Plant phenotyping methods.

Laser-induced breakdown spectroscopy for multi-elemental analysis of Nerium oleander with atomic absorption spectroscopy validation. →

This study presents a systematic quantitative multi-elemental investigation of four major plant organs (roots, stems, leaves, and flowers) of Nerium oleander using calibration-based Laser-Induced Breakdown Spectroscopy (LIBS), with the results validated by Atomic Absorption Spectroscopy (AAS). Plasma characterization was carried out using Boltzmann plot and Stark broadening analyses, while negligible self-absorption observed through the Hα emission line confirmed optically thin plasma conditions and reliable quantitative measurements. A total of nine elements were detected, including Fe, Zn, Mn, Ca, Mg, K, Na, Cu, and Ni, each exhibiting different concentration levels across the analyzed tissues. Compositional analysis using standard calibration curve-based LIBS demonstrated that elemental concentrations were non-uniform, showing marked variations between the different plant tissues. Among the detected elements, calcium emerged as the most prevalent across all tissues. The highest calcium concentration was observed in leaves (16,385 mg L-1), followed by roots (12,092 mg L-1) and flowers (11,185 mg L-1). Root tissues exhibited elevated concentrations of Fe and Mn, reaching 1918 and 513 mg L-1, respectively. In contrast, flowers showed the highest Mn concentration (1888 mg L-1), while leaves were enriched in Mg (4445 mg L-1) and K (3700 mg L-1). The highest Na concentration was observed in stems (8025 mg L-1). Trace metals, including Cu, Zn, and Ni, were detected at comparatively low concentrations, while Pb remained undetected in all samples. The strong agreement between LIBS and AAS measurements confirms the reliability of the proposed methodology and demonstrates the potential of LIBS as a rapid and non-destructive tool for elemental assessment of medicinal plants.

Why it matches plant phenotyping methods植物組織の元素濃度を取得するLIBS測定法を校正し、AASで検証しており、元素組成という植物形質の測定方法が研究の中心である。

abstractusing calibration-based Laser-Induced Breakdown Spectroscopy (LIBS), with the results validated by Atomic Absorption Spectroscopy (AAS).

A Within-Chamber Cutting Method for Real-Time Quantification of Leaf Wounding Volatiles and Gas Exchange. →

Volatile organic compounds (VOCs) released by plant leaves play key roles in stress signaling, plant-atmosphere interactions, and plant defense. Among these, wound-induced VOCs (wVOCs) are emitted within seconds of mechanical damage, herbivory, or environmental disturbance. Their emission dynamics depend strongly on the timing, severity, and method of tissue disruption. Yet, accurate quantification remains challenging due to mechanical artifacts, variable exposure conditions, and delays between injury and measurement. This study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange. A surgical-grade cutter was integrated into a portable gas-exchange chamber to enable clean, controlled cuts within a sealed chamber under stable light, humidity, CO2, and temperature conditions. A proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) continuously measured volatile emissions at the chamber outlet, minimizing delay and signal distortion. This setup resolves emission onset, peak timing, maximum rise rate, and total release with high temporal fidelity. Application of the method to Quercus rubra, Acer platanoides, and Gossypium hirsutum demonstrated its ability to resolve distinct wound-induced emission patterns across contrasting leaf types. By eliminating delays associated with conventional sampling, this method resolves the full kinetic trajectory of wound-induced emissions and overcomes major limitations of previous approaches. It provides a robust framework for studying rapid stress responses in plant physiology, ecological biochemistry, and plant-atmosphere interactions.

Why it matches plant phenotyping methods植物葉の創傷誘導揮発性物質とガス交換をリアルタイム定量する測定系を開発しており、植物のストレス生理状態の取得が研究の中心である。

abstractThis study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange.

A C-repeat binding factor-salicylic acid (CBF-SA) module links wound-induced evaporative cooling to tissue repair in plants. →

Repairing damaged tissues is essential for the survival of all organisms. In plants, tissue injury rapidly triggers defense and repair programs. However, the molecular mechanisms linking early injury cues to the later stage of wound repair remain unclear. Here, we show that wounding of Arabidopsis leaves induces localized low temperature at the injury site, likely caused by evaporative cooling, which is accompanied by an activation of cold-responsive genes. Using thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive, and real-time manner. Mechanistically, we show that C-repeat Binding Factor (CBF) transcription factors are required for the activation of the injury-associated cold response and downstream salicylic acid (SA) signaling. Our findings suggest that the CBF-SA pathway acts coordinately to promote lignin and callose deposition, thereby facilitating wound repair. Together, these findings reveal a link between a wound-induced biophysical cue and the tissue repair program.

Why it matches plant phenotyping methods熱画像とコンピュータビジョン・深層学習を組み合わせ、植物の創傷治癒を定量的・非侵襲的・リアルタイムに測定するワークフローを開発しており、表現型取得法が研究の中心である。

abstractUsing thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive, and real-time manner.

Subcellular Localization of Iron in Rhizophora mangle Leaves Revealed by Integrated Perls Reaction, TEM, STEM-HAADF, and EDS Analyses. →

Mangrove ecosystems are frequently exposed to high concentrations of iron (Fe) in sediments, resulting in Fe accumulation in plant tissues. Although Fe is an essential micronutrient involved in several metabolic processes, its excess requires efficient mechanisms of compartmentalization and storage to maintain cellular homeostasis. Histochemical detection using the Perls reaction has usually been applied to identify ferric iron (Fe 3+ ) in biological tissues; however, the combination of this technique with ultrastructural and elemental analyses remains relatively unexplored in plant cells. In this study, we investigated Fe localization in leaf tissues of Rhizophora mangle L. (Rhizophoraceae), a dominant mangrove species, by combining complementary approaches, including Perls cytochemical reaction, transmission electron microscopy (TEM), scanning transmission electron microscopy coupled with high-angle annular dark-field imaging (STEM-HAADF), and energy-dispersive X-ray spectroscopy (EDS). Perls-positive electron-dense deposits were visualized at the ultrastructural level, and their elemental composition was further characterized by EDS analyses. Fe-containing deposits were detected in the epidermis, mesophyll parenchyma, mucilage cells, and vascular tissues, as well as in multiple cellular compartments, including plastids, mitochondria, vacuoles, cell walls, intercellular spaces, and plasmodesmata, whereas sclerenchyma cells showed no detectable Fe-containing deposits. The combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level. This integrated methodology may facilitate the investigation of Fe distribution and compartmentalization in plant tissues under contrasting conditions of Fe availability.

Why it matches plant phenotyping methods植物葉の鉄分布・細胞内区画化という生理状態を対象に、複数の顕微鏡・元素分析法を統合した可視化および特性評価手法が研究の中心であるため。

abstractThe combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level.

A gold nanoparticle-boosted disposable paper-based electrochemiluminescence biosensor for in situ detection of H 2 O 2 in tomato leaves. →

This work presents a gold nanoparticle (Au NP)-boosted disposable paper-based electrochemiluminescence (ECL) biosensor for minimally invasive on-leaf in situ detection of endogenous H 2 O 2 in tomato leaves. The inherent capillary action of filter paper (FP) was employed to simplify reagent delivery, while gold nanoparticles (Au NPs) catalytically activated H 2 O 2 to generate reactive oxygen species, thereby driving the ECL signal output. This simple, low-cost paper-based platform enabled time-resolved monitoring of H 2 O 2 in stressed plants, providing a reliable in situ strategy for evaluating tomato physiology and early disease/pest warning.

Why it matches plant phenotyping methodsトマト葉内のH2O2という植物生理状態をその場で測定する紙ベースECLバイオセンサーの開発が中心であり、植物フェノタイピング手法に該当する。

abstractThis work presents a gold nanoparticle (Au NP)-boosted disposable paper-based electrochemiluminescence (ECL) biosensor for minimally invasive on-leaf in situ detection of endogenous H 2 O 2 in tomato leaves.

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.

Multiplexed Nanosensors for Real-Time Monitoring of VOC-Mediated Signalling Response in Plants. →

To enhance crop performance, intercropping strategies leverage volatile organic compound (VOC)-driven interactions with companion plants that constitutively emit VOCs. Despite the agricultural importance, the mechanisms and kinetics of VOC-mediated sensory transduction in receiver plants eavesdropping on neighbouring non-kin emitters remain largely unknown due to a lack of appropriate non-destructive analytical tools. In this work, we employ multiplexed salicylic acid (SA) and H 2 O 2 nanosensors in Brassica rapa subsp. Chinensis (pak choy) plants to visualize, in real time, reactive oxygen species (ROS) and SA signal transduction following exposure to constitutively-released VOCs from neighbouring aromatic plants-namely sweet basil and spearmint. Unique emitter-specific temporal signatures of ROS and SA were observed in receiver pak choy: sweet basil VOCs induced concomitant generation of ROS and SA at 30 min, whereas spearmint VOCs triggered SA production at 30 min, followed by ROS accumulation. The temporal data enabled the formulation of a diffusion model that quantifies the VOC perception threshold that triggers the distinct early ROS and SA signalling. Transcriptomics analysis at 2 h revealed that both emitters evoke largely distinct changes in pak choy, likely stemming from variations in speed and sequence of the early signal transduction, leading to different phenotypic outcomes. Intercropping with sweet basil led to enhanced pak choy biomass, stress resilience and secondary metabolite accumulation, whereas spearmint as companions had a limited impact. Our study captures in real time, the VOC-induced rapid signalling in receiver plants and its ensuing effect on growth. These nanosensor-enabled findings represent an important advance in deciphering how emitter-specific volatile cues are integrated into plant responses, guiding rational selection of beneficial companion plants for improved yield and nutritional profiles in sustainable agriculture.

Why it matches plant phenotyping methods植物内のROSとSAシグナルをリアルタイム可視化する多重化ナノセンサーが研究の中心であり、植物の生理状態・応答を測定する方法として適用されている。

abstractdue to a lack of appropriate non-destructive analytical tools. In this work, we employ multiplexed salicylic acid (SA) and H 2 O 2 nanosensors in Brassica rapa subsp. Chinensis (pak choy) plants to visualize, in real time, reactive oxygen species (ROS) and SA signal transduction

Raman spectroscopy resolves nitrogen-driven metabolic acclimation to urban air pollution in Quercus ilex. →

Urban air pollution may alter plant metabolism long before visible damage becomes apparent. Raman spectroscopy was evaluated as a rapid, non-destructive approach to resolve these biochemical adjustments. Mature Quercus ilex L. trees were sampled along a well-defined pollution gradient in Tuscany (Italy), spanning high, intermediate, and low levels of NO₂ and PM₁₀. Leaf Raman spectra revealed coordinated modulation of primary and secondary metabolism. Pigment-related bands (chlorophylls and carotenoids) increased toward the most polluted site, while inducible flavonoid signals showed site-dependent variation consistent with oxidative pressure in superficial tissues. These patterns were consistent with destructive biochemical analyses and chlorophyll fluorescence measurements, which indicated acclimation rather than photoinhibition damage. A composite Raman index showed a close site-level association with NO₂ exposure, suggesting that nitrogen-related urban pollution was the main exposure component linked to the observed metabolic response. Overall, Raman spectroscopy captures the chronic metabolic imprint of urban air pollution in Q. ilex, resolving coordinated pigment reinforcement and defensive activation without sample destruction. This approach provides a rapid and scalable framework for linking atmospheric chemistry to plant functional status in biomonitoring applications.

Why it matches plant phenotyping methods植物の代謝・機能状態を非破壊的に推定するRaman分光法を評価し、スペクトル指標と生化学・蛍光測定の整合性を検証しているため、手法が中心的です。

abstractRaman spectroscopy was evaluated as a rapid, non-destructive approach to resolve these biochemical adjustments.

In-situ monitoring of photosynthesis information in plant leaves using flexible wearable impedance spectroscopy →

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

Why it matches plant phenotyping methods植物葉の光合成情報を柔軟なウェアラブルインピーダンス分光でその場モニタリングする手法が中心と読めるため、植物生理形質のセンシング手法として収録する。

titleIn-situ monitoring of photosynthesis information in plant leaves using flexible wearable impedance spectroscopy

Destructive harvest validation of high-throughput measurements show that water use efficiency is unaffected by moderate drought in tobacco →

ABSTRACT Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle. PLAIN LANGUAGE SUMMARY We grew tobacco plants under either a drought or high-water treatment and harvested a portion of the plants every few days for a total of eight harvests. Throughout the experiment, we collected 3D images of the plants and continuously measured pot weight to track plant growth and water use across different developmental stages. Destructive biomass served as the gold-standard measurement. We then compared biomass and water-use estimates generated from the digital measurements with the destructive measurements. The digital approach provided accurate estimates of plant biomass and water use while requiring little hands-on labor and no plant destruction. These nondestructive methods could help plant breeders identify water-efficient plants earlier in the breeding process, accelerating the development of crops that use water more efficiently.

Why it matches plant phenotyping methods3D画像による非破壊バイオマス推定と連続的な重量測定からWUEを推定する手法を、破壊収穫と比較して検証しており、植物表現型取得法が中心である。

abstractRecent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies.

Correlation of Plant Bioelectrical Signals with Potential Ionic Energy Flow under Different Stress →

All living organisms rely on the movement of ions across cell membranes as the fundamental physical basis of their internal energy and signaling, and plants are no exception. Plants perceive, integrate, and respond to environmental stimuli through electrical signals, classified as action, variation, and system potentials, that are coupled with calcium waves, reactive oxygen species, and hydraulic and hormonal changes to coordinate whole-organism responses despite the absence of a nervous system. Yet most studies characterize these signals using a single feature, such as amplitude or spike duration, in a single tissue, an approach that cannot establish how such signals correspond to the underlying ionic activity, mobility, and structural complexity of the signaling environment, or how this correspondence varies across organs. Here, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato ( Solanum lycopersicum ) exposed to different stimulus. Electrical activity with increased stimulus strength, likely reflecting increased ionic flow, with the root showing the largest response. This suggests plant electrical signaling works as a distributed, ion-based information system, useful for stress monitoring and bio-inspired sensor design.

Why it matches plant phenotyping methods植物の電気生理シグナルを多面的に取得・解析する枠組みを中心に扱い、ストレスモニタリングへの応用可能性を示しているため、植物状態の測定方法として含める。

abstractHere, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato

The Plant Condensate Code: Emergent Phase Signatures Encode Environmental Stress and Its History →

Plant stress biology has traditionally relied on the analysis of snapshot measurements—including hormone levels, reactive oxygen species, transcripts, and physiological traits—which primarily characterize the current state of the cell, whereas the physical consequences of previous stress exposure remain considerably less accessible to direct measurement. This distinction may be particularly important under natural conditions, where plants experience recurrent, sequential, and combined stresses. This raises a fundamental question: can a cell, after physiological recovery, retain a measurable residual physical state that reflects aspects of its previous stress history and influences its response to subsequent stress? Here, we propose Environmental Phase Imprinting (EPI) as a testable biophysical hypothesis according to which environmental stress may leave a measurable imprint on the physical state of biomolecular condensates that persists after cessation of the initial exposure. EPI is not proposed as a new form of biological information or as an established mechanism of stress memory, but rather as a potential physical substrate, correlate, or consequence of previously described forms of cellular stress memory. To operationalize this hypothesis, we introduce the Plant Condensate Code (PCC), a multidimensional conceptual framework designed to move from a static “snapshot” of cellular state toward a time-resolved physical trajectory. PCC integrates complementary characteristics of condensate populations, including morphology, dynamics, molecular mobility, material state, and molecular composition, across a sequence of states encompassing baseline, stress, adaptation, recovery, and the post-stress state.We propose that the trajectory of condensate states, rather than any single measurement, may contain information about cellular stress history and may help explain differences in responses to recurrent or combined stress. Multimodal approaches, including live-cell imaging, fluorescence recovery after photobleaching (FRAP), molecular mobility analysis, microrheology, Brillouin microscopy, quantitative phase imaging, and molecular profiling, could provide complementary measurements of this physical state. PCC is further positioned within our broader conceptual research program encompassing Cytoplasmic Phase Homeostasis, Cytoplasmic Phase Sensing, and the Plant Threat Matrix (PTM)—a proposed six-state framework for describing plant physiological states under stress. Within this framework, physical measurements of condensate and cytoplasmic states may represent one possible approach for defining and quantitatively characterizing cellular physiological states. Finally, we discuss the potential application of this conceptual framework to stress-resilience phenotyping, evaluation of biostimulants, and selection for stress tolerance, while clearly distinguishing experimentally established phenomena from hypotheses and conceptual proposals. The proposed framework may provide a foundation for the development of a new direction in biophysical phenotyping of plant stress resilience, complementing molecular and physiological approaches to the study of stress memory.

Why it matches plant phenotyping methods植物ストレス履歴を測定・定量化するための新しい生物物理的フェノタイピング枠組みを中心に提案しており、単なるストレス生物学実験ではない。ただし実証ではなく概念的な方法開発提案である。

abstractTo operationalize this hypothesis, we introduce the Plant Condensate Code (PCC), a multidimensional conceptual framework designed to move from a static “snapshot” of cellular state toward a time-resolved physical trajectory.

Volatile-Based In-Field Screening of Xylella fastidiosa in Olive Plants Using a Smart E-Nose →

Xylella fastidiosa (Xf) is among the most devastating phytosanitary threats to Mediterranean agriculture, causing Olive Quick Decline Syndrome (OQDS). Since containment efficacy depends on timely intervention, scalable in-field screening tools are needed. This study evaluates a portable digital electronic nose, based on a carbon-nanotube sensor array combined with artificial intelligence algorithms, for the in-field screening of Xf through volatile organic compound (VOC) profiling. Three replicate acquisitions were performed on 130 olive trees (390 measurements) across four cultivars (Cellina di Nardò, Ogliarola Salentina, Ogliarola Barese, and Leccino) in four Italian regions (Apulia, Calabria, Lazio, and Tuscany). Plant status was assigned from the official status of the sampling area (demarcated OQDS focus versus Xf-free area) and supported by real-time quantitative PCR (qPCR) on every plant; within demarcated sites, plants with undetectable DNA in sampled twigs were retained as Xf+ following phytosanitary criteria, giving 216 infected and 174 healthy samples. The multidimensional sensor signals were processed with an optimized Shallow Neural Network. Under plant-grouped 80/20 validation, keeping each plant’s replicates in the same subset, the model achieved (93.3 ± 4.0)% accuracy, (97.7 ± 3.5)% sensitivity, and (88.2 ± 8.3)% specificity (mean ± SD). A feature-importance analysis revealed a reproducible, though not chemically resolved, VOC-related response pattern. This low-cost, portable Internet of Things (IoT) device offers a proof-of-concept screening approach for Xf surveillance, pending plant-level and external validation.

Why it matches plant phenotyping methods植物の感染状態をVOCセンサーとAIで直接推定する現地スクリーニング手法を開発・評価しており、植物病害状態の取得が研究の中心である。

abstractThis study evaluates a portable digital electronic nose, based on a carbon-nanotube sensor array combined with artificial intelligence algorithms, for the in-field screening of Xf through volatile organic compound (VOC) profiling.

Non‐Destructive and High‐Fidelity Sensing of Plant Water Content Enabled by Near‐Infrared Luminescent Metal Halides →

ABSTRACT Real‐time, accurate water monitoring is a crucial technical foundation for industrial, environmental, and biological research. However, traditional detection methods typically include destructive processes and suffer from response delays. Near‐infrared (NIR) luminescent metal halides offer a novel solution to this challenge, but they still face issues such as ultraviolet excitation and low photoelectric conversion efficiency. Herein, a luminescent material was synthesized based on the blue‐light‐excited lead‐free perovskite Cs 2 HfCl 6 :Te 4+ /Mo 4+ , in which energy transfer (ET) from Te 4+ to Mo 4+ enables highly efficient NIR luminescence in the 800–1200 nm wavelength range. Upon encapsulation with a commercial blue light chip, the fabricated NIR light‐emitting diode device achieved a photoelectric conversion efficiency of up to 16.1%. By utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content. This non‐destructive and precise NIR detection module provides a new solution for the real‐time monitoring of crop physiological status and holds broad application prospects in the fields of precision agriculture and plant science.

Why it matches plant phenotyping methods植物の含水量を非破壊・リアルタイムに推定するNIRセンシングモジュールと機械学習手法が研究の中心であり、植物生理状態の測定法を開発・検証している。

abstractBy utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content.

Using CarboTrace 480 to detect protoplastation in pigment deficient mutant of Chlorella sorokiniana →

The increasing world population necessitates new sustainable nutrient sources, making microalgae like Chlorella sorokiniana interesting due to its rich nutrient profile and sustainable cultivation methods. With genetic optimization tools like CRISPR/Cas9, microalgae as a nutrient source can be improved even further. However, degradation of the rigid cell wall of microalgae, and thereby developing protoplasts, is often necessary prior to transformation, but monitoring protoplast development in spherical, single-celled organisms like C. sorokiniana is challenging using bright-field microscopy. Carbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension. The enzymes Driselase, Glucanex, Snailase, and Saczyme were tested in different combinations to degrade the cell wall of the mutant, with Snailase as the most effective yielding ~60 % protoplasts. This study provides a quick and easy tool for monitoring protoplast development in the microalgae C. sorokiniana, the first step to improve C. sorokiniana as a sustainable nutrient source using genetic optimization tools like CRISPR/Cas9.

Why it matches plant phenotyping methods微細藻類の細胞壁状態とプロトプラスト形成を蛍光マーカーで識別する方法の開発・検証が研究の中心であり、植物状態の取得手法に該当する。

abstractCarbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension.

Nuclear diversity in Charophyceae revealed by fluorescence staining and its implications for genome size estimation. →

Main conclusion We established a protocol for reliable nuclear visualization in Charophyceae, revealed diverse nuclear organization across cell types and species, and identified suitable cells for genome size estimation via flow cytometry. Charophyceae are multicellular green algae closely related to land plants and are established model systems for understanding plant evolution. Yet key cellular parameters like genome size remain poorly characterized. We combined fluorescence microscopy, transmission electron microscopy (TEM), and flow cytometry to characterize nuclear diversity across cell types and species of Characeae and to identify a cell type suitable for genome size estimation. Among three DNA-intercalating fluorochromes, propidium iodide labeled nuclei most reliably. Nuclear morphology varied widely across cell types: mononucleated cells were found in vegetative apical cells, the coronula of oogonia, and spermatogenous filaments of antheridia, whereas multinucleation predominated in other tissues, e.g., cortical cells, spine cells, stipulodes and rhizoids. Nuclei in Chara hispida showed a significant gradient in cross-sectional area along the thallus axis. In the apical internodes, nuclei were larger and more heterogeneous, whereas in the basal internodes they were smaller and more uniform, which is consistent with possible endopolyploidy. TEM confirmed the nuclear identity of crescent-shaped structures. Relatively large nuclei were found in rhizoids and spine cells. Only Sphaerochara canadensis showed an organized nuclei pattern. Whole-thallus preparations did not yield a defined nuclear peak by flow cytometry, but antheridia of Chara tomentosa produced a sharp peak, from which a genome size of 5.32 pg (1C) was estimated. The protocol established here provides a simple, reproducible framework for visualizing nuclei and estimating genome size in Charophyceae, and helps address longstanding questions in this group, such as the mechanisms and functions of multinucleation, the site of meiosis, and genome evolution.

Why it matches plant phenotyping methodsシャジクモ類の核可視化とゲノムサイズ推定のための再現可能なプロトコルを開発・検証しており、表現型取得法が研究の中心である。

abstractWe established a protocol for reliable nuclear visualization in Charophyceae

Plant Cell-on-Chip (PCOC): Exploring the Electrical Modulation Capability of Plant Cells →

The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.

Why it matches plant phenotyping methods植物細胞の電気的生理状態を測定・解析するEISベースのオンチップ基盤を開発しており、植物状態の取得方法が研究の中心である。

abstractwe present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection

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.

Quantification of microplastic uptake and phytotoxicity in submerged aquatic plants using fluorescence spectroscopy. →

Microplastics (MPs) are persistent and ubiquitous contaminants in aquatic ecosystems, yet their interactions with submerged aquatic plants remain poorly understood. While MP-induced phytotoxicity has been extensively investigated in terrestrial plants, quantitative evidence for MP uptake and internal accumulation in submerged species is still limited. In this study, we investigated the phytotoxicity and accumulation patterns of fluorescent microplastics (FMPs) in two submerged aquatic plants, Bacopa lanigera and Rotala indica, using fluorescence spectroscopy. Plants were exposed to FMPs of two particle sizes (50 nm and 1 µm) across three exposure concentrations (0.001%, 0.01%, and 0.05%). Plant growth, chlorophyll content, fluorescence emission, and FMP accumulation were systematically evaluated. Our results demonstrated clear size- and concentration-dependent responses. Smaller particles (50 nm) showed significantly higher uptake and induced stronger phytotoxic effects than 1 µm particles, with pronounced growth inhibition and chlorophyll reduction observed at the highest concentration (0.05%). Fluorescence-based analysis enabled quantitative estimation of both surface-associated and internalized FMPs within plant tissues. Maximum surface accumulation reached 207 ppm, while internal (cross-sectional) accumulation reached up to 75 ppm, regardless of plant species. Under the respective experimental conditions, B. lanigera exhibited higher estimated FMP accumulation, whereas R. indica showed greater growth inhibition. These findings provide quantitative evidence of microplastic uptake and internal accumulation in submerged aquatic plants and highlight particle size as a critical determinant of phytotoxicity. Moreover, this study establishes a fluorescence-based methodological framework for estimating microplastic concentrations in aquatic plant tissues, contributing to improved ecological risk assessment of microplastics in freshwater ecosystems.

Why it matches plant phenotyping methods蛍光分光法による植物組織内マイクロプラスチック蓄積の定量が中心的な技術貢献であり、植物の蓄積状態と毒性関連表現型を評価している。

abstractusing fluorescence spectroscopy

Application of Remote Sensing Technologies in Crop Health Monitoring and Disease Surveillance →

The rapid detection and continuous monitoring of crop health and disease outbreaks are critical components of modern precision agriculture, essential for maintaining global food security. Traditional field-based scouting methods, while accurate, are often labor-intensive, time-consuming, and limited by spatial coverage, making them inadequate for large-scale agricultural operations. Remote sensing (RS) technologies—spanning satellite imagery, drone-based aerial platforms, and proximal sensors—offer a powerful, non-destructive, and scalable alternative for capturing high-resolution spectral and temporal data. This paper provides a comprehensive evaluation of current remote sensing applications in crop health monitoring and disease surveillance. We analyze how vegetation indices derived from multispectral and hyperspectral data, such as NDVI and red-edge parameters, serve as sensitive indicators of physiological stress and pathogen infection, often manifesting before visible symptoms appear. Furthermore, we explore the integration of machine learning and artificial intelligence algorithms in automating disease identification and severity mapping. By synthesizing recent advancements in sensor technology and data analytics, this paper demonstrates that remote sensing is indispensable for proactive, site-specific management. The findings emphasize that a multi-scale RS approach—integrating broad-scale satellite monitoring with high-resolution drone sorties—enables farmers to optimize input efficiency, minimize yield losses, and enhance the overall resilience of agro-ecosystems against biotic and abiotic stressors.

Why it matches plant phenotyping methods作物の健康・病害を対象に、リモートセンシング、センサー、植生指数、機械学習による状態・重症度推定を包括的に評価するレビューであり、フェノタイピング手法が中心です。

abstractThis paper provides a comprehensive evaluation of current remote sensing applications in crop health monitoring and disease surveillance.

IoT and Machine Learning for Crop Stress Assessment and Decision Support →

Precision agriculture increasingly requires intelligent systems capable of integrating multimodal sensing with transparent decision support to enable timely and reliable crop management. This study proposes a hybrid intelligent IoT framework integrating environmental monitoring, wearable plant physiological sensing, AI-based pest-monitoring, machine-learning-based prediction of crop physiological stress, and explainable fuzzy rule-based decision support into a unified architecture for crop stress assessment. A novel Physiological Stress Index (PSI) was developed by combining vapor pressure deficit, relative humidity, Delta-T, leaf capacitance, and relative irradiance to provide an interpretable indicator of crop physiological stress. The proposed framework was experimentally validated under real field conditions using a commercial environmental monitoring station, wearable leaf sensors, AI-enabled pest-monitoring devices, and cloud-based analytics. Correlation analysis confirmed strong relationships between PSI and the principal environmental variables (VPD: r = 0.980, Delta-T: r = 0.990, RH: r = −0.961), demonstrating the internal consistency and sensitivity of the proposed index. At the 15 min forecasting horizon, Linear Regression and Gradient Boosting demonstrated virtually identical performance: Gradient Boosting achieved a marginally lower RMSE and higher R2 (RMSE = 0.0273; R2 = 0.9810), whereas Linear Regression achieved a slightly lower MAE (MAE = 0.0186). At the 1 h forecasting horizon, Gradient Boosting achieved the strongest performance (R2 = 0.9034), indicating increasing relevance of nonlinear modelling at longer prediction horizons. The proposed framework demonstrates the feasibility of combining multimodal sensing, machine learning, explainable artificial intelligence, and edge-enabled IoT technologies to support proactive, transparent, and intelligent precision agriculture.

Why it matches plant phenotyping methods植物の生理的ストレス状態を多モーダルセンサーと機械学習で推定する方法を開発し、圃場で検証しており、フェノタイピング手法が中心である。

abstracta hybrid intelligent IoT framework integrating environmental monitoring, wearable plant physiological sensing, AI-based pest-monitoring, machine-learning-based prediction of crop physiological stress

Monitoring phases of plant stress in juvenile commercial forest cuttings using contemporary nursery sensor technologies →

Abstract Visual assessments of growing forest nursery plants are time-consuming and often result in a lack of information at a physiological level. There exists a need for health screening in nurseries, that is fast and efficient, to improve overall health monitoring and nursery productivity. Rapid handheld sensors such as rapid thermal devices, leaf porometers and moisture meters, can provide regular information at a physiological level, that can improve the understanding of the impact of stress on young plant cuttings and their decline in health over time. This paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions. Furthermore, to assess whether thermal sensors could be used as an indicator, in conjunction with other variables such as soil water content or stomatal conductance, is needed operationally for fast screening during limited planting windows. Near Infra-Red Analysis (NiRA) data was collected to understand detailed plant functions at a finer reflectance level. A relationship was found where the increase in thermal signals reflects a depletion of water content, resulting in an eventual decline in stomatal conductance and, ultimately, plant mortality. Several algorithms were used in a preliminary test, using RapidMiner software, to discriminate between the four phases of plant health decline using physiological variables and NiRA data. Both Gradient Boosting Trees (GBT) and Deep Learning (DL) showed the best performances, achieving favourable accuracies of 96.8% and 91.2% without NiRA data, 84.6% and 88.2% with NiRA data, with shorter training times. Using thermal technology weighted amongst the highest of the best performing variables using GBT, the utility and accuracy showed good discrimination between the stages of plant decline and is encouraged for future research in this field.

Why it matches plant phenotyping methods植物のストレス段階を熱センサー、ポロメータ、含水率計、NiRAおよび機械学習で測定・識別する方法の有用性と信頼性を評価しており、表現型取得・判定手法が中心である。

abstractThis paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions.

Spectral network analysis illuminates coordinated plant traits across a climate gradient →

Summary Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a powerful high‐throughput solution, simultaneously capturing leaf biochemistry, water content, and structural properties across hundreds of wavelengths. We present a framework combining hyperspectral data, inverse modeling, and network analysis to investigate population‐level variation in Streptanthus tortuosus . Using a common garden experiment with four populations, we apply supervised methods (partial least square discriminant analysis; ridge regression) to identify which spectral features differ among populations, and an unsupervised spectral network approach to characterize how wavelength correlations are organizationally structured within each population, where we treat coordination architecture itself as a population‐level phenotype that can vary with environment. The framework detects distinct, heritable spectral signatures across populations, population differences in anthocyanins, carotenoids, Chl, water content, and population‐specific network architectures. Thermally variable environments were associated with greater spectral modularity, demonstrating that trait coordination architecture varies with climate of origin. This approach addresses the phenotyping bottleneck in evolutionary ecology, providing a scalable, high‐throughput tool for characterizing genetically based population differences in both individual traits and their coordination, with broad applications for monitoring plant population responses to climate change.

Why it matches plant phenotyping methodsハイパースペクトル計測、逆モデリング、ネットワーク解析を統合し、葉の機能形質と形質協調構造を植物表現型として抽出する手法が研究の中心である。

abstractHyperspectral reflectance offers a powerful high‐throughput solution, simultaneously capturing leaf biochemistry, water content, and structural properties across hundreds of wavelengths.

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.

In-situ visualisation of the micromechanical deformation of apple tissue using 4D X-ray computed tomography with digital volume correlation. →

Continuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression, revealing how the cellular microstructure governs its mechanical response. We introduce a dimensionless number Mi that is a function of the average interfacial contact area of cells, cell wall thickness, the average cell volume and tissue porosity, to describe tissue microstructure over different development stages. Mechanical softening during maturation aligned strongly with decreasing Mi, linking microstructure to effective Young's modulus, peak stress, and toughness. DVC revealed distinctive strain-distribution signatures: in young, low-porosity tissue, strain was initially diffuse with early-onset localization indicating progressive failure, whereas mature, high-porosity tissue exhibited sharply peaked strain distributions and highly localized fracture planes indicative of brittle collapse. These findings demonstrate how pore evolution, anisotropy, and cell packing jointly determine macroscopic deformation, establishing XCT-DVC as a powerful framework for connecting plant tissue architecture to mechanical function.

Why it matches plant phenotyping methods4D XCTとDVCを用いてリンゴ組織の内部三次元ひずみ、微細構造、破壊状態を定量化する手法が研究の中心であり、植物組織の構造・力学的状態を抽出しているため。

abstractContinuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression

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

Advances in Imaging of Plant Ca2+ Signaling →

Calcium ions (Ca2+) function as ubiquitous second messengers that translate environmental and developmental cues into spatially and temporally defined cellular responses in plants. This review summarizes the cellular architecture and molecular mechanisms that generate, shape, and terminate Ca2+ signals, with emphasis on plasma-membrane channels, intracellular stores, pumps, exchangers, and organelle-associated transport systems. We also examine the development of live Ca2+ indicators, from chemical dyes and aequorin to ratiometric and single-fluorophore genetically encoded calcium indicators, and discuss principles for selecting sensors for different tissues and subcellular compartments. Recent studies have applied these tools to abiotic stress, plant immunity, polar growth, development, symbiosis, and systemic signaling. Accurate quantitative imaging nevertheless requires careful matching of sensor properties to the target cellular environment and rigorous control of motion, spectral interference, and analytical procedures. Combining improved indicators with advanced microscopy, genetic validation, and standardized data analysis should help connect distinct Ca2+ signatures with their molecular origins and physiological roles.

Why it matches plant phenotyping methods植物のCa2+シグナルを定量するライブイメージング指標、顕微鏡、解析手順を中心にレビューしており、生理状態の取得方法が主題である。

abstractWe also examine the development of live Ca2+ indicators, from chemical dyes and aequorin to ratiometric and single-fluorophore genetically encoded calcium indicators, and discuss principles for selecting sensors for different tissues and subcellular compartments.

Laser biospeckle imaging and mathematical modeling for comprehensive malting barley (Hordeum vulgare subsp. distichum L.) seed quality evaluation: viability, moisture and germination dynamics. →

Background and aims Laser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination. Methods Malting barley (Hordeum vulgare subsp. distichum L. cv. Sinfonia) seeds were analysed using five coefficients-Generalised Differences (GD), Fujii, Lasca, Frequent Motion Image (FMI), and Moment of Inertia (MI)-to assess their ability to discriminate between treatments and tissue regions, and to track changes during imbibition. Whole and longitudinally cut seeds from two treatments (control [untreated] and autoclaved [heat-inactivated]) were analysed, focusing on embryo/endosperm activity ratios. LBSA was also evaluated as a function of imbibition time and seed moisture content. Key results Four coefficients (GD, Fujii, FMI, and MI) successfully differentiated control and autoclaved seeds, as well as embryo and endosperm regions in control seeds, revealing distinct activity patterns. In control seeds, LBSA increased with imbibition time and was well described by polynomial models (quadratic for Fujii and MI; cubic for GD and FMI). GD, Fujii, and FMI required a minimum seed moisture content of 25% to detect activity, while MI was responsive only above 31.5%. In contrast, Lasca was exclusively sensitive to hydration level, fitting a relaxation curve independent of treatment. Conclusions LBSA constitutes a robust, non-destructive methodology for monitoring early germination processes. By combining coefficients, it is possible to infer physiological traits such as embryo specificity, hydration thresholds, and dynamic metabolic reactivation. This positions LBSA not only as a diagnostic tool for seed viability but also as a physiologically informative proxy for studying germination and tissue-level dynamics.

Why it matches plant phenotyping methodsレーザーバイオスペックル画像法を用いて種子の生存性、含水率、発芽動態を測定・識別し、複数係数の性能評価とモデル化を行う手法中心の研究である。

abstractLaser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination.

A Cu/ZIF-8-based flexible sensor for real-time monitoring of protocatechuic acid in plants under environmental stress. →

Understanding the dynamic regulation of endogenous metabolites in plants under environmental stress is essential for elucidating plant adaptive mechanisms and improving crop resilience. However, conventional analytical methods are typically destructive, time-consuming, and lack the capability for real-time monitoring, thereby limiting the investigation of in-vivo biochemical dynamics in plants. In particular, the in-situ, non-invasive detection of small-molecule regulators such as protocatechuic acid (PCA) remains a significant challenge. Herein, we report a wearable electrochemical sensing platform based on a Cu/ZIF-8-modified flexible printed electrode (FPE) for real-time, in-situ monitoring of PCA in plant leaves. The incorporation of Cu into the ZIF-8 framework enhances the electrical conductivity and electrocatalytic activity of the material while maintaining its porous structure, enabling sensitive detection of PCA. By integrating with reverse iontophoresis (RI), non-invasive extraction and continuous monitoring of PCA from living plant tissues are achieved. The dynamic behavior of PCA in green tea plants under light deprivation and drought stress is systematically investigated. The results reveal distinct stress-dependent response patterns, with PCA levels rapidly decreasing under both dark and drought conditions, highlighting its critical role in stress adaptation and metabolic regulation. This work establishes a versatile strategy for real-time tracking of endogenous plant metabolites and provides new insights into plant physiological responses under environmental stress. The proposed platform holds significant promise for applications in plant science, precision agriculture, and the development of stress-resilient crops.

Why it matches plant phenotyping methods植物葉内代謝物をリアルタイム・非侵襲的に取得するウェアラブル電気化学センサーと抽出・連続モニタリング系の開発が中心であり、植物の生理状態を測定する方法として適格です。

abstractHerein, we report a wearable electrochemical sensing platform based on a Cu/ZIF-8-modified flexible printed electrode (FPE) for real-time, in-situ monitoring of PCA in plant leaves.

Revisiting the Temperature Dependence of the Photorespiratory CO 2 Compensation Point (Γ*). →

Accurate estimation of the photorespiratory CO 2 compensation point (Γ*) is essential for describing the balance between Rubisco carboxylation and oxygenation and for parameterising biochemical models of photosynthesis. Γ* and the rate of CO 2 release in the light (D L ) are commonly estimated using the Laisk method, based on measurements of net CO 2 assimilation rate (A net ) at low chloroplastic CO 2 concentrations (c c ), under several sub-saturating irradiance levels. However, many widely used temperature dependence relationships for Γ* (Γ*(T)) were derived using conventional linear implementations of the Laisk method, despite the intrinsically nonlinear behaviour of the A net -c c response predicted by the photosynthetic theory. Here, we revisited the temperature dependence of Γ* and D L using the improved Laisk-FvCB framework that simultaneously constrains the nonlinear A net -c c response across multiple irradiance levels. Gas exchange of sunflower leaves was measured across a wide temperature range from 3.9°C to 42.0°C. The conventional linear implementation generated highly dispersed pairwise intersections and unstable estimates of both Γ* and D L , including some physiologically unrealistic negative D L values at low temperatures. In contrast, the mechanistically constrained Laisk-FvCB framework produced physiologically meaningful temperature responses and substantially reduced methodological artefacts associated with linear extrapolation. Using this framework, we derived a revised in vivo Γ*(T) relationship described by an Arrhenius-type function with Γ*(25) = 43.4 μmol mol -1 and an apparent activation energy of 27.7 kJ mol -1 , such that Γ*(T) = 43.4 exp[11.176 ((T - 25)/(T + 273.15))], where T is leaf temperature in °C. Comparison with other widely used Γ*(T) formulations showed substantial divergence at temperature extremes, often exceeding the variability expected from realistic interspecific differences in Rubisco specificity among C 3 species.

Why it matches plant phenotyping methods植物葉のガス交換からΓ*と光呼吸CO₂放出速度を推定する改良Laisk-FvCB手法を提示し、従来法との比較で推定の安定性と方法論的アーティファクトを検証しているため、植物表現型測定法が中心である。

abstractHere, we revisited the temperature dependence of Γ* and D L using the improved Laisk-FvCB framework that simultaneously constrains the nonlinear A net -c c response across multiple irradiance levels.

Paper-based nanozyme sensor array for volatile chemical fingerprinting of Huanglongbing-associated citrus samples. →

Citrus Huanglongbing (HLB) is one of the most destructive citrus diseases worldwide, and early diagnosis remains challenging because uneven pathogen distribution often leads to false-negative PCR results. Here, a paper-based nanozyme sensor array was developed for volatile organic compound (VOC) chemical fingerprinting of HLB-associated citrus samples. Fe/Al bimetallic NH₂-MIL-53 nanozymes were designed to regulate VOC adsorption and peroxidase-like catalytic activity. Exposure of the nanozyme sensors to VOCs reduced the catalytic oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), generating concentration-dependent colorimetric responses. By tuning the Fe/Al ratio, the nanozymes exhibited differentiated responses toward HLB-associated volatiles, including methyl salicylate, phenylacetaldehyde, and linalool, with recognition limits of 0.1-0.5 ppm. Integration with two MOF-based sensing channels formed a five-channel artificial olfactory array capable of generating multidimensional color fingerprints. The sensor array successfully discriminated healthy, asymptomatic, and infected citrus leaf samples, achieving an overall classification accuracy of 92.5%. These results suggest that the proposed platform provides a simple and low-cost approach for citrus VOC chemical fingerprinting and may provide useful information for quality-related screening in citrus production systems, while further field-oriented validation is still needed to assess its practical applicability.

Why it matches plant phenotyping methods柑橘葉のHLB感染状態をVOCセンサーアレイで識別する計測・解析法の開発が研究の中心であり、植物病害状態を直接推定している。

abstractHere, a paper-based nanozyme sensor array was developed for volatile organic compound (VOC) chemical fingerprinting of HLB-associated citrus samples.

Dual polarity-viscosity responsive fluorescent probes for lipid droplet imaging and smartphone-based on-site crop oil screening →

Herein, we rationally designed and synthesized two types of D-π-A red-emitting fluorescent probes with intrinsic intramolecular charge transfer (ICT) and twisted intramolecular charge transfer (TICT) characteristics, for traditional methods for lipid droplet detection in food crops suffer from reliance on bulky instruments, tedious sample pretreatment and the impossibility of on-site quantification. To modulate the molecular skeleton, methoxy groups were introduced, and highly specific recognition of lipid droplet microenvironments was realized through the probes' synergistic spectral response to polarity and viscosity. Typical turn-on fluorescence responses and favorable lipophilicity were displayed by both probes, thus enabling accurate localization of the hydrophobic domains of lipid droplets. Possessing ratiometric fluorescence performance, the modified probe OCH 3 -Phe-FCN efficiently alleviated signal interference from complex matrices, and toward oleic acid was a lower limit of detection presented. By combining smartphone-based RGB colorimetry and image grayscale analysis was a portable detection platform constructed, with fluorescence signals employed to accomplish visual analysis of lipid droplets in food crops. Satisfactory biocompatibility and targeting capability were validated for the as-prepared probes via cell imaging and cytotoxicity evaluations, and far superior imaging quality and signal-to-noise ratio were demonstrated by the modified probe. With the aid of density functional theory (DFT) calculations, systematically elucidated was the spectral mechanism of excited-state transitions synergistically modulated by the two microenvironmental factors. Not only does this work deepen the insight into the response mechanism of dual-responsive fluorescent probes, but also a promising technical strategy is offered for in situ spectral detection of lipid droplets in food crops.

Why it matches plant phenotyping methods食品作物の脂質滴を蛍光プローブとスマートフォン画像解析で可視化・定量する検出法および携帯型プラットフォームの開発が中心であり、植物の細胞状態を測定する技術的貢献が明確です。

abstractBy combining smartphone-based RGB colorimetry and image grayscale analysis was a portable detection platform constructed, with fluorescence signals employed to accomplish visual analysis of lipid droplets in food crops.

Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: An Integrated Dual-Stream Evidence Synthesis and Conceptual Framework for Field-Scale Validation →

Magnetic field (MF) technologies have been explored in agriculture since the 1930s, with research activity increasing markedly since 2016. However, they have not achieved mainstream adoption, partly because no MF-specific validated methodology exists for evaluating their effects under realistic field conditions. Unmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, thereby enabling, for the first time, the systematic evaluation and validation of MF treatment responses under open-field conditions. To realise this potential, however, a common evidential basis must first be established by identifying crop physiological variables that are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream evidence synthesis of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency (NUE)/nitrogen assimilation, above-ground biomass (AGB), leaf area index (LAI), and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R2 = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The review revealed a complete absence of integration between the two research domains within the reviewed corpus, despite their strong biological and methodological compatibility. The proposed framework is conceptual and remains to be experimentally validated; it provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.

Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによる作物生理形質の推定をレビューし、地上真値・植生指数・機械学習を統合した検証フレームワークを提案しており、植物表現型の取得・推定方法が中心です。

abstractUnmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale

Percent Tolerance to Phosphorus Deficiency (PTPD) as a Potential Metric for Genotypic Screening in Soybean ( Glycine max L.). →

Phosphorus (P) deficiency severely limits soybean ( Glycine max L.) productivity. This study proposed a three-stage screening framework to identify reliable traits and P-efficient genotypes. In Experiment I, percent tolerance to phosphorus deficiency (PTPD) was calculated for ten growth parameters across 98 genotypes under P-deficient and control conditions. Principal component analysis and comprehensive evaluation identified six key indicators in Experiment I, which were subsequently refined to five indicators through further analysis: SPAD at V3 and R1, photosynthetic rate at R1, shoot dry weight at R8, and seed number per plant at R8. Experiment II re-evaluated these traits using 12 contrasting genotypes under three P levels, identifying CN 15 as the most P-efficient and SN 22 as the most P-inefficient. Experiment III further revealed that CN 15 maintained superior PSII performance and exhibited a 26.2% increase in grain P-utilization efficiency under 0 µM KH 2 PO 4 treatment. This integrated framework offers a preliminary reference for screening P-efficient soybean genotypes under controlled conditions, pending field evaluation.

Why it matches plant phenotyping methodsリン欠乏耐性を評価するPTPDと三段階の形質選抜フレームワーク自体を提案・検証しており、単なる生物学的処理試験ではなく、植物形質に基づく遺伝子型スクリーニング手法が中心である。

abstractThis study proposed a three-stage screening framework to identify reliable traits and P-efficient genotypes.

Measurement of Net NH 4 + Fluxes Using the Non-invasive Micro-Test Technology (NMT) System in Rice. →

Ammonium (NH 4 + ) is the primary inorganic nitrogen source for rice ( Oryza sativa L.). Substantial progress has been made in characterizing the functions of ammonium transporters (AMTs) in roots; however, the regulatory dynamics governing subcellular ammonium compartmentation after its entry into cells, particularly its vacuolar sequestration and efflux back to the external environment, remain poorly understood. This knowledge gap stems mainly from two factors: the difficulty of applying conventional detection methods at the organellar scale and interference caused by nonspecific ion adsorption to the cell wall of intact roots. To address these challenges, we present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT). The protocol covers the preparation of protoplasts and vacuoles from rice roots, the configuration and calibration of the NMT system, and the step-by-step measurement of net NH 4 + fluxes at three distinct biological levels (intact roots, protoplasts, and vacuoles). By employing a unified sample preparation and measurement strategy, this protocol enables quantification of net uptake fluxes across the plasma membrane, characterization of net efflux dynamics under specific conditions, and indirect estimation of vacuolar sequestration capacity using the isolated vacuole system. Overall, this protocol provides a flexible and robust framework for studying NH 4 + homeostasis in plants and is readily adaptable to different crop species, treatment conditions, and experimental objectives. Owing to its modular design and compatibility with standard NMT equipment, it can be readily adopted by laboratories seeking to investigate nitrogen transport mechanisms in plants. Key features • Allows for testing of NH 4 + fluxes in roots, protoplasts, and vacuoles. • Applicable to plants grown under different culture systems, including Arabidopsis thaliana grown in dishes and rice grown in hydroponic systems. • Supports both long-term and transient stress treatments. • Real-time monitoring.

Why it matches plant phenotyping methods植物根・プロトプラスト・液胞のNH4+フラックスをリアルタイム定量するNMT測定プロトコルが研究の中心であり、植物の生理状態を取得する方法を詳細に開発・標準化している。

abstractwe present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT).

Association of morphological markers of flower buds and anthers with the pollen developmental stage in Vicia faba L. ( Fabaceae ). →

The precise identification of microspore and pollen at the optimal developmental stages to be induced towards embryogenesis (vacuolated microspores and young pollen) is essential for induction of in vitro androgenesis in plants. Such identification is not always easy, and it is especially difficult in recalcitrant species such as Vicia faba . The present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry. We measured flower bud and anther length and width, grouping them at different intervals, and performed a detailed microscopical and anatomical analysis of buds, anthers and microspores/pollen at different stages. Our results demonstrated that flower buds in V. faba exhibit complex and irregular morphologies, with considerable variation in both sepal length and shape. Furthermore, the determination of microspore and pollen developmental stages in this species is constrained by pronounced developmental asynchrony and strong genotype dependence. Although anther length measurements correlate closely with microspore and pollen developmental stages, their practical use can be challenging. Therefore, measuring flower bud length, while excluding sepals, remains the most practical criterion for routine applications. Combining this refined morphometric approach with microscopic validation appears to be the most effective strategy for improving the identification of flower buds containing microspores or pollen at developmental stages suitable for androgenesis induction in this recalcitrant legume species.

Why it matches plant phenotyping methods花蕾・葯の形態計測と顕微鏡検証を用いて、微小胞子・花粉の発達段階を推定する実用的な植物フェノタイピング手法を評価しており、方法開発・検証が中心である。

abstractThe present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry.

In situ NIR-IIb imaging of endogenous H 2 S signaling for high-resolution abiotic stress visualization in plants. →

In vivo analysis of plant stress responses remains a major challenge in precision agriculture, limiting dynamic optimization of crop growth under variable environmental conditions. Fluorescence imaging enables nondestructive tracking of stress biomarkers, but its accuracy is compromised by the low abundance of endogenous signaling molecules, tissue autofluorescence interference, and limited signal penetration depth. Here, we develop a ratiometric near-infrared IIb fluorescent probe for sensitive monitoring of endogenous hydrogen sulfide (H 2 S). This nanoprobe integrates lanthanide nanoparticles with H 2 S-responsive molecular units, enabling enhanced tissue penetration and high-resolution imaging through an absorption competition-induced emission mechanism. Under abiotic stress conditions, the probe visualizes stress-induced fluctuations of H 2 S in living plants. This work establishes an H 2 S-centered strategy for plant stress visualization and provides a foundation for developing early diagnosis platforms.

Why it matches plant phenotyping methods植物の非生物的ストレス状態を、生体内H₂Sシグナルの蛍光画像として可視化する新規NIR-IIbプローブを開発しており、表現型取得法が研究の中心である。

abstractHere, we develop a ratiometric near-infrared IIb fluorescent probe for sensitive monitoring of endogenous hydrogen sulfide (H 2 S).

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

Nanosensors and Geospatial Technologies for Early Crop-stress Detection in Precision Agriculture: A Critical Multiscale Synthesis →

Crop stress develops through a sequence that begins with molecular and biophysical perturbation, progresses through physiological dysfunction, and only later becomes visually apparent. Precision agriculture therefore requires sensors that can shorten the interval between stress onset and actionable diagnosis while preserving spatial context. This critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection. Literature published from 2000 to 1 June 2026 was selected through live searches of accessible scholarly indexes, DOI registries, publisher records, institutional repositories, and citation networks, with foundational studies retained where necessary. The evidence shows that nano-enabled interfaces can measure early biochemical, ionic, volatile, electrical, and microclimatic signals at high temporal resolution, whereas geospatial technologies reveal the distribution, persistence, and management relevance of stress across canopies and fields. Optical nanotube sensors, surface-enhanced Raman probes, electrochemical microneedles, ion-selective wearables, and flexible leaf sensors have demonstrated biologically meaningful signals before visible symptoms in controlled or pilot field settings. Yet most remain constrained by sparse sampling, crop-specific calibration, bio-interface effects, power and communication burdens, uncertain durability, and limited agronomic validation. Geospatial methods are operationally more mature, particularly thermal and multispectral imaging for water stress and hyperspectral imaging for disease and nutrient-related changes, but they often infer stress through non-specific proxies that are confounded by canopy structure, atmosphere, soil background, phenology, and co-occurring stresses. The strongest future architecture is therefore not a contest between nanoscale and landscape-scale sensing. It is a multiscale system in which physiologically specific plant sensors anchor and interpret spatial imagery, while remote sensing directs where high-specificity measurements and interventions are most valuable. Progress depends on prospective field trials, reference measurements, uncertainty-aware data fusion, interoperability, lifecycle safety assessment, and decision thresholds linked to economic and agronomic outcomes.

Why it matches plant phenotyping methods植物ストレス状態の検出に用いるナノセンサー、ウェアラブルセンサー、熱・マルチスペクトル・ハイパースペクトル画像などを中心に批判的に統合した方法レビューであり、単なる農業応用紹介ではなく、センサー性能、校正、検証、データ融合を論じている。

abstractThis critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection.

Identifying and engineering the molecular origins of lignin color for predictive staining of plant tissues →

Lignins in plant biomass are carbon-negative aromatic biopolymers which hold tremendous potential as multipurpose resources for sustainable bioeconomy, limited only by their chemical heterogeneity. Plant lignified tissues, such as sapwood and seed coats, vary in colors within and between species, indicating that specific lignin topochemistries determine the different colors. Yet, the responsible lignin chromogen(s) are unknown. We developed chemical imaging using UV-Vis microspectroscopy to link lignin color to topochemistry in isolates and plant samples. Using synthetic and technical lignins, we identified the different stable chromogens as homomeric lignin substructures varying in size, unit chemistry and interunit linkages. We controlled the accumulation of specific lignin chromogens using genetic engineering to similarly stain lignified tissues from different plant species. We established plant tissue engineering to cast plant tissues with pre-determined color by adjusting lignin topochemistries. We proved that biotechnological manipulation of the identified lignin chromogens predictably and stably stains lignified plant tissues.

Why it matches plant phenotyping methodsUV-Visマイクロ分光法による化学イメージングを開発し、植物組織のリグニン由来色をトポケミーと関連付けて予測・操作する手法が研究の中心である。

abstractWe developed chemical imaging using UV-Vis microspectroscopy to link lignin color to topochemistry in isolates and plant samples.

Volatile organic compounds as non-destructive biomarkers for postharvest quality and disease detection in vegetables. →

High perishability of vegetables associated with rapid physiological deterioration and microbial spoilage results in 30-50% post-harvest losses globally. In earlier days, post-harvest diseases were detected by visual inspection, microbial culturing, and molecular assays. These destructive methods are time-consuming and unsuitable for real-time monitoring. Volatile organic compounds (VOCs) have emerged as promising non-destructive biomarkers enhanced for early detection of quality deterioration and pathogen attack, often before visible symptoms appear. This review provides a thorough overview of current knowledge on VOC emissions in postharvest vegetables with their biosynthetic origins, classification, and roles in different kinds of stress responses and host microbe interactions. VOC alterations during spoilage and disease progression are systematically evaluated, highlighting vegetable group-specific patterns and quantitative dynamics of key biomarkers emitted naturally and due to mechanical and microbial spoilage. GC-MS, GC-IMS, PTR-MS, electronic noses, and biosensors are advanced analytical techniques that are critically compared with emphasis on their integration with machine learning for classification accuracy. Despite this significant progress, variability across cultivars and storage conditions, overlap between host- and pathogen-derived volatile metabolites, and an enduring gap between laboratory findings and commercial applications are major challenges that cannot be ignored. The development of real-time monitoring systems, vegetable-specific VOC databases, and integration with smart storage infrastructure powered by the Internet of Things and artificial intelligence must be prioritized in the future.

Why it matches plant phenotyping methods野菜の品質劣化・病害状態をVOCで非破壊推定する分析技術と機械学習を中心に比較・レビューしており、植物状態の取得手法が主題である。

abstractVolatile organic compounds (VOCs) have emerged as promising non-destructive biomarkers enhanced for early detection of quality deterioration and pathogen attack, often before visible symptoms appear.

Three-Point Bend Testing for Quantification of Maize Brace Roots Mechanics. →

Root lodging, the agronomic term for plant mechanical failure, causes yield loss in crops, including maize. Brace roots can provide structural support and assist in preventing root lodging. While the mechanics of brace roots (e.g., stiffness and strength) can play a role in their ability to prevent root lodging, there has been limited characterization of individual brace root mechanical properties. Methods to quantify root mechanics can thus be useful for characterizing maize mechanical traits and breeding new varieties with improved root anchorage and lodging resistance. Here, we describe a protocol for evaluating mechanical properties of maize brace roots. Specifically, we outline the steps necessary to perform three-point bend mechanical testing of maize brace roots using an Instron Universal Testing Stand. We describe root preparation, instrument setup, method establishment, testing, and data analysis. While we exemplify the protocol using maize brace roots, the approach can be adapted for assessing the mechanics of other plants or root types.

Why it matches plant phenotyping methodsトウモロコシの根の力学特性という植物形質を定量する三点曲げ測定プロトコルの開発・手順化が中心であり、育種利用可能な表現型測定法に該当する。

abstractHere, we describe a protocol for evaluating mechanical properties of maize brace roots.

Protocol for cell-specific ratiometric quantification of apoplastic pH in Arabidopsis seedlings. →

Apoplastic pH dynamically regulates plant intercellular communication, but its measurement in internal tissues, such as the vasculature, remains technically challenging. Here, we present a protocol for ratiometric quantification of apoplastic pH in Arabidopsis seedlings using genetically encoded sensors. We describe seedling preparation, confocal imaging, and ratiometric image processing. Companion cell-specific expression of the pH sensor enables apoplastic pH readouts in the vasculature and supports in vivo comparative analyses of apoplastic pH across genotypes, treatments, and growth conditions in young seedlings. For complete details on the use and execution of this protocol, please refer to Xiong et al. 1 .

Why it matches plant phenotyping methods植物のアポプラストpHという生理状態を、遺伝子コード型センサー、共焦点撮像、画像処理で定量するプロトコルであり、表現型取得法が中心です。

abstractHere, we present a protocol for ratiometric quantification of apoplastic pH in Arabidopsis seedlings using genetically encoded sensors.

Chitosan-Infiltrated TiO2 Nanocrystal Composite Optical Metasurfaces for Colorimetric Leaf Sensing. →

We report the design and assembly of geometrically and compositionally complex dielectric metasurfaces, termed nanocrystal-adaptive polymer composite optical metasurfaces (NCOMs), architected from nanocrystal (NC) building blocks and stimuli-responsive polymers to enable high figure-of-merit optical leaf sensors. These NCOM sensors are fabricated at scale by direct nanoimprint lithography into UV-curable TiO2 NC inks to structure optical metasurfaces in the geometry of surface relief gratings atop waveguides that support high reflectivity, narrowband guided-mode resonances. A room-temperature ligand exchange process creates a nanoporous TiO2 scaffold that is subsequently infiltrated with a stimuli-responsive polymer while retaining the metasurface geometry and optical quality. As a proof of concept, we demonstrate optical humidity sensors by incorporating a moisture-responsive biopolymer chitosan into the nanoporous TiO2 metasurfaces, resulting in a 430% improvement in sensitivity compared to conventional refractive-index sensors where the polymer is adjacent to the metasurface. The NCOM geometry is engineered to position its resonance at wavelengths above 750 nm, enabling detection with standard VIS-NIR Si photodetectors while maintaining broadband transparency across the photosynthetically active 400-700 nm wavelength range for unobstructive monitoring of crop health. NCOM sensors are mounted face-down on leaves, and the highly reflective resonances are readily distinguished from the leaf background and respond dynamically to leaf water stress, enabling passive, battery-free monitoring of leaf surface humidity.

Why it matches plant phenotyping methods葉面湿度と水ストレスを非接触・受動的にモニタリングする光学センサーの設計、製造、感度向上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。

abstractenable high figure-of-merit optical leaf sensors

Phenotyping maize stay green traits via in situ leaf hyperspectral reflectance sensing →

Advancements in stay-green phenotyping are increasingly utilizing hyperspectral sensing technology to assess crop response under extreme environmental conditions. Yet, the effectiveness of different spectral features in explaining stay green remains to be fully elucidated. This includes identifying which bands and spectral indices are more effective in capturing the genotypic differences in stay-green traits. The main objective of this study was to evaluate hyperspectral leaf reflectance as a means to estimate stay-green visual scores (SGVS) as an indicator of drought tolerance and to further understand whether chlorophyll absorption-band spectral indices can differentiate SGVS classifications during post-flowering stages of maize. The experiment was conducted over two growing seasons in Germany, comprising 18 maize genotypes under two contrasting water availability conditions. We measured leaf hyperspectral reflectance using a spectroradiometer in the second, fourth, and sixth week after flowering, along with stay-green traits measurements. We employed raw spectral reflectance, hyperspectral vegetation indices (VIs) in combination with random forest (RF) and ANN models to predict SGVS. Results showed that drought stress significantly affected stay-green-related traits and led to a 43.5% decrease in grain yield in the inbred lines. The grain dry yield (GDY) was positively correlated with stay-green visual scores (SGVS), with higher SGVS associated with higher GDY. Stay-green traits were correlated with various VIs, with the best correlation observed for the Chl_NDI (r = 0.91). Stay-green groups were successfully classified using the selected VIs, with the water-absorption band VIs performing better than the chlorophyll-absorption band VIs and other VIs. Similarly, for predicting the SGVS, the water absorption band indices (R² = 0.79 ± 0.04 and RMSE = 0.12 ± 0.01) outperformed the chlorophyll absorption band indices when using RF. Leave-one-out-location/year cross-validation revealed pronounced variation in model transferability driven by environmental and temporal domain shifts. RF consistently outperformed ANN, showing greater robustness to inter-site heterogeneity and interannual variability, whereas performance degraded most in spectrally distinct environments or atypical seasons. Interestingly, RDIS_3b (1280, 1250, 1180 nm), NDIS_2b (2190, 1510 nm), and NDWI2 (860, 1241 nm) were identified as the most critical predictors in the RF models, across merged and separated datasets. These findings demonstrate the potential of spectral signatures, particularly water-absorption band spectral indices, for quantitative phenotyping of stay-green as a proxy for drought tolerance in maize breeding programs; however, multisite, multiyear calibration is needed to enhance generalizability.

Why it matches plant phenotyping methodsトウモロコシのstay-green形質を対象に、葉のハイパースペクトル反射を用いた形質推定・分類モデルを評価し、交差検証で転移性と頑健性も検証しているため、センサー型表現型計測手法が中心である。

abstractThe main objective of this study was to evaluate hyperspectral leaf reflectance as a means to estimate stay-green visual scores (SGVS)

Hyperspectral–machine learning framework enables early and non-destructive prediction of plant resistance to pest →

The brown planthopper ( Nilaparvata lugens ) is one of the most destructive pests of rice and poses a threat to yield stability and food security. Although host-plant resistance is the most sustainable strategy for BPH management, conventional resistance phenotyping remains labor-intensive, destructive, and poorly suited for large-scale breeding. Here, we combined hyperspectral reflectance profiling of 50 rice varieties with an interpretable machine learning framework to enable non-destructive prediction of resistance phenotypes. Using post-infestation spectral profiles, we established classification models that captured resistance states shaped by constitutive traits and inducible defense responses. Among 13 evaluated algorithms, a radial basis function support vector machine achieved the best performance on full-spectrum data within the sampled variety panel, with an average accuracy of 0.939 ± 0.015 and a maximum of 0.972. Predictive wavelengths were concentrated in the green, red-edge, and near-infrared regions, corresponding to variation in pigment regulation, canopy structure, and water status. Spectral and network analyses showed that resistant genotypes exhibited more complex but less stable spectral co-occurrence networks, consistent with physiological trade-offs associated with defense. We also tested whether resistance could be predicted before pest infestation. Pre-infestation spectra retained significant predictive power, with accuracies of 0.572 ± 0.021 for five-class classification and 0.667 ± 0.021 for binary classification, indicating that constitutive defense-associated physiological states are optically detectable before visible damage occurs. Together, our results show that hyperspectral reflectance encodes both inducible responses after infestation and constitutive defense baselines present beforehand. This work establishes a scalable, non-invasive phenotyping strategy for early resistance screening within evaluated germplasm panels, while future validation across independent and variety-level held-out populations will be required before broader deployment.

Why it matches plant phenotyping methodsイネの害虫抵抗性という植物状態を、ハイパースペクトル計測と機械学習で非破壊推定する方法を開発・評価しており、表現型取得が研究の中心である。

abstractwe combined hyperspectral reflectance profiling of 50 rice varieties with an interpretable machine learning framework to enable non-destructive prediction of resistance phenotypes.

Development and Validation of Minitron III: A System for Continuous Monitoring of Crop Gas Exchange in Controlled Environments →

Optimizing environmental inputs for indoor crop production by conducting a traditional endpoint growth analysis requires significant time and resources. The most common scientific approach to assessing crop response involves the accumulation of dry mass at the end of a cropping cycle. A growth dynamics analysis also results in the accurate estimation of the crop response to the growth environment through periodic destructive sampling. Measuring crop gas exchange in the same environment in which it is grown offers a powerful alternative to accelerating the environmental optimization process, especially for vegetative crops. This work introduces Minitron III, a third-generation technology advancement capable of continuous gas-exchange monitoring from seed to harvest for small specialty crop stands. For proof of concept, 24 ‘Rouxai’ red oakleaf lettuce plants were grown from seed to harvest over a 25-day cropping cycle. Instantaneous differences in the carbon dioxide (CO 2 ) and water vapor (H 2 O V ) mole fraction between sample/reference lines flowing through/around cuvette/growth space were measured using a differential infrared gas analyzer, allowing determination of net photosynthesis based on a 0.41-m 2 cropping area. Crop stand net photosynthesis was detectable 7 days after sowing seeds, increasing gradually from 0.13 to 0.60 µmol·m −2 ·s −1 over the following week. The crop net photosynthesis rate increased robustly on a daily basis from 15 days after sowing seeds. While the net photosynthesis rate at the beginning of the photoperiod was 0.68 µmol·m −2 ·s −1 on day 15, it increased to 7.7 µmol·m −2 ·s −1 by day 25 after sowing seeds. Crop dark respiration was detectable from 17 days after sowing seeds and ranged from −0.3 to −0.9 µmol·m −2 ·s −1 . Minitron III has potential for rapid optimization of multiple environmental inputs for indoor production of specialty crops based on the near-real-time crop response to environmental inputs.

Why it matches plant phenotyping methods作物のガス交換を連続測定して光合成・暗呼吸を推定するシステム自体の開発と概念実証が中心であり、植物生理状態のフェノタイピング手法に該当する。

titleDevelopment and Validation of Minitron III: A System for Continuous Monitoring of Crop Gas Exchange in Controlled Environments

Smart Sensing Systems For Agricultural Plant Health Monitoring: Current State and Prospects →

ABSTRACT Timely and reliable assessment of plant health is essential for resilient and sustainable agriculture, yet diagnostic practice remains divided between accurate but laboratory‐bound assays and emerging field‐deployable technologies. Herein, we adopt a plant‐centric and systems‐level perspective to synthesize advances in smart sensing for plant health monitoring reported between 2000 and 2026. Rather than surveying individual devices in isolation, we organize the literature around how sensing technologies are fabricated, integrated, and translated into actionable agronomic insights. Our synthesis reveals a clear shift toward multimodal, minimally invasive sensing architectures that combine material and fabrication innovations with contact and non‐contact modalities spanning organ, canopy, and landscape scales. We find that the most impactful progress arises not from individual sensors alone, but from integrated pipelines that couple sensing hardware with edge intelligence, cross‐scale data fusion, and explainable analytics. Furthermore, persistent barriers, including calibration transfer, long‐term stability, power autonomy, dataset bias, and cybersecurity, continue to impede widespread adoption. Based on these findings, we outline design principles and research priorities needed to accelerate translation, emphasizing standardized validation against biological benchmarks, energy‐autonomous and environmentally responsible sensor systems, and artificial intelligence (AI) frameworks capable of robust generalization across crops and environments. Looking ahead, we argue that plant health monitoring will increasingly be defined by closed‐loop systems that directly link plant physiological or pathological signals to adaptive management, positioning smart sensing as a cornerstone of data‐driven and climate‐resilient agriculture.

Why it matches plant phenotyping methods植物の健康状態・生理・病理シグナルを対象とするスマートセンシング手法を、統合、検証、校正、データ解析の観点から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。

abstractwe outline design principles and research priorities needed to accelerate translation, emphasizing standardized validation against biological benchmarks

Rapid forage yield and growth rate measurement using a remotely controlled LiDAR sensor in perennial ryegrass field plots →

Biomass is a key trait in pasture plant breeding and agronomy, but measuring Dry Matter Yield or Fresh Weight across large numbers of samples is labour intensive and costly. Efficient biomass assessment systems must balance accuracy, speed, and cost, while ideally enabling non-destructive measurements. We developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution. The system operates under ambient light conditions at a ground speed of 2.7 km per hour. It was evaluated in small-plot perennial ryegrass trials at two field sites in New Zealand, across two seasons at site A and one season at site B. At site A, correlations between LiDAR-derived height and fresh weight ranged from 0.33 to 0.74 across individual measurement cycles, with an overall multilevel R² of 0.72. At site B, the multilevel correlation increased to R² = 0.88. Weekly LiDAR scans at site B were used to estimate plot-level growth rates for 60 plots, demonstrating improved temporal resolution. Statistically significant differences in growth rate within regrowth cycles were detected among plots. The platform reliably differentiates perennial ryegrass plots based on biomass and offers higher temporal resolution than traditional methods.

Why it matches plant phenotyping methodsLiDARプラットフォームを開発し、草高から牧草バイオマスと成長率を非破壊推定・検証しており、植物形質取得手法が研究の中心です。

abstractWe developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution.

IoT and Edge-AI Enabled Autonomous Agri-Robot for Precision Irrigation and Early Plant Disease Diagnosis Using Attention-Guided Lightweight CNN and Fuzzy Logic Control →

This paper presents an IoT and edge-AI enabled autonomous agricultural robot that performs early plant disease diagnosis and precision irrigation on a single mobile platform. Unlike earlier automated farming systems that rely on visible-spectrum (RGB) imagery and simple threshold-based watering, the proposed system fuses RGB and nearinfrared (NIR) imagery to compute the Normalised Difference Vegetation Index (NDVI), enabling detection of physiological plant stress several days before visible lesions appear. Leaf images are classified using a lightweight attention-guided convolutional neural network that combines a MobileNetV3 backbone with a Convolutional Block Attention Module (CBAM), allowing the network to focus on lesion-relevant channels and spatial regions while remaining compact enough for real-time inference on an ESP32-S3 edge controller. Irrigation and pesticide-spray decisions are no longer governed by a rigid binary threshold; instead, a Mamdani-type fuzzy inference engine fuses soil moisture, ambient temperature, and the NDVI-derived stress index to compute a proportional, continuously variable actuation signal, reducing both water wastage and false triggering. The robot streams sensor readings, classification results, and actuation logs to a cloud dashboard over Wi-Fi/MQTT so that farmers can monitor crop health and irrigation status remotely and receive real-time alerts. Experimental evaluation on a prototype platform shows that the proposed attention-guided model improves disease-classification accuracy over a baseline CNN, the NDVI-assisted pipeline detects stress earlier than colour-only analysis, and the fuzzy irrigation controller reduces water consumption relative to the binary threshold scheme while maintaining optimal soil-moisture levels. The results indicate that combining multispectral sensing, attention-based lightweight deep learning, and fuzzy control on a single autonomous platform is a practical and scalable route towards sustainable, resource-efficient precision agriculture.

Why it matches plant phenotyping methodsRGB/NIR画像からNDVIによる植物ストレスを推定し、葉画像から病徴を分類する取得・解析手法をロボット上で開発・評価しており、植物表現型の測定が中心である。

abstractthe proposed system fuses RGB and nearinfrared (NIR) imagery to compute the Normalised Difference Vegetation Index (NDVI), enabling detection of physiological plant stress several days before visible lesions appear.

Using hyperspectral reflectance to explore the responses of rice canopy chlorophyll fluorescence to water stress →

【Objective】Chlorophyll fluorescence is a physiological indicator reflecting crop photosynthesis and water stress. Non-destructively monitoring the changes in chlorophyll fluorescence under water stress is critical for improving irrigation management. This paper explores the applicability of canopy hyperspectral reflectance for elucidating the response of rice canopy chlorophyll fluorescence to water stress.【Method】The experiment was conducted in pots and the measurements were taken during the booting stage of rice. Three water treatments were set, including continuous flooding irrigation (CK), mild drought (MS) and severe drought (HS). Canopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform, from which we analysed the responses of chlorophyll fluorescence traits to soil water change. Prediction models were developed to estimate chlorophyll fluorescence traits using partial least squares regression (PLSR) and backpropagation neural network (BPNN), based on characteristic spectral bands.【Result】①The chlorophyll fluorescence traits Fv/Fm, Y(II), qL and Y(NPQ) varied with water stress, with significant changes observed 3-4 days after cessation of irrigation, and detectable variation identified up to day 6 after terminating irrigation. On day 6 after irrigation cessation, the HS treatment reduced Fv/Fm, Y(II) and qL by 41.3%, 46.9% and 53.1%, respectively, whereas increased Y(NPQ) by 117.5% compared with CK. ②Savitzky-Golay smoothing and multiplicative scatter correction (MSC) preprocessing effectively reduced the scattering effects on canopy hyperspectral data induced by structural variation. The characteristic spectral bands selected from the hyperspectral data were mainly distributed in the blue (400-500 nm), red and near-infrared regions. ③Compared with PLSR, the BPNN was more effective in capturing the nonlinear relationships between hyperspectral data and chlorophyll fluorescence traits. The BPNN was most accurate for estimating Y(NPQ) and qL, with the associated R2 values being 0.867 and 0.845, respectively, and less accurate for estimating Fv/Fm.【Conclusion】Canopy hyperspectral data can be used to estimate rice chlorophyll fluorescence traits. This approach provides a rapid, cost-effective, and non-destructive method for monitoring crop physiological responses to water stress.

Why it matches plant phenotyping methodsイネのクロロフィル蛍光という生理形質を、キャノピー分光反射から推定するセンサー計測・予測モデルを開発し、精度評価しており、フェノタイピング手法が中心である。

abstractCanopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform

Novel imaging approaches for visualizing root-mycorrhizal fungal interactions. →

Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding their interactions can benefit from advanced imaging techniques capable of visualizing nutrient exchange and structural colonization at subcellular resolution across large sample sizes. This review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses. Several techniques can now visualize and characterize mycorrhizal fungi and associated root structures non-destructively and in three dimensions, for example X-ray computed tomography (micro-CT), X-ray fluorescence (XRF), and X-ray absorption near edge structure (XANES) spectroscopy. Metabolic processes and nutrient exchange can be tracked through positron emission tomography (PET), fluorescent nanoparticles (FNPs), and the monitoring of electrical signalling. Artificial intelligence (AI)-powered image processing software is enabling high-throughput analysis of complex images generated from a range of sources. Mycorrhiza systems are also able to be tracked in-field at multiple scales: hyperspectral imaging can detect mycorrhizal associations at the kilometre scale, while portable MRI imagers can detect changes at the tissue scale. These converging technologies enable the direct, continuous measurement of structural and metabolic root-mycorrhizal fungi interactions, paving the way for a mechanistic understanding of these vital symbiotic partnerships and their impact on plant health and ecosystem functioning.

Why it matches plant phenotyping methods植物根と菌根の構造・代謝・栄養交換を画像およびセンサーで直接測定する手法を扱うレビューであり、植物状態の取得技術が中心である。

abstractThis review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses.

Evaluation of a Plant Quality Monitoring System Based on Visible and Near-Infrared Spectroscopy Sensors in a Controlled Environment →

The increasing demand for agricultural production requires reliable and non-destructive methods for monitoring plant physiological conditions in real time, particularly in controlled environments. Spectral sensing in the visible to near-infrared (VIS–NIR) region offers a promising approach; however, the performance of low-cost sensors is often limited by calibration accuracy, wavelength-dependent sensitivity, and insufficient validation against plant physiological indicators. This study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment. Lettuce (Lactuca sativa L.) was grown under two nutrient concentrations (600 ppm and 1200 ppm), and spectral reflectance data were collected across the 410–760 nm range. Sensor measurements were calibrated and validated against a LI-COR LI-180 reference spectrometer using linear regression, with performance assessed using the coefficient of determination (R²), root mean square error (RMSE), and spectral response consistency. The results show strong calibration performance, with wavelength-specific R² values ranging from 0.9682 to 0.9870 and RMSE values between 0.26965 and 5.19772. Although a systematic offset was observed, the AS7265x sensor preserved key spectral patterns, particularly in the green (510–560 nm) and red-edge (705–730 nm) regions. Differences in nutrient concentration were consistently reflected in both spectral responses and SPAD measurements, indicating sensitivity to plant physiological variations. These findings demonstrate that the AS7265x sensor provides reliable spectral information for relative plant monitoring and has strong potential as a cost-effective tool for plant quality assessment in controlled environments.

Why it matches plant phenotyping methodsVIS–NIR植物モニタリングシステムの校正・検証が研究の中心であり、植物の生理状態を推定するセンサー手法を評価している。

abstractThis study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment.

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.

Hyperspectral phenotyping and GWAS identify novel QTLs for soybean photosynthetic rate. →

Enhancing photosynthesis is an important approach to improve crop yields. Photosynthesis, as a key factor determining crop yield, is an important approach to increasing crop production and addressing global food security issues. Improving its efficiency is crucial in this regard. However, traditional photosynthetic phenotyping has long been a bottleneck in crop breeding due to time-consuming data collection. In this study, we simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data. By applying this model, we evaluated Pn in 219 soybean materials. A multi-environment genome-wide association study (GWAS) based on multi-environmental prediction Pn was carried out using the 3VmrMLM method, and 24 significant quantitative trait loci (QTLs) and four suggestive QTLs were identified. Among them, 24 QTLs overlapped with multiple previously reported QTL related to photosynthesis, chlorophyll content, quality, etc., or with genes related to key agronomic traits such as yield. Additionally, four new QTLs were discovered, and four candidate genes potentially associated with Pn were identified. Further, haplotype analysis identified their optimal haplotypes. This study presents a robust and nondestructive hyperspectral model for estimating the photosynthetic rate in soybeans, which is successfully applied to genetic analysis, yielding stable and biologically meaningful results. The approach offers an effective means to explore the genetic basis of photosynthesis and provides a solid theoretical foundation for large-scale, monitoring of soybean photosynthetic physiology.

Why it matches plant phenotyping methods大豆葉のハイパースペクトルデータから光合成速度を推定するモデルを開発し、検証・大規模適用しており、植物フェノタイピング手法が研究の中心である。

abstractwe simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data.

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.

Widespread Increase in Global Plant Water Stress Obscured by Greening →

Abstract Understanding the vulnerability of plants to more severe and frequent drought events and developing adaptive management strategies requires robust methods for quantifying long‐term changes in plant water stress (PWS). Most data‐driven explorations of long‐term trends in PWS have focused on alterations in canopy structure (e.g., leaf area index) or canopy structure‐dependent variables (e.g., gross primary productivity and evapotranspiration). This is largely because long‐term trends in canopy structure are relatively easy to detect from satellite observations. However, a focus on structural responses limits our ability to detect physiological stress due to challenges in isolating it from the effects of structural greening. Consequently, this difficulty hampers a comprehensive examination of long‐term PWS in the context of global greening trends. To address this gap, we developed a new process‐based metric for PWS to isolate physiological responses from structural greening, which we then used to detect global PWS trends over the past four decades. Combining site‐level and satellite observations at the half‐degree resolution across the globe, we found that accounting for greening‐related changes substantially alters the sign of long‐term PWS trends inferred from traditional approaches. Specifically, our study reveals a significant increase in PWS that is only detectable when accounting for structural greening trends. When greening trends are not accounted for, global PWS appears to have decreased over time. Overall, our results highlight the need to integrate structural dynamics and greening into PWS detection. Such an integration of observations and land models will improve our understanding of plant‐water‐energy interactions.

Why it matches plant phenotyping methods植物の生理的な水ストレスを定量化する新しいプロセスベース指標を開発し、衛星・地上観測で検証・適用しており、表現型測定法が研究の中心である。

abstractTo address this gap, we developed a new process‐based metric for PWS to isolate physiological responses from structural greening, which we then used to detect global PWS trends over the past four decades.

Design and Experimental Validation of a Low-Power IoT-Based Smart Irrigation System Using LoRa, ET0, and Crop Water Stress Index for Precision Agriculture →

Efficient irrigation management requires complementary information on atmospheric demand, soil conditions, and crop water stress. This study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework. The system combines LoRa communication, ESP32-based sensor nodes, soil and meteorological sensing, FAO-56 reference evapotranspiration (ET0), and canopy-temperature-based Crop Water Stress Index (CWSI). Irrigation decisions rely on the complementary use of ET0, in situ soil measurements, and CWSI rather than on a single indicator. A hybrid time-, event-, and query-driven acquisition strategy was implemented to adapt node activity and limit communication overhead. The system was deployed under outdoor conditions in Oujda, Morocco, demonstrating integrated sensing, wireless data transmission, crop-stress monitoring, and automated irrigation control. Energy characterization further showed distinct consumption profiles across sensing, communication, actuation, and low-power operating states, supporting the use of duty cycling to limit active node operation. The results demonstrate the feasibility of integrating environmental, soil, and crop-level information within a low-power IoT framework for adaptive irrigation management.

Why it matches plant phenotyping methods低消費電力IoT基盤の開発・実証が中心で、作物の水ストレスをCWSIで取得・監視する植物フェノタイピング要素が技術的に組み込まれている。

abstractThis study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework.

Horticultural Salinity-Stress Phenotyping and Tolerance Inference: A Critical Evidence Map and Validation Framework for AI-Related Claims →

Salinity stress constrains horticultural production in protected cultivation, hydroponics, coastal agriculture and reclaimed-water irrigation. This critical narrative review and evidence map synthesizes a DOI-verified core corpus of 160 peer-reviewed journal articles to ask how artificial intelligence (AI) can support, rather than overstate, salinity-stress inference in horticultural crops. The evidence base is uneven: 22 retained records were AI-, sensing- or phenotyping-relevant, six treated ML, deep learning, edge intelligence or agentic AI as a central method, and four directly tested salinity- or water-stress AI/sensor phenotyping in a crop-relevant system. Among the six explicit-AI records, none externally validated a salinity-specific AI model; one distinguished salinity from drought, and none reported a prospective AI-guided intervention trial. Accordingly, this article is framed as a validation and reporting framework, not as a quantitative meta-analysis of model or intervention efficacy. Across the broader corpus, evidence for salinity tolerance centres on osmotic limitation, Na+ and Cl− toxicity, K+ retention, ROS regulation, photosynthetic protection, hormonal signalling, root hydraulics, rhizosphere processes and metabolic reprogramming. The review links these mechanisms to measurable traits and to claim-specific AI validation requirements. We propose a mechanism-to-AI map, a validation ladder, an intervention maturity framework and a reporting checklist. The conclusion is deliberately conservative: AI can improve salinity research when it is constrained by rigorous metadata, physiological grounding, external validation, stress-confusion testing and explicit uncertainty, but current evidence is insufficient to support autonomous, economically validated or field-ready AI-based salinity management.

Why it matches plant phenotyping methods植物の塩ストレス表現型推定に関するAI・センシング研究を体系的に整理し、検証枠組み、検証段階、報告チェックリストを提案する方法論的レビューであり、表現型計測・推定が中心です。

abstractThis critical narrative review and evidence map synthesizes a DOI-verified core corpus of 160 peer-reviewed journal articles to ask how artificial intelligence (AI) can support, rather than overstate, salinity-stress inference in horticultural crops.

Image-based trait extraction of Chenopodium quinoa grown under salinity and drought stress. →

Above ground crop traits provide an early indication of a plant's capacity to tolerate stress, and are important for breeding programs aimed at improving stress tolerance. In this work, we present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time under control, drought, and saline conditions. We used daily sideview imaging of individual plants, followed by segmentation of the panicle, leaf and stem using the deep learning U-Net++ segmentation model. The resulting segmentations were used in regression models to estimate leaf area, fresh and dry biomass, and leaf dry weight. The regression models showed high predictive accuracy. Using these estimates, we could calculate specific leaf area and leaf weight ratio. In addition, radiation use efficiency for above-ground biomass production was calculated, providing an independent physiological check on the consistency of these predictions. Finally, using automated measurements of plant transpiration we were able to determine daily averages of whole plant stomatal conductance. The results show that image-derived morphological traits can be used to accurately estimate biomass-related traits and to derive physiologically meaningful indicators of plant performance over time. This method provides a framework for non-destructive monitoring of quinoa responses to drought and salinity.

Why it matches plant phenotyping methods画像取得、深層学習セグメンテーション、回帰による植物形質推定を中核とする高スループット表現型解析手法であり、ストレス実験での単なるルーチン測定ではない。

abstractwe present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time

Different Perspectives on the Same Target: Field and Laboratory Spectroscopy for Estimating Nitrogen Content in Sugarcane Leaves →

Proper nitrogen (N) management is essential for increasing the productivity of sugarcane (Saccharum spp.) and reducing the economic and environmental impacts associated with excessive fertilizer use. This study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions, obtained throughout the crop cycle. The experiment was conducted in Piracicaba, São Paulo, Brazil, under four N rates: 0, 60, 120, and 180 kg ha−1. Spectral measurements were taken at the foliar and canopy levels at eight evaluation times, accompanied by laboratory determination of N content. Partial Least Squares Regression (PLSR) and Random Forest (RF) models were fitted using the spectral data and days after cutting (DAC), included as a categorical factor and evaluated using 10-fold internal cross-validation, based on the metrics R2, RMSE, MAE, and Willmott’s refined agreement index (dr). The foliar data performed better with PLSR (R2 = 0.727; RMSE = 1.381 g kg−1; MAE = 1.109; dr = 0.917) than canopy data (R2 = 0.591; RMSE = 1.489 g kg−1; MAE = 1.157; dr = 0.866). PLSR also outperformed RF at both acquisition levels. The green (~550 nm) and red edge (~740 nm) regions were the most relevant for N estimation. Under the evaluated conditions, model performance was associated with the spectral acquisition level and conditions, the instrumental configuration, and the modeling strategy employed.

Why it matches plant phenotyping methodsサトウキビ葉の窒素含量を分光計とPLSR/RFで推定し、取得レベル・機器・モデル性能を比較検証しており、形質取得手法が中心である。

abstractThis study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions

Crop Phenotypic Rheology: Theoretical Framework, Dynamical Mechanisms, and the Evolution of Dynamic Crop Phenotypic Science →

Traditional crop phenotyping relies heavily on static, discrete, point-in-time measurements, a "static snapshot" approach that inherently overlooks the continuous, dynamic response patterns of living crops under fluctuating environmental conditions. This paper proposes "Crop Phenotypic Rheology", a novel interdisciplinary theoretical framework designed to systematically integrate physical rheological concepts—such as stress, strain, viscoelasticity, creep, and stress relaxation—into the spatio-temporal continuous analysis of dynamic crop phenotypes. Crop phenotypic rheology conceptualizes the crop phenotype as a complex non-linear system that continuously undergoes deformation, recovery, or phase transformation in response to time, environmental stress (e.g., drought, heat, nutrient deficit, and mechanical wind stress), and resource availability. We elucidate three fundamental rheological modes—elastic, plastic, and viscoelastic modes—and formulate the environmental stress-phenotypic strain dynamic equations governing continuous phenotypic responses. Furthermore, we explore multi-scale integration mechanisms bridging micro-scale cellular rheology, meso-scale plant posture rheology, and macro-scale canopy rheology. By overcoming the fundamental limitations of static phenotyping, crop phenotypic rheology transitions crop phenotypic research from static structural measurements to a dynamic science focused on deciphering continuous response mechanisms, providing a transformative paradigm and theoretical support for precision breeding, abiotic stress screening, and smart agronomic management.

Why it matches plant phenotyping methods動的な作物表現型を連続的に解析する新しい理論的フェノタイピング枠組みを提案し、環境ストレス応答の数理モデルとマルチスケール統合を扱うため、方法論が中心である。

abstractThis paper proposes "Crop Phenotypic Rheology", a novel interdisciplinary theoretical framework designed to systematically integrate physical rheological concepts—such as stress, strain, viscoelasticity, creep, and stress relaxation—into the spatio-temporal continuous analysis of dynamic crop phenotypes.

Graft incompatibility in fruit trees in early detection: integrating physiological, molecular, and technological approaches. →

Main conclusion This review highlights that integrating physiological, molecular, imaging, and AI-based approaches enables early and reliable detection of graft incompatibility, improving rootstock-scion selection, orchard sustainability, fruit productivity, and long-term tree performance. One of the most serious problems in fruit growing is the breaking, weakening, or dying of the tree at the graft union, either within a short period of time or after 10-15 years. This condition is often triggered by environmental factors; however, it is certainly not solely caused by environmental conditions. This problem is defined as graft incompatibility. Graft incompatibility refers to the failure of successful anatomical and physiological integration between a rootstock and a scion, primarily due to biochemical, molecular, and genetic mismatches that impair vascular reconnection and long-term stability of the graft union. Graft incompatibility remains a significant constraint in fruit tree production, resulting in reduced longevity, yield, and quality of orchards. This review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility, with special emphasis on Prunus species such as sweet cherry. Physiological and biochemical markers, including phenolic accumulation, antioxidant enzyme activities, and isozyme patterns, serve as early indicators of incompatibility. At the molecular level, transcriptomic, metabolomic, and epigenetic analyses have revealed differentially expressed genes (DEGs) and post-translational modifications associated with stress signaling, vascular reconnection, and callus formation. Imaging-based non-destructive technologies such as micro-CT, MRI, terahertz, and hyperspectral imaging now allow real-time visualization of graft-union structures without damaging plant tissues. The integration of artificial intelligence and machine learning with multi-omics datasets and imaging tools offers unprecedented potential for predictive diagnosis and compatibility assessment. Collectively, these multidisciplinary advances are reshaping the detection and management of graft incompatibility, enabling faster, more reliable, and sustainable rootstock-scion selection in fruit tree breeding.

Why it matches plant phenotyping methods果樹の接ぎ木不親和性という植物状態の早期検出法を、画像・生理・分子・AI技術の観点から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 integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility

Comprehensive Assessment of Drought Tolerance in Native Melon Germplasms from Xinjiang at the Germination Stage. →

Drought and water deficit have severely restricted melon ( Cucumis melo L.) production in Xinjiang, and large-scale systematic evaluations of drought tolerance at the germination stage are still extremely limited. Physiological and biochemical indicators related to the germination stage, including osmotic adjustment substances and antioxidant enzyme activities, have not yet been incorporated into prediction models for the rapid identification of germplasm drought resistance. To address these research gaps, this study selected 60 accessions of local melon germplasm resources in Xinjiang and used polyethylene glycol (PEG) solutions at four different concentrations (0%, 10%, 20% and 30%) to simulate drought stress conditions. Drought tolerance was evaluated to develop a method for the rapid screening of drought-tolerant germplasms. The findings demonstrated that PEG stress significantly suppressed seed germination and had both stimulatory and inhibitory effects on radicle growth. With the increase in PEG concentration, germination indices consistently exhibited a downward trend. Under 10% PEG treatment, the variation among different germplasms was relatively small, while 30% PEG completely inhibited seed germination. Notably, 20% PEG fell within the semi-lethal concentration range for all tested germplasms and yielded the maximum coefficient of variation for germination rate, which could maximally differentiate the drought resistance differences among germplasms. Therefore, 20% PEG was determined to be the optimal screening concentration. Under 20% polyethylene glycol (PEG) stress, the degree of membrane lipid peroxidation (malondialdehyde, MDA), contents of osmotic regulators (proline, Pro; soluble protein, SP), and activities of antioxidant enzymes (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; ascorbate peroxidase, APX) in the radicles of melon germplasms were universally elevated. However, the variation ranges and trends of biochemical indices among different germplasms exhibited significant differences. The proline content of melon accessions with strong drought resistance increased, the malondialdehyde (a product of membrane damage) was low, and the enzyme activities increased significantly. The proline content of non-drought-tolerant melon accessions increased less, malondialdehyde accumulated in large amounts, and the activity of some protective enzymes decreased. Correlation analysis demonstrated that Pro exerted a synergistic effect in conjunction with antioxidant enzymes (SOD, CAT) to mitigate drought stress. Cluster analysis classified the germplasm into 14 high-tolerance types, 10 medium-tolerance types, and 9 low-tolerance types. Based on extreme germination phenotypes, 27 germplasms were identified as drought-sensitive types. A prediction model for drought tolerance was established via stepwise regression: D = -0.309 + 0.053 × Pro (proline content) + 0.319 × RL (radicle length) + 0.469 × MDA (malondialdehyde) + 0.137 × SOD (superoxide dismutase), with four core indicators (RL, MDA, Pro, SOD) identified. These findings provide a scientific basis and technical support for drought tolerance breeding, parental selection, and large-scale, precise, and rapid drought tolerance screening of melon germplasms in the arid regions of Xinjiang.

Why it matches plant phenotyping methodsメロン遺伝資源の乾燥耐性を迅速にスクリーニングするため、最適PEG濃度の決定、指標選定、予測モデル構築を中心的に行っており、表現型取得・抽出法の開発に該当する。

abstractused polyethylene glycol (PEG) solutions at four different concentrations (0%, 10%, 20% and 30%) to simulate drought stress conditions. Drought tolerance was evaluated to develop a method for the rapid screening of drought-tolerant germplasms.

Probing the living Plant Cell: AFM as a tool for Biomechanical research and development →

Abstract Plants live in a physical world governed by a multitude of mechanical processes which vary over time. The unique features of plant cells, which are turgor-inflated objects surrounded by the cell wall, present an intricate perception and response system for mechanical forces. A powerful tool to investigate how plants adapt and react to these cues is Atomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties. In the context of cell wall biomechanics, there remains some controversy on appropriate AFM measurement practices and suitable use of common terminologies. Specifically, the interpretation of plant cell indentation curves and derivation of the wall elasticity modulus can be challenging and continues to spark debate. In this Expert View, we discuss recent advances of AFM in plant science as well as best practices for the use of AFM and considerations for data interpretation with a focus on mechanical probing by indentation.

Why it matches plant phenotyping methods植物細胞の表面形態と力学特性をAFMで測定・解釈する実践と標準化を扱うレビューであり、植物形質取得法が中心です。

abstractAtomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties.

Oxalate-Linked Electrochemistry Enables Early Detection of Sclerotinia Blight in Peanut Crops Using 3D-Printed Nanostructured Pt Sensors →

Abstract Sclerotinia minor (causing Sclerotinia blight) is a devastating pathogen in peanut production, with severe outbreaks causing up to 50% yield loss due to rapid oxalic acid (OA) accumulation. Early diagnosis is challenging because canopy-level symptoms typically emerge only after infection is well established, while early cues are subtle, stem-localized, and nonspecific; in contrast, molecular assays are time- and resource-intensive. Despite established mechanistic links between oxalate accumulation and disease progression, so far, there are no sensors developed or tested for detecting Sclerotinia blight in peanut plants. This paper reports a low-cost, lithography-free, and label-free electrochemical sensor for metabolite-targeted, presymptomatic monitoring of S. minor in peanut plants based on clear mechanistic links between oxalate accumulation and disease progression. The sensor platform comprises 3D-printed resin substrates with platinum (Pt) electrodes and a nanostructured reduced graphene oxide (rGO)−chitosan interface functionalized with an oxaloacetic acid (OAA) interfacial layer. Using ferri/ferrocyanide as a redox probe, the sensor exhibited a linear calibration to oxalate (prepared from OA) from 0.05 µM to 1 mM (R2 = 0.99), with a sensitivity of 6.37 µA/decade, limit of detection of 17.6 nM, and excellent coefficient of variation of 0.93−3.32% across standards (n = 4). In real plant trials, stem sap from S. minor-inoculated peanut plants produced significantly elevated voltammetric responses relative to healthy and Nothopassalora personata controls as early as five days post-inoculation (dpi), enabling longitudinal monitoring through 20 dpi (p

Why it matches plant phenotyping methods植物体内のシュウ酸を指標に、ピーナッツの病害を早期・無症状段階で検出する電気化学センサーを開発し、校正性能と実植物での識別・経時モニタリングを検証している。病害状態の取得法が研究の中心である。

abstractThis paper reports a low-cost, lithography-free, and label-free electrochemical sensor for metabolite-targeted, presymptomatic monitoring of S. minor in peanut plants

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.

Mechanisms of drought tolerance in legumes: physiological impacts, adaptive responses, and phenotyping strategies. →

Drought is recognized as the primary abiotic stress limiting global crop productivity and poses a significant threat to food security. Consequently, the genetic improvement of drought tolerance has become a priority for modern plant breeding. Developing resilient cultivars requires a fundamental understanding of the physiological, biochemical, and molecular mechanisms that plants employ to counteract water deficits. This review provides a comprehensive analysis of drought-induced effects across various developmental stages in legumes, detailing the signaling networks that facilitate stress perception and response. Furthermore, we evaluate the experimental parameters and methodologies frequently used to assess drought tolerance, weighing their respective advantages and limitations. Finally, we analyze the revolutionary role that high-throughput phenotyping could play in stress assessment and precision breeding.

Why it matches plant phenotyping methodsマメ科植物の乾燥耐性評価に用いる実験パラメータ・方法論をレビューし、高スループット表現型解析の役割も論じるため、植物フェノタイピング方法のレビューが中心である。

abstractFurthermore, we evaluate the experimental parameters and methodologies frequently used to assess drought tolerance, weighing their respective advantages and limitations.

Real-Time Growth and Diurnal Thickness Variation of a Buried Solanum tuberosum (L.) Tuber →

Real-time measurement of belowground tuber growth has not been conducted in field crops. Here, a strain-gauge sensor was used to monitor potato tuber growth and estimate mean daily tuber water loss. Over the 13 days preceding harvest, tuber thickness increased by 0.90 mm, corresponding to a daily gain of 1.33 g, or a 4.8% increase. The greatest diurnal fluctuation was 0.453 mm, corresponding to a transpirational water loss of 9.4 ml, or 3% of the tuber’s water content. Daily transpiration showed a positive correlation with air temperature and vapor pressure deficit. This sensor will enable more precise input control and higher temporal resolution than current methods for below-ground crops, supporting improved crop management, yield prediction, and harvest decisions.

Why it matches plant phenotyping methods埋没ジャガイモ塊茎の成長・厚さ変動・水分損失をひずみゲージでリアルタイム測定する手法が研究の中心であり、植物器官形質の取得法として適格。

abstractHere, a strain-gauge sensor was used to monitor potato tuber growth and estimate mean daily tuber water loss.

Nano-flow liquid chromatography-mass spectrometry for highly sensitive quantification of indole-3-acetic acid from small tissue sections prepared by laser micro dissection. →

Plant hormones play critical roles in many aspects of plant life cycles including development, growth, reproduction and responses to environmental stimuli. These processes are often associated with changes in endogenous plant hormone levels and locations. Therefore, to understand the modes of action of plant hormones, it is important to accurately quantify these chemical compounds in a high-definition tissue map. In this study, we developed a system to quantify indole-3-acetic acid (IAA), the major endogenous auxin, from small tissue samples using laser microdissection (LMD) coupled with nano-flow liquid chromatography (nano-LC)-mass spectrometry (MS), which improved detection limits, allowing quantification of IAA from a single 10 μm cryosection of maize coleoptile. Our results reveal that IAA is actively synthesized in the apical 400 μm region of the coleoptiles and is preferentially accumulated in vascular tissues. This technique can provide a precise view of the spatiotemporal distribution of plant hormones and their significance in regulating physiological responses at tissue or cellular levels.

Why it matches plant phenotyping methods植物組織中のIAAの空間分布を高感度に定量するLMD-nano-LC-MS法そのものを開発しており、植物の生理状態を組織・細胞レベルで取得する技術が研究の中心である。

abstractIn this study, we developed a system to quantify indole-3-acetic acid (IAA), the major endogenous auxin, from small tissue samples using laser microdissection (LMD) coupled with nano-flow liquid chromatography (nano-LC)-mass spectrometry (MS)

INTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES →

Selecting ideal drought-tolerant wheat varieties requires a holistic synthesis of digital phenotypes, molecular markers, and agronomic indices. This study evaluated 16 wheat genotypes for drought tolerance by integrating digital phenotyping (UAV-based thermal imaging), molecular data (DREB gene expression profiles), and 12 agronomic indices. While vegetative DREB accumulation remained mostly homogeneous, the generative stage triggered pronounced transcriptional shifts, and late-stage thermal screening revealed highly significant genotypic differences during grain filling. A multivariate PCA biplot identified early canopy temperature differences during tillering (ΔCT_TL) as the most informative non-destructive selection indicator. ΔCT_TL showed a strong positive association with terminal yield stability metrics (YSI and RSI) and a marked negative relationship with the drought sensitivity index (SDI). This early canopy temperature regulation contributed to the maintenance of yield stability in the modern hexaploid variety MFTBY-T and advanced tetraploid lines OR2-T and OR4-S. In contrast, poorly adapted ancient varieties (P5-S and S3-S) exhibited high drought sensitivity accompanied by pronounced late-stage induction of DREB1 and DREB2, suggesting a delayed stress-response mechanism activated under severe tissue dehydration. Conversely, the modern tetraploid variety KZLTN-T and hexaploid landraces appeared to rely on an early vegetative molecular priming strategy. These findings suggest that breeding programs should prioritize the incorporation of vegetative transcriptional traits associated with effective canopy temperature homeostasis into elite genetic backgrounds.

Why it matches plant phenotyping methodsUAV熱画像によるキャノピー温度の非破壊測定をデジタル表現型として用い、乾燥耐性選抜指標として評価しており、表現型取得・解析が研究の主要な構成要素である。

titleINTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES

Parameterizing steady-state C3 photosynthesis models using photosynthesis response curves: Which models to use and which parameter estimates to trust? →

Photosynthesis sustains life on Earth, yet we still lack comprehensive understanding of the biochemical and environmental factors that affect this fundamental process. Steady-state models of C3 photosynthesis provide a powerful framework but rely on reliable estimation of numerous parameters from gas-exchange data. Despite methodological advances, how model structure and data choice influence parameter accuracy and consistency remains poorly explored. Here, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements. Using synthetic photosynthesis response curves generated from the examined models with sampled parameter values, we applied Bayesian inference to quantify parameter uncertainty and estimation performance for the considered models. We showed that while key parameters of C3 photosynthesis, such as maximum rate of RuBP-saturated carboxylation and of electron transport through photosystem II, can be reliably estimated from a single A-Ci curve, other parameters, such as leaf mitochondrial respiration and CO2 compensation point, require expanded sampling of light response space. We also demonstrated the advantage of using simultaneous estimation of all model parameters over biasing the estimation by keeping some parameters fixed to prior values. Usage of barley gas-exchange data further demonstrated that parameter consistency across models can be evaluated comparing different levels of measurements and depends strongly on both model formulation and data type. Together, our study provides practical guidance for selecting photosynthesis models, designing phenotyping strategies and choosing parameterization approaches for steady-state C3 photosynthesis.

Why it matches plant phenotyping methodsガス交換応答曲線から光合成パラメータを推定するモデルとベイズ推論を体系的に比較・評価し、フェノタイピング戦略の設計を扱うため、植物表現型取得・推定法が中心である。

abstractHere, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements.

Improving the prediction of water stress-related traits in open-field tomato using multivariate models and variable importance-based indices →

The assessment of water stress levels in plants should be essential part of precise irrigation management, and a good and quick method is useful in plant phenotyping. There are many options for this task, but the effectiveness varies between crop species and environments. Apparently open field applications face the most difficulties. This study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components. Data of the abovementioned variables was collected during three consecutive growing seasons in processing tomato cultivated under different water supply regimes to provide data with varying water stress levels. Then the relation of the measured variables to 226 VIs was tested created according to the formulas collected in the Index DataBase (IDB Project, indexdatabas.de ). New VIs were also developed derived from the most important variables of the ML algorithms, customised to tomato water stress assessment. Standard normal variate and its combination with Savitzky-Golay first derivative were used for pre-processing the spectra and principal component regression (PCR), partial least squares regression (PLSR), elastic net (ENET), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGB) algorithms were tested. The newly developed indices outperformed the existing formulas, except in the case of β-carotene. The most reliable index was developed for RWC estimation; that was the difference of the reflectance on the 986 and 701 nm wavelengths. The ENET and SVR algorithms produced the best models depending on the pre-processing method. The blue, near-infrared (NIR) and green regions, respectively, were the most important regarding all models according to the variable importance analysis. The model with the best metrics was developed for chlorophyll-a (R 2 =0.82, nRMSE=11%, RPIQ=2.41), followed by RWC (R 2 =0.72, nRMSE=14%, RPIQ=2.53).

Why it matches plant phenotyping methodsハイパースペクトル反射データと多変量・機械学習モデルを用いて、トマトの水ストレス関連生理形質を推定し、新規指標も開発・評価しているため、表現型取得・推定手法が中心である。

abstractThis study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components.

Apple Crop Health Detection Based on Vegetation Indices at Shalimar, Kashmir: North-Western Himalayas →

Apple orchard health monitoring is important for supporting timely crop management under the temperate conditions of Kashmir Valley. The present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices, namely the Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Modified Soil Adjusted Vegetation Index and Enhanced Vegetation Index, in a 2 ha apple orchard at SKUAST-K, Shalimar, Kashmir, during the 2020 growing season. Sentinel-2 imagery acquired from April to September was processed using SNAP and QGIS, and mean vegetation index values were extracted for the orchard area. Monthly weather data, including average temperature and rainfall, were obtained from the on-campus meteorological observatory. Ground observations at 20 georeferenced points were used to support the interpretation of canopy development, phenological stage and visible plant health condition. All four vegetation indices showed a seasonal increase from April to July-August, followed by a decline during September-October, corresponding to canopy development, fruit maturation, harvest and senescence. Based on the monthly values presented in the study, temperature showed positive associations with the vegetation indices, with the strongest relationship observed for MSAVI, followed by NDVI and SAVI. Rainfall showed weak and non-significant associations with the indices during the study period. The results indicate that Sentinel-2-derived vegetation indices can reflect seasonal canopy dynamics in apple orchards under the studied conditions. MSAVI appeared particularly useful for representing canopy development, while field observations remained necessary for interpreting pest, disease and phenological effects.

Why it matches plant phenotyping methodsSentinel-2画像から植生指数を算出し、リンゴ樹冠の季節動態・健康状態を評価する測定ワークフローが研究の中心であり、植物状態の推定に直接用いられている。

abstractThe present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices

Multispectral and anatomical assessment of chromium and nickel accumulation in urban weeds. →

Early detection of heavy metal stress in plants is essential for effective environmental monitoring, particularly in contaminated urban areas. This study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species. Five species were selected: Trifolium pratense, Rumex acetosa, Alcea rosea, Amaranthus retroflexus, and Plantago lanceolata. Visible plant injuries were assessed using Evans Blue staining and image-based anatomical analysis, which enabled distinguishing between living, partially damaged, and dead cells. Multispectral observations using a MicaSense RedEdge-M camera allowed calculation of the Normalized Difference Vegetation Index (NDVI) to detect stress-related changes in photosynthetic apparatus. The studied species differed in their capacity to accumulate and translocate Cr and Ni. Metal bioaccumulation was low in all species (bioconcentration factor < 1), with the highest Ni accumulation observed in Plantago lanceolata. Translocation of both metals was the greatest in Trifolium pratense and Amaranthus retroflexus. Hydrogen peroxide levels increased in roots and leaves of all species, particularly in Alcea rosea. Despite the absence of visible injuries, microscopic anatomical changes were detected in T. pratense and R. acetosa, while NDVI values differed between sites. In summary, this study indicates that no simple relationship was found between physiological stress parameter values and NDVI. It is important to emphasize the need for continued research under controlled conditions with specific doses of PTEs salts. This should clearly demonstrate the relationship between plant physiological responses to stress and the results of multispectral observations.

Why it matches plant phenotyping methodsリモートセンシング、画像ベースの解剖診断、NDVIを用いた植物ストレス検出法の評価が研究目的として明示されており、植物状態の取得・推定が中心的です。

abstractThis study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species.

Phenotyping of genotypes and diagnosis of water status in cowpea using thermographic images and machine learning →

Abstract Purpose The variability in tolerance to water stress among cowpea genotypes requires fast and accurate phenotyping methods. The integration of infrared thermography with artificial intelligence is emerging as a robust solution for large-scale, non-invasive monitoring. Thus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images. Methods Ten genotypes (five varieties: Corujinha – G1, Paulistinha – G2, Sempre Verde – G3, Pintado – G4, and Rabo de Tatu – G5) and the cultivars BRS Novaera – G6, BRS Pajeú – G7, IPA 206 – G8, BRS Tapaihum – G9, and BRS Miranda – G10) were subjected to four water regimes (25%, 50%, 75%, and 100% of ETc). Thermographic images were collected at the V3 and R2 stages and processed using Deep Learning architectures (InceptionV3, SqueezeNet, VGG16, and VGG19) to extract features (vectorization). The k-NN, Decision Tree, Random Forest, SVM, Neural Network, and AdaBoost algorithms were trained to classify stress levels and genotypes. Results The vegetative stage (V3) proved more effective for diagnosis than the reproductive stage (R2), exhibiting more stable thermal signatures. The SVM algorithm, combined with the VGG16 vectorizer, achieved the best performance, achieving an accuracy greater than 0.910 in classifying water regimes. The landrace varieties exhibited thermal patterns distinct from those of the improved cultivars, enabling high-precision genotypic identification. Conclusions The proposed approach demonstrates that infrared thermography, combined with machine learning models, is an effective tool for high-throughput digital phenotyping, optimizing the selection of drought-tolerant materials and irrigation management in precision agriculture.

Why it matches plant phenotyping methods赤外線サーモグラフィ画像から水ストレス状態と遺伝型を抽出する機械学習手法を開発・評価しており、植物フェノタイピングが研究の中心である。

abstractThus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images.

Leaf- and canopy-level hyperspectral sensing of wheat-Fusarium head blight-Trichoderma gamsii interactions →

Fusarium head blight (FHB) is a major mycotoxigenic disease of wheat, causing yield and quality losses and deoxynivalenol contamination. Rapid, non-destructive tools are needed to detect FHB, monitor wheat physiological responses, and evaluate sustainable management strategies, including biological control agents. Although vegetation spectroscopy is widely used for high-throughput phenotyping, most spectral studies focus on binary disease detection, while the capacity of hyperspectral data to capture concurrent host–pathogen–biocontrol responses across leaf and canopy scales remains underexplored. Here, we tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085. Two cultivars, Bingo and Rebelde, with higher and lower FHB susceptibility, respectively, were treated with a chemical fungicide (Chem) or T. gamsii T6085 (Bioc) under FHB pressure. Leaf- and canopy-level spectra were acquired at 2, 5, and 14 days post-inoculation, alongside gas exchange, water status, and chlorophyll measurements. Permutational multivariate analysis of variance (PERMANOVA) tested whole-spectrum effects, partial least squares discriminant analysis (PLS-DA) explored class separability, and partial least squares regression (PLSR) estimated physiological traits. PERMANOVA detected genotype × inoculation × treatment interactions from 5 days post-inoculation at leaf and canopy levels. PLS-DA revealed treatment- and cultivar-dependent spectral fingerprints, but overall low-to-fair validation performance indicates that these class-specific patterns should be interpreted as exploratory and not as evidence of operational treatment discrimination. PLSR provided high accuracy for chlorophyll content and osmotic potential, moderate accuracy for CO 2 -assimilation traits, and poor accuracy for transpiration and leaf water potential. While the workflow is scalable as an experimental and analytical framework, its operational deployment will require broader validation across sites, seasons, cultivars, disease-pressure conditions, and sensing platforms.

Why it matches plant phenotyping methods小麦のFHB・生物防除応答を対象に、葉・群落ハイパースペクトル取得、分類、検証、形質推定を統合したフェノタイピング枠組みが中心である。

abstractwe tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085.

Pre-Symptomatic Crop Intelligence: A Closed-Loop Framework for Anticipatory, Confidence-Aware Decision-Making in Site-Specific Crop Protection →

Abstract Purpose Symptom-triggered crop protection acts only after damage is committed, and the intervention window has narrowed. This review reframes pre-symptomatic sensing from a detection problem into a closed decision loop, establishing the physiological lead time of a signal, set against its detection confidence and the latency of the response it can trigger, as the organizing principle for anticipatory, site-specific decision-making. Methods A systematic-narrative synthesis was conducted across major bibliographic databases through June 2026. Studies reporting pre-symptomatic capability under field or realistic conditions were retained and coded onto a coupled lead-time × confidence × actionability framework spanning sensing, inference, and actuation. Results Optical modalities were found to dominate the evidence base, while electrophysiological and volatile signals extended achievable lead time. Single modalities were insufficient to separate biotic from abiotic stress, motivating heterogeneous fusion. Edge inference and temporal onset forecasting remained immature, detection confidence was rarely quantified, and the sensing-to-actuation loop was seldom closed. Reported performance degraded sharply from laboratory to field, particularly in perennial and smallholder systems. Conclusions A unifying Pre-Symptomatic Crop Intelligence framework is proposed, governed by the principle that system value is bounded by the weakest of lead time, detection confidence, and response latency; priorities identified include lead-time-labeled benchmarks, uncertainty-aware inference, field-robust fusion, and economic evaluation for perennial crops.

Why it matches plant phenotyping methods植物の病害・ストレス状態を早期に推定するセンシング手法を体系的に整理し、検出リードタイム、信頼度、融合推論、ベンチマークを評価するレビューであり、植物状態の取得・推定方法が中心である。

abstractThis review reframes pre-symptomatic sensing from a detection problem into a closed decision loop

Medium-adaptive wideband near-field antenna for microwave detection of internal cavities in plant stems →

Abstract This paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks. Unlike conventional antennas designed for free-space operation, the proposed antenna is explicitly engineered to operate in close proximity to a lossy, anisotropic, and dispersive cylindrical medium representing wood tissues. A physics-based electromagnetic model of the stem is incorporated into the design process, enabling accurate optimization under realistic dielectric loading conditions. The antenna consists of a compact quasi-planar dipole with blended arms integrated with a medium-adaptive dual-ring balun that ensures balanced current excitation and stable impedance matching under strong near-field loading. Both simulation and experimental measurements demonstrate wideband impedance matching to a 50 Ω source over the 2.0–3.0 GHz frequency range. Surface current distribution and specific absorption rate (SAR) analyses confirm efficient electromagnetic coupling into the stem tissues with minimal radiation leakage. To evaluate the sensing capability of the proposed design, a conceptual two-element antenna system is introduced as a feasibility study for cavity detection. The detection performance is assessed through a sensitivity-driven framework based on variations in both self- and mutual-scattering parameters. A comprehensive sensitivity analysis is conducted to quantify the response of the system to changes in cavity diameter, radial position, and angular location. The results demonstrate that while the reflection coefficient is primarily sensitive to near-surface inhomogeneities, the mutual coupling between antenna elements provides strong and reliable sensitivity to internal cavity characteristics. Based on the sensitivity analysis, an optimal operating frequency band centered at 2.76 GHz and an appropriate antenna clearance are identified to maximize detection performance. The proposed antenna and sensing methodology are further validated through experimental measurements, confirming the consistency with numerical results. Simulation results demonstrate cavity-detection sensitivity, while experimental measurements validate the antenna impedance matching and mutual-coupling characteristics. The compact geometry of the antenna enables scalable multi-element configurations, establishing a practical framework for non-destructive, microwave-based monitoring of internal tree degradation in agricultural and forestry applications.

Why it matches plant phenotyping methods植物茎内の空洞という状態をマイクロ波で非破壊検出するアンテナとセンシング手法を開発し、シミュレーションおよび実験で検証しており、植物フェノタイピング手法が中心である。

abstractThis paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks.

PlaFe: an outdoor platform for crop phenotyping under progressive drought →

The selection of genotypes adapted to water stress requires experimental facilities that allow environmental control without compromising physiological and yield relevance. The objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe, which comprised sixty-two high-volume prismatic lysimeters arranged in rows 1.2 m long and spaced 0.6 m apart. Soil water dynamics were monitored weekly using a weighting system. To validate PlaFe, two soybean genotypes were exposed to two water scenarios for forty days from R2 + 7d, during two growing seasons. Two irrigation treatments were applied: irrigation to keep soil water content over 60–70 % of field capacity (EH0), and irrigation equivalent to 35 % of that applied in EH0 (EH1). Water consumption, crop biomass, and pod number were determined at maturity. On average, water stress reduced both biomass and pod numbers by 40 %. However, reproductive efficiency varied among genotypes. Canopy temperature increased by 0.56 °C as daily water consumption decreased, demonstrating its potential to assess drought. These results demonstrate PlaFe’s potential for the accurate evaluation of crop response and adaptation to diverse water scenarios without compromising the complex plant-environment interactions inherent to field conditions.

Why it matches plant phenotyping methodsPlaFeという屋外半制御型フェノタイピングプラットフォームを設計・検証しており、植物の水消費、バイオマス、莢数、群落温度などの表現型評価が研究の中心である。

abstractThe objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe

Digitalization of wheat mold odor based on controlled volatile release: A large-scale study. →

This study presents a large-scale framework for the digitalization of wheat mold odor based on controlled volatile release and standardized gas acquisition. Seven wheat-varieties harvested from 2022 to 2025 in three provinces of China were cultivated into four spoilage levels (normal, mild, moderate, and severe mildew), and a graphene-based sensor array was developed for multidimensional odor detection. A dual pre-treatment strategy integrating temperature-regulated volatilization and cooling-assisted dehumidification was implemented to ensure stable and comparable signal acquisition. Under optimized conditions, 1491 odor response curves from the first three batches were used for machine learning modeling, while an independent fourth batch (n = 503) was used for external validation. For binary classification (normal vs. moldy wheat), the optimized Light Gradient Boosting Machine achieved 95.8% accuracy, 95.5% sensitivity, 96.8% specificity, and an AUC of 0.984. This proposed approach enables rapid, non-destructive mold assessment and supports standardized grain quality monitoring.

Why it matches plant phenotyping methods小麦穀粒のカビ状態を対象に、グラフェンセンサーアレイと揮発成分取得前処理を開発し、機械学習分類と独立バッチ検証まで行っており、植物状態の取得・判定法が研究の中心である。

abstracta graphene-based sensor array was developed for multidimensional odor detection

Cell‐based crop phenotyping for future climates →

Abiotic stress tolerance has been significantly weakened in modern crops during the domestication process. Regaining tolerance has become a critical task in light of current climate trends and their impact on global food security. Abiotic stress tolerance is an extremely complex trait and is conferred at various levels of plant functional organization and developmental stages, with regulatory mechanisms operating across multiple scales, from individual cells to tissues and the entire plant. The emergence of advanced molecular tools such as single-cell RNA sequencing and spatial omics technologies has revolutionized the field, advancing our understanding of plant responses to hostile environments. However, the implementation of this knowledge in crop breeding programmes is handicapped by the lack of appropriate phenotyping platforms. Here, we argue that current phenotyping methods may be excellent tools for functional validation of previously discovered traits but have limited predictive value in stress biology. We also propose that bridging the mismatch between omics technologies and phenotyping is the only way to account for cell-specific operation of key genes conferring stress tolerance and implementing them in breeding programmes. Some practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.

Why it matches plant phenotyping methods細胞ベースの植物フェノタイピング手法を扱い、蛍光色素や電気生理学的方法の例、限界、展望を論じる方法論レビューである。

abstractSome practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.

Trait-based machine learning modeling of soluble carbohydrate content in Trachyspermum ammi exposed to organic and chemical fertilization and salicylic acid. →

Accurate assessment of biochemical traits in medicinal plants is essential for supporting environmentally responsible agriculture, improving crop quality, and enhancing the nutritional and pharmacological value of plant-derived products. Although medicinal plants are rich in bioactive compounds, conventional methods for measuring key biochemical components, such as soluble carbohydrates, are often time-consuming, destructive, and resource-intensive. Trachyspermum ammi L. (Ajwain) is valued for its antioxidant, antimicrobial, and digestive properties, highlighting the need for rapid, reliable, and non-destructive evaluation methods. Despite previous studies on fertilization effects on growth and bioactive compounds in T. ammi, research integrating morpho-physiological data with machine learning to predict key biochemical traits remains limited. In this study, we applied Multilayer Perceptron (MLP) and Gaussian Process Regression (GPR) models to estimate soluble carbohydrate content in a non-invasive and efficient manner. A dataset including morphological, biochemical, physiological, and macronutrient traits was used as input variables. Fertilization regimes and salicylic acid (SA) treatments were applied to induce variability in plant traits but were not directly included as model features, ensuring that predictions were trait-based. Models were trained and evaluated on n = 45 samples using five-fold cross-validation. Among the tested models, MLP and GPR achieved the highest predictive accuracy, particularly when the full feature set was used. Predictions based solely on biochemical and physiological traits were nearly as accurate as those using all variables, suggesting that these traits provide reliable and cost-effective estimates. Considering the limited dataset, results should be interpreted with caution, and future studies using larger, independent datasets are recommended to further assess model robustness and generalizability. These findings demonstrate the practical potential of the proposed machine learning approach for rapid, non-destructive assessment of biochemical traits in medicinal plants and may inform the development of GUI-based decision-support tools for precision agriculture and phytopharmaceutical research.

Why it matches plant phenotyping methods機械学習モデルによる植物の可溶性炭水化物含量の非破壊推定が研究の中心であり、交差検証による技術評価も行っているため、植物フェノタイピング手法として採用する。

abstractIn this study, we applied Multilayer Perceptron (MLP) and Gaussian Process Regression (GPR) models to estimate soluble carbohydrate content in a non-invasive and efficient manner.

In-bore climate control chamber for magnetic resonance imaging of living plants. →

Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging.In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions.The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B 0 homogeneity, RF attenuation as well as potential RF artefacts) were verified.Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.

Why it matches plant phenotyping methods植物の解剖学的・機能的MRI計測を可能にする環境制御チャンバーとインボア拡張を開発し、性能および植物での機能・解剖学的計測を検証しており、フェノタイピング手法が中心である。

abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.

Comprehensive evaluation of the heat tolerance of non-heading Chinese cabbage based on multifactorial statistical analysis. →

Non-heading Chinese cabbage is a cool-season crop, and high temperature has become a key factor limiting its quality and yield. Given that plant heat tolerance is a complex quantitative trait regulated by multiple genes, establishing a comprehensive evaluation system integrating multiple physiological and biochemical indicators is of great significance. In this study, 35 varieties of non-heading Chinese cabbage germplasm were used to investigate heat damage indices (HDI) and measure physiological and biochemical indicators under summer high-temperature stress, aiming to provide a basis for heat tolerance evaluation. Correlation analysis revealed significant correlations among the physiological and biochemical indicators, indicating information overlap. Principal component analysis (PCA) was subsequently employed to extract six independent composite indicators. A composite index of heat tolerance productivity, namely the Heat Tolerance Productivity Index (HTPI), was obtained through membership function analysis, and cluster analysis classified the tested germplasm into four heat tolerance levels. A regression equation for evaluating heat tolerance in non-heading Chinese cabbage was successfully established. Eleven key heat tolerance indicators were identified, and two highly heat-tolerant varieties, B21 and B32, with excellent comprehensive traits were selected. The comprehensive evaluation system established in this study not only provides an effective tool for high-throughput screening of heat-tolerant germplasm resources but also lays a solid foundation for subsequent genetic improvement and molecular breeding of heat-tolerant varieties. However, this study was conducted only at the seedling stage, and did not evaluate heat tolerance during the more sensitive reproductive stages (flowering and bolting).

Why it matches plant phenotyping methods複数の生理・生化学指標を統合し、PCA、メンバーシップ関数、回帰式による耐暑性評価システムを開発しており、植物表現型の抽出・スクリーニング手法が研究の中心である。

abstractestablishing a comprehensive evaluation system integrating multiple physiological and biochemical indicators is of great significance

A Flexible, Top-Down Approach To Assays Of Leaf Movement Rhythms. →

A growing body of literature has illustrated the importance of understanding the circadian clock due to its connection to a wide array of molecular and physiological processes. Numerous methods have been developed in order to monitor the status of the circadian clock in living tissue; however, most methods are either costly or labor intensive, and often require precise conditions that lowers throughput and limits flexibility in the size, age, or type of plant to be assayed. Here, we present an affordable and adaptable methodology for assaying circadian period using the well-characterized circadian output of leaf movement. Employing fully automated time-lapse photography using Raspberry Pi cameras and predominantly automated image post-processing, this methodology minimizes manual input to expedite circadian analysis, thus improving throughput. Additionally, the top-down setup used in this method is appropriate for a wide range of sizes and ages of plants, allowing for an expansion of the scientific questions that can be assayed by this methodology.

Why it matches plant phenotyping methods葉の運動リズムを自動画像計測・解析して概日時計の周期を評価する、植物表現型取得法の開発が中心である。

abstractwe present an affordable and adaptable methodology for assaying circadian period using the well-characterized circadian output of leaf movement

Development of auxin reporters in oilseed rape (Brassica napus) →

Auxin is a key phytohormone that regulates all aspects of plant growth, development, and environmental responses, making the precise analysis of its distribution and signaling essential for understanding plant adaptation and physiological processes. However, despite the agricultural importance of oilseed rape (Brassica napus), the lack of robust, species-specific molecular tools limits detailed studies of hormone signaling in this crop. Here, we developed and characterized reporter systems for the sensitive visualization and quantification of auxin distribution and signaling in B. napus. The DR5cc auxin signaling reporter and a novel synthetic auxin-responsive reporter, BIP3, assembled from promoter fragments of three oilseed rape IAA genes, were generated to drive GUS expression. In hairy roots, both reporters showed auxin-responsive expression in the root apical meristem that became broader after auxin treatment. In transgenic seedlings, flowers at anthesis, and 12-day-old embryos, DR5cc exhibited a more defined expression pattern than BIP3. To monitor real-time auxin dynamics under abiotic stress, DR5cc fluorescent reporters were employed in hairy roots. Mannitol and NaCl treatments induced a time-dependent increase in fluorescence, peaking at 6-12 h before returning to basal levels after 24 h. Furthermore, dual-reporter assays enabled simultaneous monitoring of auxin and cytokinin signaling, revealing distinct hormone-specific spatial responses in hairy roots. Finally, we established a quantitative DII (qDII) reporter system using degron domains from B. napus Aux/IAA proteins, providing a high-resolution quantitative readout of auxin depletion. Together, these reporter systems enable spatial, temporal, and quantitative analyses of auxin dynamics during development and stress adaptation in oilseed rape.

Why it matches plant phenotyping methodsナタネにおけるオーキシン分布・シグナルを可視化および定量するレポーター系を開発・評価しており、植物の生理状態を取得する方法が研究の中心である。

abstractHere, we developed and characterized reporter systems for the sensitive visualization and quantification of auxin distribution and signaling in B. napus.

First Field Validation of a New VNIR–SWIR-Based Six-Band Multi-Camera System for UAVs over Winter Wheat →

Abstract. Shortwave infrared (SWIR) UAV imaging remains uncommon despite its sensitivity to canopy water and protein. We report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat. The payload used narrow bandpass filters at 910, 980, 1100, 1200, 1510, and 1650 nm (FWHM 10–12 nm) and was flown at 30 m AGL, yielding 4 cm GSD. Radiometric calibration used in-flight empirical line calibration with an in-scene gray panel set, followed by independent validation on a material-distinct gray set. ASD spectroradiometer measurements were convolved with Gaussian proxy spectral response functions matched to the nominal filter passbands. Empirical line fits were near-perfect (R2 ≈ 1.000; RMSE = 0.003–0.009). Independent panel validation showed near-unity slopes for five bands from 980–1650 nm (R2 = 0.998–0.999; RMSE = 0.005–0.013). Across 36 canopy plot ROIs, camera-to-ASD agreement remained strong for five bands, with slopes of 0.943–1.079, R2 = 0.58–0.85, and RMSE = 0.010–0.023. Two SWIR normalized ratio indices showed tight cross-sensor agreement: NRI[1100,1200] (R2 ≈ 0.93; RMSE ≈ 0.010) and NRI[1650,1510] (R2 ≈ 0.90; RMSE ≈ 0.017–0.018). Post-hoc filter transmittance measurements revealed secondary long-wavelength throughput in the 910 nm channel, causing compressed slopes and elevated error (MAPE ≈ 33%); this band was excluded from accuracy claims. Panel-anchored, bandpass-aware calibration enables quantitative UAV SWIR reflectance and robust SWIR indices for precision agriculture applications. The workflow also identifies hardware-specific failure modes, supporting reproducible validation and informed redesign of filter-reconfigurable SWIR payloads.

Why it matches plant phenotyping methods冬小麦キャノピーの定量的な反射率・SWIR指数取得を目的に、UAVマルチカメラの校正、独立検証、センサー間比較、故障モード評価を中心的に実施しており、植物フェノタイピング手法の技術検証に該当する。

abstractWe report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat.

Photoelectrochemical aptasensor based on biomass-derived carbon/BiOI nanoflowers for in-situ monitoring of abscisic acid. →

A photoelectrochemical (PEC) aptasensor based on bismuth oxyiodide (BiOI) nanoflower/biomass carbon (BiOI@BC) was fabricated for in-situ detecting abscisic acid (ABA) in tomato leaves under salt stress. Shrimp shells-derived biomass carbon acted as an enhanced carrier, and the biomass carbon improves the PEC performance of BiOI by extending the visible light absorption range and promoting the charge transfer of pure BiOI nanoflower. The BiOI@BC exhibited high photocurrent, which was about 19 times in contrast to pristine BiOI, attributing to the synergistic effects of biomass carbon self-doped with N, P, and S atoms. Furthermore, a PEC aptasensing platform was developed for the sensitive and selective determination of ABA, with a wide linear range from 0.1 to 1000 pM and a remarkably low detection limit of 0.03 pM. The practical applicability of the device was further validated by on-site monitoring of ABA levels in tomato leaves under salt stress, demonstrating good stability and accuracy. This work provides a robust strategy for real-time phytohormone detection, facilitating precise crop regulation in plant biology and agriculture.

Why it matches plant phenotyping methods植物葉内のABAをその場で測定するPECアプタセンサー自体の開発と実用検証が中心であり、塩ストレス下の植物生理状態を抽出するセンサー型表現型計測に該当する。

abstractA photoelectrochemical (PEC) aptasensor based on bismuth oxyiodide (BiOI) nanoflower/biomass carbon (BiOI@BC) was fabricated for in-situ detecting abscisic acid (ABA) in tomato leaves under salt stress.

Comparison of Isotope Mass Balance and AquaCrop Model in Evapotranspiration Partitioning in a Maize Field of North China. →

Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012-2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0-5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40-100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94-96% at filling, then declining to 84-85% at maturity. The two methods agreed well during filling to maturity (differences of 2-10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions.

Why it matches plant phenotyping methodsトウモロコシの蒸散比を対象に、同位体質量収支法とAquaCropモデルを比較・検証しており、植物の水利用状態を取得する測定手法の性能評価が中心である。

abstractThe isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized.

Plant Acoustics for Evaluating Vase Life in Chrysanthemums: A Novel and Noninvasive Method →

ABSTRACT Vase life is a key determinant of cut flower quality and market value. Conventional vase life assessment relies on visual inspection and physiological monitoring over several days to weeks, making it labor‐ and time‐intensive. This study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers. Six chrysanthemum cultivars were grown under two supplemental lighting treatments (Hybrid and LED) and two planting densities (54 and 74 plants m −2 ). Acoustic monitoring was compared with optical microscopy for the assessment of xylem vessel diameter, while conventional vase‐life testing was performed in parallel. Optical microscopy validated the acoustic measurements, with both methods consistently identifying vessel radii around 10 μm. The acoustic radius (), derived from pulse settling time measurements, showed cultivar‐ and planting‐density‐specific variation. Linear mixed‐effects modelling demonstrated that the relationship between acoustic radius and vase life differed significantly among cultivars, indicating that a universal relationship across cultivars is not supported. These findings show that acoustic monitoring provides a meaningful noninvasive proxy for vase‐life‐associated stem traits and may serve as a useful cultivar‐calibrated tool for evaluating postharvest longevity in cut chrysanthemums.

Why it matches plant phenotyping methods植物の茎の道管径に基づく音響的な非破壊測定法を開発し、光学顕微鏡および花持ち試験で検証しているため、表現型取得法が研究の中心です。

abstractThis study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers.

Quantification of leaf photosynthetic traits in field conditions: Towards an efficient and reliable method for plant phenotyping and modelling ecophysiological processes →

• Rapid methods show reduced robustness in hot summer Mediterranean field conditions. • Method‑dependent differences in Vc max estimates strongly affect A - g s model outputs. • Steady‑state A / C i provides the most accurate gs simulations. • RACiR provides Vc max estimates closest to A / C i and represents a suitable option for high‑throughput phenotyping. The maximum rate of carboxylation of ribulose-1,5-bisphosphate ( Vc max ) represents a key biochemical trait and a fundamental parameter in C3 models of photosynthesis, as it enables an accurate representation of leaf carbon assimilation and gas exchange. Accurate estimation of this parameter is essential for process‑based modelling across scales, as uncertainties in Vc max may influence model behaviour when scaled from leaves to larger spatial domains. Traditionally, Vc max is derived from the response of photosynthesis ( A ) to intercellular CO 2 concentration ( C i ), known as the A / C i curve, a reliable but time-consuming and labour-intensive procedure that limits its application in high-throughput phenotyping. To address this limitation, rapid approaches such as the Rapid A/Ci Response (RACiR) and the one-point (OP) methods have been developed. However, their accuracy, reliability, and reproducibility must be carefully validated, as discrepancies arising from the use of heterogeneous data sources for model parameterization may introduce significant uncertainty. In this study, the RACiR and the OP methods were evaluated against the conventional A / C i curve in a two-year field experiment on four Cannabis sativa varieties grown under different irrigation regimes. Photosynthetic traits derived from each method were compared and integrated into a coupled A -stomatal conductance ( g s ) model to assess how method-driven differences affect model outputs. Overall, photosynthetic traits estimated from A / C i curves provided the most accurate simulations of g s , with R 2 values ranging from 0.55 to 0.84 and the lowest RMSE. In contrast, traits derived from RACiR and OP methods resulted in g s overestimations of 26.7% and 50.7%, respectively. Field application of RACiR was hindered by high failure rates under high summer temperatures, while OP estimates showed substantial variability. These results indicate that, despite the appeal of faster alternatives, the A / C i curve remains the most reliable method for estimating Vc max under Mediterranean field conditions, particularly when high accuracy is required for model-based applications.

Why it matches plant phenotyping methods植物の光合成形質（Vcmax等）を高スループットに測定する手法を比較・検証し、精度、再現性、失敗率、モデル性能を評価しており、フェノタイピング手法が中心です。

titleQuantification of leaf photosynthetic traits in field conditions: Towards an efficient and reliable method for plant phenotyping and modelling ecophysiological processes

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.

Non-destructive Three-dimensional Elemental Mapping in Intact Plant Tissues Using Confocal X-ray Microscopy →

Understanding elemental distributions in plants is critical for agricultural productivity, nutritional quality, and limiting the transfer of toxic elements into the food chain. Conventional elemental mapping techniques typically require thin sectioning or complex tomographic reconstructions, making three-dimensional analysis labor-intensive and destructive. Here, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1]. This method defines a localized 3D detection volume within the sample, enabling the generation of elemental virtual cross-sections without physical sectioning while preserving native spatial relationships. We demonstrate the capability of this technique by mapping Fe distributions in carrot (Daucus carota) roots and shoots and Cd distributions in root tips of near-isogenic wheat (Triticum aestivum) lines. Integration of multiple virtual sections enabled three-dimensional reconstructions that reveal distinct Cd translocation pathways between accumulating and non-accumulating wheat lines, tracing elemental movement from the epidermis through cortical layers into vascular tissues. The method is applicable to diverse plant morphologies, including cylindrical roots and irregular leaf and stem tissues. This approach enables high-sensitivity, non-destructive 3D elemental imaging, providing a powerful tool for studying elemental transport in plants with direct relevance to crop breeding, food safety, and agricultural sustainability [2].

Why it matches plant phenotyping methods植物組織内の元素分布という生理状態を、非破壊3D XRFで取得・再構成する手法の開発と植物試料での実証が中心である。

abstractHere, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1].

Thermal imaging as a tool for studying circadian rhythms in roots. →

Plants rely on the circadian clock to anticipate daily environmental fluctuations and to coordinate key physiological, metabolic, and developmental processes. Most if not all plant cells have semi-autonomous circadian oscillators. Roots possess a modified yet robust circadian oscillator that is entrained by external cues such as light and temperature to synchronize nutrient uptake, water transport, and metabolic activity. It has been shown that the root and shoot oscillators can communicate through long-distance signals including mobile proteins and carbon assimilates such as sucrose. Moreover, recent studies indicate root-microbe interactions; root-associated microbial communities exhibit diurnal oscillations structured by the host circadian system, while microbes can in turn modulate the circadian period and rhythmic outputs of the plant. However, in general, while the shoot circadian oscillator has been extensively characterized, much less is known about the root circadian system. Progress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity. Existing methods including luciferase reporters, quantitative RT-PCR, and microscopy remain limited by cost, destructive sampling, or require transgenic lines with reporter genes. We have developed a thermal infrared imaging platform that enables non-invasive, high resolution of circadian rhythms in roots across plant species and growth conditions. We show that our system can be used to analyse metabolite and microbial effects on root circadian regulation. This platform provides new opportunities to investigate below-ground circadian regulation and the possibilities of harnessing the root clock to enhance plant performance and resilience.

Why it matches plant phenotyping methods根の概日リズムという植物状態を、非破壊・高解像度の熱赤外画像で測定するプラットフォームを開発しており、フェノタイピング手法が研究の中心である。

abstractProgress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity.

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

Dynamic, adaptive and modular Digital Twin framework for resource-efficient Controlled Environment Agriculture. →

In Controlled Environment Agriculture (CEA), traditional fixed set-point control is replaced by dynamic control strategies. These strategies enable joint optimization of resource use efficiency and biomass output, leverage electricity price fluctuations to reduce energy costs, and employ targeted environmental stressors to enhance crop quality and physiological resilience. Implementation of dynamic control strategies, however, builds upon real-time monitoring, robust data integration and management, and high-fidelity predictive modeling. These capabilities can be effectively provided through a Digital Twin (DT). This study introduces a novel open-source DT framework designed to support dynamic control strategies in CEA, addressing challenges in scalability, generalizability and interoperability. The framework is centered on the IoT platform ThingsBoard, providing unified, scalable data acquisition and management across heterogeneous sensor and actuator networks through vendor-agnostic integration and standardized interfaces. A significant contribution is its physics-based modeling backend, built on ordinary differential equation models developed in Modelica and exported as Functional Mock-up Units (FMUs). To ensure model accuracy across varying biological conditions, a parameter estimation pipeline is developed to calibrate and adapt these FMUs against experimental data. Building on this, a dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control. Furthermore, the framework incorporates a Multirate Moving Horizon Estimation (MMHE) state estimator to estimate critical unmeasured variables, such as plant biomass. This estimator is specifically designed to handle multirate data, maintaining continuous estimates even when certain sensors provide frequent data while others are sparse or infrequent. Demonstrated through a simulation-based case study modeling lettuce growth in a vertical hydroponic farm, the DT framework's architectural feasibility and virtual modeling capabilities are verified. Using synthetic data generated from a known true parameter set, the calibrated growth model achieved a low cross-validation prediction error, with an RMSE of 0.221g and an NRMSE of 6.62% on an independent test set. The MMHE-based state estimator effectively maintained continuous biomass estimates despite sparse synthetic measurements and model mismatch. These findings underscore the framework's potential as a robust and extensible foundation for future physical DT implementations in CEA, enabling a 31 transition toward dynamic, data-driven, and energy-aware operations.

Why it matches plant phenotyping methods植物バイオマスという観測可能な植物形質を、デジタルツインの状態推定器と動的モデルで継続的に推定する方法を開発・検証しており、単なる栽培制御や routine measurement ではない。

abstracta dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control.

Decoupling Crop Stressors in the Bărăgan Plain: A Multi-Sensor Remote Sensing Framework →

Abstract With agriculture moving to a more performant and autonomous-driven sustainability, advanced digital monitoring is becoming the key to resilient land management. This study investigates hydro-climatic conditions and urban air pollution in 2024 in the Bărăgan Plain, Romania’s primary grain-producing region. Our approach uses the Google Earth Engine (GEE) platform, high-resolution multispectral imagery (Sentinel-2), and atmospheric trace gas data from Sentinel-5P TROPOMI to map spatiotemporal interactions between agricultural health and urban air pollution. To find the most robust approach we compared three machine learning algorithms: Multiple Linear Regression, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), the Enhanced Vegetation Index (EVI) was used as a predictor of the Normalized Difference Vegetation Index (NDVI). Although the models that included the EVI achieved higher accuracy (R2 of 93%), the non-EVI Random Forest model performed better (R 2 of 86%) and revealed moisture availability (NDWI) as the primary regulator of crop vigor, with an importance of 81.8%. To isolate the effect of atmospheric chemistry alone, spatial residuals from the optimized RF model were extracted and plotted. Negative residuals that produced anomalies pointed to a unique type of crop stress in which the plants were underperforming even when water was adequate. These anomalies spatially align the urban pollution plume trajectories. Even if the NO2 has a rapid distance-decay effect, the secondary pollutants (O3) reach over the agricultural land through the photochemical titration effect. Our results show that while water determines regional agricultural baselines, atmospheric chemistry from urban sources can independently cause significant crop stress even in the absence of drought. This study provides a robust proof of concept on the separation of climatic and anthropogenic stressors and lays a basic pathway for a multi-sensor diagnostic framework in the domain of remote sensing.

Why it matches plant phenotyping methods衛星マルチセンサー画像と機械学習を統合し、作物の活力・ストレスを推定する診断フレームワークが研究の中心であり、単なる農業実験のルーチン測定ではない。

abstractOur approach uses the Google Earth Engine (GEE) platform, high-resolution multispectral imagery (Sentinel-2), and atmospheric trace gas data from Sentinel-5P TROPOMI to map spatiotemporal interactions between agricultural health and urban air pollution.

Neuroscience-Inspired Plant Electrophysiology: From Signal Decoding to Plant-Computer Interfaces →

Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for this interdisciplinary evolution. This review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants. Second, we discuss the application pathways of introducing standardized sequential evoked paradigms from neuroscience—such as steady-state visual evoked potentials and event-related potentials—into the plant domain. This aims to replace traditional destructive stimuli with non-invasive, reproducible rhythmic stimulation to acquire data with high signal-to-noise ratios. In the dimension of data analysis, we explore modeling strategies that incorporate physics-informed neural networks and multi-modal heterogeneous sensor fusion technologies under the constraint of sample scarcity, aiming to resolve the equifinality problem inherent in single-modality electrical signal decoding. Building upon this decoding foundation, this paper proposes the construction of a bidirectional Plant-Computer Interface architecture, exploring the engineering feasibility of utilizing the plant itself as an active sensory node to directly drive closed-loop regulation within agricultural environments. Establishing cross-species standardized open-source datasets and unified hardware/software testing benchmarks will be the core driving force in overcoming current data fragmentation. Ultimately, the deep integration of multidisciplinary approaches will lay a rigorous scientific foundation for precision agricultural resource management and the development of next-generation bio-inspired intelligent hardware.

Why it matches plant phenotyping methods植物の電気生理シグナル取得用センサー、刺激、デコード、マルチモーダル解析、データセットとベンチマークを体系的に扱うレビューであり、植物状態の計測・推定手法が中心です。

abstractThis review first systematically summarizes the evolution of flexible wearable electrodes and ultra-high impedance amplification hardware systems tailored to the ultra-slow signal dynamics and continuous morphological growth characteristics of plants.

Predicting capsicum leaf water stress using mid-infrared ATR-FTIR spectroscopy. →

Leaf water status is a key indicator for irrigation scheduling and early stress detection, but many spectroscopic prediction studies have mainly relied on near-infrared features. Here, practical prediction models were developed using mid-infrared (MIR) ATR-FTIR spectra of capsicum (Capsicum annuum L.) leaves collected under glasshouse conditions during a 10-day gradual dehydration period, alongside an irrigated control. Spectra (4000-450 cm⁻1) were measured with minimal sample preparation, and leaf water traits were quantified using fuel moisture content (FMC), equivalent water thickness (EWT), and specific leaf weight (SLW). Water-related MIR bands at 3370 and 1641 cm⁻1 showed the most consistent response to dehydration, and simple band ratios generally provided stronger predictions than single bands. The best ratios were A1641/A2159 for FMC (R2 = 0.81; RMSE = 12.80) and A3370/A2849 for EWT (R2 = 0.72; RMSE = 0.0034) and SLW (R2 = 0.62; RMSE = 6.95 × 10⁻4), while predicted-versus-measured performance yielded R2 values of 0.72 for FMC, 0.68 for EWT, and 0.52 for SLW. These results indicate that MIR ATR-FTIR spectroscopy, when coupled with selected band ratios, can provide a rapid, low-preparation laboratory-based approach for estimating capsicum leaf water traits under controlled dehydration, supporting plant-based water stress assessment under controlled conditions and providing a basis for further irrigation-related sensing studies. However, the models are preliminary and require validation with larger independent datasets and tightly standardised measurement conditions before operational use in irrigation management.

Why it matches plant phenotyping methodsMIR ATR-FTIRスペクトルと選択バンド比を用いて、葉の水分形質を推定するセンシング・予測手法の開発と性能評価が中心である。

abstractHere, practical prediction models were developed using mid-infrared (MIR) ATR-FTIR spectra of capsicum (Capsicum annuum L.) leaves

Synchronous Luminescence Spectroscopy: A Powerful Tool for Investigation of Plant Physiology →

The study of the effect of various stresses like light stress, temperature stress, pollutant stress etc. may be performed using various spectroscopic techniques like absorption spectroscopy, fluorescence spectroscopy, fluorescence kinetics, Fourier Transform Infrared spectroscopy etc. In addition to these techniques the synchronous luminescence technique may be successfully employed to study the effect of stresses on the plant health. In the present attempt we are going to use the synchronous luminescence spectroscopy for the study of plant health and classification. As per our information the type of measurements made by us is the first report of this kind. It is seen that more information can be obtained from the analysis of synchronous luminescence spectra of the plant leaves

Why it matches plant phenotyping methods植物の健康状態・ストレス状態を評価・分類する同期発光分光法そのものが研究の中心であり、植物状態の表現型取得手法として扱われている。

abstractthe synchronous luminescence technique may be successfully employed to study the effect of stresses on the plant health

Beyond static snapshots: predicting dynamic, explainable intermediate phenotypes for climate-resilient crop breeding →

The convergence of multi-omics profiling, high-throughput phenotyping (HTP), and artificial intelligence (AI) has expanded our ability to characterize crop stress responses at unprecedented resolution (Danilevicz et al., 2025;Pacheco-Ruiz et al., 2025;Tsega and Mullualem, 2026). Researchers can now routinely identify candidate genes, construct gene regulatory networks, and train machine learning models to predict terminal phenotypes, such as yield under drought, biomass under salinity, and disease severity scores. Yet the practical impact remains limited: delivering a single improved crop variety to market still requires approximately one decade and more than 14 million euros, a timeline that has barely changed in 30 years despite the exponential growth in data generation (Pacheco-Ruiz et al., 2025). We argue that a fundamental cause of this translational gap is what we call the temporal poverty of current GP: models often predict static endpoints rather than the dynamic processes that determine them.Classical GP models, including multi-omics-informed variants, are overwhelmingly trained on traits measured at single time points, such as final yield or end-of-season stress scores. However, stress tolerance is inherently temporal, reflecting a cascade of physiological decisions whose sequence and timing ultimately determine field survival: when to close stomata, how rapidly to accumulate osmolytes, and whether to prioritize root extension or shoot preservation. A model that predicts terminal drought tolerance without distinguishing whether it arises from early water conservation, sustained photosynthesis, or post-stress recovery offers limited support for rational gene stacking or knowledge transfer across environments and crops (Danilevicz et al., 2025). Based on these observations, we hypothesize that making the dynamic trajectories of intermediate physiological traits the direct targets of GP, rather than treating them as auxiliary variables, will substantially improve prediction accuracy, mechanistic interpretability, and the rate of genetic gain for stress resilience in crop breeding.To bridge this temporal gap, we propose a dynamic explainable genomic prediction framework centered on intermediate physiological trajectories (Figure 1). The framework integrates dynamic phenotyping, environmental information, and mechanistic multi-omics anchors to support trajectory-aware prediction and climate-resilient breeding.Conventional genomic prediction focuses on static endpoint prediction, whereas the proposed framework targets dynamic intermediate phenotypes and integrates environmental and multi-omics information to enable trajectory-aware, interpretable prediction for climate-resilient crop breeding.This reframing is becoming increasingly feasible. The recent dynamicGP model combines GP with dynamic mode decomposition (DMD) to predict full temporal trajectories of multiple morphometric and colorimetric traits scored by HTP in maize and Arabidopsis thaliana (Hobby et al., 2025). DynamicGP was also shown to predict traits at time points beyond the training period. It was the only model tested with this capability and showed superior longitudinal accuracy in capturing developmental dynamics (Hobby et al., 2026). The model further revealed that traits with more temporally stable heritability can be predicted with higher accuracy, providing practical guidance on which dynamic intermediates to prioritize (Hobby et al., 2025).Mechanistic modeling offers a complementary line of evidence: computational models of grass inflorescence morphodynamics recently guided the discovery of the duo2 mutant allele in wheat, which accelerates developmental progression and improved yield by 7-11% under field conditions (Wang et al., 2026b). These two independent advances, one data-driven and one mechanism-driven, converge on the same insight: incorporating time as a central dimension in prediction can provide predictive power and mechanistic understanding that are inaccessible to static models, thereby connecting prediction with actionable breeding decisions.A realistic view of current data availability shapes this strategy. The current and near-future foundation for dynamic intermediate phenotype prediction is largely HTP.The marginal cost of repeated phenotyping with automated platforms, UAVs, and low-cost sensors has decreased substantially, and public time-series datasets covering wheat, sorghum (LeBauer et al., 2020), soybean, and several other crops are rapidly expanding. This supports a pragmatic, asymmetric integration strategy: HTP provides the dense temporal skeleton of dynamic trait trajectories, while multi-omics is deployed selectively at a small number of mechanistically critical time windows, such as the onset of stress signaling, the transition from alarm to acclimation, or the peak of a known physiological trade-off. These sparse but information-rich omics snapshots serve as "explainability anchors." When explainable AI approaches are used to link these molecular profiles to parameterized dynamic modes extracted from HTP, the resulting model can reveal which genes, transcripts, or metabolites modulate specific phases of the dynamic response and when. For instance, SHAP-based interpretation of Random Forest models applied to soybean multi-omics data revealed that the isoflavone derivative daidzin and specific drought-tolerant microbes are major contributors to phenotypic variation under drought stress, while SHAP-based interaction networks uncovered cross-omics links between metabolites and microbial taxa (Yoshioka et al., 2026). Extracting biologically meaningful signals from such comparisons requires specialized computational tools. The MODAS2 pipeline, for example, uses contrastive principal component analysis, a machine learning algorithm, to disentangle stress-responsive molecular QTLs from background genetic effects in multi-omics data, enabling the identification of salt-responsive genetic variants in maize (Liu et al., 2025). Such tools are essential for implementing the explainability-anchor strategy at the stress-transition windows targeted by our framework.Advocating for dynamic intermediate phenotypes as prediction targets does not mean abandoning terminal agronomic traits. The two approaches serve complementary roles at different stages of the breeding pipeline. In early-generation selection, when thousands of lines must be evaluated and resources for multi-environment yield trials are limited, dynamic intermediates, which are often measurable earlier, at lower cost per data point, and with higher heritability, can help enrich populations for stress-resilient candidates (Melsen et al., 2025). In later-stage trials, direct prediction of yield under target stress environments remains indispensable for final variety release decisions. In practice, the central question is not which target is superior in isolation, but whether their combined use can accelerate genetic gain for stress tolerance.Dynamic intermediate phenotypes can contribute to genetic gain through three main routes. First, many dynamic intermediates show higher heritability and earlier measurability than terminal yield under stress, enabling more accurate early-generation selection and shorter breeding cycles. Second, a dynamic physiological module, such as rapid osmotic adjustment within 48 hours of soil drying, can become a reusable building block once it has been genetically dissected and validated. Such modules could then be stacked, introgressed, or transferred across genetic backgrounds and crop species. Third, selecting on the shape of a response curve rather than a single terminal value can reduce environmental noise, because temporal patterns are often more genetically determined than absolute end-point values (Hobby et al., 2025). This framework is also inherently cross-crop: a conserved physiological module such as "stomatal response speed to soil drying" is unlikely to be restricted to a single species. Orthologous genes and conserved pathways can inform candidate selection in legumes, vegetables, and under-researched crops, extending advanced breeding methodologies beyond the major cereals (Kundu and Tanti, 2026;Wang et al., 2026b).The dynamic GP framework we advocate remains incomplete without explicit environmental inputs. At present, many AI-driven prediction models treat the environment as a categorical label or a set of static summary statistics. This approach can conflate genetic and environmental effects and limit performance forecasting across mega-environments or novel climatic scenarios. To support the development of climate-resilient varieties, models should instead incorporate environmental data as an explicit, dynamic data layer, including time series of temperature, soil moisture, and vapor pressure deficit, that co-determines the trajectory of intermediate phenotypes.Recent advances suggest that this is increasingly feasible. Enviromics and reaction-norm approaches that incorporate high-dimensional environmental covariates through penalized regression can now approach the accuracy of deep learning while retaining interpretability and an explicit description of genotype-by-environment interactions (Avagyan et al., 2025). In parallel, hybrid frameworks that couple crop growth models with whole-genome prediction can link genetic, environmental, and management inputs to dynamic physiological outputs (Laurent et al., 2025). Building on these developments, embedding environmental time series into the temporal kernels of dynamic models such as dynamicGP could, in principle, enable prospective, environment-aware forecasting of genotype-specific response curves. This would shift selection from retrospective mega-environment classification toward forward-looking prediction across single or multiple target mega-environments. However, robust extrapolation to untested environments and stress combinations remains a key open challenge. Careful envirotyping and cross-environment validation will therefore be essential before such models can guide variety deployment decisions in practice.The framework proposed here has important implications for future crop production.By shifting the breeding target from terminal yield to the temporal architecture of stress responses, breeders could develop selection strategies that are both physiologically informed and operationally efficient. Dynamic intermediate phenotypes, captured through increasingly affordable HTP platforms, can serve as early indicators of resilience, allowing breeders to discard susceptible lines long before harvest. This could shorten the breeding cycle, especially when combined with genomic selection and speed breeding, while also enabling the deliberate assembly of stress-resilience modules that are robust across environments. Empirical evidence already shows that integrating HTP-derived spectral data with genomic information can substantially improve cross-environment prediction accuracy (McBreen et al., 2025;Nannuru et al., 2025). As climate variability intensifies, the ability to design varieties with predictable temporal behavior under drought, heat, or salinity will become increasingly important for enhancing yield stability and food security.Challenges remain. Dynamic GP models have yet to be systematically validated across radically different environments, and their ability to predict trait dynamics under novel stress combinations remains unproven. Data from controlled HTP platforms must also be calibrated against field conditions. In addition, the integration of heterogeneous multi-omics datasets creates persistent bottlenecks that constrain practical application beyond proof-of-concept studies (Pacheco-Ruiz et al., 2025;Tsega and Mullualem, 2026). Multi-omics time series, even at the sparse sampling density we advocate, remain limited by high technology costs and scalability challenges that restrict their routine deployment in breeding programs (Syeda, 2025;Younas et al., 2025). However, pilot-scale time-series multi-omics studies demonstrate both feasibility and value. For example, transcriptomic and ionomic profiling of Sorghum bicolor across a 21-day micronutrient stress time course revealed iron-zinc regulatory crosstalk and conserved gene regulatory networks (Mishra et al., 2025), while high-resolution time-series transcriptomic and metabolomic profiling of salt-tolerant and salt-sensitive maize inbred lines identified the hub gene ZmGLN2 and constructed dynamic regulatory networks governing salt-responsive metabolite biosynthesis (Zhang et al., 2025). Furthermore, the MODAS2 pipeline, which uses contrastive principal component analysis to extract stress-responsive signals from multi-omics comparisons, illustrates that the computational methods needed to integrate heterogeneous omics layers are already under active development (Liu et al., 2025).Equally important is the ability of AI models to capture the non-linear genetic interactions that underpin complex stress responses. Linear mixed models, the backbone of classical GP, primarily model additive effects and therefore have limited capacity to capture epistasis, gene-by-environment interactions, and threshold-type responses (Wang et al., 2026a). In contrast, deep learning architectures can learn hierarchical, non-linear mappings from high-dimensional input spaces. When applied to dynamic intermediate phenotypes, these models could learn not only which genomic regions influence the temporal shape of a trait, but also how those regions interact with each other and with environmental triggers over time. For example, convolutional neural networks have outperformed classical methods such as LASSO and Bayes C in predicting integrative traits, with the combination of CNNs and crop model parameters further enhancing prediction accuracy (Larue et al., 2024). Fully harnessing non-linear interactions for dynamic trait prediction will likely require hybrid approaches that embed mechanistic constraints into flexible AI architectures, ensuring that predictions remain both powerful and biologically plausible.As outlined in the preceding section, integrating environmental time series into dynamic GP models remains a frontier, but the computational tools and conceptual frameworks are now within reach. The integration of environmental data with multiple omics layers for genotype-by-environment prediction has been identified as a major emerging frontier, although it is currently addressed in fewer than 20% of studies (Tsega and Mullualem, 2026). This gap underscores the urgency of the framework we advocate. These are not sequential prerequisites, but parallel investments that reinforce one another. We therefore call on the crop science community to: (i) prioritize the generation of time-series phenotypic data in multi-environment stress trials, leveraging increasingly affordable HTP platforms; (ii) adopt explainable AI as standard practice, not merely reporting prediction accuracy but also elucidating which features drive predictions, when, and through which physiological mechanisms (Danilevicz et al., 2025); and (iii) develop selection indices that explicitly reward favorable dynamic trajectories alongside terminal trait values.Multi-omics and AI should not merely describe how stress resistance appears at harvest; they must reveal how it unfolds over time and how it can be rationally assembled. Bridging this temporal gap will connect current data abundance with the practical goal of delivering climate-resilient crops to farmers' fields.In

Why it matches plant phenotyping methods動的HTP phenotypingと軌跡予測を中核とする概念・方法論的枠組みを提案し、既存モデル、環境データ統合、検証課題を体系的に論じているため。

abstractThe recent dynamicGP model combines GP with dynamic mode decomposition (DMD) to predict full temporal trajectories of multiple morphometric and colorimetric traits scored by HTP in maize and Arabidopsis thaliana

Plant Wearable Sensors: Emerging Technology for Real-Time Plant Monitoring →

Plant wearable sensors are emerging as flexible, non-invasive platforms for continuous assessment of plant physiological status and plant–environment interactions. This review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses. It summarises major sensing approaches, including capacitive, chemical, photodetector-based and piezoresistive sensors, with attention to their materials, fabrication strategies, operating principles and potential applications in plant health monitoring. Advances in flexible substrates, conductive materials, nanostructured sensing layers, biodegradable polymers and wireless communication have improved sensor compatibility with plant surfaces and enhanced the detection of physiological changes under field-relevant conditions. Integration with the Internet of Things, artificial intelligence, machine learning, cloud platforms and data analytics further supports continuous data acquisition and interpretation for precision crop management. These systems may contribute to early detection of biotic and abiotic stresses, enabling timely interventions and improved resource-use efficiency. However, broader adoption remains limited by sensor durability, environmental interference, power requirements, scalability, cost and the complexity of interpreting plant-derived signals. Continued interdisciplinary research is required to develop reliable, affordable, energy-efficient, biodegradable and multifunctional sensing platforms that support sustainable agricultural management under changing environmental conditions.

Why it matches plant phenotyping methods植物の生理状態・成長・クロロフィル・ストレス応答を測定するウェアラブルセンシング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が中核である。

abstractThis review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses.

Automated, high-throughput hyperspectral imaging enables early detection of grapevine downy mildew and monitoring of vineyard spray program performance. →

Grapevine downy mildew (GDM), caused by Plasmopara viticola, is managed largely through repeated fungicide applications, yet evaluating spray-program performance is difficult because field infections are spatially heterogeneous. However, host physiological changes can precede visible symptom development. We tested whether standardized, high-throughput VNIR hyperspectral imaging of field-grown grapevine leaf discs can capture early optical changes associated with infection and discriminate program-linked mitigation. Leaves were collected from a 2025 vineyard trial in Geneva, NY (cv. La Crescent) managed under three spray programs (conventional fungicides, biofungicides, and untreated control). Leaf discs (≈87 per program) were excised, inoculated with P. viticola, and imaged at 0.5, 1.5, and 3.5 days post inoculation (dpi) using a custom built, automated, hyperspectral imaging microscopy platform (400-980 nm). No visible sporulation occurred at 0.5 or 1.5 dpi; sporulation was first observed at 3.5 dpi and occurred only in the untreated control, whereas no discs from either treated program sporulated. Multivariate spectral analyses showed significant separation by spray program and dpi, and disease-related spectral indices exhibited program-dependent trajectories, with treated discs showing attenuated optical change relative to the control. A random-forest classifier trained on pre-sporulation spectra (0.5 and 1.5 dpi; N = 169) predicted subsequent sporulation with 0.78 accuracy and 0.77 ROC-AUC (95% CI 0.68-0.85), highlighting informative bands across visible, red-edge, and near-infrared regions. Together, these results support outcome-linked, replicate-rich hyperspectral phenotyping of vineyard spray programs using field-derived material under standardized acquisition conditions.

Why it matches plant phenotyping methods自動化VNIRハイパースペクトル撮像で、発病前のブドウ葉の光学的変化と将来の胞子形成を推定する手法を中心に検証しており、植物病害状態の表現型取得・分類に該当する。

abstractWe tested whether standardized, high-throughput VNIR hyperspectral imaging of field-grown grapevine leaf discs can capture early optical changes associated with infection and discriminate program-linked mitigation.

Hijacked hydraulics: Verticillium dahliae-induced xylem dysfunction in pepper stems revealed by integrated hydraulic, imaging, and molecular analyses. →

Xylem tissue enables efficient long-distance water transport but is a primary target for vascular pathogens. This study investigates how systemic invasion by Verticillium dahliae impairs the hydraulic function of pepper (Capsicum annuum) plants, focussing on xylem colonisation and its anatomical and physiological effects. Real-time sap flow was continuously monitored with custom-built ExoBeat sensors, while periodic stem water potential measurements allowed calculation of changes in stem hydraulic conductance as an additional indicator of xylem performance. Fungal colonisation was assessed by quantitative polymerase chain reaction, and vessel occlusions and embolised conduits were visualised using scanning electron microscopy and micro-computed tomography, complemented by direct hydraulic conductivity measurements. By 14 d post inoculation, V. dahliae had progressed from roots to aboveground tissues, coinciding with a marked decrease in sap flow, water potential, and soil-to-stem hydraulic conductance, alongside the onset of dwarfing. Direct fungal blockage and anatomical changes were the primary contributors to hydraulic dysfunction. Vessel occlusion by tyloses, gels, and air embolisms played a negligible role. This study reveals how V. dahliae progressively impairs pepper hydraulics through systemic xylem colonisation, highlighting the value of real-time sap flow monitoring. Our integrative, multidisciplinary approach offers a powerful framework to unravel the complexity of dynamic plant-fungal vascular interactions.

Why it matches plant phenotyping methodsカスタムセンサーによるリアルタイム・サップフロー測定を中心に、植物の水理機能・病原体による機能低下を定量化しており、単なる生物学的測定にとどまらない実質的なフェノタイピング手法の適用である。

abstractReal-time sap flow was continuously monitored with custom-built ExoBeat sensors

Adaptive spectral-thermal illumination management for protected tomato cultivation: a fused deep learning and pareto-based decision framework. →

This study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization to improve tomato production while reducing the carbon impact of supplemental LED lighting. The CNN-ELM model was trained using key environmental variables, including photosynthetic photon flux density (PPFD), red-to-blue light ratio, canopy temperature, CO 2 concentration, and relative humidity. Within the experimental conditions, the model achieved high predictive accuracy, with an R² of 0.976 and an RMSE of 0.712 µmol m -2 s -1 . Using these predictions, the MOEA/D algorithm generated Pareto-optimal lighting strategies, which were ranked through entropy-weighted TOPSIS and implemented via cloud-based control connected to a LoRa wireless sensor network and pulse-width-modulated LED drivers. The system was evaluated during a 110-day tomato cultivation trial and compared with single-parameter control and ambient-condition treatments. Results showed a 38.4% reduction in LED-related carbon emissions, a 22.6% increase in net photosynthetic rate, and a 31.7% improvement in harvestable yield relative to ambient conditions. Physiological analyses further indicated enhanced photosynthetic performance, radiation-use efficiency, and light utilization. Overall, the findings demonstrate that data-driven, closed-loop lighting management can simultaneously enhance productivity and reduce greenhouse gas emissions in controlled-environment agriculture when applied within the validated operational domain.

Why it matches plant phenotyping methods光合成という植物生理形質を予測するCNN-ELMモデルを中核に、センサーネットワークと閉ループ制御を統合・評価しており、単なる栽培試験ではなく形質推定手法の応用が主要内容である。

abstractThis study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization

UAV-Based Assessment of Pre- and Post-Harvest Water Stress Dynamics in Vineyards Using NDRE and GNDVI Spectral Responses →

Abstract Climate change and increasing drought frequency are intensifying water stress risks in viticultural systems, particularly in semi-arid regions where water availability directly influences grapevine productivity and quality. This study investigated pre-harvest and post-harvest water stress dynamics in a commercial Ağın Beyazı vineyard located in Eastern Türkiye using UAV-based multispectral imagery. High-resolution orthomosaics (2.31 cm pixel⁻¹) were acquired with a DJI Mavic 3 Multispectral platform and used to generate Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Red Edge Index (NDRE) maps for both phenological periods. Water stress responses were evaluated through pixel-based change detection, descriptive statistics, spatial heterogeneity metrics (Local Mean and Local Standard Deviation), and Global Moran’s I spatial autocorrelation analysis. Results revealed measurable spectral differences between pre-harvest and post-harvest periods, indicating changes in canopy physiological activity associated with water stress and post-harvest vine responses. GNDVI exhibited a higher relative change (+ 10.49%) and stronger standardized response ratio, suggesting greater sensitivity to overall canopy vigor and photosynthetic activity. In contrast, NDRE showed the highest heterogeneity response (+ 64.97%), demonstrating superior capability for identifying localized stress variability and fine-scale physiological differences within the vineyard. Spatial heterogeneity analyses indicated increasing local variability after harvest, while consistently positive Moran’s I values (≈ 0.257) revealed that stress patterns remained spatially organized rather than randomly distributed. These findings suggest that vineyard water stress is controlled not only by vine physiology but also by persistent spatial factors such as soil conditions, micro-topography, and water availability. Overall, the combined use of GNDVI and NDRE provided complementary information for vineyard water stress assessment. The proposed framework demonstrates the potential of UAV-based multispectral monitoring for precision irrigation management, early stress detection, and climate-resilient viticulture in water-limited environments.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からGNDVI・NDREを抽出し、ブドウ樹の水ストレスと空間変動を評価する測定・解析フレームワークが研究の中心である。

abstractThis study investigated pre-harvest and post-harvest water stress dynamics in a commercial Ağın Beyazı vineyard located in Eastern Türkiye using UAV-based multispectral imagery.

Integrated assessment of drought-driven vegetation declines in Olive and Citrus Orchards of Semi-Arid Morocco: A Multi-Index Remote Sensing Framework →

Abstract In the era of climate change, drought is defined as one of the most severe natural catastrophes that affects the environment, crop growth, and water resources, leading to economic losses, migration, and risks to human life. Since 2019, Morocco has suffered one of the most severe droughts in its recording history, coinciding with a broader period of precipitation deficit across the Mediterranean basin, resulting in a significant reduction in reservoir storage levels and the suspension of irrigation provided by dams in some areas due to low or absent rainfall, making drought a serious challenge to natural resources in this country. This study focused on semi-arid regions, especially on the Tensift basin in Morocco, and was conducted between 2018 and 2024. The effects of drought on arboriculture were analyzed thanks to remote sensing by using the normalized difference vegetation index (NDVI), while NDVI, TCI (temperature condition index), VCI (vegetation condition index), VHI (vegetation health index), and SPI (standardized precipitation index) were employed to assess and evaluate the health of the vegetation area, especially the arboriculture area, and the effective water stress conditions in our agricultural study area. The analyses indicated that arboriculture cover decreased markedly, from 11.17% in 2020 to 7.40% in 2023. From the analyses, it was evident that the cover of this culture had significantly declined from 11.17% in 2020 to 7.40% in 2023. From 2018 to 2024, more than half of the area suffered from drought in the agriculture of varying intensity levels, from moderate to severe. The meteorology of the evaluations confirmed the above observations by showing that there was a considerable decline in precipitation levels from about 350 mm in 2019 to less than 50 mm in 2024, coupled with continuously negative SPI-6 indices. Moreover, it was established that there was a significant effect on tree crops such as olives and citrus. Degradation of land was at its worst during 2021 when it affected more than 3,000 hectares of olives and 2,250 hectares of citrus. Similarly, 80% of farmers engaged in the production of citrus registered a decline in yield from 37% to 41%. Consequently, this study provides new information concerning the need for comprehensive evaluation and monitoring of agricultural drought in Morocco, thereby emphasizing the importance of essential factors.

Why it matches plant phenotyping methodsリモートセンシングと複数の植生・水ストレス指数を中核に、オリーブ・柑橘樹の植生健康状態や干ばつ影響を評価しており、植物状態の抽出手法の実質的応用に該当する。

abstractThe effects of drought on arboriculture were analyzed thanks to remote sensing by using the normalized difference vegetation index (NDVI), while NDVI, TCI (temperature condition index), VCI (vegetation condition index), VHI (vegetation health index), and SPI (standardized precipitation index) were employed to assess and evaluate the health of the vegetation area, especially the arboriculture area, and the effective water stress conditions in our agricultural study area.

The Origin of Dielectric Permittivity in Plants. →

Dielectric permittivity describes how a material becomes polarized in response to a time-varying electric field and provides a powerful framework for probing the physical organization of biological systems. This review aims to clarify the origin of dielectric permittivity in plants, offering a conceptually grounded interpretation while keeping mathematical formalism to the level necessary for biological interpretation. We first outline the fundamental mechanisms of polarization, their characteristic time scales, and the frequency-dependent nature of the dielectric response, including the concept of complex permittivity, together with commonly used measurement approaches in biological materials. Particular attention is given to water, whose dielectric properties play a dominant role in plant tissues. We then examine how permittivity varies across different plant organs, including leaves, fruits, and roots, highlighting the relationship between dielectric response and structural and compositional features. Modeling strategies linking microscopic organization to macroscopic dielectric behavior are also discussed. Because dielectric permittivity is intrinsically connected to plant structure and composition, non-invasive measurements offer significant potential for assessing plant physiological status, including the detection of changes induced by abiotic and biotic stresses. By bridging engineering approaches with plant physiology, this review provides a unified framework to interpret dielectric measurements in plants and supports their application in plant science and phenotyping.

Why it matches plant phenotyping methods植物の誘電率測定を植物構造・組成や生理状態の非侵襲的評価に結び付けるレビューであり、植物フェノタイピング手法の解釈と応用が中心です。

abstractThis review aims to clarify the origin of dielectric permittivity in plants

SeedMatExplorer: the transcriptome atlas of Arabidopsis seed maturation. →

Background Seed maturation is a critical developmental phase during which seeds acquire traits essential for nutritional value, desiccation tolerance, and long-term survival. Abscisic acid (ABA) signalling is a key regulator of this process, coordinating gene expression programs underlying the acquisition of seed quality traits. However, the molecular regulation of many of these traits remains poorly understood. To address this, we performed a comprehensive analysis of seed maturation in Arabidopsis thaliana, combining physiological and transcriptomic approaches across wild-type plants and mutants affected in ABA biosynthesis, signalling, and catabolism. Results We generated a high-resolution transcriptome dataset covering seed development from 12 days after pollination to the dry seed stage in wild-type and ten mutant lines. In parallel, we characterized the temporal acquisition of multiple seed traits, including germination capacity, dormancy, chlorophyll fluorescence, longevity and desiccation tolerance. Integration of these datasets using weighted gene co-expression network analysis (WGCNA) identified gene modules associated with specific trait acquisition patterns. This approach enabled the identification of coordinated transcriptional programs linked to distinct seed quality traits, extending beyond individual gene-level analyses. Notably, modules associated with desiccation tolerance and longevity were enriched for genes involved in stress responses and ABA-regulated pathways, highlighting the complex and multifactorial regulation of these traits. Conclusions This study provides a comprehensive physiological and transcriptomic framework for understanding seed maturation and the acquisition of key seed quality traits in Arabidopsis thaliana. By linking gene expression dynamics to trait development, our work offers new insights into the regulatory networks underlying seed resilience and storage capacity. The dataset is made accessible through SeedMatExplorer (https://www.bioinformatics.nl/SeedMatExplorer), an open-access web platform that enables interactive exploration and supports hypothesis generation. Together, this resource represents a valuable tool for advancing research on seed biology and improving seed performance in agricultural contexts.

Why it matches plant phenotyping methods種子成熟に伴う複数の植物形質を体系的に取得し、トランスクリプトームと統合した再利用可能なデータセットおよび探索プラットフォームを提供しており、単なる生物学的実験の routine 測定を超える。

abstractIn parallel, we characterized the temporal acquisition of multiple seed traits, including germination capacity, dormancy, chlorophyll fluorescence, longevity and desiccation tolerance.

Assessing plant water status: Part 1 - Classical methods. →

As a result of the changing climate, water scarcity poses a significant threat to crop and pasture production. Although soil water content can indicate drought, its measurements often provide limited spatial resolution and are weakly correlated with plant water status, producing misleading drought assessments. Accurately measuring plant water status is essential to understand nutrient uptake, thermal regulation and stomatal behavior. Water status, primarily determined by turgor pressure and its crucial component of leaf water potential regulate plant physiological functions. These variables depend on the energy state of water, determining essential processes such as stomatal conductance and cell expansion. Becaus directly measuring turgor pressure may be impractical, leaf water content and relative water content are reliable proxies for assessing water status. In Part 1 of a two-part review, we provide insights into using leaf water content as a reliable proxy for assessing water status and synthesize classical, destructive methods for measuring plant water status, encompassing gravimetric techniques, Scholander pressure chamber and psychrometric techniques. These classical approaches provide direct, physically interpretable and mechanically based measurements of water content, water potential and turgor-related parameters. Operational principles, procedural considerations and physiological insights accompany each method. These destructive measurements determine water status accurately, forming the essential calibration and validation backbone for modern non-destructive approaches discussed in Part 2. Integrating these classical measurements with concurrent soil moisture data provides reliable guidance for irrigation management, optimizing water usage and improving crop resilience in the face of increasingly variable climatic conditions. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methods植物の水分状態を測定する古典的方法（重量法、プレッシャーチャンバー、サイクロメトリ等）を中心に原理・手順・検証用途をレビューしており、植物生理形質のフェノタイピング方法レビューに該当する。

abstractIn Part 1 of a two-part review, we provide insights into using leaf water content as a reliable proxy for assessing water status and synthesize classical, destructive methods for measuring plant water status, encompassing gravimetric techniques, Scholander pressure chamber and psychrometric techniques.

Assessing plant water status: Part 2 – Non‐destructive and remote sensing approaches →

Precise, real time and non-destructive assessment of plant water status is important for advancing plant physiological understanding, optimizing water usage, improving crop resilience and supporting precision agriculture in the face of increasingly variable climatic conditions. Classical methods for measuring plant water status reviewed in Part 1 of this two-part review have significant limitations for field level applications, providing only discrete, single-point measurements and potentially altering plant physiology through destructive sampling. This second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations. We review techniques such as ZIM-probe, terahertz spectroscopic techniques, microwave remote sensing, infrared transmission sensor, microtensiometers, dendrometers and leaf thickness sensors, light detection and ranging (i.e. LiDAR), imaging spectroscopy, NMR relaxation, spectroscopy based on equivalent water thickness, spectral indices, derivative spectra, post-continuum removal indicators, visible and near-infrared spectroscopy, and infrared thermography. These emerging techniques facilitate high-resolution, real-time monitoring of water status across leaf, canopy and ecosystem scales. This comprehensive comparison provides guidance for selecting most appropriate technique based on experimental objectives, guiding applications ranging from single leaf to canopy scale ecosystem assessment. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methods植物の水分状態を非破壊・遠隔センシングで測定する手法を体系的に比較・評価したレビューであり、植物フェノタイピング手法が中心です。

abstractThis second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations.

Plasmodesmata display dynamic local and systemic redox responses during plant stress →

Abstract Hydrogen peroxide (H2O2) is a potent reactive oxygen species that plays a crucial role as a versatile signaling molecule for cellular function and vitality. Recent experimental evidence indicates that H2O2 affects cell-to-cell communication through plasmodesmata, tiny cytoplasmic nanopores connecting adjacent plant cells. H2O2-dependent systemic signaling has also been reported to involve plasmodesmal function in some contexts, although the dominant routes and messengers underlying rapid long-distance signaling remain under active debate. Nevertheless, direct monitoring of redox dynamics at plasmodesmata in live tissues has remained challenging. In this study, we developed a plasmodesmata-localized HyPer7 (Pd-HyPer7) reporter to investigate H2O2 dynamics at plasmodesmata in response to exogenous redox stressors and plant stresses, including cold and mechanical wounding. Pd-HyPer7 showed response characteristics that differed from the HyPer7 reporters localized to the cytosol, plasma membrane, and chloroplasts under the conditions tested, indicating that redox responses at plasmodesmata are distinguishable from these compartments. Notably, during mechanical wounding, both the cytosol and plasmodesmata showed transient redox responses with broadly similar temporal profiles in local tissues. In systemic tissues, however, the responses were temporally separated, with plasmodesmal oxidation peaking well after the cytosolic response. This timing relationship is consistent with plasmodesmata acting downstream of early systemic wound signaling, rather than simply mirroring cytosolic redox dynamics. Together, our results establish Pd-HyPer7 as a tool for monitoring plasmodesmal redox dynamics and support a model in which plasmodesmata participate in spatially and temporally regulated redox responses during plant stress.

Why it matches plant phenotyping methods植物ストレス時の原形質連絡における酸化還元動態を可視化するPd-HyPer7レポーターを開発し、植物組織での測定に適用・評価しているため、植物生理状態のフェノタイピング手法が中心である。

abstractdirect monitoring of redox dynamics at plasmodesmata in live tissues has remained challenging

Canopy Structure and Water Use Efficiency Variations Between Short- and Long-Day Strawberry Cultivars Revealed by Non-Destructive 3D Phenotyping →

Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.

Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。

abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE

Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: A Systematic Review and Framework for Field-Scale Validation →

Magnetic field (MF) technologies have been applied in agriculture for decades. However, they have not achieved mainstream adoption, partly because no validated methodology exists for evaluating their effects under realistic field conditions. UAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, it could provide the monitoring infrastructure through which MF treatment responses are, for the first time, systematically evaluated and validated under open-field conditions. To exploit this complementarity, however, a common evidential ground must first be established, identifying which crop physiological variables are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream systematic review of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency/nitrogen assimilation, above-ground biomass, leaf area index, and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R² = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The results reveal a complete absence of integration between the two research domains despite their strong biological and methodological compatibility. The proposed framework provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.

Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによる作物生理形質の推定を体系的にレビューし、地上検証と機械学習を含むフィールドスケール評価フレームワークを提案しており、植物フェノタイピング手法が中心である。

abstractUAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale

Plant phytohormone electrochemical sensing: From functional materials and interfaces to multiplexed sensor design. →

Electrochemical monitoring of plant phytohormones offers a powerful route toward real-time assessment of plant stress and physiological status, yet remains technically challenging due to ultra-low analyte concentrations, strong matrix interferences, and the limited redox activity of several key hormones. Conventional analytical techniques provide high sensitivity but are incompatible with in situ, continuous, and field-deployable measurements required for precision agriculture. This review critically examines recent advances in electrochemical sensing strategies for major plant phytohormones, including salicylic acid (SA), abscisic acid (ABA), jasmonic acid (JA), and indole-3-acetic acid (IAA), with a focus on how material design, interfacial engineering, and sensor architecture address fundamental limitations. Hybrid nanomaterials, affinity-based and direct electrochemical transduction mechanisms, and flexible or wearable platforms were critically evaluated for their potential to enhance sensitivity, selectivity, and operational stability under realistic plant and environmental conditions. Beyond individual sensor performance, particular emphasis is placed on multiplexed architectures and data-driven integration with wireless platforms and artificial intelligence, enabling the simultaneous decoding of multiple hormonal signals and their temporal dynamics. By comparing design strategies, performance trade-offs, and remaining bottlenecks, this review provides a conceptual framework for the rational engineering of next-generation electrochemical phytohormone sensors and outlines key directions toward robust, field-ready monitoring systems for smart and sustainable agriculture.

Why it matches plant phenotyping methods植物ホルモンを用いた生理状態・ストレスのセンサー計測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 critically examines recent advances in electrochemical sensing strategies for major plant phytohormones

Leaf movements as a quantitative metric for early stress detection →

Early, precise, and non-destructive stress detection is essential for maintaining crop productivity, particularly in high-density plant growth systems like controlled environment agriculture (CEA), where manual monitoring is often impractical. Using plant motion as a proxy for growth and plant health, we demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis in lettuce and five other CEA relevant crops. Leaf-movement dynamics under stress were imaged with a low-cost, scalable Raspberry Pi imaging setup and quantified using a repurposed open-source motion estimation algorithm; Tracking Rhythms in Plants (TRiP). Our system detected stress-induced changes in leaf-movement within 1 hour of stress, with the timing dependent on the nature of the stress. Sustained reductions in leaf-movement coincide with decreased biomass accumulation. This approach offers a non-invasive, rapid, scalable, and cost-effective solution for continuous crop monitoring, with potential for application in both terrestrial and space farming CEA systems. Abstract Figure Graphical abstract: Quantification of leaf-movement dynamics as a high-throughput proxy for plant physiological status, enabling early stress detection and timely intervention to mitigate yield penalties in CEA settings (image made with biorender.org).

Why it matches plant phenotyping methods低コスト撮像と既存アルゴリズムを用いて葉の動きを定量化し、植物ストレス・生理状態を早期推定する方法が研究の中心である。

abstractwe demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis

Development of a New Handheld Device for Measuring Photosynthetic Carbon Dioxide Assimilation in Plant Leaves →

With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO2 assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research.

Why it matches plant phenotyping methods植物葉の光合成CO2同化速度を測定する携帯型センサーを開発し、市販ガス分析計との比較検証まで行っており、植物表現型取得法が研究の中心である。

abstractHere, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors

Revisiting volatile organic compounds’ role in plant communication using real-time bioimaging →

Plants release Volatile Organic Compounds (VOCs) in response to insect attacks. VOC facilitates communication with neighboring, undamaged plants. In response to VOC from insect damaged plants, neighboring undamaged plants upregulate their own defenses as if they were being attacked themselves. To date, Green Leaf Volatiles (GLVs) within VOC have been widely considered a primary mediator for plant communication. GLV is a six-carbon compound which all land plants emit immediately and in large quantities after wounding. We hypothesized that GLVs’ lack of specificity and abundance is unlikely to account for key aspects of plant communication like increased sensitivity between closely related plants. To test our hypothesis, we used an Arabidopsis accession which does not produce GLVs. We also developed a non-invasive imaging technique to visualize plant communication utilizing expressions of insect stress marker gene VSP1 . Our analysis confirmed that plant communication occurs even without GLVs. Cytosolic calcium ion concentration increased before this timing, and moved towards the tip of the leaf in undamaged plants. Additionally, when plants were damaged by insects, acetophenone and alkanes accumulated the experiment’s enclosed space. This suggests that plants communicate independently of GLV using alkanes and acetophenone, which are known to attract natural enemies of herbivore insects like parasitoid wasps.

Why it matches plant phenotyping methods植物間コミュニケーションとストレス状態を可視化する非侵襲的イメージング手法の開発・適用が研究の中心である。

abstractCytosolic calcium ion concentration increased before this timing, and moved towards the tip of the leaf in undamaged plants.

Imaging Double Fertilization in Maize →

Sexual reproduction in flowering plants relies on double fertilization, a process marked by two fusion events between the male and female gametes that lead to seed formation. Because this process unfolds within the embryo sac embedded deep inside the ovule, direct observation remains technically demanding, especially in maize, where the large size of female reproductive organs presents additional obstacles. The described method enables high-resolution visualization of cellular events unfolding during maize double fertilization. The approach integrates optimized fixation, clearing and confocal imaging of embryo sacs from ears pollinated with fluorescent pollen marker lines. Precise timing of embryo sac fixation is critical, allowing capture of key events such as pollen peri-germ cell membrane break-down or gamete karyogamy. The protocol provides detailed guidance for ovule dissection, fixation, preparation and renewal of the clearing solution and confocal imaging of embryo sacs. This method offers unprecedented access to the cellular events of double fertilization in maize, establishing a robust framework for studying reproductive processes and supporting future discoveries in plant reproduction.

Why it matches plant phenotyping methodsトウモロコシの二重受精過程を高解像度で可視化する固定・透明化・共焦点 imaging プロトコルが研究の中心であり、植物の生殖状態を取得する方法として該当する。

abstractThe described method enables high-resolution visualization of cellular events unfolding during maize double fertilization.

Quantitative light element profiling in plant tissues with monochromatic X-ray fluorescence analysis: a new frontier for abiotic stress studies. →

Determining elemental concentrations in plant tissues is essential for physiological studies on abiotic stress. However, high-throughput routine analysis of light elements (sodium to calcium) in plants is challenging due to the need for complete sample dissolution and expensive and time-consuming inductively coupled plasma-mass-spectrometry (ICP-MS). Ion chromatography and ion-selective electrodes are low-cost methods but suffer from major drawbacks, including limited throughput and time-consuming sample preparation. This study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis. We quantitatively assessed sodium and potassium uptake in Arabidopsis thaliana, Oryza sativa and Lactuca sativa in salinity treatments. The new method provides reliable results from samples as small as 1 mg, making it suitable for analysis at the seedling stage. This is enabled by the high sensitivity of the system and optimized sample preparation that ensures sufficient signal even at low sample masses. We tested the accuracy and precision of the technique for other light elements to demonstrate its broad applicability. The results show that the method delivers rapid, non-destructive, and extraction-free light element analysis on small samples highly correlating with ICP-MS. The monochromatic XRF method provides accurate measurements and reproducible results for studying salinity tolerance ideally suited for investigating elemental composition of early plant developmental stages, offering new possibilities for research into early stimuli responses.

Why it matches plant phenotyping methods植物組織中の元素濃度という生理形質を測定するMXRF法の開発と、ICP-MSとの相関、精度・再現性評価が研究の中心であるため。

abstractThis study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis.

Continuous monitoring of plant transpiration dynamics with a leaf-mounted sensor across environmental conditions →

O_LITranspiration plays a central role in plant water relations and strongly influences plant growth. Continuous monitoring is essential for understanding responses to environmental conditions and improving water management in both natural and agricultural systems. Gas-exchange techniques such as infrared gas analysers (IRGAs) and porometers are widely used but are challenging for long-term or large-scale monitoring. On the other hand, the FylloClip is a low-cost, leaf-mounted capacitance sensor developed previously to monitor transpiration by detecting condensation of water vapour near the leaf surface. Here, we evaluated the potential of the FylloClip for monitoring transpiration dynamics and assessed environmental conditions that may affect its performance. C_LIO_LIThe FylloClip was tested under growth chamber, greenhouse, and tropical field conditions. We evaluated how its capacitance measurements respond to rainfall, temperature and humidity, and compared FylloClip measurements with transpiration measured with an IRGA. C_LIO_LIThere was a strong correlation (r = 0.85) between FylloClip and IRGA data. Both systems captured similar diurnal transpiration patterns, with transpiration declining simultaneously under water deficit. Rainfall and very high relative humidity produced FylloClip signals that could be misinterpreted as high transpiration, although transpiration is negligible under these conditions. C_LIO_LIOur results revealed that FylloClips capture temporal patterns of transpiration with high accuracy and resolution, providing a reliable tool for long-term, large-scale monitoring of transpiration dynamics in ecophysiological studies and precision agriculture. C_LI

Why it matches plant phenotyping methods葉面センサーによる蒸散動態測定法を開発・評価し、IRGAとの比較検証および環境条件による性能評価を行っており、植物生理形質の取得が中心である。

abstractHere, we evaluated the potential of the FylloClip for monitoring transpiration dynamics and assessed environmental conditions that may affect its performance.

Advancing the pneumatic method to assess xylem vulnerability to embolism among distinct growth forms. →

Drought-driven plant mortality is closely linked to xylem embolism. Building useful, reliable datasets of xylem vulnerability to embolism requires methods that are practical, fast, accurate, widely accessible and robust across growth forms. We tested and advanced the pneumatic method for constructing xylem vulnerability curves (VCs) across contrasting growth forms to improve inference of drought resilience. Using an automated pneumatron, VCs were constructed for three species representing a small woody shrub (Erica monsoniana), a large woody shrub (Protea repens), and a reed-like graminoid (Cannomois congesta). For graminoid culms, we compared three approaches for estimating xylem water potential (Ψ) and developed a non-invasive method that couples repeated relative water content (RWC) measurements with Ψ-RWC models to obtain high-temporal Ψ estimates. Percent air discharged (PAD)-Ψ relationships were well captured by sigmoid functions. Cannomois congesta showed the steepest curves and the least negative thresholds overall (P 50 = -2.91 ± 0.09 MPa), indicating early, rapid embolism progression, whereas Erica monsoniana was most resistant (P 12 = -5.91 ± 0.74 MPa; P 50 = -6.78 ± 0.76 MPa) with higher variability; Protea repens was intermediate. P 50 estimates were the most comparable with prior optical, pneumatic and centrifuge estimates, whereas P 12 and P 88 showed greater divergence. Ψ TLP was less variable between species, but ranked similarly (-1.49 ± 0.03, -1.53 ± 0.03, -1.59 ± 0.02 MPa for C. congesta, P. repens, and E. monsoniana, respectively). Such variation yielded systematically wider hydraulic safety margins for the three species. By demonstrating that the pneumatic method can generate reliable vulnerability curves across small and large woody shrubs and graminoids, this study broadens the comparative evaluation of xylem vulnerability across growth forms with contrasting anatomy. A practical advance is the use of repeated RWC measurements paired with Ψ-RWC relationships to improve Ψ resolution in graminoid culms while minimizing disturbance.

Why it matches plant phenotyping methods植物の木部キャビテーション脆弱性を測定する空気圧法を改良・比較検証し、反復RWC測定による非侵襲的な水ポテンシャル推定も開発しているため、植物生理形質の取得法が研究の中心です。

abstractWe tested and advanced the pneumatic method for constructing xylem vulnerability curves (VCs) across contrasting growth forms to improve inference of drought resilience.

VNIR Hyperspectral Signatures and Machine Learning for Early Detection and Classification of Barley Diseases. →

This study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections. We examined the causal agents of widespread crop diseases, including: loose smut, head blight, fusarium head blight (FHB), stem rust, net blotch, spot blotch, common root rot. Analysing disease-specific spectral characteristics with machine learning (ML) algorithms revealed the most informative spectral ranges: the green region (~520-560 nm), the red chlorophyll absorption zone (~650-680 nm), and the red-edge region (~700 nm). These ranges accurately reflect alterations in the plant's cellular structure and pigment complexes. Spectral data were processed using five ML algorithms. Random Forest (RF) proved to be the most effective for identifying and differentiating barley diseases, achieving an accuracy of up to 90.13% (MCC = 0.86). This superior performance stems from the ensemble method's robustness to noise and its ability to extract critical features from high-dimensional hyperspectral data, particularly when distinguishing diseases with overlapping spectral signatures. Furthermore, this study highlights the potential of integrating UAV-based remote sensing to delineate reference zones, proximal hyperspectral imaging (HSI), and ML for robust plant health monitoring. This combined approach shows significant promise for early disease diagnostics, enabling site-specific treatments, curbing disease progression, and reducing pesticide application. Ultimately, these findings offer practical value for the agro-industrial sector in major grain-producing countries, especially in Central Asia, where agricultural advancement is a strategic priority for sustainable development and food security.

Why it matches plant phenotyping methodsVNIRハイパースペクトル計測と機械学習により、オオムギの病害状態を植物体のスペクトル特徴から検出・分類する方法が研究の中心であり、植物フェノタイピング手法に該当する。

abstractThis study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections.

Dual-signal MOF nanozyme microneedle patch for on-site monitoring of hydrogen peroxide in postharvest lettuce. →

Real-time monitoring of H 2 O 2 in plant tissues is useful for evaluating oxidative changes during postharvest storage, but direct on-site detection in vegetables remains difficult because most assays still require tissue disruption and laboratory instruments. In this study, a dual-signal microneedle biosensor was developed by integrating polydopamine-coated Fe/Zr-MOF nanozyme (PDA@Fe/Zr-MOF) into a gelatin/sodium alginate microneedle patch for H 2 O 2 detection in lettuce. The polydopamine coating improved the peroxidase-like response of Fe/Zr-MOF through •OH generation and also contributed to photothermal conversion under 808 nm near-infrared (NIR) irradiation. After contact with lettuce leaves, the microneedles extracted interstitial fluid and allowed H 2 O 2 -triggered TMB oxidation to be read by both colorimetric imaging and thermal imaging. The two outputs were not independent recognition mechanisms, but they provided mutually supportive information and helped reduce the influence of sample color and environmental fluctuations. The sensor achieved detection limits of 0.42 μM for the colorimetric mode and 0.34 μM for the photothermal mode. During 15 days of storage at 4°C, the sensor tracked H 2 O 2 accumulation in lettuce and showed a clear relationship with spoilage progression. These results indicate that PDA@Fe/Zr-MOF-based microneedle sensing is a feasible approach for monitoring oxidative freshness changes in postharvest vegetables.

Why it matches plant phenotyping methodsレタス組織内H2O2という植物の生理状態を、マイクロニードルとカラー・熱画像で現場測定するセンサーを開発しており、取得手法が研究の中心である。

abstracta dual-signal microneedle biosensor was developed by integrating polydopamine-coated Fe/Zr-MOF nanozyme (PDA@Fe/Zr-MOF) into a gelatin/sodium alginate microneedle patch for H 2 O 2 detection in lettuce.

High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a regulatory pathway for carotenoid synthesis. →

Functional genomics have been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated cell sorting (RACS). The CRISPR/Cas-generated library consisted of 3567 microalgal mutants derived from 2397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unraveled mutations in the violaxanthin de-epoxidase (noVDE) or in the proteasome assembly chaperone 4 (noPAC4) genes. Knocking out all five known noVDEs revealed that the high carotenoid content is due to violaxanthin increase, whilst noPAC4 knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidence suggested two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this forward-genetics approach can greatly accelerate the discovery of genes and pathways.

Why it matches plant phenotyping methods微細藻類の単細胞カロテノイド含量という生理形質を、Raman-activated cell sortingでラベルフリーかつハイスループットに取得・選別するプラットフォームが研究の中心であり、単なる生物学的測定ではない。

abstractwe introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated cell sorting (RACS).

Application of hyperspectral reflectance for early detection of dry root rot and fusarium wilt in chickpea (Cicer arietinum L.) →

Abstract Early detection of soil-borne fungal diseases is essential for sustaining chickpea ( Cicer arietinum L.) productivity. This study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes. Spectral data were collected at regular intervals from 1 to 76 days after sowing (DAS) and used to derive vegetation indices including NDVI, NDWI, PRI, and DSWI. Visual symptoms appeared at 46 DAS (DRR), 42 DAS (wilt), and 43 DAS (combined stress), whereas spectral indices indicated stress-related changes earlier, typically between 36 and 40 DAS. NDVI reflected early reductions in canopy vigor, PRI captured changes in photosynthetic activity, and NDWI and DSWI indicated alterations in plant water status, with DSWI showing comparatively consistent early sensitivity. Resistant genotypes maintained relatively stable NIR reflectance and water-sensitive spectral responses, while susceptible genotypes exhibited reduced NIR reflectance and increased SWIR absorption. Significant differences (p

Why it matches plant phenotyping methodsハイパースペクトル反射測定とスペクトル指標を用いて、植物体の病害ストレスを症状発現前に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ある。

abstractThis study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes.

Subcellular metallomic networks orchestrate physiological outcomes: Single-cell mapping via an integrated SEM-FIB-TOF-SIMS platform. →

The spatial organization of essential, nonessential, and toxic metal(loid) elements (MEs) within plant cells underpins physiological function. Yet, comprehensive subcellular imaging of the full ME spectrum remains challenging due to trade-offs among spatial resolution, elemental coverage, and structural correlation. Here, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution. Applying this high-fidelity workflow to Arabidopsis , soybean, and wheat, we constructed single-cell metallome maps revealing an evolutionarily conserved subcellular architecture: chloroplasts enrich essential MEs (e.g., magnesium, iron, copper), whereas vacuoles compartmentalize nonessential [e.g., lanthanum (La)] and toxic MEs [e.g., cadmium (Cd), lead, arsenic]. We demonstrate that while this architecture remains stable under homeostasis, it undergoes dynamic, stimulus-specific, and dose-dependent remodeling under stress. Low-dose La(III) enhances pairwise and higher-order colocalizations of essential MEs within chloroplasts, correlating with improved photosynthetic efficiency and growth. High-dose La(III) induces nonphysiological La-ME associations and, critically, drives aberrant Cd(II) accumulation in chloroplasts-revealing a cross-toxicity mechanism wherein La(III) disrupts native sequestration barriers. In contrast, although high-dose Cd(II) is largely excluded from chloroplasts, it triggers a widespread redistribution of essential MEs, progressively eroding spatial organization. Thus, while both ions inhibit growth, they perturb metallomic networks via distinct mechanisms: La(III)-mediated disruption of sequestration vs. Cd(II)-induced systemic compartmental collapse. Our findings establish that subcellular ME networks are dynamically regulated and orchestrate physiological outcomes.

Why it matches plant phenotyping methods植物細胞内の金属元素分布と超微細構造を取得する統合イメージング基盤とワークフローの開発が中心であり、植物の生理状態・ストレス応答に結び付けて実証している。

abstractHere, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution.

Construction of a Virtual Sensor-Driven Digital Twin System for Plant Growth Monitoring on Rooftop Farms →

Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R2 values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms.

Why it matches plant phenotyping methods植物成長のLAI、地上部バイオマス、フェノロジー、収量を予測する仮想センサー・デジタルツインを開発し、精度検証しており、表現型推定手法が研究の中心である。

abstractThis study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China.

Rapid preliminary screening of Tomato brown rugose fruit virus based on surface-enhanced Raman spectroscopy and machine learning. →

Introduction Tomato brown rugose fruit virus (ToBRFV) represents a growing threat to global tomato production, causing severe losses in crop yield and fruit quality. Although the standard RT-qPCR assay is highly accurate, its reliance on laboratory processing, specialized equipment, and trained personnel limits its applicability for rapid on-site diagnostics. To address this limitation, this study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves. Methods Following health status confirmation via RT-qPCR, leaf extracts were directly deposited onto silver nanorod arrays for SERS spectral acquisition. Three classification models were evaluated: PCA-LDA, PLS-DA, and SVM. Results The results showed that all models were able to discriminate infected samples from healthy samples in the present dataset. Notably, the SVM model exhibited the best performance, achieving an accuracy of 91.67%, a sensitivity of 100.00%, a specificity of 81.48%, and an area under the ROC curve (AUC) of 0.993. Discussion This result suggests that SERS spectra may contain biochemical information associated with ToBRFV infection and that such information can be used for sample classification using machine learning models. In its present form, this approach is intended as a rapid, low-cost, field-deployable preliminary screening tool - not a replacement for RT-qPCR or other confirmatory molecular assays. The reported accuracy was obtained on mechanically inoculated plants of a single cultivar under controlled greenhouse conditions and should therefore be interpreted as a proof-of-concept upper bound; field-scale validation is the focus of ongoing work.

Why it matches plant phenotyping methodsSERSと機械学習を用いて感染トマト葉と健全葉を識別する植物病害状態の取得・分類法が研究の中心であり、複数モデルの性能評価も行っている。

abstractthis study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves.

Early hyperspectral detection of Carlavirus vignae in common bean under field conditions →

Abstract Virus-associated diseases are among the biological stresses that affect common bean yield, such as Carlavirus vignae ( Cowpea mild mottle virus , CPMMV), transmitted by the whitefly Bemisia tabaci . CPMMV is present in different continents, and recent outbreaks have concerned Brazilian farmers and researchers. Integrated Pest Management routines for field monitoring of viral spread are laborious and may be limited to visible symptoms. We hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance. To test this hypothesis, we used a hyperspectral sensor mounted on a drone to capture images of CPMMV-inoculated and non-inoculated field plots in 2022 and 2023, across a tolerant common bean cultivar (BRS FC420 RMD) and a susceptible one (BRS FC401 RMD). Results showed that CPMMV was detected in common bean plants by hyperspectral imaging at early infection stages (~ 6 DAI) before symptom onset and at an advanced infection stage (~ 22 DAI). Reflectance within the visible light spectrum was affected by soil cover on all flights, and in most of these, also in the near-infrared region. The main differences between CPMMV-inoculated and control plants were consistent across two years of experiments, regardless of the common bean phenological stage and genotype. Fit statistics using the sum of squared errors, R 2 and AIC indicated that reflectance from 401 to 425 nm, especially near 415 nm, differed significantly between infected and healthy plants. Such changes are associated with chlorophyll degradation and disruption of the photosynthetic apparatus, and are detectable even before symptom onset. Progress in the disease severity index also differentiated the tolerant cultivar from the susceptible one. CPMMV infection significantly reduced common bean yield by ~ 21% compared with healthy plants. CPMMV detection by hyperspectral imaging enables early scouting to optimize disease management.

Why it matches plant phenotyping methodsハイパースペクトル画像を用いて、症状発現前の感染植物の反射特性と病害状態を検出し、複数年・品種で技術性能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。

abstractWe hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance.

Unleashing the power of visible-near infrared spectroscopy: predicting Golden Delicious apple enzyme activity. →

Background Enzymatic browning is a significant reaction in fruits that affects their color, appearance, and quality. The quality of apples, as a perishable product, is mainly influenced by the activity of two browning-related enzymes, polyphenol oxidase (PPO) and peroxidase (POD), during storage. Assessment of these enzymes using conventional methods is often destructive and time-consuming, preventing rapid and non-invasive monitoring of fruit quality. In this study, a visible-near infrared (visible-NIR) spectroscopy approach was developed to predict the enzymatic activity of PPO and POD in intact Golden Delicious apples, aiming to enable rapid, non-destructive evaluation and to identify the most informative spectral regions for industrial applications. Results Both support vector regression (SVR) and decision tree (DT) algorithms achieved high performance when combined with non-linear feature selection algorithms. The best performance, in terms of elapsed time and figure of merits, was achieved by combining particle swarm optimization (PSO) with SVR and DT. However, partial least squares (PLS) models outperformed both SVR-PSO and DT-PSO. Conclusions This study is an advanced proof of concept of the use of visible-NIR spectroscopy - combined with variable selection and machine learning algorithms - for predicting browning-related enzyme activity in apples. The SVR and DT algorithms, coupled with metaheuristic strategies, reached lower performances than PLS, but the success of the variable selection strategy lays the groundwork for developing a miniaturized sensor for assessing apple quality during storage and controlling browning. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methodsリンゴ果実の酵素活性という植物器官の状態を、可視近赤外分光と機械学習で非破壊推定する手法を開発・比較検証しており、表現型取得法が中心である。

abstracta visible-near infrared (visible-NIR) spectroscopy approach was developed to predict the enzymatic activity of PPO and POD in intact Golden Delicious apples

From Sensing to Action: A Leaf Humidity–Triggered Closed Loop System for Precision Salicylic Acid Delivery to Mitigate Plant Stress →

Abstract Real-time detection of plant stress and timely delivery of protective biomolecules are essential for improving crop resilience under adverse environmental conditions. However, conventional plant monitoring systems typically rely on ambient measurements and passive treatment strategies that fail to enable targeted plant recovery based on their localized physiological conditions. As a result, current approaches largely operate as open-loop systems, where sensing and intervention are not directly integrated, limiting the ability to respond dynamically to plant stress. This study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system. The objective of this work was to develop a platform capable of monitoring transpiration driven humidity changes at the leaf surface and enabling controlled hormone delivery based on plant physiological responses. A temperature responsive hydrogel encapsulating SA was synthesized to achieve sustained biomolecule release while minimizing initial burst release. Salicylic acid release kinetics were evaluated using multiple mathematical models, with the Korsmeyer–Peppas model providing the best fit (R² = 0.9978), indicating that SA release was governed primarily by polymer relaxation and degradation mechanisms. Leaf-level relative humidity was continuously monitored on the abaxial surface under different treatment conditions. Plants treated with the hydrogel-based SA delivery system showed improved drought tolerance, with localized relative humidity increasing from approximately 20–30% in stressed plants to 60–70% after treatment, while untreated stressed plants did not show any noticeable recovery. This improvement was further supported by measurements of stomatal aperture, which showed a mean opening of 1.932 micrometers in treated plants, compared to 0.396 micrometers in untreated plants. SA treated seeds also demonstrated accelerated germination within 14 days. These findings demonstrate the potential of integrating plant wearable sensors with stimulus responsive biomaterials to establish closed-loop plant healthcare systems that couple physiological sensing with adaptive intervention.

Why it matches plant phenotyping methods葉面湿度を連続測定して植物の生理状態（蒸散・ストレス回復）を推定するセンサーと、応答型処置を統合した植物フェノタイピング／ケア基盤の開発が中心である。

abstractThis study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system.

One-year-old wood morphology and colorimetric indicators of cold hardiness in 'Frontenac' and 'Prairie Star' grapevines. →

Cold hardiness is a critical trait for grapevine survival and productivity in cold climates. This study examined the relationships among cane morphological characteristics, shoot color parameters, and cold hardiness in two grapevine cultivars ('Prairie Star' and 'Frontenac') across four dormant-season sampling times (ST 1-ST 4) and three internode diameter classes (small, normal, and large). Morphological traits, including internode length, shoot diameter, and cross-sectional area, did not show a consistent temporal trend across sampling periods, suggesting that the observed variation was primarily associated with sampling time and cane class rather than progressive structural change during dormancy. In contrast, colorimetric traits showed a clear seasonal pattern, with shoots becoming darker and redder from ST 1 to ST 4, consistent with advancing lignification and cane maturation. Cold hardiness, assessed using low-temperature exotherms of bud, phloem, and xylem tissues, increased substantially from early to mid-dormancy, with xylem tissues reaching the greatest freezing tolerance by ST 3-ST 4. 'Prairie Star' showed slightly greater xylem cold hardiness than 'Frontenac', while bud survival remained consistently high across all treatments. Strong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status. Sampling time was the primary source of multivariate variation, with cultivar and internode class contributing secondary effects. These findings demonstrate that observable cane traits, especially shoot color, reflect the progression of seasonal cold acclimation and may support the evaluation and selection of cold-hardy grapevine germplasm.

Why it matches plant phenotyping methods枝の色・形態を用いてブドウの耐寒性を非破壊推定する指標として評価しており、単なる生物学的測定ではなく表現型取得法の妥当性評価が中心です。

abstractStrong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status.

Cell Wall-Anchored MoO x @CuPc Nanoprobes Decode Organ-Level Metabolic Trade-Offs in Halophytes under Salt Stress. →

Soil salinization poses a severe threat to global food security. However, deciphering the spatiotemporal dynamics of key metabolites and ions in living plants remains a formidable challenge due to the lack of robust in vivo sensing tools. In this study, we developed a nonmetallic MoO x @CuPc core-shell nanoprobe anchored to the plant cell wall, which serves as the cornerstone of an "in vivo-in situ-long term-multitargeted" (VSLM) surface-enhanced Raman spectroscopy (SERS) platform. This design overcomes critical limitations of conventional metallic probes, such as rapid corrosion in saline microenvironments and inability to achieve stable multitarget detection, by synergizing a corrosion-resistant MoO x core with a protective CuPc shell. The optimized interface electronic coupling enables simultaneous tracking of adenosine triphosphate (ATP), salicylic acid (SA), Na + , and K + at nanomolar detection limits, with signal stability maintained over 48 h ( Suaeda salsa ( S. salsa ) under salt stress, revealing a shift from "growth-priority" to "defense-priority" resource allocation alongside coordinated ion partitioning across roots, stems, and leaves. This work presents a novel in situ and multitargeted monitoring methodology, which substantially expands the capability of SERS for complex biological systems and opens a new avenue in analytical chemistry for dynamic, multiparameter life science research.

Why it matches plant phenotyping methods植物体内の代謝物・イオンを長期・多標的に測定するSERSナノプローブ／プラットフォームの開発が中心で、塩ストレス下の植物の生理状態を直接評価しているため。

abstractwe developed a nonmetallic MoO x @CuPc core-shell nanoprobe anchored to the plant cell wall, which serves as the cornerstone of an "in vivo-in situ-long term-multitargeted" (VSLM) surface-enhanced Raman spectroscopy (SERS) platform.

Recent trends in crop water stress monitoring using remote sensing technologies: A review →

Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE)

Why it matches plant phenotyping methodsUAV搭載センサーによる作物の水ストレスや生理形質のモニタリング技術をレビューしており、表現型取得法が中心である。

titleRecent trends in crop water stress monitoring using remote sensing technologies: A review

Minimally invasive quantitative tracking of minerals, sugars, amino acids, and hormones in the leaf apoplast of Arabidopsis thaliana and Vicia faba. →

The apoplast of leaves is involved in nutrient transport, microbe-host interactions, systemic signaling, cell wall dynamics, and serves as an interface for various other physiological processes. The composition of the apoplastic solute pool, which supports many of these functions, is dynamic and shaped by developmental and environmental cues. However, due to the complexity and compartmentalization of the apoplast, analysing these fluids - and thus the associated physiological processes - remains technically challenging. This study introduces a minimally invasive method for extracting apoplastic fluids from leaves of selected dicots (e.g. Arabidopsis thaliana, Vicia faba, and many more), offering two key advantages: (i) repeated extractions from the same leaves to generate time-series data, such as every 24 hours, over consecutive days, and (ii) high spatial resolution, enabling identification of macrodomains within the leaf apoplast. For example, abscisic acid macrodomains were revealed along the leaf axis, providing insight into apoplastic hormone regulation. The method also reveals other previously unrecognized aspects, such as the accumulation of kaempferol glycosides in the apoplast after plants experienced salt stress. Finally, the method addresses the distortion of apoplast compound levels caused by dilution bias that results from the inconsistent recovery of infiltration fluid. Adding pyranine enables correction, ensuring accurate and comparable data. By integrating spatial and temporal precision, this new tool will promote a deeper understanding of plant apoplastic processes and their physiological relevance in various biological contexts.

Why it matches plant phenotyping methods葉のアポプラスト液を反復・高空間分解能で抽出し、植物の生理状態を定量する新規手法の開発が中心である。

abstractThis study introduces a minimally invasive method for extracting apoplastic fluids from leaves of selected dicots

Diagnostic system for tebuthiuron soil ecotoxicity using morphophysiological indicators of Mucuna pruriens validated by Lactuca sativa. →

This study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens, using the germination index (GI) of Lactuca sativa as a sensitive ecotoxicological validation endpoint. The experiment was conducted under greenhouse conditions using a completely randomized design with 12 treatments and 360 individual pots (independent samples evaluated via destructive sampling), which were distributed across five evaluation periods at 14, 28, 42, 56, and 70 days after sowing. Morphophysiological variables, including plant height, root length, shoot and root dry mass, chlorophyll content, nodule number, and visual phytotoxicity, were quantified and integrated with multivariate and probabilistic modeling approaches. Given the multifactorial nature of the germination index, Principal Component Analysis (PCA) was applied to identify ecological and physiological gradients associated with plant vigor, stress, and symbiotic functioning. The PCA outputs were subsequently used as inputs for Probabilistic Neural Networks (PNNs), enabling the classification and prediction of bioindicator-based ecotoxicological levels using mathematically defined low, medium, and high GI classes. Model performance was internally assessed using training and validation datasets, confusion matrices, overall accuracy, sensitivity, specificity, and ROC curves. Because no independent external dataset was available, the predictive performance should be interpreted as evidence of internal consistency rather than definitive generalizability across different soils, climates, herbicide doses, or field conditions. Multivariate analyses revealed that ecotoxicological attenuation trajectories in tebuthiuron-contaminated soils are inherently nonlinear, being structured by coordinated shifts in morphophysiological traits rather than isolated responses of individual variables. The integrated PCA-PNN framework demonstrated that aboveground traits. Particularly plant height, chlorophyll content, and shoot dry mass, were more sensitive indicators of tebuthiuron-induced stress than root traits alone. Higher GI values were associated with PCA regions characterized by increased shoot biomass, greater plant height, reduced phytotoxicity, and improved physiological performance, whereas lower GI classes corresponded to suppressed growth and multidimensional stress signatures. The progressive convergence between plant vigor and GI across evaluation periods suggests a gradual mitigation of ecotoxicological stress signals on the indicator plants, indicating transitions from acute injury to physiological adaptation states. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, these responses should not be interpreted as direct evidence of herbicide degradation, dissipation, or removal. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, the observed improvements should be interpreted as evidence of physiological adaptation and/or ecological attenuation rather than definitive proof of herbicide degradation or removal. Overall, this approach provides a robust framework for early detection of soil contamination and supports its application in monitoring and guiding soil rehabilitation processes, with potential for future validation under field conditions.

Why it matches plant phenotyping methods植物の形態・生理形質を統合し、PCA-PNNで植物ストレスおよび土壌生態毒性レベルを診断する手法の開発・内部検証が中心であり、単なる生物学的測定ではない。

abstractThis study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens

Visible–Near Infrared Spectroscopy for Nondestructive Prediction of Firmness and Moisture Content in Bronzing-Affected Jackfruit (Artocarpus heterophyllus cv. ‘Tekam Yellow’) →

Abstract The Malaysian jackfruit industry is increasingly threatened by “jackfruit-bronzing,” a disease caused by Pantoea stewartii subsp. stewartii , which manifests as yellowish-orange to reddish discoloration of the pulp while leaving the rind visually unaffected. The cv. ‘Tekam Yellow’ cultivar is particularly vulnerable, resulting in substantial postharvest losses. This study explores the feasibility of employing visible near infrared spectroscopy (Vis-NIRS) as a non-destructive method to predict internal bronzing through the estimation of rind or flesh firmness and rind, flesh or seed moisture content. Spectral reflectance data were acquired non-destructively from the rind surface of jackfruit, and the resulting spectra were used to predict rind firmness, and moisture content of rind, flesh, and seed tissues. Jackfruits at 10, 12, and 14 weeks after anthesis (WAA) were analyzed within the 500–950 nm wavelength range. Partial least squares regression (PLSR) models were developed and optimized using preprocessing techniques such as Savitzky–Golay smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC). The best-performing models yielded high determination coefficients for both calibration (Rc²) and validation (Rv²), reaching up to 0.99, with root mean square error of calibration (RMSEC) and validation (RMSEV) values as low as 0.67 N and 0.74% w.b., respectively. Destructive reference measurements were conducted in parallel and analyzed using ANOVA and Fisher’s protected least significant difference (FPLSD) test at p ≤ 0.05. Results demonstrated that Vis–NIRS applied through the rind surface provided reliable prediction of firmness and moisture-related attributes associated with internal bronzing disorder in jackfruit. The developed approach shows strong potential as a rapid and non-invasive technique for early bronzing detection and postharvest quality assessment in jackfruit.

Why it matches plant phenotyping methodsVis-NIRSによる非破壊的な植物器官の硬度・含水率推定と、内部障害の早期検出モデル開発・検証が研究の中心であるため。

abstractThis study explores the feasibility of employing visible near infrared spectroscopy (Vis-NIRS) as a non-destructive method to predict internal bronzing through the estimation of rind or flesh firmness and rind, flesh or seed moisture content.

Rapid monitoring of drought and salinity stress responses in wheat via potential Raman-derived biomarkers and traditional biochemical indicators. →

Abiotic stresses such as drought and salinity significantly constrain the productivity of in vitro-grown wheat (Triticum aestivum L.) by disrupting its biochemical and physiological homeostasis. Rapid, non-destructive, and data-driven diagnostic approaches are therefore essential for the early detection of stress conditions and for supporting sustainable crop management. In this study, Raman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments. Distinct Raman spectral features associated with pigments, proteins, carbohydrates, and lipids were analyzed alongside biochemical indicators, including proline, chlorophyll, and malondialdehyde levels. Overall, the integration of RS with machine learning provides a rapid, robust, and non-invasive framework for the early detection of drought and salinity stress in wheat. Notably, Raman intensity variations observed at 737, 996, 1051, 1064, and 1518 [Formula: see text] exhibited consistent spectral trends that closely mirrored changes in conventional biochemical stress markers, confirming that these spectral shifts directly reflect underlying physiological stress responses. To classify stress levels and to identify key Raman-derived biomarkers associated with each stress type, a machine learning approach was implemented, achieving a classification accuracy exceeding 85% in discriminating control, drought-stressed, and salinity-stressed plants. Furthermore, characteristic Raman bands, particularly those associated with C-H and amide vibrational modes, showed strong correlations with established biochemical indicators, underscoring their potential as reliable, non-invasive stress biomarkers. Collectively, these findings provide mechanistic insight into stress-induced structural and biochemical alterations and support the application of RS-machine learning integration for precision agriculture and resilient crop management under changing environmental conditions.

Why it matches plant phenotyping methodsラマン分光と機械学習により、コムギの乾燥・塩ストレス状態を非破壊的に検出・分類する方法を開発・検証しており、植物状態の取得が研究の中心である。

abstractRaman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments.

Single-Particle Tracking of Genetically Encoded Multimeric Nanoparticles Reveals Regional Heterogeneity and Osmotic Stress-Induced Convergence of Cytoplasmic Crowding in Plant Root Cells →

Abstract Macromolecular crowding is a fundamental physical property of the cytoplasm that governs intracellular diffusion and biochemical reactions. However, in situ quantitative characterization of intracellular dynamics and associated biophysical states in intact plant tissues remains challenging. Using 40-nm genetically encoded multimeric nanoparticles (GEMs) and single-particle tracking in Arabidopsis roots, we quantitatively map the regional heterogeneity of cytoplasmic diffusion dynamics and crowding along the root developmental axis: elongation zone cells exhibit a dense, low-mobility baseline, whereas maturation zone and root hair cells display higher mobility. These regions exhibit different sensitivities to osmotic stress. Notably, under severe ionic stress, both the diffusion coefficients and non-Gaussian parameters of the maturation zone and root hair cells converge toward the levels of the elongation zone cells, suggesting an intrinsic physical baseline for cytoplasmic crowding. This kinetic convergence in these cells is accompanied by vacuolar retraction and an increase in cytoplasmic thickness. Together, our study establishes a GEMs-based platform for in situ biophysical analysis in plant cells and uncovers a spatially-resolved physical landscape of cytoplasmic crowding and its dynamic reorganization under osmotic stress.

Why it matches plant phenotyping methods植物細胞内の拡散動態・細胞質クラウディングを定量するGEMs単粒子追跡法を構築し、植物根で実証した研究であり、表現型取得基盤が中心である。

abstractUsing 40-nm genetically encoded multimeric nanoparticles (GEMs) and single-particle tracking in Arabidopsis roots, we quantitatively map the regional heterogeneity of cytoplasmic diffusion dynamics and crowding along the root developmental axis

Finding Hidden Huanglongbing using an Electronic Nose. →

Background and Objective: The Huanglongbing (HLB) is one of the most destructive diseases affecting citrus worldwide. A major challenge in its management is its ability to remain asymptomatic for extended periods, delaying timely detection and control. This study aims to develop and evaluate a compact electronic nose (e-nose) system equipped with metal-oxide semiconductor (MOS) sensors for the early detection of Candidatus Liberibacter asiaticus infection in citrus leaves through Volatile Organic Compound (VOC) analysis under field-like conditions. Materials and Methods: A total of 454 Purworejo Siamese citrus leaf samples were collected from two orchards. The infection status of each sample was confirmed using conventional Polymerase Chain Reaction (PCR) prior to headspace VOC extraction. The cross-sensitive MOS sensor array converted VOC interactions into electrical signals, which were subsequently preprocessed, feature-extracted and analyzed using machine learning pipelines. Model selection and optimization were performed on baseline-shifted data. Results: A stratified 5-fold cross-validation using the Extra Trees algorithm successfully discriminated between PCR-confirmed Candidatus Liberibacter asiaticus-infected leaves and healthy controls, achieving an accuracy of 84.57% (95% confidence interval: 80.98%-88.15%). These results were obtained under field-like conditions and were further validated using headspace gas chromatography-mass spectrometry (HS-GC/MS), which revealed distinct VOC profiles for each group. Conclusion: This study demonstrates the potential of the electronic nose (e-nose) as a rapid, in-field screening tool capable of prioritizing samples for laboratory confirmation, thereby supporting effective HLB management.

Why it matches plant phenotyping methods柑橘葉の感染状態をVOCセンサーで直接推定する電子鼻を開発・評価しており、植物病害状態の取得方法が研究の中心である。

abstractThis study aims to develop and evaluate a compact electronic nose (e-nose) system equipped with metal-oxide semiconductor (MOS) sensors for the early detection of Candidatus Liberibacter asiaticus infection in citrus leaves through Volatile Organic Compound (VOC) analysis under field-like conditions.

Estimation of relative chlorophyll content in winter wheat employing hyperspectral reflectance: A comprehensive analysis of data-driven ensemble learning methods →

Ensemble learning is increasingly used for remote sensing-based plant phenotyping data. A thorough evaluation of its effectiveness in estimating relative chlorophyll content is essential to optimize model selection and enhance prediction accuracy. This study aimed to assess the performance of parallel, sequential, and hybrid ensemble learning approaches, as well as individual machine learning models, for cross-environment estimation of SPAD-based chlorophyll content using hyperspectral reflectance data. Canopy hyperspectral reflectance and SPAD measurement were collected from winter wheat during the late growth stages across two distinct environments. Parallel and sequential ensemble learning strategies were represented by random forests (RF) and eXtreme gradient boosting (XGBoost), respectively. The hybrid ensemble integrated the performance of K-nearest neighbors, support vector machine, partial least squares regression, generalized linear model (GLM), deep neural network, and Gaussian process. Gray relational analysis (GRA) was employed to evaluate band features, and the positive association between feature quality and prediction accuracy validated its effectiveness. During modeling, XGBoost (R 2 = 0.657–0.658) underperformed compared to RF (R 2 = 0.687–0.691). GLM consistently excelled across most hybrid ensemble members in most feature intervals (R 2 = 0.137–0.692, 0.512–0.723), achieving superior prediction accuracy across several feature intervals and also outperforming RF (R 2 = 0.156–0.687, 0.535–0.691). Among the six forecast combination strategies used in the hybrid ensemble, inverse rank (R 2 = 0.640–0.726) demonstrated robust performance across different feature intervals but provided only marginal improvement over the best individual ensemble member. Moreover, incorporating RF and XGBoost in the hybrid ensemble did not result in significant accuracy gains. To balance computational efficiency and prediction accuracy, GRA-based optimal features combined with GLM modeling are recommended for similar applications, rather than relying on complex ensemble learning methods. These findings offer valuable insights for estimating relative chlorophyll content and hold potential for large-scale estimation of crop traits.

Why it matches plant phenotyping methodsハイパースペクトル反射データから冬コムギの相対クロロフィル含量を推定する機械学習手法を比較・評価しており、植物形質の取得・推定方法が研究の中心である。

abstractA thorough evaluation of its effectiveness in estimating relative chlorophyll content is essential to optimize model selection and enhance prediction accuracy.

Magnetometry for Agriculture and Animal Systems: From Classical Sensors to Quantum-Enabled Biosensing. →

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture.

Why it matches plant phenotyping methods植物の生理状態を対象とする磁気センシング技術を農業向けにレビューしており、植物フェノタイピングに関連するセンサー技術が中心です。

abstractThis review surveys the evolution of magnetic sensing technologies for agricultural and animal systems

C 4 photosynthetic pathway fluxes in transgenic rice plants →

ABSTRACT Most land plants photosynthesize using the C 3 pathway, in which ribulose bisphosphate carboxylase/oxygenase (Rubisco) fixes CO 2 into 3-carbon acids. The C 4 pathway, a biochemical CO 2 -concentrating mechanism that operates in the context of specialized leaf anatomy to concentrate CO 2 around Rubisco, is more efficient. Introduction of the C 4 pathway into the C 3 crop rice could increase yield by 50%. Expression of five C 4 enzymes in transgenic rice previously led to flux through the first step. However, there was no evidence for flux later in the cycle. Here we developed new transgenic rice lines and novel protocols to detect C 4 cycle activity: CO 2 fixation into C 4 acids by carboxylation of a C 3 compound, decarboxylation, refixation of CO 2 by Rubisco, and regeneration of the C 3 donor. We demonstrate that these four core C 4 reactions are operating in rice, establishing the in vivo flux framework needed to progress towards a functional carbon-concentrating mechanism.

Why it matches plant phenotyping methodsトランスジェニックイネのC4光合成フラックスを検出する新規プロトコルを開発し、植物内での生理状態を測定・実証しており、フェノタイピング手法が研究の中心である。

abstractHere we developed new transgenic rice lines and novel protocols to detect C 4 cycle activity

Shedding light on plant proteolysis: genetically encoded fluorescent sensors as tools for profiling protease activities →

Abstract Proteolysis is a universal process, as proteases play a pivotal role in modulating numerous signaling pathways. Proteases control the fate and function of their target proteins by hydrolyzing peptide bonds within these proteins. Understanding the temporal and spatial dynamics of proteolytic events, including the proteases that execute them, is crucial for elucidating their particular roles across diverse biological processes. In this study, we developed and characterized a set of genetically encoded Förster resonance energy transfer (FRET)-based reporters for the detection of various proteolytic activities in plants. Our sensors reliably reported the activity of specific proteases, exhibiting a performance comparable to previously established detection systems. In addition, we engineered variants capable of detecting the spatial dynamics of metacaspase-triggered proteolysis after wounding and during programmed cell death in roots. We demonstrated the feasibility of these FRET-based sensors for detecting various activities in vivo with high spatiotemporal resolution. The implementation of these tools in plant research opens opportunities to explore proteolytic mechanisms with enhanced precision. Overall, these biosensors constitute a versatile toolbox for probing protease function within its native cellular context, paving the way for deeper insights into plant biology and signaling.

Why it matches plant phenotyping methods植物内のプロテアーゼ活性という生理状態を高い時空間分解能で測定するFRETセンサーを開発・検証しており、表現型取得法が研究の中心です。

abstractwe developed and characterized a set of genetically encoded Förster resonance energy transfer (FRET)-based reporters for the detection of various proteolytic activities in plants.

Derivation of crop yield response factor (Ky) based on satellite data and machine learning methods →

Deficit irrigation (DI) is a crucial strategy for optimizing water use in arid and semi-arid agriculture, yet its success depends on accurately determining the crop yield response factor (K y ). This study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators. fused Landsat 7/8/9 and MODIS data within the Google Earth Engine platform to generate high-resolution daily Crop Water Stress Index (CWSI) maps for the 2023 season in southern Iran. The Random Forest (RF) algorithm was applied to correct biases in land surface temperature (LST), achieving high accuracy (RMSE < 1.0°C, nRMSE < 3%, rMBE ≈ 0%) before CWSI calculation. Ground measurements from 12 field points, including canopy temperature and yield, were used for calibration and validation. The satellite-based CWSI showed strong agreement with field data (RMSE = 0.05, nRMSE = 11%), with values ranging from 0.18 to 0.71 at dekadal scale. Using this CWSI, K y was computed at dekadal, monthly, and seasonal scales, revealing substantial spatiotemporal variability (0.2–1.63) and an average seasonal K y of 1.05. This value is lower than the FAO-66 default of 1.2, indicating that the standard coefficient may prompt over-irrigation without yield benefits. The analysis further identified early July as the period of peak water stress sensitivity, with K y values exceeding 1.82. This ML-enhanced, satellite-based approach provides a robust tool for deriving spatially explicit K y values, offering a significant advancement for precision irrigation planning and water resource management.

Why it matches plant phenotyping methods衛星データと機械学習で作物の水ストレス状態（CWSI）を推定し、地上測定で較正・検証する手法が中心であるため、植物生理状態のセンシング型フェノタイピングとして含める。

abstractThis study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators.

A Stomata-Infiltratable SERS Nanosensor for Real-Time Monitoring of Hydrogen Sulfide Dynamics in Plants. →

Hydrogen sulfide (H 2 S) is a key gaseous regulator in plant stress responses, but its spatiotemporal dynamics in living plants remain poorly understood due to the lack of noninvasive sensing tools. Here, we report a stomata-infiltratable SERS nanosensor based on Au@Ag@SiO 2 core-shell nanoparticles for real-time monitoring of endogenous H 2 S. The sensor, with an enhancement factor of ∼6.07 × 10 9 and a detection limit of 15 nM, efficiently infiltrates leaves of diverse species (Arabidopsis, spinach, and tomato). Real-time monitoring revealed that H 2 S accumulation kinetics are stress-specific and occur within 20 min of stress onset, preceding visible phenotypic damage. Notably, the nanosensor enabled visualization of stress-induced H 2 S transmission between neighboring plants, suggesting a role for H 2 S as an airborne signal in plant-to-plant communication. Furthermore, a species-dependent kinetic framework describing systemic signal propagation was established. This work demonstrates a versatile SERS-based platform for noninvasive monitoring of gaseous signaling molecules in plants.

Why it matches plant phenotyping methods植物内のH₂S動態という生理状態をリアルタイム・非侵襲的に測定するSERSセンサーを開発し、複数種で性能と適用性を示した研究であり、測定手法が中心的である。

abstractHere, we report a stomata-infiltratable SERS nanosensor based on Au@Ag@SiO 2 core-shell nanoparticles for real-time monitoring of endogenous H 2 S.

Remote evaluation of rice nitrogen utilization efficiency using chlorophyll-related spectral indices derived from unmanned aerial vehicle imagery. →

Introduction Nitrogen utilization efficiency (NUtE) directly reflects the efficiency of nitrogen remobilization to grains, serving as a key indicator of yield formation and environmental performance. However, conventional methods for assessing NUtE rely on destructive sampling and laboratory analysis, which are labor-intensive and time-consuming, whereas most existing remote sensing studies estimate NUtE by directly regressing spectral features against the final efficiency value without decomposing it into its underlying physiological components. Methods This study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI). NUtE showed a close and near-linear relationship with the ratio of panicle nitrogen accumulation from heading to dough stage (ΔPNA dough-heading , sink indicator) to leaf nitrogen accumulation at booting stage (LNA booting , source indicator). Therefore, with multi-site field experiments across different rice cultivars and nitrogen treatments, this study employed unmanned aerial vehicle imaging to accurately estimate rice leaf and panicle nitrogen accumulations, enabling rapid, large-scale evaluation of rice NUtE. Results This proposed index showed a strong correlation with measured NUtE (R 2 = 0.72, rRMSE = 10.84%) and effectively captured the distinct patterns of NUtE across different nitrogen treatments and cultivars. Discussion Our developed indicator is generalizable across diverse conditions for high-throughput selection of nitrogen-efficient cultivars and precision nitrogen management in sustainable agriculture.

Why it matches plant phenotyping methodsUAV画像とスペクトル指標からイネの窒素利用効率を推定する手法を開発し、多地点・品種・施肥条件で精度評価しており、植物形質の取得・推定法が中心である。

abstractThis study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI).

Using shortwave infrared spectral indices to monitor short-term water stress dynamics in peach orchards →

Abstract Purpose Capturing rapid changes in water status is key to optimizing deficit irrigation in Mediterranean orchards, but thermal remote sensing is constrained by the availability of high-spatial-resolution data. This study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards. Methods An experiment was conducted in two commercial orchards in south-eastern Spain, where mild water stress was induced by withholding irrigation for four days. High-resolution hyperspectral and thermal imagery were acquired concurrently with stem water potential measurements (ψ stem ). Structural, pigment-related, and water-sensitive indices were evaluated at high (20–50 cm) and medium (30 m) spatial resolutions to analyze the effects of pixel size on stress detection. The Crop Water Stress Index (CWSI), derived from thermal imagery, served as a reference indicator. Results Those optical indices based on SWIR reflectance at 1240 nm, the Normalized Difference Water Index (NDWI₁₂₄₀) and the Simple Ratio Water Index (SRWI), showed the strongest sensitivity to ψ stem variability (R² = 0.63, p

Why it matches plant phenotyping methods桃樹の水ストレス状態を高解像度ハイパースペクトル・熱画像とスペクトル指標で推定し、茎水ポテンシャルを用いて検証しており、植物表現型取得手法が中心である。

abstractThis study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards.

Defense mechanisms of kiwifruit against Corynespora cassiicola and predictive model for estimating resistance levels. →

Brown spot disease, caused by Corynespora cassiicola , poses a major threat to kiwifruit production, leading to severe defoliation, nutrient loss, and significant economic damage. This study assessed 25 kiwifruit germplasm accessions and found 64% exhibited resistance, including three highly resistant (HR) cultivars; most commercial and wild germplasm accessions were moderately resistant (MR) or highly susceptible (HS). Three cultivars representing different resistance levels - HR 'Longshan' (LS), MR 'Jinyan' (JY), and HS 'Hongyang' (HY) - were selected for mechanistic analysis. Resistant types showed stronger structural defenses: 64.55% lower stomatal density, 60.05% more trichome branching, and 52.28% higher epicuticular wax content than susceptible ones. These traits delayed appressorium formation by 12 hours and hindered penetration peg development. After infection, resistant plants activated rapid immune responses-ROS burst, hypersensitive reaction, and extensive lignin deposition. In LS, four defense enzyme activities rose 12-48 hours earlier than in HY and reached 1.12-1.57 times higher levels. Six defense-related genes were significantly up-regulated within 48 hours post-inoculation (hpi). Stepwise regression of 22 variables identified five key predictors of resistance: stomatal density, lesion diameter at 120 hpi (cm), average Phenylalanine Ammonia-Lyase gene expression (0, 4 and 8 hpi), peroxidase enzyme average activity (36, 48 and 72 hpi), and H 2 O 2 accumulation average area (12, 24 and 36 hpi). A model based on these achieved 94.24% accuracy (R² = 0.95) in field validation, offering a reliable tool for evaluating kiwifruit resistance.

Why it matches plant phenotyping methodsキウイフルーツの病害抵抗性を推定する予測モデルを開発し、圃場で検証しており、植物の病害状態を評価する手法が中心的です。

titlepredictive model for estimating resistance levels

Discovering genetic loci associated with rate of vegetative index gain using UAV-based phenomics in spring wheat →

In wheat, the pre-heading stage determines spikelet formation, floret fertility, and canopy development, making it a critical window for early stress detection and yield potential. The genetic basis of pre-heading canopy development in wheat has remained constrained by the conventional phenotyping due to the low temporal resolution. Here, we quantified the rate of vegetation index gain (RVIs) during tillering to heading stages using UAV-mounted multispectral sensor in 196 spring wheat cultivars representing 112 years of breeding history. RVIs were calculated using six vegetation indices for consecutive two growing seasons, and genome wide association study (GWAS) was performed on RVIs, grain yield (GY) and thousand grain weight (TGW) using a wheat 37K SNP array. RVIs showed significant positive correlations with grain yield (r=0.28-0.43; p<0.001) and consistently increased in the modern cultivars compared to old cultivars. This indicated that resource remobilization during pre-heading canopy development significantly contributed to GY during modern wheat breeding. GWAS identified 67 loci, including 12 Group-I loci associated only with RVIs, and 18 Group-II loci associated with both RVIs and yield traits. Two stable loci on chr1B and chr5D consistently increased GY and RVIs across environments, and the tag SNPs were converted to selectable KASP markers. The allelic distribution on global wheat collection of ∼3000 accessions showcased that favorable alleles on both loci were dominant in cultivars compared to landraces. Similarly, favorable alleles showed more frequency in winter type than spring type. Across breeding eras both alleles showed increasing trend with chr5D reaching near fixation and chr1B remaining partially enriched in modern cultivars. Our work on capturing pre-heading canopy development, discovery of two stable loci underpinning yield and RVIs, and development of KASP markers provided a strong foundation to HTP assisted genetic dissection of GY and facilitated the understanding of canopy dynamics and yield formation.

Why it matches plant phenotyping methodsUAV搭載マルチスペクトルセンサーで生育期間中のキャノピー発達を定量化するフェノタイピング手法が、研究の主要なデータ取得・解析基盤として用いられている。

abstractwe quantified the rate of vegetation index gain (RVIs) during tillering to heading stages using UAV-mounted multispectral sensor

Detection of downy mildew infection in grapevine leaves using field spectroscopy and machine learning →

Abstract Context Downy mildew, caused by Plasmopara viticola , remains one of the most damaging diseases affecting grapevines, especially in humid viticultural regions such as the “Vinhos Verdes” in northern Portugal. Traditional detection relies on visual inspection and laboratory techniques, which are subjective and reactive, often delaying effective intervention. Aims This study aims to evaluate the potential of field spectroscopy combined with machine learning to detect downy mildew in Vitis vinifera cv. Loureiro field conditions. The focus is on providing an early, non-destructive detection method that can be used in precision viticulture, reducing the need for costly, widespread pesticide applications. Methods and Key Results Leaf spectral reflectance data were collected along the 2023 and 2024 growing seasons using a portable spectroradiometer. Measurements were obtained from both untreated and fungicide-treated grapevines, covering different infection stages. Spectral signatures from 600 grapevine leaves were used to train and validate classification models using Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Random Forest (RF) models. Both RF and PLS-LDA models showed an overall accuracy of 95.1% when trained with all spectral features from the dataset. Red edge (700–750 nm) and visible (400–700 nm) wavelengths demonstrated the highest classification contribution. Moreover, the twenty most informative wavelengths for infection discrimination were identified for each model. Conclusion The results confirm the effectiveness of field spectroscopy, making it possible to detect downy mildew symptoms in different stages of infection. This method offers a rapid, cost-effective, and sustainable tool for early disease detection, which can greatly benefit winegrowers by enabling more timely and specific interventions and reducing the reliance on chemical treatments. Implications and Impacts This study demonstrates a novel approach to precision viticulture, offering winegrowers a rapid, cost-effective, and sustainable tool for early disease detection. The methodology not only promotes more disease management strategies but also aligns with environmental and regulatory goals.

Why it matches plant phenotyping methodsブドウ葉の病害症状をフィールド分光と機械学習で直接検出する方法を開発・評価しており、植物病害状態の取得手法が中心である。

abstractThis study aims to evaluate the potential of field spectroscopy combined with machine learning to detect downy mildew in Vitis vinifera cv. Loureiro field conditions.

Quantification of Callose in Plant Tissues by Enzyme-Linked Immunosorbent Assay/ Immunofluorescence Spectrophotometry. →

The existing methods of callose quantification from plant tissues include epifluorescence microscopy, fluorescence spectrophotometry, immunofluorescence microscopy, and indirect assessment of both callose synthase and β-(1,3)-glucanase activities. However, some of these methods have significant limitations, which include being time-consuming, non-specific to callose, labor-intensive, subjective, high autofluorescence, low sensitivity, being more qualitative rather than quantitative, and requiring the acquisition of software resources and technical skills. Therefore, there is a pressing need to explore alternative methods for callose quantification in plant tissues. It was hypothesized that immunofluorescence spectrophotometry or enzyme-linked immunosorbent assay (ELISA) that uses callose-specific antibodies could overcome some of the limitations of the current callose quantification methods. Biotic stress was administered by inoculating tissue culture-derived banana plantlets with Xanthomonas vasicola pv. musacearum (Xvm) bacteria which induced callose production. Banana corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification using the new immunofluorescence spectrophotometry method. Callose production in the corms of Xvm-inoculated and control groups varied significantly in both the banana genotypes (independent sample t-test, p < 0.05). The immunofluorescence spectrophotometry method described here could be applied for the quantification of callose in different plant tissues with high specificity to callose, sensitivity, reliability, and reproducibility. Additionally, the use of a 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases.

Why it matches plant phenotyping methods植物組織中のカロース量という生理状態を定量する新規免疫蛍光分光法・ELISA法の開発と性能評価が研究の中心であり、ハイスループット化や再現性も検討している。

abstractTherefore, there is a pressing need to explore alternative methods for callose quantification in plant tissues.

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.

Identifying the underpinnings of δ 2 H discrepancies between plant stem and soil water: extraction-induced methodological artifacts versus biological fractionation effects →

Abstract. Recent studies have reported widespread presence of hydrogen isotope offset (HIO) between cryogenically-extracted plant stem and soil water, challenging the long-standing assumption that the isotopic composition of stem xylem water reliably represents that of its source water. Despite intensive researches on this topic over the past decade, it remains debated as to whether and/or to what extent HIO originates from extraction-related artifacts or from in situ isotope mixing/fractionation during water transport from soil to plants. Here, we used cryogenic vacuum distillation (CVD) to extract stem and soil water from eight species (trees, shrubs, and grasses) grown under two humidity regimes. We quantified species-specific HIO, tested its associations with ecophysiological and environmental variables, and conducted immersion-based rehydration experiments to assess CVD-induced biases. Across species, HIO ranged from −7.2‰ to 3.2‰: trees were consistently negative, whereas shrubs and grasses were near-zero to slightly positive. Rehydration experiments revealed CVD-induced δ2H biases in stem (−4.5‰) and soil water (−2.5‰). When these extraction-related biases in both stem and soil water were simultaneously corrected, species-level HIO (mean = 0.2‰) was no longer different from zero, and showed no significant correlations with ecophysiological or environmental variables. These results suggest that apparent HIO is largely driven by CVD-induced artifacts rather than ecophysiological/environmental processes that cause isotopic fractionation during water transport along the soil-xylem continuum. We conclude that simultaneously correcting CVD-induced biases in both stem and soil water is critical to avoid spurious HIO signals and to improve isotope-based estimation of plant water sources.

Why it matches plant phenotyping methods植物茎水・土壌水の同位体組成測定におけるCVD抽出バイアスを再水和実験で検証・補正しており、植物の水源推定に関わる測定法の技術的妥当性が中心である。

abstractWe quantified species-specific HIO, tested its associations with ecophysiological and environmental variables, and conducted immersion-based rehydration experiments to assess CVD-induced biases.

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.

Near-Infrared Interaction Spectroscopy Under Daylight Conditions to Assess Total Soluble Solids in On-the-Plant Strawberries →

Robust in-field sensing technologies are essential for advancing precision agriculture and autonomous field robotics toward analysing internal quality attributes of fruits and vegetables. This study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions. A compact NIR interaction instrument (750–1020 nm), designed for robotic operation, was built and tested in a polytunnel environment under varying day- and night-time conditions. The instrument was calibrated using a partial least squares regression (PLSR) model built on laboratory data collected in 2025 from 200 strawberries of a single variety. It was tested on 100 strawberries of two varieties that were measured in 2024, while still attached to the plant. During night-time operation, TSS was predicted with a standard error of prediction ( SEP ) of 0.73 % TSS and a bias of 0.65 % TSS. Under challenging daytime conditions with strong and fluctuating ambient light, measurements were more affected by additional shot noise from the ambient light, resulting in SEP s up to 1.35 % TSS and biases up to 1.45 % TSS, both of which are acceptable for most applications. The measurement time was 12 s. Robust performance was achieved by implementing rapid and continuous ambient light sampling and correction, combined with outlier rejection of spectra of insufficient quality. These findings confirm the feasibility of in-field, on-the-plant NIR spectroscopy for assessing internal fruit quality and provide practical design guidelines to support further in-field implementations of NIR spectroscopy.

Why it matches plant phenotyping methodsイチゴ果実の糖度という植物器官形質を、ロボット搭載可能なNIRセンサーで非接触測定する手法を開発・検証しており、環境光補正や性能評価も中心的に扱っている。

abstractThis study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions.

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

Integrated Fruit Phenotyping and Electronic-Nose Profiling of Five Ilex Taxa from Eastern China for Germplasm Characterization and Utilization. →

Accurate characterization of closely related Ilex taxa is essential for the conservation, documentation, and utilization of plant genetic resources. In this study, five Ilex taxa from eastern China ( Ilex rotunda Thunb., Ilex chinensis , Ilex cornuta Lindl. & Paxt., Ilex cornuta 'Fortunei', and Ilex latifolia Thunb.) were evaluated using an integrated framework combining fruit morphometric traits, CIELAB color parameters, and electronic-nose (E-nose) volatile fingerprints. Fruit transverse diameter, longitudinal diameter, single-fruit weight, fruit shape index, and peel color traits (L*, a*, b*, and chroma, C*) differed significantly among taxa (one-way ANOVA, all p I . cornuta produced the largest and heaviest fruits, I . chinensis showed the most elongated fruit shape, and I . rotunda exhibited the highest redness and chroma values. Chemometric analyses of E-nose responses further improved taxon discrimination and revealed clear divergence in volatile-response patterns. Trait-space relationships were broadly consistent with the preset phylogenetic framework, with I . rotunda showing the greatest divergence and I . cornuta and I . cornuta 'Fortunei' showing the closest similarity. These findings indicate that integrated fruit phenotyping and rapid volatile profiling provide a practical approach for Ilex germplasm identification, comparative evaluation, and resource documentation, with potential value for conservation planning and horticultural utilization.

Why it matches plant phenotyping methods果実形態・色彩形質と電子鼻プロファイリングを統合した再利用可能な分類・遺伝資源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 測定を超える応用と判断する。

abstractevaluated using an integrated framework combining fruit morphometric traits, CIELAB color parameters, and electronic-nose (E-nose) volatile fingerprints.

A Protocol for Standardizing Measurements and Enabling Global Harmonization of Herbarium Leaf Reflectance Spectra →

Reflectance spectroscopy offers a powerful approach to integrate high-throughput phenotypic data from herbarium specimens into the digital landscapes of ecology, evolution, and systematics. Because inconsistencies in instrumentation and measurement practices can increase noise and limit dataset compatibility, the International Herbarium Spectral Digitization Working Group (IHerbSpec) has published the Protocol for Spectral Digitization of Herbarium Specimens, currently in version 1.2.1, as an open resource that will continue to evolve through community use and collaboration (https://iherbspec.github.io/protocol). The protocol defines a stepwise measurement workflow, standardized filename conventions, structured metadata tables with controlled vocabularies, and guidance on tissue selection, materials, and instrumentation quality control to ensure that newly generated spectral datasets are robust and comparable. By embedding standardized practices at the point of data collection, it provides a scalable foundation for data synthesis and new quantitative insights into plant diversity across taxonomic, geographic, and temporal scales.

Why it matches plant phenotyping methodsハーバリウム葉の反射スペクトル取得を標準化する測定プロトコルであり、ワークフロー、メタデータ、組織選択、機器品質管理を中心的に扱うため、植物表現型取得法として適格。

abstractReflectance spectroscopy offers a powerful approach to integrate high-throughput phenotypic data from herbarium specimens

Airborne ion charge kinetics reveal circadian-range periodicity in detached plant fruits: a continuous, noncontact, and noninvasive measurement system →

Abstract Background Understanding temporal regulation in plant systems requires measurement approaches that capture physiological kinetics with minimal tissue perturbation. Here, we present a magnetically levitated electrode ionization chamber (MALIC) system for monitoring airborne ion charge kinetics in a continuous, noncontact, and noninvasive manner, enabling the continuous physicochemical observation of plant-derived signals without genetic modification or optical readouts. Results Using detached fruits of two apple cultivars (Yoko and Akibae), Lomb-Scargle periodogram analysis identified dominant periodic components within the circadian range (~ 24–25 h) in airborne ion charge. Phase drift analysis revealed cultivar-dependent differences in temporal stability, with Yoko exhibiting relatively consistent peak timing across successive cycles, whereas Akibae showed greater variability. These differences were further supported by phase coherence analysis, which demonstrated the tighter clustering of phase values in Yoko compared with Akibae. Quantitative analysis showed that Akibae exhibited larger amplitude and a higher coefficient of variation, indicating greater relative variability. Because the MALIC system detects net ion-related signals in the surrounding air rather than intracellular processes directly, the observed oscillations should be interpreted as a proxy for integrated physiological activity. These rhythms are consistent with temporally organized physiological processes but do not establish a direct link to endogenous circadian clock mechanisms. Conclusions The MALIC system enables the continuous, noncontact, and noninvasive measurement of airborne ion charge kinetics exhibiting reproducible circadian-range periodicity in detached plant tissues. This work establishes airborne ion charge as a previously unrecognized temporal signal at the plant–environment interface. Rather than replacing established circadian assays, the MALIC system should be considered a complementary approach that captures signals distinct from transcriptional and photosynthetic readouts. Further validation across species, cultivars, and environmental conditions, together with integrated environmental, molecular, and physiological measurements, will be essential to determine whether airborne ion charge kinetics can serve as reliable indicators of endogenous biological rhythms in plants.

Why it matches plant phenotyping methodsMALICシステムによる植物由来の空中イオン荷電動態を、非接触・連続的に測定する手法の提示と、リンゴ果実での技術的適用・解析が中心である。

abstractHere, we present a magnetically levitated electrode ionization chamber (MALIC) system for monitoring airborne ion charge kinetics in a continuous, noncontact, and noninvasive manner

In-bore climate control chamber for magnetic resonance imaging of living plants →

Abstract Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging. In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions. The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B0 homogeneity, RF attenuation as well as potential RF artefacts) were verified. Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.

Why it matches plant phenotyping methods植物のMRI計測を可能にする環境制御・MR互換チャンバーを開発し、性能および植物の解剖・通道機能計測で検証しており、フェノタイピング手法と基盤が研究の中心である。

abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.

Predicting leaf traits in wine grapes with reflectance spectroscopy. →

Estimating crop trait data is critical for predicting crop responses to environmental change, enabling more informed diagnoses of crop performance and the development of on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping-specifically reflectance spectroscopy-has emerged as a key element of plant trait research, enabling rapid estimation of plant traits. However, little is known about whether reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits, especially variation that exists among different cultivars or genotypes of the same crop. Using wine grapes (V. vinifera subsp. vinifera) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy to quantify intraspecific variation in 12 leaf traits across 12 different cultivars from seven different varieties. We find significant variability in traits across and within cultivars, especially in gas-exchange and hydraulic traits, with cultivars varying along a resource-conservative-to-resource-acquisitive trait axis. Models based on spectral reflectance data were able to differentiate and predict this fine-scale trait variation among cultivars for seven plant traits, with a predictive power range of R2 = 0.12-0.57. Models predicting leaf chemical (i.e., carbon and nitrogen concentrations), physiological (i.e., maximum rate of light-saturated photosynthesis), and morphological traits (i.e., leaf dry matter content) were more accurate in their predictions, while models predicting leaf water status were less accurate. Our results indicate that reflectance spectroscopy can capture certain dimensions of the fine-scale trait variation that exists within genetically diverse agroecosystems, though spectroscopic estimates of intraspecific variation in leaf water status are less accurate.

Why it matches plant phenotyping methods反射分光法を用いてブドウ葉の複数形質を推定し、品種内変異に対する予測性能を評価しており、植物表現型取得・推定手法が研究の中心である。

abstracthigh-throughput phenotyping-specifically reflectance spectroscopy-has emerged as a key element of plant trait research, enabling rapid estimation of plant traits.

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.

Monitoring plant moisture content and optimizing irrigation prescriptions based on UAV multimodal data →

Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractCrop Height was extracted from oblique photogrammetry point cloud data.

Image-based phenotyping of faba bean genetic resources for water deficit responses under controlled conditions →

Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.

Why it matches plant phenotyping methods多センサー表現型プラットフォームを用いた画像由来バイオマスの抽出と生物量との検証が研究の中心であり、表現型取得・検証に該当する。

abstractIn this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions.

Plant and substrate-based indices for salinity monitoring in Cestrum nocturnum →

This study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops, using Cestrum nocturnum as a model species. A greenhouse experiment was conducted with plants grown in containers and irrigated with nutrient solutions at three electrical conductivity (EC) levels (2.0, 4.5, and 7.0 dS m - ¹). Plant responses were assessed through vegetative growth, visual quality, flowering intensity, continuous stem diameter variation (maximum daily stem shrinkage, MDS), cumulative evapotranspiration (ETa), and substrate bulk EC monitored with sensors. Increasing salinity reduced vegetative growth, particularly shoot biomass, while enhancing flowering intensity at 4.5 dS m - ¹, indicating a shift from vegetative to reproductive development. The moving average of MDS (avgMDS) responded to salinity, showing both increases and decreases depending on stress intensity, and, when expressed as signal intensity (SI: control/salinity), discriminated between stress levels, establishing alert (1.10) and critical (1.38) thresholds. Salinity decreased ETa by 35% and 65% at 4.5 and 7.0 dS m - ¹, respectively, and ETa-based SI defined alert (1.20) and critical (1.55) thresholds. The hourly moving average of bulk EC (avgECb) enabled continuous assessment of salinity dynamics, minimizing the influence of substrate moisture variability. The use of avgMDS, ETa, and avgECb enables the detection and interpretation of salinity stress by integrating plant physiological responses with substrate conditions, while the combined use of two or more indices improves the robustness of the assessment, providing a quantitative framework for salinity management in potted crops.

Why it matches plant phenotyping methods植物の生理応答とセンサー計測から塩ストレスを定量検出する指標を開発し、警戒・臨界閾値を設定して技術的に評価しているため、単なる生育測定ではない。

abstractThis study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops

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

Assessing the Impact of Soil Hydrocarbon Properties on Plant Functional Types Using Hyperspectral Data in the Niger Delta →

The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L−1), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI844,447; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L−1), based on Spearman correlation coefficients (rs = 0.657 and rs = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta.

Why it matches plant phenotyping methods葉面ハイパースペクトルデータとレッドエッジ指標により植物のクロロフィル量・生理的ストレスを定量化する手法を中心的に適用しており、植物状態の測定方法として実質的です。

abstractfield-measured, leaf-scale hyperspectral data acquired across the region

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.

AI Camera Sensor-Based Detection of Crop Water Stress and Pesticide Requirement →

Artificial intelligence (AI)-enabled camera sensor systems are increasingly transforming precision agriculture by providing non-destructive, rapid, and scalable methods for monitoring crop health. Two of the most critical applications are the detection of crop water stress and the assessment of pesticide requirement through pest, disease, and symptom recognition. This literature review synthesizes published work on RGB, thermal, multispectral, and hyperspectral imaging integrated with machine learning and deep learning methods for agricultural decision support. The reviewed studies show that thermal and hyperspectral imaging are particularly effective for water stress detection, whereas RGB and multispectral systems are highly practical for identifying disease symptoms, pest infestation, and spray targets. The literature further indicates a shift from simple classification toward real-time decision support, multimodal fusion, explainable AI, and precision input application. This review discusses core sensing technologies, major algorithmic approaches, research findings from key studies, present limitations, and future research directions. Overall, AI camera sensor systems offer substantial potential for reducing water wastage, minimizing excessive pesticide use, and improving sustainable agricultural productivity.

Why it matches plant phenotyping methods作物の水ストレスや病害症状を画像・センサーから推定する手法を中心に整理したレビューであり、植物状態の取得・推定方法が中核です。

abstractThis literature review synthesizes published work on RGB, thermal, multispectral, and hyperspectral imaging integrated with machine learning and deep learning methods for agricultural decision support.

Proactive Irrigation Timing Decision-Making for Greenhouse Tomatoes via STL-LSTM Deep Learning and Plant–Soil Dual-Threshold Sensing →

Traditional irrigation management for tomatoes in solar greenhouses relies heavily on empirical manual experience and single soil moisture indicators, often leading to irrigation scheduling that lacks crop-specific physiological evidence and results in suboptimal water-use efficiency. To address these challenges, this study developed an intelligent, plant-centric irrigation decision-making framework for greenhouse tomatoes in the arid region of Xinjiang. Central to this framework is the precise identification of irrigation timing—the most critical first step and a fundamental prerequisite for achieving true on-demand irrigation. By monitoring the high-frequency dynamics of stem diameter (SD) and integrating soil moisture data, the physiological responsiveness of tomatoes to water stress was systematically analyzed. A hybrid predictive model, STL-LSTM, was constructed by coupling Seasonal-Trend decomposition using Loess (STL) with Long Short-Term Memory (LSTM) networks to forecast 24-h SD trends. Furthermore, an innovative dual-threshold irrigation mechanism was established, utilizing a physiological trigger (Maximum Daily Shrinkage, MDS > 70 μm) and a soil moisture constraint (Volumetric Water Content, VWC ≤ 17%). Results demonstrated that tomato SD exhibited distinct diurnal rhythms, with MDS and Daily Increment (DI) identified as highly sensitive indicators of plant water status. The proposed STL-LSTM model achieved superior predictive performance during the peak fruiting stage, with a coefficient of determination (R2) of 0.9184, representing an improvement of 14.8% and 27.56% over standalone LSTM and ARIMA models, respectively. The validation of the dual-threshold mechanism confirms its ability to balance real-time crop water demand with conservation requirements, effectively mitigating the risks of premature or delayed irrigation inherent in traditional methods. This research provides scientific rationale and technical support for the transition of greenhouse agriculture in arid regions towards precision irrigation and optimised water resource management.

Why it matches plant phenotyping methodsトマト茎径を植物の水分状態指標として高頻度センシングし、STL-LSTMによる予測と二重閾値の検証を行うことが中心であり、単なる灌漑実験ではない。

abstractCentral to this framework is the precise identification of irrigation timing—the most critical first step and a fundamental prerequisite for achieving true on-demand irrigation.

Integration of NIRS and GWAS identifies GhMYB86 as a potential regulator of cottonseed protein content with pleiotropic effects on fiber strength →

Abstract Cottonseed is rich in protein and oil, making the improvement of its nutritional quality essential for global food security. In this study, high-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively. Using these models, 249 upland cotton accessions were phenotyped across five environments and subjected to a genome-wide association study (GWAS) based on a 10K liquid-phase SNP array, resulting in the identification of 24 significant loci. A novel stable QTL, qPO-A07-1 , was detected, within which GhMYB86 was prioritized as a candidate gene. This gene exhibited higher expression in high protein varieties during ovule development. Functional validation demonstrated that heterologous overexpression in Arabidopsis thaliana increased seed protein content by 2.61% – 3.34%, whereas expression in Saccharomyces cerevisiae reduced triglyceride content by 30.72% relative to the control. These results demonstrate that GhMYB86 positively regulates protein accumulation while negatively affecting oil content. A kompetitive allele-specific PCR (KASP) marker targeting a promoter A/T polymorphism revealed that the AA allele was associated with higher protein content, lower oil content, and increased fiber strength across both mapping and validation populations. Furthermore, the protein- and fiber strength-favorable allele has undergone positive selection during breeding. This study provides robust phenotyping tools, reliable genetic resources and molecular markers for cottonseed nutritional quality breeding, laying a foundation for the synergistic improvement of both fiber quality and nutritional quality in cotton.

Why it matches plant phenotyping methodsNIRSによる綿実タンパク質・油分の非破壊推定モデルを開発・検証し、多数系統の表現型取得に用いており、植物形質の取得法が実質的な中心要素である。

abstracthigh-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively.

Wearable Plant Electronics Enables Early Detection of Salt Stress by Tracking K + /Na + Homeostasis and Salicylic Acid Accumulation. →

Soil salinization threatens global food security by disrupting plant ion homeostasis and triggering complex hormonal signaling cascades. Early detection of salt stress and real-time tracking of stress-responsive physiological dynamics remain major technical bottlenecks, as current methods rely on destructive sampling or single-parameter sensing that obscure the crosstalk between ionic and hormonal networks. Here, we report a wearable plant electronic system that enables noninvasive, in situ monitoring of K + /Na + homeostasis and salicylic acid (SA) accumulation, two key indicators of plant salt stress adaptation. The integrated platform combines flexible laser-induced graphene (LIG) ion-selective electrodes enhanced with a SnS 2 -MoS 2 heterostructure for stable potentiometric sensing, a reverse iontophoresis module for noninvasive analyte extraction, and a biocompatible flexible zinc-air battery for long-term power supply. Deployed on tobacco plants, the wearable device detected salt stress within 24 h by capturing the initial disruption of the K + /Na + ratio and the subsequent accumulation of SA, far preceding visible symptoms. The temporally resolved data revealed a dynamic correlation where SA accumulation coincided with a partial recovery of the K + /Na + ratio, suggesting an active role of SA in modulating ion homeostasis. This wearable plant electronic system transcends the limitations of conventional single-parameter detection, providing a powerful tool for early stress diagnosis, deciphering plant adaptive mechanisms, and advancing precision agriculture to mitigate the impact of soil salinization.

Why it matches plant phenotyping methods植物の塩ストレス状態を非破壊・リアルタイムに測定するウェアラブルセンサー基盤の開発が研究の中心であり、K+/Na+恒常性とサリチル酸蓄積という生理表現型を直接取得している。

abstractHere, we report a wearable plant electronic system that enables noninvasive, in situ monitoring of K + /Na + homeostasis and salicylic acid (SA) accumulation, two key indicators of plant salt stress adaptation.

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

Phase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen →

Soil-borne vascular pathogens pose serious threats to agriculture with complex invasion strategies that remain poorly characterized compared to foliar pathogens. While foliar pathogens like Magnaporthe oryzae employ specialized appressoria to penetrate plant surfaces through a combination of mechanical force and enzymatic degradation, the invasion mechanisms of vascular pathogens that lack classical appressoria have remained largely theoretical. The nanoscale processes governing root penetration and colonization by these pathogens are particularly challenging to visualize due to technical limitations of conventional microscopy. Here we show, using phase-contrast X-ray computed microtomography and advanced microscopy, that Fusarium oxysporum (Fo) employs distinct mitogen-activated protein kinase (MAPK) cascades to orchestrate root invasion through unprecedented morphological plasticity. We identify previously undocumented appressoria-like structures that facilitate physical penetration, while demonstrating that Fo exhibits remarkable cellular adaptability, reducing hyphal diameter by more than 20-fold (from 5 μm to 220 nm) to navigate confined plant spaces, a dramatic morphological transition previously thought impossible. By using cellulase-deficient mutants, we demonstrate that cellulolytic activity is dispensable for surface breach and submicrometric hyphal colonization, establishing that mechanical force generation rather than enzymatic degradation is the primary determinant of successful host penetration. Three-dimensional reconstruction reveals a quantitative correlation between fungal proliferation and progressive embolism formation, with distinct MAPK pathways differentially regulating penetration force generation (Fmk1), osmotic adaptation during apoplastic colonization (Hog1), and directional growth toward vascular tissues (Mpk1). These findings provide a mechanistic framework for vascular wilt pathogenesis and reveal potential targets for controlling these economically devastating plant diseases.

Why it matches plant phenotyping methods位相コントラストX線マイクロトモグラフィーと3次元再構成を中核に、根の侵入・菌糸形態・塞栓形成という植物の病態と形態を定量化しており、病理学的機構研究であるものの表現型取得法の適用が実質的です。

titlePhase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen

Real‐time monitoring of root dielectric properties for assessing crop plant damage caused by foliar application of glyphosate →

Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.

Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。

abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying

Continuous monitoring of plant water potential: sensor‐based approaches and best practices →

Summary Plant water potential is a central integrator of plant water status, linking hydraulic function with physiological performance and ecosystem water dynamics across species and systems. This review is motivated by the need to capture these dynamics under rapidly changing environmental conditions, which are often missed by discrete measurements. We evaluate the main approaches for continuous monitoring of plant water potential, including direct in situ sensors, indirect methods based on plant water content, and remote‐sensing proxies. We discuss the principles, measurement mechanisms, practical constraints, and environmental sensitivities of each approach. Relative to traditional methods, such as pressure chambers, continuous measurements offer major advantages by resolving rapid variation in water status and strengthening inference on plant–soil–atmosphere interactions. These approaches are especially valuable under dynamic field conditions, where temporal variability in vapor pressure deficit, soil moisture, temperature, and radiation strongly shapes hydraulic behavior. We conclude that continuous monitoring has substantial potential to advance plant and ecosystem science, but wider application will depend on careful interpretation and greater harmonization across comparable methodologies. By synthesizing core principles, methodological challenges and best practices, this review provides a practical framework for researchers and practitioners applying continuous water potential measurements.

Why it matches plant phenotyping methods植物の水ポテンシャルという生理形質を連続測定するセンサー手法を比較・整理し、測定原理、制約、標準化、実践指針を扱う方法論レビューであり、フェノタイピング手法が中心である。

abstractWe evaluate the main approaches for continuous monitoring of plant water potential, including direct in situ sensors, indirect methods based on plant water content, and remote‐sensing proxies.

Quantitative Assessment of Heat Shock-Induced Ferroptosis-Like Cell Death via Electrolyte Leakage in Arabidopsis thaliana Seedlings. →

We present a protocol to allow continuous assessment of cell death in Arabidopsis thaliana (L.) seedlings by measuring the release of electrolytes from dying cells upon heat shock. The electrolyte leakage assay is a well-established method to quantify the extent of cell death of plant tissues exposed to pathogen infection, since the activation of the immune response leads to compromised membrane integrity and to the release of ions from the dying cell. This prolonged release of electrolytes is considered a hallmark of regulated cell death in plants. Heat shock in plants induces ferroptosis-like cell death, which can be suppressed either pharmacologically, using inhibitors such as ferrostatin, or genetically through knockout of ferroptosis-related genes. Here, we have adapted the electrolyte leakage assay to quantify cell death in young Arabidopsis seedlings exposed to a heat shock previously shown to induce ferroptosis-like cell death. We also illustrate how this method can be used to assess activation of ferroptosis-like cell death in whole Arabidopsis seedlings using ferrostatin or knockout mutants of potential gene candidates involved in ferroptosis-like cell death. Key features • This protocol does not require any technical experience apart from gentle handling of young seedlings and is less labor-intensive than microscopy-based cell death evaluation. • Builds upon existing methods to quantify the extent of cell death upon immune response in whole seedlings subjected to heat stress. • Only requires a conductivity meter and allows the assessment of continuous cell death using multiple parallel replicates. • The protocol demonstrates how heat shock-induced ferroptosis-like cell death can be inhibited pharmacologically or genetically in whole seedlings, supported with quantitative data.

Why it matches plant phenotyping methodsArabidopsis幼植物の細胞死という植物状態を、電解質漏出で連続定量する測定プロトコルの適応・技術的提示が中心であり、ルーチン測定ではない。

abstractWe present a protocol to allow continuous assessment of cell death in Arabidopsis thaliana (L.) seedlings by measuring the release of electrolytes from dying cells upon heat shock.

Monitoring Carbon Stock Change at the Individual-Plant Scale: A Methodological Review and Integrative Framework →

With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO2 exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems.

Why it matches plant phenotyping methods個体植物スケールの炭素蓄積変化を推定するための測定・センシング・統合手法を体系的に比較し、セグメンテーションや不確実性伝播を含む統合枠組みを提案する方法論レビューであり、植物状態の取得・推定が中心である。

abstractmethodologies for repeated monitoring at this scale remain fragmented

Mechanosensory Coupling Between Human Cardiac Activity and Plant Bioelectric Potentials: A Naturalistic Self-Study with Spectral Validation →

We report a single-subject proof-of-concept naturalistic longitudinal within-office study (n ≈ 93,000 one-Hz observations from a single participant across seven daytime sessions; n here refers to time-series samples, not biological replicates) testing whether human emotional states couple to the bioelectric potentials of a co-located Kalanchoe dai-gremontiana plant through measurable physical intermediaries. Using a custom IoT platform comprising two complementary AD8232 bioelectric plant sensors (Plant1: RC lowpass, 5-second averages; Plant2: 0.1–20 Hz bandpass at 1 Hz), a Sensirion SCD41 CO₂/temperature/humidity sensor, an SGP40 volatile organic compound (VOC) sensor, a Polar H10 heart rate monitor, and HSEmotion facial expression recognition, we recorded simultaneous human emotion (valence, arousal), physiology (heart rate HR, heart rate variability RMSSD), and environmental chemistry (CO₂, VOC index) at 1 Hz alongside plant bioelectric voltage. Lagged mediation analysis confirmed partial mediation of the valence→plant relationship through heart rate (Δa = 8 s for valence→HR, r = +0.067, p < 0.001; 30% partial mediation with Plant1). Critically, a novel spectral mediation analy-sis—using short-time Fourier transform (STFT) band powers of Plant2 as outcome vari-ables—reveals that the valence→HR coupling (Δa = 8 s) is invariant across all eight plant frequency bands, while the HR→plant coupling (path b) is frequency-specific: full sta-tistical mediation occurs only in the 44–75 second oscillation band (Δb = 34 s), with partial mediation across five other bands. This frequency specificity rules out broadband me-chanical coupling and points toward a frequency-selective biological transduction mechanism analogous to Venus flytrap mechanosensory integration. HRV-derived emotion (RMSSD-based valence, independent of the camera system) provides convergent validation. VOC index is the strongest cross-session predictor of emotional state in random forest models.

Why it matches plant phenotyping methods植物のバイオ電位をセンサーで取得し、STFTによる周波数解析とスペクトル媒介分析で評価する技術的手法が研究の中心であるため、植物の生理状態を対象とするフェノタイピング手法として含める。

abstractUsing a custom IoT platform comprising two complementary AD8232 bioelectric plant sensors

Intelligent Bioelectrical Sensing and Deep Learning Framework for Non-Invasive Monitoring of Plant Alkaline Stress. →

Alkaline stress disrupts ion balance and physiological homeostasis in plants, yet its timely assessment remains challenging because conventional phenotyping methods are often destructive, discontinuous, or delayed relative to the onset of stress symptoms. In this study, we developed a non-invasive plant electrophysiological sensing framework for the identification of alkaline stress in Clivia. Thin-film patch electrodes were used to record bioelectrical signals under five alkaline gradients (pH 7.0, 7.5, 8.0, 8.5, and 9.0) in a controlled environment. The acquired signals were subjected to wavelet denoising and normalization, and were then analyzed using a dedicated deep learning model, the Spatial Channel Alkaline Stress Network (SCANet). To provide a more rigorous evaluation of generalization, model performance was assessed using plant-wise five-fold cross-validation. Under this protocol, SCANet achieved 97.51% ± 0.77% accuracy, 97.55% ± 0.75% precision, 97.51% ± 0.77% recall, and 97.52% ± 0.77% F 1 -score, outperforming representative convolutional and transformer-based baselines. Ablation experiments further showed that both the spatial reconstruction module and the channel reconstruction module contributed to performance improvement, and that a 30 s input window provided the best balance between signal completeness and discrimination. These results indicate that plant electrophysiological signals can support accurate, non-destructive identification of alkaline stress levels under controlled conditions, and that the proposed sensing-analysis framework may be useful for stress phenotyping and intelligent monitoring of plant status.

Why it matches plant phenotyping methods植物のアルカリストレス状態を対象に、非侵襲的な電気生理センシングと深層学習による表現型推定手法を開発・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。

abstractwe developed a non-invasive plant electrophysiological sensing framework for the identification of alkaline stress in Clivia.

Technological advances in imaging and modelling of leaf structural traits: a review of heat stress in wheat →

Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic carbon assimilation and crop yield. Despite the advancement in various imaging technologies at the field, canopy, plant, tissue, cellular, and subcellular levels, phenotyping of imaging-based leaf structural traits (e.g. vein density, stomatal density, and stomatal aperture) for abiotic stresses is still time-consuming and expensive without the aid of artificial intelligence (AI) and machine learning (ML). This review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits. Recent high-resolution, non-destructive imaging technologies, including confocal laser scanning microscopy, X-ray computed tomography, and optical coherence tomography, have enabled in vivo visualization of plants. Integrating these imaging techniques with AI/ML facilitates high-throughput phenotyping and the modelling of stress responses. We emphasize the potential for future research to leverage these technological advancements in imaging and AI, combining imaging data with physiological and multi-omics studies to deepen the understanding of plant heat tolerance mechanisms. Such multidisciplinary integration in leaf structure phenotyping will accelerate the development of resilient wheat varieties, offering critical insights for crop improvement in the face of climate change.

Why it matches plant phenotyping methods植物の葉構造・機能形質を対象とする画像計測技術とAI/MLによる表現型解析を中心に整理したレビューであり、植物フェノタイピング手法レビューに該当する。

abstractThis review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits.

Saving water for more crop per drop: high-throughput phenotyping reveals plastic regulation of transpiration in wheat under natural evaporative demand →

Improving transpiration efficiency (TE) offers a mechanistic pathway to enhance yield under drought by modulating the balance between carbon assimilation and water loss. This study quantified genotypic variation in TE and dissected the physiological processes underlying this variation across a diverse panel of wheat genotypes. Following an initial experiment with six cultivars, 105 genetically diverse lines were evaluated under well-watered conditions across naturally fluctuating vapour pressure deficit (VPD). Transpiration was measured at 10-minute intervals using a high-throughput lysimeter platform and normalised daily at low VPD to minimise confounding effects arising from genotypic differences in canopy size. Variation in TE was strongly associated with reduced normalised transpiration rate at high VPD (TR norm-highVPD ). No relationship was detected with maximum photosynthetic capacity, indicating that TE differences were driven primarily by regulation of water loss rather than carbon gain. High-TE genotypes achieved either greater biomass for a given water use or equivalent biomass with reduced water use, consistent with conservative stomatal regulation under high evaporative demand. A complementary experiment conducted under low VPD revealed limited genotypic variation in intrinsic TE, suggesting that genetic control of TE is predominantly expressed under high atmospheric demand. The consistency of TE–TR norm-highVPD relationships across experiments highlights TR norm-highVPD as a robust and physiologically meaningful phenotyping target. Several high-TE genotypes outperformed modern cultivars, offering novel sources of allelic variation for transpiration regulation. Collectively, these results define a mechanistically grounded, scalable phenotyping framework to target VPD-responsive water-use traits and support breeding strategies aimed at improving drought resilience and water productivity.

Why it matches plant phenotyping methods高スループットライシメータで蒸散を定量し、VPD応答性の水利用形質を検証・評価するスケーラブルな表現型解析枠組みが研究の中心である。

abstractTranspiration was measured at 10-minute intervals using a high-throughput lysimeter platform

Integrating Satellite Remote Sensing and Artificial Intelligence for the Presymptomatic Detection of Crop Diseases and Nutrient Deficiencies: A Comprehensive Narrative Review →

ABSTRACT Global food security is threatened by crop diseases and nutrient deficiencies. Traditional detection methods—visual scouting, molecular diagnostics and soil testing—are reactive and only identify problems once visible symptoms appear, which often misses intervention windows. This narrative review synthesizes 173 peer‐reviewed articles (from 2012 to 2025) to critically evaluate the synergistic potential of artificial intelligence (AI) and multisensor satellite remote sensing (RS) for presymptomatic detection. We propose a four‐principal framework: (1) sensor choice must align with pathogen infection strategy; (2) detection becomes actionable when spectral deviation exceeds twice baseline noise; (3) spectral time series can estimate epidemiological parameters (e.g., latent period, Area Under the Disease Progress Curve); and (4) explainable AI (XAI) converts black‐box predictions into interpretable diagnostics. Key findings uncovered were that multispectral sensors detect biotrophic pathogens 5–10 days pre‐symptomatically via red‐edge sensitivity; hyperspectral platforms offer 7–14 days warning and that thermal sensors detect vascular wilts 1–7 days earlier. Key challenges remain, including trade‐offs between resolution and revisit frequency, atmospheric interference causing 60%–80% optical data loss in tropical regions, spectral confusion between biotic and abiotic stresses, and limited scalability for smallholder farms (

Why it matches plant phenotyping methods衛星リモートセンシングとAIによる作物病害・栄養欠乏の早期検出手法を体系的に評価するレビューであり、植物の病害状態を推定するセンシング手法が中心です。

abstractThis narrative review synthesizes 173 peer‐reviewed articles (from 2012 to 2025) to critically evaluate the synergistic potential of artificial intelligence (AI) and multisensor satellite remote sensing (RS) for presymptomatic detection.

Monitoring Water Stress in Grapevine ( Vitis vinifera L.) Using Proximal Hyperspectral Imaging. →

This study addresses the early detection of water stress in grapevines ( Vitis vinifera L. cv. Monastrell), a key challenge for precision irrigation. The main objective is to assess the feasibility of VIS-NIR hyperspectral imaging (400-1000 nm) to anticipate water stress, relating the spectral signal to stem water potential. This study was developed over two campaigns, in 2024 and 2025, using 18 potted plants. In 2024, eight vines were irrigated, and the remaining 10 were subjected to water-deprivation treatments, whilst in 2025, all plants were irrigated, but half at a control dose and the rest at a reduced dose equivalent to 33% of the control. Images were acquired over five dates in June 2024 and over seven in June 2025 using a Specim IQ camera; stem potential was also measured to provide a physiological reference. Individual time series were developed, calculating the Mahalanoubis distance in a PCA space. Results revealed a change window between 10 and 13 June, consistent with the divergence in water potential from 17 to 24 June. PCA highlighted spectral regions related to changes in pigments, nitrogen and water content as main indicators of water stress. We conclude that HSI is a promising tool for early water stress detection.

Why it matches plant phenotyping methodsブドウの水ストレス状態を近接ハイパースペクトル画像から推定・早期検出する方法が研究の中心であり、生理学的基準との比較も行っている。

abstractThe main objective is to assess the feasibility of VIS-NIR hyperspectral imaging (400-1000 nm) to anticipate water stress, relating the spectral signal to stem water potential.

Evaluation of SAR C-band radar vegetation indices for rice crop monitoring in Tamil Nadu, India →

Rice (Oryza sativa) cultivation plays a critical role in food security across Asia, where smallholder farmers depend heavily on timely information about crop development and field conditions. Monitoring these changes using optical remote sensing is constrained by persistent cloud cover during monsoon-driven growing seasons, underscoring the necessity of Synthetic Aperture Radar (SAR) for continuous observation. This study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January). Field-scale analysis of VV, VH, and NDVI time series for 2023–2024 captured key phenological transitions, with polarization showing a strong correlation with NDVI (Farm 1: r = 0.75; Farm 2: r = 0.73). Radar Vegetation Indices (RVI, mRVI, and RVI4S1) were computed from multi-year Sentinel-1 data (2018–2023) and compared with MODIS NDVI. Although the radar indices showed high inter-correlation (r > 0.90), their relationship with NDVI remained weak (0.15–0.30). Machine learning experiments over a 1.5 × 1.5 km region (2018–2022) demonstrated that a Linear Regression model (6.92 × 10−5) outperformed Random Forest Regression (0.000258) in predicting RVI4S1 from VV and VH, indicating linear relationship between radar channels and the index. The study highlights the suitability of Sentinel-1 SAR-particularly VH polarization – for phenology tracking in smallholder contexts, especially where optical data are limited by cloud cover.

Why it matches plant phenotyping methodsSARセンシングとレーダー植生指数を用いてイネの生育・フェノロジーを推定し、光学指標との比較および機械学習による技術評価を行っており、表現型取得法が中心である。

abstractThis study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January).

Quantifying the dynamic recovery of plants through stress memory and physiological attractors. →

Climate change-induced weather variability poses a growing threat to global food security, yet plant resilience is still interpreted through static and reductionist models that treat stress as independent and transient. Here, we introduce a unified quantitative framework grounded in dynamical systems theory. We formalize three novel metrics: (1) the Phenological Weather Memory Index (PWMI), that quantifies exponentially decaying stress memory across developmental stages (with a decay constant α = 0.10 determined by cross‑validation); (2) the Treatment-Weather Resonance Coefficient (TWRC), which measures the alignment of agronomic interventions with favorable weather conditions; and (3) the Physiological State-Space Trajectory (PSST), which maps multi-trait plant physiology into low dimensional attractor basins. Analyzing 288 tomato plants across 24 cultivars under hot, sub-tropical conditions (mean VPD: 2.53 kPa, 65 heat days > 35 °C), we discovered that stress memory is strongly phase-dependent, remaining minimal during vegetative growth (PWMI = 0.009) but increasing sharply during reproductive phase (PWMI = 0.574). Despite the prolonged thermal stress, 97.9% of plants converged into a stable high-yield attractor basin, revealing a fundamental nonlinearity in plant performance. This convergence was driven by dynamic recovery, defined as the capacity of certain cultivars to rapidly forget the stress memory while maintaining internal physiological flexibility. Cultivars such as 'Pony Express', combined low PWMI with effective treatment-weather synchronization, enabling stable productivity under extreme conditions. Together, these results demonstrate that resilience is not a static trait of endurance, but an emergent property arising from temporal synchronization, rapid stress recovery and stable physiological organization. By quantifying stress "forgetting curves" and attractor dynamics, this framework provides a predictive, systems-based foundation for breeding and management strategies that prioritize dynamic recovery over stress tolerance alone.

Why it matches plant phenotyping methods植物のストレス記憶・回復・生理状態を定量化する新規指標と状態空間フレームワークを中心に提示しており、単なる生理測定ではなく表現型抽出・解析手法の開発に該当する。

abstractHere, we introduce a unified quantitative framework grounded in dynamical systems theory.

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.

Field Phenotyping of Triticale Overwintering Dynamics Under Varied Sowing Practices Using Spectral Indices →

This study aims to enhance the early warning and monitoring of frost damage in triticale (×Triticosecale Wittmack), as well as to identify frost-tolerant materials. To this end, this work focused on phenotyping the dynamics of triticale under different damage intensities using spectral indices. Sixteen triticale genotypes were planted under three sowing date (SD) treatments, with three sowing rate (SR) gradients set for each SD. The multispectral data of triticale under six frost damage intensities were acquired using an unmanned aerial vehicle (UAV) platform. A total of eight spectral indices (SIs) were extracted from samples under each intensity. In general, for each combination of SD and SR, all SIs decreased monotonically with increasing damage intensity. These indices are therefore suitable for monitoring frost damage in triticale under complex sowing scenarios. Under early frost damage, the relative decline rates (RDRs) of the SRI (Simple Ratio Vegetation Index), EVI2 (Enhanced Vegetation Index 2), NIRv (Near-Infrared Reflectance of Vegetation), and GLI (Green Leaf Index) were higher than those of other indices, indicating that they are more sensitive to early frost damage and thus more suitable for frost warning. Under frost stress, the RDRs of the indices were higher in early-sown samples than in late-sown samples. SD plays a more significant role than SR in determining the response of triticale indices to frost damage. Models were developed to detect triticale under varying damage intensities with SIs and classification algorithms—XGBoost, Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM). The SVM classifier demonstrated the best generalization performance (overall accuracy: 98.03%; F1-score: 0.98). The detection contributions of indices within the optimal model were evaluated by their respective SHAP (Shapley Additive Explanations) values. The GLI, NIRv, NDVI (Normalized Difference Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index) were identified as key indices, as they exhibit higher cumulative SHAP values. Identification models for triticale with different frost tolerance levels were established based on the time-series data of these key indices and the above four algorithms. The optimal model based on the SVM algorithm achieved an identification accuracy exceeding 90%. The average overwintering dynamics and frost damage responses of the key indices were analyzed for triticale with different frost tolerance levels under all treatments. Under frost stress, these indices and their RDRs in frost-tolerant triticale were generally higher and lower, respectively, than those in frost-sensitive triticale. These four key indices can thus assist in the identification of frost tolerance in triticale. This study aids in the early warning and monitoring of frost damage in triticale under complex planting scenarios and the evaluation of overwintering performance in triticale germplasm.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とスペクトル指標を用いて、コムギライムギの凍害強度・耐凍性を推定するモデルを開発・評価しており、植物表現型取得と解析手法が研究の中心である。

abstractThe multispectral data of triticale under six frost damage intensities were acquired using an unmanned aerial vehicle (UAV) platform.

Extraction of Plant Physiological Features Using Multispectral Imaging and Spectrophotometry: A Systematic Review Highlighting Research Gaps for Stenocereus spp. →

Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species.

Why it matches plant phenotyping methods植物生理形質推定のためのマルチスペクトル画像・分光法を対象とした系統的レビューであり、方法の比較、品質評価、検証標準化を中心に扱っている。

abstractThis systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework).

Dynamics and Crosstalks of H 2 S and H 2 O 2 Signaling in Plant Abiotic Stress Response Deciphered by a Disposable SERS Sensing Patch. →

Abiotic stresses caused by climate change pose a serious threat to global crop productivity, making the early detection of plant stress responses crucial. Hydrogen sulfide (H 2 S) and hydrogen peroxide (H 2 O 2 ), as key signaling molecules, their dynamic synergistic effects are central to understanding the mechanisms of plant stress adaptation. However, real-time tracking of the dynamic changes of these molecules remains challenging. This study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs), which cleverly combines the high sensitivity of surface-enhanced Raman scattering (SERS) with the gas enrichment capacity of the MOF, incorporates 2D plasmonic membrane assembly technology and 4-mercaptophenylboronic acid (4-MPBA) conjugation strategy, and successfully achieves real-time and synchronous detection of H 2 S and H 2 O 2 in plants. The 24 h dynamic monitoring results showed that under different stress conditions, H 2 S and H 2 O 2 in tomatoes and rice both had specific dynamic change rules, and there was a complex cross-regulation mechanism between them. By combining sensor data with partial least squares discriminant analysis (PLS-DA), the classification accuracy of stress types exceeds 95%. This non-destructive and highly sensitive detection system can provide real-time dynamic data of stress signals, bringing a breakthrough to the in-situ monitoring of plant physiological states.

Why it matches plant phenotyping methods植物内のストレスシグナルをリアルタイム測定するウェアラブルSERSセンサーの開発が研究の中心であり、植物の生理状態の表現型取得に直接結び付いている。

abstractThis study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs)

Genetically Encoded Fluorescent Biosensors Enable Noninvasive Real-Time Visualization of Nitrate Dynamics in Intact Living Plants. →

Nitrate (NO 3 - ) serves as a pivotal molecule with dual functions in nutrient supply and signaling during plant growth and development. Precise monitoring of its spatiotemporal dynamics in planta is therefore essential for dissecting the regulatory mechanisms underlying plant nitrogen metabolism. However, conventional nitrate detection methods suffer from inherent limitations, including destructive sampling, insufficient spatiotemporal resolution, and an inability to achieve real-time whole-plant monitoring. Here, we report a genetically encoded nitrate biosensor, designated NitNRCL1, constructed using a split firefly luciferase complementation system. Functional validation in both prokaryotic and eukaryotic systems demonstrates that NitNRCL1 responds to changes in nitrate availability and generates stable chemiluminescent signals in bacteria and diverse plant species. Importantly, NitNRCL1 enables non-invasive, real-time, and whole-plant monitoring of nitrate levels in living plants. Using NitNRCL1, we successfully imaged the spatiotemporal dynamics of nitrate signaling in Arabidopsis thaliana . Collectively, our findings establish NitNRCL1 as a robust and novel tool for investigating nitrate transport, signaling, and metabolic pathways in plants. This biosensor advances our mechanistic understanding of plant nitrate biology and provides a technical foundation for breeding nitrogen-use-efficient crops and developing precision fertilization strategies.

Why it matches plant phenotyping methods植物体内の硝酸動態を非破壊・リアルタイム・全身的に可視化する遺伝子コード型バイオセンサーを開発し、複数の生物・植物種で機能検証しているため、植物生理状態の取得手法が中心です。

abstractHere, we report a genetically encoded nitrate biosensor, designated NitNRCL1, constructed using a split firefly luciferase complementation system.

Biosensors for Stress Detection: A Systematic Review from Herbaceous to Woody Plants. →

Plants must constantly adapt to biotic and abiotic stressors, which the global climate change crisis has intensified. To monitor plant health and predict their ability to face these challenges, various target molecules, such as hormones, glucose, and reactive oxygen species, are used as proxies for their physiological status. This review provides a systematic assessment of the current state of biosensor technology, an innovative analytical approach designed for in situ, minimally invasive, and real-time monitoring. Using the PICO (Problem, Intervention, Comparison, and Outcome) strategy, relevant research papers were identified. The review highlights how biosensors can detect physiological responses to stress before visual symptoms manifest, offering a significant advantage over traditional, often destructive, laboratory techniques, like gas chromatography-mass spectrometer (GC-MS) or high-performance liquid chromatography (HPLC). These advancements aim to improve precision agriculture and forestry management by providing sustainable methods to assess resilience in changing environments. Finally, the challenges of translating research from model organisms to complex woody species and choosing the correct target are discussed, and future perspectives, including the integration of biosensors with Artificial Intelligence-driven predictive models for large-scale environmental monitoring, are outlined.

Why it matches plant phenotyping methods植物ストレスの生理状態をリアルタイムに評価するバイオセンサー技術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 review provides a systematic assessment of the current state of biosensor technology, an innovative analytical approach designed for in situ, minimally invasive, and real-time monitoring.

Polyacrylamide ZrOH Hydrogels for Spatially Resolved Sampling of Carboxylates Exuded from Roots Growing in Soil. →

Sampling and reliable quantification of root exudates from undisturbed soil-grown plant roots remain challenging. We further developed a non-destructive method for the sampling, 2D mapping and quantification of seven carboxylates (aconitate, citrate, fumarate, lactate, malate, oxalate, succinate) exuded from rhizobox-grown plant roots. The method described here employs polyacrylamide zirconium hydroxide hydrogels (ZrOH hydrogels) that uptake all tested carboxylates and can be eluted with an efficiency ranging from 95.3% ± 3.12% to 111% ± 1.99% . The ZrOH hydrogels have a high binding capacity for carboxylates, up to 1.82 µmol cm -2 , depending on the solution pH and carboxylate species, a concentration higher than that usually available in the rhizosphere. Moreover, the bound carboxylates on the ZrOH hydrogels remain stable and can be stored for several weeks at 4 °C before analysis. For the application, plants are cultivated in soil-filled rhizoboxes that allow for easy access with minimal disturbance to the root system. To sample root-released carboxylates, ZrOH hydrogels are carefully applied to the region of interest for 24 h. After retrieving the ZrOH hydrogels, they are cut for mapping purposes, and the gel pieces are eluted for subsequent carboxylate analysis (e.g., via Ion Chromatography-Mass Spectrometry). Our findings indicate that ZrOH hydrogels are effective for capturing and determining carboxylate concentrations in the rhizosphere. The novelty of this method lies in its ability to sample root exudates from intact soil-grown plant roots, as well as the possibility of time-resolved sampling, compared to traditional methods (soil-hydroponic hybrid approach) that are often destructive and allow only single-time sampling. Most importantly, it enables the generation of quantitative, high-resolution, millimetre-scale 2D images, facilitating the visualisation of carboxylate exudation along the root axis and its spatial distribution within the rhizosphere. Additionally, this method facilitates the sampling of root exudates at various growth stages during the growth cycle.

Why it matches plant phenotyping methods根からのカルボキシレート放出という植物の生理状態を、非破壊・空間分解・定量的に取得するハイドロゲル法の開発が研究の中心であり、単なる化学測定のルーチン利用ではない。

abstractThe novelty of this method lies in its ability to sample root exudates from intact soil-grown plant roots

Additional file 1 of Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions →

Supplementary Material 1

Why it matches plant phenotyping methodsタイトルから、幾何学的画像ベース表現型解析と生理学的解析を用いたイネ耐塩性スクリーニングの検証が主題であり、植物表現型取得法の技術検証に該当する。

titleGeometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions

Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions. →

Salinity stress in coastal areas threatens the stability of rice production in Indonesia, necessitating innovative breeding strategies to adapt to this stress. In breeding, screening methods are crucial to improve selection effectiveness. One approach is pot selection on saline soil. However, this concept requires a precise approach, so integrating image-based phenotyping (IBP) screening and validation by physiological traits provides a rapid and effective approach to assessing salinity tolerance in rice genotypes. This study aimed to identify robust IBP traits for pot salinity screening and validate them through physiological response patterns among rice genotypes under salinity conditions. Six rice genotypes were evaluated under normal and saline environments using artificial pot trials. IBP traits related to plant geometry were quantified and complemented with physiological indicators, including Na⁺/K⁺ balance, chlorophyll pigments, and proline accumulation. Based on the result, perimeter and ferret were identified as effective IBP selection criteria. Both criteria captured differences in osmotic regulation and photosynthetic performance under salinity stress. Principal component analysis clearly separated tolerant, moderately tolerant, and sensitive genotypes, with geometric traits contributing most strongly to genotype discrimination. It supported a bit of physiological responses, which revealed distinct tolerance patterns. Tolerant genotypes (Pokkali, HS4.15.1.70, and HS4.15.2.4) maintained better Na⁺/K⁺ balance, lower chlorophyll loss, and adaptive proline responses, while sensitive genotypes (IR 29 and Ciherang) showed pronounced ionic imbalance and chlorophyll reduction; HS4.45.1.66 exhibited intermediate responses. The integration of IBP and physiological traits offers a practical framework for high-throughput salinity screening.

Why it matches plant phenotyping methods画像ベース表現型形質を定量化し、塩分耐性スクリーニングの選抜基準として検証することが中心である。生理形質による妥当性検証も含む。

abstractThis study aimed to identify robust IBP traits for pot salinity screening and validate them through physiological response patterns among rice genotypes under salinity conditions.

Evaluating GNSS-T VOD sensitivity to plant water dynamics, rainfall interception, and dew in a coniferous forest →

Abstract. Monitoring forest canopy water is essential for understanding drought response and interception losses under climate change. At short timescales, canopy water is partitioned among internal plant water storage (Sp), rainfall interception (Si), and dew (Sd), which together regulate plant functioning, canopy evaporation, and precipitation partitioning, yet are rarely observed simultaneously. GNSS transmissometry (GNSS-T) has recently emerged as a low-cost, continuous, stand-scale method to observe L-band vegetation optical depth (VOD) from signal attenuation. However, interpreting GNSS-T VOD remains difficult because the signal integrates multiple water pools and is similarly affected by biomass, canopy structure, and measurement noise. Here, we applied GNSS-T in a mature Picea abies stand in Tharandt, Germany, during the 2024 growing season to separate canopy water storage into Si, Sd, and Sp. Rainfall interception was simulated with the multilayer Penman–Rutter model CanWat and used to calibrate the empirical VOD–water-storage relationship and to convert the GNSS-T noise floor into an equivalent storage detectability threshold. GNSS-T VOD tracked interception storage robustly and approximately linearly at both 30-min and event scales (R² = 0.63/0.79), and modeled Si explained VOD variability better than gross precipitation alone. The inferred attenuation coefficient b was physically consistent with the reported L-band values but varied seasonally, indicating that time-varying calibration is preferable to a fixed relationship. Dew-related wetting signals were distinguishable in VOD and yielded plausible mean nightly amounts, but short event duration and high noise caused unrealistic extremes and limited detectability. Diurnal changes in internal plant water storage were not directly detectable at sub-daily scales, indicating that realized variations in Sp remained below the GNSS-T noise floor during the study period which we could show using trait-based estimates of expected maximum plant water loss under non-stressed conditions. This storage-versus-noise framework provides a practical way to benchmark the hydraulic sensitivity of GNSS-T across sites that differ in biomass, hydraulic strategy, and climate, and further highlights noise reduction as a prerequisite for plant-hydraulic applications, especially in low-biomass ecosystems. This study further promotes GNSS-T VOD as a robust monitoring instrument for resolving sub-event interception storage, a potential avenue for constraining hydrological models.

Why it matches plant phenotyping methodsGNSS-T VODによる植物・林冠の水貯蔵量測定を中心に、校正、検出限界、ノイズ、感度を評価しており、植物の水状態を取得するセンシング手法の検証・適用研究である。

abstractGNSS transmissometry (GNSS-T) has recently emerged as a low-cost, continuous, stand-scale method to observe L-band vegetation optical depth (VOD) from signal attenuation.

Additional file 1 of Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions →

Supplementary Material 1

Why it matches plant phenotyping methodsタイトルで画像ベース表現型解析と生理解析を用いた耐塩性スクリーニングの検証を明示しており、表現型取得・検証が中心的な方法論的役割を持つ。

titleGeometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions

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+ .

Root growth and function in New Zealand pasture systems: a perspective on research needs, methods, and system integration. →

Understanding root growth and phenology is essential for improving the productivity, resilience, and sustainability of pasture-based systems. However, roots remain one of the most difficult components of plant systems to measure and monitor, particularly in managed, high-turnover pastures, such as those in New Zealand (NZ) dairy systems. As a result, root processes are often underrepresented in both experimental studies and pasture system models. This perspective paper identifies critical, but underdeveloped areas in root research, with particular focus on root phenology. Current studies are limited by insufficient temporal resolution, a lack of species- and cultivar-specific trait data in mixed swards, and weak integration of root dynamics into breeding programmes and farm system models. These constraints limit our ability to link root processes to pasture persistence, nutrient cycling, and climate resilience. To address this gap, we propose that root phenology should be treated as a dynamic functional trait that links plant responses to environmental and management drivers with ecosystem-level outcomes. This framing provides a conceptual foundation for integrating root dynamics into pasture research and modelling, particularly in systems subject to frequent defoliation and environmental variability. We further highlight opportunities arising from rapid advances in sensing technologies, automation, and data analytics, which enable continuous, high-resolution root monitoring systems at multiple scales. However, realising this potential requires integration of complementary measurement approaches and alignment with system-level research questions. In this context, NZ provides a unique platform for developing scalable, pasture-based root monitoring framework that integrates science, management and policy. We argue for a coordinated effort that bridges fundamental root biology with applied pasture management, supported by long-term datasets, methodological integration, and engagement with end users. Embedding root traits and phenological dynamics into the next generation of pasture models and decision-support tools will be critical for improving system performance and environmental outcomes. This perspective aims to stimulate a shift towards more integrated, temporally explicit approaches for studying root systems in pasture environments, with relevance to grazing system beyond NZ and across temperate regions.

Why it matches plant phenotyping methods根の成長・フェノロジーという植物形質の測定課題と、センシング・自動化・データ解析を統合した高頻度モニタリング手法を中心に論じる方法論的パースペクティブである。

abstractroots remain one of the most difficult components of plant systems to measure and monitor

Phenotyping for physiological traits under plant stress: methods, challenges, and future perspectives →

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

Why it matches plant phenotyping methods植物ストレス下の生理形質フェノタイピング手法を扱う方法論レビューであり、フェノタイピング手法が中心である。

titlePhenotyping for physiological traits under plant stress: methods, challenges, and future perspectives

Boosting innovative microbial solutions by understanding the functional benefits of endophytic rhizobacteria on tomato growth and protection using plant phenomics →

Biological control represents a valuable tool for the sustainable management of soil-borne diseases in tomato cultivation and relies on the availability of effective microbial solutions. Digital technologies can support to the ecodesign stage by accelerating the screening and selection of high-performing microbial biocontrol agents. In this study, a collection of eleven endophytic bacteria strains recruited from the tomato root endosphere and proved to be compatible with Trichoderma spp. (non-target effect), was characterized for antagonistic and biofertilization/biostimulant traits, and evaluated in planta against two major tomato pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Plant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions. Multivariate analysis of 20 digitally computed phenotypic traits helped the detection of Peribacillus sp. C5NA and Neobacillus sp. TR12 as highly effective against wilt, and capable of counteracting the reduction in leaf angle surface and chlorophyll: typical tracheofusariosis symptoms. On the other hand, Peribacillus strains TR2 and C6 treatments caused partial phenotypic recovery in plants affected by Sclerotium rot. Interestingly, Microbacterium sp. TR9, appeared to be multifaceted. It showed mild multisuppressivity against both pathogens coherently with the exhibited N-acetyl-b-glucosaminidase, polysaccharide breaking and in vitro antifungal activities. In addition, it also acted as a putative biostimulant in the absence of pathogens, increasing digital biomass, plant height, and NDVI, in line with its proven strong ability to produce ammonia, fix nitrogen, solubilize phosphates, and release indoleacetic acid. Overall, the integration of phenomics supported the high-resolution detection of plant responses and supported the identification of multifunctional microbial strains with biocontrol and biofertilization potential for sustainable tomato production.

Why it matches plant phenotyping methodsPlantEye 500によるマルチスペクトル表現型取得と20形質のデジタル解析が、微生物資材のスクリーニングおよび植物応答評価の中心的手法として用いられているため。

abstractPlant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions.

An optimized mung bean seedling model for characterizing virulence of Pseudomonas aeruginosa biofilm infections. →

Plant-based infection models provide cost effective and biologically relevant systems for investigating bacterial pathogenesis and virulence in living hosts. The mung bean seedling model enables the study of bacterial biofilms on living surfaces by allowing attachment and biofilm development on plants, but its broader use has been limited by methodological complexity and variability in experimental outcomes. Here, we present a modified mung bean seedling biofilm infection model for assessing Pseudomonas aeruginosa virulence that improves both consistency and practicality. The assay incorporates a bleach-based seed sterilization protocol that effectively reduces surface associated contaminants while maintaining high seed germination percentages. Additional refinements, including dehulling germinated seedlings, a shortened bacterial inoculation period, and plate-based incubation of seedlings at 37 °C, minimize variability in plant health outcomes while supporting development of gnotobiotic plants. Plant mortality, cotyledon emergence, and root branching were identified as rapid and quantitative measures of biofilm associated disease. Using this modified assay, reproducible differences in virulence were detected among P. aeruginosa strains, including reduced pathogenicity in a pqsR quorum sensing mutant. This simplified mung bean seedling model provides an accessible platform for studying biofilm associated virulence and screening genes involved in biofilm-mediated pathogenicity on a biotic surface.

Why it matches plant phenotyping methodsムングビーン幼植物を用いた感染・疾患表現型測定系の改良と再現性評価が研究の中心であり、植物死亡、子葉出現、根分枝を定量的な病徴指標として開発・検証している。

abstractHere, we present a modified mung bean seedling biofilm infection model for assessing Pseudomonas aeruginosa virulence that improves both consistency and practicality.

A Condition-Aware Shading Domain-Adaptive Framework for Robust Chlorophyll Inversion Across Shade Managements in Hopea hainanensis . →

Shade management, which is widely adopted in cultivation and understory regeneration, alters plant light environments, thereby degrading the trait inversion performance and posing a key challenge in plant phenotyping. To address this issue, this study reframed chlorophyll retrieval of Hopea hainanensis under shade management as an illumination-regime-dependent conditional domain shift problem, and developed a condition-aware domain adaptation framework (CAI-DAI) tailored to this setting. The results showed that chlorophyll content increased with shading intensity, accompanied by clear differences in canopy spectral distributions among shading levels, supporting the presence of condition-dependent variation under shade management. Model comparisons showed that CA-IE and CAI-DAI, which integrate conditional encoding and conditional alignment, performed better than the comparative models across fine-tuning ratios from 30% to 70%. Among them, CAI-DAI achieved the best and most stable performance, with test MAE ranging from 4.355 to 4.774 μg·cm−2 and nRMSE ranging from 16.4% to 18.2%, and R2 ranging from 0.456 to 0.585. Further evaluation at individual shading levels (S1–S4) showed that CAI-DAI produced narrower error ranges than CA-IE. It also showed smaller error fluctuations under most fine-tuning ratios. These results demonstrate that the proposed framework effectively improves robustness under heterogeneous shading conditions and limited labeled samples, providing methodological support for chlorophyll monitoring and decision-making related to shade management.

Why it matches plant phenotyping methods植物のクロロフィル含量を推定する条件認識型ドメイン適応フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。

abstractthis study reframed chlorophyll retrieval of Hopea hainanensis under shade management as an illumination-regime-dependent conditional domain shift problem, and developed a condition-aware domain adaptation framework (CAI-DAI) tailored to this setting.

Rhizo-PET: A Dedicated PET System for 4D Imaging of Carbon Dynamics in the Rhizosphere →

Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems. The system achieved a global energy resolution of 11.93 ± 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants ( N = 3) over 180 min following 11 CO 2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments ( p < 0.001). Hierarchical variability analysis showed that within-plant spatial organization ( CV TTP = 0.03) is significantly more stable than inter-plant variation ( CV TTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.

Why it matches plant phenotyping methods植物根圏における炭素動態を非侵襲・時系列で測定する専用PETシステムを開発し、性能・再現性・空間解析能力を検証した研究であり、植物状態の取得方法が中心である。

abstractWe present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems.

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.

Remote Sensing Applications in Medicinal Plant Monitoring and Quality Assessment: A Review →

As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources.

Why it matches plant phenotyping methods植物の成長・ストレス・品質などの状態を対象に、リモートセンシングのプラットフォーム、センサー、解析手法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す。

abstractThis study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods

Identification of self-incompatibility in macadamia (Macadamia SPP.) using field-bagging and fluorescence microscopy →

Self-incompatibility (SI) significantly reduces crop yield, often far below its genetic potential. Developing self-compatible varieties is the most effective strategy for overcoming SI in crops. Most macadamia ( Macadamia SPP.) species exhibit SI or partial self-incompatibility (PSI), so the efficient identification of self-compatible germplasms has emerged as a crucial topic. To characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility. That is, the degree of SI was based on the final self-incompatibility index (F_SI), which was calculated based on the open-pollination final nut set per raceme (OP_FNS) and self-pollination final nut set per raceme (SP_FNS) values (strong SI: F_SI ≥ 0.7, medium SI: 0.4 ≤ F_SI 35%). Through comprehensive analysis of the field-bagging and fluorescence-microscopy observations, thirteen varieties with strong SI (816, 778, 842, Special, 812, D, 820, 246, 772, A16, A4, 951, and 695), six varieties with moderate SI (851, 828, 508, 936, O.C, and D4), and four varieties with weak SI (915, HY, 836, and 814) were identified. Our study provides a theoretical foundation and technical support for advancing germplasm resource innovation, and the genetic improvement and breeding of self-compatible macadamia varieties.

Why it matches plant phenotyping methods自家不和合性という植物状態を対象に、圃場袋掛けと蛍光顕微鏡観察を用いた標準化分類体系を確立しており、表現型の取得・評価法が研究の中心である。

abstractTo characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility.

In-Planta Tattoo and Kirigami Sensors for Moisture-Powered Monitoring of Vapor Pressure Deficit and Growth Dynamics. →

We report a scalable, moisture-powered in-planta sensor platform for the continuous monitoring of plant hydration and growth. The system integrates two components: a leaf-mounted tattoo sensor for estimating vapor pressure deficit (VPD) and a kirigami-inspired strain sensor for tracking radial stem growth. Uniquely, the tattoo sensor serves a dual function: measuring temperature and humidity beneath the leaf surface while simultaneously harvesting power from ambient moisture via a vanadium pentoxide (V 2 O 5 ) nanosheet membrane. This moist-electric-generator (MEG) configuration enables energy-autonomous operation, delivering a power density of 0.1114 μW/cm 2 . The V 2 O 5 -based sensor exhibits high sensitivity to humidity (4.2 mV/% RH) and temperature (1.02%/°C), enabling accurate VPD estimation for over 10 days until leaf senescence. The eutectogel-based kirigami strain sensor, wrapped around the stem, offers a gauge factor of 1.5 and immunity to unrelated mechanical disturbances, allowing for continuous growth tracking for more than 20 days. Both sensors are fabricated via cleanroom-free, roll-to-roll compatible methods, underscoring their potential for large-scale agricultural deployment to monitor abiotic stress and improve crop management.

Why it matches plant phenotyping methods植物の水分状態（VPD）と茎の成長を連続測定するセンサー基盤を開発・性能評価しており、植物表現型の取得が中心的な技術貢献である。

abstractWe report a scalable, moisture-powered in-planta sensor platform for the continuous monitoring of plant hydration and growth.

Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials →

Abstract Improving selection for multiple disease resistance (MDR) and yield in maize ( Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years in near‐isogenic inbreds, near‐isogenic hybrids, and a diverse hybrid panel. VIs showed lower coefficients of variation but broad‐sense heritability ranging from 0.09 to 0.95, compared with 0.30 to 0.99 for visual disease scores and 0.21 to 0.97 for yield. Correlations between VIs and ground traits were strongest in near‐isogenic hybrids, particularly for early‐season yield prediction ( r = 0.98–0.99 in 2018; r = 0.87–0.90 in 2019), and moderate for total disease severity (e.g., r = −0.61 to −0.68 in 2018). Associations were weaker and less consistent in the diverse hybrid panel (yield r = 0.11–0.28). Disease‐specific signals were temporally structured: NLS correlated most strongly with early‐season VIs ( r = −0.59 to −0.75), whereas ATD was best detected mid‐season ( r = −0.63 to −0.66) along with NLB ( r = −0.66 to −0.75). Overall, multispectral VIs captured meaningful canopy variation related to MDR and yield, with predictive performance depending on germplasm structure and flight timing. These findings highlight the potential of drone‐based temporal phenotyping to complement visual assessments and improve selection efficiency in maize breeding programs.

Why it matches plant phenotyping methodsドローン multispectral による作物キャノピー形質の取得・予測性能を検証し、病害抵抗性と収量予測への適用を評価しており、フェノタイピング手法が中心である。

abstractWe evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years

Nanomaterials for Enhancing Agricultural Stress Resilience. →

Advances in nanomaterials design have conferred new capabilities for interfacing with plant systems, providing a versatile toolbox for probing and modulating plant responses to environmental stresses with high spatiotemporal control. These nanomaterials-based innovations are particularly important for enhancing agricultural stress resilience as they complement existing agronomic practices by addressing two long-standing technological gaps: nondestructive presymptomatic detection of stress-related biochemical signaling in plants and precise delivery of genetic and bioactive cargoes across plant barriers. This review outlines key design principles, properties, and engineering of different nanomaterial classes that enable their application in agriculture. We highlight recent advances in plant nanosensors, including corona phase molecular recognition (CoPhMoRe) sensors, plasmonic nanosensors that are surface-enhanced Raman scattering (SERS)-active, and reticular framework-based sensors that enable continuous presymptomatic monitoring of key stress-related analytes such as reactive oxygen species (ROS), phytohormones, and metabolites. In parallel, progress in nanocarriers, including carbon-based nanostructures, polymeric nanoparticles, and functional peptides, has enabled delivery of nucleic acids, growth regulators, nutrients, and agrochemicals across plant biological barriers that traditionally impede efficient plant transformation and stress mediation. Together, nanosensors and nanocarriers are highly synergistic for nanoenabled precision agriculture, where real-time monitoring serves as feedback control for responsive interventions, contributing to resilient and sustainable next-generation crop production under a changing climate. While the practical implementation of these plant nanosensors and nanocarriers still faces significant hurdles related to scalability, stability, and environmental safety, progress in rational materials design coupled with plant interface engineering suggests a clear pathway ahead to overcome these limitations and realize their full potential in the field.

Why it matches plant phenotyping methods植物ストレス状態を非破壊・連続的に検出するナノセンサーを中心に扱うレビューであり、植物の生理状態・ストレス関連 analyte の測定法が中核的である。

abstractThis review outlines key design principles, properties, and engineering of different nanomaterial classes that enable their application in agriculture.

Phase-Transition-Driven Adaptive Reconfiguration of Wearable Devices for Conformal Biointerfaces. →

Conformal integration of flexible electronics with unstandardized biological tissues is critical for next-generation wearables. However, flexible devices are predominantly fabricated in conventional planar formats, incompatible with the nonplanar, hairy, or dynamic surfaces of biological organisms. Here, we resolve this conflict by introducing a universal solid-liquid-solid phase transition strategy. This approach utilizes water-soluble polyvinyl alcohol as a substrate, which temporarily liquefies and flows to match target topography upon wetting, then solidifies in place, enabling a perfect conformal interface. Such a process helps the reconstructed devices to establish robust (interfacial toughness ∼29 J m -2 , tensile strength of ∼161 kPa), stretchable, and stress-free interfaces with skin. Furthermore, this robust interface permits reversible switching between strong to weak adhesion, while dissolving on-demand for painless, non-traumatic removal. We validate the approach with shape-adaptable sensors and electrodes that seamlessly wrap the vulnerable, peristaltic bodies of silkworms for motion tracking, and hairy, thorn-laden leaves for plant electrophysiology monitoring, expanding the utility of wearable electronics to previously inaccessible biological surfaces.

Why it matches plant phenotyping methods生体表面に適合するウェアラブルデバイスの再構成法を開発し、植物葉の電気生理モニタリングへ適用しているため、植物状態の取得方法が中心的です。

abstractWe validate the approach with shape-adaptable sensors and electrodes that seamlessly wrap the vulnerable, peristaltic bodies of silkworms for motion tracking, and hairy, thorn-laden leaves for plant electrophysiology monitoring

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.

Unlocking the dual roles of carbon monoxide by a rapid fluorescent probe: from monitoring pneumonia therapy in animals to cadmium resistance in plants. →

Carbon monoxide (CO) has long been viewed as an environmental pollutant, but recent work points to its role as an endogenous signaling molecule in infection and stress. Here, we present RDM-CO, a near-infrared fluorescent probe built by attaching an allyl formate recognition unit to a rhodamine scaffold. When CO and Pd2+ are both present, the probe undergoes a Tsuji-Trost deallylation that turns on fluorescence at 653 nm within 13 min. RDM-CO shows a 63 nm Stokes shift, good selectivity over other biologically relevant species, a detection limit of 1.35 µM, and no obvious toxicity to cells. Using this probe, we visualized endogenous CO production in LPS-stimulated macrophages and found it localized to mitochondria. We also used RDM-CO to follow CO dynamics in a mouse model of bacterial pneumonia-allowing us to monitor anti-inflammatory treatment effects-and in plant roots under cadmium stress. These experiments demonstrated that RDM-CO can be used to study CO signaling across different biological systems.

Why it matches plant phenotyping methods植物根のストレス下における内因性CO動態を可視化する蛍光プローブを開発し、植物の生理状態の測定に適用しているため、植物フェノタイピング手法が中心的である。

abstractHere, we present RDM-CO, a near-infrared fluorescent probe built by attaching an allyl formate recognition unit to a rhodamine scaffold.

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

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植物細胞膜を対象とした蛍光プローブと機能レポーターを開発し、生体植物組織で膜密度・脂質秩序・膜酸化を定量画像化する方法を提示しており、表現型取得法が中心である。

abstractHere, 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.

A rapid, high-throughput, non-invasive approach for assessing drought tolerance in rose (Rosa spp.) using RGB-derived vegetation indices →

Drought stress poses a significant threat to rose (Rosa spp.) cultivation, impacting plant vigor, floral quality and marketability. Traditional drought screening methods are often destructive and labor-intensive, limiting their application in large-scale breeding programs. This study presents a non-destructive and high-throughput phenotyping approach for assessing drought responses in rose using RGB-derived vegetation indices (VIs) obtained from multi-angle imaging. Twenty-eight diverse rose genotypes were evaluated under well-watered (WW) and induced drought (ID) conditions using a LemnaTec Scanalyzer 3D platform. A total of 56 indices from side-view (SV) and top-view (TV) images were computed to quantify canopy color, greenness and pigment-related traits. Analysis of variance revealed significant genotypic differences and strong genotype × treatment (G × T) interactions across most indices, demonstrating their sensitivity to drought-induced physiological changes. Multivariate analyses, including Principal Component Analysis (PCA) and Pearson correlation matrix evaluation, were performed to explore trait relationships and identify key traits associated with drought stress. These analyses effectively differentiated greenness-related and stress-responsive traits. In addition, the MGIDI analysis integrated all indices and identified ‘Queen Elizabeth’, ‘Jwala’, Rosa chinensis, ‘Sylvia’ and ‘Rose Sherbet’ as the top-performing drought-tolerant genotypes. Integration of leaf wilting scores validated the reliability of these indices as accurate indicators of drought response, with tolerant genotypes exhibiting lower LWS and higher greenness indices. Overall, the study demonstrates that RGB-based high-throughput phenotyping provides a rapid, efficient and scalable method for drought tolerance assessment in roses, offering a valuable tool for accelerating selection in ornamental breeding programs.

Why it matches plant phenotyping methodsRGB画像から植生指数を抽出する高スループット表現型解析手法の開発・実証が研究の中心であり、バラの干ばつ応答を評価する再利用可能なワークフローを提示している。

abstractThis study presents a non-destructive and high-throughput phenotyping approach for assessing drought responses in rose using RGB-derived vegetation indices (VIs) obtained from multi-angle imaging.

Interpreting Yield–Spectral Relationships in Wheat and Cotton Using a Harmonised Sentinel-2 Indicator Framework →

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of in-dividual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterra-nean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield varia-bility (up to R² ≈ 0.82) under controlled analytical conditions. In contrast, cotton yield var-iability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.76. Recurrence analysis confirmed the stability of these indicator families across analytical stages. Overall, the results indicate that parsimonious, physiologically interpretable indi-cator combinations can account for a substantial proportion of yield variability without reliance on black-box modelling, supporting crop-aware indicator selection for precision agriculture applications.

Why it matches plant phenotyping methodsSentinel-2のスペクトル・熱指標から作物収量を推定する統一ワークフローを構築・比較し、指標の安定性と予測性能を検証しており、収量フェノタイピング手法が中心です。

abstractThis study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework

Multi-trait selection of common bean lines resistant to Meloidogyne incognita →

Abstract Meloidogyne incognita (root-knot nematode) is one of the most damaging soilborne pathogens affecting the common bean. Control relies primarily on resistant cultivars, making accurate resistance phenotyping a key component of breeding programs. Here, we developed an integrated phenotyping approach to identify resistant genotypes in a recombinant inbred line (RIL) population. For initial screening, 361 RILs were evaluated with three replications for galling index (GI), number of galls (NG), and egg masses (EM) at 60 days after inoculation (DAI). A subset of 24 segregating RILs was further assessed in a second trial for GI, NG, EM, and reproduction factor (RF), with seven replications at 30 and 60 DAI. A multi-trait factor analytic mixed model was used to derive an overall resistance index (ORI) for genotype classification into moderately resistant (MR), intermediate (I) and susceptible (S) classes. We also assessed the potential of a qPCR-based phenotyping protocol using two contrasting RILs from the segregating subset. High heritability (> 0.8) and strong genotypic correlations among resistance components were observed in the RIL segregants, indicating a robust genetic basis for selection. MR genotypes consistently exhibited reduced GI, NG, EM, and RF, and transgressive segregants were identified within the MR group, confirming that the ORI framework effectively distinguished resistance levels. Moreover, later evaluation improved genotype classification and revealed resistance shifts. qPCR-based phenotyping consistently discriminated MR and S lines in agreement with classical phenotyping, supporting its use as a complementary evaluation tool. Overall, our results validate an integrative multi-trait strategy for more precise resistance phenotyping and genotype selection.

Why it matches plant phenotyping methodsマメの線虫抵抗性を対象に、複数の表現型指標、統合モデル、qPCRプロトコルを組み合わせた抵抗性フェノタイピング手法を開発・検証しており、手法が研究の中心である。

abstractwe developed an integrated phenotyping approach to identify resistant genotypes in a recombinant inbred line (RIL) population.

Implantable Fiber Sensor for Continuous Monitoring of Light-Regulated Ascorbic Acid Dynamics. →

Understanding how light dynamically regulates ascorbic acid (AA) levels is essential for improving crop nutritional quality. However, the dynamic regulation of AA by light in vivo remains unclear, since conventional methods rely on destructive sampling and only provide static data. To address this, we develop an implantable fiber sensor functionalized with a dual-atomic nanozyme for minimally invasive, long-term tracking of AA in living plants. The implantable fiber sensor integrates a hierarchical nanobio interface composed of a Co-Fe dual-atomic nanozyme (CoFe-DAzyme) and an antifouling hydrogel, achieving a detection limit of 0.081 μM in plant bleeding sap and remaining functional for up to 7 days postimplantation. Employing this sensor in lettuce, we uncover rapid, light-dependent AA fluctuations, directly revealing how dynamic light environments fine-tune this key nutritional metabolite. Our work not only establishes a versatile sensing platform but also provides direct mechanistic insight into the light-regulated improvement of crop nutritional quality.

Why it matches plant phenotyping methods植物体内のアスコルビン酸動態という生理形質を連続測定する埋植型センサーを開発し、性能評価と植物での実証を行っており、フェノタイピング手法が中心である。

abstractwe develop an implantable fiber sensor functionalized with a dual-atomic nanozyme for minimally invasive, long-term tracking of AA in living plants.

Cobalt oxide based microneedle sensor for direct detection of glucose from plant leaves →

Glucose is a crucial biomarker reflecting plant metabolism and growth. While glucose sensing is well-established in human healthcare, continuous in situ measurement in living plants remains challenging. In this study, we developed a non-enzymatic microneedle (MN) electrochemical sensor for the direct, real-time monitoring of glucose in plant leaves. To achieve this, we utilized a highly customizable and cost-effective fabrication strategy. A three-electrode MN system was fabricated by combining SU-8 micromolding with 3D printing of a carbon paste circuit, significantly reducing the reliance on complex cleanroom processes. The working electrode was then functionalized via a single-step co-electrodeposition of Co 3 O 4 catalysts and multiwall carbon nanotubes in a chitosan matrix. This co-deposition effectively compensated for the inherently low electrical conductivity of Co 3 O 4 . The optimized sensor exhibited reliable in vitro analytical performance. Furthermore, the MN sensor was inserted into living leaves to monitor glucose levels under varying light and dark conditions. The sensor successfully recorded dynamic real-time current signals, showing an average of 71.5 ± 11.0 nA in the dark and 9.6 ± 2.8 nA in the light. This customizable MN platform provides a practical diagnostic tool for continuous plant health monitoring.

Why it matches plant phenotyping methods植物葉内のグルコースをリアルタイム測定するマイクロニードル電気化学センサーを開発し、生葉での連続モニタリングまで実証しており、植物生理状態の取得方法が研究の中心である。

abstractwe developed a non-enzymatic microneedle (MN) electrochemical sensor for the direct, real-time monitoring of glucose in plant leaves.

Early Detection and Differentiation of Dragon Fruit Plant Diseases Using Optical Spectral Reflectance →

Dragon fruit (Hylocereus spp.) is an emerging crop in the tropics and subtropics, but its production is increasingly threatened by diseases that reduce yield and profitability. Early diagnosis of these diseases is crucial for timely intervention, yet visual symptoms often appear only after significant infection has occurred. The study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions. In this study, six major dragon fruit stem diseases: Neoscytalidium stem canker, stem sunburn, anthracnose, Botryosphaeria stem canker, Bipolaris stem rot, and bacterial soft rot were characterized by the goal of identifying unique spectral signatures for early detection and differentiation of each disease. Seventy-two potted dragon fruit plants of three distinct species were grown under four organic vermicompost treatments (0, 5, 10, 20 tons/acre) in both open-field and high-tunnel conditions together, in a randomized complete block design. A handheld spectroradiometer (350–2500 nm) was used to collect reflectance from the diseased and healthy cladodes (stem segment). Various spectral vegetative indices were computed to identify disease-specific features. The results revealed distinct spectral features for each disease. Infected cladodes consistently exhibited higher reflectance especially in the visible region (400–700 nm) and the near-infrared region (900–2500 nm) of the spectrum than healthy cladodes. The Normalized Difference Vegetative Index (NDVI), Green Normalized Difference Vegetative Index (GNDVI), and Spectral Ratio (SR) spectral indices were significantly higher in healthy plants than in diseased ones, reflecting higher chlorophyll concentration and plant biomass. Conversely, the 1110/810 ratio was lower in healthy plants than in diseased plants, suggesting a more compact internal plant structure. Statistical analysis revealed highly significant differences (p

Why it matches plant phenotyping methods光学スペクトル反射とスペクトル指数を用いて、ドラゴンフルーツの病徴を早期検出・識別する方法が研究の中心であり、感染植物の状態を直接推定している。

abstractThe study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions.

MAGMA: a Modeling Approach for Growth inhibition of MAcrophytes to predict effects of time variable exposure under laboratory conditions. →

In the European framework for assessing the ecological effects of plant protection products on aquatic organisms, standard tests usually rely on constant laboratory exposure. This Tier 1 approach may lead to overly conservative assessments for short-term exposures lasting only a few hours under realistic environmental conditions. To address this, the European Food Safety Authority proposed a Tier 2C assessment to incorporate more realistic dynamic exposure profiles through refined tests and toxicokinetic/toxicodynamic (TKTD) modeling. Currently, the only accepted TKTD model for macrophyte growth inhibition relates to the duckweed Lemna sp. We hypothesize that this model can be adapted for other macrophytes including sediment rooted macrophytes. We propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions. The model was calibrated using experimental data from Myriophyllum spicatum exposed to two herbicides with different modes of action. Validation against single and two-pulse exposure experiments showed good agreement between model predictions and observed data. The modeling approach, MAGMA can therefore serve as a valuable Tier 2C tool for predicting macrophyte growth rates under dynamic exposure conditions.

Why it matches plant phenotyping methodsマクロファイトの成長阻害を動的曝露下で予測するモデルを開発し、実験データで較正・検証しており、植物表現型（成長率）の推定手法が研究の中心である。

abstractWe propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions.

Unlocking the potential of 1D to 2D transformation in visible and near-infrared (VIS/NIR) spectroscopy for improved plant disease and stress detection: A review →

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

Why it matches plant phenotyping methods植物の病害・ストレス状態をVIS/NIR分光で検出する手法のレビューであり、植物フェノタイピング手法が中心です。

titleUnlocking the potential of 1D to 2D transformation in visible and near-infrared (VIS/NIR) spectroscopy for improved plant disease and stress detection: A review

Detection of Foliar Phosphorus in Potato Crops Using Multispectral Imagery →

Phosphorus is essential for potato cultivation; however, its low natural availability and high fixation in the soil require efficient management strategies. In this context, rapid and non-destructive methods for estimating foliar phosphorus content are important to reduce waste and improve production efficiency. This study evaluated the use of multispectral images acquired by a Survey 3 (MAPIR) camera to estimate foliar phosphorus content in potato crops at the tuberization stage, when topdressing fertilization is typically applied. To generate variability in foliar phosphorus levels, an experiment was conducted under greenhouse conditions using a randomized complete block design with eight P₂O₅ rates (0, 50, 100, 200, 400, 800, 1,600, and 3,200 kg ha⁻¹) and 40 replications. Spectral indices (NDVI, GNDVI, and GRVI) were analyzed, and regression models and machine learning algorithms (Random Forest, Multilayer Perceptron, and SMOreg) were applied to predict foliar phosphorus content. NDVI showed a significant inverse correlation with phosphorus content, attributed to the nutritional dilution effect. Among the evaluated methods, Random Forest showed the best performance, with a relative root mean square error of 19.01%. The results demonstrate that the integration of multispectral variables and machine learning techniques increases diagnostic accuracy, highlighting the potential of these technologies for nutritional monitoring of potato crops in precision agriculture.

Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習により、ジャガイモ葉のリン含量という植物生理形質を非破壊推定する手法が研究の中心である。

abstractrapid and non-destructive methods for estimating foliar phosphorus content are important

Photosynthetic traits and canopy-level thermal imaging to assess plant-water relations in two apple cultivars under waterlogging and recovery conditions →

Waterlogging is an increasingly important constraint in orchard systems under climate extremes. Understanding cultivar-specific physiological responses and identifying reliable, non-invasive indicators of plant water status are essential for improving orchard management under excess soil moisture. In this study, we evaluated the physiological, hydraulic, and canopy thermal responses of two commercially important apple cultivars, ‘Fuji’ and ‘Gamhong' grafted onto M.9, under controlled waterlogging and recovery conditions. We quantified photosynthetic traits and plant hydraulic parameters—including sap flow (SF), leaf water potential ( Ψ Leaf ), and whole-plant hydraulic conductivity ( K s )—together with canopy thermal indicators, canopy temperature ( T c ), and a modified crop water stress index ( mCWSI ) calculated using empirically derived, day-specific canopy temperature references. Waterlogging significantly reduced photosynthetic performance and hydraulic function in both cultivars, but responses differed in magnitude and recovery dynamics. ‘Fuji’ exhibited greater resilience, with smaller declines and faster recovery of gas exchange and water-relation traits, whereas ‘Gamhong’ showed earlier photosynthetic limitation and delayed recovery, indicating lower tolerance to saturated soil conditions. Leaf mass per area (LMA) increased under waterlogging, reflecting constraints on leaf expansion rather than enhanced photosynthetic activity. Among the thermal indicators, mCWSI showed the strongest correlations with Ψ Leaf , stomatal conductance ( g s ), and net photosynthetic rate ( P n ), outperforming T c as an indicator of plant water status. These findings demonstrate that canopy-based thermal metrics, particularly mCWSI when interpreted alongside physiological traits, provide a robust tool for detecting cultivar-specific responses to waterlogging stress. This multi-trait framework supports cultivar selection and precision water management in orchard systems exposed to episodic flooding.

Why it matches plant phenotyping methodsキャノピー熱画像から算出したmCWSIを生理・水分状態の指標として検証し、従来のキャノピー温度と比較しているため、表現型取得・評価法が研究の中心的要素である。

abstracta modified crop water stress index ( mCWSI ) calculated using empirically derived, day-specific canopy temperature references

Determination of Total Soluble Sugars in Pteridophytes Using the Anthrone Method. →

Sugars serve as crucial integrators of both internal and environmental signals in plants, shaping the regulation of diverse physiological processes that occur throughout the plant's life span, from early embryogenesis to later senescence stages. During evolution, plants have developed various strategies to cope with abiotic stress. For example, the accumulation of osmolytes such as soluble sugars, which help protect against oxidative stress, stabilizes cellular membranes and preserves enzymes in the dry state. Precise quantification of total sugars is therefore essential for elucidating the biochemical and physiological strategies of plants in response to different conditions. Here, we present a detailed protocol for extracting and quantifying total soluble sugars in pteridophytes (lycophytes and ferns) from small amounts of leaf tissue (10 mg fresh tissue) using the anthrone method, a colorimetric assay in which carbohydrates react with the anthrone reagent under acidic conditions to form a blue-green complex whose concentration is measurable by spectrophotometry. The procedure was adapted for small sample volumes, incorporating ethanol extraction, preparation of a glucose standard curve, and absorbance measurement at 620 nm in 96-well plates. Quantification of total sugars in pteridophytes is essential for understanding the changes in metabolic responses. Likewise, using small amounts of plant tissue optimizes sugar extraction in plants with low biomass and minimizes impact on plant populations. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Extraction of fresh plant tissue Basic Protocol 2: Reaction with anthrone and measurement Support Protocol 1: Preparation of anthrone reagent Support Protocol 2: Preparation of the glucose standard curve Basic Protocol 3: Calculation of total sugar concentration.

Why it matches plant phenotyping methods植物組織から総可溶性糖を抽出・定量する手順自体が論文の中心であり、植物の生理状態を測定する方法として小試料向けに適応されています。

abstractHere, we present a detailed protocol for extracting and quantifying total soluble sugars in pteridophytes (lycophytes and ferns) from small amounts of leaf tissue (10 mg fresh tissue) using the anthrone method

Investigating phloem transport dynamics in Arabidopsis through compartmental modelling of positron emission tomography data. →

Background Phloem is the long-distance transport tissue of vascular plants in which photoassimilates are distributed from sources (e.g., leaves) to sinks (e.g., roots, fruits). Phloem transport occurs under pressure, making it very sensitive to manipulation and almost experimentally inaccessible. Therefore, functional data on phloem speed and dynamic distribution of photoassimilates along the transport pathway are still scarce, both in trees and herbaceous plants. This study presents a methodological pipeline to image phloem transport in very thin shoots of the model plant Arabidopsis using photosynthetic uptake of 11 CO 2 and state-of-the-art positron emission tomography (PET). Results Successful application of the latest generation preclinical PET scanners allowed in vivo visualization of internal movement of 11 C-labelled photoassimilates inside primary and secondary shoots of 1 to 2 mm diameter every 5 min. Using this data as input in a compartmental model enabled estimation of (i) phloem front speed, and (ii) radial carbon partitioning between leakage-retrieval phloem, carbon storage and respiratory efflux. The methodology shows that the phloem front speed of recently fixed carbon in primary shoots was almost two-fold the speed in secondary shoots (128 vs. 70 µm s -1 ). Furthermore, it was estimated that the fraction of recently fixed 11 CO 2 that was unloaded from the phloem to the surrounding storage cells and retrieved back into the phloem was higher in primary shoots than in secondary shoots, and that allocation to the storage compartment was higher in secondary shoots. Within the primary shoot, the fraction of unloading and retrieval of the 11 C-labelled photosynthates increased towards the inflorescence. Conclusion Here, we demonstrate the synergistic application of high-resolution PET scanning and compartmental modelling as a promising approach to advance our understanding of phloem dynamics in small-dimension plants, such as the model plant Arabidopsis. With this, an opportunity is created to explore the genetic basis of phloem dynamics.

Why it matches plant phenotyping methodsPET撮像とコンパートメントモデルを組み合わせ、植物体内の師部輸送速度や炭素分配という生理形質を推定する方法論が研究の中心であるため。

abstractThis study presents a methodological pipeline to image phloem transport in very thin shoots of the model plant Arabidopsis using photosynthetic uptake of 11 CO 2 and state-of-the-art positron emission tomography (PET).

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review →

Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water management. We reviewed over 200 research articles and discussed the tools, techniques, and challenges associated with spatial and temporal phenotyping for assessing crop water stress, highlighting recent advances and emerging technologies. Emerging technologies such as artificial intelligence (AI) and Internet of Things systems are transforming crop water‐stress phenotyping by enabling real‐time monitoring and robust predictive models. Despite these advancements, challenges persist, including data gaps, platform limitations, and the need for scalable integration frameworks. The review examines key physiological and spectral indicators of crop water stress across multiple spatial and temporal scales using ground‐based sensors, unmanned aerial vehicles, and satellites. It further discusses multiscale phenotyping approaches and data fusion techniques to improve spatial resolution and prediction accuracy. Challenges in harmonizing spatial and temporal data are discussed, along with the need for interdisciplinary collaboration among the phenotyping, modeling, and agronomy domains. The review concludes by identifying future directions, including edge computing, high‐resolution imaging, and robust spatiotemporal phenotyping frameworks to enhance crop water‐stress assessment. By leveraging remote sensing, modeling, and AI, future phenotyping systems can improve water‐stress assessment, advance precision agriculture, and ensure resilience in water‐limited agroecosystems.

Why it matches plant phenotyping methods植物の水ストレス表現型評価に用いる空間・時間スケール、センサー、UAV、衛星、データ融合などの手法を中心に扱うレビューであり、対象範囲に明確に該当する。

abstractThis review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment

A Precise Reproductive Calendar of Sexual and Apomictic Genotypes of Eragrostis curvula . →

Eragrostis curvula serves as a valuable model for studying diplosporous apomixis due to its unique reproductive mode, wide ploidy range, and extensive genomic resources. A major limitation for reproductive studies in this species is the difficulty of isolating female tissues at precise developmental stages, for example, for transcriptomics studies, since different floral tissues can introduce expression noise from non-target tissues. To overcome this, we performed a detailed cytoembryological and morphometric characterization of male and female development in seven E. curvula genotypes with different ploidy levels (2X-7X) and reproductive modes (sexual, facultative apomictic, and obligate apomictic). Using differential interference contrast microscopy and methyl salicylate clarification, we described key cytological stages of male and female development. These stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars. Pistil length showed the strongest association with female developmental stage, particularly during the early phases of ovule development, enabling more precise staging. Synchrony between male and female development was also evaluated, revealing no consistent differences among reproductive modes or ploidy levels. This genotype-informed framework provides a practical tool for stage prediction and tissue selection, supporting future reproductive, developmental, and comparative studies in E. curvula and related grasses.

Why it matches plant phenotyping methods花器官の形態計測を細胞発生段階の推定に体系的に対応付け、遺伝子型別の発達カレンダーと再利用可能なステージ予測手法を構築しており、表現型取得が中心である。

abstractThese stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars.

Early Plant Stress Detection using Thermal Leaf Patterns with TAP-EfficientNet for Precision Agriculture →

The objective of this study is to employ the proposed TAP-EfficientNet model for the early detection of plant stress using thermal leaf patterns, aiming to improve diagnostic accuracy and computational efficiency in precision agriculture. Group 1 is the standard EfficientNet baseline model. Group 2 is the proposed TAP-EfficientNet model. A sample size of 500 thermal leaf images is used for each group, and data is collected across various time intervals and stress conditions (e.g., water deficit, disease). The models' classification accuracy, precision, recall, F1-score, and inference delay are all calculated. The output demonstrated that the TAP-EfficientNet model has better classification results than the standard EfficientNet model in terms of 5.4% higher accuracy, 4.8% higher precision, 6.2% higher F1-score, and [e.g., 12.5%] lower inference delay. The results of the experiment indicate that the suggested TAP-EfficientNet model can detect early plant stress more effectively than the standard EfficientNet model, making it highly suitable for real-time monitoring and deployment in precision agriculture.

Why it matches plant phenotyping methods熱画像から植物ストレス状態を推定する深層学習モデルを提案し、既存モデルと精度・推論遅延を比較検証しており、植物フェノタイピング手法が中心である。

abstractThe objective of this study is to employ the proposed TAP-EfficientNet model for the early detection of plant stress using thermal leaf patterns

Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning →

Abstract Fungal diseases such as Ascochyta pose major threats to chickpea production, causing significant losses if not detected early. Conventional diagnostic methods, including visual inspection and molecular assays, are often time-consuming, subjective, and ineffective for early detection of infection. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves, an application that remains underexplored in previous research. Hyperspectral data in the 400–1000 nm range were acquired under controlled laboratory conditions from artificially infected chickpea plants. In this study, we developed a new comprehensive processing pipeline to address critical challenges associated with hyperspectral data, including noise, artifacts, and illumination variations. Subsequently, unsupervised learning approaches, such as K-means clustering, were employed to construct a clean, well-labeled database of mean leaf spectra. Using this refined dataset, we evaluated a classification framework based on supervised learning models, leveraging selected vegetation indices, visible and infrared spectral bands, along with features derived from statistical analyses. The proposed approach achieved an overall classification accuracy exceeding 95% in distinguishing healthy chickpea plants from those infected with Ascochyta blight. Results demonstrate that HSI can capture subtle physiological changes in leaves before visible symptoms appear, offering a reliable and scalable tool for precision agriculture. This study contributes a promising step toward AI-powered early disease detection in chickpea farming, enabling timely interventions, reducing fungicide use, and supporting sustainable crop protection strategies. Future work will focus on real-world deployment and cost-effective integration into existing monitoring systems.

Why it matches plant phenotyping methodsHSIと機械学習による感染葉の生理変化・病害状態の非破壊推定パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。

abstractThis study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves

Optimization of LF-NMR-based methods for analysis of oil content and distribution in germinating oilseeds. →

Background Lipid metabolism is critical for seed germination, directly impacting their nutritional value as a food raw material. Conventional methods for oil analysis are destructive and fail to determine oil distribution. This study evaluated the feasibility of using low-field nuclear magnetic resonance (LF-NMR) coupled with magnetic resonance imaging (MRI) as a non-destructive approach for monitoring oil changes in germinating oilseeds. Results Four representative oilseed varieties - herbaceous (peanut, soybean) and woody (camellia, almond) - were investigated to analyze oil changes during germination. The accuracy of LF-NMR was validated against Soxhlet extraction and confocal laser scanning microscopy (CLSM). The results revealed that herbaceous seeds exhibited rapid oil mobilization germination, whereas woody seeds showed slower oil consumption. High correlations were observed between LF-NMR method and conventional method/CLSM imaging method (R 2 > 0.9). Notably, MRI-imaging oil ratio demonstrated the highest accuracy in quantifying both oil content and distribution. Greenness evaluation results show that LF-NMR is the greenest method for sample preparation and measurement process. Conclusion These findings confirm LF-NMR as an effective method for non-destructive monitoring of oil content and distribution during seed germination, which holds significant application potential in areas such as food raw material quality assessment and the optimization of oilseed processing pretreatment. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methods発芽油種子の油含量・分布という植物器官の状態を、LF-NMR/MRIで非破壊測定する手法を開発・検証しており、表現型取得法が研究の中心である。

abstractThis study evaluated the feasibility of using low-field nuclear magnetic resonance (LF-NMR) coupled with magnetic resonance imaging (MRI) as a non-destructive approach for monitoring oil changes in germinating oilseeds.

Hyperspectral estimation of magnesium content in Yunyan 87 and Zhongyan 100 tobacco leaves using machine learning. →

Hyperspectral remote sensing provides a rapid and non-destructive approach for monitoring plant nutrient status; however, its application for magnesium (Mg) estimation in flue-cured tobacco remains limited. In this study, two cultivars, Yunyan 87 and Zhongyan 100, were grown in a hydroponic system with five Mg concentration gradients (0, 0.2, 1, 5, and 25 mmol L -1 ). Hyperspectral reflectance data of fresh leaves were collected at different growth stages. Three preprocessing methods, including first derivative (FD), standard normal variate (SNV), and multiplicative scatter correction (MSC), were applied, and partial least squares regression (PLSR) was used to identify the optimal preprocessing strategy. Characteristic wavelengths were selected using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and genetic algorithm (GA), and were further combined with extreme learning machine (ELM), support vector regression (SVR), and radial basis function (RBF) neural network models to estimate Mg content. The results showed that spectral preprocessing significantly improved the relationship between hyperspectral data and Mg content, with optimal methods varying across cultivars and growth stages. Selected wavelengths were mainly located in the near-infrared region. The developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90. In addition, Yunyan 87 exhibited higher prediction accuracy than Zhongyan 100. These findings demonstrate that hyperspectral technology combined with feature wavelength selection and machine learning enables accurate and non-destructive estimation of Mg content in flue-cured tobacco leaves, providing a reliable tool for Mg nutrition diagnosis and precision management. However, further validation under diverse field conditions is required to enhance model robustness.

Why it matches plant phenotyping methodsタバコ葉のMg含量という植物生理状態を、ハイパースペクトル計測と波長選択・機械学習で非破壊推定する方法が研究の中心であり、モデル精度評価も行っている。

abstractThe developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90.

Leaf to Root: Harnessing leaf spectral signatures for non-destructive monitoring of soybean nodule traits. →

Soybean ( Glycine max ) root nodules, formed through symbiosis with nitrogen-fixing rhizobia, are essential for biological nitrogen fixation. While quantifying key nodulation traits, nodule number and weight, is critical for assessing symbiotic efficiency and yield potential, current methods are destructive and labor-intensive, unsuitable for longitudinal monitoring and high-throughput phenotyping. Here, we established hyperspectral leaf reflectance as a non-destructive, high-resolution tool capable of monitoring root nodule development. Using Partial Least Squares Regression models, we connected spectral data with nodule metrics from 528 unique soybean plants across 18 genotypes, inoculated with different rhizobium strains, and under different abiotic stresses. These models achieved high accuracy for predicting nodule number (R 2 = 0.75, nRMSE = 6.02%) and moderate accuracy for nodule weight (R 2 = 0.53, nRMSE = 12.38%). Crucially, spectral analyses revealed distinct hyperspectral signatures sensitive to nodule traits. While different rhizobium strains induced comparable changes in both nodule traits, and therefore produced highly overlapped spectral domains, diagnostically distinct spectral patterns were generated under drought versus salt stress, with the former suppressing nodulation more significantly than the latter. Furthermore, we demonstrated the effectiveness of our models for real-time in-situ monitoring of nodule development for individual plants. Spectral-nodule trait covariation analyses further revealed leaf signatures correlated with nodule traits primarily through systemic physiological coupling governed by carbon-nitrogen exchange dynamics and plant water status. This study showcased hyperspectral sensing as a transformative methodology, enabling the unprecedented non-destructive quantification of nodulation dynamics, revealing novel physiological insights into plant-microbe-environment interactions, facilitating breeding and management strategies for sustainable soybean production.

Why it matches plant phenotyping methods葉のハイパースペクトル反射を用いて根粒数・重量を非破壊推定するセンシング手法を開発・評価しており、植物表現型取得が研究の中心です。

abstractcurrent methods are destructive and labor-intensive, unsuitable for longitudinal monitoring and high-throughput phenotyping.

Thermography Applied to the Assessment of Podosphaera xanthii Infection in Susceptible Melon Plants →

Powdery mildew, a disease caused by the biotrophic fungus Podosphaera xanthii, is one of the most destructive diseases affecting melon crops worldwide. This pathogen causes alterations in the physiology of the host plant even before visible symptoms appear, which in turn can be detected using non-invasive imaging techniques. In this piece of work, infrared thermography was used to evaluate the temperature dynamics of melon leaves infected with P. xanthii during the first 72 h after infection. Infected leaves showed a significant decrease in temperature compared to mock-controls from 18.5 hpi onwards, before the appearance of visible mycelium. This temperature difference between mock-control and P. xanthii-infected melon leaves remained significant throughout the experiment, suggesting a sustained disruption of water-balance regulation caused by the fungus. This imbalance could be linked to haustorium-mediated interference with stomatal function or epidermal osmotic homeostasis. Overall, these results highlight thermography as a powerful and sensitive tool for detecting early physiological responses during P. xanthii infection of melon leaves. Therefore, thermography could be used as a valuable complement to ‘omics’ and other image-based phenotyping methods, helping to provide a comprehensive view of the responses that different diseases trigger in host plants.

Why it matches plant phenotyping methodsメロン葉の感染に伴う温度変化を赤外線サーモグラフィーで非侵襲的に測定し、可視症状前の病態・生理状態を評価する方法の適用が中心である。

abstractwhich in turn can be detected using non-invasive imaging techniques

A Bioluminescent Reporter System for Real-Time Monitoring of the Unfolded Protein Response in Plants. →

The unfolded protein response (UPR) is a critical mechanism for maintaining endoplasmic reticulum (ER) homeostasis under stress. Here, we developed a bioluminescent reporter system, AtbZIP60-LUC, in Arabidopsis to dynamically monitor ER stress by coupling IRE1-mediated splicing of bZIP60 mRNA to firefly luciferase (LUC) expression. Under ER stress, IRE1 removes a 23-bp sequence from bZIP60u, producing a spliced bZIP60s transcript in-frame with LUC, enabling luciferin-dependent luminescence. Transgenic AtbZIP60-LUC lines exhibited specificity for canonical ER stressors (heat, DTT, tunicamycin) but not osmotic stressors (NaCl, mannitol), confirmed by bioluminescence, qPCR, and immunoblotting. Time-course assays revealed rapid LUC induction by DTT (peak at 1 h) and delayed activation by tunicamycin (peak at 1-2 h), followed by signal decline, reflecting adaptive UPR dynamics. Heat stress optimization identified 38°C as optimal, inducing robust LUC expression after 2-3 h without compromising viability, while 42°C caused irreversible damage. Genetic validation in ire1a ire1b mutants abolished LUC induction, confirming IRE1 dependency, whereas constitutive UPR activation via maize 16-kDa γ-zein (16γz) overexpression triggered LUC expression without stress. Extending this system to tobacco and tomato, we engineered NbbZIP60-LUC and SlbZIP60-LUC, which similarly responded to heat (38°C), DTT, tunicamycin, and ER-localized protein aggregation (16γz, zeolin) in transient and stable assays. This work establishes bZIP60-LUC as versatile, non-invasive tools for real-time UPR monitoring in plants, offering insights into ER stress dynamics and enabling cross-species studies of stress adaptation mechanisms.

Why it matches plant phenotyping methods植物のERストレス状態を非侵襲的・リアルタイムに測定するルシフェラーゼレポーター法を開発し、ストレス特異性、時間応答、遺伝的依存性、複数種での性能を検証しており、表現型取得法が研究の中心である。

abstractHere, we developed a bioluminescent reporter system, AtbZIP60-LUC, in Arabidopsis to dynamically monitor ER stress by coupling IRE1-mediated splicing of bZIP60 mRNA to firefly luciferase (LUC) expression.

Towards in-field live fuel moisture content estimation using multi-wavelength terrestrial laser scanning →

The impact of climate change on vegetation dynamics and wildfire risk has been a subject of considerable research interest. Live fuel moisture content (LFMC) is a critical factor in assessing fire risk and influencing fire ignition and behaviour. Satellite remote sensing techniques provide information on LMFC dynamics, but spatial and temporal resolution hinder understanding in structurally complex forests with interconnected tree and shrub layers. Multi-wavelength terrestrial laser scanning (TLS) sensors can measure the structural and spectral properties of forests and have demonstrated their potential for monitoring LFMC. However, studies of LFMC in shrubs are scarce despite their key role in fire spread. In this study, we investigated the capacity of a dual-wavelength SALCA (Salford Advanced Laser Canopy Analyser) TLS (1063 and 1545 nm) and the single-wavelength Trimble X6 (1500 nm) to estimate LFMC in six Mediterranean forest plots (135 individual plants, 18 species). Analysis at different separate plots and individual-species levels identified key factors affecting LFMC prediction using TLS. At plot level, linking spectral indices and LFMC is challenging due to species diversity in crown structures, ages, sizes and leaf types. Our results suggest that detector heating by solar radiation could alter the sensor calibration and reduce model accuracy. Nevertheless, in some areas the multiple linear regression models achieved an R adj 2 up to 0.82 and an RMSE of 7.66%. At the species level, models showed stronger relationships with LFMC ( R adj 2 ranging from 0.43 to 0.88) and a relatively low RMSE (RMSE from 1.92 to 3.97%). Overall, univariate relationships between LFMC and individual-wavelength reflectance were not consistent across species or most plots. Considering these results, combining the capacity of dual TLS devices to estimate LFMC with the structural information that they provide, open a potential to improve field work sampling for wildfire risk assessment. Extending the research to cover a wider range of tree, shrub and herbaceous species in the future will advance our understanding of LFMC dynamics and contribute to more accurate fire behaviour modelling.

Why it matches plant phenotyping methodsTLSセンサーを用いて個体・種・プロットレベルの生植物含水率（LFMC）を推定し、モデル精度やセンサー校正の影響を評価しているため、植物状態の取得・推定方法が中心です。

abstractMulti-wavelength terrestrial laser scanning (TLS) sensors can measure the structural and spectral properties of forests and have demonstrated their potential for monitoring LFMC.

Predicting Grain Yield in Wheat Using UAV Multispectral and Ground Based Vegetation Indices →

Abstract High-throughput phenotyping using unmanned aerial vehicle (UAV) multispectral imagery offers a promising approach for predicting wheat yields under variable sowing conditions. This study evaluated the effectiveness of UAV-based vegetation indices compared to the GreenSeeker handheld sensor in estimating yield-related traits in 13 bread wheat genotypes. UAV-based multispectral indices— Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red-edge Normalized Difference Vegetation Index (RNDVI and simple ratio (SR) were captured using a MicaSense sensor at two growth stages [52 and 79 days after sowing (DAS) for timely sown; 16 and 43 DAS for late sown]. Simultaneously, NDVI was recorded using a GreenSeeker handheld sensor for direct comparison with UAV-derived NDVI. UAV-derived indices showed consistently stronger correlations with biological yield (BY), grain yield (GY), and thousand grain weight (TGW), particularly during the anthesis stage. GNDVI and SR emerged as the most predictive indices for BY and GY, while TGW showed stronger associations with early-stage indices. GreenSeeker NDVI correlations were weaker and less consistent across growth stages and sowing conditions. Genotypes such as Phule Samadhan, MACS 2496, and GS 4042 exhibited superior adaptability under late-sown heat stress, maintaining higher vegetation index values throughout. UAV-based multispectral imaging outperformed the handheld sensor in predicting key yield traits and detecting inter-genotypic variation under stress. Statistical and multivariate analyses (ANOVA, PCA, and heatmap visualization) revealed distinct inter-genotypic variability in vegetation indices, effectively distinguishing high-vigor and stress-susceptible wheat genotypes under varying sowing environments. These findings highlight UAV-based multispectral imaging as a robust, efficient, and scalable phenotyping tool for identifying stress-tolerant and high-yielding genotypes, underscoring the importance of phenological timing and optimal index selection in breeding and precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と地上センサーによる植生指数の取得・比較を中核とし、収量関連形質と遺伝子型差の推定に用いる実質的な植物フェノタイピング研究である。

abstractHigh-throughput phenotyping using unmanned aerial vehicle (UAV) multispectral imagery offers a promising approach for predicting wheat yields under variable sowing conditions.

GWAS-informed genomic selection for cold tolerance in pepper (Capsicum annuum L.) →

Abstract Background Breeding for cold tolerance in pepper ( Capsicum annuum L.) is critical to mitigate yield losses caused by unpredictable temperature fluctuations associated with climate change. However, genetic improvement of this trait is hindered by challenges in accurate phenotyping, particularly at the adult stage, and by its complex genetic architecture involving numerous minor-effect loci. While genomic selection (GS) offers a promising solution to accelerate genetic gain, its predictive ability is often limited by statistical noise from uninformative markers within whole-genome marker sets. This study aimed to overcome this limitation by developing a robust phenotypic index and implementing a genome-wide association study (GWAS)-informed GS strategy. Results We phenotyped 192 pepper accessions from a core collection for cold tolerance using a visual survival score (Surv) and a newly developed composite cold-tolerance index (CTI). Both CTI ( h 2 = 0.55) and Surv ( h 2 = 0.53) showed moderate heritability, suggesting a substantial contribution from additive genetic variance to the phenotypic variation of cold tolerance in adult plants. GWAS identified 13 candidate genomic regions associated with cold tolerance; these regions included TRM9 , CAP1 , and PP2A-2 , genes previously implicated in abiotic stress responses. A GWAS-informed GS model using a selected subset of 1,024 markers achieved a prediction accuracy of 0.78, representing a substantial improvement over that obtained with the standard model using the full marker set of 73,502 markers (0.203). Notably, a control model using a random marker set of identical size (1,024 markers) yielded an accuracy of only 0.122, confirming that the greater predictive power of the GWAS-informed GS model was driven by the genetic relevance of the selected markers rather than by lower marker density. Conclusions Our study demonstrates that assessing cold tolerance via the CTI is pivotal for overcoming the limitations of small-scale sample collection. By turning ordinal data into a continuous spectrum, the CTI can effectively unmask hidden genetic variation. Integrating this refined phenotype with GWAS-informed marker selection into prediction models significantly enhanced the accuracy of genomic prediction for cold tolerance in adult pepper plants. This integrated framework offers a practical and efficient roadmap for accelerating breeding cycles and improving selection precision for complex abiotic stress traits in pepper breeding.

Why it matches plant phenotyping methods成体ペッパーの耐寒性を測定する新規複合表現型指数（CTI）を開発し、視覚スコアとの比較および遺伝的予測への有用性を評価しており、表現型取得法が研究の中心である。

abstractThis study aimed to overcome this limitation by developing a robust phenotypic index and implementing a genome-wide association study (GWAS)-informed GS strategy.

Pierceable, Water-Resistant, and Transparent Nanofilm Electrodes Comprising Carbon Nanotubes for Long-Term Monitoring of Plant Electrophysiology. →

Trichomes are found on the surfaces of many crops, making it challenging to design sensors that monitor plant conditions without interfering with their natural functions. Notably, conventional and advanced electrodes developed for plant electrophysiology have not overcome this challenge. Herein, we present thin-film composite electrodes that are transparent, water-vapor-permeable, water-resistant, non-invasive, pierceable by trichomes, and conformal to the complex surfaces of leaves, ensuring low contact impedance without the need for adhesives. The bilayer electrodes (referred to as "nanofilm electrodes") are composed of single-walled carbon nanotubes (SWCNTs) deposited on poly(styrene-b-butadiene-b-styrene), and the thickness is varied (70-480 nm) to evaluate its influence. Furthermore, the sensing performance and long-term stability are compared with those of commercial wet electrodes and conventional poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) electrodes. The proposed SWCNT nanofilm electrodes enable stable detection of electrophysiological changes in leaves for over 2 months, as well as light-induced biopotential signals under chemical stress, highlighting their potential for long-term, real-time plant health monitoring in smart agriculture systems.

Why it matches plant phenotyping methods植物の電気生理状態を長期測定する透明・非侵襲的電極を開発し、既存電極との性能比較と安定性評価を行っており、植物フェノタイピング手法が研究の中心である。

abstractHerein, we present thin-film composite electrodes that are transparent, water-vapor-permeable, water-resistant, non-invasive, pierceable by trichomes, and conformal to the complex surfaces of leaves, ensuring low contact impedance without the need for adhesives.

Spectral prediction of anthocyanin concentration in Populus pruinosa leaves based on vegetation index. →

Introduction Populus pruinosa is the key foundation tree species in desert riparian forests in arid areas of northwestern China. Timely and accurate monitoring of the physiological status of P. pruinosa is crucial for restoring the damaged ecosystem. Anthocyanins are one of the important physiological indicators that reflect the environmental adaptability of P. pruinosa under stress. Existing studies have extensively applied hyperspectral technology for the quantitative prediction of crop leaf pigments. However, research on hyperspectral prediction of anthocyanin concentration in woody halophytes is still lacking, particularly in the integration of spectral preprocessing, species-specific vegetation index construction, and machine learning modeling. Methods In this study, the hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves collected in five months (June - October) under five groundwater depth conditions (0-2, 2-4, 4-6, 6-8, and 8-10 m). Based on first-order (FD) and second-order (SD) derivative processing, competitive adaptive reweighted sampling (CARS), https://xueshu.baidu.com/usercenter/paper/show?paperid=bea4d6371f19161f21aac22941cc4408&site=xueshu_se shuffled frog leaping algorithm (SFLA), and recursive feature elimination with cross-validation (RFECV) were used to extract spectral features of P. pruinosa leaves to construct the anthocyanin reflectance index, composite index, difference vegetation index, and normalized anthocyanin reflectance index. After that, the top 10 sets of data with high correlation with anthocyanin concentration were selected from each vegetation index to form a total data set (40 sets in total) for modeling. Twelve models were constructed using support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) methods. Results The FD and SD derivative transformations of the spectral reflectance significantly enhanced the correlation with anthocyanin concentration. The feature extraction methods SFLA and RFECV were superior in extracting the bands highly related to anthocyanin concentration, and the vegetation indices constructed based on these two methods had a high correlation with anthocyanin concentration in the red and near-infrared regions. The optimal prediction model was FD-SFLA-SVM (R 2 = 0.852, RMSE = 86.851 mg m -2 , RPD = 2.596). Discussion Unlike existing vegetation index-based studies, the research develops a systematic approach to construct vegetation indices and models for estimating the anthocyanin concentration in the woody halophyte P. pruinose in deserts. The research will provide technical support for non-destructive monitoring of the physiological status of P. pruinosa , and also contribute to the restoration of desert riparian ecosystems.

Why it matches plant phenotyping methods葉のハイパースペクトルからアントシアニン濃度という植物生理形質を推定する手法を開発・比較しており、形質取得とモデル化が研究の中心である。

abstractthe hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves

OpenAlea.HydroRoot: A modelling framework to dissect, predict and phenotype branched root hydraulic architecture →

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method “cut and flow” is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.

Why it matches plant phenotyping methods根系の水理特性を解析・予測し、表現型化するモデルとオープンソースソフトウェアを開発・提示しており、植物フェノタイピング手法が中心である。

abstractHydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA.

Sentinel plants enable quantitative monitoring of bioavailable nitrate in soils and microbial environments →

Microbial transformations of nitrogen in soils strongly influence plant nutrition and ecosystem function, yet these processes remain difficult to monitor. Existing approaches rely largely on destructive soil sampling and laboratory analysis, limiting the ability to track nitrate dynamics in situ. Here, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate. The sensor uses a synthetic nitrate-responsive promoter coupled to a ratiometric luciferase reporter, enabling high-dynamic-range measurements. Sentinel plants exhibit a dose-dependent, reversible nitrate response with high specificity over alternative nitrogen sources. In agricultural soils from multiple California field sites, sensor output closely tracked analytically measured nitrate concentrations and resolved nitrate amendments without destructive extraction. Beyond environmental sensing, sentinel plants enabled screening of nitrogen-fixing microbial communities and the detection of microbially generated nitrate in both liquid culture and soil systems. Using this platform, we identified a minimal three-member microbial consortium capable of converting atmospheric nitrogen into nitrate via sequential nitrogen fixation and nitrification. This consortium increased tissue nitrate accumulation and plant fresh weight, demonstrating that sentinel plants can both monitor nitrate availability and identify microbial communities that enhance plant growth. Significance Statement Nitrogen availability in soils fluctuates across space and time, yet most measurements rely on extracting soil samples and analyzing them in the laboratory. Such measurements provide only snapshots of nitrogen status and do not necessarily reflect the nitrogen that plants themselves experience. Here, we engineer plants that act as living nitrate sensors by converting nitrate perception into a measurable optical signal. Because these sensors operate within intact plants, they report nitrate availability as integrated through plant uptake and physiology rather than through chemical extraction alone. Using this platform, we tracked nitrate levels in agricultural soils and identified a minimal microbial consortium capable of converting atmospheric nitrogen into plant-available nitrate. This plant-based sensing strategy enables direct monitoring of nitrogen dynamics in soils and microbial environments, providing a platform for identifying microbial communities that enhance nitrogen availability for crops.

Why it matches plant phenotyping methods植物を用いた遺伝子 encoded センサーを開発し、硝酸可給性を定量する光学的表現型取得法として検証・応用しているため、植物フェノタイピング手法が中心である。

abstractHere, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate.

SPECGAN: Extracting sensitive bands from plant disease spectra based on generative adversarial network. →

Hyperspectral imaging provides detailed spectral information for non-destructive plant disease diagnosis, yet its use is limited by high dimensionality of the original spectra, as well as insufficient and imbalanced data records. These issues hinder the extraction of weak pathological signals and ultimately reduce model applicability. To overcome these challenges, this study proposes SPECGAN, a Generative Adversarial Network with a Temporal-Domain Feature Pyramid Fusion (TD-FPNF) and a residual attention mechanism. SPECGAN forms a general framework for both sensitive band extraction and data augmentation. A multi-scale convolutional module captures local narrow-band features related to biochemical changes, as well as global broadband trends linked to physiological structure. The residual attention mechanism further enhances subtle disease cues by adaptively reweighting multi-level fused features and suppressing background noise. SPECGAN accurately identifies key discriminatory bands based on gradient saliency analysis of the discriminator, while generating high-quality synthetic samples to alleviate data scarcity. Experiment results demonstrate that the sensitive bands concentrate in the green peak (520-550 nm) and red-edge (680-720 nm) regions, consistent with disease-induced physiological changes. By only inputting the top 20 bands (8% of the spectrum), the MLP classifier achieves 96.22% accuracy. Under a 14.6:1 imbalance scenario, generating 1500 synthetic samples boosts performance by 6%-13%. Overall, SPECGAN provides an efficient and interpretable approach for early diagnosis of rice bacterial leaf blight.

Why it matches plant phenotyping methods植物病害の症状・生理状態を対象に、ハイパースペクトル画像から感受性バンドを抽出し、データ拡張も行うSPECGAN手法を開発しており、病害表現型の取得・解析が中心である。

abstractthis study proposes SPECGAN, a Generative Adversarial Network with a Temporal-Domain Feature Pyramid Fusion (TD-FPNF) and a residual attention mechanism.

Drought induced metabolomics of potato leaves highlight metabolic reprogramming and promising biomarkers for smart irrigation advisories →

Smart irrigation management is essential for improving crop resilience under increasing drought frequency driven by climate change. Although satellite-based remote sensing provides valuable tools for monitoring crop water status at large spatial scales, its accuracy is often limited in mountainous and heterogeneous agricultural landscapes. In this study, we investigated drought-induced metabolic responses in potato ( Solanum tuberosum L.) to identify biochemical biomarkers that could complement satellite-based irrigation advisories in the mid-Himalayan region of India. A field experiment was conducted using a gradient of soil moisture regimes corresponding to moderate (50% field capacity), critical (25% field capacity), and extreme drought stress (5-8% field capacity). Satellite-derived evapotranspiration-based irrigation advisories were validated against in situ soil moisture measurements, revealing discrepancies attributed to the inability of satellite estimates to capture actual water loss under drought stress conditions, highlighting the need for additional ground-truth biomarkers across heterogeneous field conditions. To capture plant-level physiological responses, untargeted metabolite profiling of potato leaves was performed using gas chromatography–mass spectrometry (GC-MS). Approximately fifty metabolites belonging to amino acids, organic acids, sugars, and sugar alcohols were detected. Multivariate statistical analyses revealed distinct metabolic signatures associated with progressive drought stress. Notably, accumulation of proline, serine, isoleucine, sucrose, fructose, glucose, and polyols such as mannitol and myo-inositol reflected key metabolic reprogramming associated with osmoprotection, redox homeostasis, and energy metabolism under drought conditions. Collectively, this ensemble of stress-responsive metabolites represents a robust panel of drought stress biomarkers. As a proof of concept, proline was validated as a qualitative biomarker of plant water status through a rapid and cost-effective colorimetric biochemical assay, demonstrating its practical applicability for field-level irrigation management. These findings demonstrate that metabolomics-derived biomarkers can provide sensitive plant-level indicators of drought stress that complement satellite-based monitoring systems. The integration of biochemical diagnostics with remote sensing platforms offers a promising approach for improving drought detection and developing low-cost, field-deployable tools for smart irrigation advisories in heterogeneous agricultural landscapes. Graphical abstract

Why it matches plant phenotyping methods植物の水分状態・乾燥ストレスを示す代謝バイオマーカーを開発・検証し、迅速な比色 assay として実用化可能性を評価しており、単なる乾燥処理実験の routine 測定を超えて表現型取得法が中心的です。

abstractAs a proof of concept, proline was validated as a qualitative biomarker of plant water status through a rapid and cost-effective colorimetric biochemical assay, demonstrating its practical applicability for field-level irrigation management.

MWCNT-Au/Pt-modified screen-printed electrode for electrochemical detection of secondary metabolites in Ganoderma -infected oil palms. →

Ganoderma boninense infection affects nearly half of Indonesia's palm oil plantations, which leads to severe economic losses. Conventional detection methods like PCR are limited by cost and complexity, while electrochemical detection offers a promising alternative by targeting plant-derived metabolites. This study focuses on developing a low-cost, custom fabricated screen-printed electrode (SPE) modified with MWCNTs and Au/Pt nanoparticles to enable practical, scalable early detection of G. boninense in oil palm plantations using various electrochemical techniques. The SPE was fabricated using conductive carbon ink on sticker paper and modified with MWCNT-Au/Pt nanocomposites to enhance performance. Pt nanoparticles (2.5, 5, and 10 mM) were synthesized hydrothermally, while Au (5 mM) was prepared via citrate reduction. The nanocomposites were formed by sonication and applied via drop-casting. Characterization was performed using UV-vis, FTIR, XPS, SEM, contact angle, four-point probe, CV, and EIS. Electrochemical detection of phytol, quinoline, and stigmasterol was conducted using DPV and chronoamperometry in phosphate buffer saline. The results indicate that surface modification enhances conductivity and hydrophilicity, improving the charge transfer process in electrochemical detection. The best combination for SPE modification was the 3 : 1 ratio and 5 mM Pt concentration. The best detection results using DPV were achieved for phytol, quinoline, and stigmasterol, with respective R 2 and LOD of 0.98 and 2.85 mM, 0.95 and 2.36 µM, and 0.98 and 1.36 µM. Repeated testing on the SPE demonstrated good stability, despite being designed as a disposable test kit. Further, the SPEs demonstrate low detection limits and high sensitivity, highlighting their potential for early diagnosis and rapid screening of Ganoderma -infected oil palm trees as an accessible alternative for disease prevention.

Why it matches plant phenotyping methods感染植物由来代謝物を用いて油ヤシの感染状態を推定する電気化学センサーを開発し、性能評価・反復試験も行っているため、植物状態の取得法が研究の中心である。

abstractThis study focuses on developing a low-cost, custom fabricated screen-printed electrode (SPE) modified with MWCNTs and Au/Pt nanoparticles to enable practical, scalable early detection of G. boninense in oil palm plantations using various electrochemical techniques.

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.

In situ detection of methyl jasmonate using plant-wearable sensors to quantify genotype-dependent herbivory resistance. →

Methyl jasmonate (MeJA) is a key phytohormone regulating plant responses to herbivory and environmental stress. While conventional analytical techniques, such as liquid chromatography-mass spectrometry, provide high accuracy, their application is often limited by labor-intensive workflows, high costs, and complex sample preparation requirements. In this study, we present a wearable electrochemical sensor for the in situ monitoring of MeJA in maize (Zea mays L.) under fall armyworm (FAW; Spodoptera frugiperda [J.E. Smith]) herbivory. The sensor employs an array of microneedles functionalized with a MeJA-specific molecularly imprinted polymer (MIP). This work presents the in situ monitoring of MeJA levels within intact plant tissues using a MeJA-specific MIP integrated with a microneedle-based sensor, and demonstrates, for the first time, the use of a plant-wearable sensor to quantify genotype-dependent herbivory resistance. The sensor exhibited considerable sensitivity and selectivity, with a detection limit of 0.18 μM. Sensor performance was validated in four maize genotypes with varying levels of resistance to FAW (Mp708, BS39:0043, Tx601, GEMN0131). Time-course measurements revealed that resistant genotypes exhibited earlier and stronger MeJA induction following infestation, whereas susceptible genotypes showed delayed and attenuated responses. Sensor measurements demonstrated a strong correlation with conventional measurement data. Statistical analysis using a randomized complete block design confirmed that genotype, detection methodology, and infestation status significantly influence MeJA variability. These findings highlight the potential of the present wearable sensor as a powerful tool for studying plant defense mechanisms and advancing precision agriculture through direct monitoring of phytohormonal signaling.

Why it matches plant phenotyping methods植物体内のホルモン状態を測定するウェアラブル電気化学センサーを開発し、複数遺伝子型で性能検証・従来法との相関評価を行っており、表現型取得手法が研究の中心である。

abstractwe present a wearable electrochemical sensor for the in situ monitoring of MeJA in maize (Zea mays L.) under fall armyworm (FAW; Spodoptera frugiperda [J.E. Smith]) herbivory.

Comparative Evaluation of Ground-based, Manual, and Remote Sensing Approaches for Crop Stress Detection: A Review between Malaysia and China →

This study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China. Although sensing technologies have advanced considerably, cross-country comparisons between regions with differing levels of technological maturity remain limited. China, recognised for its advanced adoption of UAV and sensor-based agriculture, provides a benchmark against Malaysia’s developing digital agriculture landscape. Each method was assessed based on accuracy, responsiveness, scalability, and cost-effectiveness under field conditions. UAV-based remote sensing achieved the highest overall accuracy (mean 92%) and demonstrated superior scalability, enabling rapid large-area monitoring using vegetation indices such as NDVI and NDRE. Ground-based sensors, including soil moisture probes and chlorophyll meters, showed moderate accuracy (mean 81%) and were suitable for plot-level monitoring with real-time feedback. Manual inspection recorded the lowest accuracy (mean 68%) and limited scalability due to labour dependency and subjective assessment. UAV methods were particularly effective in early stress detection, with thermal imaging identifying canopy temperature anomalies 3–5 days before visible symptoms, especially in maize and coconut fields. Integrating UAV and ground-based sensing provided more comprehensive and timely assessments than individual approaches. These findings support the development of scalable precision agriculture frameworks tailored to tropical and subtropical systems.

Why it matches plant phenotyping methods作物ストレスという植物状態を対象に、手動観察・地上センサー・UAVリモートセンシングを精度、応答性、拡張性、費用で比較評価しており、センシング手法の技術評価が中心である。

abstractThis study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China.

Construction and UAV-based inversion of integrated nitrogen diagnosis index for cotton using multispectral imagery. →

Introduction Accurate and stable diagnosis of cotton nitrogen status across growth stages is essential for precision fertilization in drip-irrigated systems. However, the instability of conventional nitrogen-related indicators across different phenological stages often reduces diagnostic performance and limits their broader application. Methods A field experiment was conducted in Xinjiang, China, under four irrigation levels (60%, 80%, 100%, and 120% ET c ) and four nitrogen application rates (0, 245, 300, and 350 kg N ha -1 ). UAV multispectral imagery was acquired at the squaring, flowering, boll-setting, and boll-opening stages. Based on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed. Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) models were used to evaluate the inversion performance of INDI. In addition, the nitrogen nutrition index (NNI), derived from the critical nitrogen dilution curve, was used to validate the diagnostic stability of INDI. Results Multispectral vegetation indices were strongly correlated with LAI, LNWupper, and INDI, with red-edge- and near-infrared-based indices showing the highest sensitivity. Among the three models, XGBoost achieved the best inversion accuracy for INDI (R 2 = 0.85, RMSE = 0.61). INDI was significantly correlated with NNI across growth stages, with R 2 values of 0.58, 0.77, 0.81, and 0.70 at the squaring, flowering, boll-setting, and boll-opening stages, respectively, and the highest accuracy observed at the boll-setting stage. Moreover, the spatial distribution maps of INDI effectively distinguished nitrogen differences under different water-nitrogen treatments and were consistent with NNI-based classifications. Discussion INDI accurately captured nitrogen dynamics throughout cotton growth, and the INDI-XGBoost framework provided a robust approach for high-precision spatial nitrogen diagnosis. These results support precision fertilization management in drip-irrigated cotton fields in Xinjiang.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からワタの窒素状態を推定する指標とXGBoost反転手法を開発・検証しており、植物生理状態の取得・推定が研究の中心である。

abstractBased on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed.

Estimation of Crop Coefficients of a High-Density Hazelnut Orchard Using Traditional Methods vs. UAV-Derived Thermal and Spectral Indices →

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana®) with two planting densities (625 and 1250 trees ha−1) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling.

Why it matches plant phenotyping methodsUAVのマルチスペクトル・熱画像からキャノピー形状、被覆率、NDWI、CWSIを抽出し、作物係数との関係を較正・比較している。植物の水分状態やキャノピー特性の測定・推定が研究の中心であり、単なる灌漑試験の routine measurement ではない。

abstractMultispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI

Ultrastructural and Energy-Dispersive Spectroscopy (EDS) study of Araucaria grandifolia leaf cuticles (Aptian, Patagonia): Implications for taxonomy and paleoecology →

Transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae). Well-preserved foliar compressions of this taxon were collected from the Punta del Barco Formation (Baqueró Group, Aptian, Patagonia, Argentina). TEM sections revealed six types of cell cuticles: two representing the ordinary epidermal cells (OEC) of the upper and lower cuticle, and four related to the stomatal apparatus and associated cells: subsidiary and guard cell cuticles, and inner and outer associated OEC cuticles. Cuticles comprise either a granular A2 layer (cuticle proper) and a spongy-fibrilous B1 layer (cuticular layer), or solely a B1 spongy layer, which is similar to that of Nothopehuen brevis and Brachyphyllum garciarum , two Cretaceous Araucariaceae from Patagonia. The statistical evaluation of quantitative measurements revealed the relationships and hierarchies between cell cuticle types and ultrastructural layers, revealing for the first time the precise identity of Araucariaceae cuticles. TEM-EDS revealed a significant presence of phosphorus (P) and chlorine (Cl), highlighting the potential taxonomic and paleoenvironmental relevance of the P/Cl ratio. Additionally, the six cell cuticle types found in A. grandifolia are shown in a dichotomous key, and a cuticle three-dimensional reconstruction is provided. Finally, the paleoenvironment conditions under which the A. grandifolia plant lived during the Aptian in Patagonia are also inferred.

Why it matches plant phenotyping methods葉のクチクラ微細構造・元素組成という植物器官形質を、TEM、EDS、各種顕微鏡、定量解析、3D再構成で体系的に取得・解析しており、観察が分類・古生態の補助的な routine 測定に留まらず、方法に基づく形質記載の中心となっている。

abstractTransmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae).

Using Leaf Hyperspectral Data to Early Detect Flavescence Dorée Before the Onset of Symptoms in a Heavily Affected Vineyard of Tuscany (Central Italy). →

Advanced methods are necessary to improve the detection of flavescence dorée (FD), one of the most relevant grapevine ( Vitis vinifera ) diseases in Europe, caused by flavescence dorée phytoplasma (FDp). Detection is commonly carried out visually by agronomists/winegrowers, and is time-consuming and error-prone. The present study demonstrated that full-range hyperspectral data (i.e., 400 to 2,400 nm) collected at leaf level can be used as a tool to rapidly and nondestructively detect FD infection directly in the field. Focusing on a Sangiovese (red grape) vineyard of Tuscany (Central Italy), heavily affected by FD (incidence higher than 75%), we showed that the proposed hyperspectral approach is capable of (i) detecting FDp infection, even before the occurrence of leaf symptoms (accuracy >70%); (ii) discriminating FD-induced leaf symptoms, even between asymptomatic and lightly symptomatic leaves (accuracy 80%); and (iii) elucidating the complex physiological responses of grapevines to FDp infection, with changes in leaf parameters estimated from spectra suggesting that the disease not only impaired early-season photosynthetic efficiency but also accelerated leaf senescence, potentially impacting grapevine productivity and grape quality. Although the hyperspectral approach proposed here is not intended to replace traditional diagnostic methods (molecular analyses), it could serve as a valuable tool to support the monitoring of plants affected by FD and may represent a crucial advancement in FD management. Further and broader studies including vineyards less challenged by FD and with other grape varieties (e.g., white ones, showing leaf yellowing instead of reddening) are encouraged.

Why it matches plant phenotyping methods葉のハイパースペクトル計測により、ブドウの病徴・感染状態・生理状態を推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。

abstractfull-range hyperspectral data (i.e., 400 to 2,400 nm) collected at leaf level can be used as a tool to rapidly and nondestructively detect FD infection directly in the field.

In-situ measurements of dissolved gases in tree xylem sap as tracers for plant physiology →

Common hydrogeological methods make use of natural gases as tracers to better understand the spatial and temporal evolution of groundwater flow, to constrain water residence time, and to reconstruct environmental conditions at recharge [1-3]. Noble gases can be used as complement of the stable water isotope tracers for understanding complex hydrological systems [4,5,6].We adapted these methods to in-situ measurements of gases in tree xylem sap to better understand the plant-mediated water and gas flux between the hydrosphere, the biosphere, and the atmosphere. Using a “miniRuedi” portable mass-spectrometer [7] and tailored semi-permeable membrane probes, the partial pressures of He, Ar, Kr, N2, O2, CO2, and CH4 were continuously monitored in-situ in the soil, the tree, and the atmosphere. Diurnal variations of CO2 and O2 were observed that reflected the tree physiological activities [8]. Since transpiration by plants is a major component of the hydrological cycle, such measurement techniques offer new opportunities to better understand plant water and CO₂ dynamics, within the soil-plant-atmosphere continuum.[1] Kipfer et al. (2002), Reviews in Mineralogy and Geochemistry, 47, 615–700; [2] Brennwald et al. (2013), Advances in Isotope Geochemistry – The Noble Gases as Geochemical Tracers, 123-153; [3] Brennwald et al. (2022), Frontiers in Water, 4, 107-115; [4] Althaus et al. (2009), Journal of Hydrology, 370, 64-72. [5] Schilling et al. (2019), Reviews of Geophysics, 57, 146-182. [6] Xu et al. (2017). Hydrogeology Journal, 25(7), 2015–2029; [7] Brennwald et al. (2016), ES&T, 50, 13455-1346; [8] Marion et al. (2024), Tree Physiology, tpae062.

Why it matches plant phenotyping methods植物の木部樹液中ガスを測定する携帯型質量分析計と膜プローブを適応し、植物生理活動や水・CO₂動態を連続的に取得する測定法が研究の中心である。

abstractWe adapted these methods to in-situ measurements of gases in tree xylem sap to better understand the plant-mediated water and gas flux between the hydrosphere, the biosphere, and the atmosphere.

Real-time monitoring of plant CO2 exchange using a direct absorption-based optical sensor →

Understanding CO2 plant exchange is essential for quantifying its role in the global carbon cycle, predicting ecosystem responses to environmental change, and evaluating long-term growth under varying environmental conditions across several types of photosynthesis[1,2]. Plants exchange carbon dioxide with the atmosphere through three primary physiological processes: photosynthesis, which assimilates CO₂ during daylight to produce glucose and release O2 as a byproduct; photorespiration, a light-dependent process that recycles harmful byproducts of photosynthesis while releasing excess energy and CO2; mitochondrial respiration, which releases CO₂ and consume O2 to produce energy, occurring both day and night. These CO₂ fluxes are coupled with transpiration that facilitates the loss of water vapor from leaves through stomata[1]. These processes can be accurately quantified using gas-exchange techniques, in which a gas analyzer measures the exchange of CO₂ and H₂O between leaves and the atmosphere. In this study, we employed a self-calibrated, optical sensor based on tunable diode laser spectroscopy to monitor plant CO₂ exchange in real time. The sensor consists of a quantum cascade laser emitting at 4.234 μm as the light source and a photodetector to measure CO2 absorption along an open optical path of 10 cm. Measurements were performed using an amplitude modulation approach with first-harmonic detection at 10 kHz, employing a phase-sensitive lock-in amplifier. The optical sensor was placed inside a transparent plexiglass enclosure (525x375x300 mm3) containing a plant to monitor CO₂ exchange with the surrounding environment. A temperature and humidity sensor was also installed inside the enclosure, while a non-dispersive infrared CO₂ sensor (SEFRAM 9825) outside the enclosure was used to track ambient CO₂, temperature, and humidity. Continuous measurements were performed over approximately 20 days, covering both daytime and nighttime periods outside the laboratory, in a dedicated open area to minimize disturbances from nearby activity. Measured CO₂ concentrations inside the enclosure reflected both plant exchange and diffusive transport driven by the concentration gradient with the external environment. A differential equation model accounting for these processes was developed and applied to the experimental data to quantitatively determine the plant’s net CO₂ exchange rate.ReferencesNiu, Z., Ye, Z. W. Y., Huang, Q., Peng, C. & Kang, H. Accuracy of photorespiration and mitochondrial respiration in the light fitted by CO2 response model for photosynthesis. Front. Plant Sci. 16, 1455533 (2025).Busch, F. A., Ainsworth, E. A., Amtmann, A., Cavanagh, A. P., Driever, S. M., et al. A guide to photosynthetic gas exchange measurements: Fundamental principles, best practice and potential pitfalls. Plant Cell Environ. 47, 3344–3364 (2024).

Why it matches plant phenotyping methods植物のCO₂交換率をリアルタイムに定量する光学センサーと解析モデル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 study, we employed a self-calibrated, optical sensor based on tunable diode laser spectroscopy to monitor plant CO₂ exchange in real time.

Simulation and prediction of post-harvest ripening processes for tomatoes with different ripeness levels based on electrical characteristics. →

Detecting postharvest tomato ripeness is essential for quality control. To reveal the evolution of complex conductivity σ ∗ and complex permittivityε ∗ during tomato ripening, this study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity. Based on the Maxwell-Wagner equation, σ ∗ and ε ∗ were derived from the measured impedance and conductance data. BIS measurements were conducted on whole tomatoes at four ripening periods and their components (pericarp, chamber, core, cavity). A finite element model was implemented in COMSOL to simulate electrical field distribution and quantify tissue-specific differences. Continuous monitoring of white ripening period tomatoes was used to validate the model, yielding an average accuracy of 85.16%, peaking at 92.86% in red ripening period and dipping to 80.30% in color change period, elucidate the dynamic changes in electrical properties during tomato ripening and provide a basis for nondestructive maturity assessment.

Why it matches plant phenotyping methodsトマトの成熟状態を電気特性から非破壊推定するBIS・FEM手法を開発・検証しており、植物状態の取得・推定が研究の中心である。

abstractthis study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity.

Characterizing plant hydraulic behaviour under drought stress using vegetation modelling →