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.
ArabidopsisThermalLeafTissueGrowth / time-series analysisStress response / tolerancePlant / canopy temperature
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.
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Cadmium contamination severely affects rice growth, yield, and quality, making early stress monitoring essential for agricultural management and food safety. However, traditional detection methods are cumbersome and time-consuming, limiting their applicability to early stress diagnosis. This study developed a rapid and accurate approach for discriminating cadmium stress levels in rice. Arginine-modified flower-like silver nanoparticles (Ag NPs-Arg) were synthesized to enhance Raman signals associated with three stress-response indicators: salicylic acid (SA), malondialdehyde (MDA), and peroxidase (POD) activity. Quantitative prediction models for these physiological indicators and a stress-level discrimination model were established. Among the evaluated models, the CNN-Transformer model achieved the best predictive performance, with Rp 2 values of 0.889, 0.832, and 0.802 for SA, MDA, and POD activity, respectively. An objective weighting method was used to integrate the three biochemical reference indicators, providing a multi-indicator physiological basis for comprehensive stress assessment. The resulting stress-level assessment model achieved an accuracy of 95.83%, demonstrating its ability to capture cadmium-induced physiological changes and assess stress levels in rice.
Why it matches plant phenotyping methods携帯型Raman SERSと深層学習を開発し、イネの生理指標とカドミウムストレスレベルを推定・判別することが研究の中心であるため、植物フェノタイピング手法に該当する。
abstractThis study developed a rapid and accurate approach for discriminating cadmium stress levels in rice.
Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.
Why it matches plant phenotyping methods継続的な植物センサー、ドローン画像、樹冠構造解析、果実表現型計測を統合し、NDVIや樹冠表面粗さなどのストレス指標を抽出する方法が研究の主要部分であるため。
abstractWe conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping
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 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.
Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.
Why it matches plant phenotyping methodsオリーブ遺伝資源の標準化フェノタイピングプロトコルを体系化し、複数機関で実装・検証して大規模データセットを生成した方法論中心の研究である。
abstractthe historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses.
Reproduction assets foundThe article declares two paper-specific public assets: the GEN4OLIVE phenotypic dataset from evaluating over 500 olive accessions across five germplasm banks, hosted on the project's Olive Varieties Database, and a Zenodo-deposited methodological handbook (Extended Data) containing the 14 protocols, visual assessment, Dataset · publicData and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean
germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE
project repository.
• Repository: GEN4OLIVE Olive Varieties Database.
• Link: https://www.uco.es/ucolivo/gen4olive/olivevarieties (GEN4OLIVE Database, 2025).
Page 8 of 15
Open Research Europe 2026, 6:322 Last updated: 14 SEP 2026Open asset ↗GEN4OLIVE Olive Varieties Databasepdf-raw-page:8 lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published2 Sept 2026The Science of the total environment
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.
Whitefly-transmitted begomoviruses cause tomato leaf curl disease (ToLCD). In India, at least 15 begomoviruses are known to cause ToLCD, posing a major challenge to resistance breeding. Although several Ty resistance loci have been introgressed from wild tomato relatives, variable resistance responses are frequently observed, likely due to mixed infections and the absence of a standardized disease scoring system. Moreover, limited knowledge of the infecting begomoviruses in resistant genotypes has hindered the effective use of donor lines in breeding programs. This study evaluated 17 homozygous Ty-gene donor tomato genotypes under natural epiphytotic conditions and identified the associated begomoviruses. To quantify disease severity, a robust disease scoring system was developed using coefficient of infection (CI) by integrating symptom parameters- leaf curling, leaf smalling, stunting, and fruiting, with population-level disease incidence. Field evaluations for two years revealed that genotypes carrying both Ty-2 and Ty-3 loci showed higher resistance, though variability existed among them. Genotypes with Ty-3 alone or Ty-5 + Ty-6 combinations also displayed substantial tolerance, and five genotypes were identified as highly resistant. Molecular indexing revealed frequent mixed infections and identified multiple begomoviruses, including a newly characterized species, tomato leaf curl Ty Pusa virus, alongside tomato leaf curl New Delhi virus, tomato leaf curl Palampur virus, tomato leaf curl Gujarat virus, and tomato leaf curl Joydebpur virus. These findings highlight a shift in begomovirus predominance and possible recombination-driven emergence of new variants. This study provides an integrated framework for evaluating ToLCD resistance and emphasizes the need for continuous reassessment of resistance sources to ensure durable tomato cultivar development.
Why it matches plant phenotyping methods植物の病徴と発病率を統合した病害重症度スコアリング法を開発し、抵抗性評価に適用しており、表現型取得法が研究の中心である。
abstractTo quantify disease severity, a robust disease scoring system was developed using coefficient of infection (CI) by integrating symptom parameters- leaf curling, leaf smalling, stunting, and fruiting, with population-level disease incidence.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
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.
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
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.
This paper presents an integrated system design for autonomous quadcopter flight path generation using the MAVLink protocol and a custom Ground Control Station (GCS) for precision agricultural crop monitoring. The system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment. The 2.8 kg quadcopter (450 mm frame, 4-cell LiPo) achieves 22–25 minutes of flight time. Across five field sizes (0.5–10 ha), the Energy-Optimized path achieved 96.5% coverage efficiency with 4.2% overlap and a 12.4% energy reduction over the Boustrophedon baseline. NDVI-based crop segmentation achieved pixel accuracy of 92.5% (maize), 94.1% (rice), and 90.8% (wheat), and four-class crop-health classification achieved a weighted F1-score of 90.0%. MAVLink 2.0 command latency averaged 15.8 ms with 99.3% packet delivery at ranges up to 800 m. An ablation study showed additional gains of 1.5–3.1% coverage from wind compensation and 2.1–2.8% from terrain-following.
Why it matches plant phenotyping methods自律ドローン、マルチスペクトル撮像、NDVIセグメンテーションによる作物健康状態推定を統合し、飛行・画像解析性能を定量評価しているため、植物状態の取得・抽出が技術的に実質的な構成要素である。
abstractThe system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment.
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 5 Sept 2026
Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.
Why it matches plant phenotyping methods3Dマルチスペクトル画像・ハイパースペクトル画像と機械学習を統合し、植物形態・スペクトル形質を非破壊かつ高スループットに取得・検証する方法が研究の中心である。
abstractNon-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
Why it matches plant phenotyping methods植物葉の蛍光を自律ロボットで空間的・反復的に取得するプラットフォームを開発しており、植物表現型の取得手法が研究の中心です。
abstractWe present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.
Reproduction assets foundThe paper states its code is publicly available in the authors' SonyCSLParis GitHub repository (Plant3DImager), which implements the phenotyping perception and motion-planning pipeline. The exact full URL is split across a line break in the supplied text, so the verifiable allowed URL prefix is used.Code · public2 The code is available at https://github.com/SonyCSLParis/ 3 See for example at https://www.youtube.com/watch?v=Open asset ↗SonyCSLParis/pdf-page:4 lines:1-61Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Chlorophyll fluorescence provides sensitive information on plant photochemical responses, but its measurement requirements can limit high-throughput application. This study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying. A Photochemical Status Index (PSI) was constructed by principal component analysis from five highly correlated JIP-test energy-flux variables (RC/CS, ABS/CS, TRo/CS, ET2o/CS, and RE1o/CS). The dataset comprised 50 aggregated species-by-soil-moisture-stage observations representing ten species and five sequential soil-moisture stages. Eleven RGB-derived variables were evaluated, and a partial least-squares regression model was assessed using nested leave-one-species-out validation, with all data-dependent procedures repeated within each outer training fold. PC1 explained 96.5% of the shared variation among the fluorescence-derived fluxes. The predictors g, GLI, ExG, ExGR, and CIVE were retained in all ten outer folds. The final model yielded a pooled out-of-fold R2 of 0.469, an RMSE of 1.585, and an MAE of 1.183. However, species-specific R2 ranged from −0.179 to 0.959, and a calibration slope of 0.509 indicated prediction-range compression. These findings provide proof-of-concept evidence of moderate RGB-based approximation of fluorescence-derived photochemical status, but inconsistent species transferability and the common soil-moisture/time gradient require external validation before practical deployment.
Why it matches plant phenotyping methodsRGB画像から蛍光由来の植物光化学状態を推定する手法を構築し、種間交差検証で性能評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Structural failure of cereal stems during late-season climate extremes is a critical determinant of yield stability. In barley, breakage of the stem below the spike, known as head loss, leads to major yield losses, particularly in hot and dry regions where the crop is widely grown. Despite a predicted increase in head loss risk due to global warming, current understanding of the genetic, physiological, anatomical, and environmental factors that control head loss remains limited. Overcoming these knowledge gaps is essential to providing a systems-level strategy for barley breeders to develop climate-ready cultivars that are resilient to stem breakage and suitable for industry adoption. Here, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting, and genetic modification strategies informed by studies on hormonal regulation and cell wall composition. Coupled with genotypic data, these efforts will enable the development of a genomic selection platform to facilitate future breeding programs. The framework and tools discussed here are broadly applicable to improving stem resilience in other cereal crops.
Why it matches plant phenotyping methods茎の強度・柔軟性や穂数を対象とする表現型計測手法をレビューし、機械試験とドローンによる高スループット計測を育種基盤として論じているため、表現型手法が中心的です。
abstractHere, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · OpenAlex · checked 13 Sept 2026
Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)-derived metrics provide a potential high-throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot-level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV-derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest root mean square error (RMSE=16-18%), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.
Why it matches plant phenotyping methodsUAV画像と写真測量点群からソルガム区画の倒伏程度を推定する取得・解析フレームワークを開発し、実データで精度評価しており、植物表現型測定法が研究の中心である。
abstractThis study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots.
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。
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.
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。
Accurate and non-destructive assessment of drought stress is important for improving lettuce production and supporting timely crop management. This study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images. The study further investigates whether canopy segmentation can improve classification performance by reducing irrelevant background information. The framework was evaluated using 2190 images collected across three independent cultivation cycles in which drought stress was induced by isolating the plant root zones from the nutrient solution. In the first stage, YOLO-based object detection was used to localize individual plants, with YOLO26m achieving the highest detection performance of 99.4% mAP@0.5. The detected regions were subsequently used as spatial prompts for zero-shot canopy segmentation using the Segment Anything Model (SAM), with SAM ViT-B achieving a mean IoU of 0.9864. Six convolutional, transformer-based, and hybrid classification architectures were then evaluated independently using YOLO-cropped and SAM-segmented plant images. Segmented inputs consistently improved classification performance, with MaxViT-S achieving the highest binary test accuracy of 96.3%. The framework further distinguished time-defined pre-stress, early-stress, and late-stress periods with an accuracy of 92.4%. Plant-level generalization was further assessed using six-fold leave-one-plant-out cross-validation, resulting in a mean test accuracy of 90.25 ± 1.78% on unseen plants. These findings demonstrate that RGB-based plant-level analysis can support non-destructive drought-stress assessment and that canopy segmentation improves classification by reducing background influence.
Why it matches plant phenotyping methodsRGB画像からレタス個体の乾燥ストレス状態を推定する検出・セグメンテーション・分類フレームワークを開発し、複数サイクル、未見個体、性能指標で検証しており、表現型取得手法が中心である。
abstractThis study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images.
Background Global soybean production is constrained by scarce arable land, and standardized evaluation tools remain lacking for natural mixed saline-alkali stress, the predominant abiotic stress under field conditions. Objective This study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification. Methods Seventy-one soybean germplasms were tested under 90 mmol/L mixed saline-alkali stress (NaCl:Na 2 SO 4 :NaHCO 3 :Na 2 CO 3 = 1:9:9:1, pH 8.2) simulating natural saline-alkali soil. We quantified the saline-alkali tolerance coefficients (SATC) of 13 morphological and physiological indicators, followed by coefficient of variation (CV), principal component analysis (PCA), subordinate function, cluster analysis and regression modeling. Results Significant inter-germplasm variations in saline-alkali tolerance were detected, and indicators with CV > 0.35 ( e.g ., root length (RL), root fresh weight (RFW)) were screened as primary indices. PCA extracted five principal components with 87.18% cumulative variance contribution, and the integrated analytical pipeline categorized germplasms into five tolerance grades: eight highly tolerant, 24 moderately tolerant, 13 generally tolerant, 16 sensitive and 10 highly sensitive accessions. A high-precision prediction model was constructed ( D = 0.290 X 1 - 0.026 X 2 + 0.438 X 3 + 0.402 X 4 + 0.180 X 5 + 0.153 X 6 + 0.813 X 7 - 1.123; R 2 = 0.998, where X 1 - X 7 represent the SATC of germination rate (GR), RL, RFW, total fresh weight (TFW), shoot dry weight (SDW), root dry weight (RDW), and total dry weight (TDW), respectively). A Chi-squared Automatic Interaction Detection (CHAID) decision tree model was further developed and validated using 10-fold cross-validation, yielding a cross-validation risk value of 0.003, which was comparable to the resubstitution risk value (0.002), indicating good generalization ability and low risk of overfitting. A novel five-dimensional overlapping analysis identified RFW as the core evaluation indicator, with RDW, TFW and R/S as key auxiliary indicators. Conclusion This study delivers a standardized, reproducible technical framework for large-scale screening of saline-alkali-tolerant soybean germplasms. It facilitates global saline-alkali land utilization, accelerates worldwide soybean stress-tolerance breeding, and provides a transferable paradigm for stress tolerance evaluation in other major crops.
Why it matches plant phenotyping methodsダイズの耐塩・耐アルカリ性を評価するための形態・生理形質の統合評価体系、予測モデル、指標選定、交差検証を中心的に開発・検証しており、再利用可能な植物表現型評価手法に該当する。
abstractThis study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification.
Wheat grain number integrates fate of individual florets, strongly affected by environmental stress during sensitive stages like meiosis. Because development is asynchronous across tillers, spikelets, and florets, it is hard to distinguish stress tolerance from stress escape. Based on 2400 destructive measurements of spike and anther length together with non-destructive morphological measurements from 158 plants grown in four experiments in controlled-environment, we developed a framework to track individual floret developmental stages at plant level. We applied it in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable or heat conditions. All florets showed a common relative growth rate, producing additive delays across tillers (1-7 d), spikelets (1-5 d), and floret positions (1-6 d). This generated a developmental map for every floret based on external traits. Under control conditions, grain set probability at floret level combined both positional and developmental effects within a spike. Under heat stress, grain loss occurred only in florets at meiosis during the stress, allowing to quantify a true "stress response", while later florets escaped damage. This framework allows understanding and predicting floret development and linking it to grain set, clearly distinguishing timing effects from positional influences and separating tolerance from stress escape.
Why it matches plant phenotyping methods外部形態測定から個々の小花の発育段階を追跡する方法・発育マップを開発し、複数実験で適用しているため、表現型取得・推定が研究の中心である。
abstractwe developed a framework to track individual floret developmental stages at plant level
Localized soil-moisture deficits, that is, irregular sub-field patches where crops experience water stress well before visible wilting, are a leading cause of yield variability in row-crop agriculture. These zones are difficult to detect at the spatial resolution and revisit frequency required for timely irrigation response. This paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time. Rather than flying a fixed lawnmower survey, the platform is guided by an adaptive-sampling policy trained with Proximal Policy Optimization (PPO) that reallocates flight time and sensor dwell toward regions of emerging water stress as evidence accumulates mid-flight. We present the complete engineering pipeline: airframe and sensor design, a keyframe-based Visual SLAM front and back end that provides centimeter-scale geolocation without continuous reliance on Real-Time Kinematic (RTK) GNSS lock, a hyperspectral preprocessing and spectralindex chain (NDVI, NDRE, NDWI/NDMI) used to derive CWSI through a learned regression, the partially observable Markov Decision Process (POMDP) formulation and reward shaping used to train the sampling policy, and the fused system architecture tying these subsystems together. In simulated field trials over a 0.8-hectare test plot, the reinforcement-learning-guided policy achieved a 92% water-stress-zone detection rate versus 61% for a fixed-grid baseline, while reducing mission flight time by approximately 32%. We further report an ablation study isolating the contribution of SLAM-derived canopy structure to CWSI accuracy, a sensitivity analysis across field complexity, and a full error budget for the fused pipeline. We close with a discussion of validation limitations, broader scientific and agricultural impact, and a roadmap toward multi-UAV fleet deployment for whole-farm monitoring
Why it matches plant phenotyping methodsUAV、Visual SLAM、ハイパースペクトル画像、機械学習を統合し、作物の水ストレス状態を推定・地図化する技術パイプラインを開発・評価しており、植物表現型取得が中心である。
abstractThis paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time.
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.
Soil salinity is a major constraint on rice ( Oryza sativa L.) production, particularly during the yield-determining reproductive stage. Utilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years. By extracting 60 dynamic image-based traits (i-traits) reflecting canopy architecture and stay-green capacity, and four seed-setting rate-related traits, our genome-wide association study (GWAS) identified 95 significant loci, 35.8% of which precisely co-localized with previously reported QTLs. We further prioritized OsSLT1 ( LOC_Os01g05790 ) as a candidate gene at a reproducible suggestive locus associated with leaf-rolling-related image traits. Transgenic evaluations confirmed that it acts as a positive regulator of salt tolerance at the seedling stage. OsSLT1 was mainly detected in the nucleus, and no significant changes in Na + or K + accumulation were observed in flag leaves under the tested salt-stress condition, suggesting that OsSLT1 may regulate salt tolerance through mechanisms beyond classical shoot ion accumulation. Natural variations in the OsSLT1 promoter were associated with transcriptional divergence. The Hap2 promoter haplotype showed significantly higher stress-induced transcriptional activity. Hap2 was rare in modern indica accessions, suggesting that it may represent a potentially useful genetic resource for future salt-tolerance improvement. Supplementary information The online version contains supplementary material available at 10.1007/s11032-026-01704-2.
Why it matches plant phenotyping methodsRGB高スループット基盤による非破壊画像計測と、60種の動的画像形質の抽出が研究の主要なデータ取得・解析手法として明示されているため、GWAS中心の応用研究でも植物フェノタイピング手法の実質的応用に該当する。
abstractUtilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years.
Abstract Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs. Sixteen soybean genotypes were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using six YOLO (You Only Look Once) object-detection architectures (YOLOv7–YOLOv12) to identify the best-performing model for automated analysis. Among the tested object-detection architectures, YOLOv9 achieved the best overall performance for detecting germinated and non-germinated pollen grains in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P < 0.05), with a significant genotype × growth temperature interaction. Invitro incubation temperatures ranging from 10 °C to 45 °C produced a clear thermal response; however, no significant genotype × incubation temperature interaction was detected within either growth temperature regime. Although photosynthetic and physiological traits were measured exploring their relationship with pollen germination, their transient and complex response limited their reliability for predicting reproductive performance. The automated pipeline substantially reduced the time required to evaluate pollen germination. The pipeline processed nearly 5,000 images in approximately one hour, substantially increasing throughput and reducing reliance on manual counting. The findings demonstrate that pollen germination is a promising proxy trait for screening reproductive heat tolerance in soybean. Combining controlled environment phenotyping with YOLO-based object detection enabled efficient, accurate, and scalable pollen analysis, and represents the central methodological advance of this study. YOLOv9 performed best among the tested architectures, although discrepancies from manual counts in some images indicate that additional validation is needed. The weak associations with vegetative physiological traits further support the value of direct pollen-based phenotyping.
Why it matches plant phenotyping methods深層学習による花粉画像解析を中心に、花粉発芽という生殖形質を高速・自動測定するハイスループット表現型解析フレームワークを開発・比較・検証している。
abstractThis study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs.
Reproduction assets foundThe authors state that all data supporting the study, including annotated pollen germination images, computational and statistical codes, and analysis tools, were deposited in Zenodo with a public DOI. This is a paper-specific, publicly actionable asset. LabelMe and Ultralytics YOLO are generic third-party tools, not作者Dataset · publicCommission.
Data availability
All data supporting the findings of this study, including annotated images, computational and
statistical codes, and analysis tools, have been deposited in the Zenodo data repository. Additional
data will be made available upon reasonable request following acceptance of the manuscript.
Repository: https://doi.org/10.5281/zenodo.21685593
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Competing Interests
Authors declared no competing interests
References
1. FAOSTAT: Crops and livestock products: soybean production data. https://www.fao.org/faostat/
(2022). Accessed 15 Feb 2026.
2. Patel D, Franklin KA. TemperaturOpen asset ↗Zenodo · 10.5281/zenodo.21685593pdf-raw-page:28 lines:1-34Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Cercospora janseana (Racib.) O. Const. is a re-emerging fungal pathogen that causes Cercospora net blotch on rice. Previous research on resistance to C. janseana has primarily focused on foliar symptoms. Subsequently, sheath infection remains poorly characterized which hinders disease management efforts. This study developed and validated a reproducible sheath inoculation protocol under controlled conditions. Three inoculation methods (agar disc, spray, and drop) were evaluated with and without mechanical wounding. Lesions only formed with inoculation methods using wounding and the agar disc method produced the most consistent and uniform symptom development. Time-course analysis in the susceptible variety Cheniere revealed earlier lesion onset, more rapid expansion, and lower variability in the agar disc method compared to spray, confirming its suitability for phenotypic screening. The optimized protocol was applied across five independent trials involving four rice varieties. DG263L consistently exhibited minimal lesion development, confirming its resistance, while Cheniere showed extensive lesion growth, indicating high susceptibility. PVL03 and LaGrue displayed moderately susceptible reactions, with PVL03 developing significantly higher lesion lengths and AUDPC values than LaGrue in one-month-old plants. Although lesion onset was delayed in 45-day-old plants, disease progressed more rapidly once established. AUDPC analysis corroborated these trends, further distinguishing varietal responses. The protocol effectively discerned resistant, intermediate, and susceptible phenotypes, supporting its use in resistance screening. To our knowledge, this is the first controlled sheath inoculation method developed for Cercospora net blotch, offering a standardized approach for evaluating sheath-specific resistance and advancing the characterization of the C. janseana -rice pathosystem.
Why it matches plant phenotyping methodsイネ葉鞘の病斑を用いた抵抗性表現型の取得プロトコルを開発・検証し、品種間の病害表現型を再現性よく識別しているため、植物フェノタイピング手法が中心である。
abstractThis study developed and validated a reproducible sheath inoculation protocol under controlled conditions.
Calibrating the mutagenic dose is the first practical step of any radiation mutation-breeding programme, and it is usually summarised by the median lethal dose (LD50) or the median growth-reduction dose (GR50). We asked whether an accessible, image-based phenotyping pipeline can quantify the early radiation response of cowpea (Vigna unguiculata L. Walp.) seedlings finely enough to estimate GR50 and to rank organ- and pigment-level sensitivities. Seeds of the traditional Paraguayan landrace kumandá pyta’i were exposed to Cobalt-60 gamma rays at 0, 100, 200, 300, 400, 500, 600, and 700 Gy, grown in a greenhouse, and photographed at the early seedling stage. A single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits (total, root, and shoot length, root:shoot ratio, tortuosity, and a two-dimensional biomass proxy) and colorimetric traits (CIE L*a*b*, a normalised greenness index, and colour-class pixel fractions). Because the data departed from normality, dose effects were tested with Kruskal–Wallis, Spearman rank correlation, and Dunn post hoc tests, and GR50 was estimated by regression of each trait expressed as a percentage of the control. Total length, shoot length, and the biomass proxy declined significantly with dose (Spearman ρ = −0.40, −0.51, and −0.47; all p < 0.001), preceded by a low-dose stimulation at 100 Gy. Estimated GR50 values were ≈390 Gy for shoot length, ≈510 Gy for total length, and ≈550 Gy for the biomass proxy, within the range reported for other cowpea genotypes. Shoot elongation was more radiosensitive than root elongation, so the root:shoot ratio did not decline; tortuosity showed no dose response. Among pigment traits, the loss of greenness was the most robust signal (a* increased, ρ = +0.62, p = 5 × 10−10; green pixel fraction fell from 0.32 to near zero by 500 Gy). These results show that single-photograph phenotyping resolves a coherent, statistically supported dose response and yields a GR50 estimate usable for dose calibration. For kumandá pyta’i, doses of roughly 300–400 Gy (below GR50) are the most defensible starting window for mutation induction. The framework is reproducible and low-cost, but it is based on one greenhouse experiment and a single genotype, and should be validated across independent trials and cultivars.
Why it matches plant phenotyping methods画像取得・セグメンテーション・解析による形態および色彩形質の抽出を中心に、放射線応答とGR50を推定する低コスト画像ベース表現型解析法を提示しているため。
abstractA single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits
Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データから干ばつ耐性指標を予測する高スループット表現型解析手法が研究の中心であり、モデル性能も評価している。
abstractUsing UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening.
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
High-throughput phenotyping is key in modern breeding for rapidly and cost-effectively evaluating salt-adaptive traits. However, few studies have combined spectral reflectance indices (SRIs) with deep learning to assess field-grown wheat under salt stress. In this study, we developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY) in 32 recombinant inbred lines (RILs) and four cultivars under 150 mM NaCl field conditions. ANOVA revealed that genotype contributed the largest proportion of the treatment sum of squares across all traits (60–80%), followed by the genotype × year interaction (8–15%), whereas year contributed the smallest proportion (1–5%). Heatmap clustering of these traits clearly distinguished salt-tolerant from salt-sensitive genotypes. The study findings highlight using four key traits as screening criteria for salt tolerance in wheat. Our optimized 2D/3D spectral indices showed moderate to strong predictive power (R 2 = 0.25–0.75), outperforming earlier indices. Multi-season data improved accuracy by 15–25%, with best predictions for Chl a and TChl (R 2 = 0.34–0.75) versus Chl b and GY (R 2 = 0.25–0.64). Top models included ANN-3D-SRIs-8 for Chl a (R 2 = 0.735/0.644), ANN-3D-SRIs-3 for Chl b (R 2 = 0.611/0.549), ANN-2D-3D-SRIs-3 for TChl (R 2 = 0.713/0.619), and ANN-2D-SRIs-2 for GY (R 2 = 0.648/0.553). This framework combines optimized indices and machine learning for scalable, high-throughput phenotyping to advance precision breeding of salt-tolerant wheat.
Why it matches plant phenotyping methodsスペクトル指数とニューラルネットワークを開発・評価し、コムギのクロロフィルと収量を高スループット推定する方法が研究の中心である。
abstractwe developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY)
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
India’s economy is heavily reliant on agriculture, with a diverse array of crops grown on vast tracts of land. Fruit cultivation, especially papaya, has become more popular in recent years because of its high nutritional and financial value. To increase yield, maximize resource use and lessen reliance on chemical pesticides, modern techniques like protected cultivation and hydroponics are being used more and more. Fruit crops grown in controlled or semi-controlled environments are still susceptible to nutrient imbalances despite these developments, which can have a substantial impact on plant health, fruit quality and overall productivity. Papaya leaf nutrient deficiencies frequently show up in the early stages of growth and can result in poor fruit development and decreased yield if they are not detected in time. To support early diagnosis and better crop management, the current study focuses on creating an effective method for identifying nutrient deficiencies in papaya leaves using a deep learning (DL) framework based on transfer learning (TL). In this nutrient and micronutrient deficiency study and field work observation during year 2024 to 2026 with different climate and weather conditions done in order to tackle a new but related classification task, in the context of plant health assessment, several well-established architectures including InceptionV3, VGG19, DenseNet and Xception have been widely explored for leaf image analysis. Studies commonly utilize publicly available datasets, such as papaya leaf image repositories hosted on platforms like IEEE DataPort, to fine-tune these models for efficient feature extraction and accurate identification of nutrient and micronutrient deficiency patterns. This body of work demonstrates the growing role of transferring convolutional neural network (CNN) models in advancing automated crop monitoring and decision support systems.
Why it matches plant phenotyping methodsパパイヤ葉画像から栄養・微量栄養素欠乏という植物状態を深層学習で識別する手法の開発が研究の中心であり、植物フェノタイピングに該当する。
abstractthe current study focuses on creating an effective method for identifying nutrient deficiencies in papaya leaves using a deep learning (DL) framework based on transfer learning (TL).
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Within the context of climate change, quinoa ( Chenopodium quinoa Willd.) is a climate-resilient crop with high nutritional value. The effects of deficit irrigation on quinoa growth and physiological performance under arid conditions remain insufficiently understood. This study evaluated ten quinoa genotypes (two commercial varieties and eight accessions) under two irrigation regimes to identify traits and spectral indices associated with water-stress tolerance. We combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively. Deficit irrigation reduced plant height (18%), specific leaf area (8%), yield (43%), harvest index (26%), relative water content (7%), and dry matter accumulation (36%), while relative chlorophyll content (SPAD, Soil Plant Analysis Development) and stomatal density increased by 16% and 13%, respectively; accession ACC_23 exhibited the highest water-use efficiency (5.9 g kg -1 ). A univariate analysis of 35 vegetation indices across 13 dates showed that: Health Index(HIV), Normalized Green-Red Difference Index (NGRD), Red-Green Ratio (RG) and Plant Senescence Reflectance Index (PSRI), were the most sensitive, detecting significant differences between irrigation treatments in up to 32 of the 130 possible genotype-by-date comparisons. Integrating remote sensing into crop phenotyping represented a significant methodological improvement by enhancing phenotyping efficiency, improving detection of deficit irrigation effects, and facilitating identification of tolerant quinoa genotypes for arid production systems.
Why it matches plant phenotyping methodsリモートセンシングと多時点の植 phenotyping を統合し、35の植生指数の感度比較によって水ストレス関連形質を抽出する方法適用が、研究の主要な技術的要素として明示されています。
abstractWe combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Grass seed crops are susceptible to yellow dwarf viruses transmitted by aphids. The Willamette Valley in Oregon, United States, is the leading producer of cool-season grass seed crops globally, and industry reports have attributed seed yield loss and shortened stand longevity to aphid-transmitted yellow dwarf viruses. Genetic resources are needed for effective and sustainable management of this pest, specifically the Rhopalosiphum padi–PAV pathosystem, in grass seed production to reduce foliar insecticide applications and maintain optimum seed yield potential. High-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs. An automated video tracking procedure was optimized to evaluate host plant resistance in cool-season grass seed crops to R. padi–PAV with live plants and viruliferous and nonviruliferous aphid populations. Feeding behavior recorded with automated video tracking was strongly correlated with “ground-truthed” observations by human observers. Partial resistance (antixenosis and antibiosis) and tolerance traits were detected in select perennial ryegrass and tall fescue cultivars evaluated with traditional phenotyping methods in a greenhouse setting and with high-throughput phenotyping using automated video tracking in the laboratory. Across grass cultivars, nonviruliferous aphids had greater fitness and preference for noninfected grass plants compared with viruliferous aphids. Automated video tracking can be used as a high-throughput phenotyping method for continued evaluation of host plant resistance in grasses grown for seed production, as well as to identify resistant genotypes in other grass crops susceptible to aphid–yellow dwarf virus virus–vector systems.
Why it matches plant phenotyping methods自動動画追跡を用いてアブラムシ媒介ウイルスに対する植物抵抗性を高スループットに評価する手法を最適化・検証し、従来観察との相関および抵抗性形質の検出を示しており、表現型取得法が研究の中心である。
abstractHigh-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs.
Field / plotLeafStem / branchPhysiological trait estimationGrowth / time-series analysisStress response / toleranceWater status / transpiration
Live fuel moisture content is a key determinant of live fuel flammability, yet its destructive and discontinuous measurement limits high-temporal-resolution monitoring. This study evaluated whether leaf electrical potential can serve as a non-invasive proxy for LFMC and flammability-related traits under natural drought conditions. From February to July 2025, leaf and trunk electrical potentials were monitored weekly in Salvia rosmarinus individuals from a Mediterranean shrubland, while LFMC, essential oil yield, fatty-acid fraction, and laboratory-based flammability metrics-ignition time, combustion duration, and flame height-were assessed bi-weekly. Leaf electrical potential was strongly associated with LFMC (R 2 = 0.64, p < 0.001), decreasing as plants underwent seasonal drought-induced dehydration. Periods of high temperature and low rainfall reduced both LFMC and electrical potential, coinciding with shorter ignition times, which declined to approximately 20-30 s during the driest period. Based on the observed shifts in ignition time, combustion duration, and flame height, three empirical LFMC response zones were identified, with leaf electrical potential closely tracking transitions in plant hydration and flammability. These results suggest that plant electrophysiology may provide a promising non-invasive indicator of live fuel water status and seasonal flammability dynamics, with potential applications in wildfire risk monitoring when combined with conventional LFMC, meteorological, and remote-sensing approaches.
Why it matches plant phenotyping methods葉の電気的電位をLFMC（水分状態）および可燃性関連形質の非破壊・連続的な指標として評価しており、植物状態の取得方法の検証が研究の中心である。
abstractThis study evaluated whether leaf electrical potential can serve as a non-invasive proxy for LFMC and flammability-related traits under natural drought conditions.
Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.
Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。
abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.
Tolerance against winter freeze is the main focus of variety development in Louisiana, which represents the northernmost sugarcane-growing region worldwide. Antifreeze metabolites, xylem structure, and fiber content represent interrelated physicochemical properties contributing to freeze tolerance. This study first classified the cold tolerance of sugarcane cultivars using metabolites in juice as predictor variables. The best-fit model (XGBoost discriminant analysis) estimated the higher cold tolerance of the final on-station clone progeny to the stress tolerance-inducing wild germplasm line. Stalks of the tolerant sugarcane genotype contained higher fiber for mechanical support against cellular injury, compared to susceptible varieties. Fluorescence microscopy visualized phospholipids responsible for maintaining membrane fluidity during frost in lignin surrounding the vascular bundle. Thermal imaging is proposed for real-time monitoring of spatiotemporal temperature changes, as stalk injury is initiated by ice formation at sub-freeze temperatures during winter freeze. As additional datasets for independent prediction become available, developed methods could be used to explore the biomarkers for stress resistance in simpler multivariate discriminant analysis and the distribution of specific biomarkers in cellular components by microscopic imaging, and to trace stalk injury hot spots as a function of time and relationships with resistance markers.
Why it matches plant phenotyping methodsサトウキビの耐寒性という植物状態を、XGBoost判別モデルと画像・熱画像によって分類・評価する方法が研究の中心であり、単なる生物学的測定ではない。
titleMultivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones.
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.
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).
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
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
The use of glufosinate-resistant GM soybean has expanded, raising concerns about resistant weed development and unintended transgene flow. To support monitoring for timely management, we propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment. We evaluated the potential of spectral imaging, using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors, for early detection of glufosinate resistance in soybean. In the dose-response test, the key spectral indices including NDI, temperature difference, F v /F m , and NPQ distinguished between resistant and susceptible soybeans within 4 to 24 hours after treatment (HAT). IR thermal and CF imaging showed higher sensitivity in identifying resistance than RGB imaging by detecting spectral responses associated with physiological changes before visual symptoms appeared. Validation test with a single dose treatment of glufosinate reconfirmed that image analysis by both the naked eye and machine learning (ML) can discriminate between resistant and susceptible soybeans in a single day after glufosinate treatment. ML-based classification using IR thermal index achieved 100% accuracy as early as 6 HAT and the classification by the naked eye using IR thermal images showed 96.6% accuracy at 24 HAT. These results suggest that plant imaging enables early and non-destructive identification of herbicide-resistant individuals by detecting early spectral changes to herbicide treatment. These findings support its use as a potential alternative to conventional diagnostic methods for detecting individuals containing transgenes in herbicide-resistant GM soybean cultivation for future applications in herbicide-resistant weed monitoring.
Why it matches plant phenotyping methodsスペクトル画像（RGB、熱赤外、クロロフィル蛍光）と機械学習を用いて、薬剤処理後の植物の生理応答から耐性を早期識別する方法を開発・検証しており、植物表現型の取得が中心である。
abstractwe propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment.
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.
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.
1 Summary Embolism, the formation of air bubbles in the plant water transport system, is a mechanistic driver of plant death. The Optical Vulnerability Technique (OVT) is an imaging method for non-invasive quantification of embolism (including P50, a common metric for drought vulnerability), which can also provide detailed spatial and temporal information. Its major cost lies in the post-processing of thousands of images. Here we designed, tested, trained, and make publicly available a neural network model to automate post-processing of OVT images. Using a dataset of 65 leaves from Senecio pterophorous , we compared our model predictions to results obtained via traditional post-processing by an expert. Our model resolved P50 to within 0.027 MPa of the expert-processed data with training taking 30 minutes to 2.5 hours and model-runtime in the order of seconds to minutes, demonstrating its promise for increasing the efficiency and throughput of P50 calculation. The model’s performance in replicating the pixels that constitute embolism events was lower (mean event-frame IoU of 0.38). We invite the community to utilise our model but emphasise that it does not replace the expert-processing pipeline and that care must be taken when considering applying this and similar approaches to OVT data.
Why it matches plant phenotyping methods葉の塞栓を画像から定量化するOVTの後処理を自動化するニューラルネットワークを開発・検証しており、植物生理状態の表現型取得が研究の中心である。
abstractHere we designed, tested, trained, and make publicly available a neural network model to automate post-processing of OVT images.
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)
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.
Why it matches plant phenotyping methods感染根のカルロース沈着という植物の病態・生理状態を、染色・蛍光画像・Fiji/TWSで検出および定量する方法を最適化した手法論文であり、表現型取得が中心です。
abstractHere, we have optimized a robust and reliable method for detecting callose deposition in soybean lateral roots during Macrophomina phaseolina infections.
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.
【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
Plant stress recognition plays a vital role in precision agriculture by enabling the early detection of diseases, insect infestations, and nutrient deficiencies that adversely affect crop productivity. Although deep learning models have achieved promising results, existing CNN-, Transformer-, and hybrid architectures often struggle to capture complex spatial dependencies, distinguish visually similar stress symptoms, and handle class imbalance in multi-label classification. To address these challenges, this paper proposes Q-TriLSTM-Vision, a novel hybrid deep learning framework that integrates an EfficientNet-B0 visual encoder, a tri-stream long short-term memory (TriLSTM) network, and a lightweight quantum-inspired interference gate. Unlike quantum computing-based approaches, the proposed interference mechanism is implemented entirely using classical neural operations to enhance feature representation without requiring quantum hardware. The model further employs an entanglement-inspired attention fusion module, focal binary cross-entropy loss with label smoothing, weighted sampling, and per-class threshold calibration to improve discriminative learning and minority-class recognition. The proposed framework was evaluated on the OLID-I dataset and further validated on the PlantVillage and PlantDoc benchmark datasets. Experimental results demonstrate that Q-TriLSTM-Vision achieved Macro-F1 scores of 0.9127, 0.9624, and 0.8975 on OLID-I, PlantVillage, and PlantDoc, respectively, outperforming representative CNN-, Transformer-, and hybrid deep learning models while achieving lower Hamming loss and improved recall. Cross-validation, ablation studies, and statistical significance analysis further confirm the robustness and effectiveness of the proposed framework. Overall, Q-TriLSTM-Vision provides an accurate, computationally efficient, and reliable solution for intelligent plant stress recognition in precision agriculture.
Why it matches plant phenotyping methods植物ストレス症状を画像から認識する深層学習手法を開発し、複数の植物画像ベンチマークで検証しているため、表現型取得・抽出法が中心である。
Proper diagnosis of plant disease and nutrient deficiency is crucial in enhancing crop productivity, reducing yield losses and early agricultural interventions. This paper introduces the machine learning approach for automated identification of plant diseases and their severity by analyzing the images of plant leaves. The proposed framework involves image preprocessing, feature extraction, classification and severity estimation, which would enable the accurate identification of various types of disease and estimation of their severity levels while detecting nutrient deficiencies. An extensive image dataset of healthy, diseased and nutrient deficient leaves was used to train and test the models. As illustrated by the experimental results, the proposed framework outperforms the existing machine learning and deep learning methods for plant disease identification and nutrient deficiency detection with the classification accuracy of 98.76% and 98.14% respectively. In addition, the severity assessment module estimates well to enable accurate pesticide and nutrient application, which minimizes chemical use. The proposed framework provides a scalable, efficient, and precise approach for smart crop health monitoring and precision agriculture applications.
Why it matches plant phenotyping methods葉画像から植物病害・栄養欠乏の同定と重症度推定を行う機械学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractThis paper introduces the machine learning approach for automated identification of plant diseases and their severity by analyzing the images of plant leaves.
Broccoli is a globally significant vegetable, but climate change and soil salinization increasingly threaten its productivity. Precise seedling phenotyping is essential for selecting salt-tolerant germplasm, yet traditional manual methods are labor-intensive and error-prone. This study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings at the germination and early developmental phase under salt stress. High-fidelity 3D point clouds were reconstructed from a precision three-view imaging system using Structure from Motion (SfM) algorithms. LBD-PointNet++ introduces three core optimizations: (1) a Large Kernel Attention (LKA) mechanism using 3D sparse decomposition to capture long-range global dependencies; (2) a Dual Uncertainty and Shape-Adaptive Sampling (DUSAS) mechanism to preserve high-frequency features of fragile stems and margins; and (3) a joint Boundary-Aware Nested Contrastive and Adaptive Varifocal Joint Loss (BNCV-Loss) to effectively isolate overlapping leaves. Experimental results demonstrate superior performance, achieving an overall mean Intersection over Union (mIoU) of 88.07% across all three categories (Leaf, Stem, and Pot) and a Mean F1-score of 93.48%. Compared to state-of-the-art Transformer architectures like PTv3, LBD-PointNet++ achieves higher accuracy with less than 6% of the parameter volume and over twofold faster inference speed. Furthermore, dynamic monitoring across NaCl gradients (0-250 mmol/L) revealed a potential non-linear threshold effect, identifying 100 mmol/L as a preliminary phenotypic threshold under these conditions. Beyond this threshold, growth inhibition intensified rapidly; At 250 mmol/L, plant height decreased by 54.43% and the 3D entity volume shrank to approximately one-fifth of the control group. In summary, LBD-PointNet++ provides a high-efficiency solution for phenotypic identification and digital breeding of salt-tolerant Brassicaceae crops.
Why it matches plant phenotyping methods3D点群分割ネットワークと三視点SfM撮像を開発し、ブロッコリー幼植物の表現型形質抽出を中心的に評価しているため。
abstractThis study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.
Why it matches plant phenotyping methods高スループット表現型解析をゲノム選抜との統合という方法論的主題の一部として批判的にレビューしており、各種画像・センサープラットフォームと形質抽出を扱うため、表現型手法レビューに該当する。
abstractThis narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.
The ready-to-eat lettuce industry is rapidly expanding, increasing the need for reliable, scalable methods to assess seed germination and early growth under realistic soil conditions. This study presents an automated imaging-based approach for quantifying germination dynamics and seedling vigor using a low-cost multi-camera system under greenhouse conditions. Lettuce seeds were grown in soil either inoculated or non-inoculated with the soil-borne pathogen Rhizoctonia solani. Top-view images were acquired using commercial surveillance cameras and processed through a calibrated pipeline including geometric correction, color normalization, vegetation segmentation, clustering, and temporal tracking of emergence events. Seedling vigor was quantified through projected leaf area estimation. The proposed method enables accurate estimation of germination kinetics and growth dynamics under field-like conditions. Automated counts were validated against manual measurements at both intermediate and final time points, achieving high agreement in both cases. At the final assessment, the method reached R² = 0.98 and RMSE = 1.12, while at the midterm evaluation it achieved improved performance with R² = 0.998 and RMSE = 0.5, reflecting the lower complexity of plant structure at earlier growth stages. Results showed that pathogen inoculation significantly reduced both germination rate and seedling vigor, with up to 70% reduction in biomass accumulation. The proposed framework provides a robust, low-cost solution for high-throughput phenotyping of early plant development in soil-based systems, supporting scalable agricultural experimentation.
Why it matches plant phenotyping methods低コスト多カメラ画像システムと画像解析パイプラインを開発・検証し、発芽動態と幼植物活力を定量化しているため、植物フェノタイピング手法が中心です。
abstractThis study presents an automated imaging-based approach for quantifying germination dynamics and seedling vigor using a low-cost multi-camera system under greenhouse conditions.
Long-duration flood inundation can substantially suppress crop growth and cause yield loss, particularly in semi-arid agricultural regions increasingly affected by extreme rainfall. Timely crop damage assessment is critical for disaster response and insurance-related decision-making, but direct yield-loss observations are often unavailable during or shortly after flooding. This study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations. The study was conducted on the Tumochuan Plateau, Inner Mongolia, China, where severe rainfall beginning on 23 July 2025 caused widespread cropland inundation. Sentinel-2 EVI time series from 2022 to 2025 were fitted using a Savitzky–Golay (SG) filter, and annual area under the EVI curve (AUC) loss in 2025 relative to the 2022–2024 historical mean was used as a proxy for flood-induced crop damage. Optical features from Landsat-8/9 and Sentinel-2, together with SAR backscatter features from Sentinel-1, Lutan-1, and Gaofen-3, were incorporated into machine learning regression models. SAR features improved pixel-wise prediction, with the Random Forest model achieving the highest R2 of 0.62 using early-period features and 0.77 using later-period features. Village-scale aggregation further improved performance, yielding an early-period R2 of 0.84 across 123 and 0.78 across 122 villages. These results demonstrate the feasibility of SAR–optical and phenology-guided regression for early crop damage assessment under long-duration inundation.
Why it matches plant phenotyping methodsSAR・光学画像とフェノロジー指標を用いて作物被害を推定する回帰手法が研究の中心であり、作物状態（洪水被害）を定量化・検証しているため。
abstractThis study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations.
Corn stunt is one of the most important diseases affecting maize (Zea mays L.) production in tropical regions of the Americas. The disease is caused by a complex of pathogens transmitted by the corn leafhopper (Dalbulus maidis), and its predominantly quantitative inheritance complicates the identification of tolerant genotypes under field conditions. In this context, we aimed to perform a comprehensive phenotypic stratification of corn stunt tolerance in a tropical public maize diversity panel and to identify contrasting inbred lines for breeding and genetic studies. A total of 360 inbred lines were evaluated under natural infection using three complementary disease-response traits: survivor plant health score (SPHS), proportion of survivor plants (PSP), and whole-plant health score (WPHS). Multi-trait mixed-model analyses revealed significant genotypic variation, moderate to high broad-sense heritability, and significant genotype × environment interactions for all evaluated traits. A multi-trait index (MSI), calculated from standardized best linear unbiased predictions (BLUPs), successfully integrated the three phenotypic components and enabled robust stratification of the diversity panel, identifying 60 highly tolerant and 60 highly susceptible inbred lines. Further, a genomic principal component analysis demonstrated that these phenotypic extremes were distributed across both tropical and subtropical germplasm, indicating that tolerance is not restricted to a single genetic background. The proposed phenotypic framework provides a robust and reproducible strategy for characterizing quantitative disease tolerance, identifying valuable parental germplasm, and establishing well-defined phenotypic extremes for future investigations of the genetic architecture of corn stunt tolerance.
Why it matches plant phenotyping methods複数の植物病害応答形質を統合する統計的フェノタイピング枠組みと指標を中核として、耐性の再現可能な層別化手法を提示しているため。
abstracta comprehensive phenotypic stratification of corn stunt tolerance
Plants encounter multiple abiotic stresses. Among them, heat and drought stress play a substantial role in reducing the agricultural productivity of commercial plants. Hence, wild and underutilized plants can be a potential alternative as they are naturally tolerant to extreme climatic conditions and are a rich source of nutrition. Manual stress and disease detection is a laborious and expensive process, and hence automation in this field is required to reduce agricultural losses. This study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves, exploring various stress-induced changes using machine learning (ML) algorithms and biochemical analysis. A. senegal , an underutilized edible desert legume, was grown under controlled greenhouse conditions. After 2 months, these plants were segregated into groups and subjected to heat and drought treatments. Image acquisition was performed to obtain a dataset of 3,454 images of A. senegal leaves. Physiological parameters, such as fresh and dry leaf weight, shoot length, number of leaves, and biochemical assays like antioxidant assay (DPPH), total phenolic content (TPC), and total flavonoid content (TFC), were determined. LC-MS/MS analysis was conducted to identify over 50 phytochemical compounds. A hybrid model was developed consisting of a fine-tuned EfficientNet-based Convolutional Neural Network (CNN) followed by a Support Vector Machine (SVM) for the binary classification of A. senegal leaves. The model distinguishes between healthy and stress-affected unhealthy leaves and achieved an accuracy score of 86.6%. This report provides a significant lead toward stress phenotyping and prediction of a bipinnate leaf plant using ML algorithms. The overall study is useful to understand how the stress encountered by arid plants alters the nutritional quality.
Why it matches plant phenotyping methods画像データと機械学習モデルを用いて、アカシア葉の健全・ストレス状態を自動分類する手法を開発・評価しており、植物表現型取得が中心です。
abstractThis study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves
Reproduction assets foundThe paper's data availability statement explicitly makes the 3,454-image A. senegal leaf imaging dataset public on Zenodo and the ML implementation source code public on GitHub; both are paper-specific, public, and actionable.Dataset · publicThe plant leaf imaging data used in the work is publicly available at https://doi.org/10.5281/zenodo.16531486.Open asset ↗zenodo · 10.5281/zenodo.16531486html-lines:480-497Code · publicThe source code of the implementation is available at https://github.com/softwareinnovationslabBITS/CDRF_ASenegal_MLImagingOpen asset ↗github · softwareinnovationslabBITS/CDRF_ASenegal_MLImaginghtml-lines:480-497Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The Florida sugarcane industry is transitioning from manual to mechanical planting systems that use comparatively smaller seedcane pieces (billets) as planting material. A major limitation of mechanical planting is the increased seedcane requirement owing to mechanical damage and the increased vulnerability of seedcane pieces to soilborne pathogens that cause sett rots, particularly pineapple sett rot caused by Thielaviopsis spp. Current sugarcane breeding programs in Florida screen for major diseases, such as rusts, smut, ratoon stunting, and viruses, early in the breeding process but not for pineapple sett rot. This study aimed to isolate and identify Thielaviopsis spp. in the Everglades Agricultural Area (EAA), develop a single-bud inoculation protocol for greenhouse-based disease screening, and phenotype the current widely grown sugarcane varieties in Florida against Thielaviopsis spp. The pathogen was confirmed as T. ethacetica , consistent with previous reports from the EAA. A reproducible inoculation method was established and validated through symptom assessment, pathogen reisolation, and molecular confirmation. Using this protocol, six widely grown Florida sugarcane varieties showed significantly reduced germination (by more than 50%) and reduced above- and belowground morphological characteristics under infection, indicating susceptibility. Varietal differences were observed, with CP 03-1912 showing the highest mortality percentage and reduced growth under T. ethacetica infection. These findings highlight the vulnerability of current varieties to pineapple sett rot, especially under mechanical planting systems where smaller seedcane pieces are used. Furthermore, the developed inoculation protocol provides a scalable tool for early stage evaluation of resistance in breeding programs, offering potential to accelerate the development of varieties better adapted to mechanical planting.
Why it matches plant phenotyping methodsサトウキビの病害抵抗性を評価するための単芽接種・症状評価プロトコルを開発し、再現性を検証した研究であり、植物病害表現型の取得法が中心である。
abstractdevelop a single-bud inoculation protocol for greenhouse-based disease screening
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.
Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。
abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.
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.
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
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Plant phenomics has undergone rapid development over the past two decades, driven by advances in imaging, robotics, artificial intelligence and data analysis. Whilst field phenotyping is increasingly operational and scalable, the relevance of controlled-environment (CE) phenotyping is questioned because of concerns regarding the limited transferability of results to agricultural conditions. This Expert View first addresses the limitations and risks of using CE as surrogate of outdoor conditions. However, we argue that CE enables the disentangling of interacting environmental drivers allowing causal analysis of plant responses to multiple abiotic and biotic stresses. CE platforms also provide access to complex traits that are difficult or impossible to measure in the field whilst providing a robust framework in combination of field approaches to interpret and predict field performance. We further discuss contexts where CE remains indispensable, including quarantine and biosafety regulations together with emerging opportunities for agricultural innovation. Whilst limitations of CE systems are acknowledged, including issues of extrapolation, pot effects, environmental realism, and the indispensable need for rigorous envirotyping, we conclude that CE phenotyping should be regarded as an enabling analytical framework that complements and strengthens field phenomics for crop adaptation research under climate change.
Why it matches plant phenotyping methods管理環境フェノタイピングとフィールドフェノミクスの役割・限界・分析枠組みを論じる専門的レビューであり、植物表現型計測の方法論が中心です。
titleWhy bother with controlled-environment phenotyping when field phenomics is already up and running?
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
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.
Introduction Safeguarding the yield and quality of field crops against abiotic stresses is critical for large-scale agricultural production and precision agronomy. This study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study. Methods We developed a leaf-fruit synergistic Roll and Color Disease Index (RCDI), integrating fruit incidence with visible canopy phenotypic features to characterize the combined canopy-fruit stress status associated with BER. A three-level screening strategy was used to identify the optimal spectral feature set. A Multi-model Collaborative Cyclic Self-Training (MCC-ST) framework was subsequently developed to address the limited availability of severity-labeled samples. Results The combination of GRVI, NDVI, and SAVI was identified as the optimal spectral feature set, achieving stable within-dataset binary classification performance of approximately 97% in repeated cross-validation. Under 30 random-seed repeated stratified three-fold cross-validations, MCC-ST + DT and MCC-ST + RF achieved RCDI-based BER severity-grading accuracies of 85.00% ± 0.31% and 85.07% ± 0.42%, respectively. Compared with the corresponding original DT and RF models, MCC-ST improved repeated-validation accuracy by 16.19-4.09 percentage points. Discussion The RCDI helps bridge the observational gap between canopy signals and concealed fruit stress, while MCC-ST alleviates the bottleneck associated with label scarcity. The proposed approach provides a promising framework for crop abiotic-stress monitoring under limited-label conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトマトの果実ストレス状態・BER重症度を推定する指標と、ラベル不足に対応する機械学習フレームワークを開発・検証しており、植物表現型取得・推定手法が研究の中心である。
abstractThis study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study.
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.
Why it matches plant phenotyping methodsカスタマイズしたロボット搭載3Dマルチスペクトル画像システムを実装し、植物形態・スペクトル・ストレス状態を高精度に取得することが研究の中心である。
abstractthe present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns
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.
Climate change is intensifying abiotic stresses such as drought and heat, posing significant threats to global food security and the productivity of staple crops including maize (Zea mays L.) and rice (Oryza sativa L.). Conventional breeding approaches are often constrained by the complex genetic architecture of stress-adaptive traits and lengthy breeding cycles, highlighting the need for more efficient, data-driven strategies. This review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice. We discuss the applications of machine learning architectures, including multilayer perceptron (MLP), convolutional neural networks (CNN), random forest (RF), deep neural networks (DNN), gradient boosting methods, and explainable artificial intelligence (XAI), in improving genomic selection and capturing complex genotype–environment interactions. The review further explores the integration of AI with HTP technologies, including autonomous robotic platforms, drones, hyperspectral imaging, and LiDAR, to enable rapid, accurate, and non-destructive phenotypic assessment. In addition, we examine the role of AI-driven predictive models in identifying stress-responsive genes, improving trait prediction, and accelerating the development of climate-resilient crop varieties. Current challenges, including data heterogeneity, computational demands, model interpretability, and biological validation, are also discussed alongside emerging solutions such as multi-view learning, transfer learning, and intelligent precision design breeding. Overall, the convergence of AI, ML, multi-omics, and advanced phenotyping technologies represents a transformative framework for next-generation crop improvement, offering new opportunities to accelerate sustainable breeding programs and strengthen global food security under changing climatic conditions.
Why it matches plant phenotyping methodsAI・MLを用いた高スループット植物表現型解析と、ロボット、ドローン、ハイパースペクトル、LiDARによる表現型評価を中心的にレビューしているため。
abstractThis review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
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.
Abstract Blanket-rate agrochemical scheduling — a practice wherein the same quantity of fertilizer or pesticide is spread uniformly across an entire field irrespective of spatial or temporal crop need — persists as the dominant farm management paradigm across rural India and large parts of South Asia. This approach generates cascading inefficiencies: excess nitrogen drains into waterways, off-target pesticide deposits devastate pollinators, input costs erode thin profit margins, and wide-scale greenhouse gas release from soil microbial activity accelerates climate change. The study documented here addresses this challenge through a purpose-built, four-layer intelligent field management platform. The platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack. A fine-tuned YOLOv8-L network performs real-time pest and foliar disease localisation; a ResNet-50 backbone quantifies canopy health across five stress gradients; a two-layer stacked LSTM projects short-horizon yield trajectories; and a Deep Q-Network autonomously plans drone spray routes weighted by field-specific prescription maps. Field validation spanned two consecutive growing seasons (Rabi 2022–23 and Kharif 2023–24) across six georeferenced plots covering 4.8 ha at Baramati, Maharashtra. Outcome metrics recorded during head-to-head comparison with conventional practice included a disease detection score of 95.6% mAP, a 47.3% reduction in total nitrogen applied, a 38.1% decrease in pesticide volume, and a 22.4% uplift in harvested grain weight. Together, these field-verified numbers substantiate the operational readiness of integrated AI precision agriculture for smallholder deployment.
Why it matches plant phenotyping methodsマルチスペクトル画像・深層学習による病害局在化とキャノピー健康状態の定量化を中核機能とする統合プラットフォームであり、植物の病害状態・生育状態を直接推定して現地検証している。
abstractThe platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicData Availability The annotated image dataset (14,300 images, 23 classes), trained YOLOv8-L and ResNet-50 weights, LSTM model files, DQN policy checkpoint, and all analysis scripts are archived at https://github.com/precision-agri-ai (Zenodo DOI: 10.5281/zenodo.XXXXXXX).Open asset ↗precision-agri-ailines:161-182Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published20 Jul 2026Plant science : an international journal of experimental plant biologyCited by 0 · OpenAlex ↗
Accurate high-throughput evaluation of seed germination under abiotic stress is often hindered by subjective manual scoring and insufficient temporal resolution. This study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming. Utilizing a custom X-Y motorized imaging system, we captured continuous time-lapse data across 16 treatment combinations (0-60 mM NaCl × 0-300 mg L⁻¹ nano-SiO₂). We developed an ultra-lightweight architecture, YOLO26n-Ghost-EMA, which integrates Ghost convolutions and Efficient Multi-scale Attention. This model achieved 99.46% mAP@50 with a 4.5 ms inference time, providing a high detection accuracy while maintaining a lightweight architecture and favorable accuracy-efficiency trade-off compared with standard YOLO variants. while reducing computational demand by 35-50%. To ensure biological validity, Explainable AI (XAI) via Grad-CAM confirmed that the model precisely targets radicle protrusion zones, eliminating 'black-box' opacity. Response Surface Methodology (RSM) quantified the potent ameliorative effect of nano-SiO₂, identifying 100 mg L⁻¹ as the optimal concentration to recover germination from 58.57% to 84.28% under severe salinity (60 mM NaCl). By bridging real-time computer vision and plant stress physiology, this framework provides a scalable, high-resolution solution for precision seed biology and rapid assessment of abiotic stress.
Why it matches plant phenotyping methods深層学習とカスタム撮像システムによる発芽動態の高スループット・リアルタイム定量が研究の中心であり、植物状態（発芽・幼根突出）を画像から抽出する手法を開発・検証している。
abstractThis study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming.
TomatoMultimodalLeafClassificationDisease symptoms / severityStress response / tolerancePlant / canopy temperature
Wearable plant sensing systems for simultaneous biochemical and physiological monitoring with real-time multimodal data analysis remain limited. Here, we present FolioClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, light, CO 2 , and three volatile organic compounds (VOCs) with high selectivity. Its bookmark-inspired design enables secure attachment to plant leaves of diverse morphologies and it integrates a flexible printed circuit board for wireless data transmission. We also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated FolioClip–FolioOmni platform detects and classifies nine stresses, including light, water, CO 2 , mechanical cut, P. infestans , and A. alternata , in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.
Why it matches plant phenotyping methods葉の生理・健康状態とストレスを測定するウェアラブルセンシング装置および機械学習解析基盤を開発し、複数ストレスで性能評価しているため、植物フェノタイピング手法が中心である。
abstractWe also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection.
Background Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. Results We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. Conclusions We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity-treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.
Why it matches plant phenotyping methods植物組織の元素・生化学状態を空間的に取得するピクセル登録型マルチモーダル画像パイプラインを開発し、植物ストレス表現型解析への汎用的プラットフォームとして提示しているため。
abstractwe developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1
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
Accurate crop stress detection is essential for precision agriculture; however, most existing approaches rely on binary labels that collapse distinct stress processes water deficit, nutrient deficiency, disease, and pest damage into a single "stressed" category.We demonstrate empirically that this binary formulation is the primary barrier to classification performance: five model architectures achieve ROC-AUC values within ±0.01 of the random baseline (0.50) on binary stress classification, regardless of feature engineering strategy. Decomposing the binary label into stress-type-specific categories enables anXGBoost classifier to achieve 91.4% accuracy and a macro-averaged F1-score of 0.93 using the same underlying features.To extend coverage to visual disease symptoms, we train a MobileNetV2-based CNN on paddy leaf images, achieving 93.7% binary accuracy (healthy vs. disease_stress) with 100% healthy recall.We combine both modalities in a fusion ensemble that merges tabular and image predictions through rule-based priority logic, achieving 94.6% accuracy on the evaluated image subset.
Why it matches plant phenotyping methods葉画像から健全・病害ストレス状態を推定するCNNと、画像・表形式データの融合分類法が研究の中心であり、植物状態の取得・推定手法を評価している。
abstractTo extend coverage to visual disease symptoms, we train a MobileNetV2-based CNN on paddy leaf images, achieving 93.7% binary accuracy (healthy vs. disease_stress) with 100% healthy recall.
Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.
Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。
abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure
Reproduction assets foundThe paper explicitly states that the analysis code is publicly available in the authors' GitHub repository (jianglong26/soybean-insect-resistance), which directly reproduces this paper's phenotyping pipeline (orthomosaic processing, VI/DINOv3 feature extraction, similarity analysis, genotype ranking). The paper also声明sCode · public540 The code used for analysis is available at https://github.com/jianglong26/Open asset ↗pdf-page:16 lines:1-45Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published17 Jul 2026Zenodo (CERN European Organization for Nuclear Research)Cited by 0 · OpenAlex ↗
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerを用いた根系画像解析が研究の中心で、根長・径・体積・表面積・分枝などの植物形質をデジタル抽出しているため、実質的な植物フェノタイピング応用研究である。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerによるデジタル根系表現型計測が研究の主要な方法として明示され、根長・径・体積・表面積・分枝などの植物形質を抽出しているため。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
Whole plant leaf expansion (shoot expansion) under drought drives the trade-off between water saving for later grain production and canopy photosynthesis. Fine-tuning shoot expansion could therefore become a target of genetic progress for drought-prone environments. However, its components (axis production, i.e. tillering, leaf production on each axis, and individual leaf elongation) may have their own genotypic variability and plasticity under drought, making hard to calibrate crop simulation models and specify breeding targets. In this study, we focused on the genetic diversity of bread wheat and durum wheat to determine the links and trade-offs between the underlying processes of shoot expansion under drought and how it translates at the whole plant and canopy level. For that, we used non-destructive imaging both in the field and controlled condition platforms to determine their dynamics and analyze their relative contribution to the genotypic variability of whole-plant shoot expansion under drought. Results show that shoot expansion measured at plant level in controlled environment was associated with that measured at canopy level in the field, indicating that controlled phenotyping platforms can capture the genotypic variability of growth in the field. Both whole-plant and canopy expansion were associated with tillering rate. In addition, the sensitivity of shoot growth and tillering to soil water deficit were correlated, indicating that both tillering ability and sensitivity to water deficit drive the genotypic variability of shoot expansion. Overall, dissecting shoot- expansion dynamics allowed determining the links between shoot expansion traits under drought, and provides key targets in phenotyping, modelling and breeding for drought environments.
Why it matches plant phenotyping methods非破壊画像と管理環境・圃場のフェノタイピングプラットフォームを用いて、シュート伸長・分げつの動態を測定し、環境間での性能を比較しているため、表現型取得法の応用が研究の中心的要素です。
abstractFor that, we used non-destructive imaging both in the field and controlled condition platforms to determine their dynamics and analyze their relative contribution to the genotypic variability of whole-plant shoot expansion under drought.
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.
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
Introduction Early-stage detection and classification of lettuce heat responses are essential for non-destructive phenotyping, yet conventional assessment mainly relies on visible symptoms and manual observation. Methods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer). DSPformer integrates edge-enhanced feature extraction, dynamic multi-scale spatial-spectral representation, Peak-k selective attention, and a confusion-aware dynamic focal loss to enhance discriminative features while reducing spectral redundancy, class imbalance, and inter-class confusion. Results Under the patch-level evaluation protocol, DSPformer achieved 96.22% accuracy, 95.55% recall, 96.35% precision, and 95.95% F1-score, outperforming the compared CNN- and Transformer-based models. Day-wise evaluation showed that DSPformer reached 82.61% accuracy on Day 1 and 96.55% on Day 3, before visible heat-stress symptoms appeared on Day 6. Under a plant-level partition protocol, DSPformer maintained robust performance with 93.76 +/- 0.49% accuracy. Additional evaluation on the Indian Pines benchmark further demonstrated the applicability of DSPformer to general hyperspectral image classification. Discussion These findings suggest that hyperspectral imaging can capture heat-stress-sensitive information beyond visual phenotypes, and that DSPformer provides a promising framework for early, non-destructive lettuce heat-response screening and hyperspectral phenotyping-assisted breeding.
Why it matches plant phenotyping methodsレタスの熱ストレス応答を非破壊的に早期分類するため、ハイパースペクトル画像データセットと新規Transformer手法を開発・評価しており、植物表現型取得・抽出が中心である。
abstractMethods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer).
ABSTRACT The common bean is vital for food security, but its productivity is often limited by competition with weeds, requiring the use of herbicides. The response of genotypes to herbicides such as fomesafen and imazamox is variable, and the traditional evaluation of phytotoxicity through visual methods is subjective. Therefore, the present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide‐induced phytotoxicity in common bean genotypes, aiming to reduce the subjectivity of traditional visual assessments; (ii) characterize common bean genotypes under the effects of different herbicides and their doses in progenies and parental lines, based on morphophysiological traits and indices derived from visible RGB (red, green, and blue) digital images. The experiment was conducted in a completely randomized design under a 3 × 3 × 6 factorial scheme (herbicide × dose × genotype) with three replications, evaluating fomesafen and imazamox at doses of 0%, 100%, and 200% of the recommended rates. Data were collected on visual phytotoxicity, plant height, stem diameter, number of leaves, and image indices (Green Index, Excess Green Index, Excess Red Index, and Color Index of Vegetation Extraction). Results indicated that the triple interaction was significant, revealing the complexity of plant responses to herbicides. Canonical discriminant analysis explained 78.66% of the total variation, with the first canonical discriminant function (29.42%) contrasting structural development and vitality with stress, the second canonical discriminant function (27.59%) reflecting overall plant vigor, and the third canonical discriminant function (21.65%) capturing stress and phytotoxicity negatively affecting growth. The analysis demonstrated that image‐based indices combined with multivariate techniques are effective for quantifying phytotoxicity and distinguishing genotypes (tolerant and sensitive to herbicide effects), overcoming the limitations of visual evaluations, and should be used as a complementary tool to traditional techniques. Therefore, the parental genotype IPR Campos Gerais and the progeny F1A were tolerant to herbicides at different doses, while the parental genotype BAF36 and the progeny F2B were sensitive. Hence, the proposed methodology is effective for identifying herbicide‐tolerant and sensitive genotypes.
Why it matches plant phenotyping methodsRGB画像解析と多変量解析による除草剤誘発 phytotoxicity の表現型評価法の提案が研究の中心であり、従来の主観的評価を改善する方法開発に該当する。
abstractthe present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide‐induced phytotoxicity in common bean genotypes
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
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.
Why it matches plant phenotyping methods自動UAVのRGB・熱画像センシング platform を圃場で系統的に評価し、温度・キャノピー被覆率・高さ・LAIなどの植物形質を定量化しているため、フェノタイピング手法が研究の中心である。
abstractThis study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Field / plotCell / cellular structureWhole plant / canopy / plot / fieldGrowth / development / phenologyStress response / tolerance
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.
Heat stress limits plant productivity by disrupting transpiration, altering leaf microclimate, and activating metabolic pathways that increase volatile organic compound (VOC) emissions. VOC signatures, combined with leaf-level relative humidity (RH), provide early indicators of plant stress, but conventional analytical methods are costly, bulky, and unsuitable for continuous in situ monitoring. Here, a low-cost multimodal sensing platform based on laser-induced graphene (LIG) is reported for real-time, on-leaf detection of methanol, acetic acid, and RH under ambient conditions. The platform integrates Pt-modified LIG electrodes with PtNP/ZnONR@ZIF-8 for methanol, PtNP/ZnONR@ZIF-8/Sn3O4 for acetic acid and GO:PDMAA for RH sensing. After optimization, the sensors respectively achieved sensitivities of −75.83 Ω/log(ppm), −1.63 Ω/ppm, and −4,776.01 Ω/%RH with detection limits of 0.382 ppm, 0.318 ppm, and 1.53 %RH and more than 97% signal retention over 26 days. On-leaf measurements over 2 weeks showed methanol increasing from ~0.3–13.8 ppm to ~13.5–59.0 ppm, acetic acid from ~6.6–14.8 ppm to ~23.3–60.6 ppm, and RH decreasing from ~72.2–86.3% to ~52.5–65.1% under heat stress. These coupled chemical and microclimate changes provide direct, dynamic stress readouts during plant monitoring. By moving beyond single-analyte measurements, the proposed multimodal approach enables early stress detection, data-driven crop management, and next-generation precision agriculture applications.
Why it matches plant phenotyping methods植物の熱ストレス状態を葉上のVOCと相対湿度から連続測定するセンサー基盤を開発・性能評価しており、植物状態の取得方法が中心的である。
abstractHere, a low-cost multimodal sensing platform based on laser-induced graphene (LIG) is reported for real-time, on-leaf detection of methanol, acetic acid, and RH under ambient conditions.
The segmentation model achieved Mean IoU values of 0.7723 for Water Stress 2025, 0.9164 for Common Rust 2025, and 0.9531 on the benchmark dataset. The classifier achieved 99.54% accuracy for the five-class task; however, the improvement over the strongest baselines and the RGB + multispectral configuration was limited. Therefore, the classification component is not presented as a substantially superior classification-only model. Instead, it is interpreted as an exploratory multimodal analysis that quantifies the contribution and limitation of RGB, multispectral, Wavelet, and GLCM branches under the adopted UAV dataset protocol. For classification-only deployment, simpler alternatives such as DenseNet201 or the RGB + multispectral configuration may be more practical because they provide comparable accuracy with lower architectural or preprocessing complexity. Ablation, modality-controlled, and 21-run stability experiments showed reproducible segmentation results and clarified the behavior of the classification branches. RGB and multispectral branches mainly provided the peak classification accuracy, whereas Wavelet and GLCM branches mainly affected offline convergence rather than final accuracy. RGB, NDVI, and NDRE visualizations were also added for qualitative support. Since direct physiological ground measurements were not available for all samples, the masks are interpreted as adaptive index-guided labels rather than direct physiological ground truth. Overall, the main evidence of practical benefit is associated with UAV-based dataset construction, adaptive index-guided segmentation, and field-scale stress/disease mapping, while the classification experiments should be interpreted as modality-contribution and convergence analyses rather than proof of a practically superior complex classifier.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるトウモロコシの水ストレス・病害状態のセグメンテーション、データセット構築、再現性評価が研究の中心であり、植物表現型の取得・抽出手法として実質的です。
abstractOverall, the main evidence of practical benefit is associated with UAV-based dataset construction, adaptive index-guided segmentation, and field-scale stress/disease mapping
Abstract One of the most important challenges faced by smallholder farmers in the agricultural industry is the lack of accurate, timely knowledge to predict and detect crop health issues. Crop productivity is often threatened not only by diseases but also by environmental and physiological stresses, which contribute to significant yield losses and negatively impact the national economy. Traditional detection methods are time-consuming, costly, and require expert knowledge, creating a need for automated and intelligent systems. This work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves. This integrated system ensures early detection, reduces crop loss, improves productivity, and provides a scalable, farmer-friendly solution for sustainable agriculture.
Why it matches plant phenotyping methods葉画像から健康・病害状態を機械学習で分類する手法が研究の中心であり、植物の病害・ストレス状態を直接推定するため、植物フェノタイピング手法として採用。
abstractThis work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves.
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
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. For genomic prediction, we applied nested CV and LOOCV in which GWAS and marker selection were performed within the training set before fitting the prediction model, so that phenotypic information from the test individuals was not incorporated into the marker selection step. Compared with the full marker set of 73,502 markers, the best GWAS-selected marker sets achieved prediction accuracies of 0.237 for CTI and 0.197 for Surv in nested CV, and 0.349 for CTI and 0.294 for Surv in LOOCV. At the same marker numbers and model conditions, random marker sets showed lower accuracies of 0.205 and 0.166 for the nested CV, and 0.068 and - 0.064 for the LOOCV, respectively. Conclusions Our study demonstrates that assessing cold tolerance via the CTI helps overcome the limitations of discrete survival scoring. By turning ordinal data into a continuous spectrum, the CTI can unmask hidden genetic variation. In addition, GWAS identified candidate genomic regions and genes associated with cold response, and nested CV and LOOCV showed that GWAS-selected marker sets could achieve higher prediction accuracy than the full marker set and random marker sets of the same marker number. This integrated framework offers a practical approach for interpreting the genetic basis of adult-stage cold tolerance in pepper and improving the efficiency of genomic prediction models for complex abiotic stress traits.
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標（CTI）の開発・評価が明示され、単なる形質の routine 測定を超えて表現型取得法の中心的貢献となっている。
abstractThis study aimed to overcome this limitation by developing a robust phenotypic index and implementing a genome-wide association study (GWAS)-informed GS strategy.
Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M2 genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R2 = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R2 ≈ 0.83) fell to R2 ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R2 = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10−5).
Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル形質を抽出し、飛行高度、予測モデル、再現性、交差検証を体系的にベンチマークしているため、植物表現型取得法が中心である。
abstractEarly-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract This study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer leveraging engineered Au core/Ag shell nanocubes (Au@AgNCs) to bridge in planta pathogen tracking with airborne defense signal monitoring, enabling unprecedented decoding of plant–plant communication (PPC) kinetics. Within a Pseudomonas aeruginosa (P. aeruginosa)-infected plant biotic stress model, the analyzer achieved: (1) spatiotemporal mapping of virulence kinetics through sensitive detection of P. aeruginosa-specific virulence factor pyocyanin, establishing infection progression timelines and tissue-specific dissemination gradients. (2) Quantification of stress-responsive signaling via dual-functionalized Au@AgNCs, revealing methyl salicylate (MeSA) release kinetics and establishing a direct correlation between pathogen invasion severity and airborne alarm signal—a calibrated defense response heretofore unquantified. (3) Real-time in situ monitoring of MeSA-mediated PPC revealed fundamental plant physiological breakthroughs: First, receiver-specific signaling reprogramming occurs where healthy plants exhibit delayed yet amplified defense hormone kinetics, contrasting sharply with the immediate response of infected emitters. Second, evolutionarily constrained coordination emerges through cross-species signaling divergence, where phylogenetic adaptations in phytohormone perception circuits drive distinct defense strategies−exemplified by Solanaceae amplification versus Poaceae suppression. (4) Validation of systemic acquired resistance (SAR) in PPC-primed plants showing 63.5% reduced infection severity and two days delayed susceptibility. This analyzer integrates molecular-scale pathogen kinetics with ecosystem-level signaling networks, advancing precision agriculture through field-deployable plant immunity diagnostics.
Why it matches plant phenotyping methodsSERSセンサーアナライザーの開発・検証が研究の中心で、植物感染進行、ストレス応答、空中防御シグナル、感染重症度を時空間的に測定するため、植物フェノタイピング手法に該当する。
abstractThis study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer
Rice seedling salt-tolerance evaluation commonly relies on visual scoring or destructive assays, which are subjective, labor-intensive, and difficult to standardize for population-level analysis. This study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings. The framework integrates controlled hydroponic cultivation, RGB imaging, RicePhenoSeg-assisted annotation and trait extraction, ELMERF-based semantic segmentation, and image-derived quantification of salt-induced shoot injury. Using this framework, we constructed the Rice Seedling-Salt RGB Dataset (RSSD), which contains green shoot tissues, yellow shoot tissues, roots, and background from hydroponically grown rice seedlings. Based on RSSD, ELMERF achieved a mean Intersection over Union of 51.4% and a mean Accuracy of 89.5%, outperforming nine representative segmentation models. We further defined shoot yellowing rate (SYR) as an image-derived quantitative trait describing visible salt-induced shoot injury. The framework was applied to 261 re-sequenced rice accessions for population-level phenotyping and genome-wide association analysis. Compared with standard evaluation score and seedling death rate, SYR showed a more continuous phenotypic distribution and detected 36 significant SNPs, including a major signal near the Saltol/OsHKT1; 5 region. Notably, 34 SYR-associated SNPs were not detected by conventional visual scores. Overall, this study provides a targeted hydroponic RGB phenotyping framework for standardized rice seedling salt-stress evaluation and genetic analysis.
Why it matches plant phenotyping methods深層学習によるRGB画像セグメンテーション、形質抽出、データセット構築、性能比較を中核とし、画像由来の塩ストレス傷害形質を定量化する植物フェノタイピング手法である。
abstractThis study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits datasets, source code, and supporting data in a public GitHub repository (ELMERF), which covers the RSSD RGB image dataset, segmentation code, and phenotyping/GWAS analysis assets. RiceVarMap is a cited external SNP database, not a paper-specific asset.Code · publicThe datasets, source code, and other supporting data are openly available on the ELMERF repository (https://github.com/PhenoCodexh/ELMERF).Open asset ↗PhenoCodexh/ELMERFhtml-lines:446-478Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
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.
Abstract There are many challenges faced by desert agriculture like water scarcity, extreme temperatures, poor soil conditions and sand encroachment(sand dunes). Traditional farming methods worked exceptionally well in deserts for thousands of years. But now due to modern megacities projects, climate shifts and unpredictable weather patterns these methods cannot keep up with modern planetary pressures. Modern advanced technologies like AI and precision agriculture along with drones provide new opportunities for intelligent crop monitoring and resource management in arid environments. In this research we propose a simulation-based UAV swarm framework for sustainable desert agriculture using virtual UAV agents, synthetic crop imagery, and AI-driven analysis. We generated a synthetic crop monitoring dataset using publicly available crop disease images combined with environmental stress augmentation techniques to emulate desert farming conditions such as dehydration and heat stress. The virtual UAV swarm collects image-based sensor information from different regions of the simulated field. We created a synthetic desert farming stress dataset to emulate challenging environmental conditions including moderate and severe crop stress scenarios. When integrated within the UAV monitoring framework, the proposed approach achieved complete field coverage, a stress detection rate of 100%, and an average prediction confidence of 98.9%. The achieved results showed clearly that our proposed simulated implementation is suitable to support intelligent crop monitoring, stress detection, and resource-efficient agricultural management in desert environments.
Why it matches plant phenotyping methods仮想UAV群、合成作物画像、AI解析を統合した作物ストレス検出・監視フレームワークが研究の中心であり、植物のストレス状態を画像から推定するフェノタイピング手法として扱える。
abstractwe propose a simulation-based UAV swarm framework for sustainable desert agriculture using virtual UAV agents, synthetic crop imagery, and AI-driven analysis.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Timely quantification of crop stress physiology remains challenging because conventional assays are destructive, labor-intensive, and poorly suited for continuous monitoring and field deployment. Here, we report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress that integrates three design innovations in a single architecture: (i) a fully integrated hollow microneedle–microfluidic measurement pathway for sap access, (ii) physical isolation of the metal electrodes from direct tissue contact to reduce insertion-zone abrasion of the sensing interface, improving biocompatibility and potentially lowering fouling pathways, and (iii) lithography-free fabrication of a modular transducer on an additively manufactured substrate. The platform comprises a three-electrode gold (Au) transducer modified with a nanostructured reduced graphene oxide (rGO)–chitosan layer. The biosensing platform enabled dual sensing channels via functionalized glucose oxidase (GOx) and horseradish peroxidase (HRP) for the detection of glucose and water stress-associated hydrogen peroxide (H2O2), respectively. The glucose channel showed a strong linear calibration over the tested range, with Pearson’s r = 0.99, R2 = 0.98, sensitivity of 62.34 μA/mM, and a limit of detection (LOD) of 102.50 μM (∼1.85 mg/dL), while the H2O2 channel exhibited Pearson’s r = 0.99, R2 = 0.99, sensitivity of 3.65 μA/decade, and an LOD of 3.22 μM. Repeatability across measured standards remained high for both channels, with mean coefficients of variation of 1.31% for glucose and 1.16% for H2O2. Ex vivo measurements in plant sap, including standard-addition experiments and comparison with commercial benchmark assays, provided validation of analyte concentration determination in plant-derived samples. In planta measurements on maize plants (Zea mays L.) grown under graded watering treatments revealed statistically significant treatment-dependent glucose and H2O2 signatures over time (p
Why it matches plant phenotyping methods植物体内のグルコースとH2O2を非破壊・連続測定し、水ストレス状態を推定する電気化学センシング基盤の開発と検証が中心であり、植物フェノタイプ取得手法に該当する。
abstractwe report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Background High temperatures during the reproductive stage of soybean severely disrupt reproductive processes and reduce yield. Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean. Sixteen soybean breeding lines (genotypes) were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using a deep learning–based object detection tool to reduce the manual labor and improve accuracy. Several advanced object detection models belonging to the YOLO (You Only Look Once) family, specifically, YOLOv7–YOLOv12, were evaluated to identify the most reliable model. Results Comparative evaluations of different object detection models indicated that YOLOv9 model achieved superior performance in evaluating pollen germination relative to other YOLO models, especially for detecting germinated and non-germinated pollen in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P
Why it matches plant phenotyping methodsダイズの耐暑性評価のため、花粉発芽を対象とした画像ベースの深層学習測定法を開発・比較検証しており、フェノタイピング手法が研究の中心である。
abstractThis experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean.
Agriculture remains the backbone of global food security, yet crop diseases, nutrient deficiencies, water stress, and pest infestations cause annual yield losses estimated at 20–40% worldwide. Conventional field scouting methods are labour-intensive, time-consuming, and fail to capture the spatial heterogeneity of large farms. This paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying. A DJI Matrice 300 RTK drone equipped with a MicaSense RedEdge-MX multi-spectral camera captures high-resolution aerial imagery across five spectral bands (Blue, Green, Red, Red-Edge, Near Infrared). The captured data is processed through a custom-trained YOLOv8-based convolutional neural network (CNN) pipeline to detect 18 distinct crop diseases and stress conditions across rice, wheat, and cotton crops. Concurrently, vegetation indices (NDVI, NDRE, GNDVI, SAVI) are computed to generate prescription maps for site-specific fertilizer and pesticide application. Experimental evaluation on a 120 acre farm in Thanjavur, Tamil Nadu over two crop seasons demonstrates a disease detection accuracy of 96.3%, early stress detection 8–12 days before visible symptoms, and a 31% reduction in agrochemical usage through variable-rate application. The system achieves an end-to-end field analysis time of under 45 minutes for 100 acres. Keywords — UAV, Precision Agriculture, Crop Disease Detection, Multi-Spectral Imaging, NDVI, YOLOv8, Deep Learning, Variable-Rate Application, Remote Sensing, Smart Farming.
Why it matches plant phenotyping methodsドローンのマルチスペクトル/RGB画像とYOLOv8を用いて作物の病害・ストレス状態を推定するシステムを開発し、精度と運用性能を評価しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying.
Semi-arid regions with a high potential for rice and maize cultivation have become some of the most actively farmed areas. They now face the challenge of achieving food security despite the threats of crop water stress, nutrient loss, and environmental changes. In this paper, we develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models in Mahabubabad district, Telangana, India. Different types of crop stresses such as water stress, nutrient deficiency, and phenological anomalies were detected and classified using a hybrid CNN-LSTM architecture with an attention mechanism. The methodology was based on 874 field polygons with extensive in-situ data collection during 2023-24, incorporating multi-temporal spectral indices (NDVI, EVI, NDWI, REP), weather variables, and soil characteristics. The total classification accuracy reached 89.4% for paddy and 87.2% for maize over all stress types, showing that stress detection from satellite images is quite reliable. Water stress was the category that was detected most accurately (92.1% for paddy and 89.8% for maize), followed by nutrient stress (88.7% and 86.3%) and phenological stress (85.2% and 83.9%). The warning system made it possible to identify the problem 15-25 days before there were visible symptoms, making it possible for the farm management to respond in time. Activities of the farm that were most vulnerable to detection were air and water temperatures, precipitation, and crop growth stages for water stress 45-60 days after sowing, 30-45 days for nutrient stress, and during the reproductive phase for phenological stress. The system could be extended for industrial crop stress monitoring across the semi-arid agricultural systems which might lead to precision agriculture and climate-resilient farming practices.
Why it matches plant phenotyping methods衛星画像と深層学習を用いて作物の水ストレス・栄養ストレス・生育異常を直接推定し、精度検証と早期検出性能を評価しているため、植物表現型取得法が中心である。
abstractwe develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models
Modern agriculture is rapidly adopting Artificial Intelligence (AI) and Internet of Things (IoT) technologies to improve crop monitoring and decision-making. Many existing systems focus either on water stress detection or pest detection separately. The proposed system integrates both functions into a single platform. It uses a camera module and environmental sensors connected to a Raspberry Pi (5/4) as the main controller. A Convolutional Neural Network (CNN) model processes leaf images captured by the AI camera, while a soil moisture sensor supports water stress analysis. The system classifies crops into three categories: healthy, water-stressed, and pest-infected. Based on the output, it provides real-time recommendations for irrigation and pesticide application. This reduces manual inspection, prevents unnecessary chemical usage, saves water, and improves crop productivity.
Why it matches plant phenotyping methods植物の葉画像と土壌水分センサーを用いて、健康・水ストレス・害虫感染という植物の状態を自動分類する統合センシング基盤を開発しており、表現型取得・判定が中心的です。
abstractThe proposed system integrates both functions into a single platform.
Reproduction assets foundThe paper's CNN phenotyping/classification analysis is built directly on two public Kaggle image datasets (PlantVillage plant disease and Crop Water Stress), explicitly cited with URLs. No author code or trained model is deposited.Dataset · publicThe PlantVillage Dataset was used for
plant disease detection, and it is available at https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseasepdf-page:7 lines:1-57Dataset · publicThe Crop Water Stress Dataset was used for crop water stress analysis, and it can be accessed at
https://www.kaggle.com/datasets/harshilsharma/crop-water-stress.Open asset ↗Kaggle · harshilsharma/crop-water-stresspdf-page:7 lines:1-57Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published1 Jul 2026Proceedings of the International Conference on Business ExcellenceCited by 0 · OpenAlex ↗
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.
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
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
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
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Drought poses a global threat to food security and demands intensified efforts from breeding programs. Yet the lack of efficient methods for selecting this trait increases the cost and time required to develop new cultivars. The goal of this work was to assess the feasibility of using spectral data from RGB or multispectral sensors for drought-tolerance classification across various machine-learning models under the most practical cross-validation scenarios typical in breeding programs. The genotypes were assessed during trials conducted under either optimal (irrigated) or drought-stress conditions across two years, and evaluated using up to 10 field traits to determine their drought-tolerance classification based on membership function values related to drought. RGB and multispectral vegetation indices collected during several flights throughout the crop cycle were used to train machine learning models. We found that drought trials offer the best training data. Specificity was the metric most affected by sensor type and the nature of the training data. The multispectral sensor outperformed the RGB sensor on most evaluation metrics in both years. AdaBoost and linear discriminant analysis models demonstrated the strongest consistency across all prediction scenarios. Together, they achieved an overall accuracy, specificity, and F1-Score of 0.71, 0.56, and 0.77, respectively. The most influential vegetation indices for model performance consistently included the NIR band. Spectral information, such as vegetation indices, is a useful tool for plant researchers to complement drought tolerance evaluations in the field. This data-driven approach facilitates automation, paving the way to speed genetic gains by including early assessments of drought tolerance in breeding pipeline, and improves resource utilization efficiency.
Why it matches plant phenotyping methodsUASのRGB・マルチスペクトルデータと機械学習を用いて干ばつ耐性を分類し、センサー比較や交差検証を行うことが研究の中心であるため、植物フェノタイピング手法として適格です。
abstractThe goal of this work was to assess the feasibility of using spectral data from RGB or multispectral sensors for drought-tolerance classification across various machine-learning models under the most practical cross-validation scenarios typical in breeding programs.
Field / plotWhole plant / canopy / plot / fieldObject detectionPhysiological trait estimationStress / disease detectionGrowth / development / phenologyPigment / colour / senescenceStress response / toleranceWater status / transpiration
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.
Low temperature stress severely restricts the cultivation and distribution of pear ( Pyrus L.) germplasms, frequently resulting in frost injury and yield reduction. To accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress. In this study, 122 pear germplasms were classified into high (HR), medium (MR), and low (LR) cold-tolerance categories based on their semi-lethal temperature (LT 50 ). Further analysis of pear germplasms with different levels of cold resistance revealed that, with decreasing temperature, HR germplasms exhibited smaller increases in relative electrolyte conductivity (REC) and malondialdehyde (MDA) content and higher accumulation of proline (Pro), soluble proteins (SP), soluble sugars (SS), and peroxidase activity compared with LR germplasms. In addition, the peak values of these indicators generally occurred at lower temperatures in HR germplasms. A correlation analysis and principal component analysis indicated that physiological indices, including REC, bound water/free water ratio, SS, and MDA, as well as branch anatomical traits related to xylem and cortex proportions, were closely associated with variation in LT 50 . An integrated assessment using membership function analysis produced rankings consistent with LT 50 -based clustering, supporting the reliability of the multivariate evaluation framework. Overall, this study establishes an integrated, indicator-based approach for evaluating cold resistance in pear germplasm by integrating physiological, biochemical, and anatomical characteristics. These results provide a theoretical basis and methodological reference for screening cold resistance germplasms.
Why it matches plant phenotyping methods生理・生化学・解剖学的形質を統合し、LT50と多変量評価によってナシ遺伝資源の耐寒性を分類・スクリーニングする評価フレームワークが研究の中心である。
abstractTo accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress.
Reproduction assets foundThe article's Data Availability statement links a public Zenodo deposit containing the paper's raw phenotyping data (LT50, physiological/biochemical and anatomical measurements for pear germplasms). Supplemental files also contain germplasm characteristics and LT50 comparisons, but the Zenodo raw-data deposit is the明确,Dataset · publicThe data is available at Zenodo: liu186253. (2025). liu186253/Data: raw data (Version V11). Zenodo. https://doi.org/10.5281/zenodo.17524773 .Open asset ↗Zenodo · 10.5281/zenodo.17524773lines:636-710Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Plants face a range of biotic and abiotic stresses that reduce yield, and in agricultural countries like Bangladesh, manual detection of these stresses remains slow and error-prone. Convolutional Neural Networks (CNNs) classify plant leaves accurately, but they suffer from catastrophic forgetting when trained on tasks sequentially. This is a major obstacle in real agricultural settings, where new crops and stress conditions arrive over time. Existing continual learning (CL) studies in this domain rely on relatively heavy backbones, leaving open the question of whether lightweight CL pipelines can retain prior-task knowledge under realistic resource constraints. We investigate this question by integrating two established CL methods, Elastic Weight Consolidation (EWC) and Learning without Forgetting (LwF), into EfficientNet-B0, a backbone with only 5.3M parameters. The setup is evaluated on the Nutrispace cucurbit nutritional deficiency dataset, where three plant species (ash gourd, bitter gourd, and snake gourd) are treated as three sequential tasks, each with the same three classes: healthy, nitrogen deficiency, and potassium deficiency. Without continual learning, accuracy on the earliest task collapses to 30% by the end of training. EWC preserves over 61% accuracy on prior tasks while reaching 98% on the final task, and LwF reaches 98% on the final task with slightly lower retention on earlier ones. Pairwise Welch's t-tests confirm that both methods significantly outperform the baseline ([Formula: see text]) and that EWC retains prior-task knowledge significantly better than LwF ([Formula: see text]). These results show that lightweight CNNs paired with established CL techniques offer a workable path for plant stress classification in resource-constrained agricultural AI.
Why it matches plant phenotyping methods植物葉画像から栄養欠乏・健全状態を分類するCNNに、継続学習手法を組み込んで性能保持を評価しており、植物ストレス状態の取得・推定手法が研究の中心である。
abstractConvolutional Neural Networks (CNNs) classify plant leaves accurately, but they suffer from catastrophic forgetting when trained on tasks sequentially.
In modern agriculture, artificial intelligence (AI) is doing excellent work in crop monitoring, crop disease detection, crop yield prediction, and crop stress assessment. Various techniques such as deep learning, generative models, vision transformers, explainable AI (XAI), multimodal fusion, etc., have helped in building intelligent crop analysis. This paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets. The important challenges and open issues identified are limited field datasets, class imbalance, dataset bias, high computational complexity, privacy concerns, etc. Based on these, we suggested future work that can include foundation models, digital twin techniques, federated learning, multimodal frameworks, and interpretability architecture. This review provides a review for creating reliable, scalable, and sustainable AI-driven crop analysis systems. In addition to that, the survey seeks to give researchers and AI practitioners a comprehensive analysis of the current situation.
Why it matches plant phenotyping methods作物の病害・収量・ストレス評価を対象に、AI手法、データモード、ベンチマーク、実験方法、データセットを体系的にレビューしており、植物表現型取得・推定手法のレビューが中心です。
abstractThis paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets.
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.
TomatoMultimodalLeafClassificationStress / disease detectionStress response / tolerancePlant / canopy temperature
Wearable plant sensing systems for simultaneous biochemical and physical monitoring with real-time multimodal data analysis remain limited. Here, we present PhytoClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, three volatile organic compounds (VOCs) with high selectivity, and microenvironmental light intensity and CO2 concentration. PhytoClip features a bookmark-inspired design for secure attachment to leaves of diverse morphologies, supported by a flexible printed circuit board for data acquisition, wireless communication, and cloud-based monitoring. We develop PhytoSense, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated PhytoClip-PhytoSense platform detects and classifies nine biotic and abiotic stresses in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.
Why it matches plant phenotyping methods植物の葉に装着するマルチモーダルセンサーとオンラインMLによるストレス・病害状態の取得および分類が研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe develop PhytoSense, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection.
The paper discusses the preparation of experimental data used to measure the biopotentials of Novosibirskaya 41 wheat seedlings exposed to elevated and lowered temperatures, in order to conduct phenotyping of these plants using cluster analysis. It is noted that such a preparation is necessary for long-term experimental studies that take several calendar days (up to 10 or more), during which metabolic changes in seedling samples occur, affecting their biopotential values. The paper is based on experimental data obtained in 2020 and 2022 and their regression analysis, as reported in [13]. The results of changes in seedling biopotentials depending on their age are briefly described, and an algorithm for calculating corrective biopotential values for each magnification level of the objects is provided. Statistical regressions of changes in biopotential values depending on the need to preserve seedlings of these wheat varieties were obtained. This allowed the development of an algorithm for correcting the initial average biopotentials for these conditions without preliminary regression analysis of the data. Two data sets were generated for assessing the phenotype of the objects: the original data set, obtained through primary processing of changes in these seedling biopotentials under exposure to elevated and lowered temperatures, and the corrected data set, in the partial parameter (smax.c.) of the maximum filtered centered value (cf) of the wheat seedling biopotentials under these conditions. Plant phenotyping was performed based on the data sets using the original Eclaster program, which implements this methodical spectral clustering from the sklearn.cluster library in the Python programming environment. The clustering results presented in the form of a scatterplot demonstrate improved cluster separation for the corrected data.
Why it matches plant phenotyping methods小麦幼苗のバイオポテンシャルを用いた表現型評価のため、データ補正アルゴリズムとクラスタリング解析プログラムを開発・適用しており、表現型取得・抽出手法が研究の中心である。
abstractThe paper discusses the preparation of experimental data used to measure the biopotentials of Novosibirskaya 41 wheat seedlings exposed to elevated and lowered temperatures, in order to conduct phenotyping of these plants using cluster analysis.
Abstract Canopy temperature (Tc) is a critical physiological indicator of water and heat stress in cotton. Although weather-driven Tc forecasting is used by 60% of Australian cotton growers for irrigation scheduling, current methods typically rely on a single in situ sensor to represent an entire management area. This uniform assumption overlooks substantial spatial variability in Tc and can lead to suboptimal water application. We propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts. Experimental results show that UAV-linear forecast stress conditions at unmeasured locations as effectively as models trained on exhaustive historical data, achieving a 25-35% improvement over the standard uniform-forecast assumption. Furthermore, in a large-scale validation across 50,000 hectares of commercially active farms (practical dataset), UAV-linear improved stress-hour prediction by 22% relative to the uniform assumption while maintaining accuracy comparable to historical benchmarks. These findings show that the proposed spatio-temporal framework provides the spatial detail needed for differentiated precision management, with potential to improve crop yield and water-use efficiency.
Why it matches plant phenotyping methods綿花のキャノピー温度という植物の生理状態を、UAV熱画像とセンサーから推定・予測する手法を開発し、大規模に検証しているため、灌漑管理への応用でもフェノタイピング手法が中心です。
abstractWe propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts.
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
RiceGrowth / development / phenologyStress response / tolerance
The reliance on traditional or manual phenotyping creates significant operational bottlenecks in rice breeding due to its resource-intensive and time-consuming nature. This review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations. HTP has great potential to accelerate rice breeding by revolutionizing phenomics. This review examined the various applications of HTP in rice research, phenomics, and breeding. The use of HTP in rice has been substantiated through a range of cutting-edge technologies, such as drones, imaging systems, and sensor networks, that facilitate precise monitoring of key traits at various growth stages, assessment of responses to biotic and abiotic stresses, and the identification of genes or quantitative trait loci (QTLs) associated with essential characteristics. Also, this review discussed HTPs' contributions to current rice breeding programs and documented notable challenges in scaling them. This review offers insights into optimizing HTP strategies to advance rice research, phenomics, and rice breeding.
Why it matches plant phenotyping methodsイネのハイスループット表現型解析技術の応用、技術、課題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レビューに該当する。
abstractThis review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations.
Salt stress represents one of the main challenges for global agricultural production, and digital phenotyping has emerged as a promising alternative for identifying popcorn genotypes tolerant to salt stress. This study evaluated the accumulation of plant pigments in response to salt stress in 49 popcorn genotypes (7 inbred lines and 42 F1 hybrids). Seeds were subjected to two saline conditions: without salt stress (NS—0 mM NaCl) and salt stressed (SS—100 mM NaCl). The evaluation included physiological parameters, and morphological and colorimetric attributes based on the CIELab color space were analyzed using the GroundEye® system. Additionally, the salt stress tolerance index (SSTI) was calculated for all assessed genotypes. The SSTI ranged from 0.55 to 0.83, with values closer to 1.0 indicating higher tolerance to the stressor. Among the evaluated genotypes, L472 and four of its hybrids stood out for their salinity tolerance, as they combined efficient maintenance of chlorophyll content with higher SSTI estimates. In contrast, L217 and two of its hybrids were identified as sensitive, exhibiting some of the lowest SSTI estimates and significant accumulation of anthocyanins, which, in this study, indicated a response mechanism to oxidative damage. Digital phenotyping associated with CIELab colorimetric analysis constitutes an objective tool for identifying tolerant genotypes, thereby accelerating breeding programs aimed at developing cultivars adapted to saline environments.
Why it matches plant phenotyping methodsCIELab色空間とGroundEye®を用いた植物色素・色彩形質のデジタル表現型解析が、耐塩性遺伝子型評価の中心的手法として明示されている。
abstractdigital phenotyping has emerged as a promising alternative for identifying popcorn genotypes tolerant to salt stress
Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.
Why it matches plant phenotyping methods温室イチゴのフェノタイピング、マルチモーダルセンシング、AIによる作物状態解釈を中心に整理する方法論レビューであり、植物状態の取得・推定手法が主題。
abstractThis review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation.
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
The development of Low-Cost Phenotyping Platforms Supported by Generative Artificial Intelligence (AI) is part of a recently launched research initiative at Embrapa Vegetables in Brasília, Federal District, aimed at creating a National Platform for Adaptation to Climate Change Applied to Family Farming (Clima AF). Through Prompt Engineering and Command Chaining, this stage was designed for the visual assessment of physiological disorders in sweet potato (Ipomoea batatas) tuberous roots in the context of the Climate Emergency. The pipeline consists of four stages: 1 - Definition of an expert persona; 2 - Phenological contextualization and critical root filling period; 3 - Visual anatomical phenotyping; and 4 - Synthesis of the physiological disorders found, with a focus on heat stress. The methodology is available as open access following FAIR principles. The analysis is conducted using minimal information, such as photos that can be taken with everyday devices like smartphones and information about the harvest season. Because it is available as open access, it democratizes information and contributes to achieving climate justice for a socioeconomically vulnerable audience (family farmers).
Why it matches plant phenotyping methods生成AIとプロンプト連鎖を用い、スマートフォン画像からサツマイモ塊根の生理障害を視覚的に評価する低コスト表現型解析プラットフォームの開発であり、植物状態の取得・抽出法が中心である。
abstractThe development of Low-Cost Phenotyping Platforms Supported by Generative Artificial Intelligence (AI)
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
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
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
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.
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.
Abstract Mild stress is often difficult to distinguish from non-stress signals that may even mask the detection of stress; therefore, early diagnosis and precision grading of plant/microbial stress severity are essential for sustainable precision agriculture toward achieving optimized yields. We propose an interpretable hybrid deep learning–fuzzy decision framework combining EfficientNet B7 and Inception-ResNet-v2 with multiscale feature aggregation integrating Sparse Pyramid Pool (SPP) and Atrous Spatial Pyramid Pooling (ASPP). A Gaussian-based fuzzy inference system is incorporated to derive severity reasoning in a linguistically interpretable manner to address uncertainty and overlapping stress stages. Unlike conventional approaches evaluated only on controlled datasets, the proposed framework is validated through stringent cross-dataset generalization between the PlantVillage and PlantDoc datasets. The model demonstrates robustness under environmental disturbances and passes statistical significance tests. On the PlantVillage benchmark, the framework achieves an exact-match accuracy of $97.82\%$, a macro F1-score of $97.60\%$, and an AUC of $0.979$. When evaluated across a different domain, the performance decreases by only $4.8\%$, indicating strong generalization capability. The integration of fuzzy logic reduces adjacent-class error by $3.4\%$ and improves probability calibration with an Expected Calibration Error (ECE) of $0.021$. Grad-CAM visualizations and saliency analyses further confirm that the model focuses on biologically relevant diseased regions. These results demonstrate that combining multiscale deep feature learning with structured fuzzy reasoning enhances robustness, interpretability, and decision stability, thereby supporting human-centered agricultural monitoring systems.
Why it matches plant phenotyping methods植物のストレス重症度を画像から推定する深層学習・ファジー推論手法を開発し、異なるデータセット間で検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose an interpretable hybrid deep learning–fuzzy decision framework
Reproduction assets foundThe paper's plant-phenotyping measurements rely on two public leaf-image datasets, PlantVillage and PlantDoc, both cited with explicit public URLs in the supplied text. No author analysis code, trained models, or code deposit is mentioned.Dataset · publicthe proposed model was validated
on the PlantDoc dataset1 , a publicly available real-field plant disease dataset
that reflects practical agricultural variability.Open asset ↗pdf-page:19 lines:1-53Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
TissuePhysiological trait estimationStress response / toleranceWater status / transpiration
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.
TomatoRootMorphology / geometry measurementSegmentationRoot system architectureStress response / tolerance
Automated phenotyping of crops is a vital component of precision agriculture. As a representative economic crop, the radicle length of tomato seeds is a key phenotypic indicator for assessing seed vigor and seedling health. To reduce labor costs and improve measurement efficiency, we developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process. To achieve automatic measurement of radicle length, we propose a deep learning-based segmentation framework, termed RootNet. The framework incorporates a custom-designed module, MambaNextBlock (MNB), within skip connections to enhance long-range feature modelling, and integrates an Atrous Spatial Pyramid Pooling (ASPP) module at the bottleneck to capture multi-scale contextual information. Post-segmentation, Canny edge detection is employed to extract radicle contours, from which actual lengths are computed based on contour arc length. Experimental results show that RootNet achieved 77.75% Intersection over Union (IOU), 86.03% Precision, 88.72% Recall, and 87.46% F1-Score on the root class. Compared with manual measurements conducted using ImageJ, our method showed high agreement across 1170 radicle measurements, with an R² of 0.9785, an MAE of 0.274 mm, an RMSE of 0.344 mm, and a Bias of −0.008 mm. Bland–Altman analysis further confirmed the absence of systematic bias, with 95% limits of agreement ranging from −0.682 mm to +0.666 mm. Meanwhile, measurement efficiency was improved by approximately 680-fold. Furthermore, the method was applied to evaluate the effects of drought, salinity stress, and different concentrations of Streptomyces albidoflavus (HL4) and Streptomyces virginiae (GZ2) on radicle growth. The results indicated that drought stress, salinity stress, and undiluted HL4 inhibited radicle elongation, whereas diluted HL4, as well as both undiluted and diluted GZ2, significantly promoted radicle growth. This study provides an efficient and cost-effective solution for non-destructive crop phenotyping and intelligent agricultural management in precision farming.
Why it matches plant phenotyping methodsトマト幼根長を画像から自動抽出・測定する深層学習フレームワークを開発し、手動測定との定量的検証も行っており、植物フェノタイピング手法が研究の中心である。
abstractwe developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process.
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in coastal areas due to insufficient water resources and inadequate irrigation infrastructure. Consequently, combined salinity-drought stress poses a major threat to rice production. To investigate the effects of combined salinity-drought stress, a two-season study was conducted utilizing soil media. The first season involved screening 58 rice genotypes, while the second season focused on validating the consistency of response in 20 selected tolerant and susceptible genotypes. These included established tolerant checks (Pokkali and Salumpikit) and susceptible checks (IR 29 and IR 20). Both drought and salinity treatments were given at an electrical conductivity (EC) of 10 dSm⁻¹ and 75% field capacity at the seedling stage. The experimental design was arranged in a modified lattice design in each season, with six blocks and three replications in the first season and two blocks and five replications in the second season. The data collected are leaf symptoms, biomass weight, and shoot length. A number of 330 images captured by a smartphone camera. Machine learning models were employed to predict drought-salinity tolerance criteria. The study revealed that XGBoost model achieved an accuracy of 90.62%. The study identified two genotypes, IR18A1925-SKI-0 and Inpari 30, that exhibited insignificance to Pokkali, based on assessment of shoot length, biomass, and leaf symptoms. These two genotypes were also consistently clustered with Salumpikit. These findings highlight potential of machine learning techniques in predicting rice tolerance to combined salinity-drought stress, with the XGBoost model demonstrating superior predictive capability in this study.
Why it matches plant phenotyping methodsスマートフォン画像から葉症状・バイオマス・草丈などの表現型を取得し、機械学習で複合ストレス耐性を予測する手法の適用が研究の中心である。
titleImage-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses
While multispectral sensors offer a cost-effective and robust solution for monitoring plant responses to environmental stress, their limited spectral resolution, largely dependent on vegetation indices, can hinder accurate classification of stress severity using machine learning. This paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection that is 1) affordable for a wide range of end-users, 2) robust to environmental factors, 3) capable of automatically finding the most meaningful features that maximize the stress detection accuracy, and 4) capable of discriminating different plant stress severity. The device integrates a broadband LED and a VIS-NIR multispectral sensor to early predict plant stress through machine learning algorithms (i.e., SelectKBest, kNN, SVM, and LDA). It was trained on spectral measurements acquired from tobacco plants under salinity stress. The results demonstrated its high capability to discriminate with high accuracy different stress severity (average accuracy of 91.0 ± 3.1%).
Why it matches plant phenotyping methods植物ストレスの重症度を推定する低コスト multispectral デバイスと機械学習手法を開発・評価しており、植物状態の取得・判別が研究の中心である。
abstractThis paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection
Determining the drivers of ecological stability amid accelerating global environmental change is a critical goal of contemporary ecology. Various candidate drivers have been suggested, with recent attention turning to response diversity—the variation among organism-environment responses. However, despite conceptual interest in response diversity as a driver of stability, there remain few field tests of this relationship. Using multi-species competitive communities of floating aquatic macrophytes as an experimental model for measuring temporal stability and response diversity to nutrient loading, we show that response diversity does not promote temporal stability of total macrophyte cover, but that communities with an uneven distribution of species responses were more resistant to an exogenous shock. To quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology. We measured response diversity as the balance of positive and negative biomass growth responses to dissolved nitrate concentration, weighted by species’ relative contributions to biomass, and tested its effect on temporal stability and resistance to an unexpected pulse disturbance (a large typhoon that disrupted our outdoor mesocosms). Response imbalance predicted typhoon resistance, but species asynchrony and mean population stability best predicted community stability, with no direct or indirect effect of species responses. Overall, our results provide new experimental evidence for how the structure of species responses promotes stability, and we aim our LeafMosaic workflow to empower future field experiments using floating macrophytes to study response diversity and ecological stability.
Why it matches plant phenotyping methods浮遊水生植物の写真時系列から種組成と成長動態を抽出する、オープンソースでスケーラブルな機械学習ワークフローを開発しており、植物表現型取得・解析法が中心的です。
abstractTo quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology.
Introduction Nutrient deficiencies in coffee plants significantly impact bean quality and yield, making timely detection crucial for successful cultivation. Current assessment methods rely on manual inspection, which is labor-intensive and time-consuming, posing challenges for large-scale field management. This approach often results in inconsistent evaluations and delayed interventions. Methods This study presents CoNutriNet, an automated deep learning architecture that integrates DenseNet121 with a novel Graph-Enhanced Attention Feature Network (GEAFNet) for classifying nutrient deficiencies in coffee leaves. DenseNet121 provides deep hierarchical and regional feature representation, while GEAFNet captures local, fine-grained spatial features through Inception, Ghost, and Efficient Channel Attention (ECA) modules. Furthermore, a Graph Convolutional Network (GCN) is included to model spatial dependencies and structural variations between leaf regions. Feature representations from both pathways are concatenated and refined using a Coordinate Attention (CA) module to enhance discriminative capability. Results Evaluation on the CoLeaf dataset demonstrates that CoNutriNet achieves an accuracy of 94.5%. The integration of lightweight attention mechanisms, dense connectivity, and graph-based modeling improves both performance and computational efficiency. Conclusion These results indicate that CoNutriNet achieves and efficient performance in nutrient deficiency detection in coffee crops, highlighting its potential for deployment in agricultural environments to support precision farming and optimize yield.
Why it matches plant phenotyping methodsコーヒー葉の栄養欠乏という植物状態を画像から分類する深層学習手法を開発し、データセットで性能評価しており、表現型取得・推定が研究の中心である。
abstractThis study presents CoNutriNet, an automated deep learning architecture that integrates DenseNet121 with a novel Graph-Enhanced Attention Feature Network (GEAFNet) for classifying nutrient deficiencies in coffee leaves.
Reproduction assets foundThe paper's phenotyping analysis (coffee nutrient deficiency classification) is performed on publicly available leaf image datasets. The data availability statement links a Mendeley Data repository containing the analyzed data, which is an allowed URL. No author analysis code or trained model checkpoints are explicitlyDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/brfgw46wzb/1Open asset ↗brfgw46wzb/1lines:866-910Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published11 Jun 2026Sensing for Agriculture and Food Quality and Safety XVIIICited by 0 · OpenAlex ↗
A hybrid AI framework combining a spatial–spectral–temporal Transformer and unsupervised clustering was applied to five microgreen species—Pak Choi Cabbage, Tatsoi Mustard, Red Mizuna, Chinese Cabbage, and Arugula—grown for 3–4 weeks under water, nutrient, and combined stresses. Across five datasets collected within six months, the system achieved a Macro-F1 of 0.91 and a pre-symptomatic F1 of 0.88, enabling early detection before visible symptoms. Applications include NASA’s APH, Mars and Moon habitats, and terrestrial precision agriculture.
Why it matches plant phenotyping methods植物の水・養分ストレス状態をマルチモーダル画像から早期推定するAI手法が研究の中心であり、性能評価も示されている。
abstractA hybrid AI framework combining a spatial–spectral–temporal Transformer and unsupervised clustering was applied
Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.
Why it matches plant phenotyping methodsオクラ幼苗の点群から草丈・茎径・樹冠幅を抽出する計算フェノタイピング手法を開発し、手動測定との比較で精度検証しているため、方法が研究の中心である。
abstractthis study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model.
First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.
Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性用データを提供しており、フェノタイピング手法が中心である。
abstractFirst stable release of the RGB and NPQ pixel-wise phenotyping pipeline
Under greenhouse production conditions, variability in fertilization management, substrate properties, and microenvironmental factors can disrupt balanced nutrient uptake, often resulting in localized or transient multi-element nutrient imbalances. Hyperspectral sensing provides continuous and high-resolution spectral information for plant nutrient assessment. However, most existing studies focus on single-element deficiencies or simplified scenarios, which limits their applicability to complex nutritional environments encountered in practice. To address this limitation, we designed a series of single- and dual-element deficiency treatments in four cultivars of chrysanthemum (Chrysanthemum morifolium Ramat.), an important cut-flower crop whose ornamental quality is highly influenced by nutrient supply. Sampling was conducted at five key growth stages across three independent experiments, yielding a total of 615 data points. Each treatment included replicates and was confirmed based on characteristic deficiency symptoms. A hyperspectral-based qualitative classification framework was developed to assess nutrient imbalances under controlled greenhouse conditions. Results indicate that although some nutrient deficiencies exhibit similar visual or phenotypic symptoms, their hyperspectral responses are distinguishable, suggesting that hyperspectral data can capture subtle differences associated with distinct nutrient imbalance conditions. To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and multiple classification models were evaluated using cross-validation. The Gradient Boosting Decision Tree (GBDT) classifier combined with SMOTE showed the most consistent performance across nutrient-recognition tasks, achieving cross-validation accuracies from 0.9191 ± 0.0401 to 0.8556 ± 0.0516, balanced accuracies from 0.9595 to 0.8447, F1 from 0.9591 to 0.8496 and testing accuracies from 0.9200 to 0.8269, balanced accuracies from 0.9167 to 0.8269, F1 from 0.9140 to 0.8244. Overall, this study presents a non-destructive hyperspectral framework for classifying multi-element nutrient imbalances and demonstrates its effectiveness under greenhouse conditions, supporting hyperspectral-based nutritional assessment in ornamental crops. Further validation across diverse genotypes, seasons, and environmental conditions is needed to confirm broader applicability and model generalizability.
Why it matches plant phenotyping methodsキク葉の栄養状態という植物状態を、ハイパースペクトル計測と機械学習で非破壊的に分類する枠組みを開発・検証しており、表現型取得・抽出法が研究の中心である。
abstractA hyperspectral-based qualitative classification framework was developed to assess nutrient imbalances under controlled greenhouse conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Reliable field phenotyping for terminal heat stress (THS) tolerance in chickpea is constrained by conventional late-sowing approaches that confound reproductive stress with reduced vegetative growth. We developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour. Early flowers were removed to synchronize flowering and delay reproduction by 10-15 days, exposing flowering and pod set to high temperatures (>33 °C). Across two seasons and contrasting genotypes, DF maintained vegetative growth but significantly reduced pollen viability, pod set, and yield, with tolerant genotypes showing markedly lower yield penalties than susceptible ones. The method effectively discriminated reproductive thermotolerance and provides a simple, low-cost, and biologically grounded phenotyping tool for chickpea breeding under warming climates.•A DF-based field method selectively imposes reproductive-stage heat stress without compromising vegetative growth.•The approach reliably distinguishes heat-tolerant and susceptible chickpea genotypes under natural field conditions.
Why it matches plant phenotyping methods生殖期の耐暑性を選択的に評価するDFベースの圃場スクリーニング法を開発・検証しており、遺伝子型の識別に用いるフェノタイピング手法が中心である。
abstractWe developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour.
The integration of artificial intelligence (AI), machine learning (ML), and precision agriculture has created new opportunities for efficient and sustainable crop monitoring. These technologies enable large-scale analysis of agricultural data to assess plant health, optimize resource usage, and support data-driven decision-making. This work presents a machine-learning-based framework for assessing strawberry plant health using RGB imagery collected from unmanned aerial vehicles (UAVs). Unlike traditional object detection approaches, this study adopts a hierarchical classification strategy using convolutional neural networks, including different ResNet and EfficientNet architectures. Individual plant regions are extracted as blobs through a preprocessing pipeline, and these image tiles are used to train stage-wise binary classifiers that progressively distinguish plant health categories. To enhance reliability, model predictions are validated using field-collected ground-truth data including chlorophyll measurements and visual plant health ratings, as well as real-time deployment scenarios, where predictions are made from live UAV video feeds. Geospatial alignment associates image-based predictions with real-world measurements, enabling comprehensive evaluation of model performance. Experimental results showed that ResNet18 achieved 87.75% accuracy with an F1 score of 0.8524 for healthy plant classification and 93.75% accuracy with an F1 score of 0.6115 for unhealthy plant classification. EfficientNet-B0 demonstrated superior performance for moderately healthy and moderately unhealthy categories, achieving accuracies of 66.83% and 75.65%, with F1 scores of 0.6350 and 0.6070, respectively, highlighting the effectiveness of the hierarchical classification framework. This framework demonstrates the practical potential of RGB-based plant health monitoring integrated with geospatial alignment and field-validated measurements, offering a scalable, efficient solution for precision agriculture applications.
Why it matches plant phenotyping methodsRGB画像と深層学習を用いてイチゴ個体の健康状態を推定する分類フレームワークを開発し、地上測定・目視評価・実運用映像で検証しており、植物表現型取得が中心的です。
abstractThis work presents a machine-learning-based framework for assessing strawberry plant health using RGB imagery collected from unmanned aerial vehicles (UAVs).
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.
Abstract Background Sorghum ( Sorghum bicolor ) is a versatile C4 crop used for food and feed and as biomass for bioproducts and energy. Improving nitrogen use efficiency (NUE) in sorghum is important because fertilizer is costly and excessive fertilizer use has negative environmental impacts. Leaf senescence mediates nutrient recycling, but its dynamic progression is difficult to quantify at scale. We evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum and link these phenotypes to gene expression. Sorghum Tx430 plants were grown under four N treatments (6, 9, 12, and 15 mM), imaged from vegetative growth through grain fill, and destructively sampled for RNA-seq at four developmental stages. Results A supervised support vector machine with a radial basis function kernel classified pixels from a hyperspectral image of sorghum plants grown under different N levels into green leaf, yellow leaf, dry leaf, stalk, panicle, and background classes with 0.93 accuracy. We defined the senescence ratio as the sum of yellow and dry leaf areas divided by the green leaf area and computed it across multiple growth stages and nitrogen levels. The senescence ratio did not differ among N treatments during vegetative growth, but it declined with increasing N during boot, anthesis, and grain fill, indicating earlier senescence under N limitation. Among the genes whose expression positively correlated with senescence ratio were 13 putative transcription factors, including SbiRTX430.02G247100, a WRKY1/ZAP1 homolog and a WRKY4 homolog. Gene regulatory network analysis of the top 1% of genes associated with SbiRTX430.02G247100 showed enrichment for processes associated with leaf senescence and chlorophyll catabolism. In contrast, the network associated with the WRKY4 homolog was enriched for autophagy-related terms. Conclusions Our study shows that automated hyperspectral imaging is highly effective for monitoring dynamic plant phenotypes, such as stress-induced senescence, that are difficult to visually score with the naked eye. Here, nitrogen deficiency served as the stress condition. Still, this approach supports large-scale phenotypic data collection for any such stressor and enables analyses with greater statistical power, yielding more robust conclusions and the potential for new insights that can be applied to engineering and breeding better crops.
Why it matches plant phenotyping methodsソルガムの動的な老化表現型を高スループットに取得する hyperspectral imaging と、SVMによる画像分類・senescence ratio算出が研究の中心であり、植物状態の定量化手法を実証している。
abstractWe evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum
Reproduction assets foundThe paper's availability statement points to a public GitHub repository containing the authors' image-processing, machine-learning classification, transcriptomic analysis, and figure-generation scripts. The 148 GB hyperspectral image data is only promised 'upon acceptance' (not yet public), and the RNA-seq deposit is aCode · publicle in the NCBI SRA repository,
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under BioProject PRJNA1452908 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1452908)
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(RNA-seq raw reads SRR38119224 to SRR38119282). Scripts used for image processing,
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machine-learning classification, transcriptomic analyses, and figure generation will be
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accessible through GitHub (https://github.com/belafif2/TX430_Senescence). Image data (148
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GB) will be made available in a data repository upon acceptance. Other relevant processed data
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files and supporting figures are available as supplementary data documents.
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Competing interests
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The authors declare that they have no competing interests.
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Funding
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This work was funded Open asset ↗belafif2/TX430_Senescencepdf-raw-page:22 lines:1-54Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Cucumber is a core cultivated facility vegetable in China. Drought stress at the seedling stage severely inhibits its growth and development. The regulatory mechanism and optimal application concentration of SiO 2 nanoparticles in alleviating drought stress in cucumber seedlings remain unclear. Moreover, traditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress. To address these issues, this study focused on phenotypic detection under drought stress and analysis of the regulatory effects of SiO 2 nanoparticles. An improved compact and low-redundancy segmentation model, DDCANet, was proposed based on PointNet++-SSG. Combined with 3D point cloud technology and the Euclidean clustering algorithm, it enables automatic extraction of phenotypic parameters from cucumber seedlings treated with SiO 2 nanoparticles under drought stress. In this study, Trailing cucumber seedlings were used as experimental materials. 3D point cloud data of cucumber seedlings were collected under treatments with different concentrations of SiO 2 nanoparticles and PEG-simulated drought stress. A dataset containing 70 valid samples was constructed and labeled into two categories: Stem and Leaf. The core optimizations of the DDCANet model are as follows: Firstly, an Adaptive Density-Aware Feature Enhancement (ADFE) module is embedded to accurately capture point cloud density heterogeneity induced by SiO 2 ;Secondly, a Channel Attention and Normalization-enhanced SA Layer (CANL) is designed to strengthen the coupling of local and global drought-related phenotypic features. Thirdly, a Drought-Aware Hybrid Loss (DHL) function is constructed to alleviate the class imbalance of seedling stem and leaf point clouds under drought stress. Results show that the DDCANet model achieves a mean Intersection over Union (mIoU) of 89.01 ± 0.32% and a Stem IoU of 83.6 ± 0.45%, representing improvements of 6.55% and 9.6% respectively compared with the baseline PointNet++-SSG model, and a 30.5% improvement in stem segmentation accuracy compared with the classic PointNet model. It thus enables high-throughput, non-destructive detection of drought phenotypes in cucumber seedlings under SiO 2 nanoparticle treatment. Ablation experiments verified the positive contributions of the ADFE, CANL, and DHL modules. Furthermore, instance segmentation and phenotype extraction were completed using the Euclidean clustering algorithm to analyze the drought-alleviating effects of SiO 2 nanoparticles under PEG-simulated drought stress. Results indicate that a low concentration of 20 mg/L exhibits a weak alleviating effect, medium concentrations of 40-60 mg/L show bidirectional regulatory characteristics, and a high concentration of 100 mg/L causes negative physiological effects. The optimal application concentration is 80 mg/L, which comprehensively improves key phenotypes such as seedling height and volume under drought stress and exerts a positive regulatory effect on seedling growth under drought conditions. The DDCANet model constructed in this study provides an efficient technical tool for the accurate phenotypic detection of crop seedlings treated with SiO 2 nanoparticles under drought stress. It clarifies the optimal application concentration of SiO 2 nanoparticles, offers a precise concentration threshold and theoretical support for the scientific application of SiO 2 nanoparticles in drought-stressed cultivation of protected cucumber, and establishes a novel methodological reference for the research on phenotypic regulation of crops under drought stress via nano-agricultural technology.
Why it matches plant phenotyping methods3D点群分割モデルを開発・検証し、キュウリ幼苗の茎葉分離と形質抽出を自動化することが研究の中心であるため、植物フェノタイピング手法論文として採用。
abstracttraditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress.
Clubroot disease, caused by Plasmodiophora brassicae , is one of the major constraints in rapeseed production. Breeding disease-resistant cultivars is the best way to control this devastating disease. However, breeding reliable resistant germplasm and genes is limited. Inactivation of susceptible genes has been shown to be a new and effective strategy for developing resistant crops. Therefore, we aimed to screen key candidate susceptible genes in this study. Firstly, we established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection. At 14 days post-inoculation (dpi), the earliest time point with a clear record of scorable root swelling, remarkable variations in the speed of gall formation were observed among 85 genotypes. Secondly, genome-wide association studies (GWAS) were performed to identify genes involved in gall development. Three and two consecutive significant peaks were detected at 14 and 21 dpi, respectively. Thirdly, comparative transcriptomic analysis was conducted between 2AF195 and 2AF058 at 7 and 14 dpi; these two materials exhibit contrasting speeds of gall development. Gene clustering analysis revealed two opposite expression patterns at 14 dpi. One pattern comprised 1,383 genes downregulated in 2AF195 but upregulated in 2AF058, which were significantly enriched in 10 KEGG pathways, including Environmental Information Processing and Plant-pathogen interaction, and involved core repressors JAZ8/10 in the jasmonic acid (JA) signaling pathway, as well as nucleotide-binding site (NBS) protein-encoding genes. The opposite pattern consisted of 79 genes upregulated in 2AF195 but downregulated in 2AF058, which were enriched in an additional 10 KEGG pathways, predominantly related to Carbohydrate Metabolism and the Ubiquitin System. These genes were functionally annotated mainly as pectin methylesterases, xyloglucan endotransglucosylase/hydrolases (XTHs), and lignin biosynthesis-related enzymes. These findings demonstrated that distinct regulatory networks exist in different susceptible rapeseed genotypes. Finally, through the combined analysis of haplotype and transcriptome data, we co-localized and identified the candidate gene BnaC08g46100D , a nodulin-related gene belonging to the MtN21 transporter family. These results provide a theoretical basis for developing novel disease-resistant materials by editing the key susceptibility genes involved in root gall formation. The candidate genes identified in this study are the most promising targets for this purpose.
Why it matches plant phenotyping methods根こぶ形成を高スループットに可視化・判定する方法の確立が明示され、感染植物の病徴を測定する手法として研究の主要な技術要素になっている。
abstractwe established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
Disease incidence data of 85 rapeseed accessions at various time points following inoculation with the Xinmin strain.Open asset ↗lines:502-594Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Root anatomical traits and spatial architecture play a critical role in crop water acquisition and utilization, directly impacting drought tolerance. However, comprehensive studies examining the synergistic effects of deep root configuration and cortical tissue organization under drought stress during the seedling stage remain scarce. Additionally, the underlying physiological mechanisms are not yet well understood. Methods: In this study, we utilized a high-throughput, paper-based phenotyping platform to simulate drought stress using 10% PEG. An efficient, multi-trait evaluation framework was employed to classify the 28 tested genotypes into five drought tolerance categories. Results: This approach enabled the identification of drought-tolerant cultivars "Ruichun 1," "Ningchun 11," and "Ningchun 57," as well as the drought-sensitive cultivar "Dingxi 48." Root traits, including maximum depth, convex hull area, and plant height, demonstrated strong explanatory power and could serve as valuable phenotypic indicators for seedling stage screening. Our findings suggest that drought adaptation in spring wheat involves a strategic coupling in which specific cortical configurations facilitate the development of deep root architecture. While previous studies have often focused on individual parameters, we show that drought-tolerant genotypes optimize root growth in deeper segments of the growth medium by adjusting cortical tissue proportions, potentially minimizing metabolic costs. Discussion: This integrated perspective offers a detailed physiological framework for understanding drought resilience and moves toward a mechanism-based interpretation of resource reallocation. However, it is important to note that these results were obtained using a paper-based phenotyping platform under PEG-induced osmotic stress, reflecting the genotypic potential at the seedling stage rather than actual field drought tolerance. In conclusion, combining the paper-based high-throughput phenotyping platform with a multi-trait evaluation framework allows for the accurate classification of drought tolerance types and the efficient identification of representative spring wheat cultivars. The findings emphasize the importance of deep root configuration and optimized cortical allocation as fundamental components of the root structural basis for drought adaptation in spring wheat. These results provide clear phenotypic targets for early-stage screening, which should be further validated at later developmental stages and under field conditions before being applied in breeding programs.
Why it matches plant phenotyping methods紙ベースのハイスループット表現型解析プラットフォームと多形質評価フレームワークが、根形態を用いた耐乾性分類の中心的手法として明示されているため。
abstractwe utilized a high-throughput, paper-based phenotyping platform
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.
Artificial intelligence applied to plant phenotyping is crucial for consistent results, as stomata classification under stress impacts physiology, water use efficiency, and productivity. Manual analysis is laborious and error-prone, limiting the efficiency and accuracy of evaluations. In this context, this study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata. Soybean plants were grown under water deficit and well-watered conditions, generating significant variations in stomatal structural opening and associated gas exchange traits. To capture stomata variations, epidermal printing techniques were employed, with images obtained by microscopy. The dataset was labeled using the intelligent polygon tool of the Roboflow application, with 269 images of the adaxial and abaxial surfaces of leaves annotated in two categories: open and closed stomata. The images underwent geometric transformations to facilitate model training. The results demonstrated that the YOLOV8 neural network achieved precision recall and mAP greater than 90%, highlighting its effectiveness in detecting and classifying stomata. By integrating automated classification of aperture states (open and closed) with a defined physiological stress context in soybean, this work establishes a dataset specifically designed for functional analysis. This approach extends the applicability of deep learning toward stress-oriented plant physiology studies, offering a robust tool for evaluating crop adaptation under climate change scenarios.
Why it matches plant phenotyping methodsヨロウ豆の気孔開閉状態を画像から自動検出・分類するYOLOv8手法とデータセットを開発・評価しており、植物表現型取得が研究の中心である。
abstractthis study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata.
Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m−1. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m−1). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments.
Why it matches plant phenotyping methods若いウチワサボテンの塩ストレスを対象に、ハイパースペクトル画像と3D-CNNによる非破壊フェノタイピング手法を開発・検証しており、表現型取得・推定が研究の中心です。
abstractThe objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes.
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The nucleus is the characteristic organelle of eukaryotic organisms. Unlike the classic textbook view of static nuclei, nuclear shape is dynamic in live cells. Altered or deformed nuclear shape is a hallmark of cancer in animal cells and environmental stress in plants. Nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, little is known about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To confront this issue, we developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition. This confocal imaging method utilizes a dual fluorescently tagged marker line - nuclear envelope protein and chromatin - to perform live cell imaging of the root in model plant Arabidopsis thaliana under control and salt-stressed conditions. These captured movies are analyzed to quantify nuclear and chromatin dynamics using open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. To validate this method, we imaged and quantified chromatin movement in control and salt-stressed roots, revealing a decrease in chromatin speed under salt-stressed conditions. This method allows for quantitative live cell imaging of root nuclear shape and chromatin dynamics during plant development and environmental stress, thus enabling analysis of changes in nuclear and chromatin dynamics caused by abiotic stressors.
Why it matches plant phenotyping methodsシロイヌナズナ根の核形状・クロマチン動態を定量する共焦点ライブイメージングと画像解析パイプラインを開発し、塩ストレス条件で検証しており、植物表現型取得が中心です。
abstractwe developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition.
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
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.
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.
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
Precision agriculture demands integrated systems that couple accurate crop stress detection with targeted intervention to mitigate climate volatility and input overuse. Traditional manual scouting and uniform chemical application are spatially imprecise, labour-intensive, and environmentally burdensome. This study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS). A randomized complete block design with four replications was implemented in maize (Zea mays L.) across a 2.4 ha field in Davangere Karnataka, India. Scouting flights at 25 m altitude (1.8 cm ground sampling distance) utilized a MicaSense RedEdge-P and FLIR thermal sensor, with imagery processed through a radiometrically calibrated YOLOv8-Seg pipeline to detect early-stage disease, nutrient deficiency, and water stress. Prescription maps derived from NDRE and CWSI thresholds directly controlled a DJI Agras T40 centrifugal sprayer calibrated to ASABE S572.1 standards. The integrated system achieved an AI detection F1-score of 0.91, reduced agrochemical volume by 34.2%, and improved spray deposition uniformity (coefficient of variation = 18.4%) relative to conventional blanket spraying. Grain yield increased significantly by 11.7% (p
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とAI解析により、作物の病害、栄養欠乏、水ストレスを検出する方法を開発・現地検証しており、植物状態の取得が統合システムの中心的要素である。
abstractThis study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS).
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Drought is the major abiotic stress limiting soybean growth and yield, yet accurately identifying genotypes that sustain yield under rainfed conditions remains a major bottleneck in soybean breeding. Canopy wilting scores are widely used as a proxy for evaluating plant responses to drought stress. However, most assessments rely on leaf-level visual observations that are inherently subjective and typically based on single time-point scores, providing only a snapshot of stress expression and failing to capture their relationship with yield retention under rainfed conditions. To address these limitations, this study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4/R5) as a more quantitative and objective alternative to visual scoring, with closer relevance to yield performance under drought conditions. From 2023 to 2025, a total of 85 soybean genotypes developed by soybean breeding programs in Arkansas, Missouri, Kansas, and North Carolina, along with commercial checks, were evaluated under irrigated and rainfed conditions in Stuttgart, Arkansas. Visual canopy wilting scores were recorded at R4/R5, along with vegetation indices captured using UAV-based multispectral imagery. UAV-derived indices showed significant correlations with yield ( r = 0.22 to 0.45, p<0.05) under rainfed conditions. In contrast, visual canopy wilting scores displayed weak and inconsistent associations with yield ( r = -0.28 to 0.35, p<0.05), suggesting limited ability to capture yield retention under rainfed conditions. Unsupervised k -means clustering ( n = 2) of UAV-derived vegetation indices separated genotypes into two distinct canopy response groups that were consistent across 2023 to 2025 rainfed seasons. Significant differences were observed among clusters for several vegetation indices (ARI, CIG, CIRE, GSAVI, GNDVI, GOSAVI, OSAVI, NDVI), indicating contrasting canopy stress responses. Under rainfed conditions, these UAV-defined clusters also differed for grain yield (2023: 1,925.6 vs 1,703.1 kg/ha; 2024: 1,849.9 vs 1,229.2 kg/ha; 2025: 2,056.7 vs 1,773.8 kg/ha), whereas visual wilting scores failed to distinguish yield-retaining genotypes. Overall, UAV-based high-throughput phenotyping offers a robust and yield-relevant alternative to visual wilting scores, supporting the development of drought-tolerant soybean germplasm and cultivars.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による植生指数抽出を、目視評価と比較・検証し、干ばつ応答および収量保持に関連する表現型測定法として中心的に評価している。
abstractthis study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4/R5) as a more quantitative and objective alternative to visual scoring
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures—such as convolutional neural networks, recurrent neural networks, and transformers—across key crop genomic tasks, including gene expression prediction, alternative splicing analysis, cis ‐regulatory element identification, epigenomic profiling, and genome‐based trait prediction. In phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade‐offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning—such as data scarcity, model transparency, and computational demands—and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.
Why it matches plant phenotyping methods作物フェノミクスにおける深層学習による画像からの形質抽出を中心的にレビューしており、フェノタイピング手法の方法論的整理に該当する。
abstractIn phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery
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
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Urochloa grasses are among the most widely used forage grasses across the tropics. Spittlebugs (Hemiptera: Cercopidae) are major pests of tropical Urochloa (syn. Brachiaria) grass pastures, severely reducing forage productivity and quality. Understanding the genetic basis of host-plant resistance is essential for developing durable resistant cultivars. Here, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of response to Aeneolamia varia nymphs in 339 interspecific F1 hybrids derived from crosses between resistant sexual and susceptible apomictic Urochloa parents. Digital image analysis using both unsupervised (DQU) and supervised (DTR) quantification pipelines enabled accurate estimation of plant damage, yielding moderate to high broad-sense heritability estimates (H2 = 0.49 to 0.66). In contrast, insect survival (NTS) exhibited low to moderate correlations with all damage traits and lower heritability estimates (H2 = 0.42). Using 57,051 high-quality SNPs aligned to the genome of the hybrid cultivar Basilisk, GWAS models identified 18 quantitative trait loci (QTLs) for plant damage traits, but none for insect survival (antibiosis). Six robust QTLs on chromosomes 1, 6, 7, 27, 29, and 36 were consistently detected across models and phenotyping methods, explaining up to 21.5% of phenotypic variance. Candidate gene analysis revealed proteins involved in hormone signaling, oxidative stress response, and cell wall modification, suggesting multifaceted plant-insect interaction mechanisms. These results provide a foundational set of molecular markers associated with spittlebug response in Urochloa grasses, useful for marker-assisted and genomic selection in the forage breeding program.
Why it matches plant phenotyping methods高スループット画像表現型解析と、植物損傷を推定する2つの画像解析パイプラインが研究の中心であり、異なる手法間の比較と形質推定性能も評価している。
abstractHere, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of response to Aeneolamia varia nymphs
Reproduction assets foundThe paper's digital plant-damage images are publicly deposited in Harvard Dataverse (paper-specific phenotyping input). The RAD-Seq accession PRJEB109285 is a sequencing/omics deposit and is excluded per criteria. No author analysis code repository with explicit availability URL is stated.Dataset · publicThe digital images used for plant damage quantification are available in the Harvard Dataverse repository at the following identifier: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EGUVHA .Open asset ↗Harvard Dataverse · doi:10.7910/DVN/EGUVHAlines:387-414Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
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-adapted 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ベースのデジタル biomass 推定を検証し、植物形質評価への利用可能性を示しており、表現型取得法が中心的です。
abstractIn this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions.
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.
Effective crop health monitoring requires the integration of heterogeneous data sources that capture both environmental conditions and crop-level responses. Conventional single-modality approaches, relying either on in-ground sensor measurements or aerial imagery in isolation, fail to exploit the complementary strengths of each technique, resulting in limited diagnostic accuracy and delayed intervention. This study proposes an integrated approach that fuses data acquired by IoT-connected sensor networks with multispectral images captured by unmanned aerial vehicles (UAVs), enabling comprehensive crop health assessment across an entire field. Consider a system that deploys a network of sensors throughout a 2-hectare area to continuously watch the moisture and temperature, as well as other critical parameters, such as electrical conductivity, atmospheric humidity as well as the nutrient level in the soil. Simultaneously, a special UAV, a drone known as the DJI Phantom 4 Multispectral, take pictures of the field at five different spectral frequencies. However, the most interesting thing is the following: to do all this, the system relies on a special type of artificial intelligence known as a hybrid deep learning architecture. It resembles a twostep procedure, where the one section analyses the trends in the sensor data across time and is called one dimensional convolutional neural network, whereas the other section uses a pre-trained version of ResNet-50 and is responsible of making significant features of the images captured by the drone. Then, it is all unified with an eight-head multi-head attention mechanism, taking all the various kinds of data and forming a bigger picture. This will enable the system to memorize and establish relationships among the various kinds of data, which forms a potent source of knowledge and management of the field. The synchronized dataset comprised 2,520 data points collected over 120 days (April–August 2024), with 103 days of active data capture. The proposed hybrid deep learning architecture achieved a crop health classification accuracy of 94.5%, compared to 80% for conventional single-modality methods — a statistically significant improvement of 14.5 percentage points. The system classifies crop status into five categories: healthy, water stress, nutrient deficiency, disease, and pest infestation. The results demonstrate that cross-modal IoT–image fusion delivers earlier, more reliable diagnosis of crop stress conditions, enabling data-driven farm management decisions that support precision agriculture at scale.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とIoTセンサーデータを融合し、作物の健康状態・ストレス状態を分類する深層学習手法が研究の中心であり、植物状態の取得・推定方法として適格。
abstractThis study proposes an integrated approach that fuses data acquired by IoT-connected sensor networks with multispectral images captured by unmanned aerial vehicles (UAVs), enabling comprehensive crop health assessment across an entire field.
Hyperspectral imaging is an imaging technique that allows for acquisition of high-resolution spectral information beyond that of the visible spectrum. When applied to plants, it effectively enables non-invasive characterization of physiological status and has been widely used in agricultural settings. Marchantia is a model bryophyte species whose flat morphology and visually distinct stress-response phenotypes makes it an ideal candidate for imaging studies. Here, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing. This protocol features a streamlined data processing pipeline hosted on a web-based development platform that automates 1) the segmentation of plant area into spatially distinct regions for localized analysis of intra-specimen physiological gradients, and 2) classification of plant pixels based on their spectral signatures. All results are exported as structured CSV files for ease of further analysis as desired by the user.
Why it matches plant phenotyping methodsマーチャンティアを対象としたハイパースペクトル撮像プロトコルと、植物領域のセグメンテーション・スペクトル分類を含む処理パイプラインを開発しており、植物の生理状態取得が中心的な方法論的貢献である。
abstractHere, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicExample images used in this protocol have previously been published by Krishnamoorthi et al. (2024) 4 and can be downloaded from https://github.com/dr-daisuke-urano/PlantHyperspectralSVDOpen asset ↗PlantHyperspectralSVDlines:47-85Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings.Develops a weight-guided method to enhance interpretability of multimodal features.Designs a cross-modal dense interaction and gated fusion mechanism.Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings. Develops a weight-guided method to enhance interpretability of multimodal features. Designs a cross-modal dense interaction and gated fusion mechanism. Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features.
Why it matches plant phenotyping methodsマルチモーダル作物センシングから小麦収量を推定する encoder–decoder 手法を開発し、複数年・品種データで検証しており、表現型推定手法が研究の中心である。
abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network.
Here, we present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay. We describe steps for preparing standardized stress solutions, establishing the sand-bed assay, applying stresses either independently or in combination, and measuring multiple phenotypic traits. Furthermore, we detail procedures for ranking germplasm using a comprehensive scoring system based on a membership function. This protocol enables reproducible, large-scale screening of maize germplasm at the seedling stage.
Why it matches plant phenotyping methods幼苗耐盐碱性状の再現可能な大規模フェノタイピング用アッセイとスコアリング手順を中心に提示しており、単なる生物学的処理実験ではない。
abstractwe present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Main conclusion The progress of soybean phenotypic detection and intelligent sensing technologies has been reviewed under salt-alkali stress , and an integrated approach combining three-dimensional imaging with near-infrared spectroscopy has been proposed to construct full-spectrum three-dimensional images. The approach could provide a reference for the breeding of salt-alkali-tolerant soybean varieties and the optimization of cultivation practices. Soil saline-alkali is one of the major environmental factors limiting global agricultural development, posing a serious challenge to normal crop growth, resource use efficiency, and sustainable agricultural development. Soybeans are a vital oilseed crop and plant-based protein source, and their phenotypic traits are significantly affected by saline-alkali stress, severely limiting soybean grain yield and quality. With the rapid advancement of technologies such as intelligent sensing and big data, this progress has driven new developments in plant phenomics detection, offering fresh insights into germplasm resource evaluation, breeding, gene function, and the cultivation of salt-alkali stressed soybeans. This article introduces the impact of salinity-alkali stress on soybean "phenotype-environment-gene" information, reviews the technical progress and application fields of traditional phenotypic detection methods for obtaining various phenotypic indicators across crops, and focuses on a rapid detection method of soybean phenotype under salinity-alkali stress. This paper analyzes the current state of research on detecting phenotypic indicators of soybeans under saline-alkali stress using intelligent sensing methods, including near-infrared spectroscopy, image recognition, and three-dimensional imaging. It is anticipated that through the integration of three-dimensional imaging and near-infrared spectroscopy, forming "full-spectrum three-dimensional images" with spatial structure and spectral information, this approach will advance the breeding and cultivation of superior salt-alkali tolerant soybean varieties through "intelligent data-driven" methods.
Why it matches plant phenotyping methods植物フェノタイピング指標の迅速検出技術を中心に、画像認識、三次元 imaging、近赤外分光などをレビューし、統合的な表現型取得法を提案する方法論的レビューである。
abstractThe progress of soybean phenotypic detection and intelligent sensing technologies has been reviewed under salt-alkali stress
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.
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
Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.
Why it matches plant phenotyping methods高スループット3D・マルチスペクトル表現型計測プラットフォームの検証と応用が研究の中心であり、形態・スペクトル形質の自動取得、抽出、予測性能、遺伝子型分類を評価している。
abstractThis study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.
Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。
titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.
Indole-3-acetic acid (IAA), one of the most important phytohormones, plays critical roles in plant growth, development, and stress response. However, minimally invasive and in-situ monitoring of IAA in living plants remains challenging due to the complex biological matrix and the lack of suitable in vivo sensing platforms. Herein, we report a minimally invasive microneedle electrochemical sensor for in vivo monitoring of IAA in plant leaves. A polymer microneedle array was fabricated and coated with a conductive Au layer, followed by electropolymerization of a molecularly imprinted poly(o-phenylenediamine) (poly-OPD) recognition film using IAA as the template molecule. The resulting microneedle sensor exhibited a selective electrochemical response toward IAA with good anti-interference capability against common electroactive plant metabolites. The sensor showed a linear response toward IAA over a wide concentration range with a low detection limit (0.44 μM). The microscale structure of the microneedles enabled minimally invasive insertion into plant tissues while maintaining structural integrity and stable electrochemical performance of the sensing platform. The developed sensor was further applied for in vivo monitoring of IAA fluctuations in tomato leaves under different physiological conditions. Distinct dynamic electrochemical response patterns were observed between normal and drought-stressed leaves, demonstrating the potential of the microneedle platform for plant physiological analysis and precision agriculture applications. This work provides a promising strategy for minimally invasive phytohormone sensing in living plants and expands the application of microneedle electrochemical devices in plant bioanalysis.
Why it matches plant phenotyping methods植物葉内のIAAを非侵襲的に測定するマイクロニードル電気化学センサーを開発・性能評価し、乾燥ストレス下の生理状態を実植物で検証しており、表現型取得法が中心である。
abstractwe report a minimally invasive microneedle electrochemical sensor for in vivo monitoring of IAA in plant leaves.
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
This study presents a unified semantic segmentation framework for UAV-based multispectral crop stress mapping, focusing on the integration of water stress and rust disease conditions within a common label space. Unlike conventional approaches that address individual stress factors independently, the proposed framework harmonizes heterogeneous datasets with different annotation schemes into a single multi-class segmentation problem. To achieve this, UAV multispectral orthomosaics are processed using a patch-based strategy and a multi-head UNet++ architecture incorporating segmentation, edge-aware, and Signed Distance Transform (SDT) branches. In addition, a physics-informed output-space refinement module based on fractional partial differential equations (FPDE) is introduced to enhance spatial coherence and boundary preservation in the predicted maps. Experimental results demonstrate the effectiveness of the proposed framework within the evaluated dataset setting, particularly in terms of boundary delineation, spatial consistency, and minority-class detection. The study highlights the feasibility of integrating heterogeneous stress conditions into a unified segmentation framework and provides a foundation for future research on scalable multi-source agricultural monitoring systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物の水ストレスおよびさび病状態を推定するセマンティックセグメンテーション手法の開発が中心であり、植物状態の取得・抽出に該当する。
abstracta multi-head UNet++ architecture incorporating segmentation, edge-aware, and Signed Distance Transform (SDT) branches
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.
Abstract Background: The stink bug complex is one of the most damaging pests of soybean, reducing yield and seed quality. Genetic resistance remains the most sustainable and effective management strategy, but its quantitative inheritance and labor-intensive field phenotyping make its implementation in breeding programs challenging. Objective: This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models. Methods: A population of 304 soybean lines was evaluated in alpha-lattice design trials across two seasons under natural infestations. Five resistance-related traits, grain yield (GY), healthy seed weight (HSW), number of days to maturity (NDM), tolerance (TOL), and leaf retention (LR), were manually scored and linked to UAV-derived vegetation indices (VIs) and texture indices (TIs). Three ML models (AdaBoost, SVM, MLP) were tested to predict these traits from aerial features. Results: Results showed that VIs, particularly Visible Atmospherically Resistant Index at the 25th percentile (VARI_P25), were consistently associated with resistance-related traits, while decision tree analysis highlighted TIs at 45° and 135° as complementary sources of structural information. Prediction ability was highest for GY, HSW, and NDM, especially in flights near flowering and maturity, but remained low for TOL and LR. Integrating multiple flights modestly improved accuracy, whereas cross-season predictions were unreliable. Nonetheless, indices such as VARI_P25 provided useful cross-season correlations for HSW and TOL, enabling early screening of less promising lines. Conclusion: This pioneering study demonstrates that UAV–ML pipelines can capture genetic signals of stink bug resistance in soybean, despite environmental complexity. These findings open new avenues for resistance phenotyping, supporting more efficient breeding strategies and accelerating genetic gains in soybean improvement.
Why it matches plant phenotyping methodsUAV画像と機械学習を用いて、ダイズの抵抗性関連形質や収量を推定するHTPパイプラインを技術的に評価しており、表現型取得・推定法が研究の中心である。
abstractThis study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models.
Machine learning has become an increasingly important tool for overcoming agricultural challenges by enabling efficient and consistent classification of crop-related data. Training such supervised models requires high quality labeled datasets. This work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species on California's Central Coast: annual sowthistle ( Sonchus oleraceus ) and little mallow ( Malva parviflora ). Hyperspectral imaging provides rich spectral-spatial data cubes that can support the development of deep learning models and autonomous technology for precision weed management. Plants were grown in a greenhouse under five conditions: standard, drought, overwatering, excess fertilizer, and no fertilizer. Custom MATLAB scripts were utilized for preprocessing, including k-means clustering to define regions of interest (ROIs), and extraction of spectral metrics. Data visualization was performed using Wolfram language and MATLAB. The dataset includes both raw and ENVI-formatted hyperspectral cubes and pre-processed MATLAB outputs, supporting spectral feature engineering, benchmark development, and exploratory machine learning workflows for controlled environment stress classification.
Why it matches plant phenotyping methods植物のストレス状態を対象とするハイパースペクトル画像データセットで、ROI抽出・スペクトル指標化と機械学習ベンチマークを中心的に提供しているため、植物フェノタイピング手法・データセットとして適格。
abstractThis work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species
Reproduction assets foundThe paper is a Data in Brief article describing its own hyperspectral imaging dataset of annual sowthistle and little mallow under five abiotic stress treatments, deposited publicly on Zenodo (record 17398082). The dataset includes raw ENVI-format hyperspectral cubes, preprocessed MATLAB outputs (ROI masks, extracted植被Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: zenodo.17398082
Direct URL to data: https://doi.org/10.5281/zenodo.17398082Open asset ↗Zenodo · zenodo.17398082html-lines:92-120Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published15 May 2026Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 0 · OpenAlex ↗
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
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.
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
Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Ch ol , cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, β-aminobutyric acid, γ-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.
Why it matches plant phenotyping methods低コストRGB撮像とU-Netによる自動セグメンテーションを中核とする、ジャガイモ表現型取得プラットフォームの開発・検証であり、形態・色・植生指数形質を自動抽出している。
abstractWe present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture.
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Recent technological advances in high resolution image capture and analysis have led to increased adoption of high-throughput digital phenotyping in plant science research. High-throughput digital phenotyping provides a nondestructive method to quantify changes in plant growth and health in response to environmental factors or developmental cues. Moreover, it allows researchers to conduct large experiments in a time- and cost-efficient manner. The TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information. Leaf reflectance is presented as five vegetation indices (e.g., normalized difference vegetation index). Methods: This project evaluated nitrogen (N), phosphorus (P), and potassium (K) deficiency in greenhouse grown ornamental and vegetable plants using the TraitFinder. Plant species included celosia, coleus, marigold, petunia, and tomato. Plants were fertilized with a complete Hoagland's solution (control), and three modified solutions: Hoagland's solution without nitrogen (-N), phosphorus (-P), or potassium (-K). Each plant species was evaluated separately with eight replicate plants per treatment, organized as a randomized complete block design. Results: Treatment with -N, -P, and -K solutions resulted in reduced vegetative growth and decreased concentration of the corresponding macronutrient in leaf tissue for all species evaluated. We observed that the presence of flowers would negatively affect calculations of the vegetation indices due to their distinct spectral properties; therefore, flowers must be excluded to accurately quantify plant health parameters. In general, we observed a common trend where GLI (green leaf index) and NDVI (normalized difference vegetation index) decreased, and NPCI (normalized pigment chlorophyll index) and PSRI (plant senescence reflectance index) increased in response to macronutrient deficiency. The measure of GLI, NDVI, NPCI, and PSRI were different from the control plants, but these observations were dependent on the nutrient deficiency and species tested. Discussion: Our results underscore the importance of accounting for species-specific spectral signatures when assessing plant responses to nutrient deficiencies. This project also provides reference values for interpreting vegetation indices, offering valuable guidance for scientists implementing digital phenotyping in their experimental protocols. Digital phenotyping can significantly improve experimental throughput and provide quantitative insights into plant health.
Why it matches plant phenotyping methodsTraitFinderによる形態・スペクトル形質の取得と、花の除外や種特異的スペクトルへの対応を含むデジタルフェノタイピングの実質的な適用・評価が中心である。
abstractThe TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information.
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
Phenomics, which involves the holistic analysis of plant phenotypes using cutting-edge sensing and data processing technologies, has emerged as an AI-powered technique in plant breeding. Genomics tells us about a plant's genetic potential, whereas phenomics describes the dynamic manifestation of the genes under different environmental conditions. By combining high-throughput phenotyping technologies with AI and machine learning (ML) techniques, complex agronomic traits can be assessed rapidly, accurately and non-destructively, thus speeding up breeding. Advancements in phenotyping technologies, including hyperspectral and multispectral imaging, thermal sensors, LiDAR (Light Detection and Ranging), unmanned aerial vehicles (UAV) and ground robotic systems, provide vast amounts of information about plant growth, physiology, stress, disease, yield potential and other traits. The AI methods such as computer vision and deep learning process the data to discern patterns, forecast traits and detect stress or disease. This helps overcome the phenotyping bottleneck in conventional breeding and improves selection accuracy. AI-based phenomics integrates genomics, phenomics and environmental information to facilitate predictive breeding and mapping of genotype to phenotype to breed climate-adapted, high-yielding varieties. It also helps in precision agriculture for monitoring crops and resource allocation. Phenomics studies are being used in crops like rice, wheat, maize, barley, sorghum and soybean. In India, rice and wheat are extensively studied for high-throughput phenotyping for drought and heat tolerance. Addressing data standardisation and infrastructure issues, phenomics is a disruptive technology, bringing together the genetic potential and field performance for sustainable crop improvement.
Why it matches plant phenotyping methods植物フェノミクス、センシング、画像解析、AIによる形質推定を体系的に扱うレビューであり、フェノタイピング手法が中心です。
abstractPhenomics, which involves the holistic analysis of plant phenotypes using cutting-edge sensing and data processing technologies, has emerged as an AI-powered technique in plant breeding.
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.
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
Latest imaging technologies play a vital role in the extraction of plant phenotypic traits in high ranges. Most existing analytical methods treat these traits as independent features, overlooking the complex interaction patterns that focus on plant responses to environmental stress. Proposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits plat information graph structured interaction networks leverages perceptual similarity learning to capture higher-order phenotypic patterns. In the PGK framework, traits extracted from RGB (Red, Green, Blue) and multispectral imagery are encoded as nodes, and biologically meaningful relationships amongst trait pairs are represented as weighted edges. Extracted trait values are continuously transformed into perceptual states to enhance biological interpretability, and a graph kernel is employed to measure similarity between trait graphs. Experiments performed in an agricultural field with a precision agriculture dataset for plant stress phenotyping demonstrated that the proposed PGK achieved 93.8% classification accuracy, improving performance by 5.3 percentage points over the CNN baseline. The outcome results clearly highlight the effectiveness of the perceptual graph model for plant phenotyping and provide a robust, interpretable computational framework for sustainable crop monitoring and decision-support in precision agriculture.
Why it matches plant phenotyping methods画像由来の植物形質を抽出・関係グラフ化し、ストレス表現型分類を行う計算手法が研究の中心であるため。
abstractProposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository (marathonengineer/Agriproject) containing the datasets used in this plant stress phenotyping study. The other allowed URL (PlantCV) is a generic phenotyping library, not a paper-specific asset.Dataset · publicThe datasets used in this study are available in publicly accessible online repositories. The repository can be accessed at: https://github.com/marathonengineer/Agriproject.Open asset ↗marathonengineer/Agriprojecthtml-lines:589-657Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Global changes in agricultural and environmental systems will necessitate new crop research methodologies in the future years to ensure more effective use of natural resources and food security. The progress in next-generation sequencing has led to the emergence of multi-omics techniques as successful crop improvement strategies. Multi-omics studies using high-throughput techniques have been critical in understanding growth, senescence, yield, and biotic and abiotic stress responses in an array of crops. When multi-omics provide a high-resolution map of the molecular frameworks governing stress responses, advanced deep phenotyping systems can utilize advanced sensors to quantify dynamic physiological and morphological traits non-destructively. The systematic integration of these multi-layered datasets through association mapping and machine learning frameworks allows for the identification of superior alleles and regulatory hubs. Currently, the non-invasive imaging methods have effectively incorporated computer vision, machine learning, and deep learning components of AI. The use of machine learning and deep learning have progressively increased the effectiveness of data gathering and analysis. The supervised, unsupervised, and deep learning architectures have become effective tools for overcoming the genotype-to-phenotype gap, enabling more accurate predictions of yield and stress tolerance. Despite challenges related to data dimensionality, high infrastructure costs, and the need for standardized protocols, the convergence of these fields offers a robust architecture for predictive breeding. By linking microscopic molecular shifts to macroscopic field performance, integrated strategies accelerate the discovery of adaptive traits and the delivery of high-yielding, climate-smart cultivars. This review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
Why it matches plant phenotyping methods深層フェノタイピングと非破壊センサー・画像解析を中心に、作物形態・生理形質の高スループット計測とマルチオミクス統合を論じる方法論的レビューである。
abstractThis review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
Climate change-induced drought increasingly constrains water management in mixed-species urban gardens, requiring scalable and non-destructive approaches. This study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns. Ten garden plant species were evaluated under varying soil moisture conditions. Hierarchical cluster analysis integrating fluorescence parameters and RGB indices identified three physiologically defined response clusters, and their reproducibility using RGB indices alone was assessed. A total of 1,629 samples were augmented to 1,881 using the synthetic minority over-sampling technique (SMOTE) to address class imbalance. A support vector machine (SVM) model with a radial basis function kernel, using green leaf index (GLI), normalized green-red difference index (NGRDI), blue-green pigment index (BGI), and soil moisture (%) as predictors, achieved an accuracy of 0.91 and a Kappa coefficient of 0.84. In contrast, PLS-DA showed lower performance (accuracy 0.79, Kappa 0.65), indicating limited separability under linear assumptions. These results demonstrate that RGB indices combined with nonlinear models were able to reproduce physiologically defined drought response patterns under the given conditions. As a proof of concept, this study demonstrates the potential of the proposed framework; however, its generalizability is limited by the controlled greenhouse setting, the relatively small number of species, and the lack of external validation in heterogeneous field environments. The framework may provide a cost-effective approach for classifying plant drought responses and has the potential to support the grouping of plants with similar water requirements, which could contribute to improved irrigation management in mixed-species gardens under further validation.
Why it matches plant phenotyping methodsRGB画像指標と機械学習により、植物の干ばつ応答パターンという生理状態を分類し、蛍光測定との再現性を評価しているため、表現型取得・抽出手法が中心です。
abstractThis study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns.
Reproduction assets foundThe paper explicitly states that the authors' analysis code (data processing, feature extraction, SVM/PLS-DA modeling) is publicly deposited on Zenodo with a DOI matching an allowed URL. The phenotype datasets are only said to be in the manuscript/supplementary files, so the code deposit is the qualifying paperSpecificCode · publicThe code supporting the findings of this study, including data processing, feature extraction, and machine learning modeling is available at Zenodo: https://doi.org/10.5281/zenodo.19127295 .Open asset ↗Zenodo · 10.5281/zenodo.19127295lines:98-116Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published6 May 2026International Journal of Science, Strategic Management and TechnologyCited by 0 · OpenAlex ↗
Agriculture is the primary source of livelihood for majority of India’s population, with paddy serving as a staple food for a large segment of people. However, paddy cultivation is affected by several challenges that vary with climate, location, and farming practices. Among these, nutrient deficiencies in paddy leaves significantly impact crop yield and quality, making early detection crucial for effective farm management. The following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves . A diverse dataset of paddy leaf images showing different types and severity levels of nutrient deficiencies is collected, and a Convolutional Neural Network (CNN) is used in order for image classification. The model is trained and tested on diverse dataset, demonstrating strong performance in accurately detecting nutrient deficiencies in paddy leaves.
Why it matches plant phenotyping methodsイネ葉画像から栄養欠乏という植物状態をCNNで分類する手法とデータセット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。
abstractThe following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves .
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
Early-stage frost damage in citrus fruits is difficult to detect because external symptoms are often weak or absent, hindering intelligent robotic sorting in postharvest scenarios. To address this challenge, this study proposes a robotic multimodal tactile sensing approach inspired by human mechanoreception for frost-damage detection during grasping. A robotic gripper equipped with a 6×6 pressure matrix sensor and a piezoelectric vibration sensor was used to capture complementary tactile cues during standardized fruit handling, enabling the perception of subtle mechanical changes associated with early frost injury. Using 240 Citrus reticulata 'Hong Mei Ren' fruits under controlled experimental conditions, a Transformer-based multimodal fusion network was developed to jointly model pressure and vibration sequences for binary classification of normal and frost-damaged fruits. Across repeated stratified random-split experiments, the proposed method achieved a mean classification accuracy of 93.1%. Comparative experiments showed that the fusion model outperformed representative sequence-learning baselines, and ablation analysis confirmed that pressure-vibration fusion was more effective than either single modality alone. Attention-based temporal attribution further revealed that the most informative cues were concentrated in the initial contact and early loading stages, indicating the importance of early transient mechanical responses for frost-damage discrimination. Overall, the proposed approach demonstrates the feasibility of grasp-based robotic frost-damage detection under controlled experimental conditions.
Why it matches plant phenotyping methods柑橘果実の凍害状態を圧力・振動センサーで取得し、マルチモーダル融合により分類する手法の開発が中心であり、単なる生物学的実験の測定ではない。
abstractthis study proposes a robotic multimodal tactile sensing approach inspired by human mechanoreception for frost-damage detection during grasping.
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.
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.
Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.
Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。
abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences
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.
High-throughput phenotyping is a core prerequisite for breeding climate-resilient crops. To complete related breeding work, breeders must evaluate the performance of large-scale crop populations under seven types of field abiotic stresses including drought and high temperature, and the combined technology of unmanned aerial vehicle (UAV) imaging and artificial intelligence can provide core support to meet this demand. This review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits; second, sorting out the biological significance of 12 categories of image-derived traits; third, breaking down the seven full workflow nodes ranging from flight planning to breeding decision support. Existing prior research on six crop types including wheat and rice has confirmed that this technology can improve the speed, scale and repeatability of field screening, and delivers outstanding effects when combined with multi-environment testing, genomic tools, and breeders’ expertise. This paper also sorts out six core limitations currently restricting the real-world deployment of this technology, and puts forward six future development directions to support its large-scale application.
Why it matches plant phenotyping methodsUAV画像とAIによる作物のストレス関連形質の取得・選抜ワークフロー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が中核です。
abstractThis review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits;
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Branched broomrape (Phelipanche ramosa) is an obligate root parasitic weed that threatens tomato production in many regions. Progress in understanding host resistance mechanisms has been hindered by the parasite's subterranean life cycle and the technical limitations of traditional soil-based assays. Here, we introduce an integrated experimental framework that enables molecular, genetic, and cellular analysis of broomrape parasitism in tomato under controlled conditions. We implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots, and a dual-compartment in vitro co-culture system supporting parasite infection of transgenic hairy roots. This methodology enabled rapid functional testing of candidate host resistance genes, exemplified by CRISPR-edited mutants of the tomato transcription factor SCHIZORIZA (SlSCZ), which displayed localized lignin accumulation at the parasite entry site in the root. The observed lignification suggests a role for this gene in regulating inducible cell wall lignification against broomrape. Together, these tomato-focused integrated methods enable reproducible imaging, genetic perturbation, and high-resolution analysis of host-parasite interfaces. These provide a scalable platform for dissecting broomrape resistance and accelerating resistance gene discovery in tomato and a critical tool for combating the devastating consequences of this parasite on agriculture.
Why it matches plant phenotyping methodsトマト根上の寄生進展を非破壊・リアルタイムに観察する共培養系と再現可能なイメージングを開発し、植物の感染状態を取得する基盤が研究の中心である。
abstractWe implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots
Sugarcane is a high-value industrial crop vital for sugar and biofuel production, yet increasingly constrained by climate variability, biotic and abiotic stresses, soil degradation, and inefficient input use. Traditional breeding and crop management approaches are often slow, labour-intensive, and less precise, emphasizing the need for digital transformation in sugarcane agriculture. AI now offers powerful tools to accelerate genetic improvement, enhance stress resilience, and optimize resource-use efficiency. This review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems. ML and DL models enable automated, accurate prediction of key traits such as biomass, canopy temperature, nitrogen status, and sugar recovery using UAV, satellite, and proximal sensing data. AI-powered genomic selection approaches leveraging convolutional networks, transformers, and attention mechanisms improve prediction accuracy for yield, ratooning ability, and stress tolerance by integrating SNPs, pedigree, and multi-environment datasets. Emerging innovations such as digital twins, multimodal data fusion, reinforcement learning-based irrigation scheduling, and climate-smart advisory models further strengthen real-time crop intelligence. The integration of blockchain-enabled breeding databases, FAIR data standards, and interoperable analytics pipelines supports scalable and collaborative research. Literature analysis reveals 15-30% gains in selection efficiency, >90% accuracy in disease detection, and phenotyping cost reductions of up to 70%. Key challenges remain, including scarce annotated datasets, genotype × environment complexity, model interpretability, and adoption barriers for smallholders. A future roadmap is proposed featuring multimodal foundation models, edge-AI deployment, and explainable breeder dashboards. AI is redefining sugarcane research from reactive to predictive, enabling climate-resilient, sustainable, and profitable production systems.
Why it matches plant phenotyping methodsサトウキビ育種におけるAI応用の総説であり、高スループット表現型解析、UAV・衛星・近接センシングによる形質推定を主要な対象として扱っているため、フェノタイピング手法レビューとして適格。
abstractThis review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abiotic stresses, particularly acid and salt stress, severely limit plant productivity. Conventional detection is often hindered by physiological lags and phenotypic latency. Here, we develop a machine learning-enabled implantable plant biomarker sensor (MLIPBS) for early stress diagnosis. Featuring a foldable design, MLIPBS enables conformal integration into plant tissues for continuous monitoring of H 2 O 2 , K + , and pH. We confirm the robust sensing capabilities and favorable biocompatibility of MLIPBS through cross-species validation in lettuce, tomato, and Aloe vera. Additionally, leveraging the LightGBM architecture, we demonstrate that MLIPBS successfully classifies combined stress conditions and varying intensity levels of acid and salt stress, achieving an average accuracy of 90.5%. We further show that the system identifies stress types and intensities within 8 hours of onset, providing an early-warning window at least 48 hours before symptom manifestation. Our study provides reliable wearable tools for stress-resistant crop screening and precision management in smart agriculture.
Why it matches plant phenotyping methods植物組織内の生体指標を連続測定し、ストレスの種類・強度を分類するセンサーと機械学習システムの開発・検証が中心であり、植物ストレス状態のフェノタイピング手法に該当する。
abstractHere, we develop a machine learning-enabled implantable plant biomarker sensor (MLIPBS) for early stress diagnosis.
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.
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.
Plant stress monitoring is invaluable in realizing sustainable agriculture because it enables the people practicing it to take early measures to counteract losses in yield caused by environmental stressors like drought and nutrient deficiencies, as well as caused by pathogen infections. The proposed study presents a new Multi-Modal Vision Transformer (MMViT) architecture that is designed to combine both thermal and RGB imagery to take detection to the next level. In order to support this methodology and further studies, we are now publicly releasing a new collection of synchronized thermo-RGB image pairs of stressed and healthy plants, collected both in controlled settings and in the field. The data is labeled to differentiate various stress phenotype and contains over 4286 of images, and hence forms a substantial platform to evaluate multimodal plant phenotyping methods. Empirical evaluations indicate that MMViT model achieves a general classification of 94.3% when using the two modalities, which is better than the single-modality ViT used on the thermal images (85.5%) and the RGB images (93.3%). These experimental results emphasize the performance of multimodal fusion whereby the other spectral cues are used to complement a stress classification. The described framework, together with the useful dataset, will contribute to the advancement of precision agriculture as it is an open and data-driven instrument to monitor plant health automatically.
Why it matches plant phenotyping methods熱画像とRGB画像を統合して植物ストレス表現型を分類するモデルを開発し、公開データセットと性能評価も提示しており、植物フェノタイピング手法が中心である。
abstractThe proposed study presents a new Multi-Modal Vision Transformer (MMViT) architecture that is designed to combine both thermal and RGB imagery to take detection to the next level.
Objectives Nutritional deficiency in coffee is a major problem that compromises plant health, crop yield, and bean quality, directly threatening the economies of coffee-dependent regions. Traditional detection methods are primarily manual, time-consuming, and relied upon expert availability. Methods This study introduces a novel Deep Learning (DL)-based dual-track architecture designed for the efficient classification of nutritional deficiencies in coffee leaf. The first track utilizes a MobileNetV3 backbone integrated with a Multi-Convolutional Shape-Aware Kernel (MCSK) block to capture spatially adaptive features from leaf textures and vein patterns. The second track employs a Hierarchical Shuffled Group Attention Network (HSGAN), utilizing Efficient Channel Attention (ECA) and Local Group Attention (LGA) modules to balance fine-grained local variations with broad spatial dependencies. Finally, a Multidimensional Collaborative Attention (MCA) mechanism is applied to the fused features to enhance cross-channel interactions and feature extraction. Results The proposed model was evaluated using the CoLeaf dataset, where it achieved an accuracy score of 96.04%. This performance demonstrates an improvement over existing research and current state-of-the-art models, highlighting the architecture's ability to identify complex nutrient-related patterns in coffee leaves. Conclusion The performance of the proposed DL approach offer a solution for the automated monitoring of coffee plants. By providing a reliable alternative to manual inspection, this method presents the potential to help coffee production and support the agricultural regions worldwide.
Why it matches plant phenotyping methodsコーヒー葉の栄養欠乏という植物状態を画像から分類する深層学習手法を開発・評価しており、表現型取得・推定が研究の中心であるため。
abstractThis study introduces a novel Deep Learning (DL)-based dual-track architecture designed for the efficient classification of nutritional deficiencies in coffee leaf.
Reproduction assets foundThe paper's primary phenotyping asset is the CoLeaf coffee leaf nutrient-deficiency image dataset, which the authors state is publicly available via a Mendeley Data URL matching an allowed URL. No author analysis code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/brfgw46wzb/1 .Open asset ↗brfgw46wzb/1lines:733-764Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
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.
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.
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)
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.
Phenotyping plant traits using UAV-based multispectral imaging offers a robust and unbiased approach to assessing crop status. With approximately 70% of smallholder farmers in East and Southern Africa cultivating common beans as a key source of food and income, there is a critical need for accurate and timely measurements of crop health and yield to support data-driven management decisions and disease mitigation. Traditional phenotyping methods are labor-intensive, and existing remote sensing and machine learning approaches remain limited. This study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery. Data collected over three growing seasons (2022–2024) were used to extract canopy variables and vegetation indices (VIs) across phenological stages. For yield prediction, traditional machine learning models achieved a root mean squared error (RMSE) of 242.33 kg ha⁻¹ and an R² of 0.66 using an Extra Trees Regressor. A novel BY-GRU architecture improved performance, achieving an RMSE of 242.40 kg ha⁻¹ and an R² of 0.79. The analysis also identified 45–60 days after sowing as the optimal window for prediction. To address limitations in conventional plant health assessments, this study introduces a novel Health Index. Comparative analysis demonstrated its robustness across genotypes and stronger correlation with yield. Machine learning and deep learning models, including MaxViT, were applied to estimate the Health Index, achieving improved predictive performance. Overall, this work integrates UAV sensing and modelling to provide scalable tools for phenomics, crop management, and breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から作物の健康状態、収量、キャノピー形質を推定するセンシング・機械学習フレームワークが研究の中心であり、植物フェノタイピング手法として適格です。
abstractThis study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery.
Reproduction assets foundThe preprint's DATA AVAILABILITY section states that all processed data required to reproduce the results are publicly available in a Google Drive repository, which qualifies as a paper-specific public phenotype dataset asset. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicCommon Bean Breeding Program for
facilitating field trials. We also thank the Phenomics team for their valuable assistance with UAV-based
data collection.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY
The datasets generated and/or analyzed during the current study are publicly available at:
https://drive.google.com/drive/folders/1fN3Q9n3bK_YoXFK8VFKZ3uEb13y9iRWj?usp=sharing. This
repository includes all processed data required to reproduce the results presented in this study.
SUPPLEMENTAL MATERIAL
Supp. Figure 1. Drone-based field view of the bean trial site at CIAT Palmira Research Station: A) RGB
image and B) NDVI image.
Supp. Figure 2. Drone Features Open asset ↗pdf-raw-page:40 lines:1-46Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
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.
Field / plotGrowth chamberStress response / toleranceYield / yield components
The convergence of artificial intelligence (AI), genomics and phenomics is ushering in a new era of smart breeding a paradigm that promises to dramatically accelerate genetic gain while reducing the time and cost associated with developing elite crop varieties. Conventional plant breeding, though enormously successful over the past century, is increasingly challenged by a rapidly changing climate, a growing global population projected to reach nearly 10 billion by 2050 and the biological complexity of quantitative traits. Smart breeding leverages exponential growth in genomic data, high-throughput phenotyping platforms and the analytical power of machine learning and deep learning algorithms to navigate these challenges. This review synthesizes the current state of knowledge across three interdependent pillars: AI and machine learning for genomic selection, trait prediction and decision support; next-generation sequencing and multi-omics tools that have transformed our understanding of crop genetic architecture; and field and controlled-environment phenomics platforms that bridge the genotype phenotype gap. Further discuss integration through digital twins, knowledge graphs and federated learning frameworks and examine applications in gene editing, stress tolerance and yield improvement. Key challenges data standardization, interpretability of black-box models, regulatory frameworks and equitable access are critically assessed and a roadmap for the next decade of smart breeding is proposed. Another point highlighted in this review is the need to conduct collaborative and interdisciplinary research to achieve the full potential of smart breeding technologies. It emphasizes the necessity of capacity-building, data sharing systems and policy support to provide sustainable and inclusive agricultural growth. Moreover, the paper highlights the importance of new innovations in developing resilient and productive and future-oriented crop systems.
Why it matches plant phenotyping methodsAI・ゲノミクス・フェノミクスを統合するレビューであり、高スループット表現型解析プラットフォーム、形質予測、機械学習による表現型解析を主要な構成要素として扱っているため、方法レビューとして採択。
abstractSmart breeding leverages exponential growth in genomic data, high-throughput phenotyping platforms and the analytical power of machine learning and deep learning algorithms
Rapid identification of maize waterlogging is essential for post-disaster agricultural assessment, but most existing methods rely on multi-temporal imagery that is often unavailable immediately after extreme rainfall events. This study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogged maize from single-date multispectral imagery within pre-extracted maize planting areas. Built on DeepLabV3+, the model integrates three task-specific modules: a Spectral-Spatial Information Enhancement Module to improve feature discrimination under spectral mixing, an Adaptive Multi-Scale Pooling Module to capture heterogeneous patch sizes, and a Boundary Enhancement Module to refine transition zones. A pixel-level dataset containing 12,198 image patches was constructed from 62 multispectral scenes collected across five major maize-producing cities in Heilongjiang Province, China, during 2022–2024. On the test set, SAB-DeepLabV3+ achieved a waterlogged-class IoU of 68.30%, mIoU of 80.37%, mF1 of 88.62%, and OA of 93.49%, outperforming DeepLabV3+. Leave-one-city-out evaluation further produced an average mIoU of 76.56% and a waterlogged-class IoU of 63.45%. These results indicate that single-date high-resolution multispectral imagery can support rapid and reliable maize waterlogging mapping.
Why it matches plant phenotyping methodsマルチスペクトル画像からトウモロコシの水害状態を抽出するセマンティックセグメンテーション手法を開発し、データセットと都市間評価で検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogged maize from single-date multispectral imagery within pre-extracted maize planting areas.
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+ .
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Agrivoltaic systems co-locate photovoltaic (PV) arrays and crops, offering land-use efficiency and potential microclimate benefits, yet they introduce new challenges for computer-vision-based crop monitoring. PV structures produce strong, spatially varying shadows, specular reflections, and periodic occlusions that confound visual cues for diagnosing crop diseases and abiotic stresses. Meanwhile, agrivoltaic deployments are often distributed across farms and operators, making centralized data collection impractical due to privacy, ownership, and regulatory concerns. This paper proposes PrivAgriVolt, a novel privacy-preserving learning framework for agrivoltaic crop issue recognition that explicitly models PV-induced illumination and enables collaborative training without sharing raw images. The core algorithm integrates (i) a PV-geometry-conditioned shadow normalization module that fuses estimated array layout and sun-angle priors into a shadow-aware appearance canonization network, reducing illumination-induced domain shift across times and sites; (ii) a federated contrastive stress learner that aligns stress semantics across farms via prototype-based contrastive objectives while remaining robust to heterogeneous sensors and crop stages; and (iii) an adaptive privacy layer that combines secure aggregation with budget-aware gradient perturbation and client-level clipping to provide formal privacy guarantees while preserving fine-grained diagnostic performance. Extensive experiments on real agricultural vision benchmarks and agrivoltaic shadow variants demonstrate that PrivAgriVolt improves stress recognition and segmentation under PV shading while maintaining strong privacy–utility trade-offs.
Why it matches plant phenotyping methods作物の病害・非生物的ストレス状態を画像から認識・セグメンテーションする手法を開発し、農業用ベンチマークで評価しており、植物表現型取得が中心である。
abstractThis paper proposes PrivAgriVolt, a novel privacy-preserving learning framework for agrivoltaic crop issue recognition
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
Heat stress, particularly during the reproductive stage, poses a major challenge to rice production, as pollen development is highly sensitive to elevated temperatures. Accurate assessment of heat tolerance during this period is crucial for improving rice heat-stress tolerance but is hindered by asynchronous panicle development and imprecise staging. In this study, we identified a pair of near-isogenic lines, ZP15 and ZP17, which exhibited contrasting seed-setting rates under heat stress. We demonstrated that this divergence arises from differential tolerance during the pollen developmental stage, corresponding to a critical window (9-16 days before heading). Taking these lines as references, we established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss. Validated using heat-tolerant N22 and heat-sensitive Wushansimiao, this system was applied to assess four conventional varieties and eight hybrids. Huanghuazhan and self-bred hybrids (Yangxianyou 912, Yangxianyou 903, and Yangxian 9A/P119-8) displayed high tolerance comparable to control varieties, whereas Yangdao 6 and multiple hybrids showed pronounced sensitivity. Collectively, this work provides a precise and reproducible framework for evaluating heat tolerance during pollen development, offering a valuable tool for accelerating the breeding of heat-resilient rice varieties.
Why it matches plant phenotyping methodsイネの花粉発育期における高温耐性と受精率低下を定量評価する、再現性のある評価システムを開発・検証しており、植物表現型取得が研究の中心である。
abstractwe established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
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
Agriculture, as a basis of sustainable development, faces increasing pressure to meet rising global food demands while confronting the increasing impacts of climate change. Precision agriculture offers a data-driven approach to address these challenges by optimizing input use, improving productivity, and reducing environmental impacts. Sensor technologies play a critical role in smart and precision agriculture, offering high-resolution spatial and temporal insights into soil conditions, plant development and environmental conditions. This review highlights the current state and future potential of various sensor and imaging systems, particularly their role in monitoring soil properties, crop nutrition, plant health and detecting biotic and abiotic stressors. Special attention is given to accessible paper-based and printed electrochemical devices for on-site soil and plant analysis, as well as active handheld multispectral sensors designed for real-time canopy assessment. The integration of sensor-derived data with predictive models, IoT networks and decision-support tools enables more precise, site-specific management, improves input efficiency and supports climate-resilient agricultural practices. By examining the capabilities, limitations and future potential of these sensing platforms, this review highlights their growing importance in advancing sustainable intensification and strengthening crop production.
Why it matches plant phenotyping methods植物の発達、健康、ストレス、栄養、キャノピー状態を測定するセンサー・画像システムを中心にレビューしており、植物表現型取得技術のレビューに該当する。
abstractSensor technologies play a critical role in smart and precision agriculture, offering high-resolution spatial and temporal insights into soil conditions, plant development and environmental conditions.
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.
Climate change, particularly increasing frequency and intensity of spring frost events, poses a serious threat to viticulture by reducing yield and product quality. This study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP). A unique dataset called FGVL dataset from Sultana seedless grape vineyards in Manisa, Türkiye, following a severe frost event in April 2025. FGVL includes 418 frost-damaged grapes, 510 frost-damaged leaves, 395 healthy grapes, and 698 healthy leaves, all manually annotated by experts under natural field conditions. By integrating ASPP into YOLOv11s, proposed model improved multi-scale contextual feature extraction and achieved mAP@50 of 0.7686, demonstrating stronger performance in instance segmentation of small, overlapping, and visually similar grapevine organs. In addition, Dynamic Confidence Thresholding (DCT) strategy was introduced to improve prediction reliability in dense and visually complex vineyard scenes. Despite challenges such as background clutter, object overlap, and small target structures, model maintained stable performance with low computational demand, requiring only 6.45 GB of GPU memory. Proposed framework offers an accurate, efficient, and practically deployable early recognition system for frost damage assessment in viticulture.
Why it matches plant phenotyping methodsブドウの器官における霜害状態を画像からセグメンテーションする手法を開発・評価しており、植物の病害・障害状態の取得が研究の中心である。
abstractThis study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP).
Reproduction assets foundThe paper's Data availability statement explicitly shares the FGVL frost-damage dataset and source code in the corresponding author's public GitHub repository, matching an allowed URL.Code · publicSource code and dataset are publicly shared in GitHub repository of corresponding author. GitHub repo: https://github.com/kaanarikk/Grape-Instance-Segmentation-For-ViticultureOpen asset ↗https://github.com/kaanarikk/Grape-Instance-Segmentation-For-Viticulturelines:230-236Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
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.
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像を用いた高スループット圃場フェノタイピング枠組みを導入・検証し、植生被覆、草丈、NDVIから複数の植物形質・状態を抽出しており、フェノタイピング手法が中心的です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analysed (UAV-derived phenotyping measurements) in a public Zenodo repository with a DOI matching an allowed URL.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:197-235Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
Currently operating commercial photovoltaics (PV) systems integrated with agricultural production (Agrivoltaics) offer immense potential for the dual harvest of renewable energy and agro-products. Within controlled-environment agriculture (CEA), the use of semi-transparent photovoltaics (ST-PV) and the ability to control the microclimate and shading are beneficial for the production of high-value crops such as cucumbers. The objective of this research was to commence the cultivation of cucumbers under evolving CEA-PV systems by combining greenhouse experiments with computer vision (CV) based driven phenotyping to create an analytical framework and system control framework for the cultivation of cucumbers in an evolving CEA-PV system. The method involved using the monitored plant vigor to control in real time the irrigation and shading of the cucumber plants. The control of irrigation and shading was based on the monitored plant vigor as determined by a U-Net++ implementation for canopy segmentation, an EfficientNet-B3 implementation for stress detection, and a CNN regressor for growth trait estimation. Within the greenhouse, uniform environmental and fertigation conditions were established to evaluate the effect of four shading regimes (0%, 20%, 40%, 60%) on the cucumbers. Simulated, yet representative results predicted cucumber yields to be stable (within ±4% of full yield) with a 20% shading and a 15-20% reduction in water use compared to full sun. Yield was also observed to drop by 10-14% under higher shading of 40 to 60% due to insufficient photosynthetic activity for fruiting. The CNN based models were robust, (segmentation IoU 0.91, stress-class F1 0.92, LAI regression R²≈0.93), allowing for precise and comprehensive monitoring in an annual non-invasive fashion. The greenhouse's annual photovoltaic (PV) output was estimated to be 1,550 to 1,750 kWh/kWp which is able to exceed the energy demand resulting to a net energy surplus. The outcome demonstrates that the cucumber crop can be successfully combined with controlled environment agrovoltaic systems with moderate shading for optimum cucumber yield. Moreover, informed supervision through Artificial Intelligence (AI) helps to navigate closed-loop systems and enhance the water-use efficiency and yield stability.
Why it matches plant phenotyping methodsCNNによるキャノピー分割、ストレス検出、成長形質推定を中核とする植物フェノタイピングおよび閉ループ制御フレームワークであり、性能指標も報告されているため。
abstractcombining greenhouse experiments with computer vision (CV) based driven phenotyping to create an analytical framework and system control framework for the cultivation of cucumbers
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.
Vegetable crops are the backbone of global food and nutritional security, yet they remain among the most vulnerable agricultural commodities to climate change. Heat, drought, salinity, flooding and escalating biotic pressures now represent existential threats to vegetable productivity, quality and accessibility, particularly in developing nations. This review examines how the synergistic deployment of genomics and phenomics can accelerate the development of climate-resilient vegetable cultivars. The objective is to critically synthesize current advances in genomic tools—including next-generation sequencing (NGS), genome-wide association studies (GWAS), CRISPR/Cas9 genome editing and genomic selection—alongside high-throughput phenotyping (HTP) technologies such as multispectral imaging, thermal sensors and drone-based platforms, and to evaluate their integration as a strategic framework for vegetable breeding. Key findings demonstrate that the integration of genomics and phenomics enables high-resolution genotype-to-phenotype (G→P) mapping, significantly reduces breeding cycle duration and improves selection accuracy for complex, multigenic stress-adaptive traits across major vegetable crops including tomato, pepper, cucumber, lettuce, spinach and Brassica species. Specific discoveries include the identification of heat-tolerance QTL in tomato via GWAS-phenomics integration, CRISPR-mediated improvement of drought signaling in pepper and genomic selection models achieving prediction accuracies of 0.65 for heat stress indices in lettuce. The review also identifies critical limitations including high phenotyping costs, data integration challenges, lack of standardized protocols, regulatory hurdles for genome-edited cultivars and limited capacity in low-income countries. These findings have direct policy implications: so in order to unlock the transformative potential of genomics-phenomics integration, smallholder farmers in climate-vulnerable regions will not have access to improved cultivars. Instead, national agricultural research programs and international funding bodies should prioritize investment in affordable phenotyping infrastructure, open-access pan-genome databases, harmonized data ontologies, and regulatory frameworks that facilitate the deployment of genome-edited vegetables.
Why it matches plant phenotyping methods植物フェノタイピング技術（マルチスペクトル画像、熱センサー、ドローンプラットフォーム等）をゲノミクスとの統合という観点から批判的にレビューしており、方法論が中心的である。
abstractThis review examines how the synergistic deployment of genomics and phenomics can accelerate the development of climate-resilient vegetable cultivars.
Drought stress poses a significant challenge to food security in sub-Saharan Africa, particularly for smallholder farmers in dryland systems. Bambara groundnut ( Vigna subterranea (L.) Verdc.), an underutilised legume with inherent drought tolerance, remains underexplored in terms of its root system traits. This greenhouse study investigated the early root and shoot responses of six Bambara groundnut genotypes under well-watered (100% field capacity) and water-stressed (50% field capacity) conditions using rhizotron-based phenotyping. Significant genotypic differences ( p < 0.01) were observed in root traits such as root system depth (RSD: 11.0-19.9 cm), root system width (RSW: 6.96-12.2 cm), and root dry mass (RDM: 0.42-1.27 g). The ARC genotype exhibited a strong drought-avoidance strategy, increasing RSD from 12.2 to 19.9 cm and RDM from 0.42 to 1.16 g under stress. The Tiga Nicuru DIP-C-F7471 genotype showed adaptive plasticity, maintaining deeper roots (11.0-14.5 cm), high convex hull area (CHA), and root-shoot ratio (RSR) values, despite a reduction in RDM, suggesting a resource-conserving strategy. Principal Component Analysis (PCA) captured 93.6% of the total variability among genotypes. Root traits, particularly total root length (TRL), convex hull area (CHA), root system width (RSW), and root dry mass (RDM), were the main contributors to genotype differentiation. Strong positive correlations (r = 0.88-0.97) between root and shoot traits suggest that genotypes with more developed root systems also supported greater shoot growth, highlighting the coordinated response of above- and below-ground traits under drought stress. These findings provide valuable targets for breeding and highlight the value of rhizotron-based screening for root trait selection. Future field validation and full-season studies are recommended to confirm their relevance for improving yield stability in dryland agriculture.
Why it matches plant phenotyping methods根系・地上部形質を取得するrhizotron-based phenotypingを用い、そのスクリーニング価値を主要な貢献として扱っているため、植物フェノタイピング手法の実質的応用に該当する。
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.
ABSTRACT Let’s face it, the old way of walking fields and guessing at crop health just doesn’t cut it anymore. Food security is a growing problem, and farmers need smarter tools. That’s where new AI-powered drone system steps in. It’s built for real work, not just showing off tech for tech’s sake. Here’s how it goes: imagine your fields dotted with small IOT sensors, each one quietly watching over soil moisture and water quality. They’re running on low-cost, programmable ESP32 hardware—nothing fancy, but reliable. Most of the time, the drones stay parked. But the second a sensor picks up trouble—say, a patch of soil gets too dry or water quality drops—the system jumps into action. The drone takes off on its own and heads straight to the problem spot, guided by GPS and the sensor’s alert. Once there, it snaps high-res images of the crops below. No more guesswork—these images go through a machine learning pipeline built with Tensor-Flow and K-eras, using deep Convolutional Neural Networks. The system checks for early signs of yellowing, wilting, or pests—stuff you don’t want to miss. After that, the results and clear, practical advice—like when to water—pop up on a central dashboard. This setup isn’t about replacing farmers; it’s about giving them a break from endless scouting and letting them focus on bigger decisions. By only sending out drones when needed, it saves energy and cuts down costs. Early data shows a 40% faster response to crop diseases and up to 30% better use of resources. In the end, it’s a smart, affordable way to bring precision farming to the people who need it most, making farms more efficient—without all the extra work.
Why it matches plant phenotyping methodsドローン画像と深層学習により作物の黄化・萎れ・病害兆候を抽出するシステムが中心で、植物の健康状態・病害状態の計測に該当する。
abstractOnce there, it snaps high-res images of the crops below. No more guesswork—these images go through a machine learning pipeline built with Tensor-Flow and K-eras, using deep Convolutional Neural Networks.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Contemporary agrobiotechnology research increasingly relies on automated methods for capturing and interpreting morphophysiological and spectral plant characteristics - a field known as digital phenotyping. This approach aims to identify stable differences between genotypes cultivated under non-identical environmental conditions. We previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets, with a primary focus on crops such as soybean (Glycine max). The tool implements automated data preprocessing procedures, including synchronization of timestamps across samples and removal of noise artifacts and outliers. These features are particularly relevant for multi-month experiments involving assessments of growth parameters, fluctuations in photosynthetic apparatus area, or other biometric indicators. Support for standardized data formats (XLSX, CSV) ensures compatibility with common phenotyping systems, simplifying cross-platform integration. Thus, the tool can integrate with widely used HTPP platforms (e. g., Traitmill, HyperAIxpert, Plant Accelerator), enabling data from diverse sources to be analyzed within a single pipeline. For soybean experiments, StatFaRmer provides customizable analysis of variance (ANOVA) with visualization of diagnostic parameters (normality of distribution, homogeneity of variances) and evaluation of effect significance between user-defined groups. An example application compares growth parameters across 20 soybean cultivars under controlled stress: the tool automatically aggregated data with uneven measurement frequencies (from 1 hour to 3 days), identified anomalies in hypocotyl elongation dynamics, and computed statistical significance between groups (p < 0.01).The tool has been tested on large-scale datasets (over 2,000 measurements per experiment). StatFaRmer is implemented as a Shiny-based web application, with step-by-step deployment guides for Windows and Linux. All processing stages - from raw data to final plots - are documented to ensure transparency and compliance with research reproducibility standards. Thus, StatFaRmer offers a specialized solution for statistical hypothesis testing in soybean digital phenotyping, reducing data preparation time and minimizing risks of error when handling non-stationary time series.
Why it matches plant phenotyping methods植物デジタルフェノタイピング用の時系列解析ツールを開発・拡張し、前処理、異常値除去、統計解析、再現可能なワークフローを提供しているため、フェノタイピング手法が中心である。
abstractWe previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Bioavailability of iron, an essential micronutrient to plants, is low in alkaline or calcareous soils, which are prevalent across semi-arid production regions. Breeding efforts to increase tolerance to iron deficiency chlorosis (IDC) in sorghum, a major crop of semi-arid regions, are confounded by spatial variation of stress severity in field trials. Here we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability. In the field, severity and uniformity of stress are highly predictive of genetic signals for IDC tolerance ( R 2 > 0.6 for soil pH metrics and H 2 ). Plot-level data filtering for stress conditions based on control genotypes successfully addresses field spatial variation (unfiltered H 2 = 0.18 vs. filtered H 2 = 0.4). The controlled-environment assay proxies field stress using iron sources with differential bioavailability, evidenced by high heritability ( H 2 = 0.98) and phenotypic differential for hybrid control genotypes that matches field performance. Finally, we show that assay phenotypes are suitable for genome-wide association studies in global germplasm. Together, these field and lab phenomic approaches can be deployed to understand genetics of IDC tolerance and develop crops resilient to alkaline soils. HIGHLIGHT Stress severity and uniformity greatly impact detection of genetic signals underlying iron deficiency chlorosis tolerance in sorghum. A controlled-environment assay reduces spatial heterogeneity and improves assessment of tolerance genetics.
Why it matches plant phenotyping methods鉄欠乏性クロロシス耐性を評価するため、圃場マルチスペクトル空撮と管理環境アッセイという2つのハイスループット表現型計測法を開発・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。
abstractHere we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability.
Abstract A genome‐wide association study (GWAS) using digital images was conducted to delineate regions of the genome that govern the leaf flipping quantitative trait in soybean ( Glycine max (L.) Merr). However, converting the digital data to numerical scores for downstream analyses was challenging. We have developed an algorithm that operates in the hue, saturation, and value color space in a structured image processing pipeline that includes preprocessing, binary masking for leaf region isolation, contrast enhancement, grid‐based intensity analysis, and thresholding for detecting folded leaves, a response of soybean to drought. The outputs of this image analysis reached over 90% detection accuracy for images captured under different imaging conditions. GWAS using the processed images identified the same genetic loci underlying drought tolerance as were identified earlier by GWAS of the manually curated dataset from the same photos. This approach provides a robust, scalable, and cost‐effective tool for digital image‐based high‐throughput phenotyping.
Why it matches plant phenotyping methods大豆葉の反転表現型を画像から定量化する画像処理アルゴリズムを開発し、異なる撮像条件で精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe have developed an algorithm that operates in the hue, saturation, and value color space in a structured image processing pipeline that includes preprocessing, binary masking for leaf region isolation, contrast enhancement, grid‐based intensity analysis, and thresholding for detecting folded leaves
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Chlorophyll fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength chlorophyll fluorescence light detection and ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech ( Fagus sylvatica ) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.
Why it matches plant phenotyping methods植物ストレス状態を遠隔測定するクロロフィル蛍光LiDARセンサーを開発し、測定性能とストレス検出能力を実証しており、植物フェノタイピング手法が中心である。
abstractwe present a novel, lightweight, and portable dual-wavelength chlorophyll fluorescence light detection and ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research.
Agricultural robotics-enabled crop health monitoring faces critical trade-offs: standalone on-device models sacrifice accuracy for real-time responsiveness, while cloud-dependent approaches suffer from high latency and communication overhead. Additionally, data-driven models often lack biophysical plausibility, leading to unreliable predictions for agronomic decision-making under resource constraints. We propose a hybrid LSTM-edge correction architecture that hierarchically integrates lightweight Long Short-Term Memory (LSTM) networks on field robots with physics-informed neural networks (PINNs) at the edge. On-device LSTMs process localized sensor data (soil moisture, spectral reflectance) to generate initial crop stress probability estimates with minimal latency. Edge-based PINNs refine these predictions by embedding biophysical dynamics—modeled via coupled partial differential equations (PDEs) governing the soil-plant-atmosphere continuum (SPAC)—to ensure agronomic validity, mitigate sensor noise, and account for spatial variability. The framework is deployed on NVIDIA Jetson Nano (local inference) and AMD EPYC servers (edge processing), seamlessly integrating with existing farming infrastructures to replace rule-based thresholds with adaptive, physics-grounded control commands. A Fourier Neural Operator (FNO) optimizes the edge PINN’s computational efficiency for high-dimensional PDE solving. Experimental evaluations on two real-world datasets (soybean and citrus) demonstrate that the hybrid approach improves prediction accuracy by 18% compared to standalone LSTMs (F1-score: 0.89±0.02 for soybean, 0.83±0.03 for citrus) while maintaining real-time performance (end-to-end latency: 210 ms, energy consumption: 5.1 J/prediction). Field deployment on a 50-hectare soybean farm yields tangible agronomic benefits: 22% reduction in irrigation water usage, 18% fewer pesticide applications, and 95% system uptime under field conditions. The framework exhibits robust performance against sensor noise (≥80% accuracy at 30% noise-to-signal ratio) and outperforms cloud-based PINNs (72.8% lower energy consumption) and threshold-based methods (28–33% higher F1-score). This work advances distributed agricultural robotics by bridging data-driven machine learning and domain-specific physics, delivering a scalable, interpretable, and resource-efficient solution for precision agriculture. The hierarchical prediction-correction pipeline balances real-time responsiveness with biological plausibility, making it suitable for resource-constrained field robots. By integrating legacy sensors and adaptive actuation control, the architecture offers a practical pathway to upgrade existing farming systems, enabling data-informed interventions while reducing environmental impact.
Why it matches plant phenotyping methods作物ストレス状態を推定するLSTM・PINN・FNO統合パイプラインを開発し、実データで精度・遅延・ノイズ耐性を評価しており、植物状態の取得・推定手法が中心である。
abstractWe propose a hybrid LSTM-edge correction architecture that hierarchically integrates lightweight Long Short-Term Memory (LSTM) networks on field robots with physics-informed neural networks (PINNs) at the edge.
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
Abstract Temporal hyperspectral imaging enables non-destructive monitoring of agricultural stress through spectral signatures evolving across extended observation periods, yet processing high-dimensional spatial-spectral-temporal sequences remains computationally prohibitive for real-time deployment. Traditional machine learning methods sacrifice temporal information through dimensionality reduction, while hybrid deep learning architectures combining convolutional and recurrent networks suffer from optimization pathologies at component boundaries. We introduce PSUMamba, a dual-path bidirectional Mamba architecture that processes 204-band hyperspectral sequences across eight timepoints through linear-complexity state space models, achieving 95.05% accuracy with 99.00% AUC-ROC using 153,268 parameters. The architecture maintains perfect specificity (100%) with 93.67% sensitivity while out-performing Vision Transformer with 39-fold fewer parameters and 37.5% reduced training time. Separate spectral and temporal pathways with adaptive fusion enable specialized biochemical and physiological feature extraction without quadratic attention overhead. Ablation studies confirm temporal features dominate classification under experimental conditions, with dual-path fusion providing superior probabilistic calibration (97.35% AUC) over single-path variants. Statistical comparisons demonstrate significant improvements over PLS-DA (∆=12.07%, p=0.0001), 3D CNN (∆=15.48%, p=0.0042) and 1D CNN-LSTM (∆=33.77%, p
Why it matches plant phenotyping methods植物ストレス状態を時間分解ハイパースペクトル画像から推定する計算・センシング手法を開発し、既存手法との比較およびアブレーションで検証しているため、植物フェノタイピング手法が中心である。
titlePSUMamba: Dual-Path Bidirectional Mamba for Plant Stress Monitoring via Temporal Hyperspectral Imaging
Using nanosensors in living plants allows the real-time detection of internal reactive oxygen species (e.g., hydrogen peroxide) signaling in response to environmental stressors. The time-dependent pulse of hydrogen peroxide (H 2 O 2 ) constitutes a signaling waveform ; however, standardized methods of extracting and analyzing such waveforms quantitatively remain elusive. Here, we develop a reference-less framework to extract stress-induced H 2 O 2 waveforms in planta directly from active nanosensors for the first time. We show that waveforms extracted for 3-week-old spinach across different experimental configurations, including 2D nIR imaging and 1D spectroscopy, are identical. Using this standardized approach, we systematically validate an analytical waveform model based on H 2 O 2 reaction-diffusion transport with a large waveform data set and extract the wave velocities and propagation rate constants from different waveforms. A wave-velocity-rate constant map is created for comparative studies. Results suggest that nanosensors can identify distinct waveforms associated with specific plant stressors with the proposed framework, providing opportunities for new diagnostic tools.
Why it matches plant phenotyping methods植物内H2O2シグナル波形をナノセンサーから抽出・定量解析する標準化手法を開発し、異なる画像・分光構成と大規模データで検証しているため、植物状態の測定法が中心である。
abstractHere, we develop a reference-less framework to extract stress-induced H 2 O 2 waveforms in planta directly from active nanosensors for the first time.
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
Summary statement Cotton ( Gossypium hirsutum ) drought sensitivity depends strongly on flowering stage, but stage‐resolved, non‐destructive detection remains limited. Using controlled short‐term droughts imposed at early, mid, or late flowering, we integrated multispectral and hyperspectral canopy phenotyping with physiology and explainable machine learning to identify spectral predictors of metabolic status and recovery. Early and mid‐flowering drought responses were largely recoverable, whereas late‐flowering drought caused the most potent and least reversible losses in photosynthesis, canopy structure, and fiber quality. These results highlight late flowering as a critical vulnerability window and provide a mechanistically grounded framework for rapid phenotyping of stage‐specific drought resilience.
Why it matches plant phenotyping methodsマルチスペクトル・ハイパースペクトルによる非破壊キャノピー表現型計測と説明可能な機械学習を中核に、乾燥耐性を迅速推定する方法・枠組みを提示している。
Bacterial grain rot (BGR), caused by Burkholderia glumae, is a major disease that reduces the yield of rice (Oryza sativa L.), thereby threatening food security. Conventional phenotypic analysis methods face limitations in objectively evaluating disease resistance and understanding the genetic basis. In this study, we integrated image-based phenotypic analysis with QTL mapping to screen for QTLs and candidate genes associated with B. glumae resistance. B. glumae was inoculated into 189 recombinant inbred lines (RILs) derived from Kele (resistant) and IS592BB (susceptible), followed by visualization and quantitative analysis using DAB staining. Phenotypic parameters, including the field resistance score, ratio of diseased spikelets (%), DAB staining intensity, and ratio of diseased area (%), were measured and used for QTL mapping. On chromosome 1, within Chr01_24592710-Chr01_37274755, four QTLs-qFRS1 [LOD: 5.98, phenotype variation explained (PVE): 15.41%], qRDS1 (LOD: 5.29, PVE: 18.56%), qQDS1 (LOD: 9.58, PVE: 22.02%), and qRDA1 (LOD: 8.44, PVE: 31.51%)-were identified as overlapping. After fine-mapping we narrow down Chr01_33472174-Chr01_33838140 and a total of 16 candidate genes were screened this region. Among which OsBGq1 was found to encode a nucleotide-binding LRR receptor (NLR) domain. OsBGq1 expression increased significantly upon B. glumae infection. Additionally, RILs Kele type of Chr01_33472174-Chr01_33838140 presented increased ROS-scavenging enzyme activity and phytoalexin accumulation upon B. glumae infection, contributing to increased resistance. The integration of DAB-based quantitative phenotyping with QTL mapping is proposed to provide a more objective indicator for identifying genes associated with resistance to BGR.
Why it matches plant phenotyping methodsイネ病害抵抗性の遺伝解析が主目的だが、DAB染色を用いた画像ベースの病徴定量化を主要な手法として統合し、客観的な抵抗性指標として提案しているため、表現型取得法の実質的応用に該当する。
abstractwe integrated image-based phenotypic analysis with QTL mapping to screen for QTLs and candidate genes associated with B. glumae resistance.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
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.
Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物体レベルのキャノピー衰退・枯死を抽出し、時系列解析と生存モデルで評価するフレームワークが研究の中心であり、性能検証も行っている。
abstractWe developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Pine trees, globally distributed and economically vital evergreen conifers, are threatened by pine wilt disease (PWD) attributed to the pine wood nematode (PWN). Many studies have been conducted on phenome and transcriptome profiling in select Pinus species upon PWN infection, but a high-throughput phenotyping of PWD progression and transcriptomic analysis across diverse Pinus species remains lacking. Here, we developed a deep learning-based phenotyping program to quantify PWD symptoms and conducted a pan-transcriptome analysis using PWD-susceptible ( Pinus densiflora , Pinus koraiensis , Pinus thunbergii ) and -resistant ( Pinus parviflora , Pinus strobus , Pinus rigida × Pinus taeda ) Pinus species and a hybrid. Our results showed severe wilting of leaves within 14 weeks after PWN infection in susceptible species but not in resistant ones. Pan-transcriptomic analysis revealed the upregulation of genes involved in leaf abscission and abscisic acid responses in PWD-resistant taxa, while PWD-susceptible taxa downregulated genes associated with desiccation response after PWN infection. These findings suggest that activating genes involved in water conservation plays a role in mitigating PWD infection in Pinus trees. Notably, all five Pinus species and one hybrid exhibited upregulation of the elongation factor Tu receptor ( EFR ) gene and pathogenesis-related (PR)-3 gene upon PWN infection, suggesting a potential role of the EF-Tu receptor in detecting PWN invasion and activating the PR-3 gene. Our study introduces a novel deep learning-based phenotyping program for precise PWD symptom quantification and enhances understanding of the molecular mechanisms underlying PWD resistance. These insights contribute to high-throughput monitoring of PWD progression in Pinus forests for disease prevention and facilitate the development of PWD-resistant pine trees.
Why it matches plant phenotyping methodsPWD症状を定量化する深層学習ベースの表現型解析プログラムを開発し、症状進行の高スループット測定に用いており、植物病害表現型の取得・抽出が研究の中心である。
abstractHere, we developed a deep learning-based phenotyping program to quantify PWD symptoms
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.
Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.
Why it matches plant phenotyping methods植物ストレス状態を測定する新規ChlF LiDARセンサーの設計・性能評価が研究の中心であり、植物での検証も行っているため。
abstractwe present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research.
Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.
Why it matches plant phenotyping methods植物ストレス状態を測定する遠隔クロロフィル蛍光センサーを設計・実証しており、表現型取得手法が研究の中心である。
abstractwe present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research.
Premise Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate. To overcome this limitation, we utilized existing literature data from various plant species and stress combinations to derive biological inferences, thereby gaining a comprehensive understanding of plant responses through a computational tool. Methods Public databases were used to gather literature on the impact of various abiotic and biotic stress combinations. Then, a composite artificial neural network (ANN)-based multi-target classification and regression deep learning model was developed using machine learning algorithms. Results The model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters, with an overall accuracy of 76.33%. Predicted reductions in yield were validated in rice under combined drought and heat stress. Discussion The ANN-based model developed in this study is a valuable resource for plant researchers seeking to understand the impact of stress combinations. The tool can make use of multivariate and complex combined stress datasets.
Why it matches plant phenotyping methods植物のストレス応答として形態形質や収量変化を予測するANNベースの計算ツールを開発し、イネで予測を検証しており、表現型推定が中心である。
abstracta composite artificial neural network (ANN)-based multi-target classification and regression deep learning model was developed using machine learning algorithms
Reproduction assets foundThe paper's ANN model code (scripts, Jupyter Notebooks, example datasets) is publicly available on GitHub, and the underlying morphological combined-stress phenotype dataset is publicly downloadable from SCIPDb. Supporting Information appendices contain raw/processed training data and validation data but no explicit作者-Code · publicnteraction in plants. Applications in Plant Sciences
14(2): e70047. 10.1002/aps3.70047
Piyush Priya, Prachi Pandey, Rubi Jain, and Manu Kandpal contributed equally to this work.
DATA AVAILABILITY STATEMENT
The scripts, Jupyter Notebooks, quick start guide, and example datasets used in this study are freely available at GitHub ( https://github.com/scipdatabase/Prediction_model ). The literature sources used for data extraction and for training the ANN model are provided in the Supporting Information. For details on various stress combinations and input data features, readers may refer to the Stress Combinations and their Interactions in Plants Database (SCIPDb) (Priya et al., 2023 ), availablOpen asset ↗scipdatabase/Prediction_modellines:392-432Dataset · public), Python package scikit‐learn v1.4.2 ( https://scikit-learn.org/stable/ ), and Google Tensorflow version 2.17.0 ( https://www.tensorflow.org/ ) were used to implement the deep learning model in this study.
Data mining
The SCIPDb FTP server was utilized to download the morphological dataset for 41 distinct stress combinations ( https://db.nipgr.ac.in/plant_complete/downloads.php ; accessed on December 2021) (Priya et al., 2023 ). The dataset integrated into SCIPDb has been obtained through literature mining performed by employing relevant and carefully designed keywords (Appendix S1 ). The major search engines (Appendix S2 ) and the inclusion of various keyword variants ensured comprehensiveOpen asset ↗lines:41-51Dataset · publicdel ). The literature sources used for data extraction and for training the ANN model are provided in the Supporting Information. For details on various stress combinations and input data features, readers may refer to the Stress Combinations and their Interactions in Plants Database (SCIPDb) (Priya et al., 2023 ), available at https://db.nipgr.ac.in/plant_complete/index_orangesunset.php .
REFERENCES
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, Lilley C. J., and Urwin P. E.. 2013. Identification of genes involved in the response of Arabidopsis to simultaneous bioticOpen asset ↗lines:392-432Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
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.
Climate change-driven bark beetle outbreaks pose severe threats to Norway spruce forests across Europe, yet early detection of infested trees remains difficult with conventional field surveys or satellite imagery. Existing detection methods, such as traditional and deep learning-based methods, require extensive time, training data, and computational resources while underutilizing dense temporal observations. We present an integrated UAV framework that merges deep learning-based tree detection with multitemporal spectral analysis for early, tree-level identification of bark beetle stress. A key novelty of this research is the use of high-frequency UAV time series to enable temporal change-detection methods such as CUSUM, providing scalable early-warning capabilities previously unattainable at individual-tree resolution. Firstly, a novel Fused YOLO-SAM pipeline was developed to automatically detect and delineate Norway spruce trees using UAV imagery collected over the mixed forests. Three YOLO v12 models were trained separately on summer (June), fall (November), and combined seasonal datasets acquired with a DJI Phantom drone having 5-band camera. The fall-trained model achieved the highest detection performance (F1 = 0.827, mAP50 = 0.916) and demonstrated superior cross-seasonal generalization, producing accurate and transferable crown segmentations when integrated with the Segment Anything Model (SAM). These automatically delineated spruce canopies provided a consistent and scalable base for individual tree-level spectral analysis. To discriminate between healthy and bark beetle-infested Norway spruce trees at the individual tree level, the delineated crowns were subsequently used to extract per-tree spectral time series for stress analysis. Radiometrically calibrated multispectral bands and vegetation indices were evaluated using cumulative sum (CUSUM)-based Receiver Operating Characteristic (ROC)-Area under Curve (AUC) and effect size metrics. The results demonstrated that calibration significantly improves early-season discrimination, with near-infrared and red-edge bands achieving stable performance (mean AUC ≈ 0.75-0.80; Cohen’s d ≈ 0.9-1.2). In contrast, vegetation indices such as NDRE and MSR-RE exhibited lower discrimination and greater temporal variability. This research also performed comparisons of calibrated multispectral features with uncalibrated RGB features. The analysis indicated that selected RGB indices (notably ExG and GCC) is comparable to the performance of the multispectral bands (REG, NIR), showing positive early discrimination and mean AUC values around 0.70-0.75 which can provide operational benefit for forest monitoring and management when the expensive multispectral cameras are unavailable. Overall, this research demonstrates a scalable and operationally feasible pipeline that integrates automated tree delineation with robust spectral stress diagnostics, supporting early bark beetle detection and informed forest management under resource and data constraints.
Why it matches plant phenotyping methodsUAV画像による樹冠抽出と時系列スペクトル解析を統合し、個体レベルのトウヒの樹皮甲虫ストレスを推定する手法が研究の中心であるため。
abstractWe present an integrated UAV framework that merges deep learning-based tree detection with multitemporal spectral analysis for early, tree-level identification of bark beetle stress.
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.
Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands.
Why it matches plant phenotyping methods植物の塩ストレス耐性をスペクトルデータから定量評価するマルチスケール表現型解析フレームワークの開発であり、特徴選択、分類検証、指標構築、センサー間融合が中心的な方法論的貢献である。
abstractthis study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice.
Abstract Soil salinization constrains agricultural productivity across approximately 950 million hectares worldwide. In the Yellow River irrigation district of Ningxia, secondary salinization severely depresses maize yields. Existing crop–water–salt models lack day-by-day bidirectional coupling between salt transport and crop growth, over-simplify salt stress representation, and amplify stress through multiplicative integration. To address these gaps, we developed a fully coupled WOFOST–HYDRUS-1D model linking the Richards equation and convection–dispersion equation with crop photosynthesis, transpiration, and assimilate partitioning through a modified Maas–Hoffman function. Salt stress is transmitted via three physiological pathways, and the combined stress factor is computed using Liebig’s law of the minimum. The model was calibrated with 43 sampling points spanning low-to-high salinity gradients (1.49–6.79 g kg⁻¹) in Huinong District during 2024, and independently validated with 30 points (1.29–7.75 g kg⁻¹) in 2025. Calibration yielded R² = 0.883, RMSE = 0.744 t ha⁻¹, NRMSE = 10.96%, and NSE = 0.795; validation gave R² = 0.824, RMSE = 0.753 t ha⁻¹, NRMSE = 11.13%, and NSE = 0.810, confirming strong inter-annual parameter stability. Under high salinity (> 4 g kg⁻¹), simulated mean yield declined to 3.75 t ha⁻¹, a 53% reduction compared with low-salinity conditions. Compared with the standard WOFOST model (R² = 0.450, RMSE = 1.399 t ha⁻¹), the coupled model substantially improved accuracy.These results show that the coupled model can improve yield prediction under salinity stress and provide a useful tool for irrigation scheduling and water–salt management in salinized farmland.
Why it matches plant phenotyping methodsWOFOST–HYDRUS-1Dの結合モデルを開発し、トウモロコシの生育・塩ストレス・収量を推定する手法として独立検証しており、植物状態の推定手法が中心である。
abstractwe developed a fully coupled WOFOST–HYDRUS-1D model linking the Richards equation and convection–dispersion equation with crop photosynthesis, transpiration, and assimilate partitioning through a modified Maas–Hoffman function.
With increasingly severe soil acidification, it is essential to screen and identify acid-tolerant crop varieties to safeguard agricultural production. Integration of hyperspectral imaging with machine learning models has been extensively used in high-throughput crop phenotyping. Here, we established a multi-indicator evaluation system for quantification of acidic tolerance in rapeseed based on hyperspectral data collected from 65 rapeseed varieties under pH = 5.2 (acidic) and pH = 6.4 (normal) soil conditions. After denoising and smoothing, 34 existing vegetation indices and band combination indices were derived from which eight growth-sensitive indices were selected based on their correlations with the actual growth scores. Six key spectral bands exhibiting significant changes under acidic stress were identified, with the feature importance outputs from three machine learning classification models. Comprehensive sensitivity coefficients (SC) were derived by integrating growth-sensitive indices and key band data using principal component analysis weighted-sum (PCA-WS). Hierarchical clustering classified the 65 tested varieties into strongly-tolerant (four varieties), moderately-tolerant (26 varieties), and weakly-tolerant (35 varieties). Physiological validation based on yield and branch number showed that the strongly-tolerant varieties had 10.03 % and 17.15 % higher relative yields and 13.24 % and 11.14 % higher relative branch numbers than moderately and weakly-tolerant varieties, respectively. These results have established a hyperspectral evaluation system that can accurately evaluate the acidic tolerance of rapeseed, providing a reliable basis for screening acid-tolerant rapeseed varieties.
Why it matches plant phenotyping methodsラペシードの酸性耐性をハイパースペクトル画像から定量評価する評価システムを構築し、特徴量選択・機械学習・検証まで行っており、植物表現型取得・抽出手法が中心である。
abstractIntegration of hyperspectral imaging with machine learning models has been extensively used in high-throughput crop phenotyping.
Lettuce ( Lactuca sativa ) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled G×E interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.
Why it matches plant phenotyping methods圃場レタスを対象に、縦断ハイパースペクトル画像と自動画像処理でスペクトル形質を抽出するフェノタイピング手法・データセットが研究の中心である。
abstractLongitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicScripts used in this study can be found at https://github.com/SnoekLab/Hyperspec_Mehrem_etal_2025.Open asset ↗SnoekLab/Hyperspec_Mehrem_etal_2025pdf-page:9 lines:1-31Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
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https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
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.
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.
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.
Background: Automated monitoring of crop health and the precise detection of abiotic stress, such as herbicide damage, are demanding challenges for modern agriculture. Abiotic stresses are a demanding challenge for modern agriculture, responsible for up to 82% of yield losses in major food crops. To address this, researchers are increasingly leveraging artificial intelligence (AI) to automate the detection and management of these stressors. Methods: In particular, this paper presents an instance segmentation framework to precisely detect interveinal chlorosis and leaf curling on potato leaves, two common symptoms of herbicide damage and soft wind. Within the context of precision agriculture and the need to address the inherent ambiguity in manual leaf assessment, this study employs a partial label learning approach to refine the dataset. This method utilizes an EfficientNet-b1 model to classify ambiguous samples, generating high-confidence pseudo-labels for instances that are difficult to categorize visually. The core of the proposed framework is a Mask2Former model, which is first fine-tuned on general potato leaf dataset to enhance its segmentation capabilities and then transferred on the refined, pseudo-labeled dataset. Results & Conclusions: This two-stage approach yields a highly accurate segmentation tool, achieving 89% mAP50 and a pseudo-label classification accuracy of 95%, designed for integration into smart agriculture systems like ground level robotics or unmanned aerial vehicles for real-time, automated crop monitoring.
Why it matches plant phenotyping methodsジャガイモ葉の症状（葉間クロロシスと葉巻）をインスタンスセグメンテーションで直接抽出する画像ベースの表現型測定法を開発・評価しており、手法が中心である。
abstractthis paper presents an instance segmentation framework to precisely detect interveinal chlorosis and leaf curling on potato leaves
SoybeanRootSeed / grainMorphology / geometry measurementObject detectionGrowth / development / phenologyRoot system architectureStress response / tolerance
Introduction Nanoparticle-induced treatments can promote seed germination and improve germination potential under environmental stresses such as drought and salinity. This study aimed to investigate the effects of Zinc oxide nanoparticles (ZnONPs) on soybean seed germination and to develop a precise evaluation method. Methods We developed a full-time sequence crop growth vitality monitoring system. Using germination rate and root length as primary evaluation indicators, we conducted full-time sequence germination vitality monitoring experiments on soybean seeds treated with ZnONPs. A dataset was constructed from images documenting embryonic root growth. The developed detection model was used to evaluate image detection accuracy during germination. Germination index and embryonic root length were also calculated. Further tests were performed on seeds exposed to 600 mg/L ZnONPs dispersion, followed by treatment with different concentrations of NaCl and PEG6000 solutions. Results At a concentration of 600 mg/L ZnONPs dispersion, soybean seeds showed the highest germination rate (an increase of 28%) and the longest radicle length (an increase of 42%). Compared with deionized water, the 600 mg/L ZnONPs dispersion accelerated initial germination time, increased germination rate, and enhanced radicle length under low-concentration stress. Discussion The results indicate that, at certain concentrations, ZnONPs dispersion positively influences soybean seed germination under varying salinity and drought conditions. We examined morphological and physiological changes in ZnONPs-treated seeds under stress, establishing a preliminary foundation for evaluating crop and variety vitality. These findings provide new insights that may contribute to improving soybean germination under simulated stress conditions, serving as a preliminary theoretical reference for potential applications in arid and saline environments.
Why it matches plant phenotyping methods発芽中の画像から発芽率・幼根長を抽出する連続モニタリングシステムと検出モデルを開発し、精度評価とデータセット構築を行っており、表現型取得手法が中心である。
Monitoring crop conditions is crucial for effective crop management and provides valuable insights into soil-plant-atmosphere interactions. While some studies have used unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards, LiDAR-based time-series analysis to assess PAI variations with phenology throughout the growing season represents a significant gap in knowledge. Tracking PAI dynamics across phenological stages reflects canopy development and leaf expansion, which are directly linked to yield formation. Furthermore, the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point clouds is yet to be determined. This study aimed to demonstrate the potential of UAV-derived LiDAR time-series to monitor the PAI and tree vertical profiles at high spatial resolution throughout the growing season of a cherry orchard located in southeastern France. A time series of 14 point cloud acquisitions with a density of 3300 points/m² was collected between February and December 2022, with at least one acquisition per month, covering all phenological stages of the cherry orchard. Field measurements were collected on May 30, and October 6, to measure the PAI at twilight using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA), with 248 trees sampled. A voxel-based method was applied on the LiDAR point cloud data to create a three-dimensional grid within which PAI was estimated for each voxel. The results showed that a voxel size of at least 70 cm is required to retrieve reliable PAI estimates, while a voxel size of 100 cm produced the most accurate PAI estimates (RMSE = 0.5 m2.m-2, bias = 0.07, R2 = 0.59), when assessed against in-situ PAI measurements. The temporal variation of canopy PAI illustrated the progression of the phenological stages, including flowering, leaf development, ripening and senescence, and the response of the canopy to drought stress (reduction in PAI due to leaf rolling) during the summer. The maps of PAI successfully described the variations in leaf canopy density for different cherry varieties and allowed assessment of the vertical PAI profile at the individual tree level. The LiDAR-derived PAI maps and vertical profiles were able to detect trees exhibiting poor leaf development, which is an important health indicator for effective crop management in orchard settings. Future work should focus on applying UAV-derived observations to optimize crop models to enhancing decision-making tools for effective orchard management.
Why it matches plant phenotyping methodsUAV LiDARとボクセル解析により、樹木レベルのPAIおよび垂直プロファイルを推定し、実測値で精度検証しているため、植物表現型取得手法が研究の中心である。
abstractA voxel-based method was applied on the LiDAR point cloud data to create a three-dimensional grid within which PAI was estimated for each voxel.
Field / plotStem / branchStomata / guard-cell complexPhysiological trait estimationGrowth / time-series analysisStomatal traitsStress response / toleranceWater status / transpiration
Droughts have emerged as the primary driver of forest disturbances across Europe in the 21st century, significantly impacting both tree growth dynamics and mortality rates. Tree species are differently affected under drought, and these differences are related to species-specific plant hydraulic traits that govern water storage, hydraulic conductivity, and stomatal regulation. However, quantifying variability in these hydraulic traits across sites, species, and time remains challenging, as site measurements have historically rarely been comprehensive enough to assess the evolution of plant hydraulic behavior under drought stress. New continuous, high temporal resolution observational plant hydraulic data paired with process-based plant hydraulic modelling opens an opportunity to address this gap, by providing a framework to test and quantify theories based on first principles across species and sites.In this study, we apply the terrestrial biosphere model QUINCY, augmented by a recently developed plant hydraulic architecture module, across three eddy covariance sites in Germany covering broadleaved forest species (Aplern, Hainich, and Hartheim). The model is parameterized for three common temperate tree species present at the aforementioned sites. We constrain QUINCY across these species and sites using 30-minute resolution stem water potential measurements collected during the summer and autumn of 2023. Our results show that two groups of model parameters explain most of the simulated plant water potentials: parameters controlling plant water uptake from soil (plant ability to extract water from soil and the root distribution), and parameters regulating stomatal sensitivity to pre-dawn leaf water potential. Across species, we find ash to be more drought resistant than beech and hornbeam, as it closes its stomata earlier than other species under similar levels of drought stress, and it is characterised by a higher hydraulic capacitance per unit stem volume. Our study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters, effectively reducing uncertainty in, and providing robust constraints on, modelled responses to drought.
Why it matches plant phenotyping methods植物の水理状態・水理形質を連続観測と拡張モデルで定量化する手法の適用が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractOur study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters
Climate change is intensifying soil moisture variability, atmospheric evaporative demand, and salinity intrusion in agricultural landscapes, creating new challenges for sustainable food production. Understanding how soil hydrology and plant physiological stress interact under these conditions is essential for designing resilient irrigation strategies. This study presents a hydro-physiological assessment of wheat and maize grown under controlled combinations of soil salinity and deficit irrigation, and introduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support in semi-arid farming systems of northern India.Field experiments (2023–2025) were conducted to measure canopy temperature, air temperature, relative humidity, vapor pressure deficit (VPD), and soil moisture under varying salinity (EC levels) and irrigation regimes. These data were used to develop whole-season and stage-specific ANN models capable of capturing non-linear interactions between soil hydrology, crop physiology, and atmospheric demand. The ANN-based CWSI successfully distinguished mild-to-severe stress transitions and detected early-stage water stress acceleration during periods of high VPD, indicating a propensity toward flash drought development under combined salinity–moisture constraints.Results show that salinity amplifies crop water stress by reducing effective root-zone moisture availability, leading to higher canopy–air temperature gradients and elevated CWSI values even under moderate irrigation. Stage-specific ANN models achieved strong performance (R² = 0.87–0.94), particularly during flowering and grain filling, where hydrological stress most affects yield. The framework demonstrates how data-driven CWSI modeling can translate complex soil–plant–atmosphere interactions into actionable irrigation insights for farmers.This work highlights a scalable approach to precision irrigation scheduling, enabling reduced water use without compromising crop health in regions vulnerable to hydrological extremes and sociohydrological pressures. By linking soil hydrology, irrigation management, and physiologically informed stress indicators, the study contributes to sustainable food production strategies in a global climate change context.
Why it matches plant phenotyping methodsANNによる作物水ストレス指標（CWSI）の開発と性能評価が中心で、キャノピー温度などから植物の生理的ストレス状態を推定している。
abstractintroduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support
This paper investigated the feasibility of snapshot multispectral fluorescence imaging for nondestructive identification of cold stress in pepper plants. Fluorescence spectra were obtained by exciting the plant with a 405 nm ultraviolet LED. The plants were grown under three temperature conditions: 17 °C (control), 10 °C (moderate cold stress), and 5 °C (severe cold stress). Raw fluorescence spectra extracted from the demosaiced snapshot images were used as inputs for a deep-learning pipeline consisting of feature extraction, an encoder-decoder GRU, and a multilayer perceptron (MLP), and the results were compared with conventional machine learning classifiers, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and a Gaussian support vector machine (G-SVM). Tukey's HSD test indicated that the proposed deep-learning model achieved the highest cross-validation accuracy and consistently produced superior classification metrics (accuracy of 85.7%, precision of 85.3%, recall of 85.3%, F1-score of 85.2). The trained model was further applied to hyperspectral cubes to generate classification maps; however, moderate misclassification was observed, consistent with the overall prediction performance.
Why it matches plant phenotyping methodsスナップショット多波長蛍光画像からピーマンの低温ストレス状態を推定する画像取得・深層学習手法が研究の中心であり、性能比較と検証も実施している。
abstractThis paper investigated the feasibility of snapshot multispectral fluorescence imaging for nondestructive identification of cold stress in pepper plants.
A dynamic analysis of plant ion homeostasis is imperative for elucidating the mechanisms underlying stress resistance and enabling precision agriculture. However, conventional detection methods face challenges in simultaneously meeting the synergistic demands of in situ , real-time, minimally invasive, and high-resolution monitoring, which severely restricts advancements in this field. Microneedle sensing technology, with a focus on the "microneedle body-sensing unit-signal transmission" architecture, represents a significant paradigm shift from ex vivo destructive detection to in vivo minimally invasive dynamic monitoring. This article employs a systematic approach to elucidate the scientific implications and implementation challenges associated with in situ decoding of plant ionic signals. The proposed framework traces the technological evolution of microneedle sensing across the sensing, detection, and system layers, elucidating how it progressively overcomes key bottlenecks, including minimally invasive adaptation, specific recognition, and long-term, stable monitoring. Furthermore, typical applications of this technology in signal transduction analysis, stress-resistant variety screening, and precision field management are reviewed. It analyzes core challenges from two perspectives: fundamental research and industrialization. Ongoing advancements in material intelligence and system integration will position MN sensing technology as an indispensable tool in smart agriculture and plant physiology research. This technology provides critical support for addressing global food security challenges.
Why it matches plant phenotyping methods植物イオン状態を対象とするマイクロニードルセンシングの原理・実装・応用・課題を体系的にレビューしており、植物の生理状態を非侵襲・リアルタイムに取得するセンシング手法が中心である。
abstractMicroneedle sensing technology, with a focus on the "microneedle body-sensing unit-signal transmission" architecture, represents a significant paradigm shift from ex vivo destructive detection to in vivo minimally invasive dynamic monitoring.
The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products—food, feed, fuel and fiber—will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, commonly known as duckweeds, are one family of plants that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab‐scale PHenotyping Apparatus, ALPHA, for high‐throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of six different clones of L. gibba .
Why it matches plant phenotyping methods水生植物の成長率をハイスループットに定量するフェノタイピング装置を開発・適用しており、表現型取得システムが研究の中心である。
abstractHerein we describe the Automated Lab‐scale PHenotyping Apparatus, ALPHA, for high‐throughput phenotyping of Lemnaceae.
Abstract Agriculture plays a vital role in global food security, and maintaining plantation health is essential for maximizing productivity and sustainability. Traditional plantation monitoring methods rely heavily on manual field inspections, which are time-consuming, costly, and often inaccurate due to human limitations. Automated plantation health monitoring has emerged as a promising solution due to the quick development of machine learning and remote sensing technology. This study suggests a machine learning-based system for automatically monitoring plantation health using drone-acquired data and sophisticated image processing techniques. To categorize plantation areas as healthy, stressed, or diseased, the system examines the visual and spectral characteristics of crops. While vegetation indices like NDVI help measure plant vitality, a convolutional neural network (CNN) is used to directly understand complicated patterns from photos. According to experimental results, the suggested method achieves excellent classification accuracy and reliability, which qualifies it for widespread use in agriculture. Precision agriculture techniques are supported, early disease diagnosis is made possible, and labor effort is decreased.
Why it matches plant phenotyping methodsドローン画像・スペクトル情報から作物の健全・ストレス・病害状態を推定する機械学習／画像処理システムが研究の中心であり、植物病害状態のフェノタイピング手法に該当する。
abstractThis study suggests a machine learning-based system for automatically monitoring plantation health using drone-acquired data and sophisticated image processing techniques.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Hydrogen peroxide (H 2 O 2 ) functions as a key signaling molecule in plants responding to stress. Although numerous detection methods have been developed, simple and non-destructive techniques for the semi-quantitative monitoring of H 2 O 2 in plant tissues remain scarce. Methods In this study, we developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues, and conducted spectroscopic and in vivo toxicity tests. Furthermore, under experimentally controlled stress conditions, we utilized this probe to detect H 2 O 2 levels in four distinct plant types exposed to salt, waterlogging, cadmium, and drought stresses. Additionally, H 2 O 2 was detected in a grafting model under non-experimentally controlled stress conditions. Results The results showed that the probe demonstrated excellent selectivity, a strong linear correlation (R2 = 0.9849), and a low detection limit of 0.6450 μmol/L. Importantly, it exhibits good biocompatibility with plant tissues and effectively minimizes detection errors caused by transient H 2 O 2 fluctuations induced by environmental changes. Consequently, it provides more accurate and stress-reflective H 2 O 2 measurements. Under experimentally controlled stress conditions, the changes in relative fluorescence intensity conformed to the typical response patterns observed when plants experience graded levels of stress. Notably, even under complex grafting conditions without imposed stress gradients, applying the probe to bottle gourd (Lagenaria siceraria) rootstocks with different graft compatibility produced fluorescence dynamics consistent with the typical H 2 O 2 responses of compatible and incompatible rootstocks, and the distribution of relative fluorescence intensity within the population underscored the importance of prescreening plants for biological studies. Pearson correlation and Bland-Altman analyses confirmed good agreement between our method and the commercial assay kit. Discussion These results demonstrate that the LWS probe enables H 2 O 2 detection and, in combination with the IVIS in vivo imaging system, can screen individual plants differing in stress responses more effectively than other sensors. This non-destructive approach preserves the structural integrity of plant samples, enabling follow-up physiological, biochemical, and genomic analyses on the same specimens. This method provides a reliable prescreening platform for investigating plant stress responses at the biological level.
Why it matches plant phenotyping methods植物組織中のH2O2を非破壊的・半定量的に検出する蛍光プローブとIVIS画像化を開発し、性能検証およびストレス応答個体のスクリーニングに応用しており、植物状態の取得手法が中心である。
abstractwe developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues
Breeding rice varieties that are both salt-tolerant and high-yielding is essential for utilizing saline-alkaline lands and ensuring food security. However, However, high-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs. In this study, unmanned aerial vehicle (UAV) imaging was employed to screen salt-tolerant and high-yielding varieties among 60 rice varieties under saline-alkaline field conditions. Red-green-blue (RGB), multispectral, and thermal canopy images were acquired throughout the growing season by UAV, from which 41 phenotypic traits were extracted at each growth stage. These traits were categorized into early-stage (tillering and jointing), late-stage (booting, flowering, and maturity), and whole-growth-stage (from tillering to maturity) and subsequently used to screen salt-tolerant and high-yielding rice varieties. Results showed that: (1) An early high-throughput screening method for salt-tolerant rice varieties was developed based on the membership function and UAV phenotypes (MFuav), achieving high performance (Precision >0.8, OA > 0.7). MFuav demonstrated the highest accuracy at the early-stage, with Precision increasing by 0.29 and 0.43 compared to the late- and whole-stage models, respectively. (2) A machine learning based UAV phenotypes framework (MLuav) was developed to further improve salt-tolerance screening performance. Within this framework, the partial least squares regression (PLSR) was employed for early-stage salt-tolerance screening, which achieved a Precision of 0.97 and an OA of 0.78, outperforming the MFuav by 0.11 and 0.08, respectively. In addition, within the same MLuav framework, early-stage UAV phenotypes were further used for actual yield prediction using a Random Forest (RF) model. The model achieved a high Recall for high-yielding varieties (Recall = 1.00), ensuring that no potentially high-yielding germplasm was missed, although this was accompanied by a moderate Precision (0.51) and an overall accuracy of 0.70. (3) The MLuav consistently outperformed the MFuav in screening salt-tolerant and high-yielding varieties across all 60 rice varieties. Among the five referenced salt-tolerant and high-yielding rice varieties, the MLuav correctly screened four using early-stage phenotypes, whereas the MFuav only screened three. Overall, the proposed method enables early screening of salt-tolerant and high-yielding rice varieties, offering an efficient tool for the screening and utilization of elite stress-resilient germplasm.
Why it matches plant phenotyping methodsUAV画像から多数のイネ表現型形質を抽出し、塩耐性・収量性を早期スクリーニングする方法と機械学習フレームワークを開発・評価しており、表現型取得・解析手法が研究の中心である。
abstracthigh-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs
Aim of study: To identify different levels of devitalization in individuals of Abies religiosa (Kunth) Schltdl. & Cham. (oyamel) in the core zone of the Monarch Butterfly Biosphere Reserve (MBBR), using multispectral and thermal information captured with drone. Area of study: The study area, comprising 26 ha, is located within the federal property of the MBBR. This area also belongs to the core zone of the Protected Natural Area. Material and methods: Overflights were conducted with drones equipped with multispectral and thermographic sensors in monitoring plots. Using high-resolution images captured by drone and photogrammetric processing, the normalized difference vegetation index (NDVI), surface temperature (ST), and temperature-vegetation dryness index (TVDI) were calculated. These values were combined with field data to determine three different levels of tree devitalization (i.e., healthy, devitalized, and dead trees). Main results: The results demonstrated that this technology can be used to statistically distinguish the different levels of devitalization in oyamel individuals. Research highlights: This is the first research in Mexico that use Thermal and Multiespectral high resolution information related with field data applied to Abies religiosa and provides a methodological precedent for identify forest decline symptoms.
Why it matches plant phenotyping methodsドローン搭載のマルチスペクトル・熱赤外センサーと画像解析で樹木の活力度を推定し、健全・衰弱・枯死を識別する手法が研究の中心である。
abstractUsing high-resolution images captured by drone and photogrammetric processing, the normalized difference vegetation index (NDVI), surface temperature (ST), and temperature-vegetation dryness index (TVDI) were calculated.
Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions.
Why it matches plant phenotyping methods衛星・UAV画像、NDVI差分、樹冠セグメンテーションを用いて森林の個体レベル損傷を抽出する手法が研究の中心であるため。
abstractThis study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation.
Unmanned aerial vehicle (UAV) remote sensing has evolved from experimental imaging into an operational diagnostic infrastructure supporting climate-smart agriculture through high-resolution, flexible, and timely crop observation. This review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion to evaluate their combined potential for monitoring heterogeneous smallholder systems. A PRISMA-guided analysis of 59 studies (2013–2024) classified sensing architectures, analytical approaches, and application domains across diverse agroecological contexts. Integrated UAV–AI frameworks improve detection of crop stress, yield variability, biomass distribution, and phenological dynamics compared with conventional monitoring, particularly when multimodal sensor data are fused with satellite and ground observations. Predictive performance and diagnostic reliability increase when spectral, thermal, and structural datasets are analyzed jointly using machine-learning or deep-learning models. However, scalability remains constrained by operational, infra-structural, and regulatory factors, especially in resource-limited systems. These findings demonstrate that integrated sensing–analytics systems form a critical foundation for scalable climate-smart agricultural transformation and data-driven decision support across farm, landscape, and institutional scales.
Why it matches plant phenotyping methodsUAVセンシングとAIによる作物ストレス、収量変動、バイオマス、フェノロジーの観測・推定技術を体系的にレビューしており、植物形質・状態の取得方法が中心である。
abstractThis review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion
Brown planthopper (BPH) is a serious rice pest that threatens global food security by causing yield losses of up to 80%. Conventional methods for assessing BPH infestation are labour-intensive and lack real-time precision. This study evaluates hyperspectral remote sensing as a rapid, non-invasive approach for quantifying BPH population severity in three rice varieties: Pusa Basmati-1509, Pusa Basmati-1121, and TN-1. Leaf-level spectral measurements (350–2500 nm) acquired using a portable spectroradiometer effectively differentiated BPH population severity levels. Among 28 spectral indices evaluated, Structural Insensitive Pigment Index (SIPI), Pigment Specific Normalized Difference Index (PSND) for chlorophyll b, Pigment Specific Simple Ratio (PSSR a) for chlorophyll a, and (PSSR b) for chlorophyll b, showed high sensitivity to BPH infestation. Multivariate Regression models, including Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Random Forest (RF), were developed for severity prediction. Among the tested models, RF achieved the highest accuracy for vegetation indices-based estimation (R2 = 0.99), while PLSR showed strong relationships between hyperspectral data and BPH population severity (R2 = 0.62) and key biochemical parameters, including chlorophyll (R2 = 0.84), carotenoids (R2 = 0.77), and protein (R2 = 0.84). In contrast, flavonoids exhibited weak predictability (R2 = 0.34). Field validation confirmed model robustness, with vegetation index-based predictions achieving R2 values ranging from 0.72 to 0.86. Overall, the results demonstrate the potential of hyperspectral sensing combined with machine learning for early, non-destructive detection and monitoring of BPH stress, supporting precision pest management in rice.
Why it matches plant phenotyping methodsイネ葉のハイパースペクトル計測と機械学習により、害虫被害の重症度や関連する植物生理形質を推定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractThis study evaluates hyperspectral remote sensing as a rapid, non-invasive approach for quantifying BPH population severity in three rice varieties
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Plants are essential for global food security and ecosystem stability, exhibiting high sensitivity to environmental fluctuations and both biotic and abiotic stressors. Consequently, monitoring plant health is critical for advancing sustainable agriculture and optimizing resource management. However, traditional methods often rely on destructive sampling or intermittent measurements, failing to capture dynamic physiological processes. Recent developments in flexible wearable sensors enable real-time, in-situ, and non-invasive monitoring of key plant physiological parameters, offering new opportunities for plant phenotyping and precision agriculture. This review summarizes recent advances in material design and interface engineering of plant wearable sensors, focusing on their role in establishing biotic-abiotic interfaces for reliable signal acquisition and their applications in monitoring plant physiological signals and stress responses. Finally, we outline key challenges related to field stability and data integration, envisioning these sensors as pivotal components of intelligent environmental monitoring networks that promote sustainable agriculture and safeguard ecosystem health.
Why it matches plant phenotyping methods植物の生理状態・ストレス応答を取得するウェアラブルセンサー技術を中心に扱う、植物フェノタイピング手法のレビューである。
abstractRecent developments in flexible wearable sensors enable real-time, in-situ, and non-invasive monitoring of key plant physiological parameters, offering new opportunities for plant phenotyping and precision agriculture.
The nucleus is the characteristic organelle for eukaryotic organisms. Unlike the classic textbook view of static two-dimensional nuclei, nuclear shape is dynamic inside the live cell. The alteration or deformed nuclear shape is the hallmark of cancer in animal cells and environmental stress in plants. The nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, we have limited knowledge about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To circumvent this issue, we are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root. The live cell imaging was performed in control and salt-stressed conditions. We utilized these captured movies to analyze through open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. Using this method, we have demonstrated that chromatin velocity is decreased in salt-treated conditions. This method will be widely applied to quantitative live cell imaging of nuclear shape and chromatin dynamics during plant development and environmental stress. Summary This process aims to simultaneously record nucleus and chromatin dynamics in Arabidopsis thaliana roots and investigate changes in these dynamics in response to developmental and environmental cues.
Why it matches plant phenotyping methods植物の核形状・クロマチン動態をライブイメージングと画像解析で定量化する手法が中心であり、環境ストレス下の植物状態を測定する再利用可能なワークフローを提示している。
abstractwe are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root.
MaizeRiceSoybeanField / plotMultimodalLiDAR / point cloudRGB / grayscaleMultispectral / hyperspectralThermalWhole plant / canopy / plot / field
Plant phenotyping is essential for elucidating genotype–environment interactions, yet conventional methods remain labor-intensive and low-throughput. TraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform. Aligned with the ”Plant Phenotyping Trinity” framework, the system comprises a millimetre-accurate triaxial automation unit, a modular sensor array–RGB imaging, three-dimension laser scanner or LiDAR (3D), infrad (IR) thermal imaging, hyperspectral imaging (HSI), and photosynthesis (PS) imaging–and the dedicated software TraitNavigator suite into one cohesive system. A unified spatiotemporal synchronization mechanism enables robust time-series analysis and fusion of multisource phenotypic data across the entire crop growth period, while the DepthCropSeg algorithm and a night-time imaging module enhance trait extraction under complex conditions, providing G × E × P-ready, multimodal phenotypic datasets. Validation across soybean, maize, and rice trials demonstrated high sensitivity—detecting drought stress four days before visible symptoms, identifying glyphosate injury 24 hours ahead of manual scoring, and quantifying local adaption patterns across ecological gradients. While challenges remain in scaling to complex open-field conditions, TraitDiscover offers a scalable, data-driven approach to accelerate stress phenotyping and breeding decisions and is readily poised for deeper integration with AI to advance sustainable agriculture.
Why it matches plant phenotyping methodsマルチモーダルセンシング、画像解析、同期機構、形質抽出アルゴリズムを統合した植物フェノタイピング基盤の開発と検証が中心であり、ストレス検出や形質定量も実証している。
abstractTraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform.
Rice seedling stems are particularly vulnerable to structural damage during the seedling separation phase of mechanical transplanting, especially under non-ideal plant-machine interactions. Owing to its internal and transient nature, such damage is inherently difficult to quantify or predict. This study presents a novel modelling framework for stem damage assessment, which establishes a quantitative relationship between the maximum impact load (Fₘₐₓ) during seedling separation and internal damage severity, quantified by the damaged area ratio (Dₐᵣ). High-speed imaging and triaxial force sensors were employed to measure Fₘₐₓ across seedlings aged 20, 30 and 40 d under varying transplanting speeds. Microscopic cross-sections of stems were analysed to calculate Dₐᵣ. A composite impact force model, incorporating stem bending rigidity, lateral needle–stem offset and contact duration, was developed to support experimental design. A strong positive correlation was observed between Fₘₐₓ and Dₐᵣ across all seedling age groups (ρ > 0.93, p 8 %. Age-specific linear regression models achieved high predictive accuracy and good calibration (cross-validated R² of 0.86–0.91; RMSE of 0.33–0.73 percentage points in Dₐᵣ), while extending these models with a restricted cubic spline further reduced errors in the upper damage tail. This framework offers theoretical insights into age- and speed-dependent stem damage and practical tools for optimising transplanting parameters and supporting real-time, damage-aware control strategies to mitigate mechanical damage risk and improve seedling survival and post-transplant performance.
Why it matches plant phenotyping methods稲苗の茎損傷を画像・力センサー・断面解析で定量化し、予測モデルを開発・検証しており、植物状態の取得・推定方法が研究の中心である。
abstractThis study presents a novel modelling framework for stem damage assessment