetic carbon. Current AMF research relies on field trials, compartmented cultivation and pot cultures‒methods that are time-consuming (months to years) and unable to monitor dynamic nutrient transport, thus limiting efficient strain screening. Results We developed a real-time fluorescence imaging platform integrating sterile symbiotic microchambers with photodiode array detection. This system enables non-invasive, quantitative tracking of nutrient flux at plant-fungal interface. Distinct AMF strains exhibit significant differences in fluorescence kinetics—such as accumulation rate and peak intensity—providing measurable indicators of transport efficiency. The platform allows high-throughput functional screening of AMF strains, dramatically accelerating the identification of high-performance symbionts. Conclusion Our method overcome the temporal and technical limitations of conventional AMF screening approaches. By enabling simultaneous real-time monitoring and high-throughput analysis, it shortens screening cycles and establishes a standardized framework for (1) precision breeding of efficiency AMF strains, (2) mechanistic study of nutrient exchange, and (3) development of sustainable microbial inoculants.

Why it matches plant phenotyping methods植物と菌根菌の界面における栄養輸送をリアルタイム定量する蛍光イメージング基盤を開発しており、植物関連の生理状態を取得する方法が研究の中心である。

abstractWe developed a real-time fluorescence imaging platform integrating sterile symbiotic microchambers with photodiode array detection.

Consistent spectral reflectance signatures of photosystem II thermal tolerance in contrasting foundation tree species ↗

Photosystem II (PSII) is among the most thermally sensitive components of photosynthesis, and emerging evidence suggests that that plants in diverse biomes face increasing risk of PSII damage under future climate change. However, uncertainties in the distribution and drivers of PSII thermal tolerance (Tcrit) limit our ability to predict thermal risk in plant communities across spatial scales. Here, we evaluate whether intraspecific variation in Tcrit corresponds to leaf reflectance spectra (400-2500nm) to identify mechanisms associated with Tcrit in field conditions and evaluate the potential of its remote estimation using novel remote sensing platforms. We measured Tcrit using temperature response curves of minimal fluorescence (Fo) along with corresponding leaf reflectance spectra in two foundation tree species: Populus fremontii (US Southwest) and Metrosideros polymorpha (Hawai‘i). P. fremontii was sampled under both moderate ( 45ºC) heat. Consistent spectral signatures of Tcrit emerged across species and sampling conditions, with the strongest signatures in P. fremontii under extreme heat. These signatures allowed Tcrit estimation (R²=0.24-0.30; RMSE<1.0ºC) and classification of high- versus low-Tcrit (71-77% accuracy) in P. fremontii. Across both species, Tcrit tended to increase with spectral indices reflecting higher chlorophyll content and lower carotenoids, nonphotochemical quenching, and leaf water content. These findings suggest that variation in PSII thermal tolerance is linked to fundamental biochemical properties of leaves, which are reflected in their optical traits. As climate extremes intensify, spectral screening and scaling of Tcrit via remote sensing may support improved conservation, management, and thermal risk assessment in vulnerable ecosystems.

Why it matches plant phenotyping methods葉の反射スペクトルからPSII熱耐性（Tcrit）を推定・分類する方法を評価しており、植物生理形質の取得・リモート推定が研究の中心である。

abstractHere, we evaluate whether intraspecific variation in Tcrit corresponds to leaf reflectance spectra (400-2500nm) to identify mechanisms associated with Tcrit in field conditions and evaluate the potential of its remote estimation using novel remote sensing platforms.

Applications of High-throughput Tools in Mulberry Physiology Research: A Comprehensive Review ↗

Sericulture is the multi- dimensional activity and Mulberry (Morus spp) is the sole food for silkworm Bombyx mori. It is very important to study the physiology of mulberry for the betterment of sericulture productivity and screening of better performing lines to withstand biotic and abiotic stress. It is necessary to monitor the crop growing status continuously and non-destructively to make decisions as to changed environmental conditions. High-throughput screening, defined as the automation and scaling of experimental analyses, enables rapid, reproducible, and large-scale measurements of plant traits. Importantly, these approaches allow continuous and non-destructive monitoring of crop growth, providing valuable insights for adaptive management under variable environmental conditions. Recent technological advances have introduced a wide range of high-throughput tools into mulberry research. Phenotyping platforms such as leaf area meters, chlorophyll fluorescence imaging, and portable photosynthetic systems allow rapid assessment of photosynthetic efficiency and stress responses. High-throughput sequencing methods, including RNA-Sequencing and genome-wide association studies (GWAS), have deepened genetic insights, while genome editing technologies like CRISPR/Cas9 open avenues for targeted improvement. Remote, hyperspectral, and multispectral sensing technologies enable large-scale monitoring of canopy health, nutrient status, and early stress detection. This review synthesizes how these tools facilitate early stress detection, genotype screening, and integration with molecular datasets for precision breeding. Case studies highlight their use under drought, waterlogging, nutrient imbalances, etc. This review concludes that high-throughput phenotyping not only enhances physiological understanding but also offers a pathway to accelerated mulberry improvement programs, bridging the gap between research and practical sericulture applications.

Why it matches plant phenotyping methodsマルベリーの生理・ストレス応答を対象に、ハイスループット表現型解析ツールとプラットフォームを体系的にレビューしており、フェノタイピング手法が中心です。

abstractPhenotyping platforms such as leaf area meters, chlorophyll fluorescence imaging, and portable photosynthetic systems allow rapid assessment of photosynthetic efficiency and stress responses.

Multi-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought–Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture ↗

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate-resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought-heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches-genomics, transcriptomics, proteomics, and metabolomics-to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA-SnRK2 signaling cascade, HSF-HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This "pixels-to-proteins" paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties.

Why it matches plant phenotyping methodsリモートセンシング・高スループット表現型解析プラットフォームを中心に、作物のストレス関連形質測定とマルチオミクス統合をレビューしており、フェノタイピング手法が中核である。

titleMulti-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought–Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture

Sustained quenching not always means photoinhibition ↗

Photosynthetic light harvesting complexes (LHC) are involved in light absorption and energy dissipation. By modulating the photosystems absorption cross section, they affect their photosynthetic activity and non-photochemical quenching (NPQ) capacity. These processes have been widely studied by spectrally integrated chlorophyll fluorescence methods, which mask their associated spectral information. We explored in aspen and Arabidopsis npq mutants how the absence of these components affects the development of NPQ spectra under two contrasting conditions: in the absence and presence of photoinhibition. We proposed a new parameter to estimate the development of new emitting species (NESD) during time-spectrally resolved NPQ inductions and a pipeline to disentangle PSII energy partitioning heterogeneity. We demonstrate that LHCB, PsbS and zeaxanthin is required for NESD. By combining gas exchange with spectrally resolved kinetics, we show that under photoinhibitory conditions, however, NES develops in the absence of PsbS and zeaxanthin, and the resulting sustained quenching occurring independently of photoinhibition. Furthermore, we found that in the absence of LHCB and Curvature Thylakoid 1 a significant increase in photoinhibition was observed. This suggest that in the long term effective photoprotection requires the presence of LHCB and thylakoid plasticity, while PsbS and zeaxanthin play a major role in catalyzing LHCII-dependent quenching.

Why it matches plant phenotyping methods時間・スペクトル分解クロロフィル蛍光から新規パラメータと解析パイプラインを開発し、光合成エネルギー分配と光阻害状態を推定する手法が研究の中心である。

abstractWe proposed a new parameter to estimate the development of new emitting species (NESD) during time-spectrally resolved NPQ inductions and a pipeline to disentangle PSII energy partitioning heterogeneity.

A comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches ↗

Agriculture stands as a foundational element of life, closely linked to the progress and development of society. Both humans and animals depend on agriculture for a wide range of essential services, such as producing oxygen and food, along with vital raw materials for clothing, medicine, and other necessities. Given agriculture’s vital role in supporting individual well-being and driving global progress, protecting and ensuring the long-term sustainability of agriculture is essential. This is crucial for securing resources and maintaining environmental balance for future generations. In this context, in our review we have examined the various factors that can interfere with the normal physiological and developmental functions of plants and crops. These factors, referred to scientifically as stressors or stress conditions, include a wide range of both biotic and abiotic challenges. In this work we have systematically addressed all the major categories of stress that plants may encounter throughout their lifecycle. Additionally, because plants tend to exhibit recognizable physiological or biochemical responses to stress, we have cataloged the associated stress indicators. These indicators were identified through various assessment techniques, including both destructive and non-destructive approaches. A significant advancement highlighted in our review is the integration of Machine Learning (ML) algorithms with non-destructive methodologies, which has substantially enhanced the accuracy, scalability, and real-time capability of plant stress detection. These ML-enhanced systems leverage high-dimensional data acquired through remote sensing modalities, such as hyperspectral imaging, thermal imaging, and chlorophyll fluorescence. These ultimately help in enabling the early identification of biotic and abiotic stress signatures. Through advanced pattern recognition, feature extraction, and predictive modeling, ML facilitates proactive anomaly detection and stress forecasting, thereby mitigating yield losses and supporting data-driven precision agriculture. This convergence represents a significant step toward intelligent, automated crop monitoring systems. Finally, we conclude the article with a concise discussion of the potential positive roles that certain stress conditions may play in enhancing plant resilience and productivity.

Why it matches plant phenotyping methods植物ストレスの非破壊検出手法を、リモートセンシング、画像計測、MLとともに体系的にレビューしており、植物状態の取得・推定方法が中心である。

titleA comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning ↗

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape (Brassica napus), and analyzed using MATLAB 2021b to quantify NDI, ExG, Fd/Fₘ, and plant leaf temperature. NDI, ExG and Fd/Fₘ decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in Fd/Fₘ, while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 ​h enabled the diagnosis of herbicide MOAs with 89.6 ​% accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 ​%. The indices acquired at 6 ​h, and Fd/Fₘ and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 ​%, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.

Why it matches plant phenotyping methods植物のスペクトル画像から葉の生理状態・温度指標を抽出し、機械学習で除草剤作用機構を診断する手法の開発と精度検証が研究の中心であるため、植物フェノタイピング手法として採用。

abstractthis study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).

Examining photosynthetic induction variation among historical cotton cultivars through time-integrated limitation analyses. ↗

A thorough understanding of the biochemical, stomatal, and mesophyll components that limit photosynthetic induction is crucial for targeted improvement of crop productivity. However, compared with biochemical activation and stomatal conductance (gs), mesophyll conductance (gm) remains underexplored in induction studies. The fluorescence method (the variable J method) is a valid and widely accessible tool for gm measurement under steady-state conditions. Here, we experimentally validated the applicability of the fluorescence method under nonsteady-state conditions, demonstrating comparable induction kinetics of gm with the well-established carbon isotope method. Building on this validation, we combined the fluorescence method with gas-exchange measurements to comprehensively examine the induction kinetics of photosynthetic rate (A) and its associated components in a set of historical cotton (Gossypium hirsutum L.) cultivars. Our results showed no significant effect of the year of cultivar release on A during induction, suggesting that dynamic photosynthesis has not benefited from past selection efforts in cotton. Nonetheless, significant among-cultivar variations were observed in all measured induction traits, hinting at breeding opportunities for leveraging dynamic photosynthetic variation to boost crop productivity. Through induction-period-integrated limitation analysis, we further identified gs as the single most important limiter of photosynthetic induction across all cotton cultivars. Moreover, the analysis also demonstrated that accurately accounting for gm kinetics is essential for the unbiased acquisition of mechanistic insights into nonsteady-state photosynthetic physiology. We recommend that future induction studies incorporate gm measurements whenever possible to strengthen the knowledge base necessary for genetically enhancing dynamic carbon gain and crop yield in the field.

Why it matches plant phenotyping methods非定常条件での蛍光法による葉肉コンダクタンス測定を同位体法と検証し、ガス交換と組み合わせて光合成誘導形質を評価しており、測定法の検証が中心的です。

abstractHere, we experimentally validated the applicability of the fluorescence method under nonsteady-state conditions, demonstrating comparable induction kinetics of gm with the well-established carbon isotope method.

An activatable near-infrared probe for in situ monitoring of hydrogen peroxide in plant tissues ↗

Biotic and abiotic stresses can disrupt plant metabolic processes. This leads to the excessive accumulation of hydrogen peroxide (H₂O₂) in plants, which in turn induces oxidative stress. Therefore, detection of H₂O₂ is critical to understanding plant growth. In this study, we developed a naphthalene-based fluorescein near-infrared fluorescent probe (NAPF-AC) for the sensitive and selective detection of H₂O₂. Upon exposure to H₂O₂, the probe undergoes disruption of its push-pull electronic structure, triggering an intramolecular charge transfer process that allows for fluorescence-based detection. NAPF-AC exhibited excellent linearity (R² = 0.998) over a wide concentration range of H₂O₂ (0.1 to 100 μM), with a limit of detection (LOD) as low as 0.05 μM. In addition, NAPF-AC was successfully used for the in-situ detection of H₂O₂ in plant tissues. This study provides a powerful tool for studying H₂O₂ dynamics in plants and offers new insights into the mechanisms regulating plant growth and stress responses.

Why it matches plant phenotyping methods植物組織内のH₂O₂という生理状態を蛍光プローブで測定するセンサーを開発し、性能評価と植物組織での実証を行っており、測定法が研究の中心である。

abstractwe developed a naphthalene-based fluorescein near-infrared fluorescent probe (NAPF-AC) for the sensitive and selective detection of H₂O₂.

Dual responsive fluorescent probe for exploring hypochlorite and sulfur dioxide in different heavy metal ions related stress models ↗

The danger of heavy metal pollution has drawn the global concern of researchers in recent decades, especially multiple heavy metal pollution. Heavy metal pollution induced stress may cause oxidative stress and redox balance disruption in living organism, and further trigger other secondary stresses. Fluorescent imaging analysis is considered to be effective method for real-time visualization of complex bioactive molecules in situ due to their non-destruction, high sensitivity and specificity. It is still relatively rare that bifunctional fluorescent probe for imaging redox-correlated molecules under heavy metal stress. Herein, we designed and synthesized a smart dual responsive fluorescence probe (D-P) with dual detection sites for the individual detection of HClO and SO₂ derivatives with different channels. The probe D-P was applied to effectively dual monitor the dynamic change of HClO and SO₂ derivatives in plant, zebrafish and human cells. Importantly, probe D-P successfully observed dynamic changes of HClO and SO₂ derivatives mediated redox status in living organisms under single and combined exposure to heavy metal stress, and further during DTT-induced ER stress and hypoxia and ischemia stress, confirming that probe D-P is a powerful tool to real-time image of the dynamic balance of HClO and SO₂ derivatives for assessing the redox homeostasis under environmental stress, which can be monitored and adopted as the early warning in environmental stress conditions and disease.

Why it matches plant phenotyping methods植物内の酸化還元関連分子を可視化する二重応答蛍光プローブを開発し、重金属ストレス下の植物で動態を測定することが中心であるため、植物の生理状態を取得するフェノタイピング手法に該当する。

abstractHerein, we designed and synthesized a smart dual responsive fluorescence probe (D-P) with dual detection sites for the individual detection of HClO and SO₂ derivatives with different channels.

A turn-on near-infrared fluorescent probe for monitoring carbon monoxide in bacterial pneumonia and cadmium ion induced abiotic stress responses in plants ↗

Carbon monoxide (CO) is known as a highly toxic gas being emitted during industrial activities. Recent studies confirmed the roles of CO in various biological processes, such as bacterial pneumonia and plants response to abiotic stresses, that can be evaluated in situ using fluorescence probe technology. In this work, a Nile Red-based near-infrared fluorescence probe (Z1CO) is developed to evaluate CO production in animal and plant systems. The probe Z1CO is designed by conjugating allyl formate moiety that responds to CO specifically to Nile Red fluorophore. In the presence of CO with palladium chloride (PdCl₂), Tsuji–Trost reaction facilitates the cleavage of the allyl formate moiety, resulting in bathochromic-shift of absorption spectra and enhancement of the emission at 665 nm within 60 min. Z1CO features several advantages, including near-infrared emission, large Stokes shift (75 nm), high selectivity, low detection limit (0.84 μM) and low cytotoxicity in vitro, enabling its applications in monitoring CO production in animal and plant systems. Visualization of CO production in lipopolysaccharide (LPS)-induced acute pneumonia mouse model, monitoring of CO-mediated inflammatory treatment responses and CO functions in plants response to cadmium ion (Cd² ⁺)-induced abiotic stress are then demonstrated. This work thus provides a new tool for evaluating the roles of CO in animal and plant systems.

Why it matches plant phenotyping methods植物系でのCO産生を可視化・評価する蛍光プローブを開発し、Cd²⁺誘導ストレス下の植物応答への適用も実証しており、植物の生理状態取得が方法論の中心である。

abstractIn this work, a Nile Red-based near-infrared fluorescence probe (Z1CO) is developed to evaluate CO production in animal and plant systems.

Nondestructive presymptomatic detection of blue-fluorescing substances in tomato leaves infected with Ralstonia solanacearum using a polyvinyl alcohol hydrogel. ↗

This study proposes a nondestructive technique for the presymptomatic detection of pathogenic infections of plants, aiming to effectively prevent and control plant diseases in agriculture. The present and previous studies indicated that an increase in blue-fluorescing substances, including chlorogenic acid, in tomato leaves is a promising biomarker of infection with the pathogenic soil bacterium Ralstonia solanacearum. A soft and adhesive polyvinyl alcohol (PVA) hydrogel conformably adhered to the hydrophobic surface of the tomato leaf with a complex topography, mediated by non-volatile glycerol, enabling effective extraction of blue-fluorescing substances in a nondestructive manner. The fluorescence intensity of the PVA hydrogel increased a few days before the appearance of visible symptoms of bacterial wilt. This technique is expected to become a fundamental technology for the early detection of plant diseases.

Why it matches plant phenotyping methodsトマト葉の感染状態を、PVAハイドロゲルによる蛍光物質の非破壊抽出・検出で早期推定する手法の開発が中心であり、植物病害状態のフェノタイピングに該当する。

abstractThis study proposes a nondestructive technique for the presymptomatic detection of pathogenic infections of plants

Phenotypic Analysis and Characterization Methods for Transgenic Setaria viridis: From Morphology to Molecular Markers: A Comprehensive Review ↗

Setaria viridis (green foxtail) has become a principal C4 model for functional genomics and translational crop research because its short life cycle and reliable transformation protocols enable rapid production of transgenic lines. Linking those genotypes to traits, however, demands a multilayered phenotypization strategy. This narrative review synthesizes current approaches that span traditional morphology, developmental staging, and precision measurement of vegetative and reproductive traits. It describes frameworks for assessing physiological performance, such as gas-exchange assays and chlorophyll-fluorescence imaging, that translate genetic changes into functional outcomes. Recent advances in automated, high-throughput phenotyping platforms are outlined, illustrating how time-series imaging accelerates large-scale trait discovery. The review also examines experimental designs for evaluating abiotic-stress responses, highlights biochemical assays and metabolite-profiling techniques that reveal underlying metabolic adjustments, and details molecular-marker systems that couple genotype with phenotype. By integrating these complementary methods, researchers can build comprehensive genotype–phenotype maps in S. viridis, thereby streamlining gene validation and informing next-generation plant-biotechnology applications.

Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、形態・生理測定および自動ハイスループット画像解析プラットフォームを体系的にレビューしているため。

abstractThis narrative review synthesizes current approaches that span traditional morphology, developmental staging, and precision measurement of vegetative and reproductive traits.

Assessing the redox state of the plastoquinone pool in algae and cyanobacteria via OJIP fluorescence: perspectives and limitations ↗

The redox state of the plastoquinone pool (PQ-redox) acts as a central element in a variety of intracellular signal pathways. Several methods for determining PQ-redox have been established. Although some of these methods may be quantitative, such as those based on liquid chromatography, they are typically sensitive to sample preparation. Here, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803). The method, based on the evaluation of relative fluorescence yield at the J-step of the OJIP transient (VJ, VJ), has already been reported; however, thus far, it has been used mostly for studying dark-acclimated leaves, which limits its range of application. Here, we show that the OJIP transient can be used for semi-quantitative estimation of PQ-redox in algal and cyanobacterial cell cultures, in addition to plants. We further show that it can reflect PQ-redox in both dark-acclimated and light-acclimated samples. Our systematic comparison of Multi-Color PAM, AquaPen, and FL 6000 fluorometers demonstrates that accurate measurement of VJ and VJ parameters in suspension cultures requires low culture density and a high-intensity saturation pulse. We further show that with increasing light intensity to which the cells are exposed, the state of photosystem II (PSII) changes due to light-induced reduction of quinone A (QA-) and conformational changes, which in turn influence both the sensitivity and dynamic range of the VJ parameter towards PQ-redox estimation. A comparison of fluorescence transients in Chlorella and Synechocystis revealed high homeostatic control over PQ-redox in Synechocystis, maintained by terminal oxidases present at the thylakoid membrane. While we discuss certain limitations, our systematic assessment suggests that the OJIP method has great potential to become a routine tool for semi-quantitative PQ-redox estimation under a wide range of experimental conditions in green algae and cyanobacteria.

Why it matches plant phenotyping methodsOJIP蛍光法による植物・藻類・シアノバクテリアのPQ-redox推定を中心に、複数蛍光計の系統比較、測定条件、感度・適用範囲を評価しており、植物の生理状態を取得する方法の検証研究である。

abstractHere, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803).

The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants ↗

• A high-precision digital platform for plant phenotyping and cultivation was developed. • LED illumination based multispectral imaging system to study crop plant responses. • Different multispectral signatures were induced by plant abiotic and biotic factors. Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g. , multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

Why it matches plant phenotyping methods植物の画像取得・解析、ストレス定量、ソフトウェアを統合した高スループット表現型解析プラットフォームの開発が中心である。

abstractA high-precision digital platform for plant phenotyping and cultivation was developed.

Optimizing a protocol for salinity recovery during seedling stage in rice ↗

Salinity is one of the major abiotic stresses affecting rice production, but the levels of salinity in a given field are not constant across the growing season. Since the level of salinity in a rice field can fluctuate, fast recovery from salinity stress may be a useful trait to improve rice productivity in salinity-prone areas. To develop a protocol to screen for salinity recovery, seedling stage hydroponic experiments were conducted to measure salinity recovery over time through both destructive and high-throughput image-based phenotyping. Seven rice varieties were included that had previously been classified as tolerant or susceptible to salinity. Following exposure to seedling stage salinity, plants were transferred to solution with no added salt and allowed to recover. Green leaf area and relative growth rates (RGR) of salinity tolerant varieties and one salinity sensitive variety initiated recovery (i.e. started to increase) after 4 days of salt stress removal and required 6 days to completely recover (i.e. to resume a similar RGR to that observed in the no-salt control treatment), while the other salinity sensitive varieties took more time to recover. An optimal recovery period of 6 days after salt stress removal was identified for screening. Based on RGR and chlorophyll fluorescence values, some salinity sensitive varieties recovered while their Na+ contents remained high. Therefore, salinity tolerance may not necessarily correspond to salinity recovery ability. The protocol optimized here can be scaled up to screen diversity panels and populations and used for genetic mapping of the seedling stage salinity recovery trait.

Why it matches plant phenotyping methodsイネの塩ストレス回復性をスクリーニングするプロトコルを開発・最適化し、高スループット画像ベース表現型解析を中核的に用いているため。

abstractTo develop a protocol to screen for salinity recovery, seedling stage hydroponic experiments were conducted to measure salinity recovery over time through both destructive and high-throughput image-based phenotyping.

Development of an ecohydrological model for coupled simulation of water and carbon fluxes, crop growth, and canopy spectra over croplands ↗

Canopy spectral information, such as Sun-Induced chlorophyll Fluorescence (SIF) and hyperspectral reflectance, are closely associated with photosynthesis and canopy structure. These spectral indicators provide valuable insights into the actual growth status of crops, thereby guiding management practices in agricultural ecosystems. While considerable efforts have been devoted to simulating the processes of photosynthesis and crop growth, comprehensive and mechanistic modeling of canopy spectral information, integrated with these processes, remains underexplored in traditional crop models. Considering the recent advances in remote sensing observations which are mostly emitted or reflected signals, being able to accurately reproduce the canopy spectra is also advantageous to enhancing the model applicability. In this study, we propose an ecohydrological model (namely the Weishan model) with an integration of a water-carbon-energy fluxes module, a carbon allocation module, a reflectance spectrum module, and a SIF spectrum module for both C₃ (winter wheat) and C₄ crops (summer maize). Comprehensive model calibration and validation have been conducted based on the eddy covariance observations over a typical winter wheat-summer maize rotation cropping cropland in the North China Plain. Validation results highlight the capability and applicability of our ecohydrological model in reproducing the variation of water-carbon fluxes (i.e., evaporation, transpiration, averaged soil moisture, and gross primary productivity), crop growth variables (i.e., leaf area index and end-of-season crop yield), and canopy spectral information (i.e., top-of-canopy SIF, reflectance at near-infrared, red, and blue bands, and vegetation indices). Our model is capable of simulating canopy spectra through mechanistic representations of photosynthesis (e.g., utilizing the Farquhar biochemical model, the Ball-Berry stomatal model, and the energy balance model) and crop dynamics (e.g., phenology, leaf dynamics, carbon allocation and partitioning, biomass accumulation, and yield formation). This comprehensive framework enables the model to effectively disentangle the complex interactions among these processes within a changing environmental context. Furthermore, the model’s ability to accurately reproduce canopy spectra highlights its potential to leverage remote sensing observations to enhance the model performance. We emphasize the functionality and future applicability of our model in advancing ecohydrological and agricultural research.

Why it matches plant phenotyping methods作物のキャノピー分光情報（SIF、反射スペクトル）やLAI・収量などの植物状態を機械論的に推定するモデルを開発し、観測データで較正・検証しており、表現型推定手法が中心である。

abstractwe propose an ecohydrological model (namely the Weishan model) with an integration of a water-carbon-energy fluxes module, a carbon allocation module, a reflectance spectrum module, and a SIF spectrum module

Nucleophilic Ring-Opening of N -Benzoylaziridine Combined with Spirolactamization of Rhodol Enables Ratiometric Fluorescence Imaging of Hydropersulfides in Arabidopsis thaliana . ↗

Hydropersulfides (RSSH) are an important class of reactive sulfur species (RSS) involved in a variety of physiological processes in biological systems. However, selective detection of RSSH is challenging since the persulfide group (-SSH) exhibits reactivity akin to biologically abundant biothiols. To address this issue, we designed CRBA as a ratiometric fluorescent probe for RSSH by integrating the nucleophilic ring-opening of an N -benzoylaziridine and spirolactamization of a rhodol. The ratiometric sensing of RSSH is realized by modulating Förster resonance energy transfer (FRET) in the coumarin-rhodol dyad. Nucleophilic attack of the N -benzoylaziridine moiety in CRBA by RSSH affords the corresponding secondary amide ring-opened product, which subsequently undergoes a spontaneous intramolecular spirolactamization to disrupt the π-conjugation structure of the rhodol. The tandem reaction decreases the intramolecular FRET efficiency within the probe and causes a clear dual-emission signal change. Probe CRBA possesses outstanding selectivity toward RSSH over biothiols and H 2 S, and is capable of tracking RSSH levels in Arabidopsis thaliana roots. Moreover, we observed the upregulation of RSSH levels in heavy metal (HM)-induced stress of A. thaliana with probe CRBA , and revealed the correlation between S -persulfidation and H 2 S levels in living plants.

Why it matches plant phenotyping methods植物体内のRSSH（過硫化水素）を定量的に可視化する蛍光プローブを開発・検証し、Arabidopsisの根および重金属ストレス下の生理状態を測定しているため、植物フェノタイピング手法が中心です。

abstractwe designed CRBA as a ratiometric fluorescent probe for RSSH

Advancements in Plant Disease Detection: A Comprehensive Review of Traditional, Modern, and AI-Driven Approaches ↗

This paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches. It highlights that traditional methods, such as visual inspection, microbiological isolation, culturing, and molecular and serological techniques, are often limited by being time-consuming, subjective, or requiring specialized expertise and lab processing. These limitations can lead to significant crop yield losses, economic setbacks, and threats to food security. The review then discusses modern, non-destructive sensor technologies, which are crucial for detecting diseases in their early stages, often before visible symptoms appear. These technologies include: * Hyperspectral Imaging (HSI): Captures detailed "spectral fingerprints" of plants to detect subtle physiological changes. * Multispectral Imaging (MSI): Uses a limited number of spectral bands, often including near-infrared (NIR), to identify abnormal plant conditions more cost-effectively than HSI. * Thermal Imaging: Detects temperature fluctuations in plants caused by physiological changes during infection. * Chlorophyll Fluorescence Imaging (CFI): A non-invasive technique that detects early stress responses by analyzing chlorophyll emissions. * LiDAR and Drones: Used for aerial analysis of crop health, enabling early diagnosis and monitoring of large agricultural areas. Finally, the paper details how Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized this field through automated, highly accurate diagnostic capabilities. The document covers various deep learning architectures, including Convolutional Neural Networks (CNNs) like AlexNet, VGG, ResNet, and YOLO, which are used for image classification, feature extraction, and real-time disease localization. It also mentions the use of semantic segmentation models like U-Net for pixel-level disease mapping, and the role of transfer learning and explainable AI (XAI) in improving model performance and transparency. The review concludes with an emerging paradigm of federated learning for decentralized, privacy-preserving model training.

Why it matches plant phenotyping methods植物病害の症状・生理状態を画像およびセンサーで検出する手法を中心に扱う包括的レビューであり、植物フェノタイピング手法のレビューに該当する。

abstractThis paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches.

RGB image-based drought stress classification of garden plants using SVM model ↗

Abstract Water management in urban gardens is increasingly complex due to diverse plant species and growing drought stress under climate change. This study proposes a non-destructive method to classify drought responses of mixed garden plant species using RGB image indices and a support vector machine (SVM) model. Chlorophyll fluorescence responses were used to evaluate photosynthetic stress, while image-derived indices—green leaf index (GLI), normalized green-red difference index (NGRDI), and blue-green pigment index (BGI)—were analyzed to assess drought responses. Hierarchical clustering grouped species into three response clusters based on fluorescence and image patterns. Principal component analysis (PCA) identified NGRDI, GLI, and BGI as key variables, with NGRDI and GLI showing strong correlations with soil moisture content and BGI distinguishing cluster-specific responses. An SVM model was constructed using RGB indices and soil moisture content as input features, achieving a classification accuracy of 88.3% and an F1 score of 0.85 through five-fold cross-validation. This approach supports efficient water management and plant selection, and can be extended to precision irrigation strategies using machine learning.

Why it matches plant phenotyping methodsRGB画像指標から植物の乾燥ストレス応答を推定・分類する非破壊的手法を開発し、交差検証で性能評価しているため、表現型取得・推定法が中心である。

abstractThis study proposes a non-destructive method to classify drought responses of mixed garden plant species using RGB image indices and a support vector machine (SVM) model.

Simultaneous prediction of pungency and color values of paprika via LED-induced fluorescence. ↗

An accurate yet rapid assessment of pungency and color values is pivotal for the commercial processing of pepper, underscoring the critical need for high-throughput detection techniques. This study investigates a rapid method for determining the pungency and color value of paprika via LED-induced fluorescence spectroscopy. A simultaneous detection instrument was developed with a proposed fluorescence correction method to reduce the impact of excitation light reflected by the sample. The corrected spectra could improve the performance of pungency prediction. The best pungency model achieved a R c 2 of 0.941, R cv 2 of 0.935, RMSEC of 6813.41 SHU and RMSECV of 6959.97 SHU. The best color value model achieved an R c 2 of 0.862, R cv 2 of 0.822 RMSEC of 1.233 and RMSECV of 1.402. This study offers a practical solution for simultaneous quality assessment suitable for industrial deployment, with the potential to extend this methodology to diverse cultivars.

Why it matches plant phenotyping methodsLED誘起蛍光分光法による paprika の辛味・色価という植物器官形質の同時推定装置を開発し、補正法と予測性能を検証しており、表現型取得法が中心である。

abstractThis study investigates a rapid method for determining the pungency and color value of paprika via LED-induced fluorescence spectroscopy.

Phenotyping for Drought Tolerance in Different Wheat Genotypes Using Spectral and Fluorescence Sensors. ↗

The wheat planted at the end of the rainy season in the Cerrado suffers from a strong water deficit. A selection of genetic material with drought tolerance is necessary. In improvement programs that evaluate a large number of materials, efficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors. The experiment was conducted in 2016 using a phenotyping platform, where irrigation gradients ranging from 184 (WR4) to 601 mm (WR1) were created, allowing for the comparison of four genotypes. In addition to productivity, we evaluated plant height, hectoliter weight, the number of spikes per square meter, ear length, photosynthesis, and the indices calculated by the sensors. For most morphophysiological parameters, extreme stress makes it difficult to discriminate materials. WR1 (601 mm) and WR2 (501 mm) showed similar trends in almost all variables. The data validated the phenotyping platform, which creates an irrigation gradient, considering that the results obtained, in general, were proportional to the water levels. The similar trend between sensors (NDVI, PRI, and LIFT) and morphophysiological, plant growth, and crop yield evaluations validated the use of sensors as a tool in selecting drought-tolerant wheat genotypes using a non-invasive methodology. Considering that only four genotypes were used, none showed absolute and unequivocal tolerance to drought; however, each genotype exhibited some desirable characteristics related to drought tolerance mechanisms.

Why it matches plant phenotyping methodsセンサーを用いた非破壊フェノタイピングと灌漑勾配プラットフォームを検証し、センサー指標と植物形質・収量を比較しているため、方法が中心的である。

abstractefficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors.

Highly Sensitive and Selective Turn-On Fluorescent Carbon Dots for Real-Time Ethylene Sensing and Imaging in Plant Tissues. ↗

Ethylene is a volatile and low-reactivity plant hormone that is indispensable for regulating plant growth and fruit ripening. However, real-time and highly sensitive detection of ethylene in plants remains a significant challenge. Herein, we developed orange-emitting carbon dots (e-CDs) through solvothermal synthesis, integrating Grubbs catalyst-mediated olefin metathesis with the inherent adsorption properties of carbon dots to specifically recognize and turn-on response to a nonpolar ethylene molecule. By employing this innovative design, our fluorescent probe achieves a detection limit of 0.12 ppm and delivers a rapid (within 3 min), highly selective response in complex biological matrices. Utilizing these capabilities, we monitored endogenous ethylene release during fruit ripening and achieved spatially resolved fluorescence imaging of ethylene in both fruits and Arabidopsis leaves. This versatile sensor platform not only surpasses the sensitivity of previously reported small-molecule sensors but also broadens the applicability of carbon dots to nonpolar gas detection, providing a powerful new tool for advancing agricultural practices, food preservation, and fundamental plant physiology research.

Why it matches plant phenotyping methods植物組織内のエチレンをリアルタイム検出・空間イメージングする蛍光センサーを開発し、果実と葉で実証しており、植物の生理状態を取得する方法が中心である。

abstractwe developed orange-emitting carbon dots (e-CDs)

Are leaf chlorophyll fluorescence and Dark Green stressor-specific fingerprints in grapevine under drought or salt stress? A reanalysis study ↗

Early and accurate recognition of abiotic stress types is essential for accelerating the selection of stress-tolerant varieties and implementing effective management strategies. This study is motivated by the socio-economic relevance of vineyard and by the increasing need for stressor-specific fingerprint(s) to support the reliable identification of stress type (e.g., drought or salinity) within a high-throughput plant phenotyping domain. This paper presents a reanalysis of physiological and phenotyping data from drought and salt stress experiments in Vitis vinifera focussing the maximum photosynthetic efficiency ( F v / F m ) and leaf Dark Green color. The reanalysis suggests that salt-stressed vines might suffer additional (non-stomatal) limitations curbing net photosynthetic rate (Pn) severely as drought stress does at equivalent stomatal conductance ( g s ) levels. Through a Principal Component (PC) Analysis, physiological and colorimetric response variables were decomposed revealing that F v / F m and Dark Green dominates the non-stomatal PC (∼80%) clustering data between salt and drought experiments. Confusion matrices reveal that model based on F v / F m and Dark Green performed better (accuracy = 1, precision =1) than that based on Pn, g s , transpiration, and stem water potential. This study supports the potential use of F v / F m and Dark Green for early and non-destructive stress type identification.

Why it matches plant phenotyping methods既存の生理・色彩形質を用いてストレス種を識別する解析手法を再評価し、特徴量の比較と混同行列による性能検証を行っており、フェノタイピング手法の評価が中心である。

abstractThis paper presents a reanalysis of physiological and phenotyping data from drought and salt stress experiments

From spectrum to yield: advances in crop photosynthesis with hyperspectral imaging ↗

Ensuring global food security requires noninvasive techniques for optimizing resource use and monitoring crop health. Hyperspectral imaging (HSI) enables the precise analysis of plant physiology by capturing spectral data across narrow bands. This review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning. These advancements allow real-time monitoring of photosynthesis, chlorophyll fluorescence, and carbon assimilation, linking spectral data to plant health and agronomic decisions. Key indicators such as solar-induced fluorescence and vegetation indices enhance crop stress detection. This work compares HSI-derived metrics in differentiating nutrient deficiencies, drought, and disease. Despite its potential, challenges remain in data standardization and spectral interpretation. This review discusses solutions such as molecular phenotyping and predictive modeling, for AI-driven precision agriculture. Addressing these gaps, HSI is poised to revolutionize farming, improve climate resilience, and ensure food security.

Why it matches plant phenotyping methods植物の光合成・クロロフィル蛍光・ストレスなどを hyperspectral imaging で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的レビューに該当する。

abstractThis review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning.

Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data ↗

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R2 > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R2 of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data.

Why it matches plant phenotyping methods夜間マルチソース分光データと高スループット表現型解析プラットフォームを用い、イネ群落のクロロフィル指数を機械学習で推定する方法の開発・評価が中心である。

abstractThis study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models.

Downscaling the full-spectrum solar-induced fluorescence emission signal of a mixed crop canopy to the photosystem level using the hybrid approach ↗

Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) is essential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures, these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plant interactions in such systems can influence key physiological processes, such as photosynthesis, making SIF a valuable tool for evaluating how crop diversity affects photosynthetic function and productivity. However, detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. To address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on synthetic spectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Using the SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. To downscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum for re-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered from field experiments conducted over three years, covering various growth stages of cereal and legume monocrops and their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables, such as leaf area (LAI, R² = 0.75) and leaf chlorophyll content (LCC, R² = 0.91), and shows a general high retrieval performance for light absorption (fAPARCₕₗ, R² = 0.99 for the internal model validation). We confirmed the reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIF downscaled to the leaf level with independent leaf-level SIF measurements. While the results show a good prediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement of corresponding leaf and canopy measurements at the single plot level.

Why it matches plant phenotyping methods作物キャノピーのSIFを葉・光合成系レベルへ推定するハイブリッド逆解析法を開発し、独立測定との比較で検証しているため、植物生理表現型の取得手法が中心である。

abstractTo address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels.

Verticillium wilt resistance screening and early disease detection in eggplant using leaf injection inoculation and chlorophyll fluorescence ↗

Verticillium wilt is a major threat to eggplant production, and there is an urgent need for the rapid and accurate screening of resistant varieties to enhance breeding efficiency. Owing to the long incubation period of Verticillium wilt before visible symptoms appear, early disease detection remains challenging. In this study, a simple and efficient leaf injection method was established and optimized by evaluating key factors, such as seedling age and inoculum concentration. The results showed that the two-leaf-one-heart stage, combined with a spore concentration of 1 × 10⁷ spores/mL, provided optimal conditions for inoculation. Chlorophyll fluorescence (Chl-F) was used to monitor the early physiological changes in plants under pathogen stress. After pathogen inoculation, changes in Chl-F parameters, such as the effective quantum yield of photosystem II (ΦPSII) and the relative electron transport rate (rETR), were negatively correlated with the genotype’s resistance. These parameters served as early indicators for resistance screening and grading. Furthermore, our findings confirmed the high correlation (R = 0.92, P < 0.01) between the leaf injection and root-dipping inoculation methods in a resistance-segregating population, validating the reliability of the leaf injection method for resistance screening. This study demonstrated the effectiveness of combining the leaf injection method with Chl-F analysis for precise and early disease detection and offered valuable insights into enhancing eggplant breeding strategies for Verticillium wilt resistance.

Why it matches plant phenotyping methods葉注入接種法とクロロフィル蛍光による早期病害・抵抗性評価を開発、最適化し、既存接種法との相関で検証しているため、植物フェノタイピング手法が中心である。

abstractIn this study, a simple and efficient leaf injection method was established and optimized by evaluating key factors, such as seedling age and inoculum concentration.

A predictive model of photosynthetic rates for eggplants: Integrating physiological and environmental parameters ↗

Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive models primarily focus on environmental influences on photosynthesis, limiting their applicability to leaves exhibiting different physiological traits. To address the challenge, we introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies. Eggplant leaves were used as experimental samples. We collected 5280 Pn data of leaves with different ChlF parameters under controlled changes in temperature, [CO₂], and light intensity. The Fₒ (initial fluorescence) and Fᵥ/Fₘ (Maximum light energy conversion efficiency of PSII system) were selected as key ChlF indicators using the entropy method. Fₒ and Fᵥ/Fₘ, along with temperature, [CO₂], and light intensity, are key features, while Pn serves as a label, forming a robust modeling dataset. Then, we proposed a Convolutional Neural Network Regression model with Input Encoding and Genetic Algorithm optimization (CNNR-IEGA) to train these environment and fluorescence data and develop the predictive model for eggplant Pn.The results indicate that the model exhibits excellent performance in predicting Pn. On unknown datasets, the root mean square error of the model is only 0.97 μmol·m⁻²·s⁻¹, with a high coefficient of determination reaching 0.99. Compared to models established by other algorithms (including multiple nonlinear regression, support vector regression, and back propagation neural network), the proposed model demonstrates superior performance across training, testing, and validation sets. Furthermore, compared to models without ChlF parameters and those with single ChlF parameters, the proposed model has the highest accuracy. This demonstrates the validity of using fluorescence to characterize crop photosynthetic performance. CNNR-IEGA can serve as a basis for crop growth environment assessment, greenhouse control, and production warning, offering new theories and opportunities for the development of precision agriculture.

Why it matches plant phenotyping methods植物の光合成速度という生理形質を、クロロフィル蛍光・環境データから予測するモデルを開発し、比較検証しているため、表現型取得・推定手法が中心です。

abstractwe introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies

Employing Spectral Features to Accelerate Sorghum Phenotyping Against Sap‐Feeding Aphids ↗

ABSTRACT Current efforts to detect and evaluate crop resistance to insect pests are limited by traditional phenotyping methods, which are time‐consuming and highly variable. Sugarcane aphid (SCA; Melanaphis sacchari ) is a major pest of sorghum in North America that has emerged over the last decade and negatively impacts plant growth and development. The spectral reflectance data in visible, near infrared and shortwave infrared range (VIS–NIR–SWIR; 400–2500 nm) have been used to measure plant traits related to stress responses, nutrient dynamics, and physiological status. We examined the potential of spectral features (VIS–NIR–SWIR) to improve the current phenotyping methods in monitoring sorghum resistance mechanisms to SCA. We used eight sorghum lines that displayed varied levels of resistance to SCA and collected data from control and aphid‐infested plants. Spectral feature data were collected using a leaf spectrometer, while plant physiological and chlorophyll fluorescence parameters were measured with LICOR and MultispeQ devices. The random forest classifier model differentiated the control and aphid‐infested plants with a high accuracy of 87.4% with important spectral features in the VIS–NIR spectral range, particularly from 508 to 573 nm and 715 to 728 nm. The spectral indices exhibit significant difference in Greenness Index and Plant Senescence Reflectance Index in aphid‐infested susceptible lines (BTx623, SC1345) compared with control plants. In addition, plant physiological parameters, such as stomatal conductance and chlorophyll fluorescence, showed significantly higher value for aphid‐infested resistant line (Tx2783) compared with susceptible line (BTx623) in both treatments. Further, a partial least square regression model demonstrated medium predictive capability for plant physiological parameters related to fluorescence. In summary, spectral features at VIS–NIR range demonstrated promising results in differentiating aphid‐infested sorghum plants. This is a proof‐of‐concept study on potential of spectral sensing to develop an effective monitoring and phenotyping plant resistance to aphids.

Why it matches plant phenotyping methodsアブラムシ抵抗性という植物状態を対象に、VIS–NIR–SWIR分光センシングと機械学習による識別・生理形質推定を開発的に評価しており、表現型取得法が中心である。

abstractWe examined the potential of spectral features (VIS–NIR–SWIR) to improve the current phenotyping methods in monitoring sorghum resistance mechanisms to SCA.

Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize ↗

Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.

Why it matches plant phenotyping methodsハイパースペクトル反射データから多数の植物形質を推定する機械学習モデルを、未観測遺伝子型・季節で系統的に比較・検証しており、形質取得とモデル評価が研究の中心である。

abstractHyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings.

Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging ↗

To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures.

Why it matches plant phenotyping methods高温ストレス下の茶苗について、クロロフィル蛍光画像からFv/Fmを用いてストレス状態を分類・検出するYOLOモデルを開発・改良しており、植物状態の画像ベース表現型取得が中心である。

abstractthis study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification

Spatial Coordination between Leaf Gradient and Temperature Response in Barley ↗

The linear shape of cereal leaves creates distinct longitudinal zones that coordinate tissue maturation and resource allocation. Under abiotic stress such as heat, these longitudinal zones may differentially activate protective pathways, revealing hidden heterogeneity in stress response that remains poorly understood. Barley (Hordeum vulgare), a cold-adapted crop particularly sensitive to elevated temperatures, can serve as an ideal model for studying region-specific heat responses in leaves. Using chlorophyll fluorescence imaging, we found that non-photochemical quenching (NPQ) kinetics captured additional physiological changes beyond those detected by SPAD, highlighting the added value of chlorophyll fluorescence-based assessments. NPQ kinetics traits displayed consistent spatial gradients from tip to base, with heat stress reducing NPQ induction across leaf gradients. Genome-wide association analysis of traits derived from chlorophyll fluorescence imaging across leaf gradients identified significant SNPs within multiple candidate genes, including HORVU.MOREX.r3.3HG0262630 that was consistently detected in over 90% resampling iterations under heat stress, suggesting its key role during heat responses. Transcriptomic profiling along the leaf axis revealed both conserved and region-specific heat responses between leaf regions. Conserved activations highlighted conserved heat response pathways, including the reactivation of the Arabidopsis thermomemory module FtsH6-HSP21. In contrast, the region-by-temperature interaction analysis identified 40 genes with spatial responses indicative of resource reallocation from growth to defense, including those involved in growth and transport. The integration between spatially resolved phenotyping and transcriptional profiling underscores region-specific variation in response to heat along the leaf axis, guiding targeted strategies to enhance heat resilience in barley and other cereal crops.

Why it matches plant phenotyping methods葉の空間的な熱応答を評価するため、クロロフィル蛍光イメージングとNPQ動態による生理形質取得を中心的に実施しており、空間分解フェノタイピングが研究の主要手法である。

abstractUsing chlorophyll fluorescence imaging, we found that non-photochemical quenching (NPQ) kinetics captured additional physiological changes beyond those detected by SPAD

Cavity Filling-Based Enzyme Activatable Luminophores Leveraged In Vivo Bioimaging. ↗

Enzymes are essential biocatalysts in living organisms, with their dysregulation linked to various human and plant diseases. Recent advancements in enzyme detection and imaging have primarily focused on fluorescent sensors due to their superior sensitivity. However, achieving a balance between high sensitivity, specificity, and excellent catalytic efficiency remains a major challenge in the design of enzyme-activated fluorescent sensors. Herein, we propose a novel cavity filling-based design strategy (CFRD) to optimize enzyme-activated fluorescent sensors. Using nitroreductase as an example, we computationally designed and experimentally validated six fluorescent sensors, with HC - SF exhibiting an impressive catalytic efficiency ( k cat / K m ) of 435.54 μM -1 ·min -1 , demonstrating a substantial improvement in catalytic performance. Fluorescence imaging of HepG2 cells and zebrafish confirmed the NTR detection capability of HC - SF in living organisms. Most importantly, it enabled real-time, noninvasive monitoring of environmental stresses in plants. This strategy holds great potential for the design of enzyme-activated fluorescent sensors and offers a promising pathway for bioimaging applications.

Why it matches plant phenotyping methods植物ストレスを生体内でリアルタイム・非侵襲的にモニタリングする蛍光センサーの設計・検証が中心であり、植物の生理状態を取得する方法として該当する。

abstractwe propose a novel cavity filling-based design strategy (CFRD) to optimize enzyme-activated fluorescent sensors.

Expanding plant cell microscopy through artificial intelligence focusing on segmentation and virtual staining ↗

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.

Advancements in Plant Diagnostic and Sensing Technologies ↗

Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved.

Why it matches plant phenotyping methods植物の診断・センシング技術を体系的にレビューし、形質・構造・代謝物の測定、ストレス検出、光学・分光・化学センサーを中心に扱うため、方法論レビューとして対象範囲に該当する。

abstractThis review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies.

A Water-Soluble Probe for Visualizing Immune Activation for Microbial Resistance in Rice Crops via NIR-II Fluorescent Imaging. ↗

Rice, a vital staple crop, provides sustenance for more than half of the global population, and its production needs to be increased to keep up with the growing global population, while plant diseases threaten the sustainability of rice production, causing significant crop losses and reducing harvest quality. The widespread use of chemical pesticides has raised concerns about environmental pollution, pesticide resistance, and economic burdens. Therefore, alternative, sustainable disease management strategies are urgently needed. One promising solution involves plant immunity inducers, which activate the plant's natural defense mechanisms to combat pathogens. However, the mechanisms underlying immune activation remain poorly understood, and effective tools for the real-time visualization of immune responses are lacking. In this study, we developed a water-soluble and biosafe D-A-D (donor-acceptor-donor)-type fluorescent probe for real-time visualization of immune activation in rice via near-infrared second-window (NIR-II) fluorescence imaging. The probe, featuring a unique electronic structure, with a diphenylaminoxanthene and a benzyl group being the electron donors, the benzo[ cd ]indolium being the electron acceptor, and a butylamino group for nitric oxide (NO) detection, allows the monitoring of NO production, a key signaling molecule in plant immunity. Our results show that the probe effectively detects NO generation in response to salicylic acid (SA), an immune inducer, and visualizes immune activation and consequent microbial resistance in rice via NIR-II fluorescent imaging. This approach could provide an effective means for obtaining valuable insights into plant immune dynamics and contribute to promoting sustainable agriculture and food security.

Why it matches plant phenotyping methodsイネの免疫活性化をリアルタイム可視化するNIR-II蛍光プローブと画像計測法の開発が研究の中心であり、NO産生および植物免疫状態を測定するため、植物フェノタイピング手法に該当する。

abstractwe developed a water-soluble and biosafe D-A-D (donor-acceptor-donor)-type fluorescent probe for real-time visualization of immune activation in rice via near-infrared second-window (NIR-II) fluorescence imaging.

Monitoring of carboxylesterases using a tricyanofuran-based near-infrared fluorophore in living organisms. ↗

Carboxylesterases serve as crucial regulators of metabolic processes and environmental adaptation across species. While significant progress has been made in developing animal-compatible fluorescence probes, real-time monitoring of the catalytic activity of these enzymes in plants remains challenging due to the limited penetration of visible light in plant tissues. Herein, we designed and synthesized TCF-CEs, tricyanofuran-xanthene probes containing carboxylesterase-cleavable ester bonds that triggers near-infrared fluorescence activation within 20 min. The probe demonstrates excellent specificity, low detection limit of 1.18 × 10 -4 U, and negligible cytotoxicity, which enables effective sensing of endogenous carboxylesterase activities in live mammalian cells. Furthermore, salt-stress experiments with Arabidopsis established direct correlations between carboxylesterase levels and plant stress adaptation. As an effective near-infrared probe capable of tracking carboxylesterase activities in both mammalian cells and plant systems, TCF-CEs provides a powerful tool for investigating metabolic responses between organisms and environmental challenges.

Why it matches plant phenotyping methods植物体内のカルボキシルエステラーゼ活性を近赤外蛍光プローブで追跡する測定法を開発し、Arabidopsisの塩ストレス適応との関連を実証しており、植物の生理状態取得が中心です。

abstractwe designed and synthesized TCF-CEs, tricyanofuran-xanthene probes containing carboxylesterase-cleavable ester bonds that triggers near-infrared fluorescence activation within 20 min.

Size-tuned PEGylated NIR-II fluorescent probes for high-contrast plant imaging and TMV detection ↗

The widespread applications of fluorescence imaging in plant science still suffer from challenges including strong auto-fluorescence (chlorophyll) and tissue light scattering, resulting in low signal-to-background ratio (SBR) for in vivo bioimaging. Moreover, the relationship between the transport efficacy of fluorescence probes in plants and their sizes has been rarely investigated. To address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm). As the polyethylene glycol (PEG) chain length increased, the probes' nanosize decreased from 170 to 60 nm. Among them, SYH3 exhibited the fastest entry velocity into Epipremnum Aureum leaf and spread over the leaf veins evenly than the other two probes, of which SYH1 even could hardly entry into the leaf. Meanwhile, SYH3 demonstrated high-contrast imaging of leaf vein with an exceptional signal to background ratio (SBR, ~ 18.6) superior to that of classical NIR-I indocyanine green (ICG) (~ 3.0) and SYH2. This promising imaging ability of leaf veins achieved by size optimization laid the foundation for the early diagnosis of viral infections. In vivo experiments further confirmed that SYH3 effectively accumulated and monitored in the lesion of Tobacco mosaic virus (TMV)-infected Arabidopsis thaliana, which matched well with the green fluorescent protein (GFP)-labeled results. This work represents a significant step forward in plant bioimaging in the cutting-edge NIR-II region.

Why it matches plant phenotyping methods植物体内の葉脈・ウイルス病変を高コントラストに可視化するNIR-II蛍光プローブを開発し、サイズ最適化と既存色素との性能比較を行っているため、植物表現型取得法が中心である。

abstractTo address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm).

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning ↗

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape ( Brassica napus ), and analyzed using MATLAB 2021b to quantify NDI, ExG, F d /F m , and plant leaf temperature. NDI, ExG and F d /F m decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in F d /F m , while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 ​h enabled the diagnosis of herbicide MOAs with 89.6 ​% accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 ​%. The indices acquired at 6 ​h, and F d /F m and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 ​%, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.

Why it matches plant phenotyping methods植物への除草剤処理を目的とするが、スペクトル画像から葉温度や蛍光などの植物状態を抽出し、機械学習で作用機序を診断する画像解析手法が中心であるため、植物フェノタイピング手法として含める。

abstractTherefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).

Earlier quantification of rice blast impact via instantaneous chlorophyll fluorescence. ↗

Background Rice blast, one of the major diseases causing significant rice yield loss, downregulates the photosynthetic activity and induces aggressive spread of cell death causing food security concerns. Hence, earlier quantification of rice blast is imperative for improved management of the disease. Instantaneous chlorophyll fluorescence (e.g., sun-induced chlorophyll fluorescence under sunlight), which is mechanistically linked with photosynthesis at the photosystem scale, has shown the potential for quantifying the impact of abiotic stresses on plant physiology but remains yet to be tested for biotic stresses. Here, we assessed the potential of chlorophyll fluorescence (CF) for quantifying rice blast impact on plant physiology. In particular, we further retrieved the quantum yield of chlorophyll fluorescence (Φ F ) by normalizing the influence of the magnitude of incident radiation. Results Φ F sensitively responded to rice blast within 24 and 96 hours post-inoculation for susceptible and resistant cultivars, respectively. We confirmed that the Φ F showed strong sensitivity in response to different doses of inoculation and to cultivar difference. In addition to Φ F results, we further investigated the role of red to far-red CF ratio (CF R:FR ) in rice blast detection. CF R:FR , which was previously reported to be tightly coupled with chlorophyll contents, captured the impact of rice blast inoculation to some extent while green chlorophyll vegetation index did not show any difference across all inoculated groups. Conclusions We confirmed that the Φ F sensitively responded to rice blast inoculation and differentiated two dose levels of inoculation and low- and high-resistance levels via the comparison of two cultivars. Furthermore, the full spectrum of chlorophyll fluorescence was used to obtain the red to far-red CF ratio and showed its capability for indicating the physiological impact of rice blast. Our findings highlight the unique role of chlorophyll fluorescence in sensitively quantifying rice blast impact. Our approach is highly scalable through sun-induced chlorophyll fluorescence observations and thus will contribute to improving the large-scale management of rice blast.

Why it matches plant phenotyping methodsイネいもち病による植物生理状態をクロロフィル蛍光で定量・識別する手法を評価しており、病害影響の表現型取得が研究の中心である。

abstractHere, we assessed the potential of chlorophyll fluorescence (CF) for quantifying rice blast impact on plant physiology.

Phenotyping drought stress tolerance in citrus rootstocks using high-throughput imaging and physio-biochemical techniques. ↗

Background Drought stress, the most prevalent abiotic stress, has a significant effect on citrus production worldwide. The differential mechanisms to overcome the drought stress has been reported in citrus rootstock genotypes. This study evaluated nine citrus rootstock genotypes, including indigenous rough lemon variants, for drought tolerance. The genotypes were subjected to well-watered, drought stress, and re-watering conditions to assess morphological, physiological, and biochemical responses. High-throughput imaging techniques were employed to non-destructively assess chlorophyll fluorescence, digital leaf area, and plant tissue water content during drought stress. Results For rapid and accurate screening of rootstocks, phenomics and physio-biochemical tools were used to know morpho-physiological responses to drought. Citrus rootstock genotype X639 demonstrated superior performance under drought stress conditions. It maintained the highest growth in terms of relative shoot increment (8.09%), number of leaves (79.00), and specific leaf area (62.45 cm 2 g -1 ). X639 also excelled in root morphological parameters, including root length, projected area, diameter, surface area, volume, and number of tips, forks, and crossings. Trifoliate hybrids X639 and Troyer citrange exhibited larger stomata (54.73 and 43.82 µm 2 ) compared to mono-foliate species, with minimal impact of drought on stomatal pore area. X639 maintained the highest relative water content, membrane and chlorophyll stability indices, leaf gas exchange parameters, and antioxidant enzyme activity. RLC-1 and RLC-4 genotypes showed pronounced accumulation of leaf proline and antioxidant enzymes during drought, contributing to better recovery after re-watering. Conclusion In this study, Cleopatra mandarin, Grambhiri, and RLC-2 were identified as drought-susceptible rootstocks based on their responses. Rootstock genotypes X639 and RLC-4 proven a superior drought-tolerant genotypes. Their robust root system enables efficient water uptake and the maintenance of water relations during drought stress. The drought tolerance of X639 was evidenced by its ability to maintain plant tissue moisture, membrane and chlorophyll stability, and higher photosystem II efficiency. High-throughput imaging techniques have proven effective in rapidly assessing and differentiating drought-tolerant and drought-susceptible citrus rootstocks based on their photosystem- II efficiency, leaf area, and tissue water content during induced drought stress. These findings will contribute to the selection and development of drought-tolerant citrus rootstocks to improve citrus production under water-limited conditions.

Why it matches plant phenotyping methods高スループット画像法を用いて葉面積、クロロフィル蛍光、組織含水量を非破壊測定し、乾燥耐性スクリーニングに適用・評価しており、表現型取得法が中心的です。

abstractHigh-throughput imaging techniques were employed to non-destructively assess chlorophyll fluorescence, digital leaf area, and plant tissue water content during drought stress.

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning ↗

Abstract Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R 2 ≥ 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R 2 ≥ 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.

Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる高スループット表現型取得と、時系列モデルによる干ばつ状態および収穫形質の予測が研究の中心である。

abstractwe investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants

Machine learning-powered activatable NIR-II fluorescent nanosensor for in vivo monitoring of plant stress responses. ↗

Real-time monitoring of plant stress signaling molecules is crucial for early disease diagnosis and prevention. However, existing methods are often invasive and lack sensitivity, rendering them inadequate for continuous monitoring of subtle plant stress responses. In this study, we develop a non-destructive near-infrared-II (NIR-II) fluorescent nanosensor for real-time detection of stress-related H 2 O 2 signaling in living plants. This nanosensor effectively avoids interference from plant autofluorescence and specifically responds to trace amounts of endogenous H 2 O 2 , thereby providing a reliable means to real-time report stress information. We validate that it is a species-independent nanosensor by effectively monitoring the stress responses of different plant species. Additionally, with the aid of a machine learning model, we demonstrate that the nanosensor can accurately differentiate between four types of stress with an accuracy of more than 96.67%. Our study enhances the understanding of plant stress signaling mechanisms and offers reliable optical tools for precision agriculture.

Why it matches plant phenotyping methods植物のストレス応答を非破壊・リアルタイムに取得するNIR-II蛍光ナノセンサーを開発し、複数種およびストレス分類で検証しており、フェノタイピング手法が研究の中心である。

abstractIn this study, we develop a non-destructive near-infrared-II (NIR-II) fluorescent nanosensor for real-time detection of stress-related H 2 O 2 signaling in living plants.

PhenoGazer: A high-throughput phenotyping system to track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence and RGB imaging in controlled environments ↗

High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses. PhenoGazer, a high-throughput phenotyping system, enhances crop monitoring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics, four Raspberry Pi cameras, and blue LED lights. This system allows for comprehensive assessment of plant health and development. PhenoGazer features automated moveable upper and lower racks for continuous measurements. The lower rack, equipped with four blue LED lights and spectrometer fiber optics, captures blue light-induced chlorophyll fluorescence at night. The upper rack, carrying four spectrometer fiber optics and cameras, captures hyperspectral reflectance and RGB images during the day. This dual capability enables detailed evaluation of plant phenology, stress responses, and growth dynamics throughout the entire crop growth cycle. Fully automated and managed by a Raspberry Pi running Python scripts, PhenoGazer ensures precise control and data acquisition with minimal human intervention. Additionally, it includes continuous measurements through a datalogger to acquire photosynthetically active radiation (PAR), soil moisture and temperature, and features expansion capability for additional analog or digital sensors as desired by end users. To test the system, soybean plants representing three conditions, healthy well watered, healthy droughted, and diseased, were monitored to evaluate growth and stress responses. PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber. By combining nighttime blue light induced chlorophyll fluorescence, hyperspectral reflectance-based vegetation indices, and RGB imagery, PhenoGazer represented a significant advancement in plant phenotyping technology, enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.

Why it matches plant phenotyping methods植物ストレス応答を測定する高スループット表現型解析システムの開発・技術的実証が中心であり、複数センサーと自動取得ワークフローを統合している。

abstractPhenoGazer, a high-throughput phenotyping system, enhances crop monitoring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics, four Raspberry Pi cameras, and blue LED lights.

A way to extend the SIF extraction bandwidth for application of crop phenotyping: Considering canopy structure and illumination effects ↗

Solar-induced chlorophyll fluorescence (SIF) from hyperspectral imaging is strongly associated with agricultural indices, particularly leaf chlorophyll content (LCC), in crop phenotyping. However, confounding factors such as spectral resolution (SR), canopy structure, and illumination reduce SIF sensitivity and complicate its association with these indices. This study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy. This study employed theoretical simulations, experimental validation, and two field experiments comparing devices with varying SRs to extract SIF at 687 nm and 761 nm (SIF Red and SIF NIR ). Initially, SCOPE model was used to simulate radiance trends across different SRs. Secondly, narrow-band imager with 7 nm full width at half maximum (FWHM) were then compared with radiance from sub-nanometer and ASD spectrometers (0.3 nm and 3 nm FWHM). Finally, to modify SIF measurements, canopy structure was quantified using fluorescence escape fraction (f esc ), while absorbed photosynthetically active radiation (APAR) and intensity component from hue-saturation-intensity color model (I HSI ) were used to account for illumination. And driver factor for SIF extraction was explored. Results showed a strong relationship (R 2 ≥ 0.95) between original simulated radiance from sub-nanometer spectrometers and radiance resampled to 3, 5, and 7 nm FWHM. SIF NIR from the narrow-band imager correlated with the other spectrometers (R ≥ 0.64). While lower SR reduced SIF sensitivity, the narrow-band imager still showed potential for estimating LCC. Modified SIF NIR by f esc , APAR, and I HSI , improved correlations with LCC (R = 0.77, 0.74, and 0.86) compared to unmodified SIF NIR (R = 0.65). Estimation model based on SIF modified by f esc , APAR and I HSI presented R P 2 of 0.73, 0.75, 0.84 and outperformed SIF (R P 2 = 0.52), and photosynthetically active radiation and APAR showed R of 0.52 and 0.72 with SIF NIR , which indicated that disentangling confounding factors could enhance the sensitivity of SIF and structural effect governed SIF strongly than illumination effect. This study demonstrated the feasibility of narrow-band imagers for capturing spatial–temporal SIF variability by addressing confounding factors and can provide guidance for narrow-band instrument development for SIF extraction.

Why it matches plant phenotyping methods狭帯域ハイパースペクトル画像からSIFを抽出し、冠層構造・照明の影響を補正して葉緑素含量を推定する方法を、シミュレーションと実験で検証・改良しており、植物表現型取得が中心である。

abstractThis study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy.

PhenoGazer: A high-throughput phenotyping system to track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence and RGB imaging in controlled environments ↗

High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses. PhenoGazer, a high-throughput phenotyping system, enhances crop monitoring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics, four Raspberry Pi cameras, and blue LED lights. This system allows for comprehensive assessment of plant health and development. PhenoGazer features automated moveable upper and lower racks for continuous measurements. The lower rack, equipped with four blue LED lights and spectrometer fiber optics, captures blue light-induced chlorophyll fluorescence at night. The upper rack, carrying four spectrometer fiber optics and cameras, captures hyperspectral reflectance and RGB images during the day. This dual capability enables detailed evaluation of plant phenology, stress responses, and growth dynamics throughout the entire crop growth cycle. Fully automated and managed by a Raspberry Pi running Python scripts, PhenoGazer ensures precise control and data acquisition with minimal human intervention. Additionally, it includes continuous measurements through a datalogger to acquire photosynthetically active radiation (PAR), soil moisture and temperature, and features expansion capability for additional analog or digital sensors as desired by end users. To test the system, soybean plants representing three conditions, healthy well watered, healthy droughted, and diseased, were monitored to evaluate growth and stress responses. PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber. By combining nighttime blue light induced chlorophyll fluorescence, hyperspectral reflectance-based vegetation indices, and RGB imagery, PhenoGazer represented a significant advancement in plant phenotyping technology, enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.

Why it matches plant phenotyping methods植物ストレス応答を測定する高スループット表現型解析システムの開発・実証が中心であり、複数センサーと自動取得ワークフローを統合している。

abstractPhenoGazer, a high-throughput phenotyping system, enhances crop monitoring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics, four Raspberry Pi cameras, and blue LED lights.

MADI: A multispectral automated dynamic imager to monitor plant health ↗

High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology. Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms, using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change. Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency. Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.

Why it matches plant phenotyping methods植物の成長・生理形質を取得するマルチスペクトル自動イメージング基盤を開発し、蛍光パラメータを検証してストレス事例で実証しているため、フェノタイピング手法が中心である。

abstractHere, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency.

Phenotyping-based spectral signatures uncover barley cultivars’ sensitivity to combined mildew and drought treatment ↗

The plant’s phenotype changes under biotic and abiotic stress, reflecting its adaptations in gene expression and metabolism. For crop management, rapid detection of plant stress responses is crucial. To facilitate rapid detection of stress responses in crops, we explored the potential of UCPH’s PhenoLab for assessing barley disease resistance under both biotic and abiotic stress. We used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts. At specific wavelengths, PhenoLab spectral signatures clearly distinguished cultivars with different levels of susceptibility to the obligate biotroph pathogen Blumeria graminis (powdery mildew). Microscope phenotyping at similar reflectance and fluorescence settings parallelled the PhenoLab-derived spectral signatures. However, a specific systemic resistance response emerged three days after inoculation, detectable only by microscopy when targeting infected and non-infected leaf areas. We hypothesized that combined stresses would work additively and used phenotyping to study the response of the resistant and susceptible barley cultivar to a combination of drought with powdery mildew infection. Surprisingly, drought made the resistant cultivar less resistant and the susceptible one less susceptible according to changes in reflectance and fluorescence at defined wavelengths. The spectroscopic absorbance assay confirmed this result biochemically. This proof-of-concept study showcases the potential of holistic functional phenomics, using non-invasive imaging to identify predictive spectral signatures for barley pathogen resistance.

Why it matches plant phenotyping methodsPhenoLabの高スループット反射・蛍光イメージングを用いて、病害抵抗性と複合ストレス応答を非破壊的に評価し、スペクトルシグネチャを検証しており、表現型取得法が研究の中心です。

abstractWe used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts.

Monitoring CO as a plant signaling molecule under heavy metal stress using carbon nanodots. ↗

Carbon monoxide (CO) is widely recognized as a significant environmental pollutant and is associated with numerous instances of accidental poisoning in humans. However, it also serves a pivotal role as a signaling molecule in plants, exhibiting functions analogous to those of other gaseous signaling molecules, including nitric oxide (NO) and hydrogen sulfide (H 2 S). In plant physiology, CO is synthesized as an integral component of the defense mechanism against oxidative damage, particularly under abiotic stress conditions such as drought, salinity, and exposure to heavy metals. Current research methodologies have demonstrated a lack of effective tools for monitoring CO dynamics in plants during stress conditions, particularly in relation to heavy metal accumulation across various developmental stages. Therefore, development of a sensor capable of detecting CO in living plant tissues is essential, as it would enable a deeper understanding of its biological functions, underlying mechanisms, and metabolic pathways. In response to this gap, the present study introduces a novel technique for monitoring CO production and activity in plants using nitrogen-doped carbon quantum dots (N-CQDs). These nanodots exhibited exceptional biocompatibility, low toxicity, and environmentally sustainable characteristics, rendering them an optimal tool for CO detection via fluorescence quenching mechanism, with a detection limit (LOD) of 0.102 μM. This innovative nanomarker facilitated the detection of trace quantities of CO within plant cells, providing new insights into plant stress responses to heavy metals such as Cu, Zn, Pb, Ru, Cr, Cd, and Hg, as well as the processes involved in seed germination. Additionally, confocal microscopy validated the interaction between CO and N-CQDs, yielding visual evidence of CO binding within plant cells, further enhancing the understanding of CO's role in plant biology.

Why it matches plant phenotyping methods植物組織内のCO動態を検出する蛍光ナノセンサーを開発し、検出限界と共焦点顕微鏡で検証しており、植物の生理状態取得法が研究の中心である。

abstractthe present study introduces a novel technique for monitoring CO production and activity in plants using nitrogen-doped carbon quantum dots (N-CQDs).

Evaluating the Potential of Improving In-Season Potato Nitrogen Status Diagnosis using Leaf Fluorescence Sensor as Compared with the SPAD Meter ↗

In-season nitrogen (N) status diagnosis is an effective way to guide split N applications for improved profitability and minimized negative environmental impacts. Petiole nitrate-N concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) N status diagnosis but is limited because of destructive sampling and chemical processing needs. Leaf sensors can be used to predict PNNC and other N status indicators and overcome these challenges. The SPAD meter is a sensor commonly used to estimate leaf chlorophyll (Chl) based on transmittance, while Dualex is a newer leaf sensor that can also measure leaf flavanol (Flav) and anthocyanin (Anth) through Chl fluorescence. Limited research has been conducted to compare the two leaf sensors for potato N status assessment, despite their respective success in N status diagnosis for other crops. Therefore, the objectives of this study were to 1) compare the performance of the Dualex sensor relative to the SPAD meter for predicting potato N status indicators when only sensor data are used, 2) evaluate the potential of improving potato N status prediction using multi-source data fusion compared with only using leaf sensor data, and 3) develop practical strategies for leaf-sensor-based in-season potato N status diagnosis. The plot-scale experiments were conducted in Becker, Minnesota, USA in 2018, 2019, 2021, and 2023 involving different cultivars, N treatments, and irrigation treatments in a split plot design with three replications. Leaf sensor data and plant samples were simultaneously collected and processed multiple times at key growth stages each year. Daily weather data were also collected at the on-site weather station. Different in-season potato N status indicators including PNNC and N nutrition index (NNI) were derived from plant samples, while weather- and management-related parameters were calculated using the weather data and management records. Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical in-season potato N status diagnostic strategy was developed using linear support vector regression model with SPAD, cultivar information, accumulated growing degree days (GDDs), accumulated total moisture, and as-applied N rate to predict vine or whole plant NNI, achieving an R² of 0.80 - 0.82, accuracy of 0.75 - 0.77, and a Kappa statistic of 0.57 - 0.58 (near-substantial). Further research is also required to determine the critical N dilution curve and sufficiency ranges of NNI for potatoes based on different genetic, environmental, and management conditions to better support decision-making.

Why it matches plant phenotyping methodsジャガイモのN栄養状態という植物生理状態を、Dualex/SPADセンサーと機械学習・データ融合で推定する方法の比較、評価、診断戦略開発が研究の中心である。

abstractcompare the performance of the Dualex sensor relative to the SPAD meter for predicting potato N status indicators

Dissecting the Contributions to Non-photochemical Quenching in a Land Plant Under Fluctuating Light ↗

Abstract To safely dissipate excess excitation energy, photosynthetic organisms have evolved multiple photoprotection mechanisms. These mechanisms involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized non-photochemical quenching mutants of Nicotiana benthamiana , an allotetraploid vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant quenching/recovery behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching efficiencies of various xanthophylls and the contributions of six quenching pathways across different mutants. It also suggests that enhancing VDE, ZEP, and PsbS expression improves overall quenching efficiency, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.

Why it matches plant phenotyping methods葉の蛍光寿命測定に基づく定量的速度論モデルを構築し、光防護・消光状態を分解、予測、定量する手法が研究の中心である。

abstractBased on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant quenching/recovery behaviors under various light-dark regimes.

Exploring an automated indoor high-throughput phenotyping facility to investigate the response of faba bean to water stress ↗

Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally. However, it is known to be very sensitive to droughts, which can severely impact yield. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, reliable phenotyping of water stress responses remains a significant bottleneck in crop genetics and breeding programs. Overcoming this bottleneck requires high-throughput phenotyping platforms. In this study, we used an indoor image-based phenotyping facility, the National Plant Phenotyping Infrastructure at the University of Helsinki. The facility incorporates cutting-edge imaging technologies such as top- and side-view digital imaging for assessment of growth and development, as well as chlorophyll fluorometry for the detection of physiological responses. In this study, 44 faba bean accessions were subjected to early-stage water stress via weight-based water-holding capacity. The accessions presented a range of responses to water stress across the studied traits, including plant height, total canopy area, digital biomass, and water use efficiency. Our results also revealed a strong correlation between digital biomass and biological biomass. Here, we demonstrate the potential of a fully automated indoor phenotyping facility for screening a relatively large faba bean germplasm collection under well watered and water stressed conditions. Accessions that maintained growth and physiological performance under water stress conditions in this study may serve as valuable pre-breeding materials for the development of drought-adapted faba beans.

Why it matches plant phenotyping methods自動画像・蛍光計測を用いる屋内ハイスループット表現型解析施設の実質的な適用研究であり、デジタルバイオマスと生物学的バイオマスの検証も含むため、表現型取得手法が中心です。

abstractHowever, reliable phenotyping of water stress responses remains a significant bottleneck in crop genetics and breeding programs. Overcoming this bottleneck requires high-throughput phenotyping platforms.

Global distribution of leaf maximum carboxylation rate derived from the TROPOMI solar-induced chlorophyll fluorescence data ↗

Photosynthesis plays an important role in the terrestrial carbon cycle and is often studied using terrestrial biosphere models (TBMs). The maximum carboxylation rate at 25 °C (Vcₘₐₓ₂₅) is a key parameter in TBMs, and yet the information on the spatiotemporal distribution of this parameter is uncertain. In this study, we retrieved the global distribution of Vcₘₐₓ₂₅ at 0.25° resolution based on TROPOMI-observed solar-induced chlorophyll fluorescence (SIF) and meteorological forcing data using a parameter optimization technique. This study improves global mapping of Vcₘₐₓ₂₅ using TROPOMI's SIF and MODIS photochemical reflectance index (PRI) for accurate GPP estimation by sunlit leaves in the following aspects: the previous method relied on an empirical estimation of the ratio of SIF per unit sunlit leaf area to that per unit shaded leaf area (β), while β here was derived from a look-up table (LUT) constructed using the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model. Validated at two flux tower sites, the LUT method explained most of the variation in β with R² = 0.71 and 0.67, RMSE=0.19 and 0.15 and Slope=0.84 and 0.70 for two ground validation sites. We calculated the global ratio of SIF from sunlit to that from shaded leaves (SIF_ratio), and found that the SIF_ratio had a strong spatio-temporal variability with a global average of approximately 4.6, and that the contribution of SIF from shaded leaves to the canopy total was <20 %. The optimized Vcₘₐₓ₂₅ from TROPOMI was validated against Vcₘₐₓ₂₅ derived from concurrent flux data at 27 sites distributed globally using an independent method (R² = 0.39 - 0.65, RMSE = 6.47 - 21.74 μmol m⁻² s⁻¹ and rRMSE =0.14–0.36). Based on the improved global Vcₘₐₓ₂₅ map, we found that, spatially, Vcₘₐₓ₂₅ varies significantly with latitude and between- and within-plant function types (PFTs), and temporally, it has strong seasonal variation in all PFTs except evergreen broadleaf forests. The new global Vcₘₐₓ₂₅ dataset would be useful for improving terrestrial GPP modelling from the current state of the art of using constant Vcₘₐₓ₂₅ values by plant functional type.

Why it matches plant phenotyping methods衛星SIF・PRIと最適化手法により植物の生理形質Vcmax25を推定・全球マッピングし、複数地点で検証しているため、植物フェノタイピング手法が中心です。

abstractwe retrieved the global distribution of Vcₘₐₓ₂₅ at 0.25° resolution based on TROPOMI-observed solar-induced chlorophyll fluorescence (SIF) and meteorological forcing data using a parameter optimization technique.

Leveraging chlorophyll fluorescence uncovers potato virus Y resistance in potato and its validation through viral quantification and yield loss studies. ↗

Key message Chlorophyll fluorescence (CF) measurements have been demonstrated to be an efficient and non-invasive tool for identifying and developing PVY-resistant potato cultivars. The validity of CF measurements was confirmed through viral titer and yield-loss assays. In the quest to identify resistant sources for potato virus Y (PVY) within Indian potato germplasm, we developed a phenotyping approach leveraging plant physiological responses against PVY infection. The study evaluated 71 potato genotypes including cultivated and experimental clones, during the year 2021-2022 and 2022-23 through mechanical inoculation in experimental fields at the Punjab Agricultural University, Ludhiana. We employed a combination of serological and molecular screening, complemented with chlorophyll fluorescence (CF) measurements to classify resistant and susceptible genotypes. Out of 71 genotypes, 34 exhibited PVY resistance, with KP-16-19-14 being the highly resistant line with minimal yield loss (i.e., only 1.64% reduction) and undetectable viral titer. This genotype holds promise as a valuable resistance source for future breeding programmes. Our findings revealed that resistant genotypes maintained stable CF metrics and experienced minimal yield reductions (up to 5.15% only), with very low viral titer. In contrast, the photosynthetic efficiency was significantly declined in susceptible genotypes, which also experienced yield losses up to 58.84% with very high viral titer. Correlation coefficient and principal component analysis (PCA) revealed a strong association among the CF parameters, disease severity, viral titer, and yield losses. This emphasizes the utility of CF as a valuable tool for assessing resistance through physiological responses to PVY. Study demonstrates that photochemistry, heat dissipation, and fluorescence emission patterns of PS-II effectively differentiate resistant and susceptible genotypes. Moreover, this study highlights the potential of integrating physiological assessments with molecular diagnostics in large-scale preliminary screening to identify and develop PVY-resistant potato genotypes.

Why it matches plant phenotyping methodsPVY抵抗性判定のためのクロロフィル蛍光測定を中心的な表現型解析手法として開発・適用し、ウイルス量および収量損失で妥当性を検証している。

abstractwe developed a phenotyping approach leveraging plant physiological responses against PVY infection.

Mechanistic Modeling Reveals Adaptive Photosynthetic Strategies of Eichhornia crassipes : Implications for Aquatic Plant Physiology and Invasion Dynamics ↗

The invasive aquatic macrophyte Eichhornia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high and low light stress remains poorly resolved. This study integrates chlorophyll fluorescence and gas exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in E. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that E. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how E. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. Insights gained have implications for both understanding invasiveness and managing eutrophic aquatic systems.

Why it matches plant phenotyping methods光合成の測定データから植物の生理形質を推定する3モデルを比較・検証し、Yeモデルの精度と適用性を中心的に評価しているため、植物フェノタイピング手法の検証研究に該当する。

abstractThis study integrates chlorophyll fluorescence and gas exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in E. crassipes.

Aptamer-Based Microfluidic Assay for In-Field Detection of Salicylic Acid in Botrytis cinerea -Infected Strawberries. ↗

Rapid detection of plant infections is crucial for minimising crop loss and optimising management strategies, particularly in the context of climate change. While traditional diagnostic methods provide precise measurements of phytohormones such as salicylic acid (SA), a key regulator of plant defence responses, their reliance on bulky equipment and lengthy analysis times limits field applicability. This study presents a microfluidic-based aptamer assay for SA detection, enabling rapid and sensitive fluorescence-based readout from plant samples. A tailored sample pre-treatment protocol was developed and validated with real strawberry samples using HPLC measurements. The assay demonstrated a detection limit ranging from 10 -9 to 10 -6 mg/mL, within the relevant range for early infection diagnosis. The integration of the microfluidic platform with the optimised pre-treatment protocol offers a portable, cost-effective solution for on-site phytohormone analysis, providing a valuable tool for early infection detection and improved crop management.

Why it matches plant phenotyping methods植物試料中のサリチル酸を対象とするマイクロ流体・アプタマー測定法を開発し、実試料とHPLCで検証しており、感染に関連する植物の生理状態の取得が中心である。

abstractThis study presents a microfluidic-based aptamer assay for SA detection, enabling rapid and sensitive fluorescence-based readout from plant samples.

A Near-Infrared Fluorescent Nanosensor for Direct and Real-Time Measurement of Indole-3-Acetic Acid in Plants. ↗

Auxin, particularly indole-3-acetic acid (IAA), is a phytohormone critical for plant growth, development, and response to environmental stresses like shade avoidance syndrome and thermomorphogenesis. Despite its importance, there is no existing method that allows for convenient and direct detection of IAA in various plant species. Here, we introduce a near-infrared fluorescent nanosensor that directly measures IAA in planta using corona phase molecular recognition with high selectivity, specificity, and spatiotemporal resolution. The IAA sensor can be conveniently functionalized to living plants and localized in various tissues, including leaf, cotyledon, and root tip, with the capability to visualize intrinsic IAA distribution. The IAA nanosensor was further tested in Arabidopsis thaliana leaf with tunable levels of endogenous IAA, in which the sensor measured dynamic and spatiotemporal changes of IAA. We also showed that the IAA sensor can be used for qualitative and quantitative mapping of IAA induction and spatial movement in various plant species undergoing environmental or stress response, such as shade avoidance syndrome, high temperature stress, and gravitropism. This highlights the potential application of IAA sensor for monitoring plant health in agriculture.

Why it matches plant phenotyping methods植物体内のIAAを直接・リアルタイムに測定し、空間分布と環境応答を可視化する蛍光ナノセンサーを開発・検証しており、植物状態の取得方法が中心です。

abstractHere, we introduce a near-infrared fluorescent nanosensor that directly measures IAA in planta using corona phase molecular recognition with high selectivity, specificity, and spatiotemporal resolution.

Detection and quantification protocols for investigating the early stages of Botrytis cinerea interaction with strawberry reveal different infection strategies ↗

Abstract Botrytis cinerea is a filamentous fungus that infects over 200 species of crops causing grey mold disease with devastating losses to agriculture worldwide. The heavy reliance on synthetic fungicides in the strawberry industry has led to the emergence of fungicide resistance in B. cinerea . Therefore, understanding the fundamental biology of B. cinerea is the first step in the search for novel antifungals. Although B. cinerea is one of the most serious pathogens of strawberry ( Fragaria x ananassa ), few protocols have been specifically developed to study this pathosystem. Consequently, early development of pathogen penetration in strawberry is poorly understood. Here we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection. These assays allow comparison of treatment effect on the same fruit, and facilitate the screening of fungicides or biocontrol agents. Through real-time PCR, chlorophyll fluorescence analysis, scanning electron and confocal microscopy, we quantified the lesion and fungal biomass of B. cinerea in the early stages of infection in fruit and petals, and demonstrated that B. cinerea penetrates through stomata of strawberry achenes, revealing a previously unrecognized infection route in this host. These data provide a deeper understanding of the B. cinerea -strawberry interaction and will serve as a foundation for future studies seeking novel antifungal treatments against B. cinerea .

Why it matches plant phenotyping methodsイチゴの病徴（病斑）と感染状態を定量するアッセイを開発し、顕微鏡・蛍光解析などによる表現型取得が研究の中心である。

abstractHere we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection.

Advances in Fluorescence-based Probes for Abiotic Stress Detection in Plants. ↗

Abiotic stress poses significant challenges to the ecological environment and global food security. Early and accurate diagnosis of abiotic stress is essential for modern agriculture. Recently, fluorescence sensing technology has emerged as a valuable tool for monitoring abiotic stress due to its ease of use and capability for spatiotemporal visualization. These probes specifically bind to abiotic stress biomarkers, facilitating the detection of stress responses and advancing related biological research. However, there is a lack of comprehensive reviews on fluorescence probe for abiotic stress, which limits progress in this area. This review outlines the biological markers of abiotic stress, discusses the types and design principles of fluorescence probe, and reviews research on detecting plant responses to such stress. Its goal is to inspire the rational design of fluorescence probe for plant bioimaging, promote early diagnosis of abiotic stress, and enhance the understanding of plant defense mechanisms at the molecular level, ultimately providing a scientific basis for stress management in agriculture.

Why it matches plant phenotyping methods植物の非生物ストレス応答を蛍光プローブで可視化・検出する手法を中心に扱うレビューであり、植物状態のフェノタイピング手法レビューに該当する。

abstractThis review outlines the biological markers of abiotic stress, discusses the types and design principles of fluorescence probe, and reviews research on detecting plant responses to such stress.

MADI: A multispectral automated dynamic imager to monitor plant health. ↗

High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology . Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms , using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change . Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency . Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.

Why it matches plant phenotyping methods植物の成長・葉温・光合成効率・クロロフィル含量を取得するマルチスペクトル自動計測プラットフォームの開発と検証が中心であり、植物フェノタイピング手法として適格。

abstractHere, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency .

A practical SIF-based crop model for predicting crop yields by quantifying the fraction of open PSII reaction centers (qL) ↗

Crop models are essential for evaluating the effects of climate change on crop yields, optimizing agronomic practices, and guiding policy decisions to enhance food security. However, using traditional crop models, including both process-based and statistical models, for regional applications presents significant challenges. Process-based crop models often require extensive, locally-sensed inputs to drive the models, which are generally lacking at the regional level. Meanwhile, statistical crop models depend heavily on training data, but it is often difficult, or even impossible, to find high-quality training data on a large scale. Solar-induced chlorophyll fluorescence (SIF), a more physiologically based proxy for gross primary production (GPP), has shown good potential for estimating GPP and crop yield. We developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content. The key improvement of our research is to parameterize the fraction of open PSII reaction centers (qL) for crops, and incorporate variations in qL into the SIF-based estimation of crop GPP and yield. Using a leaf-level measurement system, we provided parameters for qL in corn and soybean. We showed that the simulated qL closely matches the measured qL, with R² > 0.95 and RMSE <0.05, even under conditions of high light and/or high temperature, whereas the performance of SIF alone significantly decreased under stress. By using SIF and qL within the mechanistic light response model, one can accurately estimate crop GPP without the need to parameterize various plant physiological processes or nutrient dynamics and management practices. This improvement substantially simplifies the model, reduces the need for driving variables and calibration data, and minimizes associated uncertainties. We applied the model to estimate corn and soybean yields in the U.S. Midwest for the period 2018–2023. A comparison with eddy covariance-based GPP measurements reveals that the simulated GPP accounts for 85 % of the variability in daily observed GPP for corn and 81 % for soybean. The model's performance at the regional scale was assessed by comparing it against county-level crop yield statistics. On average, the model captures 78 % of the county-level yield variability across more than 700 counties during the study period, achieving 76 % for corn and 81 % for soybean, with RMSE values of 14.47 Bu/Acre, and 4.09 Bu/Acre, respectively. The practical, yet mechanistic, SIF-based model introduced in this study represents a significant advance in regional and national crop yield estimation.

Why it matches plant phenotyping methodsSIF衛星観測とqL測定を統合した作物生理・収量推定モデルを開発し、葉レベル測定およびGPP・郡別収量で技術検証しており、植物状態の取得・推定手法が中心である。

abstractWe developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content.

A novel fluorescent sensor for imaging of viscosity and ClO - in plant cells and zebrafish. ↗

Viscosity and hypochlorite (ClO - ) are two crucial microenvironmental species that play significant roles in biological activities. Their abnormal levels are closely associated with numerous common diseases. Therefore, accurate and real-time detection of hypochlorite and viscosity related to inflammatory microenvironment conduces to elucidate the pathogenesis and further diagnose the disease. In this work, based on the strategy of the phenothiazine (PTZ)-dicyanoisophorone (DCO) dyad system, a new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo. The free sensor emits weak fluorescence in aqueous solution thanks to twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET). However, in a high-viscosity system, the fluorescence emission of the sensor at 459 nm was significantly enhanced. Upon introduction of ClO - in aqueous buffer solution, the PBI exhibited apparent fluorescence enhancement at 577 nm, and showed large Stokes shift (177 nm). The fluorescence responsive mechanism was confirmed using HRMS, 1 H NMR and DFT calculation analysis. Onion and lotus root cells imaging of PBI towards ClO - was implemented. Furthermore, PBI has been successfully applied to the fluorescence imaging of viscosity and exogenous/endogenous hypochlorite in zebrafish.

Why it matches plant phenotyping methods植物細胞内の粘度と次亜塩素酸を可視化する蛍光センサーの開発が中心であり、植物の生理状態を画像計測する方法として植物細胞で実証されている。

abstracta new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo.

Rapid detection and imaging of methylglyoxal in plant tissues by the near-infrared fluorescent probe SWJT-2 ↗

Methylglyoxal (MG) can be produced via various pathways in plants. MG is toxic for plant cells at high levels, however it acts as a signaling molecule at low levels, just as H 2 O 2 in plants. Therefore, MG detection is very important for investigating its roles in plant cells, especially in plants under environmental stresses. The near-infrared fluorescent probe SWJT-2 is a novel probe with high sensitivity for the rapid detection of MG in human HeLa cells, but at present it is not clear whether the probe can be used to determine MG levels in plant tissues. In this present research, we tried to apply the probe in plant research. Our results showed that 40 min treatment of SWJT-2 (80 μM) can be applied to the detection and imaging of MG levels in tobacco (Nicotiana benthamiana) tissues.

Why it matches plant phenotyping methods植物組織中のメチルグリオキサール濃度を近赤外蛍光プローブで検出・画像化する手法の植物への適用が中心であり、植物の生理状態を測定するフェノタイピング手法に該当する。

abstractThe near-infrared fluorescent probe SWJT-2 is a novel probe with high sensitivity for the rapid detection of MG in human HeLa cells, but at present it is not clear whether the probe can be used to determine MG levels in plant tissues.

Determination of rice ( Oryza sativa L.) drought stress levels based on chlorophyll a fluorescence through independent component analysis. ↗

Sensing rice drought stress is crucial for agriculture, and chlorophyll a fluorescence (ChlF) is often used. However, existing techniques usually rely on defined feature points on the OJIP induction curve, which ignores the rich physiological information in the entire curve. Independent Component Analysis (ICA) can effectively preserve independent features, making it suitable for capturing drought-induced physiological changes. This study applies ICA and Support Vector Machine (SVM) to classify drought levels using the entire OJIP curve. The results show that the 20-dimensional ChlF features obtained by ICA provide superior classification performance, with Accuracy , Precision , Recall , F1 - score , and Kappa coefficient improving by 18.15%, 0.18, 0.17, 0.17, and 0.22, respectively, compared to the entire curve. This work provides a rice drought stress levels determination method and highlights the importance of applying dimension reduction methods for ChlF analysis. This work is expected to enhance stress detection using ChlF.

Why it matches plant phenotyping methodsChlF全曲線からICAとSVMでイネの干ばつストレス状態を推定する方法が研究の中心であり、植物の生理状態を直接評価するフェノタイピング手法に該当する。

abstractThis study applies ICA and Support Vector Machine (SVM) to classify drought levels using the entire OJIP curve.

Advanced Plant Phenotyping Technologies for Enhanced Detection and Mode of Action Analysis of Herbicide Damage Management ↗

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging methods, plant phenotyping enables the precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.

Why it matches plant phenotyping methods画像ベース植物フェノタイピング技術を雑草の除草剤損傷・抵抗性評価へ応用する方法論レビューであり、センサー、撮像プラットフォーム、モデリング手法を中心に扱っている。

abstractThis review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods

From root to leaf: Multi-sensor monitoring of the soil-plant-atmosphere continuum ↗

The soil-plant-atmosphere continuum (SPAC) is the interconnected water pathway between soil, plants, and atmosphere, and plays a pivotal role in distribution of water and nutrients in terrestrial ecosystems. In order to understand and predict the dynamics between its components, especially in the context of advancing climate change, it is essential to investigate both the above- and below-ground part of the SPAC with high temporal resolution. However, while methods to observe the above-ground part of the plant are frequently employed, due to its inaccessibility, in-situ measurements of root system activity are still scarce.In this study, we employed a novel combination of sensors at the plot scale to obtain a more complete picture of the dynamics between root water uptake, plant photosynthesis and transpiration, and atmospheric conditions. During the growth season of 2023, we studied the rhizosphere beneath maize plots using spectral electrical impedance tomography, a method which has been shown to be sensitive to soil water content dynamics and root structure and activity. Water transport through the plant stem was monitored via sap flow sensors, while photosynthetic activity and atmospheric conditions were measured continuously using a sun-induced fluorescence sensor and a weather station, respectively. Time series data were analyzed across multiple time windows, focusing on environmental events such as precipitation, prolonged dry periods, and variations in cloud cover.Our results demonstrate we achieved consistently high-quality electrical impedance data throughout the monitoring period. The electrical imaging results exhibit spatially and temporally well resolved diurnal variations in the subsurface polarization behaviour, suggesting a sensitivity to root ion uptake processes. In particular, variability in polarization signatures was more pronounced near the surface early in the season, and shifted to deeper layers later in the season. We attribute this behaviour to the seasonal shift in water availability towards deeper layers, causing a deeper active root water uptake zone. Additionally, rain events promote polarization variability in shallow soil layers. Above-ground data showed cyclical variations both for sap flow and fluorescence measurements and revealed a clear connection to meteorological conditions such as cloud cover or precipitation, confirming the coupling of above-ground plant activity to the atmosphere. Together, the below- and above-ground observations provide a holistic view of the processes within the SPAC, and allow analysis of the complex relations between transpiration, photosynthesis, and root water uptake. To conclude, this study contributes to a deeper understanding of water uptake and plant activity dynamics in crop systems and may inform the breeding of adapted plant varieties, the optimization of agricultural management practices, and the calibration of physiological models describing the SPAC.

Why it matches plant phenotyping methods複数センサーを組み合わせ、根の活動・吸水、茎流、光合成などの植物状態を連続的かつ空間・時間分解して取得する監視手法が研究の中心であり、単なる routine measurement ではない。

abstractwe employed a novel combination of sensors at the plot scale to obtain a more complete picture of the dynamics between root water uptake, plant photosynthesis and transpiration, and atmospheric conditions.

Adaptation of Plasmid-ID Technology for Evaluation of N2-Fixing Effectiveness and Competitiveness for Root Nodulation in the Sinorhizobium-Medicago System ↗

Maximizing the nitrogen fixation occurring in rhizobia-legume associations represents an opportunity to sustainably reduce nitrogen fertilizer inputs in agriculture. High-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations and enable novel research approaches. Plasmid-ID technology, recently deployed in Rhizobium leguminosarum , facilitates the concurrent assessment of rhizobium nitrogen-fixing effectiveness and competitiveness for root nodulation. This study adapts Plasmid-ID technology to function in Sinorhizobium species that are central models for studying rhizobium-legume associations and form economically important symbioses with alfalfa. New Sino-Plasmid-IDs were developed and tested for stability and their ability to measure competitiveness for root nodulation and nitrogen-fixing effectiveness. Rhizobial competitiveness is measured by identifying strain-specific nucleotide barcodes using Next-Generation Sequencing while effectiveness is measured by GFP fluorescence driven by the synthetic nifH promoter. Sino-Plasmid-IDs allow researchers to efficiently study competitiveness and effectiveness in a multitude of Sinorhizobium strains simultaneously.

Why it matches plant phenotyping methods根粒形成競争と窒素固定効果という植物-微生物共生形質を高スループットに測定する技術を適応・開発し、安定性と測定性能を検証しているため、手法が中心である。

abstractHigh-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations

DART-based temporal and spatial retrievals of solar-induced chlorophyll fluorescence quantum efficiency from in-situ and airborne crop observations ↗

Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e., SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM (R = 0.87, R² = 0.76 before and R = −0.82, R² = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands.

Why it matches plant phenotyping methods3D放射伝達モデルと蛍光モデルを用いて、作物の光合成系レベルの蛍光量子効率を推定する手法を開発し、地上・航空観測で検証・適用している。植物生理状態の取得手法が研究の中心である。

abstractOur proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach

Exploring the potential of chlorophyll a fluorescence for detecting phosphorus status in cotton leaves (Gossypium hirsutum L.) to optimize phosphorus management in cotton production ↗

Phosphorus (P) is an essential macronutrient for cotton (Gossypium hirsutum L.) growth, and plays a crucial role in yield formation. In this context, P deficiency reduces yield due to the limited leaf photosynthesis caused by the disruption of photosynthetic apparatus, and thus can be detected early via photosynthesis-related chlorophyll a fluorescence before visible leaf changes. In addition, the leaf subtending to cotton boll (LSCB) is the primary source of photosynthates, contributing to the boll biomass accumulation. Therefore, it is necessary to develop methods for early assessment of P status in the LSCB, facilitating rapid intervention in cotton production. To satisfy above demand, this study conducted a field experiment to explore the impact of different P application levels [0 (Deficient P), 100 (Critical P), and 200 (Excess P) kg P₂O₅ ha⁻¹] on cotton yield, boll weight accumulation and LSCB photosynthesis. Results showed that the increase of boll weight under P application is a significant factor contributing to yield improvement, and 15-25 days post anthesis is the key development period of cotton boll regulated by P. During this key period, P deficiency decreases the I and P steps of chlorophyll a fluorescence transients (indicating the damage to oxygen-evolving complex), and thus lead to the impaired photosystem II (PSII) and the reduction of electron transfer capacity. Then, the relationship between the leaf phosphorus concentration (LPC) and JIP-test parameters was fitted by Partial Least Squares Regression (PLSR) and showed good accuracy (R²=0.61 in calibration; RMSE=0.05 %, RRMSE=14.00 % in validation). Among the JIP-test parameters, the six ones (i.e. RC/CSₘ, FV/FO, ETO/CSₘ, FV/FM, DIO/RC and PIABS) show the highest correlation to LPC. This study demonstrated that the PLSR model generated using JIP-test parameters has the potential to detect P status in cotton during the key development period, and provided a new insight for optimizing P nutrient management in cotton production.

Why it matches plant phenotyping methods綿花葉のクロロフィル蛍光からリン栄養状態を推定する生理フェノタイピング手法を開発・検証しており、PLSRモデルの検証も中心的に行っている。

abstractit is necessary to develop methods for early assessment of P status in the LSCB

Drought resistance evaluation in hulless barley seedlings: physiological markers for climate-resilient crop breeding ↗

Abstract Global climate change intensifies extreme weather-induced crop losses, necessitating drought-resilient crops. Qingke (Hordeum vulgare var. nudum), the staple barley of Tibet's climate-vulnerable plateau, offers genetic insights into stress adaptation. We established a seedling-stage drought evaluation system identifying four biomarkers: fresh weight, chlorophyll fluorescence (Fv/Fm, NPQ, RFD), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) accumulation. Systematic screening of the physiological traits revealed these parameters as optimal predictors of drought tolerance, enabling rapid germplasm classification. Application to three uncharacterized cultivars (ZY673, ZY1403, KL14) demonstrated weak drought resistance across all lines, with ZY1403 showing extreme sensitivity. This standardized protocol for hulless barley integrates photosynthetic efficiency and oxidative stress metrics, providing breeders with actionable thresholds for climate-resilient crop development in montane agroecosystems.

Why it matches plant phenotyping methods乾燥耐性を評価する標準化された生理フェノタイピング系を構築し、複数の形質をスクリーニングして予測指標・判定閾値を検証しているため、方法が研究の中心である。

abstractWe established a seedling-stage drought evaluation system identifying four biomarkers: fresh weight, chlorophyll fluorescence (Fv/Fm, NPQ, RFD), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) accumulation.

Hypoxia-activated fluorescent probes as markers of oxygen levels in plant cells and tissues ↗

O_LILow oxygen signalling in plants is important in development and stress responses. Measurement of oxygen levels in plant cells and tissues is hampered by a lack of chemical tools with which to reliably detect and quantify endogenous oxygen availability. We have exploited hypoxia-activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues. C_LIO_LIWe applied 4-nitrobenzyl (4NB-) resorufin and methyl-indolequinone (MeIQ-) resorufin to Arabidopsis thaliana whole cells and seedlings exposed to hypoxia (1% O2) and normoxia (21% O2). Confocal microscopy and fluorescence intensity measurements were used to visualise regions of resorufin fluorescence. C_LIO_LIBoth probes enter A.thaliana whole cells and are activated to fluoresce selectively in hypoxic conditions. Similarly, incubation with A.thaliana seedlings resulted in hypoxia-dependent activation of both probes and observation of fluorescence in hypoxic roots and leaf tissue. MeIQ-Resorufin was used to visualise endogenous hypoxia in lateral root primordia of normoxic A.thaliana seedlings. C_LIO_LIOxygen measurement in plants until now has relied on invasive probes or genetic manipulation. Use of these chemical probes to detect applied and endogenous hypoxia has the potential to facilitate a greater understanding of oxygen dynamics in plant cells and tissues, allowing correlation of oxygen concentrations with adaptive and developmental responses to hypoxia. C_LI

Why it matches plant phenotyping methods植物細胞・組織の低酸素状態を蛍光プローブで可視化・測定する化学的フェノタイピング手法の開発と検証が中心であり、単なる生物学的応用ではない。

abstractWe have exploited hypoxia-activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues.

"Light-Up" Near-Infrared Fluorescent Probe for Visualization of Hydrogen Sulfide Content and Abiotic Stress Response in Plants. ↗

Hydrogen sulfide (H 2 S) plays a vital role in plant physiology and stress adaptation, but the detection of endogenous H 2 S remains a challenge. In this work, a near-infrared fluorescent probe (NIR-BOD-HS) was synthesized using boron-dipyrromethene (BODIPY) as the raw material, which showed a good linear relationship in the concentration range of 0.1-70 μM and a detection limit of 56 nM. The long-wavelength emission (712 nm) reduced the interference of plant autofluorescence and improved the imaging quality. The probe combined with fluorescence imaging technology nondestructively realized the spatiotemporal distribution signal of H 2 S in the deep tissues of plants. In addition, the dynamic changes of H 2 S content during seed germination and seedling growth under abiotic stress were also demonstrated through the changes in fluorescence signals. This study helps to understand the physiological response mechanism of plants under abiotic stress and provides a scientific basis for further research on plant imaging and agricultural production.

Why it matches plant phenotyping methods植物体内H₂Sの時空間分布を非破壊的に可視化・定量する近赤外蛍光プローブと画像化手法が研究の中心であり、植物の生理状態を測定するフェノタイピング手法に該当する。

abstractIn this work, a near-infrared fluorescent probe (NIR-BOD-HS) was synthesized

Systemic Uptake of Rhodamine Tracers Quantified by Fluorescence Imaging: Applications for Enhanced Crop–Weed Detection ↗

Systemic fluorescence tracers introduced into crop plants provide an active signal for crop–weed differentiation that can be exploited for precision weed management. Rhodamine B (RB), a widely used tracer for seeds and seedlings, possesses desirable properties; however, its application as a seed treatment has been limited due to potential phytotoxic effects on seedling growth. Therefore, investigating mitigation strategies or alternative systemic tracers is necessary to fully leverage active signaling for crop–weed differentiation. This study aimed to identify and address the phytotoxicity concerns associated with Rhodamine B and evaluate Rhodamine WT and Sulforhodamine B as potential alternatives. A custom 2D fluorescence imaging system, along with analytical methods, was developed to optimize fluorescence imaging quality and facilitate quantitative characterization of fluorescence intensity and patterns in plant seedlings, individual leaves, and leaf disc samples. Rhodamine compounds were applied as seed treatments or in-furrow (soil application). Rhodamine B phytotoxicity was mitigated by growing in a sand and perlite media due to the adsorption of RB to perlite. Additionally, in-furrow and seed treatment methods were tested for Rhodamine WT and Sulforhodamine B to evaluate their efficacy as non-phytotoxic alternatives. Experimental results demonstrated that Rhodamine B applied via seed pelleting and Rhodamine WT used as a direct seed treatment were the most effective approaches. A case study was conducted to assess fluorescence signal intensity for crop–weed differentiation at a crop–weed seed distance of 2.5 cm (1 inch). Results indicated that fluorescence from both Rhodamine B via seed pelleting and Rhodamine WT as seed treatment was clearly detected in plant tissues and was ~10× higher than that from neighboring weed plant tissues. These findings suggest that RB ap-plied via seed pelleting effectively differentiates plant seedlings from weeds with reduced phytotoxicity, while Rhodamine WT as seed treatment offers a viable, non-phytotoxic alternative. In conclusion, the combination of the developed fluorescence imaging system and RB seed pelleting presents a promising technology for crop–weed differentiation and precision weed management. Additionally, Rhodamine WT, when used as a seed treatment, provides satisfactory efficacy as a non-phytotoxic alternative, further expanding the options for fluorescence-based crop–weed differentiation in weed management.

Why it matches plant phenotyping methods幼植物・葉・葉 disc の蛍光強度とパターンを定量するカスタム画像システムと解析法の開発が中心で、植物の識別可能な蛍光状態を取得する方法論的研究である。

abstractA custom 2D fluorescence imaging system, along with analytical methods, was developed to optimize fluorescence imaging quality and facilitate quantitative characterization of fluorescence intensity and patterns in plant seedlings, individual leaves, and leaf disc samples.

Tracking acetylcholinesterase inhibitor pesticides in the environment using the rapid transient of chlorophyll fluorescence. ↗

Several home pesticides are organophosphorus compounds. These compounds inhibit the enzyme acetylcholinesterase, causing harmful effects on the health of biota. Through this research, the usefulness of Glycine max (soybean) and Cichorium intybus (chicory) plants as sentinels of organophosphorus compounds in the environment was successfully tested. Different concentrations of the insecticide chlorpyrifos were tried out. Damage to plants at the photosynthetic apparatus level was evaluated by measuring the high temporal resolution variable chlorophyll fluorescence (OJIP test). Several parameters derived from this test indicated a high level of damage in both species even at the mean dose recommended for use in the field. However, a few parameters did not consistently reflect damage in leaves. A drop in the values of the maximum fluorescence (F M ), the quantum yield of electron transport flux, transport between quinones A and B (ET 0 /ABS) and the maximal quantum yield of PSII (TR 0 /ABS) could alert us about the presence of organophosphates in the environment. An increase in the dissipated energy flux per reaction center (DI 0 /RC) values was also observed. The species showed different sensitivities, with soybean plants being the most sensitive. The OJIP transient thus becomes a valuable rapid, non-destructive tool for biomonitoring this class of pesticides in the environment.

Why it matches plant phenotyping methods植物の光合成状態を測定する高時間分解クロロフィル蛍光法を、農薬による植物損傷の評価・環境バイオモニタリング手法として検証しており、表現型取得が中心です。

abstractDamage to plants at the photosynthetic apparatus level was evaluated by measuring the high temporal resolution variable chlorophyll fluorescence (OJIP test).

The rational design of a Rhodamine fluorescent probe enables the selective detection and bioimaging of salicylic acid in plants under abiotic stress. ↗

Abiotic stress severely hinders plant growth and development, resulting in a considerable reduction in crop yields. Salicylic acid (SA) serves as a central signal mediating abiotic stress responses in plants. Real-time fluorescence tracking using specific probes can enhance our understanding of the SA-triggered modulation underlying these events. However, in complicated living plant microenvironments, selective recognition and bioimaging of SA is a great challenge for scientists due to the severe background interference and SA analogues. Herein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings. Experimental findings reveal that phoxrodam demonstrates exceptional selectivity (fluorescence intensity: I Phoxrodam+SA /I Phoxrodam+SA analogues > 4.29-fold), high sensitivity (limit of detection = 6.42 nM, fluorescence quantum yield: Φ Phoxrodam+SA = 0.36) and good anti-interference properties. Furthermore, we confirmed that phoxrodam accurately detects SA in the roots of salt-stressed wheat seedlings, the low-temperature resistance of Nicotiana benthamiana and the heavy metal resistance of pea seeds, using in vivo confocal imaging. This study provides a feasible strategy for efficiently tracking plant signalling molecules and promotes the in-depth research of SA-mediated physiological mechanisms, laying a key foundation for the future development of new immune activation inducers.

Why it matches plant phenotyping methods植物体内のサリチル酸を選択的に可視化・定量する蛍光プローブ技術の開発が中心であり、植物の生理状態を取得する実質的なフェノタイピング手法に該当する。

abstractHerein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings.

In-Field Phenotyping Using the Low-Cost and Open Access Fluorescence PhotosynQ Multispeq Sensor Together with NDVI: A Case Study with Durum Wheat ↗

Durum wheat production is concentrated in Mediterranean climate regions, making it essential to develop cultivars that adapt to its changing conditions, including water and heat stress. In this regard, photosynthetic capacity estimates may help improve the selection of the most adapted cultivars. However, the cost and inherent low throughput of the usual methodological approaches makes, in many cases, phenotyping unfeasible, particularly under field conditions. This study uses leaf photosynthetic measurements taken with a low-cost handheld chlorophyll sensor (MultispeQ Photosynq) and a biomass sensitive sensor (GreenSeeker) measuring the normalized difference vegetation index (NDVI) to assess the performance of six modern durum wheat cultivars. The sensors were employed at anthesis and grain filling under two different types of management (rainfed and support irrigation) for two growing seasons. Compared to irrigated plants, rainfed trials had significantly lower photosynthetic performance during the two phenological stages evaluated. Significant genotype differences in steady-state fluorescence yield (Fs) and maximum fluorescence yield (Fm′) across treatments and crop seasons were found. This study shows that leaf chlorophyll fluorescence parameters can be used to select modern wheat cultivars with an open-source, low-cost, handheld sensor (Photosynq).

Why it matches plant phenotyping methods低コストの蛍光・NDVIセンサーを用いた圃場での作物表現型取得と品種評価が研究の中心であり、光合成性能や蛍光形質の測定法を実質的に適用している。

titleIn-Field Phenotyping Using the Low-Cost and Open Access Fluorescence PhotosynQ Multispeq Sensor Together with NDVI: A Case Study with Durum Wheat

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry ↗

ABSTRACT Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R² > 0.58), while the poro-fluorometer was efficient for predicting net CO₂ assimilation (R² > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R² > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.

Why it matches plant phenotyping methods分光法とポロフルオロメトリーによる葉の形態・生理形質の高スループット推定を開発・評価し、予測精度と頑健性を検証しているため、植物フェノタイピング手法が中心である。

abstractThis study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits

DART-based temporal and spatial retrievals of solar-induced chlorophyll fluorescence quantum efficiency from in-situ and airborne crop observations ↗

Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e. , SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM ( R = 0.87, R 2 = 0.76 before and R = −0.82, R 2 = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands. • A novel solar-induced fluorescence (SIF) downscaling method based on DART modeling. • Method removes confounding structural impacts from top-of-canopy SIF observations. • Applied to in-situ SIF measurements, it produced FQE diurnal courses of alfalfa crop. • Adapted to airborne SIF images, it mapped FQE spatial variation.

Why it matches plant phenotyping methodsDARTとFluspect-CXを統合し、作物の光合成系レベルの蛍光量子効率を推定するセンシング・解析手法を開発し、地上および航空観測で検証・適用している。植物生理状態の取得が研究の中心である。

abstractHere, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level.

Computational wide-field imaging of poplar embolism and wound-response with a deployable microscope. ↗

Low-cost, minimally invasive microscopy for tracking cellular dynamics in living plants within their natural ecosystems is crucial for addressing fundamental questions in plant ecology and biology. However, existing solutions are constrained by coarse resolution, limited field-of-view (FoV), and poor deployability in natural settings. Here, we utilize a compact, portable microscope ("miniscope") for label-free (autofluorescence) imaging in living poplar wood. We systematically implement and evaluate multiple computational methods to enhance resolution and FoV. Our optimal computational pipeline, comprising maximal intensity projection, deconvolution, and flat-field correction, increases resolution by up to 39% on-axis and up to 49% at the field edges, resolving features of 2.87 μm, averaged over a FoV of ∼1 mm (diameter), compared with a 4.34 μm baseline. We demonstrate microscopy within the tissue of a living poplar plant in our greenhouse, observing the embolism of vessel elements, wound response, and tissue deformation from moisture evaporation.

Why it matches plant phenotyping methods生体ポプラ組織の細胞動態・木部塞栓・創傷応答を観察する携帯型顕微鏡と画像処理パイプラインを開発・評価しており、植物状態の取得手法が中心である。

abstractWe systematically implement and evaluate multiple computational methods to enhance resolution and FoV.

Advanced Plant Phenotyping Technologies for Enhanced Detection and Mode of Action Analysis of Herbicide Damage Management ↗

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging, plant phenotyping enables precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.

Why it matches plant phenotyping methods画像ベース植物フェノタイピング技術を中心に、センサー、展開プラットフォーム、モデリング手法、限界と応用を包括的に扱うレビューであり、方法論的役割が明確です。

abstractThis review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications.

From fluorescence to biomass: A comprehensive analysis via crop modeling and sensing techniques ↗

User-friendly handheld plant phenotyping devices, such as the MultispeQ, provide quick and easy measurements that effectively capture the dynamic nature of photosynthesis. This study demonstrates the added value of integrating measurements of such devices with both process-based and empirical modeling approaches for estimating the maximum leaf photosynthetic capacity ( A m a x ) and biomass production (DMP) of potato crops. Utilizing leaf fluorescence measurements, such as the efficiency of photosystem II ( ϕ 2 ) and the electron transport rate, gathered from two fields in the Netherlands from May to September 2019, we determined the A m a x to be 34 kg C O 2 ha −1 hr −1 with a standard deviation of 6.6 kg C O 2 ha −1 hr −1 . By incorporating dynamic photosynthetic parameters, leaf area index (LAI) retrieval, and crop modeling techniques to scale assimilation from the leaf to the canopy level, we successfully reduced the discrepancy between simulated and measured dry matter production in 16 out of 18 cases, offering significant advantages over fixed, literature-based photosynthetic parameter values.

Why it matches plant phenotyping methods携帯型蛍光センシングと作物モデルを統合し、葉の光合成能力とバイオマスを推定する測定・解析ワークフローが研究の中心であるため、植物表現型計測法の応用として採用する。

abstractThis study demonstrates the added value of integrating measurements of such devices with both process-based and empirical modeling approaches for estimating the maximum leaf photosynthetic capacity ( A m a x ) and biomass production (DMP) of potato crops.

Interplay between high-energy quenching and state transitions in Chlamydomonas reinhardtii: a single-cell approach ↗

Studying cell-to-cell heterogeneity is essential to understand how unicellular organisms respond to stresses. We introduce a single-cell analysis framework that enables the study of intercellular heterogeneity of photosynthetic traits, particularly their interactions within individual cells that have identical genotypes, cellular contexts and histories. Our approach combines single-cell imaging of chlorophyll a fluorescence with machine learning and we study light stress responses in Chlamydomonas reinhardtii as a proof-of- concept. This framework allows us to score the extent of high-light responses such as state transitions (qT) and high-energy quenching (qE), to reveal significant cell-to-cell heterogeneity and to reveal a strong correlation between qT and qE, undetectable in bulk measurements. This study highlights the value of single-cell phenotypic analysis for for investigating light stress responses in unicellular organisms. We detail the key aspects that come into play to generalize the method to other complex stress responses involving multiple traits.

Why it matches plant phenotyping methods単一細胞のクロロフィル蛍光イメージングと機械学習を組み合わせ、光ストレス応答などの植物生理形質を抽出・評価する分析フレームワークが研究の中心である。

abstractWe introduce a single-cell analysis framework that enables the study of intercellular heterogeneity of photosynthetic traits

A Method for Estimating Fluorescence Emission Spectra from the Image Data of Plant Grain and Leaves Without a Spectrometer ↗

This study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels, using ordinary off-the-shelf cameras and a UV light. A mobile phone camera is used to detect the fluorescence emission in the blue wavelength region of rice grains. For plant leaves, a small monochrome camera is used with additional optical filters to detect chlorophyll fluorescence in the red-to-far-red wavelength region. A ridge regression approach is used to obtain a reliable estimate of the spectral distribution of the fluorescence emission at each pixel point from the acquired image data. The spectral distributions can be estimated by optimally selecting the ridge parameter without statistically analyzing the fluorescence spectra. An algorithm for optimal parameter selection is developed using a cross-validation technique. In experiments using real rice grains and green leaves, the estimated fluorescence emission spectral distributions by the proposed method are compared to the direct measurements obtained with a spectroradiometer and the estimates obtained using the minimum norm estimation method. The estimated images of fluorescence emissions are presented for rice grains and green leaves. The reliability of the proposed estimation method is demonstrated.

Why it matches plant phenotyping methods植物の蛍光スペクトル画像を分光器なしで推定する撮像・計算法を開発し、分光放射計との比較で信頼性を検証しており、植物表現型取得法が中心です。

abstractThis study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer.

Noninvasive, Presymptomatic Detection of Potato Cyst Nematode Infection in Tomato Using Chlorophyll Fluorescence Analysis. ↗

Potato cyst nematodes (PCNs) are notorious pathogens in all major potato production areas worldwide. Mainly due to the low mobility of this soil pathogen, PCN infestations are mostly observed as patches ("foci") that only cover a fraction of the acreage. In-field presymptomatic localization of these pathogens is valuable, as it would allow for the localized application of control measures. Although the mapping of foci is technically feasible, it is unpractical, as it would require the analysis of numerous soil samples. We investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, nondestructive method for early PCN detection. To this end, the impact of four Globodera pallida densities on the Chl-F of tomato was investigated in a phenotyping greenhouse for 26 days. Furthermore, the classical plant performance indicators of biomass and root surface area were compared with Chl-F. Thermal dissipation (NPQ) and an estimate of the photosynthetic rate (Φ PSII ) responded at 1 day postinoculation, and Φ PSII was most sensitive to low PCN infection levels. Chl-F parameters responded more readily to PCN infection than biomass and root surface area. The maximum quantum yield of photosystem II (F v /F m ) and the potential activity of photosystem II (F v /F 0 ) initially increased at low PCN infection levels, whereas a sharp decrease was observed at higher infestation levels. Hence, our data suggest that low PCN levels promoted plant performance before becoming detrimental at higher levels. Although Chl-F allowed for early and sensitive PCN detection, it remains to be investigated whether these signals can be distinguished from those produced by other belowground stressors in the field. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Why it matches plant phenotyping methodsクロロフィル蛍光を用いたトマトの感染状態の非破壊・早期推定が研究の中心であり、感度や従来指標との比較も評価しているため、植物フェノタイピング手法に該当する。

abstractWe investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, nondestructive method for early PCN detection.

Nondestructive Detection and Quantification of Dysprosium in Plant Tissues ↗

ABSTRACT Background The growing demand for rare-earth elements (REEs), particularly dysprosium (Dy), in part driven by clean energy technologies, underscores the need for sustainable extraction methods. Recovery of Dy, particularly from geographically distributed waste sources is challenging. This gap positions phytomining—a technique using plants to accumulate metals— as a promising alternative. However, plant species differ in their ability to accumulate metals in high concentrations, necessitating efficient screening methods. In this study, we developed a high-throughput fluorescence-based assay to detect and quantify Dy uptake in plant tissues. Results Our Dy detection method exploits Dy’s unique spectroscopic properties for sensitive and efficient analysis, enabling detection of concentrations as low as 0.3 µM. By incorporating sodium tungstate (Na WO) as a fluorescence enhancer, we achieved robust emissions at 480 and 580 nm, facilitating Dy quantification in complex plant matrices. Additionally, time-resolved fluorescence techniques reduced background autofluorescence from plant tissues, enhancing signal specificity. Validation against Inductively Coupled Plasma Mass Spectrometry (ICP-MS) demonstrated strong correlation. Greenhouse trials confirmed the method’s utility for screening Dy accumulation in living plants and highlight the potential for rapid standoff detection. Conclusions This fluorescence-based approach offers a scalable, efficient tool for identifying Dy-accumulating plants, advancing phytomining as a sustainable strategy for REE recovery.

Why it matches plant phenotyping methods植物組織中のDy蓄積という植物状態を対象に、蛍光による高スループット測定法を開発し、ICP-MSで検証しているため、フェノタイピング手法が中心である。

abstractwe developed a high-throughput fluorescence-based assay to detect and quantify Dy uptake in plant tissues.

An in situ approach for validation of canopy chlorophyll fluorescence radiative transfer models using the full emission spectrum ↗

The intensity and spectral properties of solar-induced chlorophyll fluorescence (SIF) carry valuable information on plant photosynthesis and productivity, but are also influenced by leaf and canopy structure. Physically based models provide a quantitative means to investigate how SIF intensity and spectra propagate and scale from the photosystem to the leaf and to the canopy levels. However, the validation of canopy SIF models is limited by the lack of methods that combine direct, independent, and complementary measurements of the full fluorescence spectrum at the leaf and canopy levels. Here, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra. The approach is demonstrated with measurements in a rice crop at two contrasting stages of canopy development. We measured leaf reflectance, transmittance, and fluorescence spectra in situ, and subsequently inverted leaf structural and biochemical parameters and determined the leaf fluorescence quantum efficiency (FQE) using the Fluspect-Cx model. Two FQE inversion methods (Inversion-IIA and Inversion-IIB) were tested for the forward simulation of leaf fluorescence spectra. Leaf fluorescence spectra were then scaled up to the canopy level using 1D, 2D, and 3D radiative transfer schemes (SCOPE, mSCOPE, and DART), and compared with the direct canopy fluorescence spectral observations measured under red, green, blue, and white illumination. The validation results demonstrate that accounting for 3D canopy structure, as in the DART model, is critical to successfully scale the fluorescence spectrum from the leaf to the canopy level, whereas 1D SCOPE or even 2D mSCOPE were unable to fully reproduce the canopy fluorescence spectra. The results also demonstrate that the Inversion-IIB method matches relatively well the measurements with mean relative absolute errors (MRAE) of 20 %, 37 %, and 43 % versus Inversion-IIA with mean relative absolute errors (MRAE) of 62 %, 100 %, and 108 % for DART, mSCOPE, and SCOPE, respectively. We suggest that our validation approach is transferable to other plant species and canopy geometries, providing a means to standardize and evaluate the performance of canopy SIF models and improve our understanding of canopy SIF observations.

Why it matches plant phenotyping methods葉・群落の蛍光スペクトルを用いてSIF放射伝達モデルを検証・比較する手法が研究の中心であり、植物の光合成状態に関わる生理形質を測定する。

abstractHere, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra.

High-throughput phenotyping salt tolerance in JUNCAOs by combining prompt chlorophyll a fluorescence with hyperspectral spectroscopy ↗

The planting of salt-tolerant plants is regarded as the one of important measurements to improve the saline-alkali lands. The outstanding biological properties of JUNCAOs have made them candidates to improve and utilize saline-alkali lands. At present, little attention has been paid to developing a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO. To close the gaps, three typical JUNCAOs (A.donax. No.1, A.donax. No.5 and A.donax. No.10) were evaluated by combining prompt chlorophyll a fluorescence (ChlF) with hyperspectral spectroscopy (HS). The results showed that salt stress reduced relative stem growth, water content, and total chlorophyll content but enhanced the malondialdehyde (MDA) content. It caused a significant change in chlorophyll a fluorescence kinetics with an appearance of L-, K- and J-band, implying damaging energetic connectivity between PSII units, uncoupling of the oxygen evolving complex (OEC) and inhibition of the QA⁻reoxidation. The negative impact of salt stress on JUNCAOs increased with the increasing level of salt concentration. Effect on spectral reflectance in the in the visible region with shifts on red edge position (REP) and blue edge position (BEP) to shorter wavelength was also found in salt stress plants. Combining principal component analysis (PCA) with the membership function method based on spectral indices and JIP-test parameters could well screen JUNCAOs salt tolerant ability with the highest for A.donax. NO.10 but lowest for A.donax. NO.1, which was the same as that of using conventional approach. The results demonstrate that prompt ChlF coupling with HS could provide potentials for non-invasively and high-throughput phenotyping salt tolerance in JUNCAOs.

Why it matches plant phenotyping methods塩耐性を非破壊・高スループットに評価するため、クロロフィル蛍光とハイパースペクトル計測を組み合わせた植物フェノタイピング手法が中心である。

abstractdeveloping a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO

Field integration of shoot gas-exchange and leaf chlorophyll fluorescence measurements to study the long-term regulation of photosynthesis in situ. ↗

Understanding the diurnal and seasonal regulation of photosynthesis is an essential step to quantify and model the impact of the environment on plant function. Although the dynamics of photosynthesis have been widely investigated in terms of CO2 exchange measurements, a more comprehensive view can be obtained when combining gas-exchange and chlorophyll fluorescence (ChlF). Until now, integrated measurements of gas-exchange and ChlF have been restricted to short-term analysis using portable infrared gas analyzer systems that include a fluorometer module. In this communication we provide a first-time demonstration of long-term, in situ and combined measurements of photosynthetic gas-exchange and ChlF. We do so by integrating a new miniature pulse amplitude modulated-fluorometer into an existing system of automated chambers to track photosynthetic gas-exchange of leaves and shoots in situ. The setup is used to track the dynamics of the light and carbon reactions of photosynthesis at a 20-min resolution in leaves of silver birch (Betula pendula Roth) during summertime. The potential of the method is illustrated using the ratio between electron transport and net assimilation (ETR/ANET), which reflects the internal electron use efficiency of photosynthesis. The setup successfully captured the diurnal patterns in the ETR/ANET during summertime, including a large increase in noon ETR/ANET in response to a period of high temperatures and relatively low soil moisture, pointing to a drastic decrease in electron-use efficiency. The observations emphasize the value of combined and long-term in situ measurements of ChlF and gas-exchange, opening new opportunities to investigate, model and quantify the regulation of photosynthesis in situ and the connection between ChlF and photosynthetic gas-exchange. The next steps, potential and limitations of the approach are discussed.

Why it matches plant phenotyping methods葉・シュートの光合成状態を長期・現場で取得する統合測定システムを開発・実証しており、植物生理フェノタイピング手法が中心である。

abstractIn this communication we provide a first-time demonstration of long-term, in situ and combined measurements of photosynthetic gas-exchange and ChlF.

The relationship between the ratio of far-red to red leaf SIF and leaf chlorophyll content: Theoretical derivation and experimental validation ↗

Leaf chlorophyll content (LCC) is an important indicator of photosynthetic capacity. Sun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC. The far-red to red ratio of chlorophyll fluorescence (Fᵣₐₜᵢₒ) has been used to empirically estimate LCC in some previous studies. While these studies support the use of the Fᵣₐₜᵢₒ for LCC estimation, its theoretical underpinning remains less well-defined and its effectiveness across a wider range of scenarios remains unclear. In this study, we established the relationship between the Fᵣₐₜᵢₒ and LCC using the light use efficiency (LUE)-based SIF model and spectral invariant radiative transfer theory. Firstly, the LUE-based SIF model demonstrates that the change in the leaf Fᵣₐₜᵢₒ is controlled by the ratio of the fluorescence escape fraction (i.e., fₑₛc from the photosystem to the leaf surface) at the corresponding bands. Secondly, a fₑₛc modeling approach is presented using the spectral invariant theory and thus the fₑₛc ratio is linked to LCC. Theoretical analysis shows that the Fᵣₐₜᵢₒ has a strong correlation with LCC, which explains over 90 % of the variation in Fᵣₐₜᵢₒ. Both experimental measurements and model simulations from a radiative transfer model Fluspect were used to validate the relationship between LCC and three Fᵣₐₜᵢₒ (i.e., Fratio↑, Fratio↓ and Fratiotot), which were derived from the upward and downward SIF of leaves, as well as the total SIF observed from both sides. The Fluspect simulations were used to assess the sensitivity of the Fᵣₐₜᵢₒ-LCC relationship to the leaf structure. Two types of experimental measurements, including the field measurements of three crops and the laboratory measurements of 20 tundra plants, were employed to examine the species dependence of the Fᵣₐₜᵢₒ-LCC relationship. The performance of Fᵣₐₜᵢₒ for LCC estimation was evaluated and compared with spectral indices and the PROSPECT model using the experimental measurements and leave-one-out cross-validation (LOOCV) approach. Both the Fluspect simulations and the experimental measurements indicate that the Fᵣₐₜᵢₒ is strongly correlated with LCC for a wide range of leaf scenarios. The Fᵣₐₜᵢₒ-LCC relationship remains relatively stable across different leaf structures and plant species, since the relationship is almost consistent. The LOOCV of experimental measurements shows that the Fᵣₐₜᵢₒ provides promising and robust LCC estimates, with the Fratiotot performing the best. The Fratiotot outperforms spectral indices, reducing the RMSE for LCC estimation by 19.5 %-93.9 %. Furthermore, compared to the PROSPECT model, the Fᵣₐₜᵢₒ achieves a reduction in RMSE by 30.4 %-77.8 %. These results demonstrate that the Fᵣₐₜᵢₒ is effective for estimating LCC of diverse plant species. This study advances our understanding of the relationship between the Fᵣₐₜᵢₒ and LCC, supporting the use of SIF signals for remote sensing of LCC.

Why it matches plant phenotyping methodsSIFの遠赤色/赤色比から葉クロロフィル含量を推定する理論・測定手法を開発し、放射伝達シミュレーションと複数植物種の実測で検証しており、植物形質取得が中心である。

abstractSun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC.

Multiple light sources excited fluorescence image-based non-destructive method for citrus Huanglongbing disease detection ↗

Citrus Huanglongbing (HLB) poses a significant threat to citrus orchards. Timely HLB screening of citrus trees is essential for citrus orchard management. This study developed a multi-excitation fluorescence imaging system based on the correlation between HLB stress and flavonoid fluorescence characteristics. The feasibility of using fluorescent images to classify healthy, macular (nutrient-deficient, not relate to HLB) and HLB-infected citrus fruits was explored. Initially, three-dimensional fluorescence spectra of citrus peels at maturity were scanned and the obtained Excitation-Emission Matrices (EEMs) were analyzed to screen four fluorescence characteristic regions (FCR1-FCR4) that were sensitive to HLB-infected citrus. Subsequently, four fluorescence imaging conditions (G1: EX = 365 ± 20 nm, EM = 525 ± 20 nm, G2: EX = 415 ± 20 nm, EM = 525 ± 20 nm, B1: EX = 308 ± 20 nm, EM = 450 ± 20 nm, and B2: EX = 365 ± 20 nm, EM = 450 ± 20 nm) were designed based on characteristic fluorescence bands. The imaging system primarily utilizes standard CMOS cameras and optical filters for image acquisition, offering significant advantages in terms of operational simplicity and cost-effectiveness. An HLB classification model was constructed using the Random Forest (RF) algorithm based on color feature parameters of fluorescence images, with a classification accuracy of up to 87.5 %. When the top 10 image feature parameters with the highest contribution rate were selected to construct the classification model considering the equipment cost, the accuracy is 83.33 %. This study demonstrated that fluorescence imaging utilizing flavonoid fluorescence characteristics enables non-destructive and rapid detection of citrus HLB. This approach provides valuable data and technical support for decision-making on spring orchard cleanup and control of HLB.

Why it matches plant phenotyping methods柑橘HLB感染状態を蛍光画像から推定する撮像システムと分類手法を開発・評価しており、植物状態の取得・抽出が研究の中心である。

abstractThis study developed a multi-excitation fluorescence imaging system based on the correlation between HLB stress and flavonoid fluorescence characteristics.

Early diagnosis of wheat powdery mildew using solar-induced chlorophyll fluorescence and hyperspectral reflectance ↗

Powdery mildew disease threatens wheat production worldwide, and early detection is of great significance for disease control and maximizing yield and quality. To improve early remote sensing detection of wheat powdery mildew, solar-induced chlorophyll fluorescence (SIF) parameters were extracted using three-band Fraunhofer line discrimination (3FLD) and reflectance index approaches, and vegetation index (VI) was calculated by hyperspectral reflectance. All features and feature subsets of different data sources were used as inputs to multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms to construct a wheat powdery mildew monitoring model. SVM includes linear kernel function (LK), polynomial kernel function (PK), and Gaussian radial basis function (RBF). Under wheat powdery mildew stress, wheat canopy reflectance showed a blue shift, and fluorescence weakened. The correlation between SIF−A intensity and disease index (DI) in the O²−A band extracted using the 3FLD method was the highest at −0.781, showing that the SIF parameter was useful for monitoring powdery mildew. Whether based on all features or feature subsets, the RBF model achieved the highest model accuracy, followed by the RF and the MLR. In the feature subset, the accuracy ranges of RBF, LK, and PK models are 0.740−0.871, 0.724−0.850, and 0.716−0.841 respectively. The SIF+VI in the RBF model is more useful for early and stable disease monitoring of wheat powdery mildew. This innovative technical solution is expected to support the early diagnosis of wheat powdery mildew, significantly improving disease prevention and control efficiency and effectiveness.

Why it matches plant phenotyping methods小麦の病害状態をSIF・ハイパースペクトル反射から推定する検出手法とモデルを開発・評価しており、植物表現型取得が中心である。

abstractTo improve early remote sensing detection of wheat powdery mildew, solar-induced chlorophyll fluorescence (SIF) parameters were extracted using three-band Fraunhofer line discrimination (3FLD) and reflectance index approaches, and vegetation index (VI) was calculated by hyperspectral reflectance.

Optimizing Crop Production With Plant Phenomics Through High‐Throughput Phenotyping and AI in Controlled Environments ↗

ABSTRACT Plant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches to quickly evaluate characteristics and enhance the yields of crops in smart plant facilities. HTP uses environmental parameters for accuracy, such as software sensors, as well as hyperspectral imaging for pigment data, thermal imaging for water content, and fluorescence imaging for photosynthesis rates. They provide information on growth kinetics, physiological and biochemical characteristics, and genotype–environment interaction. Artificial intelligence (AI) and machine learning (ML) are used on a large volume of phenotypic data to predict growth rates, determine the optimal time to water plants, or detect diseases, nutrient deficiencies, or pests at an early stage. The lighting used in smart plant factories is adjusted based on the specific growth phase of the plants, such as using different light intensities, spectrums, and durations for germination, vegetative growth, and flowering stages, hydroponics as the method of providing nutrients, and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) for improving certain characteristics, such as resistance to drought. These systems enhance crop production, yields, adaptability, and input use by optimizing the environment and utilizing precision breeding techniques. Plant phenomics with AI is a combination of several disciplines, promoting the understanding of plant–environment interactions in relation to agriculture problems such as resource use, diseases, and climate change. It affects their capacity to develop crops that capture inputs, minimize chemical application, and are resilient to climate change. Phenomics is cost‐effective, reduces inputs, and contributes to more sustainable agricultural practices, being economically and environmentally sound. Altogether, plant phenomics is central to CEA due to its capacity to capitalize on phenotypic data and genetic potential within agriculture to advance sustainability and food security. Through phenomic research, the next advancements are likely to be even more revolutionary in terms of agricultural practices and food systems worldwide.

Why it matches plant phenotyping methods植物フェノミクス、HTP、AI、画像・センサーによる形質取得を主題とする概説であり、フェノタイピング手法のレビューとして中心的です。

abstractPlant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA).

POTENTIAL OF THE PROLINE-FLUORESCENT METHOD FOR ASSESSING PLANT DROUGHT TOLERANCE ON THE TEST OBJECT OF OILSEED RADISH ↗

The results of the application of the chlorophyll fluorescence and proline content (PC) methods for assessing the drought tolerance of oilseed radish varieties are presented. The interval of Relative humidity of soil (RHS) 60.8% - 35.6% with raising air temperature (0.8°C day-1) and decrease leaf RWC (6.7% day-1) was investigated. An increase F0 and Fst of 1.11 and 0.42 relative fluorescence units (RFU) per 1% decrease in RHS, 2.33 and 0.88 RFU per 1°C increase in air temperature, and 0.35 and 0.13 RFU per 1% decrease in leaf RWC was showed. A decrease Fpl, Fm at 6.57 and 50.1 RFU per 1% decrease RHS, 13.8 and 105.2 RFU per 1°C increase temperature and 2.1 and 15.7 RFU per 1% decrease leaf RWC was determined. An increase PC of 0.20 and 0.07 nmol gDW-1 per RFU increase F0 and Fst and 1.14 and 8.73 nmol gDW-1 per RFU decrease Fpl and Fm was noted. An increase PC of 5.74 nmol gDW-1 per 1% decrease in RHS and 12.05 nmol gDW-1 per 1°C increase in air temperature was proved.

Why it matches plant phenotyping methods乾燥耐性評価を目的に、クロロフィル蛍光とプロリン含量の測定法を適用し、環境条件・葉のRWCとの定量関係を示しているため、植物生理状態のフェノタイピング手法が中心です。

titlePOTENTIAL OF THE PROLINE-FLUORESCENT METHOD FOR ASSESSING PLANT DROUGHT TOLERANCE ON THE TEST OBJECT OF OILSEED RADISH

Design of a pseudo-color-assisted biocompatible supramolecular sensing probe for "lighting up" endogenous salicylic acid in plants. ↗

The widely recognized phytohormone, salicylic acid (SA), serves not only as an exogenous additive for fruits and vegetables but, more crucially, as an in vivo regulator of the entire plant growth process. Consequently, it is essential to achieve both in vitro detection and in vivo imaging analysis of the plant hormone SA. In this study, a biocompatible supramolecular probe was crafted using a "label-free" SA aptamer as the host for an aggregation-induced emission (AIE) organic small molecule. Upon recognizing the target SA, the aptamer is not readily digested by the exonuclease (Exo.I), which permits the aptamer sequence to be largely preserved. Under these conditions, the aptamer can be further integrated with the AIE organic small molecule, thereby enabling it to produce fluorescence emission due to restricted intramolecular motion. The probe can efficiently accomplish in vitro detection of SA within a linear range of 0.3-70 μM, with a detection limit reaching 0.09 μM. Most importantly, building on the achievement of fluorescence imaging monitoring of SA in plants, this work has developed a fluorescence image processing program utilizing the Java algorithm. This program facilitates the one-click conversion of monochrome images to pseudo-color in conventional fluorescence imaging, thereby enhancing the ability of the naked eye to discern image details.

Why it matches plant phenotyping methods植物内在性サリチル酸を蛍光イメージングで検出・可視化するプローブと、画像を疑似カラー化する処理プログラムが中心的に開発されており、植物の生理状態を測定する方法研究に該当する。

abstractit is essential to achieve both in vitro detection and in vivo imaging analysis of the plant hormone SA.

BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation. ↗

Introduction In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation. Methods Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF's estimation capabilities using various datasets. Results and discussion The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R ² =0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions. Conclusion Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management.

Why it matches plant phenotyping methodsSIF・リモートセンシング・深層学習を統合して冬コムギ収量を推定するモデルを開発し、複数モデルとの精度比較と安定性・汎化性を検証しているため、収量表現型の抽出手法が中心である。

abstractThis study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM).

Evaluation of wheat drought resistance using hyperspectral and chlorophyll fluorescence imaging. ↗

Photosynthesis drives crop growth and production, and strongly affects grain yields; therefore, it is an ideal trait for wheat drought resistance breeding. However, studies of the negative effects of drought stress on wheat photosynthesis rates have lacked accurate evaluation methods, as well as high-throughput techniques. We investigated photosynthetic capacity under drought stress in wheat varieties with varying degrees of drought stress resistance using hyperspectral and chlorophyll fluorescence (ChlF) imaging data. We analyzed various morpho-physiological traits involved in wheat drought tolerance, including tiller number, leaf relative water content, and malondialdehyde content, to determine the relationships between drought resistance and hyperspectral and ChlF data. The results showed that the spectral first derivative ratio (FDR) between drought stress and control conditions in the 680-760 nm region was closely related to photosynthetic capacity and drought tolerance and that hyperspectral imaging can be used to monitor ChlF parameters, with bands sensitive to ChlF identified in two spectral regions (539-764 nm and 832-989 nm). The spectral first derivative at 989 nm had the strongest linear relationship with the minimal fluorescence (R 2 = 0.49). An uninformative variable elimination algorithm indicated that FDRs in the green (504-609 nm), red (724-751 nm), and near-infrared (944-946 nm) light regions had great potential as indices of drought resistance. A support vector machine model based on the FDRs of these characteristic bands identified wheat drought resistance with 97.33% accuracy. These findings provide insight into the application of high-throughput technologies in studying drought resistance and photosynthesis in wheat.

Why it matches plant phenotyping methodsコムギの乾燥耐性・光合成能力を、ハイパースペクトルおよびクロロフィル蛍光画像から推定する手法を解析・評価しており、表現型取得と予測モデルが研究の中心である。

abstractWe investigated photosynthetic capacity under drought stress in wheat varieties with varying degrees of drought stress resistance using hyperspectral and chlorophyll fluorescence (ChlF) imaging data.

Application of fluorescence i-motif DNA silver nanocluster sensor to visualize endogenous reactive oxygen species in plant cells. ↗

Key message A novel fluorescent i-motif DNA silver nanoclusters system has been developed for visualization of reactive oxygen species in plants, enabling the detection of intracellular signaling in plant cells. Reactive oxygen species (ROS) are crucial in plant growth, defense, and stress responses, making them vital for improving crop resilience. Various ROS sensing methods for plants have been developed to detect ROS in vitro and in vivo. However, each method comes its own advantages and disadvantages, leading to an increasing demand for a simple and effective sensory system for ROS detection in plants. Here, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants. Two sensors, C 20 /AgNCs and FAM-C 20 /AgNCs-Cy5, detect intracellular ROS signaling in response to stimuli, such as abscisic acid, salicylic acid, ethylene, and bacterial peptide elicitor flg22. Notably, FAM-C 20 /AgNCs-Cy5 exceeds the sensing capabilities of HyPer7, a widely recognized ROS sensor. Taken together, we suggest that fluorescent i-motif DNA/AgNCs system is an effective tool for visualizing ROS signals in plant cells. This advancement is important to advancing our understanding of ROS-mediated processes in plant biology.

Why it matches plant phenotyping methods植物細胞内ROSという生理状態を可視化する新規蛍光センサーを開発し、既存センサーと比較して性能を示しているため、フェノタイピング手法が中心である。

abstractHere, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants.

Integrating Diurnal Physiological and Structural Variations in SIF for Enhanced Daily Drought Detection in Maize ↗

Daily water stress reflects the water stress status of crops on a specific day, which is crucial for studying drought progression and guiding precision irrigation. However, accurately monitoring the daily water stress remains challenging, particularly when eliminating the impact of historical stress and normal growth. Recent studies have demonstrated that the diurnal characteristics of the crop canopy obtained via remote sensing techniques can be used to assess daily water stress levels effectively. Remote sensing observations, such as the solar-induced chlorophyll fluorescence (SIF) and reflectance, offer information on the crop canopy structure, physiology or their combination. However, the sensitivity of different structural, physiological or combined remote sensing variables to the daily water stress remains unclear. We investigated this issue via continuous measurements of active fluorescence, leaf rolling, and canopy spectra of maize under different irrigation conditions. The results indicated that with increasing water stress, vegetation exhibited significant coordinated diurnal variations in both structure and physiology. The influence of water stress was minimal in the morning but peaked at noon. The morning-to-noon ratio (NMR) of the apparent SIF yield (SIFy), in which only the effect of the photosynthetically active radiation (PAR) is eliminated and in which both structural and physiological information is incorporated, exhibited the highest sensitivity to water stress variations. This NMR of the SIFy was followed by the NMR of the normalized difference vegetation index (NDVI) and the NMR of the canopy fluorescence emission efficiency (&Phi;Fcanopy) obtained via the fluorescence correction vegetation index (FCVI) method, which primarily reflect structural and physiological information, respectively. This study highlights the advantages of utilizing diurnal vegetation structural and physiological variations for monitoring daily water stress levels.

Why it matches plant phenotyping methodsトウモロコシのキャノピーを対象に、SIF・反射スペクトル・蛍光・葉の巻きなどのリモートセンシング指標を統合し、日次の水ストレスを推定する方法を評価しており、表現型取得・抽出が研究の中心です。

abstractRecent studies have demonstrated that the diurnal characteristics of the crop canopy obtained via remote sensing techniques can be used to assess daily water stress levels effectively.

Three-dimensional tracking of organ development in live plants based on plasma membrane dyes at single-cell resolution. ↗

Plant developmental biology necessitates precise three-dimensional (3D) tracking of dynamic processes in live plants, and the 3D imaging technique in developmental bioimaging requires suitable fluorophores to achieve single-cell resolution imaging. Herein, we have designed a series of plasma membrane fluorescent dyes with a number of excellent properties and established a single-cell resolution imaging tool based on these dyes for three-dimensional imaging of various tissues and organs in living plants. The designed plasma membrane fluorescent dyes not only have the advantages of rapid wash-free staining, highly specific targeting, high brightness and high contrast imaging, ultralong imaging time and low biotoxicity, but also effectively avoid the autofluorescence interference of chlorophyll in cells, allowing for the development of a three-dimensional imaging approach of living plant organs with single-cell resolution. The three-dimensional histological structures of various organs of adult Arabidopsis thaliana, including roots, leaves, flowers, and fruits, were successfully reconstructed with single-cell resolution using this model plant. Furthermore, the 3D imaging method was employed to track the dynamic changes in tissue and organ morphology at the single-cell level during key plant developmental processes, including seed germination, root development, leaf growth, and anther development.

Why it matches plant phenotyping methods植物器官の3D形態を単一細胞解像度で取得・追跡する蛍光イメージング手法と色素を開発し、複数の器官・発生過程で実証しており、表現型取得法が研究の中心です。

abstractestablished a single-cell resolution imaging tool based on these dyes for three-dimensional imaging of various tissues and organs in living plants

Green fluorescent FM dyes with prolonged retention for 4D tracking of plasma membrane dynamics in live plants under environmental stress. ↗

Macroscopic phenotypic changes in plants are frequently employed as a means of evaluating the biological response of plants to external environmental stresses. However, the lack of effective observational tools at the microscopic cellular level hinders the ability to fully comprehend the intricacies of this response. Herein, we developed a plasma membrane fluorescent dye with target-activated green emission complemented with conventional FM dyes, and established a four-dimensional (4D) imaging approach based on this dye for spatio-temporal monitoring of plasma membrane dynamics during cellular responses to external environmental stress. A green fluorescent dye, designated FMG-DBO, was constructed by modifying the bridged unit between the aniline donor and the pyridinium acceptor. Its green emission can be combined with that of conventional FM dyes, enabling high-resolution imaging of plant leaf cells containing chlorophyll. The anchoring ability of the dyes was enhanced by incorporating a rigid diaza[2.2.2]octane unit as an anti-permeability group. The long retention time of the FMG-DBO dye in the plasma membrane enables the tracking of three-dimensional dynamics of the plasma membrane of plant cells. Consequently, an FMG-DBO-based four-dimensional imaging approach was established to monitor dynamic changes of plant cells under external environmental stress at the cellular level. The biological responses of two different drought-tolerant rice root cells to drought stress were examined by this four-dimensional imaging approach. It was observed that the two types of rice root cells exhibited disparate responses to the drought environmen. This approach offers alternative cell-level visualization tools for evaluating the biological responses of plant cells under environmental stress.

Why it matches plant phenotyping methods植物細胞の膜動態を可視化・追跡する蛍光色素と4Dイメージング手法を開発し、環境ストレス応答の観測に適用しており、表現型取得法が中心的です。

abstractdeveloped a plasma membrane fluorescent dye

A Method for Estimating Fluorescence Emission Spectra from Image Data of Plant Grain and Leaves Without a Spectrometer ↗

This study proposes a method for estimating fluorescence emission spectra from images of plant grain and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels using ordinary off-the-shelf cameras and UV light. A mobile phone camera is used to detect fluorescence emission in the blue wavelength region for rice grains. For plant leaves, a small monochrome camera is used with additional optical filters to detect chlorophyll fluorescence in the red to far-red wavelength region. A ridge regression approach is used to obtain a reliable estimate of the spectral distribution of the fluorescence emission from the acquired image data. The spectral distribution can be estimated by optimally selecting the ridge parameter without statistically analyzing the fluorescence spectra. An algorithm for optimal parameter selection is developed using a cross-validation technique. In the experiments using real rice grains and green leaves, the estimated fluorescence emission spectral distributions are compared to direct measurement by a spectroradiometer and estimation by the minimum norm estimation method. The reliability of the proposed estimation method is demonstrated.

Why it matches plant phenotyping methods植物の穀粒・葉の蛍光スペクトルを画像から推定する撮像・回帰手法を開発し、分光放射計との比較で検証しており、植物表現型取得法が中心である。

abstractThis study proposes a method for estimating fluorescence emission spectra from images of plant grain and leaves without using a spectrometer.

Coupling sun-induced chlorophyll fluorescence (SIF) with soil-plant-atmosphere research (SPAR) chambers to advance applications of SIF for crop stress research ↗

Sun-induced chlorophyll fluorescence (SIF) has recently emerged as a proxy for canopy photosynthesis of vegetation and offers a promising approach for scalable remote crop monitoring. Effective application of SIF for crop monitoring requires better understanding of the processes that cause SIF-photosynthesis decoupling at leaf and canopy scales. To answer this challenge, we developed a novel automated multi-targeting hyperspectral spectrometer (OctoFlox). First, we evaluated the performance of OctoFlox and found high stability and cross-channel comparability. Second, we performed an evaluation of different SIF retrieval methods to identify the best suited retrieval method for our system configuration for both red (SIFʀᴇᴅ) and far-red SIF (SIFꜰʀ). We then deployed OctoFlox within Soil-Plant Atmosphere Research (SPAR) controlled-environment chambers that enable measurement of canopy-scale SIF and photosynthesis with matching footprints. We analyzed the effect of the SPAR chamber tops on the light environment and found minimal impact on the spectral response. Lastly, we examined the response of SIF and canopy photosynthesis using the SPAR chambers. Soybean plants were evaluated at pre-drought, drought (irrigated at 100 % field capacity vs. 33 % field capacity for 2 weeks) and after 1 week recovery from drought. During early growing season, SIFꜰʀ and SIFʀᴇᴅ exhibited similar responses. At peak growing season (R2 growth stage), SIFꜰʀ increased during afternoon depression of photosynthesis, but SIFʀᴇᴅ decreased. We demonstrate that pairing SIF instrumentation with SPAR chambers can accelerate understanding SIF-photosynthesis relationships from diurnal to seasonal scales in relation to crop physiological responses to abiotic stress. We provide user recommendations for future applications using OctoFlox and SPAR chambers for co-measuring SIF and GPP.

Why it matches plant phenotyping methodsOctoFlox分光計の開発・性能評価、SIF検索法の比較、SPARチャンバーとの統合および作物ストレス時のSIF・光合成計測を中心とする植物フェノタイピング手法研究である。

abstractwe developed a novel automated multi-targeting hyperspectral spectrometer (OctoFlox).

Simulations of solar-induced chlorophyll fluorescence over crop canopies using the integrated APSIM model ↗

Agricultural production models predict crop yield by accounting for a variety of species, cultivar, farming management, and environmental impacts on crop photosynthesis. Without suitable constraints, however, large uncertainties may exist in simulations of crop photosynthesis. Recent advances in retrieving solar-induced chlorophyll fluorescence (SIF) at the top-of-canopy (TOC) have provided a promising measurement for crop photosynthesis. Within the framework of the APSIM (Agricultural Production Systems sIMulator) model, a SIF module was developed to connect crop photosynthesis to TOC SIF emission (SIFₜₒc) which can be measured by remote sensing platforms. The new model (APSIM-SIF) first estimates the leaf-level chlorophyll fluorescence emitted over the full SIF spectrum (SIFₜₒₜ_fᵤₗₗ) according to CO₂ assimilation in crops. The model then mechanistically decomposes the conversion from SIFₜₒₜ_fᵤₗₗ to SIFₜₒc into two factors: the SIF band conversion factor (ɛ) and the fluorescence escape ratio (fₑₛc) that represent the impact of leaf physiological status and plant structure properties, respectively. ɛ can be estimated using leaf structural and biochemical parameters as inputs; fₑₛc for near-infrared SIF can be expressed as a function of directional reflectance in the near-infrared region (RNIR), Normalized Difference Vegetation Index (NDVI), and the fraction of PAR absorbed by crops (fAPAR). The APSIM-SIF model determined more than 90% of the variation in gross primary productivity (GPP), aboveground biomass and leaf area index (LAI) measurements for maize (Zea mays L.) at two AmeriFlux sites in the U.S. Midwest and it also captured the seasonality of SIF (R² = 0.84) and GPP (R² = 0.81) well at an irrigated maize site in China. The APSIM-SIF model was also applied to the simulation of TOC SIF emission of maize and soybean (Glycine max L.) in the U.S. Midwest during the 2018 growing season. The simulated SIFₜₒc accounted for more than 75% of the variability of daily satellite SIF observations for grid squares with more than 70% crop area. The main contribution of this study lies in two aspects: (1) a physically-based framework is proposed to incorporate the SIF module to the APSIM-DCaPST model, and (2) the two important factors used in this framework (ɛ and fₑₛc) remains largely constant during the peak growing season. These findings provide a theoretically robust and operational basis for linking SIF observations with crop growth.

Why it matches plant phenotyping methods作物キャノピーのSIFを推定するAPSIM-SIFモジュールを開発し、光合成・GPP・バイオマス・LAI・季節性との技術的検証も行っているため、表現型取得・推定法が中心である。

abstractWithin the framework of the APSIM (Agricultural Production Systems sIMulator) model, a SIF module was developed to connect crop photosynthesis to TOC SIF emission (SIFₜₒc) which can be measured by remote sensing platforms.

An efficient fluorescence analysis for plant gallic acid based on dual-ligand Ag/Au nanoclusters. ↗

Gallic acid (GA) is a secondary metabolite derived from plant phenolics. It is essential to maintain normal physiological activities in plants facing adversity. This, in turn, helps maintain crop integrity. Consequently, the surveillance of GA levels in plants is of significant importance. This study developed a near-infrared (NIR) red fluorescent Ag/Au nanocluster sensor, utilizing glutathione and bovine serum albumin as dual ligands. This modification shifts the emission wavelength to the red spectrum, mitigating the interference from the plant's inherent fluorescence. The fluorescence resonance energy transfer (FRET) mechanism is exploited. When the presence of Ag + induces the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to oxTMB, which in turn quenches the fluorescence at 650 nm. GA's abundant phenolic hydroxyl group will reduce oxTMB to TMB, so the material's fluorescence will be turned on to detect GA. The established platform can quantify a wide range of GA thanks to the above reasons. In particular, this platform has a recovery of 96.09%-104.7 % for detecting GA in tomato samples with an error of no more than 3 % and a detection limit as low as 38.29 nM. On the other hand, a combination of probes and the fluorescence platform was used to visualize GA levels in tomato leaves under drought and salt stress. This can assist in elucidating the physiological adaptations of plants to environmental changes. The groundbreaking GSH/BSA-Au/Ag nanosensor shows great potential for trace detection of GA in plants and will contribute to a deeper understanding of plant physiology.

Why it matches plant phenotyping methods植物中のガリ酸濃度を定量・可視化する蛍光ナノセンサーを開発し、トマト試料で性能検証しているため、植物の生理状態を測定する方法が中心である。

abstractThis study developed a near-infrared (NIR) red fluorescent Ag/Au nanocluster sensor

Near-infrared fluorescent probe for visualization of nitroxyl in the plant response to stress. ↗

Background Nitroxyl (HNO) is an emerging signaling molecule that plays a significant regulatory role in various aspects of plant biology, including stress responses and developmental processes. However, understanding the precise actions of HNO in plants has been challenging due to the absence of highly sensitive and real-time in situ monitoring tools. Consequently, it is crucial to develop effective and accurate detection methods for HNO. Establishing such methodologies will enable researchers to elucidate the functional roles of HNO in plant physiological processes, thereby advancing our knowledge of plant resilience and adaptation under environmental stressors. Result Herein, we successfully constructed a near-infrared fluorescent probe, DCIF-HNO, based on the dicyanoisophorone platform as fluorophore and 2-(diphenylphosphino)benzoate as HNO recognition site for identifying HNO in plants. Probe DCIF-HNO exhibited rapid response, excellent selectivity, and high sensitivity to HNO in vitro spectroscopic tests, while also demonstrating low toxicity and biocompatibility. A rapid and portable smartphone sensing platform for HNO in actual samples was successfully constructed based on probe DCIF-HNO and color recognition application. Moreover, probe DCIF-HNO was successfully applied to plant cells and tissues, enabling real-time visualization and detection of HNO and revealing the complex network of HNO interactions during H 2 S/NO crosstalk in plants. Furthermore, the increase in HNO levels in plants response to high salt and Cr stress was observed using probe DCIF-HNO. Transcriptome sequencing and differential metabolites analysis were employed to gain insight into the mechanism of HNO production under Cr stress. Significance Due to the optical properties and high-resolution imaging capabilities of DCIF-HNO, this study offers a novel framework for elucidating the signaling role of HNO in plant stress responses. The precise visualization of HNO dynamics enhances our understanding of the complex molecular pathways involved in plant adaptation to abiotic stressors. This research not only advances plant physiology but also has significant implications for developing strategies to enhance agricultural resilience in challenging environmental conditions.

Why it matches plant phenotyping methods植物内HNOのリアルタイム可視化・検出プローブとスマートフォン計測基盤を開発し、ストレス応答という植物の生理状態を測定しているため、方法が中心的である。

abstractwe successfully constructed a near-infrared fluorescent probe, DCIF-HNO

In situ images of Cd 2+ in rice reveal Cd 2+ protective mechanism using DNAzyme fluorescent probe. ↗

As a common pollutant, cadmium (Cd) poses a serious threat to the growth and development of plants. Currently, there is no effective method to elucidate the protective mechanism of Cd 2+ in plant cells. For the first time, we designed a Cd 2+ fluorescent probe to observe the adsorption and sequestration of Cd 2+ in rice cell walls and vacuoles. Specifically, Cd 2+ is blocked by the Casparian strip and electrostatically attracted to hemicellulose, which is abundantly adsorbed and fixed to the cell walls of the endodermis. For Cd 2+ that successfully entered the endodermis, one part entered the cells and was compartmentalised and fixed in the vacuoles, while the other part entered the vascular bundles and precipitated in the cell walls of the sclerenchyma through the ion exchange effect. Furthermore, with prolonged exposure to Cd 2+ , compartmentalised bodies that were strongly labelled by fluorescence gradually appeared in the vacuoles, which were assumed to be a new heavy metal protective mechanism activated by plants in response to continuous Cd 2+ exposure. In conclusion, this study provides an innovative and effective method for the detection of adsorption, transportation, and accumulation of Cd 2+ in plant tissues, which can be employed for the rapid identification of crops with low Cd accumulation.

Why it matches plant phenotyping methodsイネ組織内のCd2+の吸着・輸送・蓄積を可視化する蛍光プローブを開発し、植物組織の状態を測定する手法自体が中心であるため。

abstractFor the first time, we designed a Cd 2+ fluorescent probe to observe the adsorption and sequestration of Cd 2+ in rice cell walls and vacuoles.

Developing fluorescence hyperspectral imaging methods for non-invasive detection of herbicide safeners action mechanism and effectiveness. ↗

Herbicide safeners are considered key agents for plant protection that reduce the harmful impacts of herbicides on crops and the environment in general, but traditional evaluation methods for their effectiveness are time-consuming and labor-intensive. In this study, a rapid and non-destructive method was proposed using chlorophyll fluorescence and hyperspectral imaging that combined with machine learning models. Besides, chemometric analysis was utilized to reveal the action mechanism between the wheat crop (Triticum aestivum L.) understudy and the herbicide isoproturon (ISO) and safener gibberellin acid (GA 3 ). The results showed that ISO caused oxidative stress and disrupted the photosynthesis mechanism in wheat by hindering the electron transport pathway from primary acceptor quinone to secondary acceptor. Meanwhile, GA 3 stimulated wheat to synthesize more glutathione (GSH) that accelerated the herbicide action metabolism. It's worth noting that excessive GA 3 has decreased significantly the GSH and photosynthetic pigment concentrations, while the malondialdehyde concentration was significantly (p 3 . In conclusion, the novelty of the current study came from the accurate real-time tracking method for GA 3 action mechanism and its effectiveness on ISO toxicity. Where, that model holds great value for reducing the traditional methods' limitations in safener developments.

Why it matches plant phenotyping methods植物の除草剤・セーフナー作用と有効性を、クロロフィル蛍光・ハイパースペクトル画像・機械学習で非破壊かつ迅速に追跡する手法の開発が中心であり、植物の生理状態を測定する実質的なフェノタイピング研究である。

abstracta rapid and non-destructive method was proposed using chlorophyll fluorescence and hyperspectral imaging that combined with machine learning models.

Application of fluorescence i-motif DNA silver nanocluster sensor to visualize endogenous reactive oxygen species in plant cells ↗

Abstract Reactive oxygen species (ROS) are crucial in plant growth, defense, and stress responses, making them vital for improving crop resilience. Various ROS sensing methods for plants have been developed to detect ROS in vitro and in vivo . However, each method comes its own advantages and disadvantages, leading to an increasing demand for a simple and effective sensory system for ROS detection in plants. Here, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants. Two sensors, C 20 /AgNCs and FAM-C 20 /AgNCs-Cy5, detect intracellular ROS signaling in response to stimuli such as abscisic acid, salicylic acid, ethylene, and bacterial peptide elicitor flg22. Notably, FAM-C 20 /AgNCs-Cy5 exceeds the sensing capabilities of HyPer7, a widely recognized ROS sensor. Taken together, we suggest that fluorescent i-motif DNA/AgNCs system is an effective tool for visualizing ROS signals in plant cells. This advancement is important to advancing our understanding of ROS-mediated processes in plant biology.

Why it matches plant phenotyping methods植物細胞内ROSという生理状態を可視化する新規DNA/AgNCsセンサーを開発・比較評価しており、植物表現型の取得法が研究の中心である。

abstractHere, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants.

Automated image registration of RGB, hyperspectral and chlorophyll fluorescence imaging data. ↗

Abstract Background The early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level. Results In this study, we demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping ofA. thalianagrown in Multi-well plates and an assay with detached leaf discs ofRosa × hybridainoculated with the black spot disease-inducing fungusDiplocarpon rosae. Here, we showcase the effects of (i) selection of reference image selection, (ii) different registrations methods and (iii) frame selection on the performance of image registration via affine transform. In addition, we developed a combined approach for registration methods through NCC-based selection for each file, resulting in a robust and accurate approach that sacrifices computational time. Since image data encompass multiple objects, the initial coarse image registration using a global transformation matrix exhibited heterogeneity across different image regions. By employing an additional fine registration on the object-separated image data, we achieved a high overlap ratio. Specifically, for theA. thalianatest set, the overlap ratios (ORConvex) were 98.0 ± 2.3% for RGB-to-ChlF and 96.6 ± 4.2% for HSI-to-ChlF. For theRosa × hybridatest set, the values were 98.9 ± 0.5% for RGB-to-ChlF and 98.3 ± 1.3% for HSI-to-ChlF. Conclusion The presented multi-modal imaging pipeline enables high-throughput, high-dimensional phenotyping of different plant species with respect to various biotic or abiotic stressors. This paves the way for in-depth studies investigating the correlative relationships of the multi-domain data or the performance enhancement of machine learning models via multi modal image fusion.

Why it matches plant phenotyping methodsRGB・HSI・クロロフィル蛍光画像の画素レベル登録手法と統合パイプラインを開発・評価し、高スループット植物表現型解析に直接利用しているため。

abstractwe demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping

Unravelling the different components of nonphotochemical quenching using a novel analytical pipeline. ↗

Photoprotection in plants includes processes collectively known as nonphotochemical quenching (NPQ), which quench excess excitation-energy in photosystem II. NPQ is triggered by acidification of the thylakoid lumen, which leads to PsbS-protein protonation and violaxanthin de-epoxidase activation, resulting in zeaxanthin accumulation. Despite extensive study, questions persist about the mechanisms of NPQ. We have set up a novel analytical pipeline to disentangle NPQ induction curves measured at many light intensities into a limited number of different kinetic components. To validate the method, we applied it to Chl-fluorescence measurements, which utilised the saturating-pulse methodology, on wild-type (wt) and zeaxanthin-lacking (npq1) Arabidopsis thaliana plants. NPQ induction curves in wt and npq1 can be explained by four components ( α , β , γ and δ ). The fastest two ( β and γ ) correlate with pH difference formed across the thylakoid membrane in wt and npq1. In wt, the slower component ( α ) appears to be due to the formation of zeaxanthin-related quenching whilst for npq1, this component is 'replaced' by a slower component ( δ ), which reflects a photoinhibition-like process that appears in the absence of zeaxanthin-induced quenching. Expanding this approach will allow the effects of mutations and other abiotic-stress factors to be directly probed by changes in these underlying components.

Why it matches plant phenotyping methods植物のクロロフィル蛍光によるNPQ誘導曲線から生理状態を抽出する新規解析パイプラインを開発し、植物試料で検証しているため、表現型取得・解析手法が中心です。

abstractWe have set up a novel analytical pipeline to disentangle NPQ induction curves measured at many light intensities into a limited number of different kinetic components.

Nighttime Fluorescence Phenotyping Reduces Environmental Variability for Photosynthetic Traits and Enables the Identification of Candidate Loci in Maize ↗

Abstract High-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity. However, the heritability of chlorophyll fluorescence parameters measured during the day is often low as a result of high levels of variation introduced by environmental fluctuations. To address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise. This significantly increased the heritability of fluorescence traits compared to daytime measurements, with the maximum quantum yield of photosystem II ( F v /F m ) showing an increase in heritability from 0.32 to 0.72. Genome-wide association studies (GWAS) conducted using three photosynthetic fluorescence traits measured at night across two growing seasons identified several significant single nucleotide polymorphisms (SNPs). Notably, two candidate genes near SNPs linked to multiple fluorescence traits, Zm00001eb271820 and Zm00001eb012130 , have known roles in photosynthesis regulation. Four of the significant signal nucleotide polymorphisms identified in GWAS conducted using nighttime collected data also exhibited statistically significant associations with the same phenotypes during the day. In a majority of other cases, direction of effect was consistent but greater variance in day measured data relative to night measured data resulted in the differences not being statistically significant. These results highlight the effectiveness of phenotyping photosynthetic traits at night in reducing environmental noise and enhancing the discovery of genomic intervals related to photosynthesis. While nighttime data collection may not be applicable for all photosynthetic traits, it offers a promising avenue for advancing our understanding of the genetic variation of photosynthesis in modern crop species.

Why it matches plant phenotyping methods夜間の蛍光測定による光合成形質フェノタイピングを中心に、昼間測定との比較で環境変動低減と技術的有効性を検証しているため。

abstractTo address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise.

Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning. ↗

China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440-530 nm and 680-780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple's equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.

Why it matches plant phenotyping methodsリンゴ果実の糖濃度という植物器官形質を、蛍光ハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、モデル比較・特徴抽出・精度評価も行っている。

abstractThis study used FHIS combined with machine learning (ML) to predict SC at the apple's equatorial position.

Non-invasive methods to assess seed quality based on ultra-weak photon emission and delayed luminescence. ↗

Seed quality is the set of physical, genetic, and physiological characteristics, reflecting the overall germination potential. Maintaining an optimal seed quality is essential for agriculture and seed banks to preserve genetic diversity. Compared to conventional methods (e.g., germination tests), non-invasive approaches allow a more sustainable and rapid evaluation of seed quality but this is limited by high costs. The measurement of ultra-weak photon emission (UPE) and delayed fluorescence (DL), defined as biological phenomena potentially related to the physiological status of living systems, may represent a suitable approach to estimate seed quality. To test this hypothesis, seeds of five agriculturally relevant legume species (Phaseolus vulgaris L., Lathyrus sativus L., Cicer arietinum L., Pisum sativum L., and Vicia faba L.), stored at different conditions (room temperature or -18 °C) for several years, were analysed using a LIANA© prototype to collect data regarding DL and UPE occurring after UV excitation. The obtained data were integrated with germination parameters which underline species-specific behaviours in response to storage conditions. The prediction models show variable efficiency in classifying seeds based on germination which underline species-dependent links between photon emission and seed quality. Therefore, these measurements represent novel, non-invasive, and rapid approaches to evaluate seed quality.

Why it matches plant phenotyping methods種子の生理状態・品質を非侵襲的な超微弱光放出と遅延発光で評価する測定法および予測モデルが研究の中心であり、発芽性に基づく種子形質の推定を検証している。

titleNon-invasive methods to assess seed quality based on ultra-weak photon emission and delayed luminescence.

Detecting Plant Infections: Prospects for Chlorophyll Fluorescence Imaging ↗

Phytopathogens are a significant challenge to agriculture and food security. In this regard, methods for the early diagnosis of plant diseases, including optical methods, are being actively developed. This review focuses on one of the optical diagnostic methods, chlorophyll fluorescence (ChlF) imaging. ChlF reflects the activity of photosynthetic processes and responds subtly to environmental factors, which makes it an excellent tool for the early detection of stressors, including the detection of pathogens at a pre-symptomatic stage of disease. In this review, we analyze the peculiarities of changes in ChlF parameters depending on the type of pathogen (viral, bacterial, or fungal infection), the terms of disease progression, and its severity. The main mechanisms responsible for the changes in ChlF parameters during the interaction between pathogen and host plant are also summarized. We discuss the advantages and limitations of ChlF imaging in pathogen detection compared to other optical methods and ways to improve the sensitivity of ChlF imaging in the early detection of pathogens.

Why it matches plant phenotyping methods植物病害の早期検出に用いるクロロフィル蛍光イメージングの方法、性能、限界、改善策を中心に扱うレビューであり、感染植物の生理状態・病害進行を測定するフェノタイピング手法として中心的です。

abstractThis review focuses on one of the optical diagnostic methods, chlorophyll fluorescence (ChlF) imaging.

Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies ↗

Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.

Why it matches plant phenotyping methods植物表現型に用いるスペクトル画像技術を体系的にレビューし、各手法の適用性や比較を扱っているため、方法論レビューとして中心的です。

abstractThe latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared.

Characterizing the Responses of sorghum and Camelina to Environmental Stress through a Multi-Modal Approach ↗

Due to their sessile nature, plants are unable to escape environmental factors that negatively impact health, resulting in losses to agricultural productivity. Rapid, non-invasive tools to detect plant stress response are essential for optimizing resource efficiency and mitigating the effects of extreme environmental pressures. However, many existing methods are either invasive, incompatible with other measurement techniques, or have not been applied to a wide range of varying environmental factors. In this study, we assess the physiological responses of four week old camelina (Camelina sativa) and sorghum (Sorghum bicolor) to chitosan, cold, drought, and both acute and chronic salt stress. Several plant characteristics were measured in parallel during stress exposure, including fluorescence and gas exchange parameters (MultispeQ and LI-6800), tissue electrical impedance with wearable biosensors (Multi-PIP), and biochemical properties via Fourier-transform infrared (FTIR) spectroscopy. We compiled unique profiles for whole plant physiological changes in response to environmental stress, demonstrating that certain aspects of plant health and makeup underwent alterations on differing temporal scales. This finding emphasizes the need for a comprehensive multi-modal approach to rapidly and accurately perform remote sensing of plant health in the field. Physiological parameters such as leaf impedance were also observed to rapidly change in response to treatment and can be leveraged to detect very early signs of plant perturbation. This research establishes the utility of a holistic phenotyping approach to inform agricultural strategies aimed at enhancing crop resilience under changing environmental conditions.

Why it matches plant phenotyping methods複数のセンサー・分光法を統合した非侵襲的な植物ストレス表現型取得と、マルチモーダル表現型解析の有用性が研究の中心である。

abstractRapid, non-invasive tools to detect plant stress response are essential

Improving extraction phenology accuracy using SIF coupled with the vegetation index and mapping the spatiotemporal pattern of bamboo forest phenology ↗

Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R² values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.

Why it matches plant phenotyping methodsSIFと植生指数を統合した指標、時系列再構成、閾値法による竹林フェノロジー抽出を開発・比較検証しており、植物状態の測定手法が研究の中心である。

abstractThe advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs

Visualizing plant salt stress with a NaCl-responsive fluorescent probe. ↗

Salt stress is an adverse environmental condition that harms plant growth and development. The development of salt stress probes is critical for tracking the growth dynamics of plants, molecular breeding or screening of growth regulators. The sodium chloride (NaCl)-responsive fluorescent probe Aza-CyBz is designed based on the tenet that NaCl induces formation of ordered aggregates, and the sensitive fluorescence response can enable the visualization of plant salt stress in root tip tissues and live plants. Herein, we describe a detailed three-step route for synthesis of Aza-CyBz and applications to monitoring salt stress in Arabidopsis thaliana. The procedures for operating fluorescence imaging under various stresses are also listed to eliminate interference from the oxidative mechanism of salt stress. Compared with conventional invasive approaches such as inductively coupled plasma emission spectrometry and flame photometer, our protocol can real-time monitor salt stress experienced by plants, which demands simple pretreatment procedure and staining technique. Due to near infrared fluorescence, this method provides direct visual observation of salt stress at both tissue and live plant levels, which is superior to conventional noninvasive approaches. The preparation of probe Aza-CyBz takes ~2 d, and the imaging experiments for assessing salt stress experienced by plants, including the preparation of stressed plant samples takes ~9-11 d for root tip tissues and ~23 d for live plants. Notably, acquisition and analysis visual images of salt stress in plants can be completed within 2 h and they require only a basic knowledge of spectroscopy and chemistry.

Why it matches plant phenotyping methods植物の塩ストレス状態を蛍光プローブと画像化で直接取得する手法を開発し、植物組織および個体での適用手順・比較を示した研究であり、表現型取得法が中心である。

abstractHerein, we describe a detailed three-step route for synthesis of Aza-CyBz and applications to monitoring salt stress in Arabidopsis thaliana.

Evaluating crop nitrogen status in maize leaves: A predictive modelling approach using chlorophyll fluorescence parameters. ↗

The consumption of chemical fertilizers has increased eight-fold since the 19th century, outstripping crop yields increases and, emphasizing the need for precise nitrogen (N) assessment in crops to optimize fertilization and mitigate environmental impacts. This study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves while addressing the limitations of current labor-intensive and environmentally sensitive methods. Based on a long-term experiment initiated in 2011, maize hybrid Fumin 985 was sampled in 2021 and 2022 under two crop-straw management strategies (SM: no tillage with surface straw mulch, SP: plough tillage with straw incorporation) and six N application rates. Partial least squares regression (PLSR) models were formulated using chlorophyll fluorescence parameters (ChlF) to assess leaf N content (N leaf). The results indicated that a N application rate of 270 kg ha -1 sufficed to meet crop N requirements. Leaf characteristics such as N leaf, total pigment content (TP), and leaf dry weight (DW leaf) changed significantly with increasing N application rates, influencing rapid chlorophyll fluorescence (OJIP) dynamics. Principal component analysis (PCA) reduced ChlF from 35 to 21, and four models were developed, among which, the model using ChlF and TP was more accurate than the model using DW alone. Key ChlF parameters for PLSR model performance included ABS/RC, φ(Eo), ETo/CSm, and δ(Ro)/(1-δ(Ro)). Although non-destructive N leaf detection using chlorophyll fluorescence technology proved feasible, additional leaf characteristics, such as TP, are necessary to improve model accuracy. Considering local field conditions is essential for the application of this technology at a larger scale. Precise evaluation of N status using chlorophyll fluorescence is beneficial for a more efficient N management and sustainable agriculture.

Why it matches plant phenotyping methodsトウモロコシ葉の窒素状態をクロロフィル蛍光から推定するモデルを開発・評価しており、植物形質推定手法が研究の中心である。

abstractThis study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves

In vivo two-photon FLIM resolves photosynthetic properties of maize bundle sheath cells ↗

Maize (Zea mays L.) performs highly efficient C4 photosynthesis by dividing photosynthetic metabolism between mesophyll and bundle sheath cells. In vivo physiological measurements are indispensable for C4 photosynthesis research as any isolated cells or sectioned leaf often show interrupted and abnormal photosynthetic activities. Yet, direct in vivo observation regarding bundle sheath cells in the delicate anatomy of the C4 leaf is still challenging. In the current work, we used two-photon fluorescence-lifetime imaging microscopy (two-photon-FLIM) to access the photosynthetic properties of bundle sheath cells on intact maize leaves. The results provide spectroscopic evidence for the diminished total PSII activity in bundle sheath cells at its physiological level and show that the single PSIIs could undergo charge separation as causal. We also report an acetic acid-induced chlorophyll fluorescence quenching on intact maize leaves, which might be a physiological state related to the nonphotochemical quenching mechanism.

Why it matches plant phenotyping methods二光子FLIMを用いて無傷葉の葉鞘細胞の光合成特性・生理状態を直接画像計測しており、植物生理表現型の取得法が研究の中心です。

abstractwe used two-photon fluorescence-lifetime imaging microscopy (two-photon-FLIM) to access the photosynthetic properties of bundle sheath cells on intact maize leaves.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants. ↗

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R 2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Why it matches plant phenotyping methods非画像ハイパースペクトルおよびクロロフィル蛍光センサーで葉の生物物理形質を推定し、PCA・PLSRによる予測精度も評価しており、植物フェノタイピング手法が中心である。

abstractchlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters.

Improving rice yield and quality through high-throughput phenomics, linear regression, and machine learning neural network models ↗

Abstract To explore the potential of using high-throughput plant phenomics in rice breeding programs, one hundred elite rice varieties from southern rice-growing areas in China were subjected to high-throughput phenomic analysis. A total of 88 parameters were measured and obtained using RGB imaging, fluorescence imaging, and hyperspectral imaging at four key rice growth stages: tillering, jointing, grain filling, and 20 days after grain filling. These 88 parameters, which include RGB color and morphological features, chlorophyll fluorescence characteristics, and rice surface reflectance spectra, were analyzed to characterize high yield and high grain quality in rice using subset selection regression and deep learning neural network models. A total of 39 significant linear regression models were obtained for predicting rice yield and grain quality, with R-squared values ranging from 0.86 to 0.15, and an average R-squared of 0.41. The data from the 100 rice varieties were split into training and test sets to evaluate the prediction accuracies of the models using mean absolute error between predicted and actual values. The results indicated that the deep learning neural network model can be used to refine the linear regression model, improving the prediction accuracy. These findings suggest that high-throughput plant phenomics can be effectively utilized in rice breeding programs to select for high-yielding, high-quality rice varieties.

Why it matches plant phenotyping methodsRGB・蛍光・ハイパースペクトル画像による88形質の高スループット取得と、収量・品質予測モデルの検証が研究の中心であり、再利用可能なフェノタイピング基盤と解析手法に該当する。

abstractone hundred elite rice varieties from southern rice-growing areas in China were subjected to high-throughput phenomic analysis.

Crop chlorophyll detection based on multiexcitation fluorescence imaging analysis ↗

The chlorophyll content of wheat was assessed using multispectral fluorescence imaging (MSFI). Ultraviolet (UV) light (365 nm)-induced fluorescence images at 440, 520, 690, and 740 nm, and visible light (460, and 610 nm)-induced fluorescence images at 690 and 740 nm were acquired while leaf chlorophyll content was measured using SPAD 520. The fluorescence images were processed after segmentation and channel extraction to calculate the parameters of each leaf based on fluorescence images (Fu440, Fu520, Fu690, and Fu740) obtained by UV excitation, and fluorescence images (Fu440, Fu520, Fu690, Fu740, Fb690, Fb740, and Fr740) obtained by three excitations of 365 nm, 460 nm, and 610 nm light. 12 fluorescence ratio parameters under UV excitation and 26 fluorescence ratio parameters under three excitations were calculated. The correlation analysis revealed that the fluorescence parameters (Fr740, Fu440, Fu520, Fu690, Fu740, Fb690, Fb740, Fu440/Fu520, Fu520/Fu690, and Fu740/Fr740) showed a strong correlation with the chlorophyll content. These parameters have the potential to measure the chlorophyll content. Subsequently, stepwise regression analysis (SRA) was employed to screen 16 fluorescence parameters under UV excitation and 33 fluorescence parameters under three excitations, with the objective of identifying and eliminating redundant variables. Finally, four variables (Fu520, Fu690, Fu740, and Fu690/Fu520) under UV excitation and five variables (Fr740, Fu520, Fb740, Fu740/Fu690, and Fb740/Fb690) under three excitations were selected. The partial least squares regression (PLSR) model, constructed using three excitations, demonstrated enhanced performance with an Rc2 of 0.901, Rv2 of 0.904, root mean square error (RMSE) of calibration of 4.398, and RMSE of validation of 4.267. Multiexcitation fluorescence based on three excitations techniques has better performance for evaluating chlorophyll content.

Why it matches plant phenotyping methods多励起蛍光画像を用いて小麦葉のクロロフィル含量を推定する画像取得・特徴抽出・回帰モデルを開発および検証しており、植物フェノタイピング手法が中心である。

abstractThe chlorophyll content of wheat was assessed using multispectral fluorescence imaging (MSFI).

A Vis/NIRS device for evaluating leaf nitrogen content using K-means algorithm and feature extraction methods ↗

Accurate assessing leaf nitrogen content (LNC) is crucial for actual production and fertilizer management. In this research, a portable device was designed to rapidly and non-destructively evaluate LNC with precision. Using hydroponically grown eggplants exposed to different nitrogen content nutrient solutions as experimental samples, we conducted various measurements, including chlorophyll fluorescence (ChlF) induction curves, hyperspectral images, and LNC values. Correlations between LNC and ChlF parameters were calculated, and the parameter qN obtained the highest correlation with LNC. False color images of qN were segmented using the K-Means algorithm to obtain three regions. The spectral data and the measured LNC of the corresponding region in the leaf were matched, and a LNC prediction model was developed using the partial least square regression (PLSR) algorithm with the processed spectral data as input and the measured LNC as output. The results showed that the model using standard normal variate-iteratively retains informative variables- successive projections algorithm (SNV-IRIV-SPA-PLSR) yielded the best performance, with a correlation coefficient of prediction (R²) of 0.9332, a root mean square error (RMSE) of 2.6890 mg/g, a residual prediction deviation (RPD) of 3.97 and a ratio of performance to interquartile distance (RPIQ) of 7.28. Based on the selected wavelengths from the SNV-IRIV-SPA-PLSR-VIP model, six narrow-band light emitting diodes (LEDs) were chosen as the light source for the designed device. Inexpensive modules were employed to assemble the device, and accuracy tests were conducted. The PLSR algorithm was employed to develop the device’s LNC evaluation model with the reflectance of the leaf under 6 LEDs as input (resulting in R², RMSE, RPD, and RPIQ values of 0.8075 6.6242 mg/g, 2.30 and 4.26, respectively). The model was then embedded in the core processor. To validate the device’s performance, an independent set was used, resulting in R² of 0.7559, RMSE of 7.4771 mg/g, RPD of 2.07, and RPIQ of 3.57, respectively. The proposed device could rapidly and accurately determine LNC in plants, surpassing other devices in terms of portability and cost. This research offers a potential solution for plant fertilizer management.

Why it matches plant phenotyping methods葉の窒素含量という植物形質を対象に、Vis/NIRS・蛍光・ハイパースペクトル計測、画像分割、特徴抽出、予測モデル、携帯デバイス化と独立検証を中心的に開発・評価しているため。

abstracta portable device was designed to rapidly and non-destructively evaluate LNC with precision

Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages ↗

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.

Why it matches plant phenotyping methods携帯型蛍光センサーでトウモロコシの窒素状態・バイオマス・窒素吸収を推定し、複数の機械学習モデルを比較・クロスサイト検証しており、植物表現型取得と推定手法が中心である。

abstractPotential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season.

Evaluating crop nitrogen status in maize leaves: A predictive modelling approach using chlorophyll fluorescence parameters ↗

The consumption of chemical fertilizers has increased eight-fold since the 19th century, outstripping crop yields increases and, emphasizing the need for precise nitrogen (N) assessment in crops to optimize fertilization and mitigate environmental impacts. This study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves while addressing the limitations of current labor-intensive and environmentally sensitive methods. Based on a long-term experiment initiated in 2011, maize hybrid Fumin 985 was sampled in 2021 and 2022 under two crop-straw management strategies (SM: no tillage with surface straw mulch, SP: plough tillage with straw incorporation) and six N application rates. Partial least squares regression (PLSR) models were formulated using chlorophyll fluorescence parameters (ChlF) to assess leaf N content (N leaf). The results indicated that a N application rate of 270 kg ha⁻¹ sufficed to meet crop N requirements. Leaf characteristics such as N leaf, total pigment content (TP), and leaf dry weight (DW leaf) changed significantly with increasing N application rates, influencing rapid chlorophyll fluorescence (OJIP) dynamics. Principal component analysis (PCA) reduced ChlF from 35 to 21, and four models were developed, among which, the model using ChlF and TP was more accurate than the model using DW alone. Key ChlF parameters for PLSR model performance included ABS/RC, φ(Eo), ETo/CSm, and δ(Ro)/(1–δ(Ro)). Although non-destructive N leaf detection using chlorophyll fluorescence technology proved feasible, additional leaf characteristics, such as TP, are necessary to improve model accuracy. Considering local field conditions is essential for the application of this technology at a larger scale. Precise evaluation of N status using chlorophyll fluorescence is beneficial for a more efficient N management and sustainable agriculture.

Why it matches plant phenotyping methodsトウモロコシ葉のクロロフィル蛍光から葉内窒素状態を非破壊推定するモデルを開発・評価しており、植物形質取得と予測手法が研究の中心である。

abstractThis study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves

Cross-variety seed vigor detection using new spectral analysis techniques and ensemble learning methods ↗

During the storage process, seeds deteriorate with aging, and their vigor gradually decreases, affecting the field emergence rate, uniformity, and crop yield. Therefore, it is critical to accurately and quickly identify seed vigor. Spectroscopic techniques are widely used in research to evaluate seed vigor rapidly. However, current research predominantly focuses on single varieties, with limited exploration of generalized detection methodologies for multi-variety seeds. In this paper, autofluorescence and reflectance spectroscopy techniques were used to detect several varieties of wheat seeds and analyze the changing pattern of their spectra after artificially accelerated aging treatment. The analysis compares the effectiveness of feature modeling of autofluorescence and reflectance spectra in cross-variety detection. By integrating these two spectral features through ensemble learning, a seed vigor detection model capable of cross-variety identification was developed and validated with an optimal accuracy of more than 87.5 %. Furthermore, for seed vigor group detection, the detection strategy was designed to discriminate small batches of seeds, and the final detection accuracy reached 93.9 % on average. In summary, this study shows that ensemble learning can combine the advantages of autofluorescence and reflectance spectroscopy, enhance the discrimination ability of combined datasets, and provide an effective reference for cross-variety seed vigor detection.

Why it matches plant phenotyping methods小麦種子の活力という植物形質を、蛍光・反射分光とアンサンブル学習で検出する手法の開発・検証が中心であり、単なる生物学的実験のルーチン測定ではない。

abstractSpectroscopic techniques are widely used in research to evaluate seed vigor rapidly.

Evaluating waterlogging stress response and recovery in barley (Hordeum vulgare L.): an image-based phenotyping approach. ↗

Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.

Why it matches plant phenotyping methods画像ベース表現型解析を用いた湛水ストレス評価プロトコルの開発と、別施設・異なるセンサーによる再現性検証が中心であるため、収録対象。

abstractwe present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping.

Methodology to enable high-throughput imaging of Arabidopsis seedlings on cover glass-bottom multiwell plates. ↗

High-throughput imaging enables rapid collection of large datasets and is used widely in many systems. However, this is not often used in plant-based systems due to issues related to the need to mount tissues and autofluorescence of plant metabolites. We therefore developed methodology enabling high-throughput imaging of Arabidopsis roots. In this system, growth media supplemented with India Ink (to block autofluorescence from cotyledons) is poured directly into multi-well coverglass-bottom plates and seedlings grown such that the roots grow down with the gravity vector and along the coverglass, effectively mounting themselves for imaging. This method enables high-throughput imaging of Arabidopsis roots.

Why it matches plant phenotyping methodsアラビドプシス根の高スループット画像取得を可能にする培養・固定・撮像法を開発しており、植物表現型取得手法が研究の中心である。

abstractWe therefore developed methodology enabling high-throughput imaging of Arabidopsis roots.

Turn-on fluorescence detection of carbon monoxide in plant tissues based on Cu 2+ modulated polydihydroxyphenylalanine nanosensors. ↗

As an important signaling molecule, carbon monoxide (CO) plays an important role in plant growth and development including affecting stomatal movement, stress response and root development. Thus, it is necessary to develop fluorescent probes that can be used to detect CO in live plant tissues and further enable a deep-understanding of its biological function, mechanism and metabolism. In this paper, a novel and sensitive fluorescent probe based on Cu 2+ modulated polydihydroxyphenylalanine nanoparticles (PDOAs) has been developed for the detection of CO. The fluorescence of PDOAs can be effectively quenched by Cu 2+ through the multi-coordination interaction. In the presence of CO, Cu 2+ can be effectively reduced to Cu + , which resulted in the release of free PDOAs and the Cu 2+ -quenched bright green fluorescence was restored obviously. Through this ingenious strategy, the abiotic CO can be accurately detected and identified with high selectivity, rapid response time within 5 min and an ultralow detection limit of 72.4 nM. Due to the admirable biocompatibility, the nano-material based probe has been successfully applied for in vivo imaging CO in the root tip and leave tissues of lettuce. To the best of our knowledge, this is the first example of a fluorescent probe-based methodology for the sensitive tracking of CO in plant tissues.

Why it matches plant phenotyping methods植物組織内のCOという生理状態を可視化・定量する蛍光プローブを開発し、レタス組織でin vivoイメージングまで実証しており、植物フェノタイピング手法が中心である。

abstracta novel and sensitive fluorescent probe based on Cu 2+ modulated polydihydroxyphenylalanine nanoparticles (PDOAs) has been developed for the detection of CO.

Non-destructive estimation of flesh oil content in avocado (Persea americana Mill.) using fluorescence images from 365-nm UV light excitation. ↗

The flesh oil content (OC) is a crucial commercial indicator of avocado maturity and directly correlates with its nutritional quality. To meet export standards and optimize edible characteristics, avocados must be harvested at the appropriate stage of physiological maturity. The significant variability in OC during maturation, without any external morphological indicators, poses a longstanding challenge. Currently, harvesting maturity is optimized through time-consuming, destructive laboratory methods like freeze-drying and chemical extraction, which use representative samples to estimate the maturity of entire orchards. In this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar. We developed a surface fluorescence index that strongly correlates with OC, achieving correlation coefficients up to - 0.91. Our non-destructive and rapid approach achieved a cross-validation accuracy with an R 2 value of 0.81, enabling the classification of avocados with low and high OC. This pioneering method shows considerable potential for further improvement and refinement. This study lays the groundwork for developing a portable, cost-effective, and real-time method for non-destructive in situ monitoring of avocado OC in the field and its integration into large-scale post-harvest grading systems.

Why it matches plant phenotyping methodsアボカド果実の油分含量という植物器官形質を、365 nm蛍光画像から非破壊推定する手法を開発・検証しており、形質取得法が研究の中心である。

abstractIn this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar.

Development of bright fluorescent auxin ↗

Polar transport of the phytohormone auxin plays a crucial role in plant growth and response to environmental stimuli. Small-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth. In this study, we developed a new fluorescent auxin probe, BODIPY-IAA2, which effectively visualizes auxin distribution in various plant tissues. We designed this probe to be transported by auxin transporters while lacking the ability to elicit auxin signaling. Using BODIPY as the fluorophore provides bright and stable fluorescence signals, making it suitable for live-imaging under standard fluorescent microscopy. We tested the probe with auxin reporter lines in Arabidopsis and performed yeast two-hybrid assays. The results showed that BODIPY-IAA2 did not activate auxin signaling through the auxin receptor TIR1. However, BODIPY-IAA2 did mildly compete with both exogenous and endogenous auxins for transport, indicating that the probe is transported by auxin transporters in vivo. The probe not only enables visualization of its tissue distribution but also allows sub-cellular staining, including the endoplasmic reticulum and tip regions in elongating cells in moss. We also observed unusual staining patterns in the main root of non-model parasitic plants where genetic transformation is not feasible. Our new fluorescent auxin probe demonstrates significant potential for detailed studies on auxin transport and distribution across diverse plant species.

Why it matches plant phenotyping methods植物体内のオーキシン分布を可視化する蛍光プローブを開発し、植物組織・細胞でのライブイメージング性能を検証しており、表現型取得手法が研究の中心である。

abstractSmall-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth.

Chlorophyll fluorescence: a smart tool for maize improvement ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsトウモロコシ改良におけるクロロフィル蛍光を「smart tool」として扱っており、植物の生理状態を測定する方法が中心と読める。抄録がないため確信度は限定的。

titleChlorophyll fluorescence: a smart tool for maize improvement

Quantification of electron transport-related oxidative signals by time and wavelength-resolved redox biosensors and chlorophyll fluorescence ↗

Reductive and oxidative signals transmitted from the photosynthetic electron chain to target proteins through the redox signaling network are key regulators of carbon assimilation and downstream metabolism. However, despite their crucial role in activating and inhibiting photosynthetic activity, their relation to photosynthetic efficiency is hardly quantified due to the methodological gap between traditional spectroscopic approaches for investigating photosynthesis and biochemical analyses used in the redox regulation field. Here, we simultaneously quantified redox signals and photosynthetic activity by exploring time and wavelength-resolved fluorescence spectra that capture biosensor and chlorophyll fluorescence signals. Using a set of potato plants expressing genetically encoded redox biosensors, we demonstrated how reductive and oxidative signals are amplified with elevated light intensities and revealed the tight connection between electron transport rate (ETR) and the generation of peroxiredoxin-related oxidative signals. These results demonstrate how full spectrum analysis can pave the way for the integration of genetically encoded biosensors in photosynthesis research and demonstrate light-dependent activation of inhibitory oxidative signals in major crop plants.

Why it matches plant phenotyping methods時間・波長分解蛍光スペクトルを用いて、植物のレドックスシグナルと光合成活性を同時定量する測定法を開発・実証しており、植物生理状態の取得が研究の中心です。

abstractHere, we simultaneously quantified redox signals and photosynthetic activity by exploring time and wavelength-resolved fluorescence spectra that capture biosensor and chlorophyll fluorescence signals.

Direct and indirect technical guide for the early detection and management of fungal plant diseases. ↗

Fungal plant diseases are a major threat to plants and vegetation worldwide. Recent technological advancements in biotechnological tools and techniques have made it possible to identify and manage fungal plant diseases at an early stage. These techniques include direct methods, such as ELISA, immunofluorescence, PCR, flow cytometry, and in-situ hybridization, as well as indirect methods, such as fluorescence imaging, hyperspectral techniques, thermography, biosensors, nanotechnology, and nano-enthused biosensors. Early detection of fungal plant diseases can help to prevent major losses to plantations. This is because early detection allows for the implementation of control measures, such as the use of fungicides or resistant varieties. Early detection can also help to minimize the spread of the disease to other plants. The techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases. These techniques can help to ensure food security and protect our valuable plant resources.

Why it matches plant phenotyping methods植物病害の早期検出技術を体系的に扱うレビューで、蛍光画像、ハイパースペクトル、サーモグラフィーなど、植物の病害状態を観測する手法が中心的に論じられている。分子診断も含むが、植物病害フェノタイピング手法のレビューとして採用可能。

abstractThe techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases.

Classification of tomato seedling chilling injury based on chlorophyll fluorescence imaging and DBO-BiLSTM. ↗

Introduction Tomatoes are sensitive to low temperatures during their growth process, and low temperatures are one of the main environmental limitations affecting plant growth and development in Northeast China. Chlorophyll fluorescence imaging technology is a powerful tool for evaluating the efficiency of plant photosynthesis, which can detect and reflect the effects that plants are subjected to during the low temperature stress stage, including early chilling injury. Methods This article primarily utilizes the chlorophyll fluorescence image set of tomato seedlings, applying the dung beetle optimization (DBO) algorithm to enhance the deep learning bidirectional long short term memory (BiLSTM) model, thereby improving the accuracy of classification prediction for chilling injury in tomatoes. Firstly, the proportion of tomato chilling injury areas in chlorophyll fluorescence images was calculated using a threshold segmentation algorithm to classify tomato cold damage into four categories. Then, the features of each type of cold damage image were filtered using SRCC to extract the data with the highest correlation with cold damage. These data served as the training and testing sample set for the BiLSTM model. Finally, DBO algorithm was applied to enhance the deep learning BiLSTM model, and the DBO-BiLSTM model was proposed to improve the prediction performance of tomato seedling category labels. Results The results showed that the DBO-BiLSTM model optimized by DBO achieved an accuracy, precision, recall, and F1 score with an average of over 95%. Discussion Compared to the original BiLSTM model, these evaluation parameters improved by 9.09%, 7.02%, 9.16%, and 8.68%, respectively. When compared to the commonly used SVM classification model, the evaluation parameters showed an increase of 6.35%, 7.33%, 6.33%, and 6.5%, respectively. This study was expected to detect early chilling injury through chlorophyll fluorescence imaging, achieve automatic classification and labeling of cold damage data, and lay a research foundation for in-depth research on the cold damage resistance of plants themselves and exploring the application of deep learning classification methods in precision agriculture.

Why it matches plant phenotyping methodsトマト苗の低温障害をクロロフィル蛍光画像から定量・分類する画像ベースの表現型取得および深層学習手法が中心であり、性能評価も実施している。

abstractThis article primarily utilizes the chlorophyll fluorescence image set of tomato seedlings, applying the dung beetle optimization (DBO) algorithm to enhance the deep learning bidirectional long short term memory (BiLSTM) model, thereby improving the accuracy of classification prediction for chilling injury in tomatoes.

Tracing the nitrogen nutrient status of crop based on solar-induced chlorophyll fluorescence ↗

Accurate and non-destructive monitoring of wheat nitrogen nutrition is of great significance for field fertilizer management to ensure crop yield and quality, reduce environmental pollution, and improve economic benefits. Compared with spectral vegetation indices (which are sensitive to greenness and structural parameters), or active fluorescence (which is limited to small-scale studies), solar-induced chlorophyll fluorescence (SIF) provides a direct measure of crop response to environmental stress and photosynthetic characteristics. However, there has been few studies comparing agronomic parameters, photosynthetic parameters, vegetation indices and SIF as an indicator of nitrogen status. In this paper, we therefore explore these measures as tools for monitoring nitrogen nutrition. During the 2016–2017 growing season, we conducted a field experiment in Rugao, Jiangsu Province, China, using winter wheat (Triticum aestivum L.) and different nitrogen treatments. The sensitivity of SIF indices, vegetation indices, photosynthetic parameters and agronomic parameters to crop nitrogen status were compared. Our results demonstrated that, compared with vegetation indices and agronomic parameters, the ratio of SIF emission peaks (FY₆₈₇/FY₇₆₁) responded to nitrogen status most rapidly at both the leaf and canopy scales, as soon as the fourth day after treatment (DAT4). A wheat nitrogen nutrition index (NNI), based on FY₆₈₇/FY₇₆₁, was used to construct a leaf dry matter (LDM-based NNI) diagnostic model, which will be beneficial for monitoring and diagnosing the nitrogen nutrition status of wheat leaves. Our results also illuminate the physiological mechanism that enables SIF to be used as a tool to monitor nitrogen nutrient status, primarily through changes in the proportion of light energy distribution. These findings provide theoretical and technical support for monitoring and diagnosing wheat nitrogen nutrition status based on SIF technology.

Why it matches plant phenotyping methodsSIFを用いた小麦の窒素栄養状態の非破壊・葉および群落スケール測定を中心に、他指標との感度比較と診断モデル構築を行っており、植物表現型取得法の技術的評価・応用に該当する。

abstractsolar-induced chlorophyll fluorescence (SIF) provides a direct measure of crop response to environmental stress and photosynthetic characteristics

Integrated sensing and machine learning: Predicting saccharine and bioenergy feedstocks in sugarcane ↗

Predicting saccharine and bioenergy feedstocks in sugarcane enables growers and industries to determine the precise time and location for harvesting a better-quality product in the field. On one hand, Brix, Purity, and total recoverable sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for first-generation (1 G) bioethanol. Conversely, Cellulose, Hemicellulose, and Lignin are the primary constituents of straw, directly contributing to second-generation (2G) bioethanol. However, analyzing these materials in the laboratory is a time-consuming and non-scalable task. Therefore, we propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks. We collected samples of stalks and leaves/straw during the maturity stage while simultaneously collecting remote sensing data. The ML models played a crucial role in predicting 1 G (R² = 0.88–0.93) and 2 G (R² = 0.56–0.82) feedstocks. Notably, remote sensing data could serve as important features for the models, mainly through the spectral bands (Blue, Green, and RedEdge), DTemp and ChlF. Hence, the best features can be further implemented within a framework to predict sugarcane feedstocks. Our study marks a significant advancement in the industrial-scale prediction of sugarcane feedstocks, providing stakeholders with invaluable prescriptive harvesting strategies for both primary products and by-products.

Why it matches plant phenotyping methodsサトウキビの茎・葉由来の飼料成分を、複数センサーと機械学習で非侵襲的に推定・マッピングする方法を中心に開発しており、植物形質の取得・推定が主題である。

abstractwe propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks.

Dynamic responses of chlorophyll fluorescence parameters to drought across diverse plant families. ↗

Chlorophyll fluorescence measurement is a quick and efficient tool for plant stress-level detection. The use of Pulse amplitude modulation (PAM), allows the detection of the plant stress level under field conditions. Over the years, several parameters estimating different parts of the chlorophyll and photosystem response were developed to describe the plant stress level. Despite all fluorescence parameters being based on the same measurements, their relationship remains unclear, and their response to drought stress is significantly influenced by the incoming light intensity. In this study, we use six different annual plants from different families, both C3 and C4 photosynthesis types, to describe the plant response to drought through the fluorescence parameters response (NPQ, Y(NPQ), and qN). To describe the dynamic response to drought, we employed light-response curves, adapting and fitting an equation for each curve to compare the drought response for each fluorescence parameter. The results demonstrated that the non-photochemical quenching (NPQ) and the quantum yield of non-photochemical quenching [Y(NPQ)] maximal values decrease when the PSII functionality (F v /F m ) is lower than ~0.7. The basal fluorescence level ( F 0 $$ {F}_0 $$ and F s ) $$ {F}_s\Big) $$ remained unaffected by the stress level and stayed stable across the various plants and stress levels. Our results indicate that the response of different stress parameters follows a distinct order under continuous drought. Consequently, monitoring just one parameter during long-term stress assessments may result in biased analysis outcomes. Incorporating multiple chlorophyll fluorescence parameters offers a more accurate reflection of the plant's stress level.

Why it matches plant phenotyping methodsPAMクロロフィル蛍光を用いた植物ストレス状態の測定について、複数パラメータの応答を比較し、動的評価のための光応答曲線モデルを適用・適合している。蛍光パラメータによる表現型取得と評価が中心で、単なるルーチン測定ではない。

abstractChlorophyll fluorescence measurement is a quick and efficient tool for plant stress-level detection.

Near-Infrared Fluorescent Carbon Nanotube Sensors for the Plant Hormone Family Gibberellins ↗

Gibberellins (GAs) are a class of phytohormones, important for plant growth, and very difficult to distinguish because of their similarity in chemical structures. Herein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA 3 and GA 4 , triggering near-infrared (NIR) fluorescence intensity changes. Using a new coupled Raman/NIR fluorimeter that enables self-referencing of nanosensor NIR fluorescence with its Raman G-band, we demonstrated detection of cellular GA in Arabidopsis, lettuce, and basil roots. The nanosensors reported increased endogenous GA levels in transgenic Arabidopsis mutants that overexpress GA and in emerging lateral roots. Our approach allows rapid spatiotemporal detection of GA across species. The reversible sensor captured the decreasing GA levels in salt-treated lettuce roots, which correlated remarkably with fresh weight changes. This work demonstrates the potential for nanosensors to solve longstanding problems in plant biotechnology.

Why it matches plant phenotyping methods植物ホルモン濃度という植物の生理状態を、開発したナノセンサーとNIR測定系で検出する手法が研究の中心であり、植物内での適用実証も行っている。

abstractHerein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA 3 and GA 4 , triggering near-infrared (NIR) fluorescence intensity changes.

Developing a prediction method for physicochemical characteristics of Pontianak Siam orange (Citrus suhuiensis cv. Pontianak) based on combined reflectance-Fluorescence spectroscopy and artificial neural network ↗

The slightly sweet and acidic taste offered by Pontianak Siam oranges is influenced by the total soluble solids (TSS) and acidity in the fruit, in which, measuring these attributes is commonly performed using instruments that potentially damage the fruit's structure, thus, impractical for fresh fruit products. Moreover, the process of classifying the quality of fresh oranges has been based on physical appearance, leading to subjective results. Correspondingly, the objective of the study is to develop a prediction method for the physicochemical characteristics of Pontianak Siam oranges based on VIS-NIR-Fluorescence spectroscopy and an artificial neural network (ANN) model. The method is applicable to classify oranges based on physicochemical characteristics without damaging the fruit's structure. As a result, the best model for classifying the maturity level of Pontianak Siam oranges was obtained using a dataset with all feature combined spectra, attaining a training accuracy of 0.99 and testing accuracy of 1. The best model for predicting TSS was obtained using all feature combined spectra dataset, attaining R2 training = 0.89 and R2 testing = 0.91. The best model for predicting acidity was obtained using all feature reflectance spectra datasets, attaining R2 training = 0.96 and R2 testing = 0.97. The best model for predicting fruit firmness was obtained using all feature reflectance spectra dataset, attaining R2 training = 0.97, R2 testing = 0.89. Overall, the combination of Vis-NIR reflectance and fluorescence spectroscopy have the potential to be applied for non-destructive assessment of citrus quality in terms of visual classification and maturity parameters prediction.

Why it matches plant phenotyping methodsVIS-NIR反射・蛍光分光とANNを用いて、柑橘果実の成熟度、TSS、酸度、硬度を非破壊推定する手法の開発が研究の中心であり、植物形質の取得・推定方法を評価している。

abstractthe objective of the study is to develop a prediction method for the physicochemical characteristics of Pontianak Siam oranges based on VIS-NIR-Fluorescence spectroscopy and an artificial neural network (ANN) model.

A fluorescence detection method for postharvest tomato epidermal defects based on improved YOLOv5m ↗

BACKGROUND: Tomato quality visual grading is greatly affected by the problems of smooth skin, uneven illumination and invisible defects that are difficult to identify. The realization of intelligent detection of postharvest epidermal defects is conducive to further improving the economic value of postharvest tomatoes. RESULTS: An image acquisition device that utilizes fluorescence technology has been designed to capture a dataset of tomato skin defects, encompassing categories such as rot defects, crack defects and imperceptible defects. The YOLOv5m model was improved by introducing Convolutional Block Attention Module and replacing part of the convolution kernels in the backbone network with Switchable Atrous Convolution. The results of comparison experiments and ablation experiments show that the Precision, Recall and mean Average Precision of the improved YOLOv5m model were 89.93%, 82.33% and 87.57%, which are higher than YOLOv5m, Faster R‐CNN and YOLOv7, and the average detection time was reduced by 47.04 ms picture⁻¹. CONCLUSION: The present study utilizes fluorescence imaging and an improved YOLOv5m model to detect tomato epidermal defects, resulting in better identification of imperceptible defects and detection of multiple categories of defects. This provides strong technical support for intelligent detection and quality grading of tomatoes. © 2024 Society of Chemical Industry.

Why it matches plant phenotyping methods蛍光画像取得装置と改良YOLOv5mによってトマト表皮の欠陥・腐敗・亀裂を検出する方法が研究の中心であり、比較実験とアブレーション実験による技術検証も行っている。

abstractAn image acquisition device that utilizes fluorescence technology has been designed to capture a dataset of tomato skin defects

Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging. ↗

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models-AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2-are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.

Why it matches plant phenotyping methods深層学習とクロロフィル蛍光画像から、ダイズ幼植物の塩ストレス状態を分類する画像ベースの表現型取得・解析手法が研究の中心である。

abstractThis study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings.

Spectral algal fingerprinting and long sequencing in synthetic algal-microbial communities ↗

O_LISynthetic biology has made progress in creating artificial microbial and algal communities, but technical and evolutionary complexities still pose significant challenges. C_LIO_LITraditional methods for studying microbial and algal communities, such as microscopy and pigment analysis, are limited in throughput and resolution. In contrast, advancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints, offering more precise and comprehensive analysis than conventional flow cytometry. C_LIO_LIThis study demonstrates the use of full-spectrum cytometry for analyzing synthetic algal-microbial communities, facilitating rapid species identification and enumeration. The workflow involves recording individual spectral signatures from monocultures, utilizing autofluorescence to distinguish them from noise, and subsequent creation of a spectral library for further analysis. The obtained library is used then to analyze mixtures of unicellular cyanobacteria and synthetic phytoplankton communities, revealing differences in spectral signatures. The synthetic consortium experiment monitored algal growth, comparing results from different instruments and highlighting the advantages of the spectral virtual filter system for precise population separation and abundance tracking. This approach demonstrated higher flexibility and accuracy in analyzing multi-component algal-microbial assemblages and tracking temporal changes in community composition. C_LIO_LIBy capturing the complete emission spectrum of each cell, this method enhances the understanding of algal-microbial community dynamics and responses to environmental stressors. With development of standardized spectral libraries, our work demonstrates an improved characterization of algal communities, advancing research in synthetic biology and phytoplankton ecology. C_LI

Why it matches plant phenotyping methods藻類の個体スペクトル計測とスペクトルライブラリを用いて、群集の構成・個体数・増殖を高スループットに測定する技術が研究の中心であり、植物状態の取得法として実質的です。

abstractadvancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints

Leveraging Image Analysis for High-throughput Phenotyping of Legume Plants ↗