Plant phenotyping methods.

Vision Transformers Enable Advanced Plant Phenotyping in Controlled Environments →

Reliable plant segmentation in high-throughput phenotyping must transfer across species and imaging conditions without repeated model tuning or extensive reannotation. We compare three segmentation strategies using images from Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory: (i) fixed color-based thresholding, (ii) supervised U-Nets trained from scratch, and (iii) pretrained vision transformers fine-tuned for binary segmentation. Models were evaluated on a held-out test set and a generalization set that comprised unseen species. On the held-out test set, thresholding, the best U-Net, and the best vision transformer achieved mean Dice scores of 58.3, 96.6, and 97.3, respectively. On the generalization set, the corresponding Dice scores were 56.5, 86.2, and 95.7. Thresholding remained effective on some datasets but failed when plant appearance changed. Supervised U-Net training resolved within-distribution errors but failed to generalize to novel species and backgrounds. Pretrained vision transformers consistently produced high-accuracy segmentations across the evaluated species, views, soil backgrounds, and tray types. These results benchmark the practical progression from fixed rules to task-specific supervision and pretrained visual representations for controlled-environment plant phenotyping.

Why it matches plant phenotyping methods植物フェノタイピングにおける画像セグメンテーション手法を比較・検証し、異なる種や撮像条件への汎化性能をベンチマークしているため、方法が研究の中心である。

abstractReliable plant segmentation in high-throughput phenotyping must transfer across species and imaging conditions without repeated model tuning or extensive reannotation.

From occlusion to 3D: amodal completion-assisted single-view wheat reconstruction →

Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. We construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW, which is captured under controlled indoor scenarios, by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD- \(L_1\) and CD- \(L_2\) values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for 3D wheat phenotyping under occlusion and provides a systematic reference for applying 3D generative models to agricultural phenotyping.

Why it matches plant phenotyping methods遮蔽下の単一画像から小麦器官を3D再構成し、形質推定精度を改善する手法を開発・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。

abstractTo address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat.

Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth →

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

Temporal profiling of floret developmental asynchrony for wheat-fertility studies. →

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

A multi-scale dataset combining 3D plant architecture, leaf gas exchange, and whole-plant fluxes in young oil palm under controlled climate scenarios →

Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO 2 and H 2 O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO 2 concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.

Why it matches plant phenotyping methods3D LiDARによる植物構造計測と生理計測を統合したデータセットで、モデルの較正・ベンチマーク・評価を主目的としており、植物フェノタイピング手法と再利用可能なワークフローが中心である。

abstractthree-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds

Utilizing Vegetation Indices Derived from VNIR-SWIR Hyperspectral Data to Characterize Growth, Maturation, and Senescence in Wheat and Barley →

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.

Why it matches plant phenotyping methodsRGB画像、赤外線サーモグラフィー、VNIR–SWIRハイパースペクトルを統合した高スループット表現型解析ワークフローが中心的に記述され、複数の植物形質・状態を定量化している。

abstractA high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence.

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

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

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

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

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

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

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

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

AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize →

Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.

Why it matches plant phenotyping methods葉の気孔・葉脈形質を自動画像解析で同時定量する高スループット表現型解析プラットフォームが研究の中心であり、形質抽出手法も具体的に記述されている。

abstractusing a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis

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

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

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

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

Early detection of plant pathogens in the asymptomatic phase: A scoping review of hyperspectral imaging combined with machine learning →

• First PRISMA-ScR mapping of 79 HSI-ML asymptomatic detection studies (42 species, 74 pathogens) • Controlled-to-field accuracy gap quantified: 91.4% vs. 86.3% (5.1 pp, p = 0.0163 ) • 56.8% of studies omit temporal sampling documentation (CV = 139%) • SWIR underutilization (11.8%) reflects economic, not scientific, barriers • DBVS proposed as standardized temporal metric for cross-study comparability Plant disease management requires non-invasive detection methods capable of identifying infections before visible symptom manifestation, thereby enabling timely intervention. Hyperspectral imaging combined with machine learning and deep learning (HSI-ML) achieves 90.2% classification accuracy in controlled environments for asymptomatic plant detection; however, systematic characterization of methodological practices across this rapidly expanding field remains absent. This PRISMA-ScR compliant scoping review mapped 79 peer-reviewed studies (2010–2025) encompassing 42 plant species and 74 pathogenic agents using a Population-Concept-Context framework. Visible-near-infrared (VNIR) systems dominated deployment (61.8%, n = 49 ), while short-wave infrared (SWIR) systems remained substantially underutilized (11.8%, n = 9 ) due primarily to economic rather than scientific constraints. Among 67 unique algorithms identified, machine learning methods accounted for 30.7% (SVM, random forests, and PLS-DA predominant), whereas deep learning represented 28.4% (2D-CNN, 3D-CNN, and hybrid architectures). Critical methodological gaps emerged: 56.8% of studies omitted temporal sampling documentation (detection latency range: 1–56 days post-inoculation; coefficient of variation = 139%). Platform-stratified analysis revealed controlled environments achieved 91.4% ± 6.6% classification accuracy ( n = 48 ) versus 86.3% ± 9.1% for field/UAV deployments ( n = 26 ), representing a significant 5.1 percentage-point performance decrease ( p = 0.0163 ). Detection accuracy exhibited a weak negative correlation with detection timing ( ρ = − 0.33 , p = 0.067 ), though this association did not reach conventional statistical significance. Methodological heterogeneity—rather than algorithmic limitations—constitutes the primary barrier to field operationalization. Adoption of Days Before Visible Symptoms (DBVS) as a standardized temporal metric could resolve an estimated 40–50% of cross-study variance currently attributed to inconsistent asymptomatic-phase definitions.

Why it matches plant phenotyping methods植物病害の無症状感染をHSIと機械学習で検出する手法群を対象に、79研究の方法、精度、時間指標、標準化課題を体系的に評価したレビューであり、フェノタイピング手法が中心です。

titleEarly detection of plant pathogens in the asymptomatic phase: A scoping review of hyperspectral imaging combined with machine learning

GREENTRIBE: A modular multi-sensor high-throughput plant phenotyping framework for indoor facilities →

Indoor high-throughput plant phenotyping (HTPP) platforms require flexible, interoperable architectures to support reproducible trait acquisition across changing controlled-environment experiments. This paper presents GREENTRIBE, a modular multi-sensor indoor HTPP framework that integrates sensing, robotic coordination, data communication, semantic data management, and crop modelling within a unified phenotyping pipeline. Rather than treating these elements as independent modules, GREENTRIBE connects distributed sensor acquisition, robot-assisted operation, lightweight message exchange, ontology-based data organization, and process-based model assimilation through a layered architecture implemented with CAN, ROS 2, MQTT, OpenSILEX, and STICS. The platform combines a multiscale sensing network with a sensor-independent communication protocol, enabling heterogeneous imaging, environmental, and plant-monitoring devices to be configured within indoor experimental designs. To support traceable and reusable workflows, GREENTRIBE implements an ontology-driven data management layer aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) metadata standards. The architecture further links computer vision and artificial intelligence pipelines with the STICS crop model, allowing multimodal observations to be transformed into biologically interpretable phenotypic information under explicit genotype, environment, and management contexts. Platform validation demonstrated reliable communication, efficient multimodal data handling, and standardized metadata management across the sensing-to-information pipeline. Under the configured acquisition schedule, GREENTRIBE achieved a maximum full data cycle of approximately 200 ms, no measurable losses up to the CAN master, high metadata completeness, and support for 1704 scheduled daily acquisition events from seven devices. Overall, GREENTRIBE provides a modular and interoperable indoor phenotyping framework for reproducible experiments and standardized multimodal phenotypic data generation.

Why it matches plant phenotyping methods植物フェノタイピングのための多センサーHTPP基盤を中心に開発・統合し、通信性能、データ処理、メタデータ管理を検証しているため。

abstractThis paper presents GREENTRIBE, a modular multi-sensor indoor HTPP framework that integrates sensing, robotic coordination, data communication, semantic data management, and crop modelling within a unified phenotyping pipeline.

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

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

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

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

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

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

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

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

Why bother with controlled-environment phenotyping when field phenomics is already up and running? →

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?

Comparative Evaluation of Wheat Cultivars for Root Architecture Traits Based on Rhizo-Vision Explorer Measurement Under Water Stress →

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.

Comparative Evaluation of Wheat Cultivars for Root Architecture Traits Based on Rhizo-Vision Explorer Measurement Under Water Stress →

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.

Tillering structures the genotypic variability of wheat vegetative growth and its plasticity under water deficit. →

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.

EDISP: a hybrid CNN-ViT framework for robust maize leaf disease detection and classification. →

Introduction Maize is one of the most important food crops in the world, and foliar diseases can lead to significant yield losses if identification is not performed on time. Experts conducting manual inspections find it less effective and more subjective. Deep learning-based approaches utilizing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been demonstrated as a viable approach to automate disease diagnosis. Thus, while CNNs fail to capture wider context due to their local feature focus and ViTs need larger datasets and tend to miss finer-grained details. To overcome these limitations, we present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning. Methods The EDISP framework brings together the strengths of CNNs and ViTs to overcome their individual weaknesses. It is trained on a dataset that includes both controlled-environment and real-field maize leaf images, which helps it handle different environmental conditions. The data undergoes thorough preprocessing, including normalization, augmentation, and stratified splitting into training, validation, and test sets to support generalization. The CNN focuses on detailed local disease features, while the ViT captures broader contextual information across the maize leaf surfaces. Results The proposed EDISP model significantly outperforms standalone CNN and ViT Models in multiple performance metrics, achieving an overall classification accuracy of 99.40%, precision of 99.43%, recall of 99.38%, and an F1-score of 99.40%. Experimental results demonstrate that EDISP excels in identifying maize leaf diseases, including Common Rust, Gray Leaf Spot, Northern Leaf Blight, and Healthy leaves, with minimal false positives and negatives. External validation with an independent dataset further highlights the model's robustness and ability to generalize to real-world conditions. Discussion The EDISP hybrid architecture, integrating CNNs and ViTs, provides a stronger method for accurate, automated maize leaf disease detection. Its robust performance, consistent results on controlled and field datasets shows robustness in diverse environments. However, EDISP's effectiveness may be limited by image quality, lighting, or disease types not seen in training. These results highlight the promise of hybrid deep learning in precision agriculture and offer a scalable solution for disease detection, supporting farmers without expert diagnostic resources.

Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から検出・分類するCNN-ViT手法の開発と独立データセットによる検証が研究の中心であり、植物フェノタイピング手法に該当する。

abstractwe present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning.

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

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

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

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

A multiregional image–text dataset and benchmark for vision-language modeling of plant diseases →

Plant diseases remain a major challenge to global food production, and timely, accurate, and scalable detection of plant stress is critical to reducing these losses. Recent advances in digital imaging and artificial intelligence offer unprecedented opportunities for precision crop disease detection and management. Yet, existing plant disease datasets remain often fragmented across crop and disease systems, and are largely dominated by controlled-environment imagery. The lack of standardized, interoperable, and representative datasets limits reproducibility, transferability, and scalability of AI systems, thereby constraining their deployment in operational agricultural applications. Here we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource that includes LeafNet 2.0, a large-scale multimodal digital image dataset comprising 255,855 image–text pairs across 37 crop species, 197 crop–disease classes, and 9 geographic regions spanning tropical, subtropical, and temperate agricultural systems. Unlike conventional datasets, LeafNet 2.0 integrates biologically grounded symptom descriptions with image-level annotations of early and late disease stages, enabling symptom-aware analysis of disease progression under realistic field conditions. We further introduce LeafBench 2.0 as part of LeafMD, a visual-question answering benchmark covering nine fine-grained plant pathology tasks, including pathogen classification, lesion characterization, symptom interpretation, and disease severity assessment. Evaluation across 16 vision–language models revealed substantial performance gaps between coarse disease recognition and fine-grained pathological reasoning, while agriculture-adapted models consistently outperformed several larger general-domain architectures on symptom-oriented tasks. Together, LeafNet 2.0 and LeafBench 2.0 establish LeafMD as a multimodal resource for developing disease-aware agricultural foundation models and studying fine-grained pathological reasoning in real-world environments.

Why it matches plant phenotyping methods植物病害の画像・症状記述データセットとベンチマークを構築し、病徴解釈・病変特徴・病害重症度評価を対象にモデル性能を評価しており、植物状態の取得・評価手法が中心である。

abstractHere we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource

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

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

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

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

Pollen Germination as a High-Throughput Phenotyping Tool for Assessing Heat Tolerance in Soybean →

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.

Evaluation of RGB-Derived Indices for Cotton Leaf Phenotyping under a Standardized Imaging Setup →

Low-cost RGB imaging is accessible for phenotyping, but color varies with devices and illumination. We tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage. Leaves (n=80) from three growth-chamber experiments were imaged in a closed light-tent with an in-frame gray/white/black card, then corrected in Adobe Photoshop. Mean leaf RGB values (manual ROIs) were used to compute 15 RGB/CIELAB indices, which were screened against SPAD, specific leaf area (SLA), vein density, water content (WC), stomatal density, and stomatal size using Pearson r and second-order regression (adj. R², NRMSE). The strongest relationships were for SLA (h_ab; adj. R²=0.666), vein density (TGI; adj. R²=0.610), and SPAD (G; adj. R²=0.558). WC was moderately associated with c_ab (adj. R²=0.344), while stomatal traits were weakly explained, consistent with scale limits of top-down mean-color metrics. Standardized consumer RGB imaging can therefore support rapid first-pass screening of pigment- and structure-related leaf traits.

Why it matches plant phenotyping methods標準化スマートフォンRGB撮像と色補正・指数計算を用いて葉形質を推定し、SPAD、SLA、葉脈密度などとの関係を定量評価しているため、画像フェノタイピング手法の検証が中心である。

abstractWe tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage.

Next-Generation Crop Breeding: Harnessing Genomics, Phenomics and Machine Learning: A Review →

Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.

Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。

abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding

Leaf movements as a quantitative metric for early stress detection →

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

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

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

An in situ image-based phenotyping system for hydroponic maize seedling roots based on DB-UNet and customized skeleton-based analysis. →

monitoring capabilities, and existing models have limited accuracy in root segmentation. To address these issues, we developed a crop root phenotyping system integrating crop cultivation and data collection. We also proposed a DB-UNet model for hydroponic maize root segmentation. DB-UNet builds a CNN-ViT dual-branch parallel structure during encoder downsampling level. The lightweight ViT branch uses sequential downsampling to achieve global topological dependency modeling while reducing computational costs. An attention fusion module dynamically calibrate dual-branch features weights, achieving complementary fusion of local root edge details and global context information. we constructed a mixed loss function combining Dice loss, Focal loss, and structural consistency KL loss to solve class imbalance, hard sample segmentation, and semantic divergence of dual-branch features. On our custom hydroponic maize root dataset, DB-UNet achieved an mIoU of 91.02%, an FG IoU of 82.78%, and a Centerline-Dice of 97.72%.Compared to classic UNet, mIoU, FG IoU, and Centerline-Dice increased by 0.92%, 1.84%, and 1.99%, respectively. Plant-level five-fold cross-validation further showed that DB-UNet maintained stable segmentation performance across different plant-level partitions. Based on DB-UNet segmentation results, we propose a custom skeleton-based algorithm for multi-trait root phenotyping, enabling the extraction of total root length and root branch points. Root area is calculated from binary mask pixel statistics. Compared to the traditional Zhang-Suen algorithm, the average relative error of root length measurement is reduced to 3.14%, which is 8.42 percentage points lower than the traditional method. Furthermore, we analyzed relationships between segmentation accuracy metrics and phenotypic relative errors. Higher segmentation quality generally led to lower phenotypic relative errors and more reliable trait measurements. In particular, Centerline-Dice was closely associated with root length estimation, whereas pixel-level segmentation consistency was more closely related to root area measurement. Pearson and Spearman correlation analyses showed a strong positive correlation between maize plant height and total root length, with coefficients of 0.8466 and 0.8634, respectively.

Why it matches plant phenotyping methods画像ベースの根セグメンテーションと骨格解析を開発・検証し、根長や分枝点などの形質を抽出するシステムが研究の中心であるため。

abstractwe developed a crop root phenotyping system integrating crop cultivation and data collection.

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

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

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

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

Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection—Challenges and Opportunities →

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security.

Why it matches plant phenotyping methods植物病害の症状・状態をハイパースペクトル画像と深層学習で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。

abstractThis paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026.

A compact multimodal and imaging system for presymptomatic plant stress detection in NASA-controlled space agriculture →

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

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

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

Why it matches plant phenotyping methodsハイパースペクトル反射測定とスペクトル指標を用いて、植物体の病害ストレスを症状発現前に推定する方法を評価しており、表現型取得・抽出が研究の中心である。

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

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

Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-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.

Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement. →

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.

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

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

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

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

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

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

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

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

Artificial Intelligence Technologies in Plant Factories over the Last Decade: Machine Vision, Nutrient Intelligence, Control, and Digital Twins →

Plant factories have evolved from automated cultivation facilities into data-driven crop production systems. Over the last decade, artificial intelligence has been applied to non-destructive crop monitoring, sensor correction, nutrient-solution diagnosis, growth prediction, environmental control, digital twins, and product-level inspection. This review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces. The main argument is that plant-factory AI should not be understood only as image-based phenotyping; practical systems require an integrated intelligence stack connecting visual perception, sensor calibration, nutrient modeling, control, remote operation, and industrial inspection. Remaining challenges include dataset scarcity, model generalization, sensor drift, explainability, energy-aware control, and closed-loop decision-making.

Why it matches plant phenotyping methods植物工場における機械視覚・センサー補正・作物モニタリングなどのフェノタイピング関連技術を中心に扱うレビューであり、方法論的役割が明確。

abstractThis review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces.

Artificial Intelligence Technologies in Plant Factories over the Last Decade: Machine Vision, Nutrient Intelligence, Control, and Digital Twins →

Plant factories have evolved from automated cultivation facilities into data-driven crop production systems. Over the last decade, artificial intelligence has been applied to non-destructive crop monitoring, sensor correction, nutrient-solution diagnosis, growth prediction, environmental control, digital twins, and product-level inspection. This review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces. The main argument is that plant-factory AI should not be understood only as image-based phenotyping; practical systems require an integrated intelligence stack connecting visual perception, sensor calibration, nutrient modeling, control, remote operation, and industrial inspection. Remaining challenges include dataset scarcity, model generalization, sensor drift, explainability, energy-aware control, and closed-loop decision-making.

Why it matches plant phenotyping methods植物工場におけるAI技術レビューで、非破壊的な作物モニタリング、機械視覚、深層学習、センサー補正など、植物形質取得に関わる方法を中心的に扱っている。

abstractThis review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces.

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

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

Why it matches plant phenotyping methods植物のアルカリストレス状態を対象に、非侵襲的な電気生理センシングと深層学習による表現型推定手法を開発・検証しており、フェノタイピング手法が研究の中心である。

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

Multimodal AI for plant stress classification across multiple plant species →

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.

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

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

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

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

Smart Breeding: Integrating AI, Genomics and Phenomics for Next-Generation Crops: A Review →

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

PHYTOTRONIC COMPLEX WITH AN INTELLIGENT SYSTEM FOR DETECTING PLANT DISEASES BASED ON A MULTIMODAL NEURAL NETWORK →

Robotic phytotronic systems in the agroindustrial complex ensure a reduction in the energy intensity of technological processes of intensive plant cultivation. Automated control of phytotrons using neural network computer vision provides early detection of abnormalities in plant development, contributing to the elimination of diseases and thermotherapy.

Why it matches plant phenotyping methods植物の発育異常・病気をニューラルネットワークによるコンピュータビジョンで早期検出する自動化フェノタイピングシステムが中心であり、植物状態の画像ベース推定に該当する。

abstractAutomated control of phytotrons using neural network computer vision provides early detection of abnormalities in plant development

YOLO-based high-throughput phenotyping pipeline for soybean nodulation traits in genomic research. →

). Accurate quantification of nodule traits is essential for understanding host-microbe interactions and genetic determinants of nodulation. However, traditional manual or semi-quantitative approaches are labor-intensive, subjective, and unsuitable for large-scale studies. Here, we present a high-throughput phenotyping pipeline based on the YOLO deep learning architecture for the automated detection and extraction of soybean root traits. The pipeline quantifies nodule count, dimensions, and spatial distribution, enabling measurement of 24 distinct nodulation-related traits. Using root images from 21-day-old hydroponically grown soybean plants, the model achieved a precision of 0.94, a recall of 0.95, and an F1 score of 0.94 for nodule detection, maintaining accuracy across count ranges. It processes 50 root images in 37 seconds on a single GPU (45 GB memory), representing a ~227-fold improvement in efficiency compared to manual scoring (~2 h 20 min). As proof of concept, we applied this pipeline in a genome-wide association study (GWAS) using the FarmCPU approach and identified 50 significant SNPs associated with multiple nodulation traits, including novel ones. Several candidate genes linked to these loci suggest potential new regulators of nodulation. This YOLO-based phenotyping framework provides a robust, scalable, and reproducible tool for trait discovery and genetic analysis, advancing research in legume genomics and crop improvement. To promote the adoption of this user-friendly nodulation phenotyping pipeline and to support its further development, we have made all essential resources publicly available at: https://github.com/Salk-Harnessing-Plants-Initiative/soybean-nodule-detection.

Why it matches plant phenotyping methodsYOLOを用いた根粒形質の自動取得パイプラインを開発し、検出精度・処理速度を検証した方法中心の研究である。

abstractHere, we present a high-throughput phenotyping pipeline based on the YOLO deep learning architecture for the automated detection and extraction of soybean root traits.

Non‐Invasive Estimation of Short‐Term Changes of Transpiration Using a Combination of 3D Imaging and Energy Balance Modelling →

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.

A ROBUST MULTI-SENSOR DEEP REGRESSION FRAMEWORK FOR PREDICTIVE PLANT GROWTH MODELLING IN CONTROLLED ENVIRONMENTS →

The analysis of plant growth and development in controlled agricultural environments has become increasingly important to ensure sustainable and efficient food production. Traditionally, plant monitoring relied on manual observations and basic statistical approaches, which provided limited insights into the complex interactions between environmental factors and plant physiology. To address these challenges, this study proposes a machine learning–driven analytical framework designed for comprehensive plant development analysis. The system integrates data preprocessing, exploratory data analysis, classification, regression, and hybrid deep learning approaches within a unified pipeline. Classification algorithms such as Support Vector Classifier (SVC), Bernoulli Naive Bayes (BNC), and Multinomial Naive Bayes (MNC) are employed to identify plant growth stages. Regression models, including Decision Tree Regressor (DTR), Support Vector Regressor (SVR), and Ridge Regressor (RR), are utilized to estimate growth-related parameters. Furthermore, two hybrid models are introduced to enhance predictive performance. The Deep Feature Probabilistic Classifier (DFPC) combines Feed Forward Neural Networks (FFNN) with Gaussian Naive Bayes (GNB) for improved classification accuracy, while the Hybrid Deep Ridge Predictor (HDRP) integrates FFNN with Ridge Regressor to achieve precise regression outcomes. Experimental results demonstrate that the DFPC model attains an accuracy of 94.42%, whereas the HDRP model achieves an R² score of 0.999. These findings highlight the effectiveness of combining deep learning and machine learning techniques for accurate plant growth analysis and informed decision-making in controlled agricultural systems.

Why it matches plant phenotyping methods植物の成長段階と成長関連パラメータを推定する機械学習・深層学習パイプラインが研究の中心であり、表現型推定手法の開発に該当する。

abstractClassification algorithms such as Support Vector Classifier (SVC), Bernoulli Naive Bayes (BNC), and Multinomial Naive Bayes (MNC) are employed to identify plant growth stages.

Field and lab phenomics facilitate detection of genetic variation for iron deficiency chlorosis tolerance in sorghum →

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.

From seed to field: advancements in controlled environment, robotics and plant phenotyping →

Abstract Plant phenotyping attempts to objectively measure a plant’s reaction to its environment as encoded by its genotype. It has become an essential tool for deepening our understanding of plant responses to environmental stimuli. Understanding the plant’s reaction to, for example, a warmer climate is crucial to ensure food production for future generations. Breeders and researchers rely on automated high-throughput phenotyping for optimizing crops. Ideally, above- and below-ground traits are observed simultaneously. The newly established controlled environment facility at the Technology Center for Phenotyping of the Fraunhofer IIS in Merkendorf provides several climate chambers with individually controllable conditions for up to 400 individual plants to allow simulation of even extreme climatic conditions all year around. Comprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide. In combination with automated data pipelines, distinct plant traits can be extracted from the sensor data. By bridging above- and below-ground phenotyping, this facility not only advances plant science but also contributes to the breeding of more resilient and productive crops. Collaborators are welcome to unlock the transformative potential of these unique phenotyping capabilities, exploring traits such as root length, leaf area, biomass, and more.

Why it matches plant phenotyping methodsX線・光学カメラと自動データパイプラインによる地上部・地下部形質の高スループット取得を中核とするフェノタイピング施設・プラットフォームの紹介であり、方法と測定基盤が中心。

abstractComprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide.

The Application of YOLOv8n, YOLOv8s, and YOLOv8m for Potato Leaf Diseases Classification →

This paper investigates the performance of YOLOv8 classification models: YOLOv8n, YOLOv8s, and YOLOv8m for potato leaf disease classification. Two datasets were used: the Plant-Village (Potato) dataset collected under controlled environmental conditions and the Potato Leaf Disease Dataset captured under uncontrolled field conditions. The models were evaluated independently on each dataset and subsequently on a combined dataset to assess their robustness and generalization. The experimental results demonstrate strong classification performance across all datasets, with test accuracies of 90% on the Potato Leaf Disease Dataset collected under uncontrolled environmental conditions and 96% on the combined dataset. Compared with other models in the literature, including EfficientNetV2B3, MobileNetV3-Large, VGG-16, ResNet50, and DenseNet121, the YOLOv8n model achieved the best performance on the uncontrolled dataset, attaining 90% accuracy, 92% precision, 88% recall, and an F1-score of 90% on the uncontrolled dataset. On the combined dataset, YOLOv8m achieved 96% accuracy, 96% precision, 94% recall, and F1-score of 95%, demonstrating strong generalization and robustness under diverse imaging conditions.

Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から分類するYOLOv8手法を中心に、複数データセットで性能・頑健性・汎化性を評価しているため、植物病害フェノタイピング手法に該当する。

abstractThis paper investigates the performance of YOLOv8 classification models: YOLOv8n, YOLOv8s, and YOLOv8m for potato leaf disease classification.

Hyperspectral estimation of magnesium content in Yunyan 87 and Zhongyan 100 tobacco leaves using machine learning. →

Hyperspectral remote sensing provides a rapid and non-destructive approach for monitoring plant nutrient status; however, its application for magnesium (Mg) estimation in flue-cured tobacco remains limited. In this study, two cultivars, Yunyan 87 and Zhongyan 100, were grown in a hydroponic system with five Mg concentration gradients (0, 0.2, 1, 5, and 25 mmol L -1 ). Hyperspectral reflectance data of fresh leaves were collected at different growth stages. Three preprocessing methods, including first derivative (FD), standard normal variate (SNV), and multiplicative scatter correction (MSC), were applied, and partial least squares regression (PLSR) was used to identify the optimal preprocessing strategy. Characteristic wavelengths were selected using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and genetic algorithm (GA), and were further combined with extreme learning machine (ELM), support vector regression (SVR), and radial basis function (RBF) neural network models to estimate Mg content. The results showed that spectral preprocessing significantly improved the relationship between hyperspectral data and Mg content, with optimal methods varying across cultivars and growth stages. Selected wavelengths were mainly located in the near-infrared region. The developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90. In addition, Yunyan 87 exhibited higher prediction accuracy than Zhongyan 100. These findings demonstrate that hyperspectral technology combined with feature wavelength selection and machine learning enables accurate and non-destructive estimation of Mg content in flue-cured tobacco leaves, providing a reliable tool for Mg nutrition diagnosis and precision management. However, further validation under diverse field conditions is required to enhance model robustness.

Why it matches plant phenotyping methodsタバコ葉のMg含量という植物生理状態を、ハイパースペクトル計測と波長選択・機械学習で非破壊推定する方法が研究の中心であり、モデル精度評価も行っている。

abstractThe developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90.

Machine Learning Integrates Multispectral Phenotyping and Ionic Signatures to Reveal Stage‐Specific Drought Resilience in Cotton →

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。

abstractstage‐resolved, non‐destructive detection remains limited

A review of remote sensing-based crop yield estimation: machine learning techniques and environmental, algorithmic, and hardware limitations →

Advancements in agricultural technologies have increasingly emphasized technical innovations aimed at improving the predictability and reliability of agricultural outputs. These aspects encompass developments in agricultural machinery, automation technologies, biotechnology, and controlled environment farming systems. This article focuses on Remote Sensing (RS)-based approaches applied to agricultural yield estimation for both crops and plants. RS technologies offer enhanced precision and scalability, making them particularly effective for large-scale agricultural monitoring and analysis. A systematic classification of RS-based methodologies employed for crop yield estimation is presented in this study. These methodologies are categorized into: (i) Sensor-Based approaches, (ii) Platform-Based approaches, (iii) Analytical and Modeling-based methods, and (iv) Machine Learning (ML)-driven models. Based on findings reported across multiple studies, it is observed that Deep Learning (DL)-based architectures consistently achieve superior performance across key evaluation metrics, including accuracy, precision, recall, and F1-score. This performance advantage stems from their capacity to learn hierarchical representations, capture complex non-linear relationships, scale efficiently with large datasets, and reduce reliance on manual feature engineering. Following this classification, our article presents a comprehensive discussion of the limitations associated with these methodologies. These challenges are organized into four major categories: (i) Environmental, (ii) Algorithmic, (iii) Hardware and Operational, and (iv) Wireless Sensor Networks (WSNs) related limitations. The adopted classification framework helps readers identify and address the key challenges associated with effective yield estimation in crops and plants. Moreover, the article concludes by outlining several future research directions intended to support and guide both early-career and experienced researchers in this domain.

Why it matches plant phenotyping methods作物収量という植物形質のリモートセンシング推定法を体系的に分類・比較し、環境・アルゴリズム・ハードウェア上の限界を論じる方法論レビューであり、フェノタイピング手法が中心である。

titleA review of remote sensing-based crop yield estimation: machine learning techniques and environmental, algorithmic, and hardware limitations

Computer Vision-Based Monitoring and Data Integration in a Multi-Trophic Controlled-Environment Agriculture Demonstrator →

Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO2) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments.

Why it matches plant phenotyping methodsステレオ画像およびRGB-Dによる植物生長の非破壊定量と、データ統合型モニタリング基盤の実装が中心的に記述されており、植物フェノタイピング基盤として収載対象です。

abstractStereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth

Non-destructive estimation of chlorophyll content in wasabi (Eutrema japonicum) leaves using spectral reflectance and deep learning models →

Accurate, non-destructive estimation of chlorophyll content is essential for monitoring crop physiological status and supporting precision cultivation management. This study investigated the feasibility of combining leaf spectral reflectance with deep learning models to estimate chlorophyll content in hydroponically grown wasabi (Eutrema japonicum), while also deriving insights applicable to other leafy crops. A total of 179 leaf samples were collected under diverse nutrient conditions, including variations in pH, sulfur levels, and macronutrient composition across two growing seasons. Spectral data were preprocessed using second-order trend removal followed by a fractional-order derivative (FOD) transformation based on the Grünwald-Letnikov definition to enhance subtle spectral features. Three regression models-a one-dimensional convolutional neural network (1D-CNN), a Vision Transformer (ViT), and a Swin Transformer (SWIN)-were evaluated. The 1D-CNN achieved the highest accuracy when low-order fractional derivatives (0-0.4) were applied, highlighting its sensitivity to localized spectral variations, whereas SWIN performed best with minimally processed original spectra, and ViT showed relatively stable performance across preprocessing methods. These findings indicate that optimal preprocessing strategies depend on model architecture, providing practical guidance for selecting suitable combinations of spectral preprocessing and deep learning models when designing chlorophyll monitoring systems for wasabi and other crops.

Why it matches plant phenotyping methodsスペクトル反射と深層学習を組み合わせ、ワサビ葉のクロロフィル含量という植物形質を非破壊推定する手法を開発・比較評価しており、フェノタイピング手法が中心である。

titleNon-destructive estimation of chlorophyll content in wasabi (Eutrema japonicum) leaves using spectral reflectance and deep learning models

Multi-crop early detection of spider mite damage using hyperspectral data and XGBoost →

• XGBoost classified healthy and spider mite–infested leaves of cucumber and strawberry • Classification accuracy remained above 70% even with a reduced set of wavelengths • A combined model detected spider mite infestations across two crop species effectively The two-spotted spider mite is a globally significant pest affecting over 150 crop species, including cucumbers and strawberries. Its feeding activity leads to chlorophyll degradation and physiological changes in leaf tissue, which alter spectral reflectance properties and enable image-based detection. In this study, hyperspectral imaging (HSI) under controlled conditions was used to classify healthy and spider mite-infested leaves of cucumber and strawberry plants, including asymptomatic infested leaves. Spectral data were analyzed and classified with three supervised machine learning algorithms built on extreme gradient boosting (XGBoost) models. The study had three objectives: (1) to assess the ability of XGBoost to classify multiple infestation states, (2) to evaluate model performance with a reduced set of effective wavelengths, and (3) to determine whether infestation across both crops can be classified using a single, merged model. Using all wavelengths, results showed that classification accuracy was 93% for cucumber leaves, 84% for strawberry leaves, and 87% when combined. With five most effective wavelengths, classification accuracy reached 70% for cucumber leaves, 65% for strawberry leaves, and 65% for cucumber and strawberry leaves combined. The most effective wavelengths were consistently selected from the red-edge and near-infrared (NIR) spectral regions, which highlights their importance for early detection. To the best of our knowledge, this is the first known study to successfully apply a combined machine learning model for early spider mite detection across two different crop species using hyperspectral data under controlled conditions. The results show the potential of machine learning for multi-crop pest detection and could lay the groundwork for practical, sensor-based tools in precision agriculture.

Why it matches plant phenotyping methodsハイパースペクトル画像とXGBoostにより、植物葉のダニ感染状態を直接推定し、波長削減と作物間モデル性能を評価しているため、病害・害虫状態のフェノタイピング手法が中心である。

titleMulti-crop early detection of spider mite damage using hyperspectral data and XGBoost

Optimizing artificial lighting for convolutional neural network-based crop monitoring with low-cost RGB imaging in indoor cultivation →

This study investigated the effect of different red:blue (R:B) spectral light ratios on the performance of a multi-task convolutional neural network (CNN) model developed for the automatic classification of four horticultural species and their corresponding phenological stages under controlled artificial lighting conditions. The model was trained and tested using RGB images acquired under five distinct spectral treatments (R:B 1, 3, 5, 7, and 9), and its performance was evaluated using accuracy, precision, recall, F1-score, and Matthews correlation coefficient (MCC). For species classification, the best results were obtained with an R:B 1, achieving an accuracy of 86%, precision of 87%, recall of 85%, F1-score of 85%, and MCC of 0.81. In terms of phenological stage classification, the highest performance was observed at R:B 3 and R:B 5, both yielding 93% accuracy and F1-score, precision and recall above 92%, and an MCC of 0.86. These findings demonstrate that the multi-task CNN model is capable of learning robust and generalizable representations, maintaining high classification performance even under non-optimal spectral conditions. The integration of optimized artificial lighting with intelligent classifiers proves to be a strategic approach for automated monitoring systems in indoor and precision agriculture. Future research should explore the impact of additional spectral components (e.g., green or far-red wavelengths) and the adoption of more advanced neural architectures to further enhance the system’s robustness and scalability.

Why it matches plant phenotyping methodsRGB画像とCNNによる植物の種およびフェノロジー段階の自動分類手法を開発・評価しており、植物状態の取得・推定が研究の中心である。

abstracta multi-task convolutional neural network (CNN) model developed for the automatic classification of four horticultural species and their corresponding phenological stages

A two-phase evaluation system integrating hydroponic and field screening identifies nutrient-efficient sweetpotato ( Ipomoea batatas (L.) Lam.) germplasm. →

Sweetpotato ( Ipomoea batatas (L.) Lam.) is a crucial crop for global food security. However, its sustainable production is hindered by low nutrient use efficiency. Reliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking. To bridge this gap, we established a novel two-phase evaluation system integrating hydroponic seedling screening with multi-nutrient field validation. We conducted principal component and regression analyses of 35 germplasms lines under controlled deficiencies of nitrogen (N), phosphorus (P), and potassium (K). Five conserved seedling traits were identified, including leaf number per plant, shoot fresh weight, root fresh weight, shoot dry weight, and net photosynthetic rate (Pn). These traits consistently correlated with tolerance to N, P, or K deficiency, thereby supporting their utility as reliable early indicators of nutrient stress. Field validation further confirmed that storage root fresh and dry weight, nutrient content, accumulation, and use efficiency varied significantly among nutrient treatments and genotypes, serving as key indicators of field performance. This integrated approach successfully identified elite germplasm with specific nutrient use efficiency: XN1985-7 as a low-N-tolerant and N-efficient utilization genotype, XN17104-132 as low-K-tolerant and K-efficient utilization, XN2141-3 as low-P-tolerant and P-efficient utilization, and notably XN2153-5, which exhibited concurrent tolerance to low N, P, and K with broad-spectrum efficiency. Our integrated two-phase framework provides a scalable model for screening nutrient-efficient germplasm in root crops, thereby contributing to sustainable breeding programs.

Why it matches plant phenotyping methods栄養効率遺伝資源を評価するための二段階スクリーニング系を構築し、複数の形態・生理形質を初期指標として検証しているため、植物フェノタイピング手法が中心的です。

abstractReliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking.

Comprehensive phenomics and vegetative yield analysis of global kale (Brassica oleracea var. acephala) germplasm in controlled environment agriculture. →

BACKGROUND: Kale (Brassica oleracea var. acephala) is a high value leafy vegetable with an extensive domestication history and germplasm diversity, making it an ideal target for genetic improvement. To meet growing food security needs particularly with controlled environment agriculture (CEA) systems, specialized breeding strategies are required. The goal of this study was to survey the phenotypic architecture of a global kale germplasm collection under commercial CEA conditions. This study establishes a phenotypic baseline and serves as a hypothesis generating resource for future genetic and physiological studies in kale and other leafy vegetables grown under CEA. RESULTS: A total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods. Significant differentiation was observed across all traits, with coefficient of variation ranging from 2.5% to 180.7%, confirming broad genetic variability among accessions. Trait correlation networks and hierarchical clustering grouped phenotypes into seven biologically corresponding modules including leaf, stem and root morphology, plant architecture, hyperspectral indices, and seedling growth. These modules highlight coordinated phenotypic patterns among traits. Integrative yield analyses combining partial least squares variable importance in projection with differential trait analysis identified 28 phenotypes most strongly associated with total aboveground fresh weight, a robust proxy for CEA vegetative yield. Principal component analysis further distilled these traits into three orthogonal components explaining 87.1% of total yield variation. These components represented modules related to plant organ size, canopy structure, and density, emphasizing their biological contribution to harvestable biomass. CONCLUSIONS: This study generates a foundational phenomics resource and comprehensive dissection of kale’s yield architecture under CEA conditions. The composition of traits identified constitutes a targeted set of breeding traits to be further validated for improved leafy vegetable yield. By integrating large-scale germplasm resources with phenomics, this work establishes the utility of a high-throughput phenotypic analysis for further leafy crop research and improvement.

Why it matches plant phenotyping methods大規模なハイスループット植物表現型解析を中核とし、113形質の取得、統合解析、再利用可能なフェノミクス資源の構築を行っているため。

abstractA total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods.

Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions →

3D phenotyping refers to the quantitative characterization of a plant's structural and morphological traits in three-dimensional space, allowing for a detailed analysis of plant architecture and growth patterns. In recent years, rapid advancements in non-destructive, high-throughput 3D imaging technologies have enabled the precise measurement of these traits. Initially focused on single-plant traits under controlled conditions, the field has now expanded towards robust applications in real-world field environments, enabling large-scale analyses of plant canopies and complex structures. This study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies. It compares sensor technology and its application in controlled environments (Chamber-Crop Phenotyping, CCP) and field conditions (Field-Crop Phenotyping, FCP). Technologies such as Multiview stereo (MVS) reconstruction, LiDAR, and laser triangulation have enhanced plant phenomics by enabling high-throughput, non-destructive measurements of key traits such as canopy structure, leaf area, and stem diameter. This review highlights the strengths of the CCP, where environmental variables and flexibility are tightly controlled, facilitating precise trait measurement, and contrasts it with the challenges of the FCP, where unpredictable factors, such as occlusion, wind, light variability, and terrain complexity, complicate data acquisition. Various sensor platforms, including ground-based robotic systems and unmanned aerial vehicles (UAVs), have been discussed regarding their ability to overcome occlusion and limited sensor range in real-world conditions. The need to transition these technologies from laboratory environments to real-world agricultural applications is emphasized, highlighting their potential to improve crop management and plant breeding through accurate phenotypic trait extraction. Finally, current research gaps and future directions for integrating advanced sensor platforms and analytical techniques in both CCP and FCP settings are identified, emphasizing the need to enhance the scalability and robustness of 3D phenotyping for field applications.

Why it matches plant phenotyping methods3D作物フェノタイピングのセンサー技術、点群処理、対象形質、検証上の課題を中心に扱う方法論レビューであり、植物形質の取得手法が明確に中心である。

abstractThis study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies.

Precise Evaluation of Transpiration Patterns in Relation to Grain Yield Under Drought Stress in Faba Bean. →

Faba bean ( Vicia faba L.) is a key crop for sustainable agriculture in temperate cropping systems due to its nitrogen-fixing ability and high protein content, but its productivity is increasingly threatened by drought stress driven by climate change. Precise phenotyping under semicontrolled conditions is crucial for understanding drought responses. High-throughput precision phenotyping enables efficient evaluation of many genotypes, revealing detailed water-use patterns as a basis for breeding productive, drought-resilient cultivars. In this study, faba bean genotypes were grown in a precision phenotyping facility comprising 120-L containers filled with mineral soil to simulate field-like growth conditions. Each container was placed on a high-precision gravimetric scale to record water use in real time in relation to 3-D spectral image information. Precise measurement of genotype-specific transpiration behavior using gravimetric methods enabled detailed insights into the transpiration patterns of different genotypes in response to ambient temperature and humidity fluctuations throughout the day and night, and across the whole-life cycle. The results showed that total water use, water-use efficiency, and consequently yield were particularly influenced by specific transpiration parameters, such as the maximum transpiration rate and the vapor pressure deficit threshold at which stomatal conductance was declined. The results revealed genetically determined variation for transpiration responses to drought stress. Genotypes that reduced water loss earlier tended to achieve higher grain yields and use water more efficiently. The findings show that precise automated phenotyping can identify previously undiscovered genetic variation for breeding drought-tolerant faba bean varieties, which are crucial for ensuring productivity under increasingly water-limited conditions.

Why it matches plant phenotyping methods高精度重量計と3-Dスペクトル画像を統合した自動表現型解析施設で、遺伝子型別の蒸散・水利用形質を測定・解析しており、表現型取得手法が研究の中心です。

abstractHigh-throughput precision phenotyping enables efficient evaluation of many genotypes, revealing detailed water-use patterns as a basis for breeding productive, drought-resilient cultivars.

Deep learning–driven hyperspectral imaging for drought stress detection in dragoon lettuce for space production →

Sustainable plant cultivation is critical for supporting long-duration space missions by ensuring reliable food production in extraterrestrial environments where resources are severely limited and growth systems operate in closed-loop conditions. With crew members managing multiple critical mission tasks and having minimal time for plant care, autonomous stress detection systems must provide reliable, interpretable diagnostics to enable rapid, informed decision-making for crop management. This study utilized a custom hyperspectral imaging (HSI) system designed for space applications to develop an AI-driven diagnostic framework. We propose a novel SAM-ViT-3PE architecture that uniquely combines sparse spectral band selection with 3D spatial-spectral patch embedding, preserving rich spatial-spectral information typically lost in conventional ROI-averaged approaches. A key temporal finding identified Day 3 After Treatment (DAT 3) as the critical threshold where drought stress signatures become distinctly detectable, with accuracy dramatically improving from 72.2% to 95.9%. By focusing analysis on data from DAT 3 onward, the SAM-ViT-3PE model achieved superior performance compared to traditional ML methods and standard deep learning approaches, with accuracy of 95.4%, precision of 96.6% and recall of 94.1%. Furthermore, Explainable AI using Integrated Gradients enabled interpretable diagnostics through physiologically meaningful spectral bands and spatial stress patterns. These results demonstrate that the AI-enhanced HSI framework provides both high-accuracy autonomous detection and scientifically grounded interpretability essential for trustworthy crop management in resource-constrained space environments.

Why it matches plant phenotyping methods植物の干ばつストレス状態を hyperspectral imaging と深層学習で検出する診断フレームワークを開発・評価しており、植物表現型取得が研究の中心である。

abstractThis study utilized a custom hyperspectral imaging (HSI) system designed for space applications to develop an AI-driven diagnostic framework.

Phenotyping of Syndrome “Basses Richesses” in Sugar Beet by Morphological and Spectral Traits →

Syndrome “Basses Richesses” (SBR) is a rapidly emerging sugar beet disease in central Europe that has a severe economic impact on the sugar beet industry and thus requires control. The cultivation of tolerant varieties is a promising method to reduce SBR. Digital plant phenotyping can support the screening process for tolerant varieties by characterizing traits of interest and quantifying tolerance. This research provides foundational work for digitally phenotyping SBR. Morphological and spectral traits were analyzed with machine learning, supporting disease monitoring and screening for tolerant varieties under controlled conditions. A susceptible sugar beet variety was infected with the dominant causal agent of SBR, ‘ Candidatus Arsenophonus phytopathogenicus’ (ARSEPH). Hyperspectral images of the canopy were recorded weekly between 20 and 62 days after inoculation and segmented by leaves and petioles. Sixty-seven days after inoculation, each leaf was two-dimensionally (2D) and each taproot three-dimensionally (3D) imaged by angle-corrected 2D imaging and structured-light 3D scans, respectively. The results indicated substantial decreases in leaf area (19.7%), leaf length (6.9%), leaf blade length (13.1%), and leaf blade width (12.1%) resulting from ARSEPH infection. The most important wavelengths for machine learning classification of ARSEPH-infected sugar beet were from the petioles (97% accuracy) in the range 623 to 659 nm and 421 to 432 nm. The 22 most relevant taproot 3D parameters were evaluated with Boruta-SHAP based on their importance to characterize SBR-induced taproot deformation. Certain value and spatial regions were characteristic, indicating thresholds for 3D parameters and taproot regions to analyze when comparing varieties. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Why it matches plant phenotyping methodsSBR耐性品種のスクリーニングを目的に、ハイパースペクトル画像、2D画像、構造化光3Dスキャン、機械学習を用いた植物形態・スペクトル形質の取得と評価が研究の中心である。

abstractDigital plant phenotyping can support the screening process for tolerant varieties by characterizing traits of interest and quantifying tolerance.

Dynamic light intensity improves light use efficiency of lettuce in vertical farming: quantifying light interception through 3D phenotyping analysis →

Vertical farming offers a promising solution to global food security and urbanization challenges, yet its widespread adoption is hindered by high costs, particularly for lighting. Addressing this requires enhancing light use efficiency (LUE) through intelligent control strategies. While numerous studies have investigated the effects of light intensity on lettuce growth, relatively few have explored the potential benefits of stage-specific light regulation. In this study, we first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception. Utilizing this quantitative framework, we conducted a dynamic light experiment with lettuce in a commercial plant factory to evaluate four dynamic light-intensity strategies. The proposed 3D phenotyping pipeline demonstrated promising performance for canopy information extraction, with RMSEs for plant height, canopy diameter, and projected leaf area of 0.79 cm, 1.05 cm, and 44.3 cm², respectively. The “high-low-high” dynamic lighting strategy, applying higher light intensity during the early and late growth stages and lower intensity during the mid-growth stage, successfully optimized canopy morphology for better light capture. This treatment significantly increased shoot fresh and dry weights by 28 % and 65 %, respectively, compared to constant lighting. Furthermore, it enhanced LUE based on incident and intercepted light integrals by 67 % and 19 %, while reducing electricity consumption per unit of fresh weight by 24 %. Nutritional quality analysis showed the treatment increased soluble sugars and starch contents. By integrating advanced 3D phenotyping with dynamic light intensity control, this study demonstrates a prototype for intelligent decision-making to enhance yield and energy use efficiency in practical vertical farming.

Why it matches plant phenotyping methods自動3Dフェノタイピングパイプラインを開発し、マルチビュー再構成で植物体形態と光遮断を定量化、精度評価も実施しており、フェノタイピング手法が研究の中心である。

abstractwe first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception.

Automated classification of plant water status through morpho-kinematic monitoring of plant movement →

Plant motion provides valuable indicators of physiological responses to water stress. In this study, we present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants subjected to varying irrigation regimes under controlled conditions. Four water availability treatments were imposed − Full Control (FC), Stress Control (SC), Mild Stress (SM), and Severe Stress (SS) − varying in timing, frequency, and intensity of irrigation protocols. Using dense optical flow on time-lapse RGB images, we extracted MK features that link leaf age to motion dynamics. These high-dimensional temporal features were compressed into descriptive and trend-based characteristics for classification. Multi-classification problem was divided into nine sub-tasks, for which feature selection and multiple machine-learning models were tested applying Leave-One-Sample-Out cross-validation. The best models were organised into four explainable hierarchical cascades. The presented system captures enough information to successfully distinguish among subtle differences in plants’ response to water availability dynamics (best architecture cascade obtained 0.93 out of fold balanced accuracy). The framework associating leaf age with MK features along with feature engineering allowed explainability – e.g., central rosette’s features were selected almost twice the expected frequency (19 out of 58) in tasks involving the stress-adapted control (SC), while features capturing linear trends in motion were generally selected over twice as often as simple descriptive statistics (44 vs. 19), proving essential for distinguishing most stress conditions. The MK approach proved effective for differentiating water stress levels, positioning it as a powerful tool for digital phenotyping and a solid foundation for developing advanced temporal-aware models.

Why it matches plant phenotyping methods画像時系列とdense optical flowから植物のモルフォ・キネマティック形質を抽出し、水分状態を分類する手法の開発・検証が研究の中心であるため。

abstractwe present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants

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

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

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

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

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

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

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

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

Computer Vision-Based Monitoring and Data Integration in a Multi-Trophic Controlled-Environment Agriculture Demonstrator →

Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO$_2$) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments.

Why it matches plant phenotyping methods植物成長をステレオ画像およびRGB-Dで非侵襲的に定量するコンピュータビジョン監視基盤を実装しており、植物フェノタイピングが統合監視システムの主要な技術要素である。

abstractStereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth

IoT-Based Plant Growth Chamber with YOLOv8 for Anthracnose Disease Severity Classification in Chili Pepper →

Plant growth chambers provide controlled environments for agricultural research, enabling precise monitoring of crop diseases under optimal microclimate conditions. This paper presents an integrated IoT-based smart plant growth chamber system utilizing YOLOv8 machine learning for the automated classification of anthracnose disease severity in chili peppers (Capsicum annuum L.). The system integrates multiple subsystems, including environmental control, a robotic camera with 2-axis movement, a gateway for data communication, and remote monitoring capabilities through a cloud server and a web interface. Dataset labeling was performed using LabelImg and Roboflow, with data augmentation increasing training samples from 70% to 86%. Three YOLOv8 models were evaluated: YOLOv8L (150 epochs), YOLOv8N (100 epochs), and YOLOv8N (398 epochs). Based on our test so far, the YOLOv8L model achieved the best performance with mAP of 67.4% and successfully detected 44 out of 102 test samples (43% detection rate) across multiple disease severity scores (0-9). The system enables both onsite and remote access, automatic data logging, real-time image capture with PyQt5-based GUI, and environmental parameter control (temperature: 5-50°C, humidity: 40-90%RH, light: 0-15,000 lux), which can be manually set and automatically set based on the requirements of the user. This integrated approach demonstrates practical deployment of edge AI and IoT technologies for precision agriculture and disease monitoring applications.

Why it matches plant phenotyping methods植物の病害症状を画像から重症度分類するYOLOv8ベースの撮像・解析システムが研究の中心であり、植物状態の表現型推定手法として適格。

abstractThis paper presents an integrated IoT-based smart plant growth chamber system utilizing YOLOv8 machine learning for the automated classification of anthracnose disease severity in chili peppers (Capsicum annuum L.).

A machine learning based model for the precise regulation of tomato seedling growth for automatic grafting. →

Introduction The morphological characteristics of grafting seedlings affect the quality of automatic grafting. Because of the non-uniform and unstable lighting conditions in greenhouses, it is difficult to implement targeted control over seedlings. In contrast, plant factories are able to cultivate grafted seedlings in a more optimal environment by adjusting environmental factors like light. This research aims to propose an intelligent control method for seedling growth, in order to precisely cultivate seedlings that meet the requirements of different grafting machines. Methods This research established an evaluation method for tomato seedlings (suitable for automatic grafting) and scored seedlings that underwent light recipe transitions at different time points. Based on the comprehensive weighting of tomato seedlings suitable for automatic grafting, combined with the growth data of seedlings under different light environments, six machine learning algorithms were used to establish growth prediction models. Results The results indicate that the length of the hypocotyl and the diameter of the stem are crucial factors influencing whether the seedling can be mechanically grafted. And the transition of light recipes during cultivation can regulate seedling quality. XGBoost achieved the best accuracy for predicting rootstock and scion growth, with R 2 values of 0.9253 and 0.9334, respectively. A smart light control system was established and grafting experiments were conducted. The results showed that the automatic grafting success rate and post-grafting survival rate of light- regulated seedlings were 8.3% and 1.4% higher than those of commercially available seedlings, respectively. Discussion This demonstrates the feasibility of the model and highlights the practical application of the system in precision agriculture.

Why it matches plant phenotyping methodsトマト苗の接ぎ木適性を評価する方法と、胚軸長・茎径などの形質を予測する機械学習モデルを開発し、光制御へ適用しているため、表現型取得・推定手法が中心である。

abstractThis research established an evaluation method for tomato seedlings (suitable for automatic grafting)

Microclimate-Controlled Smart Growth Cabinets for High-Throughput Plant Phenotyping →

Climate change is driving urgent demand for resilient crop varieties capable of withstanding extreme and changing conditions. Identifying resilient varieties requires systematic plant phenotyping research under controlled conditions, where dynamic environmental impacts can be studied. Current growth cabinets (GC) provide this capability but remain limited by high costs, static environments, and scalability. These limitations pose a challenge for climate change-based phenotyping research which requires large-scale trials under a variety of dynamic climate conditions. Presented is a microclimate-controlled smart growth cabinet (MCSGC) platform, addressing these limitations through four innovations. The first is dynamic microclimate simulation through programmable environmental ‘recipes’ reproducing real climactic variability. The second is interconnected scalable multi-cabinet for parallel experiments. The third is modular hardware able to reconfigure for different plant species, remaining cost-effective at <$10,000 AUD. The fourth is automated data collection and synchronisation of environmental and phenotypic measurements for Artificial Intelligence (AI) applications. Experimental validation confirmed precise climate control, broad crop compatibility, and high-throughput data generation. Environmental control stayed within ±2 °C for 97.42% while dynamically simulating Hobart, Australia, weather. The MCSGC provides an environment suitable for diverse crops (temperature 14.6–31.04 °C, and Photosynthetically Active Radiation (PAR) 0–1241 µmol·m−2·s−1). Multi-species cultivation validated the adaptability of the MCSGC across Cannabis sativa (544.1 mm growth over 34 days), Beta vulgaris (123.6 mm growth over 36 days), and Lactuca sativa (19-day cultivation). Without manual intervention the system generated 456 images and 164,160 sensor readings, creating datasets optimised for AI and digital twin applications. The MCSGC addresses critical limitations of existing systems, supporting advancements in plant phenotyping, crop improvement, and climate resilience research.

Why it matches plant phenotyping methods植物フェノタイピング用のスマート成長キャビネットを開発し、環境制御、拡張性、自動データ収集、作物適応性を実験的に検証しており、フェノタイプ取得基盤が研究の中心である。

abstractPresented is a microclimate-controlled smart growth cabinet (MCSGC) platform, addressing these limitations through four innovations.

Hybrid Multi-Scale Neural Network with Attention-Based Fusion for Fruit Crop Disease Identification →

Unobserved fruit crop illnesses are a major threat to agricultural productivity worldwide and frequently cause farmers to suffer large financial losses. Manual field inspection-based disease detection techniques are time-consuming, unreliable, and unsuitable for extensive monitoring. Deep learning approaches, in particular convolutional neural networks, have shown promise for automated plant disease identification, although they still face significant obstacles. These include poor generalization across complicated visual backdrops, limited resilience to different illness sizes, and high processing needs that make deployment on resource-constrained edge devices difficult. We suggest a Hybrid Multi-Scale Neural Network (HMCT-AF with GSAF) architecture for precise and effective fruit crop disease identification in order to overcome these drawbacks. In order to extract long-range dependencies, HMCT-AF with GSAF combines a Vision Transformer-based structural branch with multi-scale convolutional branches to capture both high-level contextual patterns and fine-grained local information. These disparate features are adaptively combined using a novel HMCT-AF with a GSAF module, which enhances model interpretability and classification performance. We conduct evaluations on both PlantVillage (controlled environment) and CLD (real-world in-field conditions), observing consistent performance gains that indicate strong resilience to natural lighting variations and background complexity. With an accuracy of up to 93.79%, HMCT-AF with GSAF outperforms vanilla Transformer models, EfficientNet, and traditional CNNs. These findings demonstrate how well the model captures scale-variant disease symptoms and how it may be used in real-time agricultural applications using hardware that is compatible with the edge. According to our research, HMCT-AF with GSAF presents a viable basis for intelligent, scalable plant disease monitoring systems in contemporary precision farming.

Why it matches plant phenotyping methods植物病害症状を画像から識別する新規深層学習手法を開発し、複数データセットで性能評価しており、植物状態の表現型推定が研究の中心である。

abstractWe suggest a Hybrid Multi-Scale Neural Network (HMCT-AF with GSAF) architecture for precise and effective fruit crop disease identification

Automated classification of plant water status through morpho-kinematic monitoring of plant movement →

Plant motion provides valuable indicators of physiological responses to water stress. In this study, we present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants subjected to varying irrigation regimes under controlled conditions. Four water availability treatments were imposed − Full Control (FC), Stress Control (SC), Mild Stress (SM), and Severe Stress (SS) − varying in timing, frequency, and intensity of irrigation protocols. Using dense optical flow on time-lapse RGB images, we extracted MK features that link leaf age to motion dynamics. These high-dimensional temporal features were compressed into descriptive and trend-based characteristics for classification. Multi-classification problem was divided into nine sub-tasks, for which feature selection and multiple machine-learning models were tested applying Leave-One-Sample-Out cross-validation. The best models were organised into four explainable hierarchical cascades. The presented system captures enough information to successfully distinguish among subtle differences in plants’ response to water availability dynamics (best architecture cascade obtained 0.93 out of fold balanced accuracy). The framework associating leaf age with MK features along with feature engineering allowed explainability – e.g., central rosette’s features were selected almost twice the expected frequency (19 out of 58) in tasks involving the stress-adapted control (SC), while features capturing linear trends in motion were generally selected over twice as often as simple descriptive statistics (44 vs. 19), proving essential for distinguishing most stress conditions. The MK approach proved effective for differentiating water stress levels, positioning it as a powerful tool for digital phenotyping and a solid foundation for developing advanced temporal-aware models.

Why it matches plant phenotyping methods画像時系列から光学フローで植物の運動形質を抽出し、水ストレス状態を分類する画像ベース表現型解析手法の開発・評価が研究の中心である。

abstractwe present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants

Machine learning integrated visible diffuse reflectance spectroscopy for in-situ analysis of phosphorus status in Arabidopsis plants under soilless culture. →

Phosphorus (P) is a vital macronutrient for plant growth, but its limited availability in soil due to fixation renders up to 80 % of fertilizers ineffective. Visual symptoms for P deficiency appear late or remain inconclusive, complicating timely intervention. The conventional methods are often time-consuming, costly, and labour-intensive. Diffuse reflectance spectroscopy offers a rapid, label-free alternative, though its application is challenged by weak P spectral response. In this study, Arabidopsis thaliana (Col-0) plants were subjected to soilless culture under controlled phosphorus-sufficient (P+) and deficient (P-) conditions. Leaf reflectance spectra were analyzed using Linear Discriminant Analysis (LDA), and the selected wavelengths were used to train three machine learning classifiers such as Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN). Among these, the SVM model demonstrated best performance, achieving a classification accuracy of 97.78 %. Independent validation using biochemical, morphological, and combined datasets, yielded classification accuracies of 100 %, 71.88 %, and 100 %, respectively. This approach offers a rapid, and non-destructive alternative to conventional techniques for sustainable nutrient management in agriculture.

Why it matches plant phenotyping methods植物のリン栄養状態を可視拡散反射分光と機械学習で非破壊推定し、分類性能を検証する方法研究であり、表現型取得・推定手法が中心である。

abstractDiffuse reflectance spectroscopy offers a rapid, label-free alternative

Linking stomatal function with photosynthetic light reactions and stress response in faba bean →

Faba bean ( Vicia faba L.) is a key protein crop, but its cultivation and yield stability are hindered by a number of environmental stresses. Stomata regulate gas exchange between the plant and atmosphere, playing a central role in photosynthesis and mediating plant responses to a wide range of environmental stressors. This study aimed to investigate variations in photosynthetic regulation in faba bean, and to examine leaf temperature and the response to short-term acute ozone (O₃) exposure as proxies for stomatal function. Here, we used a high-throughput plant phenotyping (HTPP) platform to screen 196 faba bean genotypes for photosynthetic and stomatal function under controlled conditions. A subset of extreme genotypes, identified based on relative leaf tempreture from the initial screening, was exposed to a 450 ppb O₃ treatment. Our results revealed strong positive relationship between photosynthetic efficiency and relative leaf temperature. A three-fold difference in relative leaf temperature was observed among genotypes. The O₃ treatment caused signicantly less damage in genotypes with higher leaf temperature compared to those with lower leaf temperature (p < 0.001). By combining a HTPP platform with elevated O₃ stress treatment, we identified faba bean genotypes with contrasting stomatal responses to the O₃ exposure. Our results advance understanding of the regulation mechanisms of photosynthetic light reactions and the role of stomatal function in modulating faba bean responses to environmental stressors. • High-throughput phenotyping reveals large variation in leaf temperature among faba bean genotypes. • Leaf temperature strongly affects photosynthetic regulation in faba bean. • The tested genotypes with higher leaf temperatures display increased ozone tolerance.

Why it matches plant phenotyping methodsHTPPプラットフォームを用いて196遺伝子型の葉温度、光合成、気孔機能を高スループット測定し、表現型に基づく選抜とオゾン応答評価を行っており、フェノタイピング手法の応用が研究の主要部分です。

abstractHere, we used a high-throughput plant phenotyping (HTPP) platform to screen 196 faba bean genotypes for photosynthetic and stomatal function under controlled conditions.

A derivative approach for efficient hydroponic vertical farm monitoring using hyperspectral vision →

Vertical indoor hydroponic farms offer sustainable solutions in land scarce countries to foster agriculture productivity for addressing growing demand. Such farms require extensive controllability of the growing conditions to ensure year round-cultivation of diverse crops within the space available. Continuous monitoring of the crops and early remedial measures are essential to ensure non-compromised, high-quality yield from these farms. Currently, most farms rely on human vision based monitoring, which is quite subjective and time-consuming and could be ineffective in identifying crop stresses at early stages. Hence, efficient management of these farms requires advanced automated systems to monitor crop health, including possible stress factors such as nutrient, water, and light deficiencies at early stages to enable timely intervention. This research, in this context, explores innovative strategies using assessment parameters such as spectral ratios and derivative reflectance derived from hyperspectral images for crop monitoring. Customized spectral index for nutrient deficiency detection and approaches for quantification of derivative spectra for stress detection are developed. These strategies can be used to rapidly detect the stresses at the early stages non-destructively (within hours in case of light and water deficiencies) and could promptly guide in timely remedial actions. The proposed method offers automation possibilities for non-invasive monitoring systems utilizing hyperspectral vision. This non-invasive imaging system integrated on a robotic platform is envisaged to revolutionize the development of unmanned indoor hydroponic farms for a sustainable future.

Why it matches plant phenotyping methodsハイパースペクトル画像から栄養・水・光ストレスを早期検出・定量する手法を開発しており、植物状態の取得方法が研究の中心である。

abstractCustomized spectral index for nutrient deficiency detection and approaches for quantification of derivative spectra for stress detection are developed.

Robotic system with tactile-enabled leaf tracking for high-resolution hyperspectral imaging device for autonomous corn leaf phenotyping in controlled environments →

Hyperspectral imaging of individual corn leaves provides valuable data for analyzing nutrient content and diagnosing diseases. However, existing leaf-level imaging techniques face challenges such as low spatial resolution and labor-intensive processes. To address these limitations, this study developed a robotic system integrated with a high-resolution line-scanning hyperspectral imaging device to autonomously scan a corn leaf. The hyperspectral imaging device used a vision-based tactile sensor for active leaf tracking throughout the scanning process, ensuring high image quality. Additionally, the device incorporated an in-hand leaf manipulation mechanism that ensured the leaf was properly positioned on the tactile sensing area at the start of every scanning. The scanning process was executed by a robotic arm equipped with an RGB-D camera and integrated with the Segment Anything Model (SAM), enabling autonomous leaf detection, localization, grasping, and scanning. The system was tested on V10-stage corn plants and the success rate was 91.4 % with an average 4.8 s for leaf detection and localization and an average leaf scanning time of 38.3 s.

Why it matches plant phenotyping methodsトウモロコシ葉の高解像度ハイパースペクトル画像取得を自動化するロボット・触覚追跡システムを開発し、検出・走査成功率や処理時間で評価しており、表現型取得手法が中心である。

abstractthis study developed a robotic system integrated with a high-resolution line-scanning hyperspectral imaging device to autonomously scan a corn leaf.

Hierarchical 3-D Scene-Graph-Based Semantic-Metric SLAM for Plant Inspection and Fruit Counting in Intelligent Hydroponics System →

The synergistic development of Internet of Things(IoT), robotics, and Artificial Intelligence (AI) is reshaping the technological paradigms of interdisciplinary laboratories and industrial ecosystems. IoT-enabled vertical farming systems demonstrate significant advantages, achieving yield enhancement while reducing carbon emissions compared to traditional agriculture, thereby providing innovative solutions for sustainable food production. The advancement of robotic technologies further expands the application dimensions of mobile intelligent sensors in vertical farm IoT networks. Based on an autonomous farming system that integrates Unmanned Aerial Vehicle (UAV), sensors, and modular vertical farming units, this study proposes a three dimensional Scene Graph (3DSG)-based hierarchical mapping method for the dynamic monitoring of plant and fruit growth. Through feedback mechanisms, the system optimizes growth conditions by adjusting lighting and nutrient delivery, while the hierarchical mapping architecture reduces detection errors and enables comprehensive 3D visualization. The main contributions of this research include: 1) Pioneering application of 3DSG technology to establish a multi-dimensional spatiotemporal representation model for plant growth processes, supporting interpretable analysis and traceable monitoring; 2) Establishing an uncertainty model through error propagation by systematically analyzing sensor models (covering various common sensor combinations) and integrating these models into 3D object pose estimation algorithms. This highlights the necessity of hierarchical abstraction levels. The system is validated through simulations and real-world experiments, providing a quantitative evaluation of object pose estimation; and 3) An IoT-driven intelligent vertical farming architecture that integrating mobile robotic perception networks and environmental regulation devices, enabling dynamic acquisition and closed-loop control of plant growth parameters. Open-source code is available at https://github.com/allenthreee/scene_graph, video link: https://youtu.be/dhc8RLmX7hc.

Why it matches plant phenotyping methods植物・果実の成長監視と計数を目的に、3Dシーングラフ、SLAM、センサー融合、誤差伝播モデルを開発・検証しており、表現型取得手法が研究の中心である。

titleHierarchical 3-D Scene-Graph-Based Semantic-Metric SLAM for Plant Inspection and Fruit Counting in Intelligent Hydroponics System

Optimized substrate selection for enhanced orchid growth based on high-throughput lysimetric arrays. →

Orchids are highly valued ornamental plants whose growth conditions directly impact the economic returns of the horticultural industry. The substrate, acting both as a physical support and a nutrient reservoir, is critical for orchid development. Therefore, the careful selection of an appropriate growth substrate is of paramount importance. However, existing research on the relationship between orchid growth and substrate properties relies mainly on manual measurements of physiological indicators, with limited application of high-throughput phenotyping (HTP) platforms. In this study, we evaluated three distinct substrate types, peat soil mixed with perlite, pine bark, and river sand, which were applied to two orchid species, Cymbidium goeringii and Cymbidium faberi . Using the high-throughput Plantarray lysimetric system, we continuously recorded environmental parameters (photosynthetically active radiation, humidity, and temperature) as well as key growth metrics (biomass accumulation, canopy conductance, and transpiration rate). This platform enabled precise and rapid quantification of orchid growth indicators. The results show that the type of substrate significantly affects orchid growth. Under controlled conditions, mixed substrates that provide balanced nutrition and excellent drainage enhanced orchid growth compared to other substrates. Additionally, when the data obtained from the HTP platform were compared with those from traditional manual measurements, the automated system showed higher reliability and accuracy. This study not only provides practical guidance for selecting cultivation substrates for orchids, but also establishes a robust scientific framework for integrating advanced phenotyping technologies into orchid cultivation practices.

Why it matches plant phenotyping methodsPlantarray高スループット表現型計測システムによる生長・生理形質の連続測定と、手動測定との信頼性・精度比較が研究の中心であり、基質効果の単なる生物実験にとどまらない。

abstractUsing the high-throughput Plantarray lysimetric system, we continuously recorded environmental parameters (photosynthetically active radiation, humidity, and temperature) as well as key growth metrics (biomass accumulation, canopy conductance, and transpiration rate).

An Effective Method for Bacterial Leaf Streak Disease Severity Estimation in Controlled and Field Environments in Small Grains →

Abstract Background Wheat ( Triticum aestivum ) is one of the most economically important crops in the United States. However, over the past two years, wheat production has suffered up to a 40% reduction in final yield due to pathogen infections worldwide. A major emerging threat in the Great Plains and Canadian Prairies, including South Dakota, North Dakota, and Minnesota, is bacterial leaf streak (BLS)/black chaff disease caused by Xanthomonas translucens spp., which has led to substantial yield losses in the last decade. Absence of both effective chemical controls and competitive highly resistant varieties makes BLS disease management very difficult. A critical step missing in this process is the establishment of a reliable and reproducible infection protocol for resistance evaluation under both controlled and field conditions. Currently, no protocols are published, and the methods published as part of research manuscripts lack detailed procedures, equipment specifications, and have major drawbacks for applications limited to controlled environment and discrepancies in field disease ratings scales. Therefore, we are presenting here a robust and reproducible BLS disease infection protocol and, disease severity rating scale for estimation of BLS disease in both controlled and field conditions. Results After Three days of inoculation with X. translucens pv. undulosa ( Xtu ), wheat plants developed initial water-soaked symptoms at inoculation sites. Over seven days, symptoms progressed to chlorosis and necrosis, frequently covering entire leaves of highly susceptible genotypes, whereas limited to no symptoms on resistant genotypes. Disease severity was consistently scored on a 1–9 scale, enabling clear differentiation of resistant, moderately resistant, and susceptible genotypes. Pathogen re-isolation confirmed infection fidelity. Field validation at the booting stage produced comparable symptom progression on flag leaves, with severity scored at 7, 14, and 21 days post-inoculation. The same protocol was successfully adapted for Pantoea ananatis and Xanthomonas prunicola , demonstrating the adaptability of the method. The protocol was repeated across five independent trials and produced reproducible results in both controlled and field environments. Conclusion We describe a simple, reproducible, and cost-effective inoculation protocol for evaluating BLS severity in wheat. The method reliably distinguishes resistance responses across environments and can be extended to other bacterial pathogens affecting small grains. Its affordability, accessibility, and reproducibility make it a valuable tool for large-scale germplasm screening and resistance breeding. Key Features • A detailed and systemic infection protocol is devised for different cultivars of wheat. • Plants can screen at seedling and adult-plant stage. • No specific equipment required. • Using an inexpensive pipeline to ensure uniform symptoms. • This protocol is validated for other bacterial species that are reported to cause bacterial leaf streak symptoms on small grains ( Pantoea spp and Xanthomonas prunicola on small grains (wheat and Barley).

Why it matches plant phenotyping methods植物の病徴・重症度を再現性よく取得・評価する感染プロトコルと重症度評価尺度を開発し、管理環境および圃場で検証しているため、植物フェノタイピング手法が中心です。

abstractwe are presenting here a robust and reproducible BLS disease infection protocol and, disease severity rating scale for estimation of BLS disease in both controlled and field conditions.

Instance-level phenotype-based growth stage classification of basil in multi-plant environments →

Climate change, shrinking arable land, urbanization, and labor shortages increasingly threaten stable crop production, attracting growing attention toward AI-based indoor farming technologies. Accurate growth stage classification is essential for nutrient management, harvest scheduling, and quality improvement; however, conventional studies rely on time-based criteria, which do not adequately capture physiological changes and lack reproducibility. This study proposes a phenotyping-based and physiologically grounded growth stage classification pipeline for basil. Among various morphological traits, the number of leaf pairs emerging from the shoot apex was identified as a robust indicator, as it can be consistently observed regardless of environmental variations or leaf overlap. This trait enables non-destructive, real-time monitoring using only low-cost fixed cameras. The research employed top-view images captured under various artificial lighting conditions across seven growth chambers. YOLO automatically detected multiple plants, followed by K-means clustering to align positions and generate an individual dataset of crop images–leaf pairs. A regression model was then trained to predict leaf pair counts, which were subsequently converted into growth stages. Experimental results demonstrated that the YOLO model achieved high detection accuracy with mAP@0.5 = 0.995, while the A convolutional neural network regression model reached MAE of 0.13 and R² of 0.96 for leaf pair prediction. Final growth stage classification accuracy exceeded 98%, maintaining consistent performance in cross-validation. In conclusion, the proposed pipeline enables automated and precise growth monitoring in multi-plant environments such as plant factories. By relying on low-cost equipment, the pipeline provides a technological foundation for precision environmental control, labor reduction, and sustainable smart agriculture.

Why it matches plant phenotyping methodsバジルの葉対数という植物形質を低コストカメラ画像から自動推定し、生育段階へ分類する画像解析パイプラインを開発・評価しており、フェノタイピング手法が中心である。

abstractThis study proposes a phenotyping-based and physiologically grounded growth stage classification pipeline for basil.

Modeling Approaches for Digital Plant Phenotyping Under Dynamic Conditions of Natural, Climatic and Anthropogenic Factors →

Methods, algorithms, and models for the creation and practical application of digital twins (3D models) of agricultural crops are presented, illustrating their condition under different levels of atmospheric CO2 concentration, soil, and meteorological conditions. An algorithm for digital phenotyping using machine learning methods with the U2-Net architecture are proposed for segmenting plants into elements and assessing their condition. To obtain a dataset and conduct verification experiments, a prototype of a software and hardware complex has been developed that implements the process of cultivation and digital phenotyping without disturbing the microclimate inside the chamber and eliminating the subjectivity of measurements. In order to identify new data and confirm the data published in open scientific sources on the effects of CO2 on crop growth and development, plants (ten species) were grown at different CO2 concentrations (0.015–0.03% and 0.07–0.09%) with a 10-fold repetition. A model has been built and trained to distinguish between cases when plant segments need to be combined because they belong to the same leaf (p-value = 0.05), and when they belong to a separate leaf (p-value = 0.03). A knowledge base has been formed, including: 790 3D models of plants and data on their physiological characteristics.

Why it matches plant phenotyping methods植物のデジタル表現型取得を中心に、U2-Netによる分割・状態評価アルゴリズム、ソフトウェア／ハードウェア複合体、検証実験、3Dモデルデータベースを開発しているため。

abstractAn algorithm for digital phenotyping using machine learning methods with the U2-Net architecture are proposed for segmenting plants into elements and assessing their condition.

Temporal profiling of meiotic asynchrony between florets for wheat-fertility studies →

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 true stress tolerance from stress escape. Based on morphological and destructive measurements of spike and anther length in four controlled-environment experiments, we developed a framework to track all floret developmental stages at plant level, and particularly meiosis. We applied this framework in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable and heat stress conditions. All florets showed a common relative growth rate, producing stable and additive developmental delays across tillers, spikelets, and floret positions. This generated a developmental map for every floret based on simple external traits. Under favorable 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 offers a quantitative tool to understand and predict floret development and link it to grain set, clearly distinguishing timing effects from positional influences and separating true tolerance from stress escape.

Why it matches plant phenotyping methods小花の発達段階を形態測定から追跡・推定する定量的フレームワークを開発し、植物体レベルで検証・適用しているため、表現型取得法が中心的です。

abstractwe developed a framework to track all floret developmental stages at plant level, and particularly meiosis.

Integration of temperature control and imaging-based prediction for grafting-related morphological regulation in tomato seedlings in a closed transplant production system →

• ADT and DIF precisely regulated elongation and thickening in tomato seedlings. • Canopy traits imaging predicted grafting traits with high accuracy (R 2 > 0.9). • CTPS-imaging integration offers real-time monitoring for grafting suitability. • First trial on tailored production with imaging for an automated grafting. Tomato seedling growth and quality are crucial determinants of the success of grafting and transplant establishment. This study aimed to investigate temperature control strategies in a closed transplant production system (CTPS) and their integration with imaging-based prediction to produce grafting-ready seedlings. Scion ‘Dotaerang Dia’ and rootstock ‘B-Blocking’ were grown under combinations of average daily temperatures (ADTs; 24 and 26 °C) and difference between day and night temperatures (DIFs; –8, –4, 0, +4, and +8 °C). Morphological traits crucial for grafting, including the epicotyl length (EPL) and diameter (EPD) of scions and hypocotyl length (HYL) and diameter (HYD) of rootstocks, and canopy traits, including leaf area index (LAI) and canopy height (CH), were evaluated. Higher ADTs and positive DIFs promoted elongation, whereas lower ADTs and negative DIFs restricted elongation and improved compactness. Compact seedlings with a higher dry matter content are advantageous for grafting, whereas seedlings with greater elongation and dimensional synchrony better meet the requirements of robotic grafting. Imaging-based monitoring using multispectral-derived LAI and light detection and ranging (LiDAR)-derived CH accurately predicted grafting-related traits ( R 2 > 0.9 for EPL, EPD, and HYD); however, HYL predictions were less reliable under negative DIFs. Leave-one-environment-out cross-validation confirmed robust performance for diameter traits across environments. These findings collectively indicate that CTPS enable precise morphological regulation of tomato scions and rootstocks through temperature control, whereas imaging-based phenotyping allows a basis for real-time prediction of grafting suitability. This integration establishes a scalable and automation-ready framework for grafted transplant production, offering a technological foundation for developing automated grafting strategies.

Why it matches plant phenotyping methods画像計測（マルチスペクトルによるLAI、LiDARによる樹冠高）から接ぎ木関連形態形質を予測・検証し、リアルタイムな接ぎ木適性評価を実現する方法が研究の中心である。

abstractCanopy traits imaging predicted grafting traits with high accuracy (R 2 > 0.9).

Artificial pre-harvest sprouting chamber with moderate humidity better simulates field sprouting for soft winter wheat. →

Pre-harvest sprouting, germination of the seed on the spike, causes reduced grain quality and marketability in US wheat. Methods devised to induce and study pre-harvest sprouting vary significantly from one another in procedure, induction time, and/or measurement, and often fail to accurately reflect natural sprouting, which varies over years and locations. An artificial sprouting chamber and protocol with a shorter exposure time and relative humidity more relevant to field conditions was significantly correlated ( p p = 0.001) with those subjected to overhead irrigated field sprouting tests over three years. Four pairs of the ten varieties were genetically related but showed significantly different sprouting and alpha amylase activity. These varietal pairs may prove useful in studying the genetics of pre-harvest sprouting. While the study was performed in wheat, other susceptible crops such as rice, barley, rye, and sorghum could be tested using this method.

Why it matches plant phenotyping methodsコムギの穂発芽という植物状態を測定する人工チャンバーとプロトコルを開発し、圃場試験との相関で妥当性を検証しており、表現型取得法が研究の中心である。

abstractMethods devised to induce and study pre-harvest sprouting vary significantly from one another in procedure, induction time, and/or measurement

Digital twin for lettuce growth in six climate zones (‘Climate 2050′) →

This study investigates lettuce growth under extreme environmental conditions by simulating the weather in six climate zones in a plant growth chamber, including Lleida, Adelaide, Paris, San Luis, Singapore, and Fairbanks. The experiment involved weekly exposure to a new city’s climate, simulating “non-terrestrial weather stress,” which is also motivated from the vantage point of space plant growth and its process-control limitations. These simulated conditions shed light on ‘Climate 2050’, when Earth will probably have harsher and more fluctuating conditions. For the period investigated, the real temperature changes could be reproduced well and in real-time in the growth chamber, the actual rain fall was mimicked, and the lighting period was adjusted to the real sunshine exposure in the respective city. The virtual move of the lettuce plant from between six climates with their own profile in temperature, lighting time, and water is assumed to create stress beyond the variability of a weather change within a single climate. Machine learning models, including linear regression, random forest regression, and boosted decision tree regression, were employed to predict weekly lettuce biomass and yield. This study successfully demonstrated the application of machine learning algorithms for predicting lettuce growth under the given range of six climate conditions. Among the tested models, random forest regression consistently delivered the most accurate and reliable biomass predictions, achieving an R² of nearly 99 % and MAPE of 6 % in all scenarios. By introducing tuned correction factors for conditions like drought stress, fertilisation, and mixed soil composition, the accuracy and flexibility of models are enhanced. This research highlights the value of integrating real-time data with machine learning through a digital twin framework, offering a promising direction for climate-resilient agriculture and space-based plant growth systems.

Why it matches plant phenotyping methodsデジタルツインと機械学習を中核に、レタスのバイオマスおよび収量を予測する再利用可能な計算ワークフローを構築・評価しており、植物形質推定法が中心である。

abstractMachine learning models, including linear regression, random forest regression, and boosted decision tree regression, were employed to predict weekly lettuce biomass and yield.

Use of Machine Learning Combined With UV-VIS-NIR Spectroscopy to Monitor Okra Plant Growth and Development in Controlled Light Environment →

Climate change has led growers with uncertainty on crop growth, development, quality and yield. Thus there is a critical need to set up proper tools to help growers follow up their crop during the growth period and ensure better production at the end. In this context we used predictive machine learning models for predictions of Okra development in a controlled lighting environment based on UV-VIS-NIR spectroscopy. Fluorescence and reflectance spectroscopy data was collected from several leaves of Okra grown under different artificial lighting condition, then vegetation spectral indices were computed and used as features for the prediction of four growth and development parameters namely Plant Height (PH), Leaf Number (LN), stem diameter (SD) and Leaf Area Index (LAI). The different trained machine learning models explicitly Linear regression, K-nearest Neighbor, Support Vector Machine, Single Tree, Random Forest, Gradient Boosting, extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) give good performance in the prediction of PH (R2 ranged from 0.93 to 0.97), LN (R2 ranged from 0.88 to 0.94), SD (R2 ranged from 0.95 to 0.98) with the tree-based algorithm outperformed the others. However, these trained models give poor performance on the prediction of LAI (R2 ranged from 0.25 to 0.37). Furthermore, the most responsive features and vegetation spectral indices were also identified using Shapley Additive Explanations. This work aims to help growers to follow up their crops development and moreover intend to be used as a decision tool in an overall horticultural management process to engineer their crop development.

Why it matches plant phenotyping methodsUV-VIS-NIRおよび蛍光・反射分光データから、機械学習でオクラの草丈、葉数、茎径、LAIを推定する手法が研究の中心であり、性能評価も実施しているため。

abstractwe used predictive machine learning models for predictions of Okra development in a controlled lighting environment based on UV-VIS-NIR spectroscopy.

Multimodal phenotyping reveals structural–physiological coordination mechanisms underlying light-use efficiency in lettuce →

Improving light-use efficiency (LUE) is essential for boosting crop productivity, particularly in controlled-environment agriculture. Despite recent advances, most studies still rely on destructive measurements or one-dimensional data, which limits insight into the structural–physiological coordination underlying LUE. We established a multimodal phenotyping platform to dissect the phenotypic regulatory network of LUE in lettuce ( Lactuca sativa L.). Integrating hyperspectral imaging with multiview three-dimensional (3D) reconstruction, we developed a noninvasive, high-throughput system that simultaneously estimates 3D plant architecture, photosynthetic physiology—net photosynthetic rate (A) and relative chlorophyll content (SPAD)—and aboveground biomass (AGB) across 35 cultivars. A modeling pipeline combining StandardScaler (SS) normalization, genetic algorithm (GA) feature selection, and artificial neural networks (ANN) achieved robust prediction of A (R²=0.72), SPAD (R²=0.87), and AGB (R²=0.85). Spectral contribution analysis revealed distinct sensitivities: SPAD across 400–700 nm, A near 430 and 680 nm, and AGB across 500–580 nm. The 426–430 nm blue band emerged as a key region: high-efficiency cultivars showed distinctive reflectance (42.93–59.03 %), consistent with superior photosynthetic performance. Structurally, high-efficiency types exhibited “large-and-loose” canopies, with greater plant height (+64.37 %), projected area (+59.42 %), and convex-hull volume (+166.3 %), alongside reduced compactness (−23.48 %). Network analysis indicated progressively tighter coupling between spectral and structural traits from low- to high-efficiency groups, consistent with adaptive coordination for light capture and use. These results identify actionable phenotypic markers for selecting high-LUE cultivars and provide a transferable platform for phenomics-driven breeding and management in controlled-environment crops. • A multimodal framework enables non-destructive, high-throughput phenotyping in lettuce. • 66 key spectral and structural features linked to light-use efficiency were identified. • A photosynthetic trait network reveals coordination of pigments and canopy architecture. • Breeding targets for blue-light response and canopy structure optimization are proposed.

Why it matches plant phenotyping methodsレタスの構造・生理形質を推定するマルチモーダル表現型プラットフォームを開発し、非破壊・高速測定と予測性能を評価しており、表現型取得手法が研究の中心である。

abstractWe established a multimodal phenotyping platform to dissect the phenotypic regulatory network of LUE in lettuce ( Lactuca sativa L.).

Severity of Charcoal Rot Disease in Soybean Genotypes Inoculated with Macrophomina phaseolina Isolates Differs Among Growth Environments. →

Charcoal rot of soybean caused by Macrophomina phaseolina is a major disease of economic significance around the world. The objective of this test is to evaluate and identify new method(s) that can identify resistant and susceptible (S) genotypes when inoculated with the fungus that causes charcoal rot in nonfield environments. Four independent experiments were performed to determine the variability in disease severity when soybean genotypes are inoculated with isolates up to a total of 100 different variants of the charcoal rot fungus in laboratory, greenhouse, and growth chamber tests. Linear mixed models were fit to area under the disease progress curve values from the four experiments and model predictions of disease progress were used to determine the best method to classify moderately resistant (MR) and S genotypes. In a growth chamber study using a modified cut-tip inoculation method, 28 of the 32 M. phaseolina isolates tested differentiated MR and S with >87% accuracy. In a study where 16 field-grown soybean genotypes were stem-wound inoculated with one isolate, the MR genotypes were correctly classified, but not all S genotypes were. Correct classification of MR genotypes dramatically increased with plant age, approaching 100% accuracy at 120 days after planting. In a study of stem-wound inoculation of field-grown soybean genotypes with 20 M. phaseolina isolates, MR were identified with more than half the isolates having >75% accuracy in detecting MR genotypes. In a study of greenhouse-grown soybeans stem-wound-inoculated with 100 isolates, classification was less accurate than samples grown in the field, with median correct classification P = 0.0006), and the rank correlations with the growth chamber study were weak. The results showed that, except for the growth chamber study, the nonfield environments did not consistently identify the same soybean lines as being S or MR to charcoal rot as were identified as in naturally infested field testing because of differences in isolates, environment, soybean varieties, and methods (or all the above). Stakeholders will benefit more from the use of the field assessment method in naturally infested soil to identify reliable sources of resistance than from the nonfield methods.

Why it matches plant phenotyping methodsダイズの炭腐病重症度を用いて、接種法・栽培環境・分離株による抵抗性判別法を比較検証しており、植物病害表現型の取得と分類性能が研究の中心である。

abstractThe objective of this test is to evaluate and identify new method(s) that can identify resistant and susceptible (S) genotypes when inoculated with the fungus that causes charcoal rot in nonfield environments.

A high-throughput phenotyping dataset for GWAS analysis of maize under combined drought and heat stress →

This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.

Why it matches plant phenotyping methods高スループット画像表現型データセットが中心で、画像から農業形質を抽出したデータ、処理済み画像、解析スクリプトを提供しているため。

titleA high-throughput phenotyping dataset for GWAS analysis of maize under combined drought and heat stress

Combined effect of salt stress and high light in plants: from basic statistical approach to machine learning methods. →

Infrared thermal imaging offers a rapid and sensitive approach to assessing temperature changes in plants caused by salt stress, even in the early stages of exposure. Given the increasing prevalence of salt contamination in the environment, it is essential to accurately estimate salinity levels, as the effects strongly depend on salt concentration: moderate salinity elicits a reversible, osmotic driven rise in leaf temperature, whereas higher salinity induces a larger, sustained temperature increase indicative of ion toxicity related stress. We propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions. Thermal measurements are then used to train machine learning models used to perform multi-class classification to distinguish between four different salt concentrations. To test the proposed method, we cultivated Arabidopsis thaliana plants under controlled conditions. Data collected from the prepared samples were used to assess the accuracy of various approaches and classifiers with lead-one-out cross-validation. This experimental evaluation shows that the optimal performance is achieved when the datasets used for training consist of longer sequences of thermal data provided to models using neural networks.

Why it matches plant phenotyping methods植物の熱画像から塩ストレスの重症度を推定・分類する画像計測と機械学習手法が研究の中心であり、植物状態の表現型取得・推定に該当する。

abstractWe propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions.

Biomarker genes for model-based prediction of drought-stress perception levels in rice. →

Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4-6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.

Why it matches plant phenotyping methodsイネの乾燥ストレス知覚レベルという植物状態を、遺伝子発現バイオマーカーと機械学習で推定する手法を開発し、複数アクセッションで検証しているため、方法論が中心である。

abstractWe therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles

Wireless Sensor Network: New Concept of Spatial-Temporal Monitoring Plant–Environment Interactions →

We present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant–environment interactions. Each plant carries in-canopy microclimate sensors (temperature, relative humidity, illuminance) paired with nearby ambient references, yielding real-time canopy-ambient differentials. The system is easy to install: at planting or sowing, sensors are fixed at positions that will lie within the developing canopy, and a separate ambient reference area is designated and kept free of vegetation. As plants grow, they envelop the sensors, thereby capturing growth dynamics over time. The sensors accuracy was validated against a commercial weather station and portable system that measures gas exchange, temperature and light (LI-COR 6800/6400), and the system’s ability to resolve plant physiological activity was confirmed using the PlantArray functional phenotyping platform with independent whole-plant transpiration and biomass references. Under controlled growth-room conditions and across two contrasting Cannabis cultivars, daily transpiration strongly predicted biomass gain (R² > 0.9). Microclimate signals mirrored physiology: midday canopy air was cooler by 4–7 °C, more humid by 18–25 % RH, and increasingly shaded as biomass accumulated, with temperature, RH, and light attenuation showing saturating logarithmic relationships with growth. The network operated for months unattended with low packet loss and predictable power use. It provides 4D (x–y–z–time) coverage, where x and y denote horizontal location, z the vertical position within the canopy, and time the dynamics, enabling resolution of where changes occur and how they evolve, and supplying high-frequency labeled data. This system complements, rather than replaces, precision instruments and high-end phenotyping platforms, providing a scalable layer for continuous tracking across wide areas. We outline practical constraints and next steps toward field pilots, modest energy harvesting, expanded sensor suites, and integration with machine learning for predictive crop management.

Why it matches plant phenotyping methods植物キャノピー内のセンサー網を開発し、植物生理・蒸散・バイオマスを連続推定する方法として検証しており、植物フェノタイピング手法が中心である。

abstractWe present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant–environment interactions.

An HPC Framework for Multi-Modal Plant Phenotyping Integrating Controlled Environment and Open Field Studies →

This paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science. Our framework addresses the unique challenges in high-throughput plant phenotyping (HTP) research, which integrate multi-mode imaging sensors such as RGB, hyperspectral, LiDAR, thermal, and X-ray computed tomography (CT) across indoor and outdoor environments. Our primary objective is to transform raw sensor data into research-ready plant phenotypical traits. This directly supports downstream agricultural research, such as in plant breeding, crop management, etc. The framework is structured into two main phases. The initial data processing occurs on non-HPC systems, utilizing operating system and license-specific software due to the specialized algorithms and knowledge required for each imaging data pipeline. The subsequent HPC phase refines these processed datasets into tabular formats, preparing them for statistical analysis in agronomy, plant science, and bioinformatics. The current implementation of the second phase utilizes a hybrid parallelization model across HPC nodes and threads. However, its performance could be significantly enhanced by implementing more efficient algorithms and optimizing resource allocation. We discuss current bottlenecks, including technical challenges related to sensors, imaging platforms, and computational pipelines, and propose immediate solutions.

Why it matches plant phenotyping methodsHPCを用いて多モーダルセンサーデータから植物形質を抽出する計算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基盤として該当する。

abstractThis paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science.

Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review →

Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.

Why it matches plant phenotyping methods植物フェノタイピングにおける成長形質の予測・表現・高スループット計測を扱う方法論レビューであり、方法論が中心です。

abstractThis review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions.

Simulating light quantity and quality over plant organs using a ray-tracing method to investigate plant responses in growth chambers →

Ray-tracing models enable the assessment of light quantity and quality intercepted by plant organs, supporting biological studies in growth chambers with varying light conditions. However, their validation within canopies and clear usage methods remain limited. This work establishes a reliable method for using these models. The method includes i) accounting for the intensity and spectrum of light sources in the calibration procedure; ii) a generic calibration strategy using a few well-placed light measurement points based on chamber geometry. It evaluates the method to simulate light phylloclimate at the organ scale across biologically relevant wavebands of contrasted widths and properties. Using the SEC2 light simulation framework, three virtual experiments were conducted in a growth chamber, with and without rose plants. Inputs included chamber geometry, material optical properties, lamp emissions, and digitised plant mock-ups. Simulations were compared with spectral measurements at various chamber positions and sensor orientations, both without plants and inside a canopy. Results showed high accuracy in replicating spatial light variability, with RMSE ranging 0.011 to 0.021 and 0.014–0.038 μmol m⁻²s⁻¹nm⁻¹ across different wavebands and sensor orientations, for vertical and horizontal transects, respectively. Applying this approach to a case study demonstrated its effectiveness in formulating new biological hypotheses regarding the role of local light in regulating bud outgrowth. This was achieved by highlighting differences in phylloclimate induced by variations in plant architecture. This work thus provides a comprehensive framework for facilitating the application of ray-tracing models in growth chamber studies.

Why it matches plant phenotyping methods植物器官スケールの光環境を推定するレイトレーシング手法を開発・較正し、実測値との検証まで行っており、植物の器官状態・アーキテクチャに関わる再利用可能な計測ワークフローが中心です。

abstractThis work establishes a reliable method for using these models.

Can high-throughput 3D and multispectral phenotyping detect early grapevine responses to water stress events? →

Phenotyping is pivotal in biological and agronomical research, enabling the characterization of phenotypic traits in living organisms. Recent advancements have led to the development of innovative platforms that enhance the precision of phenotyping, integrating genetic and ecophysiological analyses for a comprehensive understanding of plant growth under controlled conditions. These technologies are instrumental in studying plant responses to environmental stresses, such as drought, which disrupts water balance in plants. This study focuses on the adaptability of grafted grapevines (Vitis vinifera L.) to drought stress, emphasizing the rootstock influence on scion performance. The experimental trial was performed at 'PhenoPlant,' a cutting-edge phenotyping platform at the University of Torino, DISAFA. PhenoPlant is a non-invasive, high-throughput tool that employs advanced technologies, including a PlantEye sensor for 3D vision and multispectral imaging, measurement of the potted-plant evapotranspiration by gravimetric technique, water potential assessment and Infra-Red Gas Analysis for leaf-to-atmosphere gas exchange detection. Grapevine responses to drought stress across eleven scion/rootstock combinations, featuring clones of Nebbiolo and Pinot Noir grafted onto rootstocks with varying drought tolerance were assessed. A 13-day drought-recovery experiment on grafted 1-year old plants, three months after in-pot-transplanting revealed significant differences in drought responses among rootstock/scion combinations. Drought-tolerant rootstocks (e.g., 1103 P, 110 R, 140Ru, M2) maintained stable spectrometric indices (e.g.: GLI, Green Leaf Index) mirroring morpho-physiological ones (e.g., Leaf Surface Angle - SA, Stomatal Conduction - gs, Stem Water Potential and Evapotranspiration), unlike their less tolerant counterparts (e.g., Kober 5BB, SO4, 420 A, Gravesac). In particular, after 10 days of water removal, a reduced variation in some traits was observed in tolerant combinations (SA: 39-44°; GLI ≈ 0.33-0.35; gs: 34.5-45.4 mmol H₂O·m⁻²·s⁻¹), while decreasing markedly in sensitive ones (SA: 27-35°; GLI: 0.28-0.32; gs: 8.6-10.8 mmol H₂O·m⁻²·s⁻¹), underscoring the rootstock's crucial role in drought response, independently from scion cultivar. These findings are vital for a fast and early assessment of multiple rootstock/scion combinations to optimize grapevine management and breeding programs for enhanced performance under water-limited conditions. Intrinsic limitations of the measurement system and aspects to be considered to export results from the platform to the vineyard are presented and discussed.

Why it matches plant phenotyping methods3D・マルチスペクトルセンサーを用いる高スループット表現型解析プラットフォームを中心に、干ばつ応答の早期評価と測定系の限界を検討しているため。

titleCan high-throughput 3D and multispectral phenotyping detect early grapevine responses to water stress events?

A controlled environment assay for the rapid evaluation of verticillium stripe resistance in canola →

Abstract Verticillium stripe disease is an emerging threat to canola production in Western Canada. Accurately assessing verticillium stripe resistance in breeding germplasm collections is crucial for identifying sources of genetic resistance. Currently, field phenotyping is the most widely used method for evaluating verticillium stripe resistance; however, achieving uniform disease pressure under field conditions presents significant challenges. Here we report a novel controlled environment (CE) soil-less assay for the rapid evaluation of verticillium stripe resistance in canola. The CE results were validated in a field trial which showed a strong correlation between the field data and the CE results. This cost-effective and time-efficient assay enables accurate assessment of verticillium stripe symptoms in a one-month testing cycle.

Why it matches plant phenotyping methodsカノーラの病害抵抗性・症状を迅速に評価する新規の環境制御アッセイを開発し、圃場データとの相関で妥当性を検証しており、表現型取得法が中心である。

abstractHere we report a novel controlled environment (CE) soil-less assay for the rapid evaluation of verticillium stripe resistance in canola.

Full-time sequence assessment of okra seedling vigor under salt stress based on leaf area and leaf growth rate estimation using the YOLOv11-HSECal instance segmentation model. →

Introduction With the growing severity of global salinization, assessing plant growth vitality under salt stress has become a critical aspect in agricultural research. Methods In this paper, a method for calculating the leaf area and leaf growth rate of okra based on the YOLOv11-HSECal model is proposed, which is used to evaluate the activity of okra at the seedling stage. A high-throughput, Full-Time Sequence Crop Germination Vigor Monitoring System was developed to automatically capture image data from seed germination to seedling growth stage, while maintaining stable temperature and lighting conditions. To address the limitations of the traditional YOLOv11-seg model, the YOLOv11-HSECal model was optimized by incorporating the HGNetv2 backbone, Slim-Neck feature fusion, and EMAttention mechanisms. Results These improvements led to a 1.1% increase in mAP50, a 0.6% reduction in FLOPs, and a 14.1% decrease in model parameters. Additionally, Merge and Cal modules were integrated for calculating the leaf area and growth rate of okra seedlings. Finally, through salt stress experiments, we assessed the effects of varying NaCl concentrations (CK, 10 mmol/L, 20 mmol/L, 30 mmol/L, 40 mmol/L, 50 mmol/L, and 60 mmol/L) on the leaf area and growth rate of okra seedlings, verifying the inhibitory effects of salt stress on seedling vitality. Discussion The results demonstrate that the YOLOv11-HSECal model efficiently and accurately evaluates okra seedling growth vitality under salt stress in a full-time monitoring manner, offering significant potential for broader applications. This work provides a novel solution for full-time plant growth monitoring and vitality assessment in smart agriculture and offers valuable insights into the impact of salt stress on crop growth.

Why it matches plant phenotyping methodsYOLOベースの画像解析モデルと自動撮像システムを開発し、オクラ幼苗の葉面積・葉成長率を連続推定することが研究の中心であるため。

abstracta method for calculating the leaf area and leaf growth rate of okra based on the YOLOv11-HSECal model is proposed

Phenotypic evaluation of worldwide germplasm of arugula (Eruca sativa Mill.) and identification of underlying latent factors contributing to phenotypic variation under indoor farming conditions →

Eruca sativa (arugula) is often consumed fresh in regions where raw salads are a dietary staple. Studies investigating the phenotypic diversity of E. sativa have been reported in the past differentiating them by gene pools according to geographical origins. We expanded the scope of analysis to include deep phenotypes, and the diversity of germplasm. Furthermore, there is no report of such crop being evaluated in a large scale under indoor farming conditions. In this study, 185 accessions were subjected to phenotypic evaluation across 68 phenotypic traits. High-throughput phenotyping machines and image processing platforms employed were efficient to measure vegetative yield-, hyperspectral-, and plant architecture-related traits of E. sativa . Wide phenotypic variations were evidenced in the collection and significant differences were observed between accessions in majority of the traits evaluated. The population genetic structure divided the germplasm collection into three major continental clusters (Asia, Africa, and Europe). In addition, the three major continental clusters also showed significant differences in the tendency to flower early, vegetative leafy plant yield, plant height, vegetative index, hairiness and leaf blade color. Factor analysis revealed nine underlying latent factors contributing approximately 70 % of the total phenotypic variations, with each potentially enhancing crop’s productivity and quality. Based on desirable agronomic traits that are suitable for controlled environment agriculture (CEA), bivariate analysis was conducted using four latent factors (Total yield-, plant height-, post-harvest-, and flowering-related). Subsequently, three ideal accessions (ERU12, PI 178901, and PI 251491) were highlighted as high-yielding, short, long shelf-life crops for potential future plant breeding and genetic improvement.

Why it matches plant phenotyping methods大規模な遺伝資源評価が主目的だが、高スループット表現型測定機器と画像処理を用いて、収量・ハイパースペクトル・草姿形質を測定するワークフローが substantive に記述されており、植物表現型取得の応用研究として採用する。

abstractHigh-throughput phenotyping machines and image processing platforms employed were efficient to measure vegetative yield-, hyperspectral-, and plant architecture-related traits of E. sativa .

Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review. →

Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.

Why it matches plant phenotyping methods植物フェノタイピングにおける成長形質の予測モデル、パターン認識、時空間表現を中心に扱う包括的レビューであり、方法論が主題です。

abstractThis review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions.

Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory →

This study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R2) of 0.98. A spatial resolution of 0.078 mm/pixel was derived by referencing a scale ruler and processing pixel counts, eliminating outliers in the data. Image post-processing focused on extracting the canopy boundary and calculating the crop canopy area. By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R2, prediction speed, training time, and model size. Among them, the Wide Neural Network model emerged as the most optimal. It demonstrated remarkable predictive accuracy with an R2 of 0.95, RMSE of 27.15 g, and MAPE of 11.74%. Furthermore, the model achieved a high prediction speed of 60,234.9 observations per second, and its compact size of 7039 bytes makes it suitable for efficient, real-time deployment in practical applications. This model offers substantial support for managing crop growth, providing a solid foundation for refining cultivation processes and enhancing crop yields.

Why it matches plant phenotyping methods作物キャノピー画像から面積を抽出し、その形質を用いた収量予測モデルを比較・最適化しており、画像取得・抽出と予測ワークフローが中心的な方法論的貢献である。

abstractprecise crop canopy image capture and background interference removal

Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems. →

As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop productivity and food security. Traditional farming methods struggle with efficiently managing nutrients and water while ensuring high-quality products, leading to resource wastage and food safety concerns. This study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems, thereby enhancing resource management and product quality. We integrated a physics-based model with machine learning algorithms to create a dynamic hybrid framework. The model was validated with real-time data from aeroponic systems, showing good predictive performance, particularly for fresh weight and total leaf area. In contrast, predictions of nitrate content and water consumption were less accurate, due in part to smaller training datasets and limitations of the physics-based component under soilless conditions. Despite these challenges, the hybrid model offers a promising solution for optimizing controlled environment agriculture, addressing critical challenges in modern agriculture by improving efficiency and sustainability.

Why it matches plant phenotyping methods植物の生体重と葉面積という形態・成長形質を推定するハイブリッド計算モデルを開発し、実データで検証しており、形質推定手法が研究の中心である。

abstractThis study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems

Strawberry harvest date prediction using multi-feature fusion deep learning in plant factory →

Strawberries have high consumer demand due to their palatability and nutritional benefits. Commercial strawberry production in plant factories with artificial lighting (PFALs) is gaining popularity as a viable strategy for improving economic viability through high-quality fruit production. Accurate information of the optimal harvest date is crucial for optimizing harvesting decisions. While numerous studies utilize deep learning to assess strawberry ripeness, they typically only categorize generalized ripeness levels instead of predicting specific harvest dates, leaving a gap with the practical needs of growers. In this study, we proposed a two-stage multi-feature fusion model for strawberry harvest date prediction and integrated it with a web application to facilitate practical production management in PFALs. The model consists of a fruit segmentation network and a ripeness prediction network. A time-series image dataset of single fruits was constructed to continuously track the ripening process of strawberries, and a five-stage division of strawberry ripeness stages depending on optimal harvest dates was defined. A U-Net based segmentation network with post-processing was developed to automatically extract only the target fruits, which showed a reliable performance with a mIoU of 0.977. A multi-feature fusion network called Triple-Branch Attention Fusion (TBAF) was built to predict the ripeness categories with information on optimal harvest dates. The results showed that the TBAF model with fusion of color, attention-enhanced, and low-level shape features exhibited the highest performance compared to baseline models, with an overall accuracy of 0.859 and an F1 score of 0.859. In addition, a user-friendly web application was developed with the deployment of deep learning models and inspection video processing workflow to support strawberry harvesting in PFALs. Overall, this study demonstrated a prototype approach utilizing deep learning to provide essential information for grower’s decision making in practical strawberry production.

Why it matches plant phenotyping methodsイチゴ果実の画像から成熟段階・最適収穫日を推定するセグメンテーションおよび深層学習ワークフローを開発しており、植物状態の取得・推定手法が中心である。

abstractA U-Net based segmentation network with post-processing was developed to automatically extract only the target fruits

A novel approach to water stress assessment in plants: New bioimpedance method with PSO-optimized Cole-Cole impedance modeling →

In order to characterize plant water deficiencies, this paper presents a custom-developed bioimpedance (BIS) measurement setup designed for in vivo studies that extracts plant leaf parameters using a novel optimization approach based on the particle swarm optimization (PSO) algorithm. The system performs, four-electrode measurements on plant leaves and employs a custom multi-objective cost function to validate parameters for the Double Shell Cole-Cole model. The experiment consisted of two parts: first, pepper plants (Capsicum annuum L.) as a model plant were exposed to drought stress in a light chamber, and their impedance and physiological parameters were measured. In the second part of the experiment, detached pepper leaves were allowed to dry naturally, and impedance measurements were recorded at hourly and tri-hourly intervals. Impedance spectrum measurements from 230 samples (1 Hz to 100 kHz), collected during both experiments, demonstrated that extracellular fluid resistance increases linearly with water loss. The proposed PSO-optimized Double Shell model showed a stronger correlation between extracellular fluid resistance and water loss compared to the widely used Zfit algorithm, which exhibited higher coefficient of variation in the Cole-Cole parameters. Both algorithms showed a significant negative correlation between relative water content and extracellular fluid resistance, but only the proposed PSO-based model detected a relationship between cell membrane capacity and membrane stability index. Additionally, extracellular fluid resistance correlated with photosynthetic efficiency. The results highlight the effectiveness of impedance measurements for assessing plant water status and support the reliability of proposed PSO-based optimization for bioimpedance analysis.

Why it matches plant phenotyping methods植物の水分状態を測定するバイオインピーダンス計測系とPSO最適化モデルを開発・比較検証しており、植物フェノタイプ取得手法が研究の中心である。

abstractthis paper presents a custom-developed bioimpedance (BIS) measurement setup designed for in vivo studies that extracts plant leaf parameters using a novel optimization approach based on the particle swarm optimization (PSO) algorithm.

Morphometric analysis of turfgrass using digital three‐dimensional technology and its application in breeding →

Abstract Advancements in digital three‐dimensional (3D) imaging technology have enabled precise, high‐throughput, and non‐destructive phenotyping of plant morphology. In this study, we developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis. By employing a structure from motion approach, we reconstructed detailed 3D models of zoysiagrass using four industrial cameras and an automated imaging platform. A machine learning algorithm was applied to accurately isolate plant components from non‐plant elements. From these segmented models, we extracted key morphological traits—height, spread area, color, and volume—providing a comprehensive dataset for breeding applications. As a digitally derived trait, volume offers new potential in characterizing plant architecture and assessing yield‐related traits non‐destructively. Additionally, we developed a small‐scale, low‐cost prototype system using Raspberry Pi and LEGO‐based components, demonstrating the scalability and adaptability of 3D phenotyping systems across various experimental settings and budgets. Although 3D phenotyping under controlled conditions using potted plants is not directly transferable to field‐based evaluation, it provides essential, reproducible data that bridge early‐stage screening and later field validation in breeding programs. These digital morphological measurements are expected to enhance the precision, repeatability, and objectivity of turfgrass evaluation. As 3D technologies continue to evolve and integrate with genomic and environmental data, digital phenotyping will play an increasingly important role in accelerating turfgrass improvement and promoting data‐driven plant breeding.

Why it matches plant phenotyping methods植物形態を対象とする3D画像フェノタイピングシステムを開発し、機械学習による分離と形態形質抽出、さらに低コスト試作機まで扱っており、フェノタイピング手法が研究の中心である。

abstractwe developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis.

Sensl: a synthetic biology sensor for tracking strigolactone signaling in rice. →

Strigolactones (SLs), a special class of plant hormones and rhizosphere signals, play an indispensable role in regulating important agronomic traits of rice (Oryza sativa), one of the most important crops in the world (Wang et al., 2018; Chen et al., 2022). Genetically or chemically modifying the SL pathway could significantly alter plant architecture and crop yield of rice through the regulation of shoot branching (Zou et al., 2006; Arite et al., 2007; Lin et al., 2009; Wang et al., 2020). SLs can also coordinate with other plant hormones, such as auxin, brassinosteroid, gibberellin, and abscisic acid (ABA), to regulate rice growth and metabolic processes (Sang et al., 2014; Fang et al., 2020; Liu et al., 2020; Sun et al., 2023). Moreover, SLs have been shown to participate in integrating N, Pi, as well as sucrose signals to influence rice development and environmental adaptation (Shi et al., 2021; Patil et al., 2022; Barbier et al., 2023). In light of the pivotal role of SLs in rice breeding, it is necessary to develop precise molecular tools for real-time monitoring of SL signaling dynamics in rice. Up to now, strategies for in vivo and in situ tracking of SLs have been limited. Researchers largely depend on mass spectrometry (MS) to quantify SLs in plant cell lysate (Xie et al., 2013; Halouzka et al., 2020; Yoneyama et al., 2022), which is often compromised by SLs' limited stability, high MS costs, and scarce analytical standards (Floková et al., 2020). Recent advances in genetically encoded ratiometric reporter systems, such as StrigoQuant (Samodelov et al., 2016) and pRATIO (Khosla et al., 2020; White et al., 2022), along with engineered fluorescent biosensors utilizing SL receptors (Chesterfield et al., 2020), provide promising alternatives. However, the above-mentioned sensors are only successfully applied in transient expression systems, of which the application in intact plants has not been achieved. Although the fluorescent biosensor Strigo-D2 enables SL signaling monitoring in Arabidopsis seedlings (Song et al., 2022), there remains a critical gap in high-throughput, noninvasive tools for SL signaling detection in vital crops like rice, where SLs are essential for regulating several important agronomic traits. We first demonstrated that Sensl's response is dependent on bioactive SLs and proteasome activity. Next, we validated Sensl's ability to detect fluctuations in endogenous SL levels in intact living rice. We also pursued extensive Sensl exploration in distinguishing SL stereochemistry and detecting cross talk between SLs and other phytohormones. Additionally, we developed a streamlined in vitro method for assessing OsD53 degradation using Sensl. Collectively, our results highlight the significant value of Sensl as a versatile instrument for unraveling SL signaling in rice by combining species specificity, ratiometric precision, and in vivo–in vitro dual-mode compatibility. The study deployed rice (O. sativa L. ssp. japonica cv Nipponbare) as the wild-type (WT) background and for generating Sensl transgenic lines. Rice seeds were subjected to surface sterilization using 20% (v/v) sodium hypochlorite solution with 0.01% Triton X-100 for 20 min. Following sterilization, seeds were rinsed five times with sterile double-distilled water and then germinated for 30 h in sterile water at 28°C in the dark. Germinated seeds were hydroponically cultivated in Yoshida rice nutrient salt mixture (Coolaber, NSP1040) within a growth chamber maintained under controlled conditions (14 h : 10 h, 28 : 22°C, light : dark photoperiod, 200 μmol photons m−2 s−1). To assess Sensl's specificity and dose–time response to SLs, 5-d-old rice seedlings were sprayed with luciferase–hormone mixtures combining various SL analogs with 0.06 mg ml−1 d-Luciferin in a 1 : 1 ratio. To investigate the proteasome-dependent degradation of Sensl, 5-d-old rice seedlings were pretreated with 50 μM MG132 for 5 h before being sprayed with luciferase–hormone mixtures. For the Pi deprivation response assay 4-deoxyorobanchol (4DO) content detection, luminescence intensity analysis, and quantitative real-time polymerase chain reaction (qRT-PCR)), rice seedlings were initially cultured in a standard Pi nutrient solution for 12 d. Afterward, the seedlings were transferred to a standard Pi (NSP1040) or Pi-deficient (NSP1040-P) liquid medium and grown for an additional 2 wk (14 d) under identical temperature and light conditions in a growth chamber, replenishing the culture medium every 3 d. For assessing short-term hormonal responses, a range of plant hormones was mixed with 0.06 mg ml−1 d-Luciferin substrate in a 1 : 1 ratio. The solutions contained 50 μM ABA, 20 μM brassinolide (BL), 50 μM 1-aminocyclopropane-1-carboxylic acid (ACC), 10 μM GA3, 2 μM tZ, 10 μM jasmonoyl-isoleucine (JA-Ile), 2 μM indole acetic acid (IAA), 500 μM salicylic acid (SA), 1 μM rac-GR24, 2 μM KAR1, and 0.1% (v/v) dimethyl sulfoxide (DMSO). Five-day-old rice seedlings were sprayed with these mixtures. In the context of the long-term hormonal responses, 5-d-old rice seedlings were immersed in Yoshida rice nutrient solution supplemented with various hormones (at the same concentration of the short-term response) for 6 h. The specialized Sensl plasmid, based on the pUC19 backbone, was engineered and synthesized by the Beijing Genomics Institute. For optimal expression in rice plants, the DNA sequences encoding for firefly luciferase (FLUC) from Photinus pyralis and Renilla luciferase (RLUC) from Renilla reniformis were adapted using the O. sativa codon preference database accessible at http://www.kazusa.or.jp/codon/. Sensl encompasses a fusion of the OsD53 protein to FLUC, forming a sensor module. This is linked to the normalization reporter element, RLUC, through a self-cleaving 2A peptide. The entire construct is under the transcriptional control of a robust promoter derived from the rice ACTIN1 gene. For conducting bioluminescence imaging of Sensl responses, rice seedlings were sprayed with 0.3 mg ml−1 d-Luciferin, potassium salt (40902ES01; YEASEN, Shanghai, China). After a 5-min equilibration period, imaging was executed using the BLT PlantView100 (Guangzhou Biolight Biotechnology Co, Guangzhou, China). Quantitative analysis of the bioluminescence intensity was performed using ImageJ (2.1.0). The luminescence intensity within each region of interest (ROI) was meticulously quantified, ensuring ROI dimensions remained consistent across all images to facilitate accurate comparison. The YEASEN Dual-Luciferase Reporter Assay Kit (11402ES60) was employed for the examination of dual luciferase reporter genes. At the designated time points, tissue samples from rice seedlings under different conditions were collected and rapidly frozen in liquid nitrogen. Tissues were then pulverized using a homogenizer at 45 Hz for 30 s. Following the complete disruption, 300 μl of lysis buffer was added to the samples in Eppendorf tubes. The samples were then incubated on ice for c. 5 min to ensure thorough lysis of the material. The samples were centrifuged at 12 000 g (Thermo Fresco 17) for 1 min, and the supernatant was subsequently transferred. A 20 μl aliquot of the lysate was dispensed into a well of a white 96-well assay plate. First, 100 μl of firefly luciferase assay reagent was added, and the plate was shaken to mix. The activity of FLUC was measured. Following this, 100 μl of RLUC assay reagent was introduced, the plate was again shaken to mix, and the activity of RLUC was assessed. Assays were performed using the Spark multimode microplate reader (Tecan, Mannedorf, Switzerland). The cell-free protein degradation assay was conducted in accordance with the procedures outlined by Wang et al. (2009). Total proteins were isolated utilizing a degradation buffer comprising 25 mM Tris–HCl (pH 7.5), 10 mM NaCl, 10 mM MgCl2, 4 mM phenylmethylsulfonyl fluoride, 5 mM dithiothreitol, and 10 μM ATP. The proteasome inhibitor MG132 and various hormones were added to the mixture as specified. The samples were then incubated on a ThermoShaker TS1 with constant shaking (28°C, 300 rpm). At predetermined time points, a 30 μl aliquot of the reaction mixture was taken and instantly frozen in liquid nitrogen to halt the reaction. Subsequently, a dual-luciferase reporter assay kit was used to measure the luciferase activities. Plant samples were first ground in liquid nitrogen, and then, 100 mg of powder was accurately weighed and transferred into a 2-ml centrifuge tube. Extraction buffer of 1.5 ml (isopropanol: formic acid = 99.5: 0.5, v/v, with a final concentration of 2 ng L–1 of 13-13C-GR244DO as internal standard) was added with vortexing for resuspension of samples, and a 30 min ultrasonic treatment was applied to improve extraction efficiency. Then, after 15 min centrifugation at 14 000 g, 1.4 ml supernatants was transferred and dried in a LABCONCO CentriVap vacuum centrifugal concentrator and resuspended with 60 μl methanol solvent (water : methanol = 20 : 80, v/v). Then the contents of 4DO were detected by a triple quadrupole mass spectrometer and calculated with a calibration curve made with standards and internal standards. Positive ionization mode data was acquired using a Sciex Triple Quad™ 7500 LowMass quadrupole mass spectrometer (AB SCIEX) coupled with a Nexera Series (Shimadzu) UHPLC. Samples were separated with a 100 × 2.1 mm, 2.5 μm XSelect™ HSS T3 column (Waters, Milford, MA, USA). Five microliters of samples were loaded each time, and the flow rate was set at 300 μl min–1, with the column oven set at 40°C. Solvents for the mobile phase were 0.1% formic acid in acetonitrile (A) and 0.1% formic acid in water (B). The gradient of the mobile phase was: 0 min, 60% B; 0–1 min, 60%; 1–3 min, 40% B; 3–5 min, 20% B; 5–7 min, 0% B; 7–8.5 min, 0% B; 8.5–8.6 min, 60% B; 8.6–11 min, 60% B. The autosampler was set at 10°C. MS was operated in positive electrospray ionization mode. Multiple reaction m

Why it matches plant phenotyping methodsイネ体内のストリゴラクトンシグナルを非侵襲・定量的に追跡する生物発光センサーを開発し、実植物での検出性能を検証しており、フェノタイピング手法が研究の中心である。

abstracta critical gap in high-throughput, noninvasive tools for SL signaling detection in vital crops like rice

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

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

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

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

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

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

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

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

A detailed plant model in CFD that resolves the microclimate around individual leaves →

Plant factories require effective ventilation to promote proper plant growth. Computational Fluid Dynamics (CFD) is commonly used to evaluate ventilation strategies in these environments. Traditionally, porous models have been employed to study ventilation in plant factories. However, this study proposes an alternative approach using actual plant geometry, consisting of leaves and stems, which reduces the need for fitting parameters typically used in porous models. The study focuses on basil, with plant geometry based on experimental data to ensure accurate representation. This new plant model accounts for the heat and mass balance of each leaf, assigning individual temperature and humidity values. Radiative heat exchange was also included in the plant model by using the solar ray tracing algorithm to solve for shortwave radiation and the surface to surface radiation model for longwave thermal radiation. Validation was conducted in a small plant factory-like environment (1500 mm × 420 mm x 800 mm) under night-like conditions without shortwave radiation and day-like conditions, with shortwave radiation. Key variables such as transpiration rate and leaf temperature were measured and simulated. The coefficient of variation between measured and simulated transpiration rates ranged from 10 % to 15 % for night-time and 15 % for day-time. Root mean square deviations for leaf temperature were 0.4–0.6 °C at night and 0.5–1.6 °C during the day. A different test case, with air supplied from the bottom instead of the side, demonstrated the new model's capabilities. Overall, the new plant model visualises airflow around and through the canopy, and shows promise for improving ventilation strategies in vertical farming systems.

Why it matches plant phenotyping methods個葉形状を用いたCFD植物モデルを開発し、葉温度と蒸散速度を実測値と比較検証しているため、植物状態の取得・推定手法が研究の中心である。

abstractthis study proposes an alternative approach using actual plant geometry, consisting of leaves and stems

Machine learning-driven root plant phenotyping using imaging solution for space farming applications →

Producing food is one of the challenges in space exploration due to limited storage capacity and long travel duration. Extreme environmental conditions such as microgravity, elevated CO2 levels, irregular light exposure, and fluctuating air temperatures pose significant challenges to conventional plant growth and make it susceptible to stress, particularly in root systems, which struggle to absorb water and nutrients efficiently. This study will focus on root phenotyping of the plants (wheat and lettuce) grown in a near-space environment, and the impact of environmental stressors on the plants using image-based technology will be carried out. A specialized growth chamber is designed, incorporating three automated multi-modal imaging systems (MIS): Visible and Near-Infrared (VNIR) wavelength range (400-1000 nm), Micro CT Scan, and RGB cameras used to observe the impact of stress on microgravity on plants. Machine learning and deep learning techniques were also employed to optimize the discriminant classifier within the multi-modal imaging system. Through comparative analysis of these imaging techniques coupled with artificial intelligence techniques, this study aims to deepen our understanding of how microgravity and other space-induced factors affect root systems. This work will also present the challenges and potential faced that can contribute valuable insights for plant growth under space conditions.

Why it matches plant phenotyping methods根の画像ベース表現型計測システムを開発・比較し、機械学習による解析も行うことが中心であるため、植物フェノタイピング手法論文として含める。

abstractThis study will focus on root phenotyping of the plants (wheat and lettuce) grown in a near-space environment

Synergistic 3D, multispectral, and thermal image analysis via supervised machine learning for improved detection of root rot symptoms in hydroponically-grown flat-leaf parsley →

Root rot in hydroponically-grown leafy vegetables is difficult to detect via conventional manual and machine vision-based approaches as symptoms of infection are not clearly visible on the canopy at earlier stages of infection. Hence, the present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection. Herein, flat-leaf parsley seedlings were grown in an experimental hydroponic vertical farm and inoculated with Pythium irregulare and Phytophthora nicotianae . Subsequently, the seedlings were imaged via 3D, multispectral, and thermal sensors at various stages of growth to obtain twenty-six image-based plant features. Following a preliminary screening of redundant features via regression analysis, data for seventeen image features associated with morphometric, spectral, and thermal attributes was co-analyzed using supervised machine learning by Support Vector Machines (SVM). Exhaustive feature selection using different SVM kernels and maximum feature thresholds was performed to identify optimal feature subsets. It was observed that combining parameters obtained from all three imaging platforms enabled better identification of infected samples (>99%) than using a higher number of attributes from individual imaging systems. In addition, model performance was improved considerably by including temporal information during model training. Hence, it may be inferred that fusion of data from multiple imaging systems and using it with temporal information can enable better real-time high-throughput monitoring of root rot.

Why it matches plant phenotyping methods複数の画像センサーから植物形質を取得し、機械学習で根腐病症状を推定する統合的フェノタイピング手法の開発・評価が研究の中心である。

abstractthe present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection

Monitoring and Risk Prediction of Low-Temperature Stress in Strawberries through Fusion of Multisource Phenotypic Spatial Variability Features. →

Capturing crop physiological information by phenotyping is a key trend in smart agriculture. However, current studies underutilize spatial structural information in phenotypic imaging. To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability, we conducted controlled experiments on 'Toyonoka' strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions. The results revealed that NPQ/1D-Parallel/TENT, Y(NO)/2D-Region/INEM, and qP/1D-Parallel/TENT presented the highest mutual information, with the maximum net photosynthetic rate (P max ), relative electrolyte conductivity (REC), and total chlorophyll content (Chl a ​+ ​b ), respectively. The difference between the Photosynthetic Physiological Potential Index (PPPI) and relative negative accumulated temperature (RNAT)/650 effectively was used to calculate the cold damage risk (CDRI). An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest, achieving an R 2 of 0.98, an RMSE of 0.337, a classification accuracy of 92.13 ​%, and a Kappa coefficient of 0.904. qP/1D-Parallel/TENT contributed the most to the model. This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.

Why it matches plant phenotyping methodsイチゴの低温ストレスを対象に、表現型画像の空間変動特徴を抽出・融合し、光合成生理や冷害リスクを推定する手法を開発・評価しており、表現型取得・解析が研究の中心である。

abstractcurrent studies underutilize spatial structural information in phenotypic imaging

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

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

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

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

Lettuce architectural phenotypes extraction from multimodal images by low-light sensitivity and strong spatial perception →

Accurate measurement of structural phenotypes, such as plant height and canopy width, is crucial for the scientific management of lettuce cultivation in Plant Factories with Artificial Lighting (PFALs). In this study, we developed a multimodal image fusion model using visible images (RGB), depth images (Depth), and infrared images (IR) to extract lettuce phenotypes in PFAL environments. We proposed a Residual Space information enhancement module (DRS) and a fusion feature supplement method with adaptive weight optimization for IR features (IRC) to address the weak spatial perception of traditional RGB-based models and the feature loss of RGB due to illumination disturbance. Three lettuce varieties (Bixiao, Huqian, and Mondai) were selected as experimental subjects to evaluate the robustness of our proposed model. In ablation experiments, the benchmark model improved by DRS increased by 1.6% and 0.9% in terms of MAP0.75 and MAP0.5:0.95, respectively. The benchmark model improved by IRC increased by 0.2%, 0.6%, and 1.2% in terms of MAP0.5, MAP0.75, and MAP0.5:0.95, respectively. Furthermore, MAP0.5, MAP0.75, and MAP0.5:0.95 values increased by 0.3%, 3.3%, and 2.3% when the two modules were combined, respectively. Compared with manually measured plant height and canopy width, the Root Mean Square Error (RMSE) of the average plant height prediction results for the three varieties is 0.74, and the Mean Squared Error (MSE) is 0.55. For canopy width, the RMSE of the model’s prediction results was 0.70, and the MSE was 0.49. In the lighting influence experiment, our method outperformed the unimproved model by approximately 0.3–4% in terms of MAP0.5, MAP0.75, and MAP0.5:0.95 across multiple datasets. Our proposed model effectively addresses lighting disturbance, enhances the robustness of the baseline model against varying lighting conditions, improves spatial perception capability, facilitates the separation of adjacent plant features in the model’s feature extraction stage, enhances the model’s detection ability, and ultimately improves phenotype extraction capability.

Why it matches plant phenotyping methodsマルチモーダル画像融合モデルを開発し、レタスの草丈・群落幅という植物表現型を画像から抽出・推定しており、手法開発と技術評価が研究の中心である。

abstractIn this study, we developed a multimodal image fusion model using visible images (RGB), depth images (Depth), and infrared images (IR) to extract lettuce phenotypes in PFAL environments.

Precise evaluation of transpiration patterns in relation to grain yield under drought stress in faba bean →

Abstract Background Faba bean ( Vicia faba L.) is a key crop for sustainable agriculture in temperate cropping systems due to its nitrogen-fixing ability and high protein content, but its productivity is increasingly threatened by drought stress driven by climate change. Precise phenotyping under semi-controlled conditions is crucial for understanding drought responses. High-throughput precision phenotyping enables efficient evaluation of many genotypes, revealing detailed water-use patterns as a basis for breeding productive, drought-resilient cultivars. In this study, faba bean genotypes were grown in a precision phenotyping facility comprising 120-liter containers filled with mineral soil to simulate field like growth conditions. Each container was placed on a high-precision gravimetric scale to record water use in real-time in relation to 3-dimensional spectral image information. Results Precise measurement of genotype-specific transpiration behavior using gravimetric methods enabled detailed insights into the transpiration patterns of different genotypes in response to ambient temperature and humidity fluctuations throughout the day, night and across the whole life cycle. The results showed that total water use, water use efficiency, and consequently yield were particularly influenced by specific transpiration parameters, such as the maximum transpiration rate and the vapor pressure deficit threshold at which stomatal conductance was interrupted. Conclusion The results revealed genetically determined variation for transpiration responses to drought stress. Genotypes that reduced water loss earlier tended to achieve higher grain yields and use water more efficiently. The findings show that precise automated phenotyping can identify previously undiscovered genetic variation for breeding of drought-tolerant faba bean varieties, which are crucial for ensuring productivity under increasingly water-limited conditions.

Why it matches plant phenotyping methods高精度重量センサーと3次元スペクトル画像を用いた自動表現型解析施設で、遺伝子型別の蒸散特性を技術的に取得・評価しており、植物表現型測定が研究の中心である。

abstractHigh-throughput precision phenotyping enables efficient evaluation of many genotypes, revealing detailed water-use patterns as a basis for breeding productive, drought-resilient cultivars.

Infrared Thermography in Plant Factories: Solving Spatiotemporal Variations Via Machine Learning →

ABSTRACT Infrared thermography (IRT) for real‐time stress detection in plant factories (PFs) remains largely unexplored. Hence, this study investigates the feasibility of implementing IRT in PFs, using machine learning (ML) to address the challenges in information processing. Herein, purple basil plantlets were subjected to root dehydration within a pilot‐scale PF, and canopy temperature was monitored at regular intervals using a thermal camera. Subsequently, eight ML models using the ‘support vector machines’ algorithm were tested for stress detection. Our findings revealed that differences in canopy temperature due to microenvironmental variations led to inaccurate representation of stress. Nonetheless, binary classification models trained using plants at medial and high stress overcame this issue by identifying stressed samples with 81%–94% accuracy. However, although models trained with medially stressed samples performed well for all stress levels, models trained using highly stressed samples failed to identify medial stress reliably. Additionally, ternary and quaternary classification models were able to identify unstressed samples but could not distinguish between different levels of stress. Hence, binary classification models trained using medially stressed samples overcame spatiotemporal variations in canopy thermal profile most effectively and provided probabilistic estimates of plant stress within the PF most consistently.

Why it matches plant phenotyping methods植物工場での赤外線サーモグラフィーと機械学習による植物ストレス推定が研究の中心であり、手法の実装・評価と精度検証を行っている。

abstracteight ML models using the ‘support vector machines’ algorithm were tested for stress detection

Multivariate analysis unveils antioxidant-nutrient trade-offs in Maize Hybrids: A hierarchical framework for acid soil tolerance evaluation →

Abstract Background and Aims Acid soils, characterized by nutrient deficiencies and metal ion toxicity, severely limit maize yields. Cultivating acid soil tolerant maize represents a promising strategy to address these edaphic constraints. Methods Through controlled pot experiments, 50 maize hybrids were subjected to acidic soil stress (AS) and optimal soil conditions (CK), evaluating 15 morpho-physiological traits at the V5 stage. Multivariate statistical approaches were employed to identify critical tolerance indicators, with subsequent field validation conducted on four selected genotypes. Results Acidic soil stress induced significant alterations across all measured parameters compared to control conditions and revealed substantial genotypic variation in stress responses. Cluster analysis classified the 50 hybrids into five distinct tolerance categories, with two predominant adaptation strategies. Antioxidant-dependent resistance characterized by elevated peroxidase (POD), ascorbate peroxidase (APX), and catalase (CAT) activities. This strategy prioritized oxidative defense at the expense of biomass production (acid-sensitive varieties). Nutrient optimization strategy demonstrated by superior nitrogen and phosphorus acquisition efficiencies, enabling sustained growth under stress conditions (acid-tolerant varieties). Stepwise regression identified six critical evaluation parameters: plant height, fresh weight, stem diameter, leaf area, total nitrogen and phosphorus accumulation, complemented by antioxidant enzyme profiles and reactive oxygen species levels. Conclusion This study establishes a comprehensive evaluation framework incorporating 11 validated indicators for screening adaptive maize varieties in acid soil conditions. Field validation confirmed the accuracy of multivariate analysis in selecting acid soil tolerant varieties.

Why it matches plant phenotyping methods酸性土壌耐性品種のスクリーニングに向け、複数の植物形態・生理形質を統合する評価フレームワークを構築し、圃場で検証しているため、統計的な表現型評価手法が中心である。

abstractMultivariate statistical approaches were employed to identify critical tolerance indicators, with subsequent field validation conducted on four selected genotypes.

Evaluation of heat stress response in pepper (Capsicum annuum L.) seedlings under controlled environmental conditions using a high-throughput 3D multispectral phenotyping →

• Chili pepper seedling responses to heat stress were analyzed under controlled environmental conditions. • Morphological and spectral traits were identified through non-destructive 3D imaging. • PCA and clustering revealed three distinct response patterns to heat stress among genotypes. • A novel PC distance-based approach quantified heat stress stability among genotypes. • The developed phenotyping method provides an efficient tool for large-scale heat tolerance screening in breeding programs. Climate change-driven heat stress presents a significant threat to global pepper production, highlighting the urgent need for efficient methods to assess heat tolerance in breeding programs. This study presents a robust approach for assessing heat stress responses in pepper integrating high-throughput phenotyping and multivariate analysis. Twenty pepper genotypes were evaluated under controlled temperature conditions (40/35 °C day/night) for 14 days using the TraitFinder system equipped a pair of 3D multispectral scanner. Principal component analysis (PCA) of morphological and spectral traits revealed progressive divergence between control and heat-treated groups, with the maximum separation observed at day 10 (ΔC = 2.05). Three distinct response groups were identified based on Euclidean distances in the PCA space: low response (five genotypes), moderate response (nine genotypes), and high response (six genotypes). The PC-based distance metric showed strong correlations with conventional stress tolerance indicators, including biomass retention ( r = 0.66) and root system maintenance ( r = 0.48). Notably, genotype 'Pep17 (GPC121710)' demonstrated enhanced growth under heat stress (26 % increase in 3D leaf area), while 'Pep06 (GPC003350)' showed marked growth reduction (27 % decrease). This study validated the integration of high-throughput phenotyping with PCA-based metrics for the quantitative assessment of heat stress responses. The method offers an efficient tool for identifying heat-tolerant pepper genotypes and holds potential for application to other crops and stress conditions, supporting climate resilience breeding programs.

Why it matches plant phenotyping methods3Dマルチスペクトル画像による高スループット形質取得と、PCA距離指標による耐暑性評価を中心に開発・検証しており、植物フェノタイピング手法が中核である。

abstractThis study presents a robust approach for assessing heat stress responses in pepper integrating high-throughput phenotyping and multivariate analysis.

Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model →

Indoor gardening within sustainable buildings offers a transformative solution to urban food security and environmental sustainability. By 2030, urban farming, including Controlled Environment Agriculture (CEA) and vertical farming, is expected to grow at a compound annual growth rate (CAGR) of 13.2% from 2024 to 2030, according to market reports. This growth is fueled by advancements in Internet of Things (IoT) technologies, sustainable innovations such as smart growing systems, and the rising interest in green interior design. This paper presents a novel framework that integrates computer vision, machine learning (ML), and environmental sensing for the automated monitoring of plant health and growth. Unlike previous approaches, this framework combines RGB imagery, plant phenotyping data, and environmental factors such as temperature and humidity, to predict plant water stress in a controlled growth environment. The system utilizes high-resolution cameras to extract phenotypic features, such as RGB, plant area, height, and width while employing the Lag-Llama time series model to analyze and predict water stress. Experimental results demonstrate that integrating RGB, size ratios, and environmental data significantly enhances predictive accuracy, with the Fine-tuned model achieving the lowest errors (MSE = 0.420777, MAE = 0.595428) and reduced uncertainty. These findings highlight the potential of multimodal data and intelligent systems to automate plant care, optimize resource consumption, and align indoor gardening with sustainable building management practices, paving the way for resilient, green urban spaces.

Why it matches plant phenotyping methods植物の画像・環境センシングと機械学習を統合し、植物形質の抽出および水ストレス推定を行う方法論が研究の中心である。

abstractThis paper presents a novel framework that integrates computer vision, machine learning (ML), and environmental sensing for the automated monitoring of plant health and growth.

Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions. →

Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring and assessment of microclimate and plant health/stress. Drone applications have emerged as transformative technology with significant potential for CEA. However, adoption and practical implementation of such technologies remain limited, particularly in arid regions. Despite their advantages in agriculture, drones have yet to gain widespread utilization in CEA systems. This study investigates the effectiveness of drone-based thermal imaging (DBTI) in optimizing CEA performance and monitoring plant health under arid conditions. Several WSN sensors were deployed to track microclimatic variations within the CEA environment. A novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI. The crop water stress index (CWSI) was computed based on Tc extracted from DBTI. Findings revealed that DBTI effectively distinguished between all treatments, with Tc detection exhibiting a strong correlation (R 2 = 0.959) with sensor-based measurements. Results confirmed a direct relationship between CWSI and Tc, as well as a significant association between soil moisture content and CWSI. This research demonstrates that DBTI can enhance irrigation scheduling accuracy and provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing to improved water and food security.

Why it matches plant phenotyping methodsドローン熱画像から作物の樹冠温度と水ストレス指数を抽出する手法を開発し、センサー測定との相関で検証しており、植物状態の取得法が中心である。

abstractA novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI.

Spinach ( Spinacia oleracea L.) Growth Model in Indoor Controlled Environment Using Agriculture 4.0. →

Global trends in health, climate, and population growth drive the demand for high-nutrient plants like spinach, which thrive under controlled conditions with minimal resources. Despite technological advances in agriculture, current systems often rely on traditional methods and need robust computational models for precise plant growth forecasting. Optimizing vegetable growth using advanced agricultural and computational techniques, addressing challenges in food security, and obtaining efficient resource utilization within urban agriculture systems are open problems for humanity. Considering the above, this paper presents an enclosed agriculture system for growth and modeling spinach of the Viroflay ( Spinacia oleracea L.) species. It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. Data were collected over 60 days, recording temperature, humidity, substrate moisture, and light spectra information. The experimental results demonstrate the effectiveness of polynomial regression models in predicting spinach growth patterns. The best-fitting polynomial models for leaf length achieved a minimum Mean Squared Error (MSE) of 0.158, while the highest MSE observed was 1.2153, highlighting variability across different leaf pairs. Leaf width models exhibited improved predictability, with MSE values ranging from 0.0741 to 0.822. Similarly, leaf stem length models showed high accuracy, with the lowest MSE recorded at 0.0312 and the highest at 0.3907.

Why it matches plant phenotyping methods葉長・葉幅・葉柄長という植物形質の予測モデルを中心に構築・評価しており、単なる生育実験のルーチン測定ではなく、計算的な形質推定手法の応用に該当する。

abstractIt encompasses a methodology combining data science, machine learning, and mathematical modeling.

LettuceP3D: A tool for analysing 3D phenotypes of individual lettuce plants →

Lettuce is one of the major raw vegetables in the world, with diverse species and large differences in morphological structures. Achieving automated, high-throughput acquisition and intelligent analysis of 3D lettuce phenotypes using advanced phenotyping techniques and equipment is of great significance. Based on the high-throughput phenotyping platform MVS-PhenoV2 installed in a plant imaging room, this study constructed a method for automated analysis of 3D phenotypes of lettuce around the needs of lettuce DUS (distinctiveness, uniformity, and stability) testing and feature digitisation. Aiming at the characteristics of lettuce leaves which are mostly curved, the point cloud segmentation model SoftGroup was improved, which can realise lettuce single plant segmentation and leaf segmentation with high accuracy. Additionally based on lettuce 3D point clouds, plant orientation correction algorithm, leaf hole completion algorithm, leaf vein extraction algorithm, and leaf margin extraction algorithm were proposed. Finally, a pipelined automated analysis software tool LettuceP3D was developed for automated analysis of lettuce 3D phenotypes, which can automatically analyse 16 phenotypic indicators related to lettuce plant (e.g. plant height, plant width, and compactness) and leaf (e.g. leaf length, leaf margin perimeter, and leaf margin undulation) phenotypic characteristics. The study was validated on seven types of lettuce: Butterhead, Crisphead, Looseleaf, Oakleaf, Romaines, Stem, and Wild Relatives. Results show that the mIoU for plant and pot semantic segmentation reaches 97.2%, and the AP for leaf instance segmentation reaches 86.7%. Through comparison with measured values, the average R2 of the algorithm exceeds 0.95. The software operates without manual interaction, processing single plant data in approximately 2s, which demonstrates a high processing efficiency. This phenotype analysis method proposed in this study is applicable for quantifying the morphological characteristics of lettuce in seven types, providing quantitative indicator data support for lettuce DUS testing, variety identification, and multi-omics studies.

Why it matches plant phenotyping methods3D画像・点群解析によるレタスの形態形質抽出手法と自動解析ソフトウェアを開発し、精度検証まで行っており、フェノタイピング手法が研究の中心である。

abstractthis study constructed a method for automated analysis of 3D phenotypes of lettuce

Maximizing nitrogen stress tolerance through high-throughput phenotyping in rice →

Nitrogen (N) is a significant nutrient element limiting rice yield and quality, a major staple crop consumed worldwide. N deficiency negatively affects the growth and development of rice by impacting vital physiological processes. Plants have developed multiple resilience strategies, including enhanced nitrogen use efficiency (NUE) to cope with N-deprived situations. NUE in rice is less than 40 %, and increased N application leads to high production costs and ecosystem damage. Improving NUE has been one of the major challenges of agriculture research in the recent past. NUE is an obfuscated trait governed by diverse physiological traits and controlled by complex genetic mechanisms. In recent years, a combination of multi-omics techniques (phenomics and genomics) has enhanced the N resilience maximization efforts of the agricultural research community. Phenomics technology has displayed the ability to perform systematic, organism-wide phenotyping of N stress response in diverse crops over the entire life cycle using non-invasive sensors on high throughput platforms (HTPs) in a more precise manner. These HTPs augment precision phenotyping (at the spatiotemporal scale) of component traits of NUE, which are difficult to phenotype mainly due to its dynamic interactive nature with the environment. Phenomics has drastically reduced the phenotype-genotype gap by optimally utilising other omics data for breeding climate smart cultivars with enhanced N stress tolerance. This review focuses on the recent advances in HTP-based phenotyping of NUE-related traits to identify novel QTLs/genes/signaling pathways associated with improved NUE both in controlled environments and field conditions.

Why it matches plant phenotyping methods高スループット表現型解析プラットフォームによる窒素利用効率関連形質の測定・解析を主題とするレビューであり、植物フェノタイピング手法が中心です。

abstractThis review focuses on the recent advances in HTP-based phenotyping of NUE-related traits to identify novel QTLs/genes/signaling pathways associated with improved NUE both in controlled environments and field conditions.

A recursive segmentation model for bok choy growth monitoring with Internet of Things (IoT) technology in controlled environment agriculture →

The increased adoption of controlled environment agriculture (CEA) and soilless growing systems (SGS) offers new opportunities to advance the year-round production of high-quality specialty crops through the development and implementation of precision agriculture solutions. Traditionally, crop monitoring in CEA soilless systems is a critical time-consuming task requiring specialized personnel. Nevertheless, traditional crop monitoring methods do not allow frequent data collection to capture the plant growth dynamics throughout the crop cycle. Automated crop monitoring systems may allow continuous monitoring of the crop with frequent data collection and a more efficient and informed management of the crop. In this study we developed an integrated Internet of Things (IoT) and computer vision system tailored for CEA-SGS, enabling continuous monitoring and analysis of plant growth throughout the crop cycle. The core innovation of this research is the implementation of a recursive image segmentation model that processes sequential image data to accurately track temporal changes in plant growth. The vision system developed is supported by an IoT framework designed to capture high-resolution imagery at predetermined temporal frequencies. Tested on bok choy grown in an NFT (nutrient film technique) SGS, the integrated system developed successfully segmented individual plants and tracked leaf coverage area throughout their growth cycle. The quantitative analysis of Intersection over Union (IoU) scores among the segmentation approaches showed that the recursive model began with an IoU score of 0.99 during the early growth stages and maintained a robust performance, achieving a score of 0.90 at later stages. These findings demonstrate that the recursive segmentation approach significantly enhanced the precision of the bok choy crop monitoring. The outcome of this study facilitates the potential development of decision support systems for the efficient management and optimization of crops in soilless CEA systems.

Why it matches plant phenotyping methods植物の成長状態（葉面積）を画像から抽出する再帰的セグメンテーション手法とIoT画像取得システムを開発・評価しており、表現型取得法が研究の中心です。

abstractIn this study we developed an integrated Internet of Things (IoT) and computer vision system tailored for CEA-SGS, enabling continuous monitoring and analysis of plant growth throughout the crop cycle.

Early detection of bacterial canker in tomato plants using spectroscopy for smart agriculture applications →

Clavibacter michiganensis subsp. michiganensis (Cmm) causes bacterial canker in tomatoes, causing severe yield loss. It would be of practical research interest within smart agriculture to develop an effective and quick method to distinguish pre-symptomatic infected tomato plants from healthy ones to take protective measures in time. In this study, artificially inoculated tomato plants with Cmm were grown in a temperature-controlled chamber. Using the Relief method, 25 wavelengths in the visible spectrum (cyan and red regions) showed the highest statistical differences, between healthy and asymptomatic infected tomato plants, two days before the first appearance of the foliar symptoms, in each plant. In addition, inoculated tomato plantlets showed differences in contrast to healthy ones, in the near-infrared spectrum, thirteen days after the inoculation with Cmm. The spectral data were used for the creation of early detection models of healthy and inoculated pre-symptomatic plants, in a specific number of days before the appearance of the first symptoms, in each plant, and in a specific number of days-post inoculation, using two ML algorithms (SVMs and kNN). The algorithms proved effective and robust in the discrimination of the two classes of the two instances mentioned. Furthermore, three patterns of data-preprocessing followed before the training of the algorithms, i.e. the case of multidimensionality, the application of PCA, and the use of Relief method. Finally, six models were created for datasets that contain spectral data of asymptomatic inoculated with Cmm and healthy tomato transplants, all of which showed very high overall accuracy, ranging from 92 to 100%.

Why it matches plant phenotyping methodsトマトの感染植物におけるスペクトルから無症状段階の病害状態を検出・分類する手法の開発とモデル評価が研究の中心であり、植物表現型（病害状態）の取得に該当する。

abstractdevelop an effective and quick method to distinguish pre-symptomatic infected tomato plants from healthy ones

LettuceP3D: A tool for analysing 3D phenotypes of individual lettuce plants →

Lettuce is one of the major raw vegetables in the world, with diverse species and large differences in morphological structures. Achieving automated, high-throughput acquisition and intelligent analysis of 3D lettuce phenotypes using advanced phenotyping techniques and equipment is of great significance. Based on the high-throughput phenotyping platform MVS-PhenoV2 installed in a plant imaging room, this study constructed a method for automated analysis of 3D phenotypes of lettuce around the needs of lettuce DUS (distinctiveness, uniformity, and stability) testing and feature digitisation. Aiming at the characteristics of lettuce leaves which are mostly curved, the point cloud segmentation model SoftGroup was improved, which can realise lettuce single plant segmentation and leaf segmentation with high accuracy. Additionally based on lettuce 3D point clouds, plant orientation correction algorithm, leaf hole completion algorithm, leaf vein extraction algorithm, and leaf margin extraction algorithm were proposed. Finally, a pipelined automated analysis software tool LettuceP3D was developed for automated analysis of lettuce 3D phenotypes, which can automatically analyse 16 phenotypic indicators related to lettuce plant (e.g. plant height, plant width, and compactness) and leaf (e.g. leaf length, leaf margin perimeter, and leaf margin undulation) phenotypic characteristics. The study was validated on seven types of lettuce: Butterhead, Crisphead, Looseleaf, Oakleaf, Romaines, Stem, and Wild Relatives. Results show that the mIoU for plant and pot semantic segmentation reaches 97.2%, and the AP for leaf instance segmentation reaches 86.7%. Through comparison with measured values, the average R 2 of the algorithm exceeds 0.95. The software operates without manual interaction, processing single plant data in approximately 2s, which demonstrates a high processing efficiency. This phenotype analysis method proposed in this study is applicable for quantifying the morphological characteristics of lettuce in seven types, providing quantitative indicator data support for lettuce DUS testing, variety identification, and multi-omics studies.

Why it matches plant phenotyping methods3D画像・点群解析によるレタス個体および葉の形態形質抽出を中心に、分割・補完・特徴抽出アルゴリズムと解析ソフトウェアを開発・検証しているため。

abstractthis study constructed a method for automated analysis of 3D phenotypes of lettuce

Modelling crop traits and fluxes under multiple abiotic stressors →

Agriculture is the largest consumer of freshwater, accounting for approximately 70% of the total global usage. As the human population continues to grow, demand for water will be exacerbated by a changing climate and shifting temperature and precipitation regimes. Dynamically modelling crop physiological function will be crucial to optimising crop management strategies. In this study we synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat (Triticum aestivum) in growth chambers within a controlled environment experiment under water and/or nitrogen stress conditions. Results showed that plants which had first received nitrogen fertiliser and were subsequently droughted presented the lowest water fluxes, and the lowest leaf chlorophyll content and photosynthetic capacity (Vcmax) values. Partial least squares regression (PLSR) analysis of hyperspectral reflectance data revealed key wavelengths sensitive to six different plant traits and fluxes (including relative water content, leaf nitrogen, stomatal conductance), with strong correlations between measured and modelled values (R2 = 0.84; p

Why it matches plant phenotyping methodsハイパースペクトル・熱画像データとPLSRを用いて、複数の植物形質および水フラックスを推定する手法が研究の中心であり、測定値とモデル値の相関も評価しているため。

abstractwe synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat

Seeing the Unseen: A Novel Approach to Extract Latent Plant Root Traits from Digital Images →

A novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits, revealing latent patterns related to dense root clusters in digital images. Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm, 27 ARTs were extracted reflecting dense root cluster size and spatial location. These ARTs were then used independently and in combination with Traditional Root Traits (TRTs) to classify wheat genotypes differing in drought tolerance. ART-based models outperformed TRT-only models in drought classification (e.g., 96.3% vs. 85.6% accuracy). Combining ARTs and TRTs further improved accuracy to 97.4%. Notably, 4 selected ARTs matched the performance of all 23 TRTs, offering 5.8× higher information density (0.213 vs. 0.037 accuracy/feature). This superiority reflects the ability of ARTs to capture richer, more complex architectural information, evidenced by higher internal variability (35.59±11.41 vs. 28.91±14.28 for TRTs) and distinct data structures in multivariate analyses; PERMANOVA confirmed that ARTs and TRTs provide complementary insights. Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes, ART offers a scalable, customisable toolset for high-throughput phenotyping of plant roots. By bridging conventional, visually derived traits with autonomous computational analyses, this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception. ART thus emerges as a promising framework for revealing hidden features in plant imaging, with broader applications across plant science to deepen our understanding of crop adaptation and resilience.

Why it matches plant phenotyping methodsデジタル画像から根の潜在形質を抽出するART手法を開発し、圃場・環境条件で検証した、中心的な植物フェノタイピング研究。

abstractA novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits

Genetic dissection of the root system architecture QTLome and its relationship with early shoot development, breeding and adaptation in durum wheat →

Root system architecture (RSA), shoot architecture, and shoot-to-root biomass allocation are critical for optimizing crop water and nutrient capture and ultimately grain yield. Nevertheless, only a few studies adequately dissected the genetic basis of RSA and its relationship to shoot development. Herein, we dissected at a high level of details the RSA–shoot QTLome in a panel of 194 elite durum wheat (Triticum turgidum ssp. durum Desf.) varieties from worldwide adopting high-throughput phenotyping platform (HTPP) and genome-wide association study (GWAS). Plants were grown in controlled conditions up to the seventh leaf appearance (late tillering) in the GROWSCREEN-Rhizo, a rhizobox platform integrated with automated monochrome camera for root imaging, which allowed us to phenotype the panel for 35 shoot and root architectural traits, including seminal, nodal, and lateral root traits, width and depth, leaf area, leaf, and tiller number on a time-course base. GWAS identified 180 quantitative trait loci (QTLs) (−log p-value ≥ 4) grouped in 39 QTL clusters. Among those, 10, 11, and 10 QTL clusters were found for seminal, nodal, and lateral root systems. Deep rooting, a key trait for adaptation to water limiting conditions, was controlled by three major QTLs on chromosomes 2A, 6A, and 7A. Haplotype distribution revealed contrasting selection patterns between the ICARDA rainfed and CIMMYT irrigated breeding programs, respectively. These results provide valuable insights toward a better understanding of the RSA QTLome and a more effective deployment of beneficial root haplotypes to enhance durum wheat yield in different environmental conditions.

Why it matches plant phenotyping methodsGROWSCREEN-Rhizoと自動カメラによる根・地上部形態の高スループット画像計測が研究の中心的手法として明記されている。

abstractadopting high-throughput phenotyping platform (HTPP) and genome-wide association study (GWAS)

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

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

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

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

Non-destructive Detection of Growth and Quality in Basil Seedlings Grown in a Plant Factory →

Background and objective: Basil is one of the high-value crops cultivated in plant factories. The production of uniform and healthy seedlings has a direct impact on the yield and quality of the final harvest. The objectives of this study were; (1) to ascertain whether non-destructive detecting parameters [projected canopy area (PCA) and vegetation indices (VIs)] could predict changes in growth and quality of basil seedlings, and (2) to determine the feasibility of grading basil seedlings based on PCA to establish a baseline for stable yields after transplanting.Methods: Basil seedlings were grown in 2- and 3-day irrigation cycles for 25 days after sowing, and the growth parameters and image analysis parameters using a multispectral camera were examined at regular intervals. The correlations between growth parameters and PCA/VIs were investigated to detect growth and quality of basil seedlings. At the time of transplanting, the basil seedlings were classified into grades A-D based on their PCA values, and the growth and yield after transplanting of basil seedlings in each grade were evaluated.Results: The basil seedlings in the 3-day irrigation treatment showed higher growth, and the correlation between the PCA values and the leaf area and fresh weight resulted in a coefficient of determination greater than 0.93. Among the VIs, the VARI, GI, and NGRDI were correlated with the growth and quality with coefficients of determination greater than 0.6. And, the growth and yield after transplanting were dependent on the seedling grade based on PCA values at the time of transplanting.Conclusion: This study confirmed that it is possible to predict the growth and quality of basil seedlings using non-destructive image analysis, and that the grading criteria for basil seedlings that can be expected to produce stable yields after transplanting can be determined using image analysis.

Why it matches plant phenotyping methodsマルチスペクトル画像から投影キャノピー面積と植生指数を抽出し、バジル幼苗の生育・品質予測および定量的な苗分類を検証しており、表現型取得手法が研究の中心である。

abstractThe objectives of this study were; (1) to ascertain whether non-destructive detecting parameters [projected canopy area (PCA) and vegetation indices (VIs)] could predict changes in growth and quality of basil seedlings, and (2) to determine the feasibility of grading basil seedlings based on PCA

Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology →

Crop monitoring can promptly reflect the growth status of crops. However, conventional methods of growth monitoring, although simple and direct, have limitations such as destructive sampling, reliance on human experience, and slow detection speed. This study estimated the fresh weight of lettuce (Lactuca sativa L.) in a plant factory with artificial light based on three-dimensional (3D) reconstruction technology. Data from different growth stages of lettuce were collected as the training dataset, while data from different plant forms of lettuce were used as the validation dataset. The partial least squares regression (PLSR) method was utilized for modeling, and K-fold cross-validation was performed to evaluate the model. The testing dataset of this model achieved a coefficient of determination (R2) of 0.9693, with root mean square error (RMSE) and mean absolute error (MAE) values of 3.3599 and 2.5232, respectively. Based on the performance of the validation set, an adaptation was made to develop a fresh weight estimation model for lettuce under far-red light conditions. To simplify the estimation model, reduce estimation costs, enhance estimation efficiency, and improve the lettuce growth monitoring method in plant factories, the plant height and canopy width data of lettuce were extracted to estimate the fresh weight of lettuce in addition. The testing dataset of the new model achieved an R2 value of 0.8970, with RMSE and MAE values of 3.1206 and 2.4576.

Why it matches plant phenotyping methods3D再構成からレタスの生体重・草丈・キャノピー幅を推定するモデルの開発と検証が研究の中心であり、植物表現型取得・推定手法に該当する。

abstractThis study estimated the fresh weight of lettuce (Lactuca sativa L.) in a plant factory with artificial light based on three-dimensional (3D) reconstruction technology.

Hyperspectral Segmentation of Plants in Fabricated Ecosystems →

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed-scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Down-stream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach not only provides a scalable solution for spectral segmentation but also facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Why it matches plant phenotyping methods植物のハイパースペクトル画像を対象に、少量アノテーションで高精度に分割する解析ワークフローを開発し、植物状態のモニタリングに用いる方法研究である。

abstractThis study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed-scale convolution neural networks.

Frost tolerance improvement in pea and white lupin by a high-throughput phenotyping platform. →

The changing climate could expand northwards in Europe the autumn sowing of cool-season grain legumes to take advantage of milder winters and to escape the increasing risk of terminal drought. Greater frost tolerance is a key breeding target because sudden frosts following mild-temperature periods may produce high winter mortality of insufficiently acclimated plants. The increasing year-to-year climate variation hinders the field-based selection for frost tolerance. This study focused on pea and white lupin with the objectives of (i) optimizing an easy-to-build, high-throughput phenotyping platform for frost tolerance assessment with respect to optimal freezing temperatures, and (ii) verifying the consistency of genotype plant mortality responses across platform and field conditions. The platform was a 13.6 m2 freezing chamber with programmable temperature in the range of −15°C to 25°C. The study included 11 genotypes per species with substantial variation for field-based winter plant survival. Plant seedlings were evaluated under four freezing temperature treatments, i.e., −7°C, −9°C, −11°C, and −13°C, after a 15-day acclimation period at 4°C. Genotype plant mortality and lethal temperature corresponding to 50% mortality (LT50) were assessed at the end of a regrowth period, whereas biomass injury was observed through a 10-level visual score based on the amount of necrosis and mortality after recovery and regrowth. On average, pea displayed higher frost tolerance than white lupin (mean LT50 of −12.8 versus −11.0°C). The genotype LT50 values ranged from −11.6°C to −14.5°C for pea and from −10.0°C to −12.0°C for lupin. The freezing temperature that maximized the genotype mortality variation was −13°C for pea and −11°C for lupin. The genotype mortality at these temperatures exhibited high correlations with LT50 values (0.91 for pea and 0.94 for lupin) and the biomass injury score (0.98 for pea and 0.97 for lupin). The frost tolerance responses in the platform showed a good consistency with the field-based winter survival of the genotypes. Our study indicates the reliability of genotype frost tolerance assessment under artificial conditions for two cool-season grain legumes, offering a platform that could be valuable for crop improvement as well as for genomics and ecophysiological research.

Why it matches plant phenotyping methods凍結チャンバーを用いた植物の耐霜性評価プラットフォームを最適化し、圃場条件と検証しており、表現型取得法が研究の中心である。

abstractoptimizing an easy-to-build, high-throughput phenotyping platform for frost tolerance assessment

Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review →

Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.

Why it matches plant phenotyping methods植物フェノミクスにおける植物形質の予測・表現モデルと高スループット表現型解析への応用を体系的にレビューしており、方法論が中心である。

abstractThis review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field →

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars ( Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. Highlight Integrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

Why it matches plant phenotyping methodsUAV画像由来の植生指数と機械学習を用いて、圃場で根系形質を非破壊推定する方法が研究の中心であり、実データへの応用と技術的概念実証を含む。

abstractUAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass.

U3-YOLOXs: An improved YOLOXs for Uncommon Unregular Unbalance detection of the rape subhealth regions →

Agricultural production in high latitudes could be limited by cold climate. Plant factory allows continuous production all year round, where the detection of plant growth is one of the most important tasks. To achieve non-destructive detection of rape in our plant factory, a feasible approach is to automatically detect subhealth areas from the rape images. However, this task faces the following challenges: (1) Uncommon problem: the subhealth regions on rape are the domain-specific objects, so the mainstream transfer learning-based detector is unreliable; (2) Unregular problem: the subhealth regions are difficult to detect due to their variable shapes, sizes and colors. (3) Unbalance problem: localization and classification of subhealth regions each have low-/high-quality bounding box unbalance and easy/hard sample unbalance. In this paper, a novel deep object detector based on the YOLOXs, called U³YOLOXs, is proposed for the detection of subhealth regions on rape at the bolting stage. Specifically, a domain-specific self-supervised pre-training strategy in the backbone is developed for the Uncommon problem; next, a coordinate attention mechanism in the multi-scale neck network is built for the Unregular problem; finally, the focal EIoU and the focal loss in the decoupled head are designed for the Unbalance problem. The experimental results show that the mAP of our U³YOLOXs is 94.38 % with a latency of 20.4 ms per image, which achieves an optimal accuracy-speed tradeoff. Compared to the YOLOXs it achieves a significant improvement of 9.27 % on mAP at the cost of only 3.55 % increase in latency. Experimental analysis further shows the effectiveness of each improvement, and the reliability of porting U³YOLOXs to edge devices for agricultural production.

Why it matches plant phenotyping methodsアブラナ画像から生育不良（subhealth）領域を非破壊検出する深層学習手法を開発し、精度・速度と各改良要素を評価しており、植物状態の取得手法が中心である。

abstractTo achieve non-destructive detection of rape in our plant factory, a feasible approach is to automatically detect subhealth areas from the rape images.

High-throughput phenotyping of soybean ( Glycine max ) transpiration response curves to rising atmospheric drying in a mapping population. →

In soybean (Glycine max ), limiting whole-plant transpiration rate (TR) response to increasing vapor pressure deficit (VPD) has been associated with the 'slow-wilting' phenotype and with water-conservation enabling higher yields under terminal drought. Despite the promise of this trait, it is still unknown whether it has a genetic basis in soybean, a challenge limiting the prospects of breeding climate-resilient varieties. Here, we present the results of a first attempt at a high-throughput phenotyping of TR and stomatal conductance response curves to increasing VPD conducted on a soybean mapping population consisting of 140 recombinant inbred lines (RIL). This effort was conducted over two consecutive years, using a controlled-environment, gravimetric phenotyping platform that enabled characterizing 900 plants for these responses, yielding regression parameters (R 2 from 0.92 to 0.99) that were used for genetic mapping. Several quantitative trait loci (QTL) were identified for these parameters on chromosomes (Ch) 4, 6, and 10, including a VPD-conditional QTL on Ch 4 and a 'constitutive' QTL controlling all parameters on Ch 6. This study demonstrated for the first time that canopy water use in response to rising VPD has a genetic basis in soybean, opening novel avenues for identifying alleles enabling water conservation under current and future climate scenarios.

Why it matches plant phenotyping methods高スループットの重力測定フェノタイピングプラットフォームを用いて、蒸散・気孔コンダクタンス応答曲線を技術的に取得し、遺伝解析に利用しており、表現型取得法が研究の中心である。

abstractHere, we present the results of a first attempt at a high-throughput phenotyping of TR and stomatal conductance response curves to increasing VPD

Unlocking the power of gene banks: diversity in base growth temperature provides opportunities for climate-smart agriculture →

Abstract Implementation of context-specific solutions, including cultivation of varieties adapted to current and future climatic conditions, were found to be effective in establishing resilient, climate-smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesising that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world’s largest banana gene bank in terms of base temperature (T base ) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. 116 gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype-specific T base were modelled. Growth response of two genotypes was validated under greenhouse conditions, and gas exchange capacity measurements were made. The model revealed genotype-specific T base , with 30 % of the accessions showing a T base below the reference of 14 °C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T base , although within subgroup diversity was revealed. Greenhouse validation further showed low T sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate-smart varieties.

Why it matches plant phenotyping methodsバナナの温度応答と成長を定量化する高スループット表現型計測設備・手法を提示し、温室条件で検証しているため、表現型取得法が中心的です。

abstract116 gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation.

Water status assessment in grapevines using plant electrophysiology →

Traditional methods for assessing vine water status, such as the Scholander pressure chamber, are time-consuming, punctual and labour-intensive. The development of alternative methods which are accurate, reliable and can provide real-time information on vine water status is a necessity for farmers all over the world. This study proposes the use of plant electrophysiology as a novel approach for real-time water status assessment in grapevines. We conducted four climate chamber experiments with potted grapevines under different irrigation regimes. Various morphological and physiological assessments were performed in parallel with electrophysiological measurements to correlate classic water status assessment methods with plant electrophysiological signals. Two machine learning approaches based on classification and regression were employed to train the prediction models. Results obtained from both models indicate significant differences in irrigation status between well-watered and water-deficit plants, with the latter showing reduced growth and physiological activity, confirming the water stress status of the plant. While the binary classification model successfully differentiates between well-watered and water-deficit plants, its practical use is limited. Therefore, a regression model was developed to directly predict predawn leaf water potential. To the best of our knowledge, this is the first time that electrical signals are correlated with vine water potential measurements. The findings presented here thus provide a promising new tool for future real-time and remote monitoring of vine water status to manage irrigation and adapt agronomic strategies. Nevertheless, validation and optimisation of the models are still necessary, particularly under field conditions.

Why it matches plant phenotyping methodsブドウの水分状態という植物生理形質を、植物電気生理信号と機械学習でリアルタイム推定する手法を開発・検証しており、表現型取得が研究の中心である。

abstractThis study proposes the use of plant electrophysiology as a novel approach for real-time water status assessment in grapevines.

Advancing lettuce physiological state recognition in IoT aeroponic systems: A meta-learning-driven data fusion approach →

Automatically identifying key physiological factors in plants, such as leaf relative humidity (LRH), chlorophyll content (Chl), and nitrogen levels (N), is vital for effective aeroponic management and improving growth, yield, quality, and sustainability. Meta-learning (MetaL) solutions utilize data fusion and intelligent processing, ensuring fast and consistent outcomes. This paper aims to develop a novel MetaL framework that leverages multimodal data sources—including spectral, thermal, and IoT environmental data—to enable real-time, non-invasive identification of LRH, Chl, and N content in aeroponically grown lettuce. The research examined various spectral reflectance indices (SRIs) and thermal indicators from plant characteristics. Model-based feature selection was implemented using back-propagation neural networks (BPNN), decision trees (DT), and gradient boosting machines (GBM) to identify key attributes and optimize hyperparameters. The experimental findings indicated that deploying GBM-based top variables as the foundational model, combined with BPNN as the meta-model, significantly improved the accuracy of analyzing the assigned factors. The prediction scores (R²) for LRH, Chl, and N increased to 0.875 (RMSE=0.879), 0.886 (RMSE=0.694), and 0.930 (RMSE=0.184), respectively, compared to applying BPNN-based features alone as a standalone model. Overall, the designed methodology contributes to more accurate predictions of plant physiological states, enabling proactive steps toward sustainable aeroponic agriculture.

Why it matches plant phenotyping methodsスペクトル・熱画像・IoTデータを融合し、レタスの生理状態を非破壊推定するMetaL手法の開発が中心であり、植物表現型の取得・推定方法に該当する。

abstractThis paper aims to develop a novel MetaL framework that leverages multimodal data sources—including spectral, thermal, and IoT environmental data—to enable real-time, non-invasive identification of LRH, Chl, and N content in aeroponically grown lettuce.

Dissecting durum wheat time to anthesis into physiological traits using a QTL-based model →

Fine tuning crop development is a major breeding avenue to increase crop yield and for adaptation to climate change. We used an ecophysiological model that integrates our current understanding of the physiology of wheat phenology to predict the development and anthesis date of 91 recombinant inbreed lines (RILs) of durum wheat with genotypic parameters controlling vernalization requirement, photoperiod sensitivity, and earliness per se estimated using leaf stage, final leaf number, anthesis date data from a pot experiment with vernalized and nonvernalized treatments combined with short- and long-day length. Predictions of final leaf number and anthesis date of the QTL-based model was evaluated for the whole population of RILs in a set of independent field trials and for the two parents, which were not used to estimate the parameter values. Our novel approach reduces the number of environments and the time required to obtain the required data sets to develop a QTL-based prediction of model parameters. Moreover, the use of a physiologically based model of phenology gives new insight into genotype-phenology relations for wheat. We discuss the approach we used to estimate the parameters of the model and their association with QTL and major phenology genes that collocate at QTL.

Why it matches plant phenotyping methodsコムギの発育・出穂形質を予測する生理学的QTLモデルを開発し、独立圃場試験で予測を検証しており、形質取得・推定手法が研究の中心である。

abstractWe used an ecophysiological model that integrates our current understanding of the physiology of wheat phenology to predict the development and anthesis date of 91 recombinant inbreed lines (RILs) of durum wheat

Physiological phenotyping of transpiration response to vapour pressure deficit in wheat →

Abstract Background Precision phenotyping of short-term transpiration response to environmental conditions and transpiration patterns throughout wheat development enables a better understanding of specific trait compositions that lead to improved transpiration efficiency. Transpiration and related traits were evaluated in a set of 79 winter wheat lines using the custom-built “DroughtSpotter XXL” facility. The 120 l plant growth containers implemented in this phenotyping platform enable gravimetric quantification of water use in real-time under semi-controlled, yet field-like conditions across the entire crop life cycle. Results The resulting high-resolution data enabled identification of significant developmental stage-specific variation for genotype rankings in transpiration efficiency. In addition, for all examined genotypes we identified the genotype-specific breakpoint in transpiration in response to increasing vapour pressure deficit, with breakpoints ranging between 2.75 and 4.1 kPa. Conclusion Continuous monitoring of transpiration efficiency and diurnal transpiration patterns enables identification of hidden, heritable genotypic variation for transpiration traits relevant for wheat under drought stress. Since the unique experimental setup mimics field-like growth conditions, the results of this study have good transferability to field conditions.

Why it matches plant phenotyping methodsカスタム構築したフェノタイピング施設により、蒸散量・水利用・蒸散効率を連続的かつ高解像度に測定する手法とプラットフォームが研究の中心であるため。

abstractTranspiration and related traits were evaluated in a set of 79 winter wheat lines using the custom-built “DroughtSpotter XXL” facility.

PosNet: Estimating lettuce fresh weight in plant factory based on oblique image →

Fresh weight is a crucial indicator for assessing crop growth in plant factory. To date, the majority of non-destructive techniques employed for estimating crop fresh weight rely on the top view images. Nevertheless, these approaches are limited in performance due to the constrained and non-open environment of plant factories. In this paper, we propose a novel position-guided network (PosNet) to estimate the fresh weight of crop using oblique view images. Precisely, we first build the crop (i.e., lettuce) dataset by positioning the camera vertically in an oblique angle, with the Mask R-CNN framework trained for individual lettuces segmentation from the background. The segmented lettuce images are then fed into the PosNet for network training and testing purpose. By integrating the shallow feature extraction module and the position information extraction module, the proposed PosNet attains superior performance on assessing the lettuce fresh weight from oblique view images against other models. We further conducted the ablation studies and generalization testing to verify the efficacy and robustness of the proposed network model. Moreover, by comparing three variations (i.e., position, growth stage and posture orientation) of the lettuce separately and the sensitivity analysis of oblique shooting angles, our method demonstrates plausible adaptability for lettuce fresh weight estimation. Taking the accuracy, robustness, generalization capability, and adaptability into account, the integration of PosNet with oblique images not only enjoys great potentials in assessing the fresh weight, but also provides a practical support for agronomic management of crops cultivated in plant factories.

Why it matches plant phenotyping methods斜め画像からレタス個体の生 fresh weight を推定する画像解析ネットワークを開発し、精度・頑健性・汎化性・適応性を検証しており、植物表現型取得手法が中心である。

abstractwe propose a novel position-guided network (PosNet) to estimate the fresh weight of crop using oblique view images.

Identifying indicators of apple bud dormancy status by exposure to artificial forcing conditions. →

Dormancy in temperate fruit trees is a mechanism of temporary growth suspension, which is vital for tree survival during winter. Studies on this phenomenon frequently employ scientific methods that aim to detect the timing of dormancy release. Dormancy release occurs when trees have been exposed to sufficient chill, allowing them to resume growth under conducive conditions. This study investigates dormancy dynamics in two apple (Malus × domestica Borkh.) cultivars, 'Nicoter' and 'Topaz', by sampling branches in an orchard over 14 weeks (2019 to 2020) and over 31 weeks (2021 to 2022) and subjecting them to a 42-day budbreak forcing period in a growth chamber. Temporal changes in budbreak percentages demonstrated dormancy progression in the studied apple cultivars and allowed the three main dormancy phases to be distinguished: paradormancy (summer dormancy), endodormancy (deep dormancy) and ecodormancy (spring dormancy), along with transition periods between them. Using these data, we explored the suitability of several alternative methods to determine endodormancy release. Tabuenca's test, which predicts dormancy release based on the differences in dry weights of buds with and without forcing, showed promise for this purpose. However, our data indicated a need for considerable adjustments and validation of this test. Bud weight and water content of buds in the orchard did not align with budbreak percentages under forcing conditions, rendering them unsuitable for determining endodormancy release in 'Nicoter' and 'Topaz'. Shoot growth cessation did not seem to be connected with either dormancy progression or dormancy depth of the studied cultivars, whereas leaf fall coincided with the beginning of the transition from endo- to ecodormancy. This work addresses methodological limitations in dormancy research and suggests considering the mean time to budbreak and budbreak synchrony as additional criteria to assess tree dormancy status.

Why it matches plant phenotyping methodsリンゴの休眠状態を評価するため、強制開花試験や複数の判定法を比較・検証し、休眠解除の評価基準を提案しているため、植物フェノタイピング手法が中心です。

abstractUsing these data, we explored the suitability of several alternative methods to determine endodormancy release.

Delving into the Potential of Deep Learning Algorithms for Point Cloud Segmentation at Organ Level in Plant Phenotyping →

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms.

Why it matches plant phenotyping methods植物フェノタイピングにおける3D点群の器官レベル分割モデルを比較評価し、取得環境・センサー別の性能を分析することが中心であるため、方法検証および方法論レビューとして収載する。

abstractThe task of plant segmentation is crucial in plant phenotyping

The Selection of Lettuce Seedlings for Transplanting in a Plant Factory by a Non-Destructive Estimation of Leaf Area and Fresh Weight →

Selecting uniform and healthy seedlings is important to ensure that a certain level of production can be reliably achieved in a plant factory. The objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce and to determine the feasibility of using a simple image analysis to select robust seedlings that can produce a uniform and dependable yield of lettuce in a plant factory. To vary the range of the leaf area and shoot fresh weight of lettuce seedlings, we applied two- and three-day irrigation intervals during the period of seedling production and calculated the projected canopy size (PCS) from the top-view images of the lettuce seedlings, although there were no significant growth differences between the irrigation regimes. A high correlation was identified between the PCS and shoot fresh weight for the lettuce seedlings during the period of seedling production, with a coefficient of determination exceeding 0.8. Therefore, the lettuce seedlings were classified into four grades (A–D) based on their PCS values calculated at transplanting. In the early stages of cultivation after transplanting, there were differences in the lettuce growth among the four grades; however, at the harvest (28 days after transplanting), there was no significant difference in the lettuce yield between grades A–C, with the exception of grade D. The lettuce seedlings in grades A–C exhibited the anticipated yield (150 g/plant) at the harvest time. In the correlation between the PCS and leaf area or the shoot fresh weight of lettuce during the cultivation period after transplanting and the entire cultivation period, the R2 values were higher than 0.9, confirming that PCS can be used to predict lettuce growth with greater accuracy. In conclusion, we demonstrated that the PCS calculation from the top-view images, a straightforward image analysis technique, can be employed to non-destructively and accurately predict lettuce leaf area and shoot fresh weight, and the seedlings with the potential to yield above a certain level after transplanting can be objectively and accurately selected based on PCS.

Why it matches plant phenotyping methods非破壊画像解析による投影キャノピーサイズから葉面積・生体重を推定し、移植苗を選抜する手法が研究の中心である。

abstractThe objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce

Identification of phenotypic and transcriptomic signatures underpinning maize crown root systems →

Maize is pivotal in supporting global agriculture and addressing food security challenges. Crop root systems are critical for water uptake and nutrient acquisition, which impacts yield. Quantitative trait phenotyping is essential to understand better the genetic factors underpinning maize root growth and development. Root systems are challenging to phenotype given their below-ground, soil-bound nature. In addition, manual trait annotations of root images are tedious and can lead to inaccuracies and inconsistencies between individuals, resulting in data discrepancies. To address these issues, we have developed an automated phenotyping pipeline for field-grown maize crown roots by leveraging open-source software. Phenotypic variation of 20 maize genotypes from the Wisconsin Diversity panel was significant for numerous root traits, suggesting a genetic basis for the observed developmental deviations. In addition, juvenile root traits from controlled environment conditions exhibited inconsistent correlation with field-grown adult root traits, underscoring the developmental plasticity prevalent during maize root morphogenesis. Transcripts involved in hormone signaling and stress responses were among differentially expressed genes in roots from 20 maize genotypes, suggesting many molecular processes may underlie the observed phenotypic variance. This study furthers our understanding of genotype-phenotype relationships, which is relevant for informing agricultural strategies to improve maize root physiology.

Why it matches plant phenotyping methods圃場栽培トウモロコシの冠根形質を抽出する自動フェノタイピング・パイプラインの開発が研究の中心であり、根形質測定を遺伝子型比較に適用しているため。

abstractwe have developed an automated phenotyping pipeline for field-grown maize crown roots by leveraging open-source software.

Attenuated total reflection Fourier-transform infrared spectroscopy reveals environment specific phenotypes in clonal Japanese knotweed. →

Background Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP. Results Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters. Conclusions Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

Why it matches plant phenotyping methodsATR-FTIRとケモメトリクスによる植物ストレス・生理状態の推定が中心で、根の水ポテンシャル予測モデルを構築・評価しているため、単なる環境処理実験ではない。

abstractATR-FTIR spectroscopy coupled with SVM

Internal quality prediction technology for 'Sulhyang' strawberry fruit using organic analysis and hyperspectral imaging. →

In recent years, hyperspectral imaging combined with machine learning techniques has garnered significant attention for its potential in assessing fruit maturity. This study proposes a method for predicting strawberry fruit maturity based on the harvest time. The main features of this study are as follows. 1) Selection of wavelength band associated with strawberry growth season; 2) Extraction of efficient parameters to predict strawberry maturity 3) Prediction of internal quality attributes of strawberries using extracted parameters. In this study, experts cultivated strawberries in a controlled environment and performed hyperspectral measurements and organic analyses on the fruit with minimal time delay to facilitate accurate modeling. Data augmentation techniques through cross-validation and interpolation were effective in improving model performance. The four parameters included in the model and the cumulative value of the model were available for quality prediction as additional parameters. Among these five parameter candidates, two parameters with linearity were finally identified. The predictive outcomes for firmness, soluble solids content, acidity, and anthocyanin levels in strawberry fruit, based on the two identified parameters, are as follows: The first parameter, p s , demonstrated RMSE performances of 1.0 N, 2.3 %, 0.1 %, and 2.0 mg per 100 g fresh fruit for firmness, soluble solids content, acidity, and anthocyanin, respectively. The second parameter, p 3 , showed RMSE performances of 0.6 N, 1.2 %, 0.1 %, and 1.8 mg per 100 g fresh fruit, respectively. The proposed non-destructive analysis method shows the potential to overcome the challenges associated with destructive testing methods for assessing certain internal qualities of strawberry fruit.

Why it matches plant phenotyping methodsイチゴ果実の内部品質形質を、ハイパースペクトル画像と機械学習で非破壊推定する手法の開発・性能評価が研究の中心である。

abstractThis study proposes a method for predicting strawberry fruit maturity based on the harvest time.

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design. →

Abstract Background Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. Results Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass ( $$R^2 = 0.82$$ R 2 = 0.82 ) and root surface area ( $$R^2 = 0.8$$ R 2 = 0.8 ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. Conclusion Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

Why it matches plant phenotyping methodssEITによる根系形質の非侵襲的・空間分解測定と、測定設計および再構成性能の評価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。

titleQuantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

Time-resolved tracking of cellulose biosynthesis and microfibril network assembly during cell wall regeneration in live Arabidopsis protoplasts →

Plant cell walls are composed of polysaccharides among which cellulose is the most abundant component. Cellulose is processively synthesized as bundles of linear β-1,4-glucan homopolymer chains via the coordinated action of multiple enzymes in cellulose synthase complexes (CSCs) embedded within the plasma cell membrane. Plant cell walls are composed of multiple layers of cellulose fibrils that form highly intertwined extracellular matrix networks. However, it is not yet clear as to how cellulose fibrils synthesized by multiple CSCs are assembled into the intricate cellulose network deposited on plant cell surfaces. Herein, we have established an in vivo time-resolved imaging platform for visualizing cellulose during its biosynthesis and assembly into a complex fibrillar network on the surface of Arabidopsis thaliana mesophyll protoplasts as the primary cell wall regenerates. We performed total internal reflection fluorescence microscopy (TIRFM) with fluorophore-conjugated tandem carbohydrate binding modules (tdCBMs) that were engineered to specifically bind to nascent cellulose fibrils. Together with a well-controlled environment, it was possible to monitor in vivo cellulose fibril synthesis dynamics in a time-resolved manner for nearly one day of continuous cell wall regeneration on protoplast cell surfaces. Our observations provide the basis for a novel model of cellulose fibril network development in protoplasts driven by complex interplay of multi-scale dynamics that include: rapid diffusion and coalescence of short nascently synthesized cellulose fibrils; processive elongation of single fibrils; and cellulose fibrillar network rearrangement during cell wall maturation. This platform is valuable for exploring mechanistic aspects of cell wall synthesis while visualizing cellulose microfibrils assembly.

Why it matches plant phenotyping methods生細胞上のセルロース微 fibril の形成・ネットワーク構築を時系列で可視化するイメージング基盤を確立しており、植物状態の取得手法が研究の中心である。

abstractwe have established an in vivo time-resolved imaging platform for visualizing cellulose during its biosynthesis and assembly into a complex fibrillar network

Utilizing Artificial Intelligence for Plant Phenotyping in Soilless Farming: An Innovative Deep Learning Approach on a Unique Dataset →

In the dynamic landscape of agriculture, the convergence of technological advancements and sustainable practices has become imperative. The emergence of soilless farming, driven by hydroponics and aeroponics, presents a promising solution to address food security challenges while mitigating environmental concerns. Central to the success of soilless farming is efficient plant phenotyping, which traditionally relies on labour-intensive methodologies. However, this paradigm is shifting with the integration of Artificial Intelligence (AI) and Deep Learning techniques. This research endeavours to pioneer an innovative approach to plant phenotyping in soilless farming by harnessing the power of AI. Leveraging a unique dataset meticulously curated from controlled hydroponic and aeroponic environments, our study aims to redefine the boundaries of agricultural research and practice. By employing state-of-the-art Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we dissect complex phenotypic traits encompassing leaf morphology, biomass accumulation, and physiological responses. Through iterative model refinement and validation, we strive to develop a robust framework capable of real-time phenotypic assessment across diverse plant species and growth stages. By synthesizing diverse environmental conditions and perturbations, we augment the original dataset, enhancing model generalization and adaptability. Moreover, through transfer learning techniques, Furthermore, the scalability and accessibility of AI technologies pave the way for democratizing agricultural innovation, fostering inclusive growth and resilience in the face of global challenges.

Why it matches plant phenotyping methodsAI/CNN・RNNと独自データセットを用いた植物形質推定フレームワークの開発・検証が研究の中心であり、葉形態、バイオマス、生理応答を対象とするため含める。

abstractThis research endeavours to pioneer an innovative approach to plant phenotyping in soilless farming by harnessing the power of AI.

Simulating the effects of low-temperature stress during flowering stage on leaf-level photosynthesis with current rice models →

Photosynthesis is crucial for crop growth and yield, yet remains a limitation in enhancing crop production under global climate change. Crop models have been widely used to project the impacts of climate change on crop yields, including extreme temperature events. However, most crop simulation models performed poorly on leaf-level photosynthesis simulation under low-temperature stress (LTS). Here, two rice cultivars, Huaidao 5 and Nangeng 46, were treated under seven LTS intensities (from 27/21 °C to 12/6 °C) and five LTS durations (from 3 to 9 days) during the flowering stage for three years under environment-controlled conditions. The results showed that the observed maximum photosynthetic rate (Pₘₐₓ) and initial light-use efficiency (ε) decreased with increasing intensities and durations of LTS, and Pₘₐₓ and ε were linearly correlated (R² = 0.94). Experimental data under different LTS have been tested by four widely used rice models, including RiceGrow, ORYZA2000, RICEPSM, and GEMRICE, indicating that the effects of LTS durations on Pₘₐₓ and ε have not been captured well by most existing models. By integrating accumulated cold degree days (ACDD, °C·d) as the LTS index with a cultivar-specific low-temperature threshold parameter (Tₕ), algorithms of temperature factor (FT) and ε were improved, and the effects of LTS on Pₘₐₓ and ε were quantified with a double exponential decay function in this research. Compared with the original models, the normalized root mean square error (NRMSE) between observed and simulated photosynthetic rates in the four improved models decreased by about 80 % under LTS treatments, indicating the better performance of the improved models. In addition, the recovery factor of LTS damage to photosynthesis (FLTᵣₑcₒᵥₑᵣ) algorithm was developed to simulate the recovery of the leaf-level photosynthetic rate (P) after LTS treatments. This study could provide an effective quantitative tool for accurately estimating rice photosynthetic production and grain yield under future climate conditions.

Why it matches plant phenotyping methods低温ストレス下のイネ葉光合成を推定するモデル改良と検証が研究の中心であり、植物生理形質の定量的フェノタイピング手法に該当する。

abstractBy integrating accumulated cold degree days (ACDD, °C·d) as the LTS index with a cultivar-specific low-temperature threshold parameter (Tₕ), algorithms of temperature factor (FT) and ε were improved

Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars. →

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce ( Lactuca sativa ) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests ( R 2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.

Why it matches plant phenotyping methods低コスト画像・蛍光画像・組込みカメラを用いた生育モニタリングとキャノピー形状からの植物重量予測が研究の中心で、フェノタイピング手法とその予測性能を評価している。

abstractThis study explores cost-effective imaging techniques for rapid evaluation of seedling vigor

The Use of Imaging to Quantify the Impact of Seed Aging on Lettuce Seed Germination and Seedling Vigor. →

The decline in seed quality over time due to natural aging or mishandling requires assessing seed vigor for resilience in adverse conditions. Accelerated aging (AA) methods simulate seed deterioration by subjecting seeds to high temperatures and humidity. Saturated salt accelerated aging (SSAA) is an AA method adopted for small seeds like lettuce ( Lactuca sativa ). In this study, we subjected seeds of two lettuce cultivars ('Muir' and 'Bauer') to SSAA by sealing them in a box containing 40 g/100 mL of a sodium chloride (NaCl) solution in a dark growth chamber at 41 °C for 24, 48, and 72 h with a control. We monitored their vigor using embedded computer cameras, tracking the projected canopy size (PCS) daily from sowing to harvest. The cultivar 'Muir' exhibited consistent PCS values across the treatments, while 'Bauer' showed PCS variations, with notable declines after prolonged aging. The germination rates dropped significantly after 48 and 72 h of SSAA. A nonlinear regression model revealed a strong relationship between PCS and shoot dry weight across harvests and cultivars ( R 2 = 0.93, RMSE = 0.15, p < 0.001). The research found that the projected canopy size and shoot dry weight increased over time with significant differences in treatments for the cultivar 'Bauer' but not for 'Muir,' with the canopy size being a strong predictor of dry weight and no significant impact from the SSAA treatments. This study highlights cultivar-specific responses to aging and demonstrates the efficacy of our imaging tool in predicting lettuce dry weight despite treatment variations. Understanding how aging affects different lettuce varieties is crucial for seed management and crop sustainability.

Why it matches plant phenotyping methods埋め込みカメラで投影キャノピーサイズを時系列取得し、乾物重を予測する画像計測ツールの有効性を評価しており、画像ベースの植物表現型計測が研究の中心です。

abstractWe monitored their vigor using embedded computer cameras, tracking the projected canopy size (PCS) daily from sowing to harvest.

Automated Imaging to Evaluate the Exogenous Gibberellin (Ga 3 ) Impact on Seedlings from Salt-Stressed Lettuce Seeds. →

Salinity stress is a common challenge in plant growth, impacting seed quality, germination, and general plant health. Sodium chloride (NaCl) ions disrupt membranes, causing ion leakage and reducing seed viability. Gibberellic acid (GA 3 ) treatments have been found to promote germination and mitigate salinity stress on germination and plant growth. 'Bauer' and 'Muir' lettuce ( Lactuca sativa ) seeds were soaked in distilled water (control), 100 mM NaCl, 100 mM NaCl + 50 mg/L GA 3 , and 100 mM NaCl + 150 mg/L GA 3 in Petri dishes and kept in a dark growth chamber at 25 °C for 24 h. After germination, seedlings were monitored using embedded cameras, capturing red, green, and blue (RGB) images from seeding to final harvest. Despite consistent germination rates, 'Bauer' seeds treated with NaCl showed reduced germination. Surprisingly, the 'Muir' cultivar's final dry weight differed across treatments, with the NaCl and high GA 3 concentration combination yielding the poorest results ( p 3 applications in improving germination rates. However, at elevated concentrations, it induced excessive hypocotyl elongation and pale seedlings, posing challenges for two-dimensional imaging. Nonetheless, a sigmoidal regression model using projected canopy size accurately predicted dry weight across growth stages and cultivars, emphasizing its reliability despite treatment variations ( R 2 = 0.96, RMSE = 0.11, p < 0.001).

Why it matches plant phenotyping methodsRGB画像による自動撮像と投影キャノピー面積から乾物重を推定する回帰モデルを提示し、成長段階・品種をまたいで性能検証しているため、表現型取得・推定手法が中心的である。

abstractseedlings were monitored using embedded cameras, capturing red, green, and blue (RGB) images from seeding to final harvest.

Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes. →

Background Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450-850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen. Results M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data. Conclusions HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually.

Why it matches plant phenotyping methodsイネ葉の病斑および病原菌胞子形成量をハイパースペクトル画像から定量する手法が研究の中心であり、植物病態の表現型測定法として明確に該当する。

abstracthyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars

Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory →

Introduction Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system. This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R 2 ) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R 2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

Why it matches plant phenotyping methods植物の画像から生体重を非破壊推定する撮像プラットフォームと機械学習モデルを開発・比較しており、表現型取得手法が研究の中心である。

abstractThis research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory.

“Image-Spectral” fusion monitoring of small cotton samples nitrogen content based on improved deep forest →

Accurate monitoring of nitrogen nutrition is crucial for improving cotton yield and quality, as well as the ecological environment. The mainstream method for monitoring nutrition is to establish traditional machine learning (ML) models using a single data source. However, this approach has limitations such as limited access to feature information, model-fitting problems, and limited generalization. Deep learning (DL), on the other hand, has shown promise in complex nonlinear modeling tasks due to its flexible structure. However, it has its limitations, such as the fact that agronomic sample collection and testing are usually labor- and material-intensive, resulting in sample sizes that are too small to meet its training conditions. Therefore, there is an urgent need for DL models that can effectively integrate features from multiple data sources and accurately monitor crop nitrogen content, especially in scenarios with small samples. In this study, we conducted indoor pot experiments using the cotton variety Xinluzao 53 and subjected it to six nitrogen treatments. The data sources for our analysis included hyperspectral and digital images of the cotton leaves. To enhance representation learning capabilities, we enriched the multi-class base learners within each layer of the deep forest (DF) model and introduced skip connections. These enhancements improved the quality of inversion for both hyperspectral and digital image datasets. We then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning. Our focus was on three levels of fusion: feature-level fusion, decision-level fusion, and secondary decision-level fusion. This approach aimed to further enhance the accuracy and stability of nitrogen content inversion. The DF model satisfied the training condition for small samples. Compared to traditional ML algorithms and the original DF algorithm, the improved DF model achieved an increase in validation set R² of 13.4–28.5% and 10.9–14.9%, respectively. These findings highlight the enhanced accuracy and stability of the improved DF model. Additionally, compared to the optimal inversion model using two single data sources, the “Image-Spectral” three-level fusion models exhibited improvements in validation set R² of 8.6–9.3%, 10.5–11.2%, and 11.8–12.5% for feature-level, decision-level, and secondary decision-level fusion, respectively. The improved DF and three-level fusion model collectively contributed to the increased accuracy of cotton nitrogen content inversion. Among these models, the secondary decision-level fusion model demonstrated the most marked improvement. This methodology provides valuable insights into monitoring crop phenotypic parameters in situations with limited sample sizes.

Why it matches plant phenotyping methods綿葉のハイパースペクトル画像とデジタル画像から窒素含量を推定する融合モデルを開発・比較検証しており、植物形質推定手法が研究の中心である。

abstractWe then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning.

High-throughput phenotyping of soybean transpiration response curves to rising atmospheric drying in a mapping population →

Abstract In soybean, limiting whole-plant transpiration rate (TR) response to increasing vapor pressure deficit (VPD) has been associated with the ‘slow-wilting’ phenotype and with water- conservation enabling higher yields under terminal drought. Despite the promise of this trait, it is still unknown whether it has a genetic basis in soybean, a challenge limiting the prospects of breeding climate-resilient varieties. Here we present the results of a first attempt at a high- throughput phenotyping of TR and stomatal conductance response curves to increasing VPD conducted on a soybean mapping population consisting of 140 recombinant inbred lines (RIL). This effort was conducted over two consecutive years, using a controlled-environment, gravimetric phenotyping platform which enabled characterizing 900 plants for these responses, yielding regression parameters (R 2 from 0.92 to 0.99) that were used for genetic mapping. Several quantitative trait loci (QTL) were identified for these parameters on chromosomes (Ch) 4, 6 and 10, including a VPD-conditional QTL on Ch 4 and a ‘constitutive’ QTL controlling all parameters on Ch 6. This study demonstrated for the first time that canopy water use in response to rising VPD has a genetic basis in soybean, opening novel avenues for identifying alleles enabling water conservation under current and future climate scenarios.

Why it matches plant phenotyping methods制御環境の重量計測フェノタイピング基盤を用いて、900個体の蒸散・気孔コンダクタンス応答曲線を高スループット取得し、再利用可能な生理形質抽出を実施しているため、方法の適用が研究の主要部分です。

abstractHere we present the results of a first attempt at a high- throughput phenotyping of TR and stomatal conductance response curves to increasing VPD

Assessing the hydromechanical control of plant growth. →

Multicellular organisms grow and acquire their shapes through the differential expansion and deformation of their cells. Recent research has addressed the role of cell and tissue mechanical properties in these processes. In plants, it is believed that growth rate is a function of the mechanical stress exerted on the cell wall, the thin polymeric layer surrounding cells, involving an effective viscosity. Nevertheless, recent studies have questioned this view, suggesting that cell wall elasticity sets the growth rate or that uptake of water is limiting for plant growth. To assess these issues, we developed a microfluidic device to quantify the growth rates, elastic properties and hydraulic conductivity of individual Marchantia polymorpha plants in a controlled environment with a high throughput. We characterized the effect of osmotic treatment and abscisic acid on growth and hydromechanical properties. Overall, the instantaneous growth rate of individuals is correlated with both bulk elastic modulus and hydraulic conductivity. Our results are consistent with a framework in which the growth rate is determined primarily by the elasticity of the wall and its remodelling, and secondarily by hydraulic conductivity. Accordingly, the coupling between the chemistry of the cell wall and the hydromechanics of the cell appears as key to set growth patterns during morphogenesis.

Why it matches plant phenotyping methods個体植物の成長速度・弾性特性・水力伝導度を高スループットで定量するマイクロ流体デバイスを開発しており、表現型取得法が研究の中心である。

abstractwe developed a microfluidic device to quantify the growth rates, elastic properties and hydraulic conductivity of individual Marchantia polymorpha plants in a controlled environment with a high throughput

Hyperspectral imaging reveals small-scale water gradients in apple leaves due to minimal cuticle perforation by Venturia inaequalis conidiophores. →

Effects of Venturia inaequalis on water relations of apple leaves were studied under controlled conditions without limitation of water supply to elucidate their impact on the non-haustorial biotrophy of this pathogen. Leaf water relations, namely leaf water content and transpiration, were spatially resolved by hyperspectral imaging and thermography; non-imaging techniques-gravimetry, a pressure chamber, and porometry-were used for calibration and validation. Reduced stomatal transpiration 3-4 d after inoculation coincided with a transient increase of water potential. Perforation of the plant cuticle by protruding conidiophores subsequently increased cuticular transpiration even before visible symptoms occurred. With sufficient water supply, cuticular transpiration remained at elevated levels for several weeks. Infections did not affect the leaf water content before scab lesions became visible. Only hyperspectral imaging was suitable to demonstrate that a decreased leaf water content was strictly limited to sites of emerging conidiophores and that cuticle porosity increased with sporulation. Microscopy confirmed marginal cuticle injury; although perforated, it tightly surrounded the base of conidiophores throughout sporulation and restricted water loss. The role of sustained redirection of water flow to the pathogen's hyphae in the subcuticular space above epidermal cells, to facilitate the acquisition and uptake of nutrients by V. inaequalis, is discussed.

Why it matches plant phenotyping methodsリンゴ葉の水分含量・蒸散をハイパースペクトル画像と熱画像で空間定量し、非画像手法で校正・検証している。病原体研究ではあるが、感染葉の生理状態を取得する画像計測法が実質的に中心である。

abstractLeaf water relations, namely leaf water content and transpiration, were spatially resolved by hyperspectral imaging and thermography; non-imaging techniques-gravimetry, a pressure chamber, and porometry-were used for calibration and validation.

Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms →

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Why it matches plant phenotyping methods機械視覚とYOLOv8を用いて、トウガラシの生育段階を画像から推定する手法を開発・比較しており、植物状態の取得・抽出が研究の中心である。

abstractThis study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks.

Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN →

Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.

Why it matches plant phenotyping methods画像を用いたレタスの自動生育モニタリングとANNによる評価手法の開発・検証が中心であり、植物フェノタイピング手法に該当する。

abstractobservation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month

Evaluation of variation in seedling root architectural traits and their potential association with nitrogen fixation and agronomic traits in field pea accessions →

Abstract Root system architecture (RSA) plays a central role in water and nutrient acquisition in plants. Plasticity and genetic variation in RSA can be used as an adaptive strategy to optimize plant performance under variable environments. We quantified phenotypic variation for seedling RSA among 44 diverse pea ( Pisum sativum L.) genotypes, including breeding lines and germplasm accessions, grown under controlled conditions for 14 days using two‐dimensional hydroponic root imaging. Root image analysis revealed significant genotypic variability among the lines for all root traits, namely root length (RL), root diameter (RD), root volume, root surface area, number of tips, network width (NW), network depth (ND), and network convex area. Significant positive correlations were observed among the evaluated root traits, ranging from 0.5 to 0.9. Pea lines were ranked based on estimated means for root traits, with lines E20, F1, and F8 showing high rankings, while E4 and F5 received low rankings for most traits. To associate root traits with nitrogen (N) fixation and field agronomic performance, we performed redundancy analysis (RDA). The quantified root traits accounted for significant variation in the agronomic traits ( R 2 = ∼30%, p

Why it matches plant phenotyping methods二次元根画像解析を用いた幼植物の根系形態形質の定量が研究の中心であり、遺伝子型間比較と農業形質との関連解析に用いられているため、画像ベースの表現型解析の実質的応用と判断します。

abstractRoot image analysis revealed significant genotypic variability among the lines for all root traits

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars →

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

Why it matches plant phenotyping methods複数の高スループット根フェノタイピング基盤と従来法を比較し、根系構造形質およびモデルパラメータの一致性・再現性を評価することが中心の研究である。

abstractHowever, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved.

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants. →

Canopeo app was developed as a simple, accurate, rapid, and free tool to analyze ground cover fraction (GCF) from red-green-blue (RGB) images and videos captured in the field. With increasing interest in tools for plant phenotyping in controlled environments, the usefulness of Canopeo to identify differences in growth among Arabidopsis thaliana mutants in a controlled environment were explored. A simple imaging system was used to compare Arabidopsis mutants based on the FLAVIN-BINDING, KELCH REPEAT, F-BOX-1 (FKF1) mutation, which has been identified with increased biomass accumulation. Two FKF1 lines such as null expression (fkf1-t) and overexpression (FKF1-OE) lines were used along with wild type (Col-0). Canopeo was used to phenotype plants, based on biomass estimations. Under long-day photoperiod, fkf1-t had increased cellulose biosynthesis, and therefore biomass. Resource partitioning favored seedling vigor and delayed onset of senescence. In contrast, FKF1-OE illustrated a determinative growth habit where plant resources are primarily allocated for seed production. This study demonstrates the use of Canopeo for model plants and highlights its potential for phenotyping broadleaved crops in controlled environments. The value of adapting Canopeo for lab use is those with limited experience and resources have access to phenotyping methodology that is simple, accessible, accurate, and cost-efficient in a controlled environment setting.

Why it matches plant phenotyping methodsCanopeoを用いたRGB画像からの地上被覆率・バイオマス推定をArabidopsisの表現型解析に適応・実証しており、画像ベース表現型計測が研究の中心である。

titleCanopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

Non‐destructive, whole‐plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress →

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.

Why it matches plant phenotyping methods低コストで非破壊的に植物全体の生理形質を経時測定する手法とシステムを開発し、ソルガムで実証しているため、フェノタイピング手法が中心である。

abstractWe developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers.

High-throughput phenotyping for terminal drought stress in chickpea (Cicer arietinum L.) →

Most chickpea cultivation occurs in rainfed environments, where unpredictable rainfall leads to drought stress, consequently reducing growth and productivity. Fast and robust image-based screening methods would greatly facilitate drought tolerance research. In this study, an experiment was conducted in a climate-controlled environment, using radio frequency-enabled ID (RFID) tagged plant carriers on Lemnatec's high-throughput phenotyping platform. The agro-physiological characteristics of six chickpea genotypes under drought stress conditions imposed at the early podding stage were explored. Using non-destructive techniques, including Red, Green, and, Blue (RGB), Near-Infrared (NIR), Infrared (IR), and chlorophyll fluorescence (Fv/Fm) imaging, data was captured at various stages of drought stress, quantified as the fraction of transpirable soil water, using LemnaGrid software. Traits such as plant phenology, yield, yield components, and physiological parameters (e.g., leaf temperature and photosynthetic characteristics) for both drought-stressed and well-watered plants were recorded manually. Seed yields ranged from 9.9–18.1 g plant−1 under WW, and 2.6–13.7 g plant−1 under DS. DS decreased yield the most in ICC 1882 and RSG 888 (73.7 %) and the least in ICC 4958 (24.3 %) relative to WW. Our findings revealed significant genotype × water treatment interactions for all manually recorded traits. Moreover, strong positive correlations were observed between manually recorded and image-based traits, i.e., between aboveground dry weight and projected area, aboveground dry weight and convex hull area, plant height and caliper length, photosynthetic rate, and chlorophyll fluorescence, stomatal conductance and NIR reflectance, IR thermometer temperature and IR imaging temperature. Notably, the strong positive correlations between NIR reflectance and stomatal conductance, and between chlorophyll fluorescence and photosynthesis, underscore the immense potential of harnessing image-based screening methods in breeding programs to enhance drought tolerance.

Why it matches plant phenotyping methods画像・センサーを用いたハイスループット表現型取得と、手動測定との相関による技術評価が研究の中心であり、干ばつ応答の植物形質を抽出している。

abstractFast and robust image-based screening methods would greatly facilitate drought tolerance research.

Strong prevalence of light regime-specific QTL in Arabidopsis detected using automated high-throughput phenotyping in fluctuating or constant light. →

Plants have evolved and adapted under dynamic environmental conditions, particularly to fluctuating light, but plant research has often focused on constant growth conditions. To quantitatively asses the adaptation to fluctuating light, a panel of 384 natural Arabidopsis thaliana accessions was analyzed in two parallel independent experiments under fluctuating and constant light conditions in an automated high-throughput phenotyping system upgraded with supplemental LEDs. While the integrated daily photosynthetically active radiation was the same under both light regimes, plants in fluctuating light conditions accumulated significantly less biomass and had lower leaf area during their measured vegetative growth than plants in constant light. A total of 282 image-derived architectural and/or color-related traits at six common time points, and 77 photosynthesis-related traits from one common time point were used to assess their associations with genome-wide natural variation for both light regimes. Out of the 3000 significant marker-trait associations (MTAs) detected, only 183 (6.1%) were common for fluctuating and constant light conditions. The prevalence of light regime-specific QTL indicates a complex adaptation. Genes in linkage disequilibrium with fluctuating light-specific MTAs with an adjusted repeatability value >0.5 were filtered for gene ontology terms containing "photo" or "light", yielding 15 selected candidates. The candidate genes are involved in photoprotection, PSII maintenance and repair, maintenance of linear electron flow, photorespiration, phytochrome signaling, and cell wall expansion, providing a promising starting point for further investigations into the response of Arabidopsis thaliana to fluctuating light conditions.

Why it matches plant phenotyping methods自動ハイスループット表現型解析システムを用い、画像由来の形態・色形質と光合成形質を大規模に取得しており、表現型取得基盤の適用が研究の中心である。

abstracta panel of 384 natural Arabidopsis thaliana accessions was analyzed in two parallel independent experiments under fluctuating and constant light conditions in an automated high-throughput phenotyping system upgraded with supplemental LEDs.

Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches →

Early and accurate disease diagnosis is pivotal for effective phytosanitary management strategies in agriculture. Hyperspectral sensing has emerged as a promising tool for early disease detection, yet challenges remain in effectively harnessing its potential. This study compares parametric spectral Vegetation Indices (VIs) and a nonparametric Gaussian Process Classification based on an Automated Spectral Band Analysis Tool (GPC-BAT) for diagnosing plant bacterial diseases using hyperspectral data. The study conducted experiments on tomato plants in controlled conditions and kiwi plants in field settings to assess the performance of VIs and GPC-BAT. In the tomato experiment, the modeling processes were applied to classify the spectral data measured on the healthy class of plants (sprayed with water only) and discriminate them from the data captured on plants inoculated with the two bacterial suspensions (108 CFU mL−1). In the kiwi experiment, the standard modeling results of the spectral data collected on nonsymptomatic plants were compared to the ones obtained using symptomatic plants’ spectral data. VIs, known for their simplicity in extracting biophysical information, successfully distinguished healthy and diseased tissues in both plant species. The overall accuracy achieved was 63% and 71% for tomato and kiwi, respectively. Limitations were observed, particularly in differentiating specific disease infections accurately. On the other hand, GPC-BAT, after feature reduction, showcased enhanced accuracy in identifying healthy and diseased tissues. The overall accuracy ranged from 70% to 75% in the tomato and kiwi case studies. Despite its effectiveness, the model faced challenges in accurately predicting certain disease infections, especially in the early stages. Comparative analysis revealed commonalities and differences in the spectral bands identified by both approaches, with overlaps in critical regions across plant species. Notably, these spectral regions corresponded to the absorption regions of various photosynthetic pigments and structural components affected by bacterial infections in plant leaves. The study underscores the potential of hyperspectral sensing in disease diagnosis and highlights the strengths and limitations of VIs and GPC-BAT. The identified spectral features hold biological significance, suggesting correlations between bacterial infections and alterations in plant pigments and structural components. Future research avenues could focus on refining these approaches for improved accuracy in diagnosing diverse plant–pathogen interactions, thereby aiding disease diagnosis. Specifically, efforts could be directed towards adapting these methodologies for early detection, even before symptom manifestation, to better manage agricultural diseases.

Why it matches plant phenotyping methods植物の病徴・健全性をハイパースペクトルデータから推定する手法を比較・評価しており、疾病表現型の取得と分類が研究の中心である。

abstractThis study compares parametric spectral Vegetation Indices (VIs) and a nonparametric Gaussian Process Classification based on an Automated Spectral Band Analysis Tool (GPC-BAT) for diagnosing plant bacterial diseases using hyperspectral data.

Setup and characterisation according to NEMA NU 4 of the pheno PET scanner, a PET system dedicated for plant sciences. →

Objective. The pheno PET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber. Here, we present the setup of pheno PET, its data processing and image reconstruction together with its performance. Approach. The performance characterization follows the national electrical manufacturers association (NEMA) standard for small animal PET systems with a number of adoptions due to the vertical oriented bore of a PET for plant sciences. In addition temperature stability and spatial resolution with a hot rod phantom are addressed. Main results. The spatial resolution for a 22 Na point source at a radial distance of 5 mm to the center of the field-of-view (FOV) is 1.45 mm, 0.82 mm and 1.88 mm with filtered back projection in radial, tangential and axial direction, respectively. A hot rod phantom with 18 F gives a spatial resolution of up to 1.6 mm. The peak noise-equivalent count rates are 550 kcps @ 35.08 MBq, 308 kcps @ 33 MBq and 45 kcps @ 40.60 MBq for the mouse, rat and monkey size scatter phantoms, respectively. The scatter fractions for these phantoms are 12.63%, 22.64% and 55.90%. We observe a peak sensitivity of up to 3.6% and a total sensitivity of up to S A , tot = 2.17%. For the NEMA image quality phantom we observe a uniformity of % STD = 4.22% with ordinary Poisson maximum likelihood expectation-maximization with 52 iterations. Here, recovery coefficients of 0.12, 0.64, 0.89, 0.93 and 0.91 for 1 mm, 2 mm, 3 mm, 4 mm and 5 mm rods are obtained and spill-over ratios of 0.08 and 0.14 for the water-filled and air-filled inserts, respectively. Significance. The pheno PET and its laboratory are now in routine operation for the administration of [ 11 C]CO 2 and non-invasive measurement of transport and allocation of 11 C-labelled photoassimilates in plants.

Why it matches plant phenotyping methods植物専用PETスキャナーの構築、データ処理・画像再構成、性能評価を中心に扱い、植物体内の光合成産物の輸送・配分を非侵襲測定するフェノタイピング基盤である。

abstractThe pheno PET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber.

Towards portable MRI in the plant sciences. →

Plant physiology and structure are constantly changing according to internal and external factors. The study of plant water dynamics can give information on these changes, as they are linked to numerous plant functions. Currently, most of the methods used to study plant water dynamics are either invasive, destructive, or not easily accessible. Portable magnetic resonance imaging (MRI) is a field undergoing rapid expansion and which presents substantial advantages in the plant sciences. MRI permits the non-invasive study of plant water content, flow, structure, stress response, and other physiological processes, as a multitude of information can be obtained using the method, and portable devices make it possible to take these measurements in situ, in a plant's natural environment. In this work, we review the use of such devices applied to plants in climate chambers, greenhouses or in their natural environments. We also compare the use of portable MRI to other methods to obtain the same information and outline its advantages and disadvantages.

Why it matches plant phenotyping methods植物の水分動態・構造・ストレス応答を測定する携帯型MRIの利用法をレビューし、他手法との比較や利点・欠点を論じる方法論レビューである。

abstractIn this work, we review the use of such devices applied to plants in climate chambers, greenhouses or in their natural environments.

Development of a Standardized Soybean Cyst Nematode Screening Assay in Pennycress and Identification of Resistant Germplasm →

The prospect of incorporating pennycress as an oilseed cover crop in the Midwest's corn-soybean rotation system has drawn researcher and farmer attention. The inclusion of pennycress will be beneficial as it provides an excellent soil cover to reduce soil erosion and nutrient leaching while serving as an additional source for oilseed production and income. However, pennycress is an alternative host for soybean cyst nematode (SCN), which is a major biological threat to soybean that needs to be addressed for sustainable pennycress adoption into our current production systems. To develop a standardized SCN resistance screening strategy in pennycress, we tested and optimized five parameters: (i) germination stimulants, (ii) inoculation timing, (iii) inoculation rate, (iv) experimental incubation time, and (v) susceptible checks. The standardized SCN resistance screening protocol includes the following: (i) treating pennycress seeds with gibberellic acid for 24 h, (ii) transplanting seedlings 12 to 15 days after initiating germination and inoculating 10 to 12 days after transplantation, (iii) inoculating at a rate of 1,500 eggs/100 cc soil (1,500 eggs per plant), (iv) processing roots at 30 days after inoculation, and (v) using susceptible pennycress accession Ames 32869 to calculate the female index. The standardized protocol was used to quantify the response of a diverse set of pennycress accessions for response against SCN HG type 1.2.5.7 and HG type 7. While there were no highly resistant pennycress lines identified, 15 were rated as moderately resistant to HG type 1.2.5.7, and eight were rated moderately resistant to HG type 7. The resistant lines identified in this study could be utilized to develop SCN-resistant pennycress cultivars. The study also opens a new avenue for research to understand SCN-pennycress interactions through molecular and genomic studies. This knowledge could aid in the successful inclusion of pennycress as a beneficial cover/oilseed crop in the United States Midwest.

Why it matches plant phenotyping methodsペニークレスのSCN抵抗性という植物状態を評価する標準化スクリーニング法を開発・最適化し、実際の評価にも適用しているため、フェノタイピング手法が中心的です。

abstractTo develop a standardized SCN resistance screening strategy in pennycress, we tested and optimized five parameters

Non-destructive real-time analysis of plant metabolite accumulation in radish microgreens under different LED light recipes. →

Introduction The future of human space missions relies on the ability to provide adequate food resources for astronauts and also to reduce stress due to the environment (microgravity and cosmic radiation). In this context, microgreens have been proposed for the astronaut diet because of their fast-growing time and their high levels of bioactive compounds and nutrients (vitamins, antioxidants, minerals, etc.), which are even higher than mature plants, and are usually consumed as ready-to-eat vegetables. Methods Our study aimed to identify the best light recipe for the soilless cultivation of two cultivars of radish microgreens (Raphanus sativus, green daikon, and rioja improved) harvested eight days after sowing that could be used for space farming. The effects on plant metabolism of three different light emitting diodes (LED) light recipes (L1-20% red, 20% green, 60% blue; L2-40% red, 20% green, 40% blue; L3-60% red, 20% green, 20% blue) were tested on radish microgreens hydroponically grown. A fluorimetric-based technique was used for a real-time non-destructive screening to characterize plant methabolism. The adopted sensors allowed us to quantitatively estimate the fluorescence of flavonols, anthocyanins, and chlorophyll via specific indices verified by standardized spectrophotometric methods. To assess plant growth, morphometric parameters (fresh and dry weight, cotyledon area and weight, hypocotyl length) were analyzed. Results We observed a statistically significant positive effect on biomass accumulation and productivity for both cultivars grown under the same light recipe (40% blue, 20% green, 40% red). We further investigated how the addition of UV and/or far-red LED lights could have a positive effect on plant metabolite accumulation (anthocyanins and flavonols). Discussion These results can help design plant-based bioregenerative life-support systems for long-duration human space exploration, by integrating fluorescence-based non-destructive techniques to monitor the accumulation of metabolites with nutraceutical properties in soilless cultivated microgreens.

Why it matches plant phenotyping methods蛍光センサーによる非破壊・リアルタイムな植物代謝状態の定量推定が研究の明示的な中心で、分光法による検証も行っているため、植物フェノタイピング手法の応用・検証として採用。

abstractA fluorimetric-based technique was used for a real-time non-destructive screening to characterize plant methabolism.

Investigating the Effects of Full-Spectrum LED Lighting on Strawberry Traits Using Correlation Analysis and Time-Series Prediction. →

In crop cultivation, particularly in controlled environmental agriculture, light quality is one of the most critical factors affecting crop growth and harvest. Many scholars have studied the effects of light quality on strawberry traits, but they have used relatively simple light components and considered only a small number of light qualities and traits in each experiment, and the results were not complete or objective. In order to comprehensively investigate the effects of different light qualities from 350 nm to 1000 nm on strawberry traits to better predict the future growth trend of strawberries under different light qualities, we proposed a new approach. We introduced Spearman's rank correlation coefficient to handle complex light quality variations and multiple traits, preprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network. We took 500 strawberry plants as samples and cultivated them in 72 groups of dynamically changing light qualities. Then, we recorded the growth changes and formed training and testing sets. Finally, we discussed the correlation between light quality and plant trait changes in consistency with current studies, and the proposed prediction model achieved the best performance in the prediction task of nine plant traits compared with the comparison models. Thus, the validity of the proposed method and model was demonstrated.

Why it matches plant phenotyping methods植物9形質の将来予測を目的に、データ前処理法とInformer予測モデルを中心的に提案・評価しており、形質取得・推定手法が実験の単なる routine 測定ではない。

abstractpreprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network.

“Chamber #8” – a holistic approach of high-throughput non-destructive assessment of plant roots →

Introduction In the past years, it has been observed that the breeding of plants has become more challenging, as the visible difference in phenotypic data is much smaller than decades ago. With the ongoing climate change, it is necessary to breed crops that can cope with shifting climatic conditions. To select good breeding candidates for the future, phenotypic experiments can be conducted under climate-controlled conditions. Above-ground traits can be assessed with different optical sensors, but for the root growth, access to non-destructively measured traits is much more challenging. Even though MRI or CT imaging techniques have been established in the past years, they rely on an adequate infrastructure for the automatic handling of the pots as well as the controlled climate. Methods To address both challenges simultaneously, the non-destructive imaging of plant roots combined with a highly automated and standardized mid-throughput approach, we developed a workflow and an integrated scanning facility to study root growth. Our “ chamber #8 ” contains a climate chamber, a material flow control, an irrigation system, an X-ray system, a database for automatic data collection, and post-processing. The goals of this approach are to reduce the human interaction with the various components of the facility to a minimum on one hand, and to automate and standardize the complete process from plant care via measurements to root trait calculation on the other. The user receives standardized phenotypic traits and properties that were collected objectively. Results The proposed holistic approach allows us to study root growth of plants in a field-like substrate non-destructively over a defined period and to calculate phenotypic traits of root architecture. For different crops, genotypic differences can be observed in response to climatic conditions which have already been applied to a wide variety of root structures, such as potatoes, cassava, or corn. Discussion It enables breeders and scientists non-destructive access to root traits. Additionally, due to the non-destructive nature of X-ray computed tomography, the analysis of time series for root growing experiments is possible and enables the observation of kinetic traits. Furthermore, using this automation scheme for simultaneously controlled plant breeding and non-destructive testing reduces the involvement of human resources.

Why it matches plant phenotyping methods植物根系の非破壊X線イメージング、施設自動化、データ処理、根形態形質計算を統合したフェノタイピング手法・プラットフォームの開発が中心である。

abstractwe developed a workflow and an integrated scanning facility to study root growth.

High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought →

Abstract Advances in informatics, robotics, and imaging techniques make it possible to use state-of-the-art digital reconstruction technologies for high-throughput plant phenotyping (HTPP) affected by stress factors, as well as for the ontology of their structural and functional traits. Digital imaging of structural and functional features of the aboveground part of plants is non-destructive and plants can be monitored throughout their entire life cycle. In the experiment with tomato plants (Solanum lycopersicum L.; cv. Gruzanski zlatni) grown in controlled environmental conditions and affected by gradual soil dehydration, we evaluated phenotypic traits and phenotypic plasticity by the PlantScreenTM platform using digital imaging of plant optical signals. In this study, 25 different morpho-physiological traits of the plant were evaluated during the precise control and monitoring of the water content in the soil. Different levels of plant water supply induced statistically significant differences in the formation of individual phenotypic traits. Several plant traits have been identified that are characterized by low variability in both well-hydrated and water-stressed conditions, as well as traits with high phenotypic plasticity. Geometric traits (especially Isotop, Round-2top, and Compside) showed a relatively low level of drought-induced phenotypic plasticity. However, functional and chemometric characteristics (ΔF/F′m, Rfd, Water-1, and ARI-1) showed the potential to exhibit rapid plasticity in water-stressed conditions. Our results confirmed that a high-throughput phenotyping methodology coupled with advanced statistical analysis tools can be successfully applied to characterize crop stress responses and identify traits associated with crop stress tolerance.

Why it matches plant phenotyping methodsPlantScreenプラットフォームによるデジタル画像計測と25種類の形態・生理形質の抽出が研究の中心であり、干ばつ応答への実質的なフェノタイピング手法適用に該当する。

abstractwe evaluated phenotypic traits and phenotypic plasticity by the PlantScreenTM platform using digital imaging of plant optical signals.

Comparing high-cost and lower-cost remote sensing tools for detecting pre-symptomatic downy mildew (Pseudoperonospora cubensis) infections in cucumbers →

Downy mildew of cucumber caused by the oomycete Pseudoperonospora cubensis (P. cubensis) is currently the most destructive disease of this crop, causing high yield losses. Visual evaluation by experts is the most established method for the detection of P. cubensis infection, but depends on visual disease symptoms. Detection of fungal infection before visual symptoms appear is highly desirable, allowing to deploy disease control measures before extensive crop damage occurs. The capacity of remote sensing approaches, such as hyperspectral imaging or high-resolution spectrometry, have proved to be powerful tools for detecting plant fungal diseases at pre-symptomatic stages over the last years. However, these approaches are expensive and processing the massive amount of multidimensional data, in case of spectroscopy and hyperspectral imaging, remains complex. Affordable, easy to handle, devices enabling the identification of P. cubensis infected cucumber plants at the pre-symptomatic stage in-situ may represent a high-value alternative for farmers. This study compared the performance of a high-cost high-resolution spectroradiometer (FieldSpec4) and a lower-cost leaf-clip sensor (DUALEX) for the early detection of P. cubensis infection in cucumber. Screening of cucumber plants, grown in a growth chamber, 24 h and 48 h post inoculation, allowed to identify a subset of spectral bands and leaf pigments for differentiating between infected and healthy cucumber plants at the pre-symptomatic stage. In the case of the FieldSpec 4, models based on spectral bands at 402, 576, 690, 708, and 723 nm differentiated between cucumber plants infected with P. cubensis and healthy plants with a F1 value of 0.67. The use of the lower-cost leaf-clip DUALEX sensor differentiated between infected and healthy cucumber plants with a F1 value of 0.60. Furthermore, the epidermal flavonol content obtained with the +leaf-clip DUALEX sensor was shown to be decreased in cucumber plants infected with P. cubensis compared to those of healthy plants, highlighting its role as biomarker for detecting P. cubensis infection at the pre-symptomatic stage. Results of this study will help to develop affordable remote sensing tools suitable for detecting P. cubensis infection at the pre-symptomatic stage in-situ.

Why it matches plant phenotyping methodsキュウリの病害状態を非破壊スペクトル・葉クリップセンサーで検出し、高価・低価格センサーの性能比較と検証を主目的とするため、植物フェノタイピング手法として中心的である。

abstractThis study compared the performance of a high-cost high-resolution spectroradiometer (FieldSpec4) and a lower-cost leaf-clip sensor (DUALEX) for the early detection of P. cubensis infection in cucumber.

Optimization of field asymmetric ion mobility spectrometry-based assessment of Aphanomyces root rot in pea →

When plants are infected with pathogens, disease response can result in changes in the profiles of volatile organic compounds (VOC). These changes in volatile organic compounds (VOC) profiles can be utilized for disease detection and quantification. In this study, field asymmetric ion mobility spectrometry (FAIMS) was used to evaluate the VOC profile variability in a pea near isogenic line (Pisum sativum L.) inoculated with zoospores of Aphanomyces euteiches Drechs, which causes Aphanomyces root rot disease. Pots were filled with silica sand and six plants per pot were grown under controlled conditions in a randomized complete block design with four replications. Four treatments, namely non-inoculated, 1 × 10⁵, 1 × 10⁶, and 2.79 × 10⁶ zoospores ml⁻¹ were applied to plants at 5 and 7 days after emergence. FAIMS was used to collect volatile profiles at 2, 4, 7 and 9 days after inoculation. Specific regions of interest – extracted from the ion current intensity from the FAIMS spectra – were analyzed using ANOVA. Similarly, multiple regions of interest were evaluated using principal component analysis and k-means clustering. Ion current profiles and curvature profiles were incorporated into the analysis using k-means clustering. Other ground reference data such as root rot index and physiological parameters were also recorded. The results showed a biomarker in a specific region of interest demonstrating ample ability to quantify and differentiate treatment effects during non-destructive sampling at 14 DAE (7 DAI). Data from this region could be used for early and non-destructive quantification and differentiation of treatment effects based on zoospore inoculation levels. The k-means clustering of ion current and curvature profiles showed patterns based on the treatments. These findings demonstrated that FAIMS could be used as a tool to assess plant-pathogen interactions using volatile biomarkers to evaluate disease responses and severity under controlled conditions.

Why it matches plant phenotyping methodsFAIMSによる揮発性成分プロファイルを用いて、エンドウの病害応答・重症度を非破壊かつ早期に定量・識別する手法を評価しており、植物表現型取得法が中心である。

abstractFAIMS was used to collect volatile profiles at 2, 4, 7 and 9 days after inoculation.

Assessing the High Temperature Effects on Stomatal Production. →

The production of stomata, the epidermal pores of plants, is influenced by diverse environmental signals including high temperature. To assess its impact on stomatal formation, researchers need to grow plants in a carefully designed regime under controlled conditions and capture clear, microscopic views of the epidermis. Here, we describe a procedure to study the effect of high temperature on stomatal formation. This method can generate high-quality epidermal images of cotyledons, leaves, and hypocotyl of young Arabidopsis seedlings, which allow the determination of the pattern, density, and index of stomata on these tissues. Besides temperature, the protocol can serve as a general approach to examine stomatal phenotype and the effect of other external signals on stomatal formation.

Why it matches plant phenotyping methods若いシロイヌナズナの表皮画像を取得し、気孔のパターン・密度・指数を定量する手順が中心であり、気孔表現型の測定法として収載対象です。

abstractHere, we describe a procedure to study the effect of high temperature on stomatal formation.

Phenotypic Identification of Abiotic Stress at Rice Seedling Stage →

Experimental Principle: The purpose of this experiment is to simulate non-biotic stress survival conditions in nature artificially, compare transgenic plants with control plants, and observe the differences in phenotypic and physiological responses to non-biotic stress. Through this comparison, we can explore the function and mechanism of the target gene in non-biotic stress, thereby better understanding the survival and adaptability of transgenic plants under stress conditions.Experimental Materials: This experiment involves independent transgenic and control plants, with three families for each type. If the transgenic plants are mutant materials, the transgenic plants should be pure families separated from heterozygous offspring, and the control materials should be the corresponding negative families. If the transgenic plants are overexpression or RNAi materials, three overexpression single-copy families or RNAi families and their corresponding control families need to be prepared. The selection and preparation of these materials are crucial for the accuracy and reliability of the experiment.Keywords: Rice, Seedling Stage, Non-Biotic Stress, Phenotype, Physiological Response Materials and Reagents:Small buckets (30 cm in height, 20 cm in diameter) for planting rice seedlings.Clay and river sand (mixed in a 1:1 ratio) as planting medium.Rice seeds for planting in the experiment.Industrial salt (NaCl) (laboratory analytical pure reagents can be used for small-scale trials) to simulate salt stress conditions.Compound fertilizer as a base fertilizer for the planting medium.Sodium Hypochlorite (NaClO): For seed disinfection.Tap Water: For seed rinsing and hydroponic culture.Yoshida Culture Medium: For hydroponic cultivation and nutrient provision.pH Meter: For measuring and adjusting the pH of solutions.Electronic Balance: For precise weighing of solid materials.Measuring Cylinders and Pipettes: For accurate measurement of liquid volumes.Sterile Equipment: Such as sterile gloves, masks, alcohol lamps, etc., for seed treatment and aseptic operations.Petri Dishes: For seed cultivation and observation.Microscope: For observing the microstructure of seeds and seedlings.Notebook and Marking Pens: For recording experimental data and observationsInstrumental Equipment:Rainproof and light-transmitting shed to simulate natural light and precipitation conditions.Artificial cold storage room (temperature range 0-10°C) for simulating low-temperature stress.Artificial climate chamber (temperature range 0-50°C) for simulating high-temperature stress.Thermostatic Water Bath: For maintaining a constant temperature of solutions.Centrifuge: For separating liquids from solid particles.Spectrophotometer: For measuring the absorbance of solutions, such as chlorophyll content determination.Autoclave: For sterilizing culture media and experimental tools.Microscope: For observing the microstructure of seeds and seedlings.Data Analysis Software: For processing and analyzing experimental data.Image Analysis Software: For analyzing plant phenotype changes in photographs.Temperature and Humidity Data Loggers: For monitoring temperature and humidity conditions in the experimental environment.Data Recorders: For logging various parameters during the experiment, such as temperature and time. Experimental Steps:Material Preparation: Place the seeds in a 50°C oven for 2 to 3 days to break dormancy; then wash the seeds with distilled water, soak them in a 0.3% NaClO (sodium hypochlorite) solution for 6 hours, rinse with tap water 2 to 4 times, and then place them in an artificial climate chamber at 33°C with tap water (pH 5.5-5.8) for germination for 3 to 4 days. When the seeds germinate to 2-3 cm, transfer them to a PCR plate with the bottom cut off, floating on tap water (pH 5.5-5.8) for growth. After one week, transfer them to Yoshida (Yoshida S, 1972) culture medium, adjust the pH to 5.5-5.8, and change the culture medium once a week. If soil cultivation is used, first sow on a seedling tray and transplant after the 3-4 leaf stage. Growth cycle external environmental conditions: 2/3 time (27°C) light conditions, 1/3 time (25°C) dark conditions.I. Seedling Drought Phenotype IdentificationSoak and germinate the rice seeds, select seedlings with consistent growth and approximately 2-3 cm in length, and carefully transplant them into small buckets (plant transgenic family seedlings and control seedlings in each bucket, 12-20 plants of each type).When the seedlings grow to the 4-leaf stage, carefully pour off the surface water or stop watering, take photos for recording. Then, observe each plant individually at the same time every day and record the number of days of leaf rolling. When the control group plants show irreversible full leaf rolling (still rolled in the morning, the specific time is determined based on air temperature and humidity), re-water and observe and record phenotypic changes, take photos, and count the survival rate.II. Seedling Simulated Drought (Osmotic Stress) Phenotype IdentificationWhen the rice seedlings reach the four-leaf stage (21 days from sowing), treat with 20% PEG, set up three repetitions, and place them in the same light incubator under the same conditions. When the control group plants show irreversible full leaf rolling (still rolled in the morning, the specific time is determined based on air temperature and humidity), re-water (note that this is a return to Yoshida nutrient solution), observe and record phenotypic changes, take photos, and count the survival rate.III. Seedling Salt Tolerance Phenotype IdentificationPlant 15-20 transgenic seedlings and 15-20 control seedlings in small buckets, and cultivate until they are healthy and consistent in size.When the seedlings grow to the 4-leaf stage, carefully pour off the surface water, then irrigate with 50 mM NaCl solution, continue for 3-4 days, then change to 100 mM NaCl solution, and after another 3-4 days, change to 200 mM NaCl solution.During the treatment, take photos for recording, and observe and record the time of leaf rolling or wilting on a per-plant basis. Record each bucket's control group and compare with the same bucket's transgenic line. After the control seedlings are almost all wilted, examine the survival rate and individual green leaf area. In addition, you can measure the salt sensitivity index [(fresh weight under normal growth - fresh weight under stress) / fresh weight under normal growth], Na+/K+ concentration, and other indicators to judge the phenotype.Note: In high-salt stress sandy soil, seedlings are difficult to recover growth; the hydroponic experimental method is the same as above, during stress, add 100mM-140mM NaCl to the hydroponic solution, and when recovering, change to normal Yoshida hydroponic solution.IV. Seedling Cold Tolerance Phenotype IdentificationPlant 15-20 transgenic seedlings and 15-20 control seedlings in small buckets, and cultivate until they are healthy and consistent in size. The formal experiment should be repeated three times and set a non-stress control.When the seedlings grow to the 4-leaf stage, subject them to low-temperature stress (set 2-6°C low-temperature environment in the growth chamber) for about a week. Observe and record the time of leaf wilting, compare each bucket's control with the same bucket's transgenic line. After the control seedlings are almost all wilted, return to normal growth for one week, and examine individual green leaf area and fresh weight, family survival rate, and average low-temperature sensitivity index [(fresh weight under normal growth - fresh weight under stress) / fresh weight under normal growth].V. Seedling High-Temperature Tolerance Phenotype IdentificationPlant 15-20 transgenic seedlings and 15-20 control seedlings in small red buckets, and cultivate until they are healthy and consistent in size. The formal experiment should be repeated three times and set a non-stress control.When the seedlings grow to the 4-leaf stage, subject them to high-temperature stress (set 40-45°C high-temperature environment in the growth chamber), stress for one to two days, and after the control seedlings are almost all wilted, return to normal growth for one week, and examine individual green leaf area and fresh weight, family survival rate, and average high-temperature sensitivity index [(fresh weight under normal growth - fresh weight under stress) / fresh weight under normal growth].VI. Germination Tolerance to Low Oxygen Phenotype IdentificationFor low-oxygen stress screening, use 1.5% (v/v) sodium hypochlorite for disinfection for 15 minutes, rinse with ultrapure water 8 times, then load into 10 cm centrifuge tubes filled with high-pressure sterilized ultrapure water and seal. Load 5 seeds with consistent germination and length into each centrifuge tube, place in an incubator for dark culture for 7 days (28°C), allowing the coleoptile to grow fully, and use the length of the coleoptile after 7 days of germination as an indicator of germination tolerance to waterlogging.VII. Seedling Alkali Tolerance Phenotype IdentificationThe same hydroponic method as above is used, with three alkali levels set: 10, 20, 30 mmol·L−1. At these three alkali levels, three different pH environments are formed by adjusting the ratio of sodium carbonate and sodium bicarbonate: pH 8.65, pH 9.55, and pH 10.50. Specifically:For the 10 mmol·L−1 alkali level, the ratio of sodium carbonate to sodium bicarbonate is 9:1, i.e., 9 parts sodium carbonate and 1 part sodium bicarbonate.For the 20 mmol·L−1 alkali level, the ratio of sodium carbonate to sodium bicarbonate is 5:5, i.e., equal amounts of sodium carbonate and sodium bicarbonate.For the 30 mmol·L−1 alkali level, the ratio of sodium carbonate to sodium bicarbonate is 1:9, i.e., 1 part sodium carbonate and 9 parts sodium bicarbonate.Under each alkali level, according to the different c(NaHCO3+Na2CO3)/(mmol·L−1)pHn(NaHCO3):n(Na2CO3)c(NaHCO3)/(mmol·L−1)c(Na2CO3)/(mmol·L−1)10A108.659:019110B109.555:055510C1010.51:091920A208.659:0118220B209.555:05101020C2010.51:0921830A308.659:0127330B309.555:05151530C3010.51:09327 pH, the specific concentrations of sodium carbonate and sodium bicarbonate are shown in the table below.When the rice seedlings reach the four-leaf stage (21 days from sowing), treat with different modes, set up three repetitions, and place them in the same light incubator under the same conditions. When the control group plants show irreversible full leaf rolling (still rolled in the morning, the specific time is determined based on air temperature and humidity), re-water (note that this is a return to Yoshida nutrient solution), observe and record phenotypic changes, take photos, and count the survival rate. VIII. Photography Method Notes:When conducting drought and salt treatments, the soil in the small buckets should be a 1:1 evenly mixed clay and river sand soil, and a suitable amount of base fertilizer (compound fertilizer) should be mixed before soil installation.Try to select healthy seedlings with consistent growth for transplantation, and plant more pots, selecting pots with consistent plant growth for treatment.When watering, avoid impacting the soil and keep the soil surface flat.The experiment should use a random block design and set up three repetitions.Different rice varieties have different tolerances to various stress levels, so a pre-experiment is needed before the formal experiment to measure their tolerance. References:Yoshida, S. (1972). A laboratory manual for physiological studies of rice. International Rice Research Institute.Bray, E. A., Bailey-Serres, J., & Weretilnyk, E. (2000). Responses to abiotic stresses. In Biochemistry and molecular biology of plants (pp. 1158-1249). American Society of Plant Biologists.Munns, R., & Tester, M. (2008). Mechanisms of salinity tolerance. Annual Review of Plant Biology, 59, 651-681.Zong.W.,Du,H.,Ma.S.Q.and Xiong.L.Z.(2018).Phenotypic identification of abiotic stress at rice seedling stage.Bo-101 e1010179.Doi:10.21769/BioProtoc.1010179.(in Chinese)

Why it matches plant phenotyping methodsイネの乾燥・塩・低温・高温などのストレス表現型を、葉巻き、萎凋、緑葉面積、生存率、子葉鞘長などで評価する具体的な取得・記録プロトコルが中心であり、植物表現型測定法として収録対象。

titlePhenotypic Identification of Abiotic Stress at Rice Seedling Stage

Strong prevalence of light regime‐specific QTL in Arabidopsis detected using automated high‐throughput phenotyping in fluctuating or constant light →

Plants have evolved and adapted under dynamic environmental conditions, particularly to fluctuating light, but plant research has often focused on constant growth conditions. To quantitatively asses the adaptation to fluctuating light, a panel of 384 natural Arabidopsis thaliana accessions was analyzed in two parallel independent experiments under fluctuating and constant light conditions in an automated high‐throughput phenotyping system upgraded with supplemental LEDs. While the integrated daily photosynthetically active radiation was the same under both light regimes, plants in fluctuating light conditions accumulated significantly less biomass and had lower leaf area during their measured vegetative growth than plants in constant light. A total of 282 image‐derived architectural and/or color‐related traits at six common time points, and 77 photosynthesis‐related traits from one common time point were used to assess their associations with genome‐wide natural variation for both light regimes. Out of the 3000 significant marker‐trait associations (MTAs) detected, only 183 (6.1%) were common for fluctuating and constant light conditions. The prevalence of light regime‐specific QTL indicates a complex adaptation. Genes in linkage disequilibrium with fluctuating light‐specific MTAs with an adjusted repeatability value >0.5 were filtered for gene ontology terms containing “photo” or “light”, yielding 15 selected candidates. The candidate genes are involved in photoprotection, PSII maintenance and repair, maintenance of linear electron flow, photorespiration, phytochrome signaling, and cell wall expansion, providing a promising starting point for further investigations into the response of Arabidopsis thaliana to fluctuating light conditions.

Why it matches plant phenotyping methods自動ハイスループット表現型解析システムを用い、画像から多数の植物形態・色関連形質を抽出するワークフローが研究の中心的な基盤となっているため、QTL解析を目的とした応用でも植物フェノタイピング手法の実質的な適用に該当する。

abstracta panel of 384 natural Arabidopsis thaliana accessions was analyzed in two parallel independent experiments under fluctuating and constant light conditions in an automated high‐throughput phenotyping system upgraded with supplemental LEDs.

Universal modeling for optimizing leafy vegetable production in an environment-controlled vertical farm →

An empirical growth-response model (GRM) that can accurately predict leafy vegetable (e.g., kailan) shoot fresh weight, in terms of photosynthetic photon flux density (PPFD) and certain cultivation duration counted from sowing, in an environment-controlled vertical farm, was developed. This GRM was constructed as the product of three independent models including light-time-biomass response model (LTBRM), dry-weight-based shoot/seedling ratio (DSSR) and shoot fresh/dry weight ratio (SFDR), which were established separately, through using various mathematical models to fit the experimental growth data and selecting the optimal ones, respectively. The robustness verification, and the validation tests on GRM proved that this model is qualified for precisely forecasting kailan shoot fresh weight at the seedling stage. The framework built in this study can be introduced as a universal modeling approach in indoor farming to (i) quickly assess seedling productivity throughout the farm once PPFD distribution and duration are known, (ii) cooperate with artificial intelligence technology for growth prediction, (iii) confirm the transplantation date according to designated transplanting criteria to minimize electric energy loss, and (iv) increase final productivity by 29.41% and profit by 12.99% in vertical farms using an appropriate strategy (taking kailan as an estimated example). GRM could thus act as an excellent auxiliary tool for monitoring plant growth; it can also be a strategy to boost vegetable production in resource-dependent regions to handle unexpected food supply chain disruptions.

Why it matches plant phenotyping methods植物のシュート生重量という明示的形質を予測する成長応答モデルを開発し、頑健性検証と妥当性検証を実施しているため、単なる生産実験ではなく計算的な表現型推定手法が中心である。

abstractAn empirical growth-response model (GRM) that can accurately predict leafy vegetable (e.g., kailan) shoot fresh weight

Phenotypic data of Populus species selected for genomic selection and breeding program using the Advanced Plant Phenotyping Laboratory at ORNL →

This dataset contains the phenotypic measurements of Populus genotypes selected for genomic selection and breeding program. Populus species in this study includes P. trichocarpa, P. deltoides and a hybrid between these two species.

Why it matches plant phenotyping methodsPopulus遺伝資源の表現型測定データセットを提供する研究であり、植物フェノタイピングデータ自体が中心的な成果である。

titlePhenotypic data of Populus species selected for genomic selection and breeding program using the Advanced Plant Phenotyping Laboratory at ORNL

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network →

The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

Why it matches plant phenotyping methods画像処理とANNによる葉面積の非破壊推定手法を開発・比較検証しており、植物形質取得が研究の中心です。

abstractThis study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model.

Monitoring drought induced photosynthetic and fluorescent variations of potatoes by visible and thermal imaging analysis →

Accurate determination of photosynthetic parameters is critical to evaluate crop physiological and growth processes. This study aimed to estimate photosynthetic and fluorescence variables of potatoes by visible and thermal imaging fusion analysis. Two-years pot experiments were conducted in a climate chamber with different irrigation treatments. Multi-modal image features of crop canopy were extracted from both visible and thermal images by color component extraction, discrete wavelet transformation, gray level co-occurrence matrix, and local binary pattern algorithms. Extracted features were subsequently employed to build partial least squares regression (PLSR) models for estimation of transpiration rate (Tr), net photosynthetic rate (An), stomatal conductance (GSW), electron transport rate (ETR), and maximum photochemical efficiency under Photosystem II (Fv'/Fm'). Results showed that Mask Region-Convolutional Neural Network (Mask R-CNN) performed satisfactorily on canopy segmentation with intersection over union of 87.29 % and 86.93 % in visible and thermal images, respectively. Three different types of models that either using only visible image features (PLSRRGB), or only thermal image features (PLSRT) or both visible and thermal image features (PLSRRGB₊T) as inputs were compared. Results showed that PLSRRGB₊T had superior estimation performance in terms of R² and RMSE. It achieved the highest R² of 0.85 with An and the lowest R² of 0.66 with GSW for Zhongshu 5, while it had the highest R² of 0.86 with Fv'/Fm', and the lowest R² of 0.71 with Tr and An for D681. This implied the potential of visible and thermal image-driven method for quick and accurate estimation of photosynthetic traits of crops grown in controlled environment.

Why it matches plant phenotyping methods可視・熱画像を融合し、画像特徴量と回帰モデルによってジャガイモの光合成・蛍光・蒸散などの生理形質を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractThis study aimed to estimate photosynthetic and fluorescence variables of potatoes by visible and thermal imaging fusion analysis.

Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions. →

Early detection of pathogenic fungi in controlled environment areas can prevent major food production losses. Grey mould caused by Botrytis cinerea is often detected as an infection on lettuce. This paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers. The aim was focused on the potential of using vegetation indices for the early detection of grey mould and on evaluating their changes during disease development in lettuce grown under different lighting conditions. The experiment took place in controlled environment chambers, where day/night temperatures were 21 ± 2/17 ± 2 °C, a 16 h photoperiod was established, and relative humidity was 70 ± 10% under different lighting conditions: high-pressure sodium (HPS) and light-emitting diode (LED) lamps. Lettuces were inoculated by 7-day-old fungus Botrytis cinerea isolate at the BBCH 21. As a control, non-inoculated lettuces were grown under HPS and LEDs (non-inoculated). Then, the following were evaluated: Anthocyanin Reflectance Index 2 (ARI2); Carotenoid Reflectance Index 2 (CRI2); Structure Intensive Pigment Index (SIPI); Flavanol Reflectance Index (FRI); Greenness (G); Greenness 2 (G2); Redness (R); Blue (B); Blue Green Index 2 (BGI2); Browning Index 2 (BRI2); Lichtenthaler Index 1 (LIC1); Pigment Specific Simple Ratio (PSSRa and PSSRb); Gitelson and Merzlyak (GM1 and GM2); Zarco Tejada-Miller Index (ZMI); Normalized Difference Vegetation Index (NDVI); Simple Ratio (SR); Red-Eye Vegetation Stress Index (RVSI); Photochemical Reflectance Index (PRI); Photochemical Reflectance Index 515 (PRI515); Water Band Index (WBI); specific disease index for individual study (fD); Healthy Index (HI); Plant Senescence Reflectance (PSRI); Vogelmann Red Edge Index (VREI1); Red Edge Normalized Difference Vegetation Index (RENDVI); and Modified Red Edge Simple Ratio (MRESRI). Our results showed that the PSRI and fD vegetation indices significantly detected grey mould on lettuce grown under both lighting systems (HPS and LEDs) the day after inoculation. The results conclusively affirmed that NDVI, PSRI, HI, fD, WBI, RVSI, PRI, PRI515, CRI2, SIPI, chlorophyll index PSSRb, and coloration index B were identified as the best indicators for Botrytis cinerea infection on green-leaf lettuce ( Lactuca sativa L. cv Little Gem) at the early stage of inoculated lettuce's antioxidative response against grey mould with a significant increase in chlorophyll indices.

Why it matches plant phenotyping methods植物体の病害状態を植生指数で直接推定し、異なる照明条件下で早期検出性能を評価しているため、病害フェノタイピング手法の実質的な適用・評価である。

abstractThis paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers.

Genome-Wide Association Analysis of Freezing Tolerance and Winter Hardiness in Winter Wheat of Nordic Origin. →

Climate change and global food security efforts are driving the need for adaptable crops in higher latitude temperate regions. To achieve this, traits linked with winter hardiness must be introduced in winter-type crops. Here, we evaluated the freezing tolerance (FT) of a panel of 160 winter wheat genotypes of Nordic origin under controlled conditions and compared the data with the winter hardiness of 74 of these genotypes from a total of five field trials at two locations in Norway. Germplasm with high FT was identified, and significant differences in FT were detected based on country of origin, release years, and culton type. FT measurements under controlled conditions significantly correlated with overwintering survival scores in the field ( r ≤ 0.61) and were shown to be a reliable complementary high-throughput method for FT evaluation. Genome-wide association studies (GWAS) revealed five single nucleotide polymorphism (SNP) markers associated with FT under controlled conditions mapped to chromosomes 2A, 2B, 5A, 5B, and 7A. Field trials yielded 11 significant SNP markers located within or near genes, mapped to chromosomes 2B, 3B, 4A, 5B, 6B, and 7D. Candidate genes identified in this study can be introduced into the breeding programs of winter wheat in the Nordic region.

Why it matches plant phenotyping methods凍結耐性の高スループット測定法を圃場越冬性と比較・検証し、植物の生理状態を評価する方法として明示しているため、GWASだけでなく表現型測定法の妥当性評価が中心的に含まれる。

abstractFT measurements under controlled conditions significantly correlated with overwintering survival scores in the field ( r ≤ 0.61) and were shown to be a reliable complementary high-throughput method for FT evaluation.

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize. →

In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and Bi-Directional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatial-spectral-temporal characteristics for water stress classification.

Why it matches plant phenotyping methodsトウモロコシの水ストレスという植物状態を、マルチスペクトル・時系列画像と深層学習で推定する手法を提案・評価しており、表現型取得・抽出が研究の中心である。

abstractWe propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features.

Rhizonet: Image Segmentation for Plant Root in Hydroponic Ecosystem →

ABSTRACT Digital cameras have the ability to capture daily images of plant roots, allowing for the estimation of root biomass. However, the complexities of root structures and noisy image backgrounds pose challenges for advanced phenotyping. Manual segmentation methods are laborious and prone to errors, which hinders experiments involving several plants. This paper introduces Rhizonet, a supervised deep learning approach for semantic segmentation of plant root images. Rhizonet harnesses a Residual U-Net backbone to enhance prediction accuracy, incorporating a convex hull operation to precisely outline the largest connected component. The primary objective is to accurately segment the biomass of the roots and analyze their growth over time. The input data comprises color images of various plant samples within a hydroponic environment known as EcoFAB, subject to specific nutrition treatments. Validation tests demonstrate the robust generalization of the model across experiments. This research pioneers advances in root segmentation and phenotype analysis by standardizing processes and facilitating the analysis of thousands of images while reducing subjectivity. The proposed root segmentation algorithms contribute significantly to the precise assessment of the dynamics of root growth under diverse plant conditions.

Why it matches plant phenotyping methods植物根画像から根バイオマスと成長を推定するセグメンテーション手法を開発し、実験間の汎化性能も検証しており、植物フェノタイピング手法が研究の中心です。

abstractThis paper introduces Rhizonet, a supervised deep learning approach for semantic segmentation of plant root images.

A multiscale approach to investigate fluorescence and NDVI imaging as proxy of photosynthetic traits in wheat →

With the development of the digital phenotyping, repeated measurements of agronomic traits over time are easily accessible, notably for morphological and phenological traits. However high throughput methods for estimating physiological traits such as photosynthesis are lacking. This study demonstrates the links of fluorescence and reflectance imaging with photosynthetic traits. Two wheat cultivars were grown in pots in a controlled environment. Photosynthesis was characterised by gas-exchange and biochemical analysis at five time points, from booting to 21 days post anthesis. On the same days imaging was performed on the same pots, at leaf and plant scale, using indoor and outdoor phenotyping platforms, respectively. Five image variables (Fv/Fm and NDVI at the whole plant level and Fv/Fm, {Phi}(II)532 and {Phi}(NPQ)1077 at the leaf scale) were compared to variables from A-Ci and A-Par curves, biochemical analysis, and fluorescence instruments. The results suggested that the image variables are robust estimators of photosynthetic traits, as long as senescence is driving the variability. Despite contrasting cultivar behaviour, linear regression models which account for the cultivar and the interaction effects, further improved the modelling of photosynthesis indicators. Finally, the results highlight the challenge of discriminating functional to cosmetic stay green genotypes using digital imaging. HighlightA temporal and multi-scale study of fluorescence and NDVI imaging used as a proxy for photosynthetic parameters

Why it matches plant phenotyping methods蛍光・NDVI画像を用いて光合成形質を推定し、ガス交換・生化学分析等と比較検証することが研究の中心であるため、植物フェノタイピング手法研究に該当します。

abstractThis study demonstrates the links of fluorescence and reflectance imaging with photosynthetic traits.

Non-Targeted Spectranomics for the Early Detection of Xylella fastidiosa Infection in Asymptomatic Olive Trees, cv. Cellina di Nardò. →

Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 ( Xf ), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf -infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530-560 nm and 1380-1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.

Why it matches plant phenotyping methodsHSRとケモメトリクスにより、無症状オリーブ葉からXylella感染状態を推定する手法を中心に扱っており、植物病害状態の非破壊センシング手法開発に直接つながる。

abstractAsymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics.

Predicting physiological traits of rice from hyperspectral data under CO 2 and drought treatments →

Using hyperspectral technology for high-throughput plant phenotyping is a potentially useful method in crop sciences. To examine its effectiveness, we collected leaf-level hyperspectral and ground-reference data from rice plants grown in controlled-environments under drought and CO 2 treatments at Ag Alumni Seed Phenotyping Facility at Purdue University. By applying RReliefF, we found that short-wave infrared region (SWIR) was important for leaf water potential (LWP), near-infrared region was linked with specific leaf area (SLA) and both red-edge and SWIR regions were related to gas exchange traits (net assimilation [A n ], stomatal conductance to water vapor [gsw] and transpiration [E mm ]). Based on those results, we found that LWP and SLA were moderately predictable and gas exchange traits were predictable (R 2 \(\geq\) 0.60 and root mean squared error of prediction for A n , gsw and E mm were 7.706 \(\mu\)molm -2 s -1 , 0.282 molm -2 s -1 , and 3.906 mmolm -2 s -1 in validation datasets, respectively) by using partial least squares regression. Furthermore, treatment effect on A n from cross-validation predictions agreed with ground-reference data. In contrast, photosynthetic parameters (V cmax and J max ) could not be estimated from hyperspectral data. Hyperspectral data can provide potential insights about plant growth and water status. When the effect of treatments is pronounced, model predictions are consistent with ground-reference data.

Why it matches plant phenotyping methodsイネの生理形質をハイパースペクトルデータから推定する方法を開発・検証しており、形質取得と予測性能評価が研究の中心である。

abstractUsing hyperspectral technology for high-throughput plant phenotyping is a potentially useful method in crop sciences.

A Plate Growth Assay to Quantify Embryonic Root Development of Zea mays . →

Murashige-Skoog medium solutions have been used in a variety of plant plate growth assays, yet most research uses Arabidopsis thaliana as the study organism. For larger seeds such as maize ( Zea mays ), most protocols employ a paper towel roll method for experiments, which often involves wrapping maize seedlings in wet, sterile germination paper. What the paper towel roll method lacks, however, is the ability to image the roots over time without risk of contamination. Here, we describe a sterile plate growth assay that contains Murashige-Skoog medium to grow seedlings starting two days after germination. This protocol uses a section of a paper towel roll method to achieve uniform germination of maize seedlings, which are sterilely transferred onto large acrylic plates for the duration of the experiment. The media can undergo modification to include an assortment of plant hormones, exogenous sugars, and other chemicals. The acrylic plates allow researchers to freely image the plate without disturbing the seedlings and control the environment in which the seedlings are grown, such as modifications in temperature and light. Additionally, the protocol is widely adaptable for use with other cereal crops. Key features • Builds upon plate growth methods routinely used for Arabidopsis seedlings but that are inadequate for maize. • Real-time photographic analysis of seedlings up to two weeks following germination. • Allows for testing of various growth conditions involving an assortment of additives and/or modification of environmental conditions. • Samples are able to be collected for genotype screening.

Why it matches plant phenotyping methodsトウモロコシ幼苗の根発達を経時的に画像化・定量するプレートアッセイ自体を開発・提示しており、植物表現型取得が中心である。

titleA Plate Growth Assay to Quantify Embryonic Root Development of Zea mays .

Monitoring root rot in flat-leaf parsley via machine vision by unsupervised multivariate analysis of morphometric and spectral parameters →

Abstract Use of vertical farms is increasing rapidly as it enables year-round crop production, made possible by fully controlled growing environments situated within supply chains. However, intensive planting and high relative humidity make such systems ideal for the proliferation of fungal pathogens. Thus, despite the use of bio-fungicides and enhanced biosecurity measures, contamination of crops does happen, leading to extensive crop loss, necessitating the use of high-throughput monitoring for early detection of infected plants. In the present study, progression of foliar symptoms caused by Pythium irregulare -induced root rot was monitored for flat-leaf parsley grown in an experimental hydroponic vertical farming setup. Structural and spectral changes in plant canopy were recorded non-invasively at regular intervals using a 3D multispectral scanner. Five morphometric and nine spectral features were selected, and different combinations of these features were subjected to multivariate data analysis via principal component analysis to identify temporal trends for early disease detection. Combining morphometric and spectral features enabled a clear segregation of healthy and diseased plants at 4–7 days post inoculation (DPI), whereas use of only morphometric or spectral features allowed this at 7–9 DPI. Minimal datasets combining the six most effective features also resulted in effective grouping of healthy and diseased plants at 4–7 DPI. This suggests that selectively combining morphometric and spectral features can enable accurate early identification of infected plants, thus creating the scope for improving high-throughput crop monitoring in vertical farms.

Why it matches plant phenotyping methods3Dマルチスペクトルスキャナーで植物キャノピーの形態・スペクトル特徴を取得し、根腐病の症状を早期検出する手法が研究の中心であるため。

abstractStructural and spectral changes in plant canopy were recorded non-invasively at regular intervals using a 3D multispectral scanner.

Phenotypic Responses of Diverse Maize Inbred Lines In Multiple Abiotic Stress Conditions →

Global warming poses a substantial threat to food security, necessitating the development of crops resilient to the adverse effects of stress including, drought, heat, and their combined impact. To understand maize responses to individual and combined stress conditions of drought and heat at early vegetative stages, we did a comprehensive evaluation of the phenotypic responses of 47 diverse maize inbred lines. The plants were stressed for 13 days beginning at 7 days after planting, with heat stress conditions of 38/28°C day/night cycles, and the drought condition was achieved by reducing the water pot volume from 88 to 40% over the course of the experiment. The Bellwether Phenotyping Facility, a high-throughput phenotyping platform, was used to capture daily RGB images of the plants throughout the experiment. We extracted morphological and color-related traits from the images, and particular interest was placed on traits that exhibited high broad-sense heritability throughout the experiment. This approach allowed for the collection of a robust dataset of phenotypic responses. Our results revealed distinct responses among the maize genotypes under different stress conditions, with the combined drought and heat treatment leading to the most severe impairments in plant height and leaf area. To gain further insights, we applied a time series analysis to the extracted traits and created groups with dynamically-similar response patterns. This research is foundational for understanding maize stress responses to heat and drought conditions and will inform future work towards identifying important genes and molecular mechanisms, particularly in the context of combined stressors.

Why it matches plant phenotyping methods高スループット画像基盤を用いた形態・色形質の抽出と時系列解析が研究の中心であり、ストレス応答評価のための再利用可能な表現型データセットを構築している。

abstractThe Bellwether Phenotyping Facility, a high-throughput phenotyping platform, was used to capture daily RGB images of the plants throughout the experiment.

Physiological and Digital Phenotyping of Drought Tolerance in Brassica Crops →

Climate change poses a significant threat to agricultural systems, with drought becoming increasingly prevalent in the Canadian prairies. This study addresses the urgent need to enhance crop resilience, focusing on Brassica carinata , a promising industrial feedstock crop used for the production of biofuels. Our research aims to comprehensively evaluate drought adaptive capacity in B. carinata through a combination of physiological and digital phenotyping methods. Under controlled conditions, we utilized a high-throughput phenotyping platform, the Plantarray system, to screen B. carinata germplasm. This system facilitated precise measurements of physiological traits, soil conditions, and atmospheric parameters, enabling the assessment of drought response. Concurrently, we conducted a field phenotyping experiment with 47 B. carinata Nested Association Mapping (NAM) founder lines and two B. napus checks, under irrigated and non-irrigated conditions. Aerial imagery obtained through Unmanned Aerial Vehicles (UAVs), complemented by phenological observations and manually recorded phenotypic data, was systematically gathered. Digital phenotypes extracted from aerial images are analyzed to identify a digital phenotype(s) for drought tolerance. Our study also explores the correlation between indoor physiological data and field performance of B. carinata lines, in an effort to identify parameters that can serve as reliable predictors of seed yield under drought stress. Overall, we believe this research provides valuable insights for enhancing crop resilience to drought.

Why it matches plant phenotyping methodsPlantarray高スループット表現型解析とUAV画像からのデジタル形質抽出が研究の主要手法であり、乾燥耐性評価への実質的な適用を行っている。

abstractwe utilized a high-throughput phenotyping platform, the Plantarray system, to screen B. carinata germplasm

Physiological and Digital Phenotyping of Drought Tolerance in Brassica Crops →

Climate change poses a significant threat to agricultural systems, with drought becoming increasingly prevalent in the Canadian prairies. This study addresses the urgent need to enhance crop resilience, focusing on Brassica carinata, a promising industrial feedstock crop used for the production of biofuels. Our research aims to comprehensively evaluate drought adaptive capacity in B. carinata through a combination of physiological and digital phenotyping methods. Under controlled conditions, we utilized a high-throughput phenotyping platform, the Plantarray system, to screen B. carinata germplasm. This system facilitated precise measurements of physiological traits, soil conditions, and atmospheric parameters, enabling the assessment of drought response. Concurrently, we conducted a field phenotyping experiment with 47 B. carinata Nested Association Mapping (NAM) founder lines and two B. napus checks, under irrigated and non-irrigated conditions. Aerial imagery obtained through Unmanned Aerial Vehicles (UAVs), complemented by phenological observations and manually recorded phenotypic data, was systematically gathered. Digital phenotypes extracted from aerial images are analyzed to identify a digital phenotype(s) for drought tolerance. Our study also explores the correlation between indoor physiological data and field performance of B. carinata lines, in an effort to identify parameters that can serve as reliable predictors of seed yield under drought stress. Overall, we believe this research provides valuable insights for enhancing crop resilience to drought.

Why it matches plant phenotyping methodsPlantarray高スループット生理フェノタイピングとUAV画像からのデジタル形質抽出が、乾燥耐性評価の中心的手法として明示されている。

abstractUnder controlled conditions, we utilized a high-throughput phenotyping platform, the Plantarray system, to screen B. carinata germplasm.

Phenotypic Responses of Diverse Maize Inbred Lines In Multiple Abiotic Stress Conditions →

Global warming poses a substantial threat to food security, necessitating the development of crops resilient to the adverse effects of stress including, drought, heat, and their combined impact. To understand maize responses to individual and combined stress conditions of drought and heat at early vegetative stages, we did a comprehensive evaluation of the phenotypic responses of 47 diverse maize inbred lines. The plants were stressed for 13 days beginning at 7 days after planting, with heat stress conditions of 38/28°C day/night cycles, and the drought condition was achieved by reducing the water pot volume from 88 to 40% over the course of the experiment. The Bellwether Phenotyping Facility, a high-throughput phenotyping platform, was used to capture daily RGB images of the plants throughout the experiment. We extracted morphological and color-related traits from the images, and particular interest was placed on traits that exhibited high broad-sense heritability throughout the experiment. This approach allowed for the collection of a robust dataset of phenotypic responses. Our results revealed distinct responses among the maize genotypes under different stress conditions, with the combined drought and heat treatment leading to the most severe impairments in plant height and leaf area. To gain further insights, we applied a time series analysis to the extracted traits and created groups with dynamically-similar response patterns. This research is foundational for understanding maize stress responses to heat and drought conditions and will inform future work towards identifying important genes and molecular mechanisms, particularly in the context of combined stressors.

Why it matches plant phenotyping methods高スループット画像基盤を用いた植物形態・色形質の抽出と時系列解析が研究の中心であり、ストレス応答の表現型データセットを構築している。

abstractThe Bellwether Phenotyping Facility, a high-throughput phenotyping platform, was used to capture daily RGB images of the plants throughout the experiment.

Phenotypic Responses of Diverse Maize Inbred Lines In Multiple Abiotic Stress Conditions →

Global warming poses a substantial threat to food security, necessitating the development of crops resilient to the adverse effects of stress including, drought, heat, and their combined impact. To understand maize responses to individual and combined stress conditions of drought and heat at early vegetative stages, we did a comprehensive evaluation of the phenotypic responses of 47 diverse maize inbred lines. The plants were stressed for 13 days beginning at 7 days after planting, with heat stress conditions of 38/28°C day/night cycles, and the drought condition was achieved by reducing the water pot volume from 88 to 40% over the course of the experiment. The Bellwether Phenotyping Facility, a high-throughput phenotyping platform, was used to capture daily RGB images of the plants throughout the experiment. We extracted morphological and color-related traits from the images, and particular interest was placed on traits that exhibited high broad-sense heritability throughout the experiment. This approach allowed for the collection of a robust dataset of phenotypic responses. Our results revealed distinct responses among the maize genotypes under different stress conditions, with the combined drought and heat treatment leading to the most severe impairments in plant height and leaf area. To gain further insights, we applied a time series analysis to the extracted traits and created groups with dynamically-similar response patterns. This research is foundational for understanding maize stress responses to heat and drought conditions and will inform future work towards identifying important genes and molecular mechanisms, particularly in the context of combined stressors.

Why it matches plant phenotyping methods高スループット画像基盤を用いた形態・色形質の抽出と時系列解析が研究の中心であり、植物ストレス応答の表現型データセット構築にも該当する。

abstractThe Bellwether Phenotyping Facility, a high-throughput phenotyping platform, was used to capture daily RGB images of the plants throughout the experiment.

Utilizing high throughput phenotyping to evaluate and demonstrate herbicide and adjuvant efficacy →

High throughput phenotyping has wide applications to evaluate genetic traits, plant growth and development in different biotic and abiotic stress environments, as well as under different agriculture management strategies. Additionally, understanding product efficacy and identifying the mode of action prior to field testing would improve product pipeline development for agriculture manufacturers and distributors. WinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness. We have evaluated pesticide product efficacy in the presence and absence of adjuvants. Adjuvants are materials added to a pesticide to enhance performance by improving absorption, spreading, sticking, and penetration properties of the pesticide’s active ingredient(s). We have measured a statistically significant increase in pesticide efficacy with the addition of an adjuvant in multiple, independent case studies showing the increased plant health benefit. In one study, we observed a 31% decrease in diseased oat plant tissue [when defined as pixels with Normalized Difference Vegetation Index (NDVI) value <0.3] when an adjuvant was added to a commercial fungicide compared to the untreated control and a 23% improvement when compared to the fungicide alone. Multispectral imaging has also allowed us to build interactive, 3-D models that demonstrate product coverage and penetration. Combining quantitative measurements with interactive, illustrative models, we can more effectively communicate product efficacy results to retail owners and growers. Future directions include evaluating biological product efficacy in which more nuanced plant responses are observed in biotic and abiotic stress environments.

Why it matches plant phenotyping methodsマルチスペクトル画像を用いて植物の健康状態・病害組織を定量化し、農薬・アジュバント効果評価へ実質的に適用しているため、表現型取得法の応用研究として含める。

abstractWinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness.

Utilizing high throughput phenotyping to evaluate and demonstrate herbicide and adjuvant efficacy →

High throughput phenotyping has wide applications to evaluate genetic traits, plant growth and development in different biotic and abiotic stress environments, as well as under different agriculture management strategies. Additionally, understanding product efficacy and identifying the mode of action prior to field testing would improve product pipeline development for agriculture manufacturers and distributors. WinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness. We have evaluated pesticide product efficacy in the presence and absence of adjuvants. Adjuvants are materials added to a pesticide to enhance performance by improving absorption, spreading, sticking, and penetration properties of the pesticide's active ingredient(s). We have measured a statistically significant increase in pesticide efficacy with the addition of an adjuvant in multiple, independent case studies showing the increased plant health benefit. In one study, we observed a 31% decrease in diseased oat plant tissue [when defined as pixels with Normalized Difference Vegetation Index (NDVI) value <0.3] when an adjuvant was added to a commercial fungicide compared to the untreated control and a 23% improvement when compared to the fungicide alone. Multispectral imaging has also allowed us to build interactive, 3-D models that demonstrate product coverage and penetration. Combining quantitative measurements with interactive, illustrative models, we can more effectively communicate product efficacy results to retail owners and growers. Future directions include evaluating biological product efficacy in which more nuanced plant responses are observed in biotic and abiotic stress environments.

Why it matches plant phenotyping methodsマルチスペクトル画像とNDVIによる植物の健康状態・病害組織の定量化を、農薬・アジュバント効果評価の中心的な手法として適用しているため。

abstractWinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness.

High-Throughput Plant Phenotyping System Using a Low-Cost Camera Network for Plant Factory →

Plant phenotyping has been widely studied as an effective and powerful tool for analyzing crop status and growth. However, the traditional phenotyping (i.e., manual) is time-consuming and laborious, and the various types of growing structures and limited room for systems hinder phenotyping on a large and high-throughput scale. In this study, a low-cost high-throughput phenotyping system that can be flexibly applied to diverse structures of growing beds with reliable spatial–temporal continuities was developed. The phenotyping system was composed of a low-cost phenotype sensor network with an integrated Raspberry Pi board and camera module. With the distributed camera sensors, the system can provide crop imagery information over the entire growing bed in real time. Furthermore, the modularized image-processing architecture supports the investigation of several phenotypic indices. The feasibility of the system was evaluated for Batavia lettuce grown under different light periods in a container-type plant factory. For the growing lettuces under different light periods, crop characteristics such as fresh weight, leaf length, leaf width, and leaf number were manually measured and compared with the phenotypic indices from the system. From the results, the system showed varying phenotypic features of lettuce for the entire growing period. In addition, the varied growth curves according to the different positions and light conditions confirmed that the developed system has potential to achieve many plant phenotypic scenarios at low cost and with spatial versatility. As such, it serves as a valuable development tool for researchers and cultivators interested in phenotyping.

Why it matches plant phenotyping methods低コストカメラネットワークと画像処理による高スループット植物表現型計測システムの開発・評価が研究の中心である。

abstracta low-cost high-throughput phenotyping system that can be flexibly applied to diverse structures of growing beds with reliable spatial–temporal continuities was developed.

Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits →

The efficiency of hyper- and multispectral imaging (HSI and MSI) has gained considerable attention in research on plant phenotyping. This is due to their ease of use while being considered a nondestructive technology. Unlike current point-scanned spectroscopy, both HSI and MSI extract spatial and spectral information while covering a wide range of a plant body. Moreover, it is necessary to equip the extracted information with multivariate calibration techniques, followed by model evaluation. To date, the application of HSI and MSI for monitoring plant growth under a controlled environment is emerging and showing a good trend. Our systematic literature review discusses spectroscopy imaging techniques and their chemometric approaches as a sustainable sensor technology to detect plant traits. In conclusion, we also explore the possibility of carrying out HSI and MSI during plant trait analysis.

Why it matches plant phenotyping methods植物形質検出のためのHSI/MSI画像分光法とケモメトリクスを扱う系統的レビューであり、フェノタイピング手法が中心です。

abstractOur systematic literature review discusses spectroscopy imaging techniques and their chemometric approaches as a sustainable sensor technology to detect plant traits.

Lettuce Plant Trace-Element-Deficiency Symptom Identification via Machine Vision Methods →

Lettuce is one of the most widely planted leafy vegetables in plant factories. The lack of trace elements in nutrient solutions has caused huge losses to the lettuce industry. Non-obvious symptoms of trace element deficiency, the inconsistent size of the characteristic areas, and the difficulty of extraction in different growth stages are three key problems affecting lettuce deficiency symptom identification. In this study, a batch of cream lettuce (lactuca sativa) was planted in the plant factory, and its nutrient elements were artificially controlled. We collected images of the lettuce at different growth stages, including all nutrient elements and three nutrient-deficient groups (potassium deficiency, calcium deficiency, and magnesium deficiency), and performed feature extraction analysis on images of different defects. We used traditional algorithms (k-nearest neighbor, support vector machine, random forest) and lightweight deep-learning models (ShuffleNet, SqueezeNet, andMobileNetV2) for classification, and we compared different feature extraction methods (texture features, color features, scale-invariant feature transform features). The experiment shows that, under the optimal feature extraction method (color), the random-forest recognition results are the best, with an accuracy rate of 97.6%, a precision rate of 97.9%, a recall rate of 97.4%, and an F1 score of 97.6%. The accuracies of all three deep-learning models exceed 99.5%, among which ShuffleNet is the best, with the accuracy, precision, recall, and F1 score above 99.8%. It also uses fewer floating-point operations per second and less time. The proposed method can quickly identify the trace elements lacking in lettuce, and it can provide technical support for the visual recognition of the disease patrol robot in the plant factory.

Why it matches plant phenotyping methodsレタスの栄養欠乏症状を画像から抽出・分類する機械視覚手法を開発し、特徴抽出法と複数の機械学習モデルを比較評価しており、植物表現型取得が中心である。

abstractWe collected images of the lettuce at different growth stages, including all nutrient elements and three nutrient-deficient groups (potassium deficiency, calcium deficiency, and magnesium deficiency), and performed feature extraction analysis on images of different defects.

Design, development, and assessment of a High-Throughput Screening (HTS) system for the macroscopic root water uptake modeling →

Climate change is responsible for the increasing frequency and intensity of abiotic stresses generating water scarcity conditions. There is a need to breed plants adapted to future environmental conditions and resistant to water stress. This study presents a High-Throughput Screening (HTS) system for continuously and simultaneously monitoring plant stress response to drought in a semi-controlled environment. The HTS system combines a gravimetric weighing system with soil moisture and atmospheric sensors. In operative terms, the system was tested on the Sage (Salvia officinalis L.) under two soil water deficit treatments managed according to a feedback control irrigation scheduling. The system was able to model the sage water stress function following the root water uptake macroscopic approach. The threshold of soil water status below which crop water stress occurred was also identified. The gravimetric-based daily evapotranspiration (ETₐ) and the time domain reflectometry (TDR)-based root water-uptake (RWU) rates showed a high correlation during the drying when the evaporation flux is minimal. Moreover, the effects of soil bulk density on the root density and the plant biomass were evaluated, indicating the importance of carrying out a homogeneous procedure of the pot-filling process.

Why it matches plant phenotyping methods植物の乾燥ストレス応答と根の吸水を連続・同時測定するHTSシステムを開発し、センサー測定とモデル化を評価しており、表現型取得法が研究の中心である。

abstractThis study presents a High-Throughput Screening (HTS) system for continuously and simultaneously monitoring plant stress response to drought in a semi-controlled environment.

CNN Based Plant Disease Identification Using PYNQ FPGA →

Plant disease is an ongoing challenge for smallholder farmers, which threatens income and food security. The recent revolution in smartphone penetration and computer vision models has created an opportunity for image classification in agriculture. Convolutional Neural Networks (CNNs) are considered state-of-the-art in image recognition and offer the ability to provide a prompt and definite diagnosis. In this paper, the performance of a pre-trained ResNet34 model in detecting crop disease is investigated. The developed model is deployed as a web application and is capable of recognizing 7 plant diseases out of healthy leaf tissue. A dataset containing 8,685 leaf images; captured in a controlled environment, is established for training and validating the model. Validation results show that the proposed method can achieve an accuracy of 97.2% and an F1 score of greater than 96.5%. This demonstrates the technical feasibility of CNNs in classifying plant diseases and presents a path towards AI solutions for small holder farmers.

Why it matches plant phenotyping methods葉画像から植物病害を分類するCNNモデルの開発・検証とデータセット構築が研究の中心であり、植物の病害状態を直接推定するため。

abstractA dataset containing 8,685 leaf images; captured in a controlled environment, is established for training and validating the model.

Tillering structures the genetic variability of wheat vegetative growth and its plasticity under water deficit →

Leaf expansion under drought drives the trade-off between water saving for later grain production and canopy photosynthesis. Fine-tuning leaf expansion could therefore become a target of genetic progress for drought-prone environments. However, its components (branching, leaf production and elongation) may have their own genetic 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 leaf growth under drought and how it translates to leaf expansion at the whole plant and canopy level. For that, we used non-destructive imaging both in the field and controlled condition platforms to determine the dynamics of the components of shoot expansion and analyze their relative contribution to the genetic variability of whole-plant shoot expansion under drought. Results show that leaf 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 genetic 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 genetic variability of shoot expansion. Overall, dissecting leaf 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非破壊イメージングを用いた圃場・制御環境プラットフォームで植物体および群落の生長形質を測定し、制御環境プラットフォームが圃場の遺伝的変異を捉えられるかを検証しており、フェノタイピング手法の適用・技術検証が中心的です。

abstractFor that, we used non-destructive imaging both in the field and controlled condition platforms to determine the dynamics of the components of shoot expansion and analyze their relative contribution to the genetic variability of whole-plant shoot expansion under drought.

Early-Stage Phenotyping of Sweet Potato Virus Disease Caused by Sweet Potato Chlorotic Stunt Virus and Sweet Potato Virus C to Support Breeding. →

Sweet potato virus disease (SPVD) is a global constraint to sweetpotato ( Ipomoea batatas ) production, especially under intensive cultivation in the humid tropics such as East Africa. The objectives of this study were to develop a precision SPVD phenotyping protocol, to find new SPVD-resistant genotypes, and to standardize the first stages of screening for SPVD resistance. The first part of the protocol was based on enzyme-linked immunosorbent assay results for sweet potato chlorotic stunt virus (SPCSV) and sweet potato virus C (SPVC) with adjustments to a negative control (uninfected clone Tanzania) and was performed on a prebreeding population (VZ08) comprising 455 clones and 27 check clones graft inoculated under screenhouse conditions. The second part included field studies with 52 selected clones for SPCSV resistance from VZ08 and 8 checks. In screenhouse conditions, the resistant and susceptible check clones performed as expected; 63 clones from VZ08 exhibited lower relative absorbance values for SPCSV and SPVC than inoculated check Tanzania. Field experiments confirmed SPVD resistance of several clones selected by relative absorbance values (nine resistant clones in two locations; that is, 17.3% of the screenhouse selection), supporting the reliability of our method for SPVD-resistance selection. Two clones were promising, exhibiting high storage root yields of 28.7 to 34.9 t ha -1 and SPVD resistance, based on the proposed selection procedure. This modified serological analysis for SPVD-resistance phenotyping might lead to more efficient development of resistant varieties by reducing costs and time at early stages, and provide solid data for marker-assisted selection with a quantitative tool for classifying resistance.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Why it matches plant phenotyping methodsSPVD抵抗性を評価するための精密な表現型判定プロトコルを開発・標準化し、スクリーンハウスと圃場で信頼性を検証しているため、植物フェノタイピング手法が研究の中心である。

abstractThe objectives of this study were to develop a precision SPVD phenotyping protocol

A hyperspectral plant health monitoring system for space crop production →

Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.

Why it matches plant phenotyping methods植物の健康状態・乾燥ストレスを推定するハイパースペクトル画像計測システムを設計・実装し、分類性能を予備検証しており、フェノタイピング手法が研究の中心です。

abstractA new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm).

Close-range multispectral imaging with Multispectral-Depth (MS-D) system →

In this work, a Multispectral-Depth (MS-D) imaging system for close range plant inspection is presented. The proposed system is comprised of multispectral cameras calibrated with respect to an RGB-D camera. It is ultimately tested and quantitatively compared to state-of-the-art feature-based methods for multispectral image registration. The results show that MS-D system outperforms state-of-the-art methods in all experimental trials, from registration of checkerboard images (where the accuracy of the MS-D system was on a sub-pixel level) to registration of feature-rich plant images, both real and synthetic. While the greatest registration error of the MS-D system amounted to 9 pixels, registration error of the feature-based method was up to 19 times greater. Additionally, contrast to the state-of-the-art feature matching approaches, the MS-D system, once calibrated, is applicable as is, without the need for recalibration. As a part of this work, MS-D has been deployed in encapsulated growth chambers for rapid data collection and in a small indoor organic farm for automated plant monitoring. As a part of the experimental plant monitoring study, it was shown that vegetation indices calculated with the MS-D system can be used to estimate water stress in Spathiphyllum plants equally well as with the spectroradiometer, human operated device for measurement of plant vegetation indices. The biggest relative change between the vegetation indices calculated for the plants exposed to the short term water stress and the control group was found in values of NDRE, amounting to 94.8%, followed by the relative change of 64.9% observed for the values of SR.

Why it matches plant phenotyping methodsMS-Dマルチスペクトル・深度画像システムを開発し、画像レジストレーション性能を定量比較するとともに、植物モニタリングと水ストレス推定へ応用しており、植物表現型取得法が中心的である。

abstracta Multispectral-Depth (MS-D) imaging system for close range plant inspection is presented

Scales of development for wheat and barley specific to either single culms or a population of culms →

Accurate assessment of plant development is essential for agronomic management and scientific research, and crop development scales with repeatable and reproducible protocols are required to achieve this. Development scales currently in use are ambiguous, subjective and qualitative, they do not describe all stages in a crop's lifecycle, they do not explicitly distinguish between culm-level and crop-level development, and they are incompatible with modern analytical and computational technologies. Here we propose two new scales of wheat and barley development: the Single Culm Development Scale (SCDS) to define progression through the lifecycle of an individual culm, and the Population of Culms Development Scale (PCDS) to identify the timing of stages and duration of phases within a crop canopy. These development scales merge and fill gaps within existing scales currently in use, describe development in terms that are unambiguous, objective and quantitative, and better interface with crop simulation models, automated image analysis and other computational tools and analytical methods. The SCDS and PCDS are paired with definitive protocols to measure each stage that were developed and tested using different operators in controlled environment and field experiments.

Why it matches plant phenotyping methodsコムギ・オオムギの個体茎および群落の発育段階を客観的・定量的に測定する新しい発育スケールとプロトコルを開発し、異なる操作者および環境で検証しており、植物表現型測定法が研究の中心です。

abstractHere we propose two new scales of wheat and barley development: the Single Culm Development Scale (SCDS) to define progression through the lifecycle of an individual culm, and the Population of Culms Development Scale (PCDS) to identify the timing of stages and duration of phases within a crop canopy.

Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat →

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.

Why it matches plant phenotyping methods自動デジタルフェノタイピングによる生育速度・老化速度の抽出が塩耐性評価の主要な測定基盤であり、580系統への大規模適用を通じて植物形質を取得しているため。

abstractusing automated digital phenotyping performed under controlled environmental conditions

Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil →

Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms.

Why it matches plant phenotyping methods植物の栄養欠乏症状を画像とCNNで分類し、Grad-CAMで症状への注目を検証する手法が中心であるため、植物状態の画像ベース・フェノタイピング研究に該当します。

abstractphotos were taken to determine N, P and K deficiencies in basil and control plants

Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework. →

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R2 values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.

Why it matches plant phenotyping methods画像からイネ苗の生育形質を推定するCNNベースのエンドツーエンド手法を開発・比較評価しており、植物表現型取得が研究の中心である。

abstractthere is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images

Digital Twins and Data-Driven in Plant Factory: An Online Monitoring Method for Vibration Evaluation and Transplanting Quality Analysis →

The plant factory transplanter is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a digital twin model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the plant factory transplanter digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of the X, Y, and Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

Why it matches plant phenotyping methods植物工場移植機のデジタルツインを中心に、苗トレイ画像から健全苗を認識・評価する画像解析手法を技術的に提案・検証しており、植物状態の取得が中心的です。

abstracta digital twin model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed

A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion →

We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).

Why it matches plant phenotyping methods植物を多視点撮像し、SfM再構成から質量を推定するロボット型フェノタイピング手法の設計・実証が中心である。

abstractWe propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring

Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field →

Abstract Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. We show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allowed translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits matched between indoor and field conditions. Genomic prediction resulted in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguished genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for the diversity of future climates.

Why it matches plant phenotyping methodsロボット化した屋内フェノタイピングによる適応形質の高スループット測定と、圃場条件への推定・検証が研究の中心であるため。

abstractRobotized indoor phenotyping allows measuring such traits at high throughput for speed breeding

Suitable Methods of Inoculation and Quantification of Fusarium Root Rot in Lentil. →

Lentil ( Lens culinaris L. subsp. culinaris ) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 10 6 spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot ( r = 0.92 and β 1 = 0.96) than root rot ( r = 0.62 and β 1 = 0.67).

Why it matches plant phenotyping methodsレンズマメの根腐病の病徴・重症度を対象に、視覚評価尺度とRGB画像による自動スコアリング手法を開発・比較検証しており、植物表現型取得が中心である。

abstractThis work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments.

Rising vapor‐pressure deficit increases nitrogen fixation in a legume crop →

Summary Atmospheric vapor‐pressure deficit (VPD) is increasing in many regions and has a large impact on plant productivity. A VPD increase leads to raising transpiration rate (TR) and soil‐water demand, risking productivity penalties. Like water, nitrogen is critical to productivity, but the effect of VPD on legume nitrogen fixation is undocumented. To address this, we developed a portable system for quantifying nitrogen fixation noninvasively and at a high temporal resolution by tracking the rate of hydrogen gas evolution by root nodules. Combining field and controlled‐environment experiments where we measured leaf gas exchange and H 2 production by nodules, we confirmed the ability of the system to track nitrogen fixation dynamics. Raising VPD from 0.5 to 3 kPa within c. 2.5 h under well‐watered conditions increased nitrogen fixation by up to 25% in addition to TR, consistent with the hypothesis that raising VPD in that range might have alleviated nitrogenase feedback inhibition. Genotypic differences were found in this response, indicating a potential for breeding. Our study provides evidence for an important environmental effect on nitrogen fixation that is not taken into account in current crop and vegetation models, pointing to untapped avenues for better understanding climate change effects on legumes and nitrogen cycling.

Why it matches plant phenotyping methods根粒からの水素放出を追跡して窒素固定を非侵襲・高時間分解能で定量する携帯システムを開発し、その追跡能力を検証しており、植物生理状態の取得法が研究の中心である。

abstractwe developed a portable system for quantifying nitrogen fixation noninvasively and at a high temporal resolution by tracking the rate of hydrogen gas evolution by root nodules

Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory →

This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage.

Why it matches plant phenotyping methods植物工場の苗の画像取得・3D再構成・MRCNNによる表現型解析システムの開発と性能評価が研究の中心であり、表現型取得手法が実質的に扱われている。

abstractThis work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes.

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought →

Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gₛ) of a large number of individual plants. Several methods to measure E and gₛ exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min⁻¹) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30–50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gₛ measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gₛ. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

Why it matches plant phenotyping methods植物の蒸散速度と気孔コンダクタンスを推定する熱画像ベースの表現型計測法を開発し、ガス交換システムとの相関で検証しているため、方法が中心的である。

abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment

Multi-objective optimal regulation model and system based on whole plant photosynthesis and light use efficiency of lettuce →

Lettuce growth and light energy consumption in a plant factory with artificial lighting (PFAL) were studied, and whole plant photosynthetic rate (ACO₂) and light use efficiency (LUE) data were obtained on different days after planting (DAP) under different photosynthetic photon flux densities (PPFDs). Genetic algorithm‐support vector regression (GA-SVR) was used to construct the ACO₂ and LUE prediction models. The coefficient of determination (R²) between the predicted and measured values of the ACO₂ model was 0.97 and the root mean square error (RMSE) was 0.42 μmol·mol⁻¹·plant⁻¹·min⁻¹, and R² between the predicted and measured values of the LUE model was 0.97 and RMSE was 0.36%. The ACO₂ and LUE prediction models were used as the objective functions, and multi-objective search was performed by the non-dominated sorting genetic algorithm II (NSGA-II) and the distance-based knee point detection method were used to obtained the optimal equilibrium solution for different DAPs. The optimal equilibrium solutions were used as the basis to establish the light regulation model based on lettuce DAP with R² of 0.99. To validate the effect of model regulation, a lettuce light regulation system was built using an artificial climate chamber for a 30-day system validation. The results showed that compared with the traditional quantitative light supplementation method, the dry matter of model regulation significantly increased by 39.23% and 29.48% compared with quantitative PPFD150 (μmol·m⁻²·s⁻¹) and PPFD200 (μmol·m⁻²·s⁻¹). Model regulation increased the number of total light quanta consumed by 1.39% over PPFD150 and decreased by 23.96% over PPFD200; however, plant productivity increased by 35.35% and 33.14%, respectively. Model regulation significantly reduced the number of light quanta consumed per unit mass of lettuce production by 24.35% for PPFD150 and 41.54% for PPFD200, and LUE of light-emitting diode energy into dry matter was significantly increased by 33.49% and 75.09%. Therefore, the light regulation model based on multi-objective optimization in this study could improve crop yield and increase LUE.

Why it matches plant phenotyping methodsレタスの全植物光合成速度と光利用効率という生理形質を予測するGA-SVRモデルを開発し、光制御システムとして検証しており、形質取得・推定手法が研究の中心である。

abstractGenetic algorithm‐support vector regression (GA-SVR) was used to construct the ACO₂ and LUE prediction models.

Fully Automatic Controlled Environment Agriculture using Machine Learning based Plant Size Estimator →

Due to climate change, the global food supply crisis has become an urgent international problem. Given the circumstances, a controlled environment that artificially adjusts the climate for agriculture has attracted considerable attention as a solution to the problem. Various kinds of research have been proposed to develop the technology of controlled environments. However, the accuracy and scalability problem of these methods is a burden for the expansion to real-world scenarios. In particular, it is necessary to research and implement the computer vision-based algorithm, which is the key technique that enables the controlled environment system to be fully automatic. To solve the aforementioned problem, I propose a novel controlled environment agriculture system. The proposed system is composed of a plant life cycle regression module and a device control module. The system predicts the actual size of the plants and outputs the life cycle indicator of the plant, which is the growth rate of the plants. Based on the life cycle indicator, the device control module adjusts the essential factors, such as the amount of water and the strength of UV (Ultra Violet) light, for photosynthesis. As the proposed system is aware of the life cycle of plants, it can provide fully automatic controlled environments. I also propose and demonstrate the application machine to show how the proposed method can be applied to the real world. Through the experiments, it is shown that the proposed PLCR outperforms the existing state-of-the-art methods on the COCO dataset.

Why it matches plant phenotyping methods植物サイズと成長率を画像ベースで推定する手法が研究の中心であり、制御環境農業への応用も示しているため、植物フェノタイピング手法として採用する。

abstractI propose a novel controlled environment agriculture system. The proposed system is composed of a plant life cycle regression module and a device control module.

Real-Time Plant Health Detection Using Deep Convolutional Neural Networks →

In the twenty-first century, machine learning is a significant part of daily life for everyone. Today, it is adopted in many different applications, such as object recognition, object classification, and medical purposes. This research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves. Typically, farmers are unaware of diseases on plant leaves and adopt manual disease detection methods. Their production often decreases as the virus spreads. However, due to a lack of essential infrastructure, quick identification needs to be improved in many regions of the world. It is now feasible to diagnose diseases using mobile devices as a result of the increase in mobile phone usage globally and recent advancements in computer vision due to deep learning. To conduct this research, firstly, a dataset was created that contained images of money plant leaves that had been split into two primary categories, specifically (i) healthy and (ii) unhealthy. This research collected thousands of images in a controlled environment and used a public dataset with exact dimensions. The next step was to train a deep model to identify healthy and unhealthy leaves. Our trained YOLOv5 model was applied to determine the spots on the exclusive and public datasets. This research quickly and accurately identified even a small patch of disease with the help of YOLOv5. It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. This research is beneficial for farmers since it allows them to recognize diseased leaves as soon as they noted and take the necessary precautions to halt the disease’s spread. This research aimed to provide the best hyper-parameters for classifying and detecting the healthy and unhealthy parts of leaves in exclusive and public datasets. Our trained YOLOv5 model achieves 93 % accuracy on a test set.

Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する深層学習手法が研究の中心であり、植物の健康状態・病害状態という表現型を直接推定しているため。

abstractThis research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves.

A Simple Protocol for Mapping the Plant Root System Architecture Traits. →

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar tolerance to environmental challenges. An experimental protocol is presented for setting up the hydroponic system, plantlet growth, RSA spreading, and imaging. The approach used a magenta box-based hydroponic system containing polypropylene mesh supported by polycarbonate wedges. Experimental settings are exemplified by assessing the RSA of the plantlets under varying nutrient (phosphate [Pi]) supply. The system was established to examine the RSA of Arabidopsis, but it is readily adaptable to study other plants like Medicago sativa (Alfalfa). Arabidopsis thaliana (Col-0) plantlets are used in this investigation as an example to understand the plant RSA. Seeds are surface sterilized by treating ethanol and diluted commercial bleach, and kept at 4 °C for stratification. The seeds are germinated and grown on a liquid half-MS medium on a polypropylene mesh supported by polycarbonate wedges. The plantlets are grown under standard growth conditions for the desired number days, gently picked out from the mesh, and submersed in water-containing agar plates. Each root system of the plantlets is spread gently on the water-filled plate with the help of a round art brush. These Petri plates are photographed or scanned at high resolution to document the RSA traits. The root traits, such as primary root, lateral roots, and branching zone, are measured using the freely available ImageJ software. This study provides techniques for measuring plant root characteristics in controlled environmental settings. We discuss how to (1) grow the plantlets, and collect and spread root samples, (2) obtain pictures of spread RSA samples, (3) capture the images, and (4) use image analysis software to quantify root attributes. The advantage of the present method is the versatile, easy, and efficient measurement of the RSA traits.

Why it matches plant phenotyping methods根系形態形質の取得・撮影・画像解析を一体化した測定プロトコルが研究の中心であり、RSA形質を定量化する方法を提示している。

abstractAn experimental protocol is presented for setting up the hydroponic system, plantlet growth, RSA spreading, and imaging.

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought. →

Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gs) of a large number of individual plants. Several methods to measure E and gs exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min−1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30–50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gs measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gs. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

Why it matches plant phenotyping methods植物の蒸散速度・気孔コンダクタンスを推定する熱画像ベースの表現型計測法を開発し、ガス交換測定と比較検証しているため、方法が研究の中心である。

abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment

Developing broad-spectrum resistance in cassava against viruses causing the cassava mosaic and the cassava brown streak diseases. →

Growing cassava in Africa requires resistance against the viruses causing cassava mosaic disease (CMD) and the viruses causing cassava brown streak disease (CBSD). A dominant CMD2 resistance gene from a West African cassava landrace provides strong resistance against the cassava mosaic viruses. However, resistance against cassava brown streak viruses is limited to cassava varieties that show tolerance to the disease. A recently identified cassava germplasm that cannot be infected with cassava brown streak viruses provides a new source of the resistance required to protect cassava from CBSD. We present a synopsis of the status of virus resistance in cassava and report on the research to combine resistance against CBSD and CMD. We improve the lengthy and erratic screening for CBSD resistance by proposing a virus infection and screening protocol for the viruses causing CBSD and CMD, which allows a rapid and precise assessment of cassava resistance under controlled conditions. Using this approach, we classified the virus responses of cassava lines from Africa and South America and identified truly virus-resistant clones that cannot be infected with any of the known viruses causing CBSD even under the most stringent virus infections. A modification of this protocol was used to test seedlings from cassava crosses for resistance against both diseases. A broad-spectrum resistance was identified in a workflow that lasted 9 months from seed germination to the identification of virus resistance. The workflow we propose dramatically reduces the evaluation and selection time required in a classical breeding workflow to reach the advanced field trial stage in only 9 months by conducting selections for virus resistance and plant multiplication in parallel. However, it does not bypass field evaluations; cassava resistance assessment prior to the field limits the evaluation to candidates with virus resistance defined as the absence of symptoms and the absence of the virus. The transfer of our virus screening workflow to cassava breeding programs enhances the efficiency by which resistance against viruses can be selected. It provides a precise definition of the plant's resistance response and can be used as a model system to tackle resistance in cassava against other diseases.

Why it matches plant phenotyping methodsカッサバのウイルス抵抗性を迅速・精密に評価する感染・スクリーニングプロトコルと育種ワークフローの開発が中心であり、症状および抵抗性応答という植物状態を測定している。

abstractWe improve the lengthy and erratic screening for CBSD resistance by proposing a virus infection and screening protocol for the viruses causing CBSD and CMD, which allows a rapid and precise assessment of cassava resistance under controlled conditions.

Exploring plant responses to abiotic stress by contrasting spectral signature changes →

In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes ( Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control ( C ), ii) water stress ( WS ), and iii) combined water and heat shock ( WS+T ). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions ( WS and WS+T ); L CT genotypes showed an anisohydric response compared that of H CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.

Why it matches plant phenotyping methods葉レベルのスペクトル反射変化を用いて遺伝子型のストレス応答を識別する測定・スクリーニング手法が研究の中心であり、育種向けの再利用可能な表現型取得法として提示されている。

abstractdaily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes

NAPPN Annual Conference Abstract: Limitations of Solar-Induced Chlorophyll Fluorescence (SIF) for Estimating Photosynthesis Under Stress →

High-throughput measurements of photosynthesis of plants grown under various conditions may provide important insights into the plasticity of the photosynthetic performance of plants. Remote sensing of photosynthetic activity [i.e., solar-induced chlorophyll fluorescence (SIF)] and its derivatives are the next generation of remote techniques, enabling high-throughput photosynthesis measurements under field conditions. We hypothesized that by measuring SIF simultaneously with measurements of whole-plant water relations in a standardized controlled drought experiment, we would be able to quantify photosynthetic activity and to detect water stress at an early stage. A functional-phenotyping platform was used to apply the controlled drought treatment and to monitor the growth and water balance of tomato introgression lines (ILs). A new SIF-derived index, electron transport rate (RS-ETRi), was found to be negatively correlated with whole-plant stomatal conductance (Gsc) under non-stressed conditions; whereas a positive correlation was observed between those factors under drought stress. No significant relationships were found between SIF and plant biomass or Gsc. SIF 687 responded to drought earlier than any of the other measured vegetation indices (VIs). SIF parameters could not differentiate between IL lines; whereas differences between ILs were clearly identified by the gravimetric water-relations measurements. We concluded that SIF did not provide any advantage over commonly used methods for detecting physiological differences between the ILs. Overall, although SIF plays a significant role in photosynthesis, the relationship between SIF and photosynthesis is complex and we believe it would be an oversimplification to use SIF to quantify photosynthetic activity.

Why it matches plant phenotyping methodsSIFを用いた光合成・水ストレス推定法の性能評価が研究の中心であり、他の測定法との比較を含む技術的検証であるため。

abstractRemote sensing of photosynthetic activity [i.e., solar-induced chlorophyll fluorescence (SIF)] and its derivatives are the next generation of remote techniques, enabling high-throughput photosynthesis measurements under field conditions.

Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud →

Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of big data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3D point cloud. This study utilized four 3D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN (DGCNN) for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (R2) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques.

Why it matches plant phenotyping methodsLiDAR点群と深層学習による器官分割・形質抽出を開発し、手測定で妥当性検証しており、植物フェノタイピング手法が研究の中心です。

abstractHowever, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3D point cloud.

AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming Dataset →

Agriculture 4.0 will be data-driven and utilize modern technology in order to monitor plant life cycles and traits resulting in better yield. Pheno-parenting, a new concept derives certain methodologies adopted in plant phenotyping which monitors plants at various stages of their life cycle and supports their growth by deploying modern tools and technologies. In this work, a small-scale Hydroponic system was set up for plant life cycle image data collection and analysis, which consists of thirty plants of three species (i.e., Petunia, Pansy, Calendula) with ten plants from each species grown. A Deep Neural Network (DNN) based approach has been adopted to analyse the key tasks such as plant species recognition, growth analysis, health analysis, and yield stage identification. Calendula plants have been correctly recognised with above 95% detection accuracy in all test cases The result thus obtained indicates, side-view images are more effective at identifying species and tracking growth. On the other hand, top-view photographs do a better job of capturing the texture and colour characteristics of leaves and budding flowers. The Growth Development Index (GDI) metric, which first rises with an increase in nutrient input up to 31 ml and then saturates, has been proposed to provide a better understanding of plant growth, health, and productivity.

Why it matches plant phenotyping methods植物のライフサイクル画像データセットを構築し、深層学習で成長・健康・収穫段階などの形質を抽出する手法が研究の中心であるため。

abstractA Deep Neural Network (DNN) based approach has been adopted to analyse the key tasks such as plant species recognition, growth analysis, health analysis, and yield stage identification.

The Use of Spectral Imaging to Follow the Iron and pH-Dependent Accumulation of Fluorescent Coumarins. →

Plants challenged with iron deficiency produce in their roots and secrete into the rhizosphere several small molecules named coumarins that derive from the phenylpropanoid pathway. Coumarins are biosynthesized in different root cell types and transported to the root epidermis prior to their secretion in the surrounding media. Taking advantage of the natural fluorescence of most coumarins glycosides when exposed to UV light, we developed a method to uncover their individual cellular localization and accumulation. This approach couples spectral imaging acquisition and linear unmixing analysis. In this protocol, we describe guidelines, experimental setup, and conditions for the analysis of coumarins localization and accumulation in Arabidopsis thaliana root seedlings grown in control and iron deficiency conditions, at both acidic and alkaline pH.

Why it matches plant phenotyping methods植物根におけるクマリンの細胞局在・蓄積を測定するスペクトル画像取得と線形アンミキシング解析のプロトコル開発が中心であり、鉄欠乏・pH応答という植物の生理状態を可視化する方法である。

abstractwe developed a method to uncover their individual cellular localization and accumulation

Classifying cadmium contaminated leafy vegetables using hyperspectral imaging and machine learning. →

Cadmium (Cd) is a toxic element that can accumulate in edible plant tissues and negatively impact human health. Traditional Cd quantification methods are time-consuming, expensive, and generate a lot of toxic waste, slowing development of methods to reduce uptake. The objective of this study was to determine whether hyperspectral imaging (HSI) and machine learning (ML) can be used to predict Cd concentrations in plants using kale ( Brassica oleracea ) and basil ( Ocimum basilicum ) as model crops. The experiments were conducted in an automated phenotyping facility where all environmental conditions except soil Cd concentration were kept constant. Cd concentrations were determined at harvest using traditional methods and used to train the ML models with data collected from the imaging sensor. Visible/near infrared (VNIR) images were also collected at harvest and processed to calculate reflectance at 473 bands between 400 to 998 nm. All reflectance spectra were subject to the feature selection algorithm ReliefF and Principal Component Analysis (PCA) to generate data and provide input to evaluate three ML classification models: artificial neural network (ANN), ensemble learning (EL), and support vector machine (SVM). Plants were categorized according to Cd concentrations higher or lower than the safety threshold of 0.2 mg kg -1 Cd. Wavelengths with the highest ranks for Cd detection were between 519 and 574, and 692 and 732 nm, indicating that Cd content likely altered the plants' chlorophyll content and altered leaf internal structure. All models were able to sort the plants into groups, though the model with the best F1 score was the ANN for the validation subset that utilized reflectance from all wavelengths. This study demonstrates that HSI and ML are promising technologies for the fast and precise diagnosis of Cd in leafy green plants, though additional studies are needed to adapt this approach for more complex field environments.

Why it matches plant phenotyping methods葉菜のCd濃度をHSI画像と機械学習から推定する手法が研究の中心であり、植物の化学的状態を非破壊的に評価する方法としてモデル比較・検証も行っている。

abstractThe objective of this study was to determine whether hyperspectral imaging (HSI) and machine learning (ML) can be used to predict Cd concentrations in plants

Using leaf spectroscopy and pigment estimation to monitor indoor grown lettuce dynamic response to spectral light intensity. →

Rising urban food demand is being addressed by plant factories, which aim at producing quality food in closed environment with optimised use of resources. The efficiency of these new plant production systems could be further increased by automated control of plant health and nutritious composition during cultivation, allowing for increased produce value and closer match between plant needs and treatment application with potential energy savings. We hypothesise that certain leaf pigments, including chlorophylls, carotenoids and anthocyanins, which are responsive to light, may be good indicator of plant performance and related healthy compounds composition and, that the combination of leaf spectroscopy and mathematical modelling will allow monitoring of plant cultivation through noninvasive estimation of leaf pigments. Plants of two lettuce cultivars (a green- and a red-leaf) were cultivated in hydroponic conditions for 18 days under white light spectrum in climate controlled growth chamber. After that period, plant responses to white light spectrum ('W') with differing blue wavelengths ('B', 420 - 450 nm) percentage (15% 'B15', and 40% 'B40') were investigated for a 14 days period. The two light spectral treatments were applied at photon flux densities (PFDs) of 160 and 240 µmol m -2 s -1 , resulting in a total of four light treatments (160WB15, 160WB40, 240WB15, 240WB40). Chlorophyll a fluorescence measurements and assessment of foliar pigments, through destructive ( in vitro ) and non-destructive ( in vivo ) spectrophotometry, were performed at 1, 7 and 14 days after treatment initiation. Increase in measured and estimated pigments in response to WB40 and decrease in chlorophyll:carotenoid ratio in response to higher PFD were found in both cultivars. Cultivar specific behavior in terms of specific pigment content stimulation in response to time was observed. Content ranges of modelled and measured pigments were comparable, though the correlation between both needs to be improved. In conclusion, leaf pigment estimation may represent a potential noninvasive and real-time technique to monitor, and control, plant growth and nutritious quality in controlled environment agriculture.

Why it matches plant phenotyping methods葉分光と数学モデルによる葉色素の非破壊推定・モニタリングが中心的な植物表現型取得手法であるため。

abstractthe combination of leaf spectroscopy and mathematical modelling will allow monitoring of plant cultivation through noninvasive estimation of leaf pigments

Limitations of Solar-Induced Chlorophyll Fluorescence (SIF) for Estimating Photosynthesis Under Stress →

High-throughput measurements of photosynthesis of plants grown under various conditions may provide important insights into the plasticity of the photosynthetic performance of plants. Remote sensing of photosynthetic activity is the next generation of fast scanning techniques, enabling high-throughput photosynthesis measurements under controlled conditions. We hypothesized that by measuring SIF simultaneously with whole-plant water relations in a standardized controlled drought experiment, we would be able to quantify photosynthetic activity and to detect water stress at an early stage. A functional-phenotyping platform was used to apply the controlled drought treatment and to monitor the growth and water balance of tomato introgression lines (ILs). A new SIF-derived index, electron transport rate (RS-ETRi), was found to be negatively correlated with whole-plant stomatal conductance (Gsc) under non-stressed conditions. No significant relationships were found between SIF and plant biomass or Gsc. SIF 687 responded to drought earlier than any of the other measured vegetation indices. SIF based indices could not differentiate between introgressed lines of tomato; whereas differences between Introgression Lines were clearly identified by the water-relations measurements. We concluded that SIF did not provide any advantage over commonly used methods for detecting physiological differences between the Introgression Lines. Overall, although SIF plays a significant role in photosynthesis, the relationship between SIF and photosynthesis is complex and we believe it would be an oversimplification to use SIF to quantify photosynthetic activity on close canopy spatial resolution level.

Why it matches plant phenotyping methodsSIFによる植物の光合成・水ストレス推定を機能フェノタイピング基盤で検証し、他の指標や水分生理測定と比較しているため、フェノタイピング手法の技術評価が中心です。

abstractRemote sensing of photosynthetic activity is the next generation of fast scanning techniques, enabling high-throughput photosynthesis measurements under controlled conditions.

Quantifying physiological trait variation with automated hyperspectral imaging in rice →