Plant phenotyping methods.

Video-based fruit detection and tracking: effects of scanning conditions on fruit load estimation ↗

Automated fruit counting and yield estimation systems are necessary for efficient orchard management. This study presents a computer vision system based on video multi-object-tracking for fruit load estimation in apple orchards and provides a comprehensive analysis of the system performance under diverse scanning conditions. The system integrates fruit detection, tracking, localization within orchard, and fruit load map generation. Experiments were carried out in an experimental apple orchard containing 420 apple trees. Data was collected with two different RGB-D sensors (Azure Kinect DK and ZED 2) at three different scanning distances (125 cm, 175 cm, and 225 cm) on two different dates prior to the harvest. Comparing the two evaluated sensors, Azure Kinect provided more consistent performance across different dates. Results also show that the longer scanning distance improves accuracy due to seeing the full tree view gives better fruit counts than close partial views. Between the two dates, best results were achieved near harvest due to fruit color at this stage, achieving a Mean Absolute Percentage Error (MAPE) of 6.91 % and a determination coefficient (R 2 ) of 0.733 (using ZED2 sensor at 225 cm distance). Finally, a test comparing scanning from one or both sides of the tree row showed that bilateral scanning improved fruit load estimation at the stretch level by incorporating information from both sides of the canopy. The results of this work demonstrate the effectiveness of the video fruit tracking systems as a useful tool for automating fruit load estimation.

Why it matches plant phenotyping methods動画ベースの果実検出・追跡手法を開発・評価し、リンゴ樹の果実負荷量を推定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として含める。

abstractThis study presents a computer vision system based on video multi-object-tracking for fruit load estimation in apple orchards and provides a comprehensive analysis of the system performance under diverse scanning conditions.

Automated Video Tracking to Phenotype Plant Resistance to Aphid-Transmitted Yellow Dwarf Viruses in Grass Seed Crops ↗

Grass seed crops are susceptible to yellow dwarf viruses transmitted by aphids. The Willamette Valley in Oregon, United States, is the leading producer of cool-season grass seed crops globally, and industry reports have attributed seed yield loss and shortened stand longevity to aphid-transmitted yellow dwarf viruses. Genetic resources are needed for effective and sustainable management of this pest, specifically the Rhopalosiphum padi–PAV pathosystem, in grass seed production to reduce foliar insecticide applications and maintain optimum seed yield potential. High-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs. An automated video tracking procedure was optimized to evaluate host plant resistance in cool-season grass seed crops to R. padi–PAV with live plants and viruliferous and nonviruliferous aphid populations. Feeding behavior recorded with automated video tracking was strongly correlated with “ground-truthed” observations by human observers. Partial resistance (antixenosis and antibiosis) and tolerance traits were detected in select perennial ryegrass and tall fescue cultivars evaluated with traditional phenotyping methods in a greenhouse setting and with high-throughput phenotyping using automated video tracking in the laboratory. Across grass cultivars, nonviruliferous aphids had greater fitness and preference for noninfected grass plants compared with viruliferous aphids. Automated video tracking can be used as a high-throughput phenotyping method for continued evaluation of host plant resistance in grasses grown for seed production, as well as to identify resistant genotypes in other grass crops susceptible to aphid–yellow dwarf virus virus–vector systems.

Why it matches plant phenotyping methods自動動画追跡を用いてアブラムシ媒介ウイルスに対する植物抵抗性を高スループットに評価する手法を最適化・検証し、従来観察との相関および抵抗性形質の検出を示しており、表現型取得法が研究の中心である。

abstractHigh-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs.

Time-Resolved Phenotyping Reveals Heterogeneous Rice Seed Germination Dynamics in Shallow-Water Culture ↗

Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U 2 -Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h −1 . B-1 accumulated 63.71% of its final visible area during 72–80 h, whereas B-3 accumulated 73.51% during 60–72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.

Why it matches plant phenotyping methods連続画像から個々のイネ種子の発芽・組織面積・成長速度を抽出する時間分解フェノタイピングワークフローを開発しており、表現型取得と解析手法が研究の中心である。

abstractWe developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture.

RT-ZSDR: A real-time zero-shot segmentation and dense reconstruction framework for plant phenotyping robot ↗

Annotation scarcity, poor model generalization and lagged data processing remain key bottlenecks hindering the practical deployment of phenotyping robots. To address these issues, we developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge. Diverging from conventional semantic SLAM, our core contribution is RT-ZSDR, a framework featuring two key methodological novelties. First, we introduce the ForeCut pipeline for target extraction, which innovatively fuses DINO features with 3D geometric spatial information, leveraging multi-view semantic-spatial consistency to achieve annotation-free, zero-shot dense segmentation and reconstruction. Second, we designed a hardware-coupled loop closure strategy utilizing the robotic arm's kinematic feedback as prior constraints to significantly improve loop closure recall. Supported by edge computing Jetson Orin NX, the tracking and segmentation process takes approximately 0.24 s per frame after an initialization period of 1.82 s. RT-ZSDR's phenotypic measurements demonstrated strong correlations with reference baseline in both laboratory settings (n=90, PlantEye measurements as reference baseline; R 2 =0.990, 0.939, 0.725, and 0.861 for plant height, projected leaf area, surface area, and volume) and practical greenhouse environments (n=48, manual measurements as reference baseline; R 2 =0.965, 0.862 for plant height and stem diameter). Additionally, evaluated against COLMAP benchmarks (n=24), the system achieved a mean 3D reconstruction F1-score of 0.816.

Why it matches plant phenotyping methods植物フェノタイピングロボット向けに、ゼロショット分割・3D再構成・エッジ処理を開発し、植物形質を基準測定およびベンチマークと比較検証しており、取得・抽出手法が研究の中心である。

abstractwe developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge.

Precisely tracking and counting of field rice seedlings based on UAV platform with DMNP-YOLO and Improved-Bytetrack ↗

Accurate and non-destructive counting of rice seedlings is crucial for yield estimation and precision agriculture, yet remains challenging in UAV videos due to dense distribution and strong temporal appearance similarity. This study proposes an efficient tracking-based rice seedling counting framework that integrates an improved Yolov11n detector with a robust multi-object tracking strategy to achieve reliable video level counting. The proposed detector, termed DMNP-YOLO, enhances feature representation, localization robustness, and computational efficiency through Dynamic Snake Convolution, a multi-scale feature attention module, Shape-IoU combined with Normalized Wasserstein Distance, and BatchNorm scaling factor based structured channel pruning, resulting in reductions of 40.5% in Params and 15.2% in GFLOPs while achieving a precision of 0.901 and an mAP@0.5 of 0.921. Building upon accurate frame-level detections, a trajectory based counting mechanism is realized by embedding an Anchor–Angle–Distance association strategy into ByteTrack, which explicitly enforces geometric and temporal consistency across frames, significantly improving tracking stability in dense seedling scenes. As a result, Multi-Object Tracking Accuracy is increased by 5.3 percentage points, identity switches are reduced by 33.3%, and counting accuracy is improved by 3.7 percentage points. Extensive experiments demonstrate that the proposed tracking-based counting framework achieves a mean absolute error of 16.47, a mean absolute percentage error of 6.48%, and an R² of 0.95969. Field scale validation further confirms its practical applicability, achieving an overall rice seedling counting accuracy of 93.4% and demonstrating strong robustness in real world agricultural environments.

Why it matches plant phenotyping methodsUAV画像と検出・追跡アルゴリズムにより圃場のイネ幼苗数を推定する手法を開発し、精度検証と実圃場検証を行っており、植物表現型の取得方法が研究の中心である。

titlePrecisely tracking and counting of field rice seedlings based on UAV platform with DMNP-YOLO and Improved-Bytetrack

High-Throughput Panicle Counting of Wild Rice Accessions for Germplasm Evaluation: An AI-Driven UAV Phenotyping Framework ↗

Wild rice (Oryza spp.) harbors abundant genetic variation and represents an important germplasm resource for improving yield-related traits in cultivated rice. Panicle number is a key phenotypic trait for evaluating tillering capacity and yield potential in wild rice. However, existing approaches for acquiring panicle-number phenotypes remain limited by low efficiency, high dependence on manual operation, and cumbersome matching between plant targets and accession identifiers. In this study, we proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation. The framework integrates field plant localization, accession identifier binding, flight route planning, plant-by-plant video acquisition, spatiotemporal registration, video slicing, and panicle detection and tracking, enabling structured panicle-number outputs indexed by accession identifier. To address the small scale, loose structure, morphological variation, and wind-induced swaying of wild rice panicles in UAV imagery, a wild rice panicle detection model was constructed, and WRPD-Tracker was developed for cross-frame identity association and non-redundant counting. The wild rice panicle detection model achieved an AP@50 of 91.56%, representing a 6.16-percentage-point improvement over the DEIM baseline, with 3.70 M parameters and 6.55 G FLOPs, while WRPD-Tracker achieved a HOTA of 65.1% and a MOTA of 79.0%, representing a 5.8-percentage-point improvement in HOTA over the baseline tracker. At the final counting level, UAV-based counts were highly consistent with manual ground counts, with an R² of 0.992 and an MAE of 0.37 panicles. This framework enables batch acquisition of panicle-number phenotypes in wild rice and provides quantitative support for germplasm evaluation, panicle-number trait comparison, and subsequent yield-related phenotypic studies.

Why it matches plant phenotyping methodsUAV画像とAIによるイネ穂数の取得・追跡・計数手法を開発し、手動計数と技術検証しており、植物フェノタイピング手法が研究の中心です。

abstractwe proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation.

From Seedling to Maturity: AI‐Based Mustard Crop Growth Tracking Using UAV Time‐Series Images ↗

ABSTRACT Accurate monitoring of plant phenology is essential for agricultural decision‐making, as deciding the right time of fertilizer application, the maturity of the plant, and climate variation. Traditional manual monitoring often fails to capture the temporal variations across large fields. UAV‐based imaging combined with deep learning can provide a solution for automated phenological assessment. In this study, we propose an AI‐driven framework on UAV‐captured Indian mustard plants to predict the phenological progress. We applied deep learning methods, EfficientNet‐B0, and a proposed hybrid model of a Vision Transformer + LSTM to predict phenological growth from UVV‐captured images of the Indian mustard plant. Both models were trained under a supervised regression setup with extensive augmentation and optimization strategies. The results of the study show that the ViT + LSTM outperforms the EfficientNet‐B0 model in terms of prediction accuracy for mustard plant phenology. The R 2 = 0.9974, minimal errors MSE = 0.0111, and RMSE = 0.149 indicate that the ViT + LSTM provides more accurate phenological predictions on temporal and spatial dependencies. Correlation Analysis confirmed a strong linear and monotonic relationship with the ground truth, with r = 0.9989 and ρ = 0.9946. Gram‐CAM visualization showed that the ViT + LSTM captures the meaning area of the plant. These results were further validated using statistical measures such as Pearson's r and Spearman's ρ , which confirmed the reliability and consistency of the model's predictions.

Why it matches plant phenotyping methodsUAV画像から植物の生育フェノロジーを推定する深層学習手法を開発し、複数モデルの精度比較と統計的検証を行っており、フェノタイピング手法が中心である。

abstractUAV‐based imaging combined with deep learning can provide a solution for automated phenological assessment.

BeeMonitor: Automated IoT video surveillance and an AI-powered video processing system for monitoring the foraging and nesting behavior of cavity-nesting solitary bees ↗

1. Solitary bee species that use artificial trap nests are important for agricultural crop production and as indicators of habitat quality. Quantifying cavity-nesting solitary bee foraging and nesting behavior is essential for real-time analysis of population numbers and pollination activity, as well as understanding how environmental conditions shape reproductive success and population dynamics. However, manual observation is labor-intensive, prone to observer bias, and unable to deliver continuous data. Existing automated systems either require individual bee marking or detect presence without resolving nest-tube-level entry and exit events. 2. We developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video, using Osmia cornifrons (the horn-faced mason bee) as a model system. A low-cost Raspberry Pi handles solar-powered field recording, while the software combines object detection (YOLOv26), a custom multiple-object tracker (BeeTrack), and a Random Forest classifier trained on trajectory-derived features to distinguish genuine events from incidental detections. 3. Over a 29-day deployment, hardware reliability averaged 97.5% recording coverage. The pipeline achieved 91.3% precision and 87.3% recall (F1 = 0.893), generalizing robustly under leave-one-video-out cross-validation (mean F1 = 0.904 ± 0.045). Detected foraging trips correlated strongly with brood cell counts (R² = 0.849, p < 0.001, n = 19), and a Random Forest model (AUC = 0.820 ± 0.062) identified solar radiation as the dominant driver of foraging activity, followed by temperature. 4. BeeMonitor demonstrates that automated computer vision can reliably extract ecologically relevant behavioral data from continuous video, enabling real-time analysis of pollinator behavior and abundance at a temporal and spatial resolution unattainable through manual observation. Its modular design supports adaptation to other species and monitoring contexts.

Why it matches plant phenotyping methods映像からハチの巣穴への入退出や採餌行動を自動抽出するハードウェア・コンピュータビジョン基盤を開発し、精度検証も行っているため、動物対象ではあるが植物フェノタイピングの範囲外です。

abstractWe developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video

Plant−Plant Communication for Systemic Acquired Resistance under Biotic Stress Spatiotemporally Tracked by an In Situ Surface-Enhanced Raman Spectroscopy Aerosol Spraying Analyzer ↗

Abstract This study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer leveraging engineered Au core/Ag shell nanocubes (Au@AgNCs) to bridge in planta pathogen tracking with airborne defense signal monitoring, enabling unprecedented decoding of plant–plant communication (PPC) kinetics. Within a Pseudomonas aeruginosa (P. aeruginosa)-infected plant biotic stress model, the analyzer achieved: (1) spatiotemporal mapping of virulence kinetics through sensitive detection of P. aeruginosa-specific virulence factor pyocyanin, establishing infection progression timelines and tissue-specific dissemination gradients. (2) Quantification of stress-responsive signaling via dual-functionalized Au@AgNCs, revealing methyl salicylate (MeSA) release kinetics and establishing a direct correlation between pathogen invasion severity and airborne alarm signal—a calibrated defense response heretofore unquantified. (3) Real-time in situ monitoring of MeSA-mediated PPC revealed fundamental plant physiological breakthroughs: First, receiver-specific signaling reprogramming occurs where healthy plants exhibit delayed yet amplified defense hormone kinetics, contrasting sharply with the immediate response of infected emitters. Second, evolutionarily constrained coordination emerges through cross-species signaling divergence, where phylogenetic adaptations in phytohormone perception circuits drive distinct defense strategies−exemplified by Solanaceae amplification versus Poaceae suppression. (4) Validation of systemic acquired resistance (SAR) in PPC-primed plants showing 63.5% reduced infection severity and two days delayed susceptibility. This analyzer integrates molecular-scale pathogen kinetics with ecosystem-level signaling networks, advancing precision agriculture through field-deployable plant immunity diagnostics.

Why it matches plant phenotyping methodsSERSセンサーアナライザーの開発・検証が研究の中心で、植物感染進行、ストレス応答、空中防御シグナル、感染重症度を時空間的に測定するため、植物フェノタイピング手法に該当する。

abstractThis study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer

GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth ↗

Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intricate non-rigid deformations, exhibit changing topology as new organs emerge, and often lack explicit temporal correspondences between consecutive data acquisitions due to newly formed tissue. Methods designed for general scenes struggle to model topology changes and asynchronous organ growth characteristic of plants. To address these challenges, we introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series. Our approach decomposes a plant into its constituent organs and aligns each organ into its own canonical coordinate frame, isolating intrinsic growth patterns from global plant motion. We then learn a shared continuous neural deformation field that models temporal dynamics across all organs, conditioned on learnable per-organ latent codes capturing organ identity and growth characteristics. The resulting modular yet unified representation naturally accommodates the asynchronous development of plant organs while remaining grounded in the practical setting of organ-level plant tracking. We evaluate GrowFields on growth sequences from four plant species, assessing geometric fitting and organ tracking accuracy using manually annotated leaf-tip trajectories. Results demonstrate consistent improvements in spatial precision, temporal coherence, and morphological fidelity over a range of existing representations.

Why it matches plant phenotyping methods植物の3D時系列観測から器官追跡と成長動態を抽出する、オルガン認識型4Dニューラルフィールド手法の開発・評価が中心である。

abstractwe introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series.

Additively Manufactured in planta Integrated Microneedle–Microfluidic Sensing: Nondestructive Electrochemical Tracking of Glucose and Water Stress in Agricultural Crop Plants ↗

Abstract Timely quantification of crop stress physiology remains challenging because conventional assays are destructive, labor-intensive, and poorly suited for continuous monitoring and field deployment. Here, we report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress that integrates three design innovations in a single architecture: (i) a fully integrated hollow microneedle–microfluidic measurement pathway for sap access, (ii) physical isolation of the metal electrodes from direct tissue contact to reduce insertion-zone abrasion of the sensing interface, improving biocompatibility and potentially lowering fouling pathways, and (iii) lithography-free fabrication of a modular transducer on an additively manufactured substrate. The platform comprises a three-electrode gold (Au) transducer modified with a nanostructured reduced graphene oxide (rGO)–chitosan layer. The biosensing platform enabled dual sensing channels via functionalized glucose oxidase (GOx) and horseradish peroxidase (HRP) for the detection of glucose and water stress-associated hydrogen peroxide (H2O2), respectively. The glucose channel showed a strong linear calibration over the tested range, with Pearson’s r = 0.99, R2 = 0.98, sensitivity of 62.34 μA/mM, and a limit of detection (LOD) of 102.50 μM (∼1.85 mg/dL), while the H2O2 channel exhibited Pearson’s r = 0.99, R2 = 0.99, sensitivity of 3.65 μA/decade, and an LOD of 3.22 μM. Repeatability across measured standards remained high for both channels, with mean coefficients of variation of 1.31% for glucose and 1.16% for H2O2. Ex vivo measurements in plant sap, including standard-addition experiments and comparison with commercial benchmark assays, provided validation of analyte concentration determination in plant-derived samples. In planta measurements on maize plants (Zea mays L.) grown under graded watering treatments revealed statistically significant treatment-dependent glucose and H2O2 signatures over time (p

Why it matches plant phenotyping methods植物体内のグルコースとH2O2を非破壊・連続測定し、水ストレス状態を推定する電気化学センシング基盤の開発と検証が中心であり、植物フェノタイプ取得手法に該当する。

abstractwe report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress

A review: research progress on intelligent technologies for orchard yield monitoring ↗

Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable in-situ monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity-quality monitoring frameworks for actionable crop load management.

Why it matches plant phenotyping methods果実数・サイズ・体積・空間分布などの植物形質を対象に、センシング機器と画像解析・深層学習による収量推定法を体系的にレビューしており、フェノタイピング手法が中心である。

abstractThis review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions.

AgroPulse: A Real-Time Field Intelligence System for Crop Disease Tracking and Notification System ↗

Agriculture faces significant challenges due to plant diseases, which directly affect crop yield, quality, and farmer income. Early detection of agricultural diseases is critical to prevent large-scale crop losses and reduce excessive use of pesticides. Traditional disease detection methods rely on manual inspection by farmers or agricultural experts, which is time-consuming, subjective, and often inaccurate, especially during early stages of infection. With the advancement of Machine Learning (ML) and Internet of Things (IoT) technologies, automated and intelligent solutions for crop disease detection have become feasible. The IoT-Based Crop Disease Recognition and Field Notification System proposes an intelligent system that combines machine learning–based image analysis with IoT-enabled monitoring to detect crop diseases at an early stage. The system uses an ESP32 microcontroller integrated with an ESP-CAM module to capture images of plant leaves. These images are analyzed using trained machine learning models to identify disease patterns and abnormalities. The detection results are communicated through an IoT platform, enabling remote monitoring and real-time alerts. An LCD display provides local status information, while a buzzer generates immediate alerts when a disease is detected. The system is designed to be cost-effective, scalable, and suitable for deployment in real agricultural environments. By enabling early disease identification and timely intervention, the proposed solution helps improve crop productivity, reduce losses, and promote smart and sustainable agricultural practices.

Why it matches plant phenotyping methods植物葉の画像を機械学習で解析し、病徴・異常を検出するシステムが研究の中心であり、植物病害状態の画像ベースフェノタイピングに該当する。

abstractThe system uses an ESP32 microcontroller integrated with an ESP-CAM module to capture images of plant leaves.

Editorial: Plant phenotyping for agriculture ↗

Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-

Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。

titleEditorial: Plant phenotyping for agriculture

Cross-Day Grape Cluster Tracking Using Branch-Based 3D Alignment in Vineyards ↗

In Japan, the quantity of domestically produced fruit has been gradually decreasing, while wholesale prices have continued to rise due to declining production volumes and a shift toward high-quality varieties. To address these trends, improving quality and reducing labor through automation have become urgent challenges. In precision viticulture, monitoring the growth of grape clusters plays a key role in yield estimation, disease management, and optimal harvest timing. Although recent advances in deep learning and 3D reconstruction have enabled accurate fruit detection and modeling in vineyards, tracking the same clusters on different days remains challenging because of branch movement, fruit growth, and varying imaging conditions. This study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters. Stable vine structures, such as trunks and main branches, are reconstructed using Structure from Motion, and their spatial correspondences are estimated through SIFT-based matching and similarity transformation. Once the coordinate systems of different days are aligned, the grape clusters detected by CenterNet are associated based on spatial proximity in the unified 3D space. Experiments over multiple observation days demonstrated that the proposed method successfully maintained the consistent tracking of grape clusters throughout the growth period. These results indicate that branch-based alignment effectively stabilizes multi-day observations and facilitates the temporal monitoring of fruit growth, supporting automated phenotyping and future field robot applications in viticulture.

Why it matches plant phenotyping methodsブドウ房の経日追跡を目的とする3D画像アライメント手法を開発し、果実成長の時系列モニタリングと自動フェノタイピングへの利用を実験的に検証しているため、フェノタイピング手法が中心である。

abstractThis study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters.

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

Quantitative Live Cell Imaging of Nuclear Shape and Chromatin Dynamics During Development and Environmental Stress in Arabidopsis thaliana Root. ↗

The nucleus is the characteristic organelle of eukaryotic organisms. Unlike the classic textbook view of static nuclei, nuclear shape is dynamic in live cells. Altered or deformed nuclear shape is a hallmark of cancer in animal cells and environmental stress in plants. Nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, little is known about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To confront this issue, we developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition. This confocal imaging method utilizes a dual fluorescently tagged marker line - nuclear envelope protein and chromatin - to perform live cell imaging of the root in model plant Arabidopsis thaliana under control and salt-stressed conditions. These captured movies are analyzed to quantify nuclear and chromatin dynamics using open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. To validate this method, we imaged and quantified chromatin movement in control and salt-stressed roots, revealing a decrease in chromatin speed under salt-stressed conditions. This method allows for quantitative live cell imaging of root nuclear shape and chromatin dynamics during plant development and environmental stress, thus enabling analysis of changes in nuclear and chromatin dynamics caused by abiotic stressors.

Why it matches plant phenotyping methodsシロイヌナズナ根の核形状・クロマチン動態を定量する共焦点ライブイメージングと画像解析パイプラインを開発し、塩ストレス条件で検証しており、植物表現型取得が中心です。

abstractwe developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition.

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

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

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

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

A novel approach to assessing the tracking accuracy of crop phenology for multi-orbit and multi-feature Sentinel-1 time series ↗

: This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90% of GDD progression of in situ reported phenological stages, while systematic differences of around 5% by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.

Why it matches plant phenotyping methodsSAR時系列から作物フェノロジーを追跡・定量化する不確実性評価フレームワークが研究の中心であり、圃場レベルの植物状態測定法として開発・検証されている。

abstractThis study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale.

Characterization of Rhizosphere Oxidation Associated with Root Development in Rice Using Planar Oxygen Optodes ↗

Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.

Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、イネ根の発達と根圏酸化を時系列・空間的に測定しているため、植物フェノタイピング手法が研究の中心である。

abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.

Dim Green Light Enables Day-and-Night Monitoring of Leaf Movements ↗

Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 μmol m -2 s -1 ) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.

Why it matches plant phenotyping methods植物の夜間画像取得用の低強度緑色照明を開発・生理影響評価し、葉運動の定量と統合的な画像解析ワークフローを実証しており、フェノタイピング手法が中心です。

abstractHere, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce).

Image-Based Analysis for Identification of Plant Leaf Pathologics Using Deep Learning ↗

This project introduces a Convolutional Neural Network (CNN) as the proposed system for plant disease prediction, with a comparative analysis conducted against two existing models: Recurrent Neural Networks (RNN_GRU) and Artificial Neural Networks (ANN_MLP). The proposed CNN model is specifically designed to address the limitations of traditional approaches, such as lower accuracy and slower prediction times, particularly when handling complex image data. The system allows users to upload images of plant leaves, select the type of plant (e.g., potato, tomato, grape), and choose between RNN_GRU, ANN_MLP, or the newly developed CNN model for disease prediction. Additionally, users can run all three models simultaneously to compare their outputs, enabling a comprehensive evaluation of performance. Predictions are securely stored in a SQLite database, along with metadata such as confidence scores, prediction times, timestamps, and a unique group ID for efficient retrieval and management. Built using Flask, the application provides a professional-grade user interface with features like secure authentication, prediction history tracking, and deletion of past predictions. Comparative analysis demonstrates that the proposed CNN model significantly outperforms RNN_GRU and ANN_MLP in terms of accuracy, prediction speed, and overall reliability, making it a more effective tool for real-time agricultural applications. This advancement highlights the potential of CNNs in transforming agricultural practices by providing faster, more accurate, and reliable disease predictions, thereby contributing to improved crop health, reduced losses, and increased agricultural productivity.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNN手法を開発し、既存モデルとの精度・速度比較を行っているため、植物フェノタイピング手法が中心である。

abstractThis project introduces a Convolutional Neural Network (CNN) as the proposed system for plant disease prediction

Tracking Recovery: Temporally-Matched 3D Gaussian Splatting of Ecosystems after Prescribed Burns ↗

Land management and stewardship teams continue to lack the tools to capture 3D spatiotemporal insights of the ecosystems they oversee. For wildfire management at the wildland-urban interface, teams face challenges in capturing vegetation growth over time after a fuel reduction program and connecting seasonal changes to the vegetation distribution across the treated area. Current approaches rely on triangle meshes or point clouds generated from photogrammetry or LiDAR surveys on drones or hiked traverses. However, the difficulties in optimizing these meshes lead to large triangles that inadequately approximate the bulk vegetation shape, and the point cloud data is often too sparse for local plant-scale understanding. To address this gap, we extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals. We develop a new method to match the 3DGS reconstruction of these scenes across months, associating plant growth across seasons interactively in 3D. To achieve the centimeter-level matching, we adapt the Umeyama algorithm and the iterative closest point algorithm from point cloud maps to the 3DGS scene, leveraging the probabilistic interpretation of the 3D Gaussian data structure and robustly handling visual and geometric changes associated with vegetation phenology over time. We have applied our method to recent pile burns at Stanford’s Jasper Ridge ’Ootchamin ’Ooyakma Biological Preserve at monthly intervals. We demonstrate differences in ecological response where some piles featured the unexpected return of a rare and threatened bushmallow, and others remained more barren. This pile burn microcosm implicates the need for plant-level 3D spatiotemporal models to understand ecosystem recovery to fire mitigation practices. Please visit the project page for the spatiotemporal alignment video and more information: https://danineamati.github.io/burn-ecorecovery.github.io/ Please visit the Stanford Data Repository for the field data: https://doi.org/10.25740/ws901xs0162

Why it matches plant phenotyping methods植物の3D画像再構成と時系列位置合わせ手法を開発し、植物の個体・葉・草丈・成長を抽出することが中心であるため、植物フェノタイピング手法研究に該当します。

abstractwe extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals.

An integrated framework to elucidate mechanisms underlying host-branched broomrape infection. ↗

Branched broomrape (Phelipanche ramosa) is an obligate root parasitic weed that threatens tomato production in many regions. Progress in understanding host resistance mechanisms has been hindered by the parasite's subterranean life cycle and the technical limitations of traditional soil-based assays. Here, we introduce an integrated experimental framework that enables molecular, genetic, and cellular analysis of broomrape parasitism in tomato under controlled conditions. We implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots, and a dual-compartment in vitro co-culture system supporting parasite infection of transgenic hairy roots. This methodology enabled rapid functional testing of candidate host resistance genes, exemplified by CRISPR-edited mutants of the tomato transcription factor SCHIZORIZA (SlSCZ), which displayed localized lignin accumulation at the parasite entry site in the root. The observed lignification suggests a role for this gene in regulating inducible cell wall lignification against broomrape. Together, these tomato-focused integrated methods enable reproducible imaging, genetic perturbation, and high-resolution analysis of host-parasite interfaces. These provide a scalable platform for dissecting broomrape resistance and accelerating resistance gene discovery in tomato and a critical tool for combating the devastating consequences of this parasite on agriculture.

Why it matches plant phenotyping methodsトマト根上の寄生進展を非破壊・リアルタイムに観察する共培養系と再現可能なイメージングを開発し、植物の感染状態を取得する基盤が研究の中心である。

abstractWe implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots

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.

Noise-Resilient Hybrid EfficientNet–Vision Transformer Framework with Adaptive Symmetric Cross-Entropy Loss for Robust Plant Disease Detection ↗

The errors of human annotation and the noise of the environment such as lighting changes, occlusions and cluttered backdrop limit the correct detection of the plant diseases in the field condition. The research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification. The hybrid EfficientNet-Vision Transformer (ViT) network proposed is based on an EfficientNet-B4 branch of CNN and a branch of Vision Transformer (ViT) network, which focuses on capturing fine-grained lesion features and global contexts information. A data augmentation pipeline based on CycleGAN is used to introduce field-style distortions (e.g., (lighting shifts, shadowing, debris and partial occlusions), to be more robust to environmental noise, and an Adaptive Symmetric Cross-Entropy (ASCE) loss identifies and down-weights uncertain samples with normalized prediction entropy. The training is done in two phases, Stage 1 pretraining with clean images of PlantVillage and Stage 2 with increasingly noisy samples. The framework is tested in two different noise conditions, and these include the controlled synthetic label noise with PlantVillage and the real environmental noise with PlantDoc. The proposed model has an accuracy of 94.5% on the clean PlantVillage test set. It achieves 85.0% accuracy on the PlantVillage dataset under the 20% synthetic label noise protocol, outperforming ResNet-50V2 (76.5%), DenseNet-121 (78.9%), and Co-Teaching (79.5%). Macro-precision, macro-recall and macro-F1 of the model on the external PlantDoc field dataset are 0.718, 0.681, 0.681, respectively with a top-1 accuracy of 72.0, which is a manifestation of cross-domain generalization. The lesion-centric Grad-CAM images indicate that the model places emphasis on symptomatic areas of leaves and represses reactions of background soil, shadows, and clutters. The suggested hybrid EfficientNet-ViT architecture offers, in general, a robust and explainable solution to precision agriculture and intelligent crop tracking systems that are resistant to noise.

Why it matches plant phenotyping methods植物の病徴画像から病害状態を推定する深層学習手法を開発し、ノイズ条件・外部データセットで性能検証しているため、植物フェノタイピング手法が中心である。

abstractThe research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification.

A Guide to Reproducible Cellulose Synthase Density and Speed Measurements in Arabidopsis thaliana . ↗

Cellulose synthase complexes (CSCs) play a central role in plant cell wall formation. Their dynamic behavior at the plasma membrane leads to the deposition of cellulose microfibrils into the apoplastic space, thereby shaping the architecture and mechanical properties of the cell wall. Although previous imaging studies have provided important insights into CSC dynamics and localization, standardized and reproducible workflows for quantitative measurements of CSC speed and density remain limited. Here, we present a reproducible live-cell imaging and analysis workflow for quantifying the speed and density of fluorescently labeled CSCs at the plasma membrane in Arabidopsis thaliana . The protocol integrates optimized spinning-disk confocal imaging, surface-based projection of z-stack recordings, automated detection of diffraction-limited CSCs foci, and kymograph-based speed measurements using freely available tools in Fiji. While selected steps, such as region of interest definition and parameter selection for spot detection or trajectory analysis, remain user-guided, these decisions are constrained to well-defined stages within an otherwise standardized pipeline, thereby reducing variability and improving reproducibility across experiments. The workflow has been validated across multiple tissues, reporter lines, genetic backgrounds, and perturbation conditions in Arabidopsis and enables robust comparative analysis of CSC dynamics. Beyond CSCs, this workflow is expected to be adaptable to other fluorescently labeled proteins that appear as diffraction-limited foci at or near the plasma membrane. Key features • Enables accurate CSC speed and density measurements during both primary and secondary cell wall formation using spinning-disk confocal time-lapse imaging. • Combines surface-projection, kymograph analysis, and high-throughput particle detection to quantify CSC dynamics even in crowded or low-signal plasma membrane regions. • Provides a standardized analysis workflow validated across multiple Arabidopsis genotypes, including inducible systems and mutant backgrounds that possess altered cell wall biosynthesis. • Applicable to any fluorescently labeled diffraction-limited foci at or near the plasma membrane, extending the workflow beyond CSCs.

Why it matches plant phenotyping methods植物細胞内のセルロース合成酵素複合体の速度・密度という観測可能な状態を、ライブイメージングと自動解析で定量する再現可能な手法を開発・検証した研究であり、方法が中心です。

abstractHere, we present a reproducible live-cell imaging and analysis workflow for quantifying the speed and density of fluorescently labeled CSCs at the plasma membrane in Arabidopsis thaliana .

Quantitative live cell imaging of nuclear shape and chromatin dynamics during development and environmental stress in Arabidopsis thaliana ↗

The nucleus is the characteristic organelle for eukaryotic organisms. Unlike the classic textbook view of static two-dimensional nuclei, nuclear shape is dynamic inside the live cell. The alteration or deformed nuclear shape is the hallmark of cancer in animal cells and environmental stress in plants. The nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, we have limited knowledge about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To circumvent this issue, we are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root. The live cell imaging was performed in control and salt-stressed conditions. We utilized these captured movies to analyze through open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. Using this method, we have demonstrated that chromatin velocity is decreased in salt-treated conditions. This method will be widely applied to quantitative live cell imaging of nuclear shape and chromatin dynamics during plant development and environmental stress. Summary This process aims to simultaneously record nucleus and chromatin dynamics in Arabidopsis thaliana roots and investigate changes in these dynamics in response to developmental and environmental cues.

Why it matches plant phenotyping methods植物の核形状・クロマチン動態をライブイメージングと画像解析で定量化する手法が中心であり、環境ストレス下の植物状態を測定する再利用可能なワークフローを提示している。

abstractwe are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root.

Energy-autonomous IoT-based wireless sensor networking architecture for plant health monitoring and precision irrigation in sugarcane ↗

Sugarcane farming demands precise irrigation and vigilant health monitoring to maximize productivity, yet conventional approaches often fall short in efficiency and scalability. This paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time. Data is processed locally and relayed to a cloud server, enabling automated irrigation decisions informed by the Crop Water Stress Index (CWSI) and growth tracking through advanced image analysis. The system achieved a soil moisture measurement accuracy with a strong correlation (R² = 0.96) to gravimetric methods and a plant height measurement accuracy with a mean absolute error of 1.8 cm. Designed for energy independence, the system operates seamlessly in off-grid environments. Field results demonstrate key findings: 98.7% data transmission reliability, early stress detection 24-48 hours before visible symptoms, 15% water savings through precision irrigation, and continuous operation for 180+ days on battery backup. These outcomes position this solution as a practical advancement for modern, sustainable sugarcane cultivation.

Why it matches plant phenotyping methods植物の健康状態・温度・草丈をセンサーと画像解析で取得し、精度検証まで行うIoTフェノタイピング基盤が研究の中心であるため。

abstractThis paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time.

Multifactorial analysis of simultaneous organelle movement reveals cell-specific motility of peroxisomes and mitochondria. ↗

The movement, distribution, and interactions of organelles are cell-type specific, responding to fluctuating metabolic and environmental cues and governing the efficiency of plant physiology and stress response. The directional motility of various plant organelles is predominantly driven by the actomyosin system, yet the distinct functionality of these organelles across plant tissues presupposes organelle-specific regulation of motility, which requires the detection of subtle shifts in dynamics. Meanwhile, studies that comprehensively characterize and directly compare the simultaneous movement of multiple types of organelles within the same cell are limited. Here, we visualized peroxisomes, mitochondria, chloroplasts, Golgi bodies, and actin filaments simultaneously in tobacco (Nicotiana tabacum) to evaluate organelle organization and motility within the context of one another. Quantitative analysis of multiple motility factors enabled us to identify peroxisome motility in tobacco mesophyll as distinct from other organelles. Further analysis in Arabidopsis (Arabidopsis thaliana) revealed that both mitochondria and peroxisomes are slower in mesophyll cells compared to epidermis in normal growth conditions, but their motility patterns are unique from one another across leaf tissue after plants experienced conditions that induce photorespiration, a metabolic pathway requiring the concerted action of chloroplasts, peroxisomes, and mitochondria. Our quantitative analysis of thousands of organelles across species, cell type, and physiological conditions unveils distinct modulation of motility according to organelle identity and function. The extensive combinatorial characterizations of plant organelle movement provide a fundamental resource for the future discovery of molecular mechanisms driving the movement and distribution of diverse organelles.

Why it matches plant phenotyping methods複数オルガネラを同時可視化し、運動性を定量抽出する画像解析ワークフローが研究の中心で、植物細胞の生理状態を表す測定法として実質的に適用されている。

abstractHere, we visualized peroxisomes, mitochondria, chloroplasts, Golgi bodies, and actin filaments simultaneously in tobacco (Nicotiana tabacum) to evaluate organelle organization and motility within the context of one another.

Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow. ↗

BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.

Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。

abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.

Ultra-flexible PINE arrays for month-long, continuous intracellular ion flux monitoring in plants with nanomolar accuracy ↗

High-precision in vivo monitoring of ion fluxes is essential yet challenging studying plant electrophysiology such as growth regulation, signal transduction and stress responses. Existing methods for probing ion dynamics are limited by low sensitivity, high invasiveness that interferes physiological processes, and the inability to accurately resolve ion homeostasis with required spatial and temporal resolution. Here, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays manufactured on 1.2-μm-thick polymer substrates, which enable ultrasensitive and selective measurement of ionic current for month-long via scalable nanofabrication techniques. The fabricated PINE arrays have a smaller dimension than typical plant cells as well as less stiffness, facilitating minimally invasive integration with living plant cells. This subcellular-scale plant-electronic interface allows for reliable, selective detection of K + flux with a detection limit of ∼10⁻⁸ M, and thus allows continuous, stable monitoring of tomato stem cells over six weeks, capturing dynamic potassium fluctuations during all key growth stages. More importantly, the method permits long-term, real-time tracking of ion-specific dynamics without disrupting plant cellular structure or altering endogenous ion concentrations. Therefore, PINE provides unprecedented access to ion homeostasis and signaling networks, making it an excellent platform for precision agriculture and a foundational tool for future digital plant engineering.

Why it matches plant phenotyping methods植物細胞内のK+フラックスを長期間・リアルタイムに測定する超柔軟ナノ電極アレイを開発し、感度・選択性・長期安定性を実証した研究であり、植物生理状態の取得手法が中心である。

abstractHere, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays

Tracking Recovery: Temporally-Matched 3D Gaussian Splatting of Ecosystems after Prescribed Burns ↗

Land management and stewardship teams continue to lack the tools to capture 3D spatiotemporal insights of the ecosystems they oversee. For wildfire management at the wildland-urban interface, teams face challenges in capturing vegetation growth over time after a fuel reduction program and connecting seasonal changes to the vegetation distribution across the treated area. Current approaches rely on triangle meshes or point clouds generated from photogrammetry or LiDAR surveys on drones or hiked traverses. However, the difficulties in optimizing these meshes lead to large triangles that inadequately approximate the bulk vegetation shape, and the point cloud data is often too sparse for local plant-scale understanding. To address this gap, we extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals. We develop a new method to match the 3DGS reconstruction of these scenes across months, associating plant growth across seasons interactively in 3D. To achieve the centimeter-level matching, we adapt the Umeyama algorithm and the iterative closest point algorithm from point cloud maps to the 3DGS scene, leveraging the probabilistic interpretation of the 3D Gaussian data structure and robustly handling visual and geometric changes associated with vegetation phenology over time. We have applied our method to recent pile burns at Stanford’s Jasper Ridge ’Ootchamin ’Ooyakma Biological Preserve at monthly intervals. We demonstrate differences in ecological response where some piles featured the unexpected return of a rare and threatened bushmallow, and others remained more barren. This pile burn microcosm implicates the need for plant-level 3D spatiotemporal models to understand ecosystem recovery to fire mitigation practices. Please visit the project page for the spatiotemporal alignment video and more information: https://danineamati.github.io/burn-ecorecovery.github.io/

Why it matches plant phenotyping methods植物の3D画像再構成と時系列位置合わせを開発し、植物の個体・葉・草丈・成長を抽出することが中心であるため、植物フェノタイピング手法として含める。

abstractwe extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals.

Rapeseed seedling counting and geospatial localization system integrating visual tracking and real-time kinematic positioning ↗

Accurate estimation of rapeseed emergence requires reliable counting and spatially precise localization under field conditions. The video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing. Seedling centers are extracted frame by frame, clustered along the row direction with an adaptively estimated neighborhood radius and centroid-based inter-frame matching, then tracked and counted within a drifting spatiotemporal window. A calibrated projection chain links image coordinates to world coordinates by fusing visual trajectories with RTK reference points, thereby generating centimeter-level geospatial identities. Field experiments conducted on 12 videos covering 120 rapeseed varieties at the East Anhui Experimental Station of Anhui Agricultural University demonstrate strong performance: the detector achieves an AP of 93.6 % with a processing speed of 110 FPS; HAD-DBSCAN reaches 0.967 clustering accuracy while preserving row integrity under uneven density and delayed emergence; the tracking module attains a tracking accuracy (Pₜᵣ) of 92.5 %, a tracking precision (Pₘₜ) of 93.1 %, an ID switch rate (WID) of 7.4 %, and a counting precision (Pc) of 92.8 %; Geolocation yields a mean error of 2.84 cm with quasi-normal residuals centered near zero. These results establish a unified framework for efficient seedling counting and multi-temporal plant-level monitoring, enabling growth analysis to support high-throughput phenotyping.

Why it matches plant phenotyping methods圃場画像から菜種幼苗の検出・追跡・計数・高精度位置推定を行う方法を開発し、性能検証しており、植物表現型取得が研究の中心である。

abstractThe video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing.

Rapeseed seedling counting and geospatial localization system integrating visual tracking and real-time kinematic positioning ↗

Accurate estimation of rapeseed emergence requires reliable counting and spatially precise localization under field conditions. The video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing. Seedling centers are extracted frame by frame, clustered along the row direction with an adaptively estimated neighborhood radius and centroid-based inter-frame matching, then tracked and counted within a drifting spatiotemporal window. A calibrated projection chain links image coordinates to world coordinates by fusing visual trajectories with RTK reference points, thereby generating centimeter-level geospatial identities. Field experiments conducted on 12 videos covering 120 rapeseed varieties at the East Anhui Experimental Station of Anhui Agricultural University demonstrate strong performance: the detector achieves an AP of 93.6 % with a processing speed of 110 FPS ; HAD-DBSCAN reaches 0.967 clustering accuracy while preserving row integrity under uneven density and delayed emergence; the tracking module attains a tracking accuracy ( P tr ) of 92.5 %, a tracking precision ( P mt ) of 93.1 %, an ID switch rate ( W ID ) of 7.4 %, and a counting precision ( P c ) of 92.8 %; Geolocation yields a mean error of 2.84 cm with quasi-normal residuals centered near zero. These results establish a unified framework for efficient seedling counting and multi-temporal plant-level monitoring, enabling growth analysis to support high-throughput phenotyping.

Why it matches plant phenotyping methods画像検出・追跡・クラスタリング・RTK測位を統合し、圃場での rapeseed 苗の計数と個体位置推定を技術的に開発・検証しており、植物表現型取得が中心である。

abstractThe video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing.

Dense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection ↗

Accurately estimating the number of cotton bolls is vital for plant phenotyping, offering essential insights for both breeders and growers. This trait offers valuable phenotypic information on plant productivity and supports crop management decisions to optimize yield and profitability for growers. Manual counting of bolls in the field, however, is impractical because it is labor-intensive and time-consuming. This study presented a video-based cotton boll counting approach that integrated a transformer-based detector (RT-DETR) with multi-object tracking techniques. To prevent double-counting bolls across frames, two motion estimation methods, FlowFormer and TAPIR were explored to predict the movement of bolls between adjacent frames and a two-stage association process combining Intersection over Union (IoU) and Euclidean distances was developed to track bolls across time. To further enhance counting accuracy, a virtual counting line was introduced to reduce ID switch errors. Experimental results demonstrated the effectiveness of the RT-DETR model, achieving an mAP0.5 exceeding 0.93 for dense boll detection. Furthermore, both FlowFormer and TAPIR can be used for tracking cotton bolls in the videos while the tracking performance of the FlowFormer-based method was slightly higher than that of the TAPIR-based method with an MOTA of 73.36 % and an IDF1 of 79.89 %. The tracking approach integrating RT-DETR and FlowFormer exhibited a relatively strong correlation between the predicted and the ground-truth boll number with an R² of 0.60 and an MAPE of 14.34 % on multi-plant plots. In single-plant plots, the approach achieved a high correlation with an R² of 0.97 and a MAPE of 10.33%. These findings indicated the potential of the proposed approach as an effective, automated tool to support breeding programs and yield assessments in cotton production. Both the code and dataset can be accessed at: https://github.com/UGA-BSAIL/Dense_cotton_boll_counting.

Why it matches plant phenotyping methods綿花のボール数という植物生産形質を、動画検出・追跡とロボット収集で自動推定する手法の開発・評価が研究の中心であり、mAP、MOTA、IDF1、R²、MAPEによる技術検証も行っている。

abstractThis study presented a video-based cotton boll counting approach that integrated a transformer-based detector (RT-DETR) with multi-object tracking techniques.

Dense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection ↗

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

Why it matches plant phenotyping methods綿花のボール数という植物器官の形質を、トランスフォーマー追跡とカスタム表現型ロボットで取得・推定する手法が題名で明示されており、方法開発・プラットフォーム研究が中心と判断できる。

titleDense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection

SoybeanInsGS: A high-precision, data-efficient point cloud instance segmentation pipeline for mature soybean plants via cross-view instance tracking and instance-aware 3DGS ↗

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

Why it matches plant phenotyping methods成熟ダイズ個体を対象とする点群インスタンスセグメンテーション・パイプラインの開発であり、植物画像から個体を分離・再構成する手法が中心です。

titleSoybeanInsGS: A high-precision, data-efficient point cloud instance segmentation pipeline for mature soybean plants via cross-view instance tracking and instance-aware 3DGS

Computer Vision-Based Leaf Growth Monitoring System of Aeroponic-Grown Potato Plant ↗

Leaf area is a key indicator of plant health and development. However, manual measurement is time-consuming and labor-intensive, especially when monitoring aeroponic-grown potato plants with multiple leaves over extended periods. This study applied a computer vision-based system to automate leaf growth monitoring using the YOLO (You Only Look Once) v8 framework. A dataset was collected from a controlled aeroponic system: 25 images of young leaves (4,869 individual leaf segments) and 35 of mature leaves (12,368 segments). Based on evaluation of various YOLOv8 model configurations, the best model achieved a mask mAP@50 of 0.396 and 0.250 for young leaves and mature leaves, respectively. The challenge to track the mature canopy was due to severe leaf occlusion and self-similarity in dense foliage. Despite the challenge, this study demonstrates proof of concept for tracking early leaf growth and highlights the significant computer vision challenges posed by dense, mature canopies in aeroponic systems.

Why it matches plant phenotyping methods植物の葉面積・葉成長をコンピュータビジョンで自動追跡する手法の開発と評価が中心であり、植物表現型の取得方法を直接扱っている。

abstractThis study applied a computer vision-based system to automate leaf growth monitoring using the YOLO (You Only Look Once) v8 framework.

ST-DETrack: Identity-Preserving Branch Tracking in Entangled Plant Canopies via Dual Spatiotemporal Evidence ↗

Automated extraction of individual plant branches from time-series imagery is essential for high-throughput phenotyping, yet it remains computationally challenging due to non-rigid growth dynamics and severe identity fragmentation within entangled canopies. To overcome these stage-dependent ambiguities, we propose ST-DETrack, a spatiotemporal-fusion dual-decoder network designed to preserve branch identity from budding to flowering. Our architecture integrates a spatial decoder, which leverages geometric priors such as position and angle for early-stage tracking, with a temporal decoder that exploits motion consistency to resolve late-stage occlusions. Crucially, an adaptive gating mechanism dynamically shifts reliance between these spatial and temporal cues, while a biological constraint based on negative gravitropism mitigates vertical growth ambiguities. Validated on a Brassica napus dataset, ST-DETrack achieves a Branch Matching Accuracy (BMA) of 93.6%, significantly outperforming spatial and temporal baselines by 28.9 and 3.3 percentage points, respectively. These results demonstrate the method's robustness in maintaining long-term identity consistency amidst complex, dynamic plant architectures.

Why it matches plant phenotyping methods植物画像から個体枝を追跡・抽出する手法を開発し、アブラナ dataset で性能検証しているため、植物表現型取得の中心的研究である。

abstractAutomated extraction of individual plant branches from time-series imagery is essential for high-throughput phenotyping

AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas ↗

Timely detection of crop diseases in large, heterogeneous agricultural fields is difficult, as aerial imagery is often corrupted by illumination, weather, and crop-stage variations. This paper introduces AgroVisionNet, an AI-powered drone and computer vision approach that synthesises high-resolution drone imagery with in-field IoT/environmental sensor data to enhance early disease detection. The core of the proposed model is a hybrid CNN-Transformer backbone to extract spatial and contextual data from drone images, and an adaptive fusion layer to fuse time-aligned sensor readings and to make a decision, using visual and environmental evidence. Particularly, a multimodal drone–sensor dataset is collected across multiple crops and field conditions. Beyond widely used deep models for plant/crop disease identification, such as VGG16, ResNet50, Inception V3, and DenseNet121, experiments are conducted using the same training and evaluation framework. It is shown that AgroVisionNet achieves higher classification accuracy and F1-score, while inference remains feasible on an NVIDIA Jetson Nano using TensorFlow Lite. Moreover, by generating Grad-CAM plots, the study demonstrates that the proposed approach identifies disease-affected areas and, in this sense, provides interpretable information required by agronomists. These outcomes suggest that AI-based crop health tracking can be robust and field-ready by integrating drone imagery, sensor fusion, and edge computing.

Why it matches plant phenotyping methods植物の病害状態をドローン画像とセンサーから推定する手法を開発し、データセット収集、比較評価、エッジ実装、可視化まで行っており、病害フェノタイピング手法が中心である。

abstractThis paper introduces AgroVisionNet, an AI-powered drone and computer vision approach that synthesises high-resolution drone imagery with in-field IoT/environmental sensor data to enhance early disease detection.

Tracking Myrosinase Regulation across Multiscale Interactions with Fluorescent Glucosinolates. ↗

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

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

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

Chlamydomonas Cellular Phenotypes Enable in vivo Validation of Computationally Designed Therapeutic ADA1 Variants ↗

Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.

Why it matches plant phenotyping methodsChlamydomonasの運動性・代謝・デンプン蓄積を対象に、ハイスループット追跡、ラマン分光、染色による定量的フェノタイピング手法を確立し、治療タンパク質評価のプラットフォームとして検証しているため。

abstractWe established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function.

Chlamydomonas Cellular Phenotypes Enable in vivo Validation of Computationally Designed Therapeutic ADA1 Variants ↗

Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.

Why it matches plant phenotyping methodsChlamydomonasの細胞表現型を対象に、ハイスループット運動追跡、Raman分光、染色による定量的・再現可能な表現型評価系を構築し、ADA1変異体スクリーニングに適用しているため、フェノタイピング手法が中心である。

abstractWe established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function.

Epidermal Cell Dynamics Regulates Rice Lamina Joint Morphogenesis and Leaf Angle Formation through OsZHD1 and OsZHD2 Regulation. ↗

The lamina joint is a critical determinant of leaf angle and crop architecture. While epidermal cells play a fundamental role in organ morphogenesis, influencing the overall shape and function of plants, their impact on lamina joint morphology has been largely overlooked. A live-imaging system for the rice lamina joint epidermis is established in this study, enabling precise tracking of cellular dynamics during leaf angle formation. It is found that asymmetric elongation between the lateral and medial edges, determined by spatial differences in the longitudinal elongation and number of epidermal cells, is a key factor in leaf angle formation. Mutations in the homeobox genes OsZHD1 and OsZHD2 disrupt the growth patterns of lamina joint epidermal cells, resulting in a decreased leaf angle. Epidermis-specific restoration of OsZHD1 expression rescues the reduced leaf angle phenotype of oszhd1 oszhd2, confirming the pivotal role of epidermal development in lamina joint morphogenesis. Transcriptomic analysis indicates that OsZHD1 and OsZHD2 regulate auxin activity, which modulates leaf angle by restricting lamina joint epidermal growth. This study underscores the significance of epidermal cells in shaping the lamina joint and elucidates the critical role of OsZHD1 and OsZHD2 in regulating epidermal cell behavior and leaf angle formation.

Why it matches plant phenotyping methodsイネ葉舌関節表皮の細胞動態を追跡するライブイメージング系を確立し、葉角形成に関わる形態・成長を定量的に解析しており、表現型取得法が研究の中核に含まれる。

abstractA live-imaging system for the rice lamina joint epidermis is established in this study, enabling precise tracking of cellular dynamics during leaf angle formation.

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.

High-throughput monitoring of root diameter reveals the temporal dynamics of root decomposition. ↗

Background and aims Increasing C storage in cultivated soils requires a better understanding of C dynamics, particularly at depth, where root litter decomposition dynamics is expected to be slower than in ploughed layers. Methods We assessed the effect of barley root diameter on root decomposition in situ using a non-invasive method at different depths. Temporal decreases in root diameter and length were measured using images acquired by optical scanners buried at depths of 20, 50 and 90 cm from seeding and for 1.5 years. A parallel root litterbag experiment was performed to measure root mass loss. Results Root decomposition was observed on the scanned images before the flowering stage, with up to 85 % of the maximum root volume achieved being lost at harvest. Thinner roots ( Conclusions Optical scanner-based image analysis complements litterbags by enabling individual root tracking and in situ decomposition assessment without root manipulation. This method offers the opportunity to measure root decomposition at various soil depths over long periods, and could improve the estimation of root-derived soil C inputs.

Why it matches plant phenotyping methods埋設光学スキャナーと画像解析による根径・根長・根体積の非侵襲的経時測定が研究の中心で、根の分解を個別追跡する再利用可能な植物表現型取得法を実証している。

abstractTemporal decreases in root diameter and length were measured using images acquired by optical scanners buried at depths of 20, 50 and 90 cm from seeding and for 1.5 years.

Real-time and non-invasive monitoring of plant signaling by means of optical coherence tomography ↗

This work demonstrates the use of optical coherence tomography (OCT) for studying a plant’s long-range signaling in real time, in vivo , and non-invasively. This feat is achieved using OCT as a novel technique to visualize minute cellular displacements and deformations within the plant’s leaves. The use of bespoke registration algorithms enables tracking displacements with a precision greater than 0.1 μm. This measurement precision is one order of magnitude better than the typical ~1-μm optical resolution of OCT images. In the present work, OCT is used to analyze the time evolution of deformations incurred by wounding. The use of OCT enabled to 1) visualize, in real time, the propagation and evolution of the morphological changes associated with slow wave potentials (onset, peak, and recovery); 2) compute propagation speeds (~0.07 cm s −1 ); and 3) distinguish the type of deformation incurred (transient bending of the leaf due to changes in turgor cell pressure). This proof-of-concept study thus exemplifies the potential of OCT as a convenient and complementary tool to study the plant’s response mechanisms in vivo and in real time.

Why it matches plant phenotyping methodsOCT画像と登録アルゴリズムを用いて葉内の微小変位・形態変形を非侵襲的に取得・定量する手法が研究の中心であり、植物表現型の測定法として明確に該当する。

abstractThis work demonstrates the use of optical coherence tomography (OCT) for studying a plant’s long-range signaling in real time, in vivo , and non-invasively.

Droplet-based microfluidics platform for investigation of protoplast development of three exemplary plant species ↗

Abstract Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum , Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L⁻¹) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.

Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を高解像度で追跡・定量するドロplet型マイクロ流体プラットフォームが研究の中心であり、植物状態の取得・解析手法を開発・評価している。

abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.

Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction ↗

Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (gmax) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture.

Why it matches plant phenotyping methods深層学習によるインスタンスセグメンテーション、3D再構成、色解析を統合し、トマト果実のサイズと成熟度を推定するフェノタイピング手法の開発・評価が中心である。

abstractThis study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings.

Live-Cell Monitoring of Piecemeal Chloroplast Autophagy. ↗

When plants undergo senescence or experience carbon starvation, leaf cells degrade proteins in the chloroplasts on a massive scale via autophagy, an evolutionarily conserved process in which intracellular components are transported to the vacuole for degradation to facilitate nutrient recycling. Nonetheless, how portions of chloroplasts are released from the main chloroplast body and mobilized to the vacuole remains unclear. Here, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes. This protocol enabled us to track changes in chloroplast morphology during chloroplast-targeted autophagy on a timescale of seconds, and it could be adapted to monitor the dynamics of other intracellular processes in plant leaves. Key features • This protocol enables real-time monitoring of chloroplast morphology in living Arabidopsis leaves. • The method is based on confocal microscopy of transgenic plants that express fluorescent protein markers for specific organelles or suborganellar compartments. • We used this protocol to monitor the piecemeal autophagic degradation of chloroplasts, but it could also be extended to other intracellular phenomena.

Why it matches plant phenotyping methods生きた植物葉の葉緑体形態を共焦点画像でリアルタイム取得・追跡するプロトコルが中心であり、植物の形態状態を測定するフェノタイピング手法に該当する。

abstractHere, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes.

Tracking plant growth using image sequence analysis ↗

Automated plant phenotyping can help to monitor the growth process of crops, eliminating the high costs associated with traditional manual approaches. Using low-cost devices (e.g., digital cameras), RGB images can be captured under field or greenhouse conditions to track various phenotypes. In this paper, we focused on a particular task – tracking plant growth by identifying and monitoring plant nodes in greenhouse-grown crops. We used a setup where a digital camera captured images at one-hour intervals, with object detection algorithms employed to facilitate rapid and cost-effective tracking of nodes. The main challenge addressed in this paper involved tracking nodes that were hidden temporarily caused by diurnal leaf movements– leaves obscure some nodes at different times throughout the day. Because a node may be hidden for a few hours but visible at other times during the day, one can predict its location while it is hidden. We proposed two approaches, clustering and linear interpolation, for estimating hidden node locations. We collected a set of greenhouse datasets for different crops and conducted empirical comparisons of our methods. Results showed that our approach predicted the node location with an average error of less than 4 cm. • Automated plant phenotyping reduces the cost of traditional manual monitoring. • Object detection handles challenges caused by leaf movement and occlusion. • Hidden node locations are predicted using clustering and linear interpolation. The proposed methods achieve an average prediction error of less than 4 centimeters.

Why it matches plant phenotyping methods植物ノードを画像から検出・追跡し、遮蔽時の位置を推定する手法の開発と比較検証が中心であり、再利用可能な植物表現型取得ワークフローに該当する。

abstractAutomated plant phenotyping can help to monitor the growth process of crops

Advancing wheat crop analysis: A survey of deep learning approaches using hyperspectral imaging ↗

As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following GitHub Repository .

Why it matches plant phenotyping methods小麦のハイパースペクトル画像と深層学習による植物形質・状態推定を扱う方法論レビューであり、データセット、手法、疾病検出、収量推定を中心に整理している。

abstractThis review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation.

Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm. ↗

Arabidopsis is a widely used model plant to study physiology and development. Live imaging is an important technique to visualize and quantify processes in plant growth and cell division, where accurate cell tracking is essential. The commonly used software TrackMate adopts a tracking-by-detection approach, applying Laplacian of Gaussian (LoG) for blob detection and a Linear Assignment Problem (LAP) tracker for tracking. However, its performance declines when cells are densely arranged. To overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA) that incorporates knowledge of Arabidopsis root cellular patterns and spatial relationships among volumes. Our method follows a coarse-to-fine strategy: first performing relatively simple line-level tracking of nuclei, then refining associations based on the linear arrangement of cell files and their spatial relationships. We evaluated the method on long-term live imaging datasets of Arabidopsis root tips, and with minor manual correction, it achieved accurate nuclear tracking. To the best of our knowledge, this represents the first successful attempt to address a long-standing problem in time-lapse microscopy of the root meristem by providing an accurate tracking method for Arabidopsis root nuclei.

Why it matches plant phenotyping methods植物の3Dタイムラプス画像から根の細胞核を追跡する計算手法を開発・評価しており、植物の成長・細胞分裂の定量化を支える画像ベースの表現型取得法が中心です。

abstractTo overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA)

Label-free structural imaging of plant roots and microbes using third-harmonic generation microscopy ↗

Abstract Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.

Why it matches plant phenotyping methodsTHG/3PFによる根の構造を高時空間分解能でラベルフリー取得するイメージング手法を開発・実証しており、植物表現型取得が中心である。

abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution

Design and Development of a Neural Network-Based End-Effector for Disease Detection in Plants with a 7 DOF Robot Integration ↗

Agriculture and robotics have managed to integrate, using artificial intelligence in plant tracking and pest detection, as well as robotic arms and remote-controlled robots for harvesting, significantly reducing the human workload. Robots are typically designed to perform specific tasks, making their adaptability very difficult to integrate into agriculture due to the constant changes of plants, such as plant growth. As a result of the general and current functions in agro-robotics, continuous monitoring with deep learning is aimed at knowing the condition of the plants and that the mobility of the robots does not impede the plant’s growth while being monitored for it to adapt to the monitoring environment. Deep learning and a robotic arm are used for real-time plant monitoring. Image database will be used for training, accuracy, recall and F1 indicators are used to evaluate the network. The robot has kinematics that allows it to change its size to monitor plant health and growth. Early detection of plant leaf anomalies and diseases using a deep learning system. Integrating the various systems, provided an intelligent and effective solution for detecting anomalies and diseases in the leaves of plants subjected to intelligent robotic monitoring.

Why it matches plant phenotyping methods植物葉の異常・病害を画像から検出する深層学習システムと、監視用ロボットアームの統合開発が中心であり、植物の健康状態・病害という表現型を取得する方法論的研究である。

abstractDeep learning and a robotic arm are used for real-time plant monitoring.

Monitoring the Uptake and Localization of Organic Compounds in Plant Tissues Using a Hydroponic 14C-radiolabelling Assay and Phosphor Imaging. ↗

Phytoremediation, the use of plants to mitigate environmental contaminants, offers a sustainable and cost-effective approach to cleaning contaminated sites. Developing methods that aid in elucidating the mechanisms behind plant uptake and metabolism of pollutants is crucial for improving phytoremediation practices. This article describes a method to assess the uptake and transformation of organic contaminants by plants using radiolabeled compounds. 14 C-labelled organic compounds, such as model 14 C-naphthenic acids, are used to trace their absorption, translocation, localization, and metabolism in plant tissues. We have previously used this method with multiple plant species, including Elymus trachycaulus and Salix interior. These observations are corroborated here with the model plant, Arabidopsis thaliana, grown hydroponically in modified Hoagland solutions. Radiolabel uptake was monitored via liquid scintillation counting and phosphor-imaging, which allows for visualization and quantification of radiolabeled compounds within plant tissues. This method details the preparation of plant materials, the use of radiolabeled compounds, and the process of analyzing the distribution and fate of contaminants within plants. The method also includes strategies for assessing compound exudation and allows for the evaluation of both plant uptake and translocation of environmental contaminants. This approach provides insight into plant-mediated remediation processes and can be applied to the study of a wide range of environmental contaminants and plant species.

Why it matches plant phenotyping methods植物組織における汚染物質の吸収・移行・局在を定量・可視化する放射標識およびホスファーイメージング法が中心で、植物の生理状態を測定する再利用可能な手法を詳述している。

abstractThis article describes a method to assess the uptake and transformation of organic contaminants by plants using radiolabeled compounds.

Weakly labelled spatial-temporal sweet pepper data: Enabling higher quality detection, segmentation, and tracking. ↗

Accurate monitoring of crop phenotypic traits is essential for efficient farm management and automation in agriculture. Multi-object tracking (MOT) and video instance segmentation (VIS) offer promising approaches to enhance agricultural robotic-vision systems, yet a major limitation is the scarcity of high-quality spatial-temporal datasets. In this paper, we introduce BUP-ST20, a novel weakly labelled spatial-temporal dataset for sweet pepper tracking and segmentation captured on a robotic platform. Our dataset is generated by leveraging still image annotations and utilizing a neural radiance field approach (PAg-NeRF) to automatically obtain consistent object semantics and identities across video sequences. BUP-ST20 contains 16,240 images from 275 sequences, with weak labels for training and validation, and human-annotated ground truth for evaluation. We describe how this pseudo-labelling approach can be adapted to any robotic platform with the required inputs, greatly reducing the annotation requirements for dataset creation, with a focus on agriculture and horticulture. Utilizing BUP-ST20, we evaluate state-of-the-art MOT approaches and propose two novel tracklet matching criteria, enhancing robustness in frame-skipped scenarios and low frame rate cameras. When we decrease the frame rate to approximately 1 frame per second our offline MOT based matching criteria is able to improve performance by an absolute value of 19.63, outlining its validity as a tracklet aggregation technique in this scenario. Our experiments demonstrate the effectiveness of the dataset in benchmarking MOT and VIS techniques within the agricultural domain. This also allows us to highlight challenges such as occlusion, shape variations, and weak-labelling limitations. BUP-ST20 serves as a valuable resource for further advancements in robotic crop monitoring and agricultural automation, while demonstrating the ability to create future weakly labelled datasets using robotic platforms.

Why it matches plant phenotyping methods植物の追跡・セグメンテーションを対象とする時空間データセットを開発し、ロボット撮像、弱ラベル生成、ベンチマークまで扱っており、植物フェノタイピング手法が中心である。

abstractOur experiments demonstrate the effectiveness of the dataset in benchmarking MOT and VIS techniques within the agricultural domain.

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

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

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

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

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.

Droplet-based microfluidics platform for investigation of protoplast development of three exemplary plant species ↗

Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum, Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L −1 ) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.

Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を単一細胞レベルで追跡・定量するマイクロ流体プラットフォームが研究の中心であり、植物状態の取得手法として適格。

abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.

Au Bipyramids as NIR-II Contrast Agents for In Vivo Plant Imaging. ↗

In vivo plant imaging is crucial for understanding plant biology and the influence of external factors on plant health. While exogenous contrast agents are widely used in animal bioimaging to enhance contrast, track flows, and provide molecular specificity, their application in plant tissue remains challenging and underexplored. Herein, we highlight the shortcomings of contrast agents in optical coherence tomography of plant tissue while demonstrating the successful use of Au bipyramids (AuBPs) as effective NIR-II contrast agents in photoacoustic imaging, enabling spatiotemporal flow tracking in live Buttercrunch lettuce over 5 days. Furthermore, rigorous plant health studies showed no adverse effects of the AuBPs on the physiological properties of Buttercrunch lettuce 7 days postinfiltration. The use of AuBPs as contrast agents in plant imaging, combined with the versatility of their surface chemistry, opens the possibilities for studies with molecular specificity.

Why it matches plant phenotyping methods植物体内の流れを可視化・追跡する光音響イメージング用造影剤を開発・実証しており、植物表現型状態の取得法が研究の中心である。

abstractdemonstrating the successful use of Au bipyramids (AuBPs) as effective NIR-II contrast agents in photoacoustic imaging, enabling spatiotemporal flow tracking in live Buttercrunch lettuce over 5 days.

Live Leaf-Section Imaging for Visualizing Intracellular Chloroplast Movement and Analyzing Cell-Cell Interactions. ↗

In response to environmental changes, chloroplasts, the cellular organelles responsible for photosynthesis, undergo intracellular repositioning, a phenomenon known as chloroplast movement. Observing chloroplast movement within leaf tissues remains technically challenging in leaves consisting of multiple cell layers, where light scattering and absorption hinder deep tissue visualization. This limitation has been particularly problematic when analyzing chloroplast movement in the mesophyll cells of C 4 plants, which possess two distinct types of concentrically arranged photosynthetic cells. In response to stress stimuli, mesophyll chloroplasts aggregate toward the inner bundle sheath cells. However, conventional methods have not been able to observe these chloroplast dynamics over time in living cells, making it difficult to assess the influence of adjacent bundle sheath cells on this movement. Here, we present a protocol for live leaf section imaging that enables long-term and detailed observation of chloroplast movement in internal leaf tissues without chemical fixation. In this method, a leaf blade section prepared either using a vibratome or by hand was placed in a groove made of a silicone rubber sheet attached to a glass slide for microscopic observation. This technique allows for the quantitative tracking of chloroplast movement relative to the surrounding cells. In addition, by adjusting the sectioning angle and thickness of the unfixed leaf sections, it is possible to selectively inactivate specific cell types based on their size and shape differences. This protocol enables the investigation of the intercellular interactions involved in chloroplast dynamics in leaf tissues. Key features • Thin leaf sections prepared while still alive enable prolonged microscopic observation of chloroplast movement within the leaf tissue. • Selective cell inactivation can be achieved by adjusting the slice thickness and angle. • This method is applicable to a wide range of plant species.

Why it matches plant phenotyping methods生葉切片のライブイメージングにより、葉内部の葉緑体運動を長時間観察・定量追跡する手法を開発しており、植物表現型の取得が研究の中心である。

abstractHere, we present a protocol for live leaf section imaging that enables long-term and detailed observation of chloroplast movement in internal leaf tissues without chemical fixation.

Bioluminescent sentinel plants enable autonomous diagnostics of viral infections ↗

Plants engineered with synthetic genetic programs can transform how we monitor and manage the extension of crop pests and diseases. Here, we establish a bioluminescent platform in Nicotiana benthamiana for autonomous viral sensing based on the fungal bioluminescence pathway (FBP). We first demonstrate that recombinant viruses can deliver missing pathway components, enabling spatially resolved tracking of infection dynamics. Leveraging this starting point, we developed a dual-output sentinel circuit that uses a protease-responsive Bioluminescence Resonance Energy Transfer (BRET) module to report infection through a virus-triggered spectral shift in luminescence. In the absence of infection, plants emit a stable yellow glow indicating system integrity. Upon infection with potyviruses, cleavage of the BRET fusion by the virus-encoded NIa-Pro protease activates a distinct colour change detectable with low-cost imaging. This modular design is compatible with other pathogens carrying specific proteases and supports future multiplexing strategies. Our results highlight the potential of synthetic sentinel gene circuits as autonomous biosensors for precision crop protection.

Why it matches plant phenotyping methods植物のウイルス感染状態を発光変化として検出するセンチネル回路と低コスト画像診断プラットフォームの開発が中心であり、病害状態のフェノタイピング手法に該当する。

abstractHere, we establish a bioluminescent platform in Nicotiana benthamiana for autonomous viral sensing based on the fungal bioluminescence pathway (FBP).

Spatio-Temporal Consistent Semantic Mapping for Robotics Fruit Growth Monitoring ↗

Automatic fruit growth monitoring plays a vital role in advancing precision agriculture. Tracking the evolution of fruits over time is essential to monitor their development and optimize production. The ability to recognize fruits over periods of time, even with drastic scene changes, is a required capability of agricultural robots. This paper presents a system that allows long-term fruit tracking in 3D data. It generates instance-segmented 3D representations of plants at various growth stages over time, utilizing only consumer-grade RGB-D cameras installed on a mobile robot. Our approach first performs instance segmentation on each image in a sequence. Then, by exploiting geometric information and depth maps, we track the same instances throughout the sequence. We produce a 3D point cloud containing instances, exploiting odometry information and 3D semantic mapping. Once our robot performs a new recording at a different plant growth stage, it associates each fruit with the previously built 3D cloud and update the model. We validate the system in a real-world glasshouse environment in Bonn, Germany. Experimental results demonstrate that our system outperforms existing baselines even though it relies only on annotated images and operates at frame-rate, allowing the deployment on a real robot.

Why it matches plant phenotyping methodsRGB-D画像と3Dセマンティックマッピングによる果実の長期追跡・成長モニタリング手法が研究の中心であり、植物器官の成長状態を抽出するため、植物フェノタイピング手法として適格です。

abstractThis paper presents a system that allows long-term fruit tracking in 3D data.

LiDAR and hyperspectral-based structured population models show future forest fire frequency may compromise forest resilience ↗

AbstractForest disturbances are accelerating biodiversity loss and altering tree productivity worldwide. Post-disturbance recovery time is critical for identifying vulnerable areas and targeting conservation but varies with environmental conditions. Monitoring recovery at scale requires tracking tree dynamics, yet traditional ground-based approaches are resource-intensive. We present a pipeline to parameterise integral projection models (IPMs) using LiDAR and hyperspectral data to assess post-fire recovery across large, forested areas. Focusing on the fire-adapted Picea mariana, we model passage times to reproductive heights and life expectancy under different fire regimes as indicators of recovery. To do this, we combined hyperspectral-derived species maps and LiDAR-based crown heights to track individual tree survival and growth at the Caribou-Poker Creek Research Watershed (BONA) from 2017-2023. We incorporated fire history, aspect, slope, elevation, and surrounding canopy height into our models and found partial support for their expected effects on survival and growth. Once accounting for topography and competition, we estimated passage times to reproductive maturity (11-22 years). Life expectancy in the absence of fire is shortest on North-facing slopes with recent fire (579 years). Sensitivity analyses highlight fire history and aspect as key modulators of population resilience, with elevation exerting strong influence on life expectancy across all conditions. Our results demonstrate that remotely sensed IPMs can effectively quantify forest recovery at scale, revealing that in some contexts, stands of P. mariana may not recover between fire disturbances. We discuss the implications of these findings for resilience-based forest management and highlight both the challenges and opportunities of using LiDAR and hyperspectral data to build demographic models for forecasting forest dynamics.

Why it matches plant phenotyping methodsLiDARとハイパースペクトルを用いて個体樹木の樹冠高、生存、成長を追跡し、森林回復を定量化するリモートセンシング・モデル化パイプラインが研究の中心であるため。

abstractWe present a pipeline to parameterise integral projection models (IPMs) using LiDAR and hyperspectral data to assess post-fire recovery across large, forested areas.

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis ↗

Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola ( Brassica napus L., Brassica rapa L. , Brassica juncea L. ) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties. Consequently, scoring blackleg disease severity of infected plants is a key metric for identifying and selecting resistant varieties. Traditionally, blackleg severity is scored by expert raters who evaluate disease in stem cross sections using established rating scales and reference images; however, human raters are expensive and inconsistent in their scoring. Here, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants. We find that expert ratings are largely inconsistent across raters and across years for the same rater, creating substantial noise in susceptibility ratings. Meanwhile, our trained machine learning model performs more consistently than the median rater while maintaining a similar heritability as expert raters for the blackleg susceptibility trait. This model can be used to standardize blackleg susceptibility scoring across locations and years to improve canola breeding outcomes across affected regions. Core Ideas Canola Blackleg is a fungal disease affecting yield of canola, and accurate scoring of Blackleg severity is important for tracking disease and breeding for resistant varieties. The standard practice of utilizing expert raters is expensive, and scores assigned are inconsistent across raters and years. Our deep learning model for assigning blackleg severity scores is more accurate than the median expert rater, opening the door for improved breeding of new resistant varieties.

Why it matches plant phenotyping methods感染植物の画像から黒脚病の重症度という植物病害表現型を推定する深層学習手法を開発し、専門家評価との一貫性・遺伝率を検証しており、表現型取得・評価法が研究の中心である。

abstractHere, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants.

Tracking savanna vegetation structure in South Africa by extension of GEDI canopy metrics with Landsat, Sentinel-2, and PALSAR ↗

1Adaptive management of savanna ecosystems requires frequent monitoring of woody vegetation structure, and although vegetation structure and changes may be captured with repeat airborne lidar, it is spatially and temporally limited across African savannas. As an alternative, this study evaluates the extension of spaceborne waveform lidar canopy metrics (RH98, Cover, Foliage Height Diversity) from the Global Ecosystem Dynamics Investigation (GEDI) across the Greater Kruger region in South Africa using moderate resolution optical sensors (Landsat and Harmonized Landsat Sentinel-2 [HLS]), L-band Synthetic Aperture Radar (PALSAR-1 and -2), and topographic and soil covariates. We compared the performance of 14 predictor sets incorporating different sensor combinations and temporal processing methods (LandTrendr and CCDC) in random forest models using temporal cross-validation to assess extrapolation accuracy. The most parsimonious fusion model (LandTrendr + SAR + topography/soils) achieved RMSEs of 3.04 m for RH98, 13.38% for Cover and 0.34 for FHD, which was comparable to more complex models using HLS and CCDC. All models demonstrated good temporal transferability with minimal bias but tended to overestimate low values and underestimate high values, which muted the estimated magnitude of change. Annual canopy structure maps derived from the best model captured expected spatial patterns and were used in model-based estimators to quantify changes in areas impacted by elephants, timber harvesting, fuelwood extraction, and woody encroachment. Extending GEDI metrics with moderate-resolution sensors thus offers a viable approach for large-scale savanna monitoring and detecting change in high impact areas.

Why it matches plant phenotyping methodsGEDIと光学・SARセンサーを融合して樹冠構造形質を推定し、複数モデルの性能検証と年次マッピングを行うことが中心であり、植物形質の計測手法として適格です。

abstractthis study evaluates the extension of spaceborne waveform lidar canopy metrics (RH98, Cover, Foliage Height Diversity)

( Invited ) Point-of-Care Diagnostics for Human and Plant Diseases ↗

Sensitive and rapid disease diagnostics that can be performed near the patient becomes increasingly important for more effective disease management and control. After COVID, point-of-care (POC) and at-home tests are more desired than ever before to prepare for the next pandemic. This talk will highlight our recent efforts in advancing POC diagnostic technologies for human health and the new era of digital agriculture. Progresses in three key areas will be summarized and presented: 1) transforming smartphones into next-generation optical imaging and sensing devices, 2) fundamental mechanism study of CRISPR reactions and its miniaturization for in-field nucleic acid testing, and 3) novel plant sensors for early detection of plant diseases and continuous monitoring of plant physiology. Together, these technologies could find future implementations in the broad spectrum of one health (human, plant, animal, and the environment), enhancing our capability for early disease surveillance, tracking, prevention, and treatment across species and societal barriers.

Why it matches plant phenotyping methods植物病害の早期検出と植物生理の連続モニタリングを行う新規センサー技術が講演の主要内容であり、植物状態の取得手法を扱うため対象に含める。

abstractnovel plant sensors for early detection of plant diseases and continuous monitoring of plant physiology.

Real-time monitoring system for evaluating the operational quality of rice transplanters ↗

Currently, rice transplanters are extensively employed for the mechanized cultivation of rice seedlings. However, few technologies or systems are available to monitor the operational quality parameters, i.e., the number of missing seedlings, row spacing, plant distance, etc., of rice transplanters. The performance of rice transplanters is directly linked to the growth quality of the seedlings and has a crucial effect on the final yield. Therefore, monitoring the various issues that arise during the operation of rice transplanters in a timely and accurate manner to ensure the quality of the transplanting process is particularly important. To address the above issues, this paper develops a real-time monitoring system for rice transplanters. The system architecture includes embedded devices, an image capture module, and a data upload module. A rice seedling detection model based on an enhanced YOLOv5-Lite neural network is developed, and comparative experimental results demonstrate that the proposed model achieves an mAP@0.5 of 81.9 % for rice seedling detection, which is higher than that of the original YOLOv5-Lite model. We additionally propose a RANSAC-based algorithm to detect rice seeding paths in real time, and the rice seeding path detection results are used to determine the row spacing and plant distance. Specifically, a distance mapping algorithm based on triangular transformations is developed to calculate the row spacing and plant distance in a field. We subsequently calculate the number of missing seedlings between adjacent plants on the basis of the spacing between plants in the same row. Furthermore, a rice seedling tracking and counting algorithm based on an improved ByteTrack algorithm is developed to determine the missing seedling rate, as well as the seeding quantity. We integrate the developed algorithms into a real-time monitoring system and test them at Qixing Farm. The experimental results indicate that the monitoring system achieves an accuracy of 99.2 % for seedling quantity counting and an accuracy of 90.3 % for missing rate counting, with a processing speed of 3.95 frames per second.

Why it matches plant phenotyping methodsイネ苗の検出・追跡・計数、欠株率、条間および株間距離を画像から推定するリアルタイムシステムを開発・評価しており、植物形質取得が研究の中心である。

abstractthis paper develops a real-time monitoring system for rice transplanters

(ID-ICPMB05) Running on Empty: Mitochondria Without DNA Exhibit Differential Motility and Connectivity. ↗

Plant mitochondria are in continuous motion. While providing ATP to other cellular processes, they also constantly consume ATP to move rapidly within the cell. This movement is in part related to taking up, converting and delivering metabolites and energy to and from different parts of the cell. Plant mitochondria have varying amounts of DNA, even within a single cell, from none to the full mitochondrial genome. Because mitochondrial dynamics are altered in an Arabidopsis mutant with disrupted DNA maintenance, we hypothesised that exchanging DNA templates for repair is one of the functions of their movement and interactions. Here, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana. In addition to staining mitochondrial DNA with SYBR Green, we have developed and implemented a fluorescent mitochondrial DNA binding protein that will enable future understanding of mitochondrial dynamics, genome maintenance and replication. We demonstrate that mitochondria without mtDNA have altered physical behaviour and lower immediate connectivity to the rest of the population, further supporting a link between the physical and genetic dynamics of these complex organelles.

Why it matches plant phenotyping methods植物ミトコンドリアのDNA可視化と位置追跡を行うイメージング手法を開発・実装し、運動性や接続性という細胞内状態を定量化しているため、方法開発が中心である。

abstractHere, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana.

Bioorthogonal Tracking of Spatiotemporal Lignification Dynamics in Plant Cell Walls Using Alkyne-Tagged Glycosylated Monomers ↗

Lignin, a major component of plant cell walls, plays a critical role in structural support and stress resistance. Despite its importance, the transport and deposition dynamics of the glycosylated lignin monomer during lignification in living cells remain poorly understood, hindering advances in biomass utilization. To address this challenge, alkyne-labeled glycosylated lignin precursors (pGCA ALK and CF ALK ) were synthesized by introducing propargyl groups at the ortho position of aromatic rings. These precursors were successfully incorporated into lignin polymers in flax (a herbaceous plant) and ginkgo (a gymnosperm), enabling the real-time tracking of lignification via fluorescent click chemistry. Quantitative imaging revealed that lignification initiates at cell corners and the middle lamella and then progressively extends into secondary cell walls. Distinct deposition patterns were observed: parenchyma cells exhibited continuous lignin accumulation, whereas fiber tracheids underwent rapid lignification, followed by cell death. Specialized pit structures displayed "tunnel-like" lignin deposition in longitudinal pits and unilateral patterns in transverse pits. In vitro synthesis of dehydrogenation polymers (DHP) and extraction of the cellulolytic enzyme lignin (CEL) from ginkgo confirmed the biocompatibility of labeled monomers. LC-MS analysis further demonstrated that alkynyl groups formed oxygen-containing cyclic structures without disrupting natural β-O-4 and β-5 lignin linkages. Application of this labeling method in biomass utilization indicated that lower overall fluorescence intensity correlates with more efficient lignin removal during pretreatment. These results provide new insights into the spatiotemporal dynamics of lignification and establish a bioorthogonal platform for lignin research, offering promising strategies for optimizing plant biomass in industrial applications.

Why it matches plant phenotyping methods蛍光クリック化学による生細胞内リグニン形成の時空間追跡と定量イメージング手法を開発・適用しており、植物状態の取得方法が研究の中心である。

abstractenabling the real-time tracking of lignification via fluorescent click chemistry.

PhenoVision : A framework for automating and delivering research‐ready plant phenology data from field images ↗

Abstract Plant phenology plays a fundamental role in shaping ecosystems, and global change‐induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high‐quality observations of when plants undergo seasonal transitions such as leaf‐out, flowering and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global‐scale research‐grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist—data spanning all angiosperms and drawn from across the globe—to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy on held‐out test images for flower (98.5%) and fruit presence (95%), as well as a high level of agreement with an expert annotator (98.6% for flowers and 90.4% for fruits). Key to producing research‐ready phenology data is post‐calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, for example leaf phenology, and further integration with other resources to form a global central database integrating all in situ plant phenology resources.

Why it matches plant phenotyping methods植物の開花・結実状態をフィールド画像から推定する視覚モデルを開発・検証し、校正、品質指標、研究用データ資源まで整備しており、フェノタイピング手法が中心である。

abstractto train a computer vision model (PhenoVision) to detect the presence of fruits and flowers

Orange yield estimation using object tracking and 3D reconstruction ↗

The labor-intensive nature of agriculture, particularly in tasks such as yield estimation of fruits, is a significant challenge. Yield estimation is crucial for the better management of the resources and for taking adequate measures for the transportation, storage, and export of the fruits. It also helps the farmers to estimate the total pricing of the yield. However, counting fruits directly on trees for yield estimation presents an obstacle due to their dispersed nature and often dense foliage. Therefore, we propose that reasonably accurate fruit yield estimation can be automated with a handheld camera. The dataset is curated by capturing and annotating 1451 images of orange trees. The dataset is augmented and processed in different ways to evaluate the performance of YOLOv8 for the detection of oranges. Then the Byte tracker is deployed to track oranges in consecutive video frames. Further, we have classified the fruits into two categories, ripe and unripe using MobileVit. The 2D fruits detected by YOLOv8 are projected to a 3D space for a more detailed analysis of the scene. Subsequently, the clustering algorithm is applied to the 3D projections of the detected objects to estimate per tree yield. On images, YOLOv8 nano has achieved a precision of 78.2% and recall of 69.7% on the test set. Moreover, for ripeness stage classification, MobileVit has achieved an accuracy of 97.8% and 86.7% on a test set containing 2 classes and 3 classes, respectively. Testing our proposed solution on videos shows that the algorithm is achieving good results on trees with less leaf occlusion. This paper demonstrates that preprocessing techniques can aid the detection model to achieve high detection rates. Furthermore, per tree yield of an orange orchard can be estimated by using video input. This offers an automated solution to the laborious task of fruit yield estimation in agricultural settings, that can help to optimize orange production.

Why it matches plant phenotyping methodsオレンジ果実の検出・追跡・3D再構成・熟度分類から樹体ごとの収量を推定する手法が研究の中心であり、データセット構築と性能評価も行っているため、植物表現型計測手法として採用する。

abstractwe propose that reasonably accurate fruit yield estimation can be automated with a handheld camera.

A long term time lapse microscopy technique for Arabidopsis roots. ↗

Time lapse microscopy is a transformative technique for plant cell and developmental biology. Light sheet microscopy, which manipulates the amount of light a sample is exposed to in order to minimize phototoxicity and maximize signal intensity, is an increasingly popular tool for time lapse imaging. However, many light sheet imaging systems are not designed with the unique properties of plant samples in mind. Recent advances have decreased the cost and increased the technical accessibility of light sheet microscopy, but plant samples still require special preparation to be compatible with these new systems. Here, we apply a novel light sheet microscopy system to regenerating Arabidopsis roots damaged via laser ablation. To adapt this system for Arabidopsis roots we establish a new protocol for sample mounting, as well as an automated root tip tracking system that requires no additional proprietary software. The methods presented here can be used to increase researcher access to long-term time-lapse imaging in Arabidopsis biology.

Why it matches plant phenotyping methodsArabidopsis根の長期タイムラプス撮像系を植物試料向けに適応し、試料マウント法と自動根端追跡を開発しており、表現型取得・抽出法が中心である。

abstractTo adapt this system for Arabidopsis roots we establish a new protocol for sample mounting, as well as an automated root tip tracking system that requires no additional proprietary software.

Dual-Axial Force Measurements of Non-Tethered Plants ↗

Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force-measurement systems have limited capacity to capture weak (sub-mN) forces in freely moving plant organs - such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Crucially, unlike many force-measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods such as cantilevers. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing to extract the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (such as growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with bean ( Phaseolus vulgaris ) shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not - an open question since Darwin’s first observations.

Why it matches plant phenotyping methods自由に成長する植物器官が発生する力を、非拘束・二軸で測定し、カメラ画像から3D位置と力を抽出する新規計測システムの開発・実証であり、植物の機械的状態を測る方法が中心である。

abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.

Spatiotemporal modeling of host-pathogen interactions using level-set method. ↗

Phenotyping host-pathogen interactions is crucial for understanding infectious diseases in plants. Traditionally, this process has relied on visual assessments or manual measurements, which can be subjective and labor-intensive. Recent advances in image processing and mathematical modeling enable the precise and high-throughput phenotyping of plant symptoms. Among many challenges, considering local deformations of symptoms and host tissues is difficult in plant pathology. In this study, we address this question using a level-set method. We propose an innovative approach in plant pathology that allows one to reconstruct the continuous deformation of leaf and lesion contours from daily image sequences of inoculated leaves. We consider pea stipules inoculated by the fungal pathogen Peyronellaea pinodes as an example pathosystem. After extracting lesion and stipule contours from daily visible images, we use the level-set method to track their deformations within image sequences. The visual assessment of model adequacy, along with the Jaccard Index and relative error metrics, demonstrated strong overall performance. Results showed a gradual decrease in model accuracy over time for leaf contours, while lesion contours exhibited a higher relative error on the first targeted date. These findings highlight the robustness of our method while identifying specific challenges in early lesion detection. We finish by discussing the interest in this method based on partial differential equations for the study of host-pathogen interactions, especially the development of original phenotyping methods in plant pathology.

Why it matches plant phenotyping methods植物の葉・病斑輪郭を画像から抽出し、level-set法で時系列変形を追跡する病害表現型計測法の開発・検証が中心である。

abstractWe propose an innovative approach in plant pathology that allows one to reconstruct the continuous deformation of leaf and lesion contours from daily image sequences of inoculated leaves.

Tracking Lesion Growth in the Field: Imaging and Deep Learning Reveal Components of Quantitative Resistance ↗

Summary Measuring individual components of pathogen reproduction is key to understanding mechanisms underlying rate-reducing quantitative resistance (QR). Simulation models predict that lesion expansion plays a key role in seasonal epidemics of foliar diseases, but measuring lesion growth with sufficient precision and scale to test these predictions under field conditions has remained impractical. We used deep learning-based image analysis to track 6889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars across two field seasons, enabling 27,218 precise and objective measurements of lesion growth in the field. Lesion appearance traits reflecting specific interactions between particular host and pathogen genotypes were consistently associated with lesion growth, whereas overall effects of host genotype and environment were modest. Both host cultivar and cultivar-by-environment interaction effects on lesion growth were highly significant and moderately heritable ( h 2 ≥ 0.40). After excluding a single outlier cultivar, a strong and statistically significant association between lesion growth and overall QR was found. Lesion expansion appears to be an important component of QR to STB in most—but not all—wheat cultivars, underscoring its potential as a selection target. By facilitating the dissection of individual resistance components, our approach can support more targeted, knowledge-based breeding for durable QR.

Why it matches plant phenotyping methods深層学習画像解析を中心に、圃場で個々の病斑の成長を大規模かつ定量的に測定する手法を適用しており、植物病害状態の表現型取得が研究の中核である。

abstractWe used deep learning-based image analysis to track 6889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars across two field seasons, enabling 27,218 precise and objective measurements of lesion growth in the field.

Preliminary development of a new multispectral vision-based, automated apple grading system towards in-field fruit presorting ↗

While Machine vision technology has been widely implemented for fruit quality inspection at packing line facilities, but appropriate in-orchard pre-sorting technology has yet to be developed. This study represents a novel effort to leverage advanced real-time multispectral vision coupled with artificial intelligence to develop a new, automated apple grading system that inspects size, color, and surface defects simultaneously, towards in-orchard application. The system consists of a multispectral imaging chamber on a compact screw conveyor, which acquires five-band images from singulated apples traveling and rotating on the conveyor. Online experiments are conducted on different varieties of apples in diverse quality conditions at different conveyor speeds. A deep learning-based computer vision algorithm pipeline is developed to segment and track each apple on the conveyor while assessing its quality attributes (size, color, and surface defects) from different, multiple views, and grade the fruit based on full-surface quality information into three quality categories. The system will evolve into a fully integrated machine prototype for automated, in-orchard sorting.

Why it matches plant phenotyping methodsリンゴ果実のサイズ・色・表面欠陥という器官形質を、マルチスペクトル画像と深層学習で取得・評価する自動システムの開発が中心であり、単なる農業実験の routine 測定ではない。

abstractThis study represents a novel effort to leverage advanced real-time multispectral vision coupled with artificial intelligence to develop a new, automated apple grading system that inspects size, color, and surface defects simultaneously

Carbon-phosphorous exchange rate constrains growth of arbuscular mycorrhizal fungal networks ↗

Symbiotic nutrient exchange between arbuscular mycorrhizal (AM) fungi and their host plants varies widely depending on their physical, chemical, and biological environment. Yet dissecting this context dependency remains challenging because we lack methods for tracking nutrients such as carbon (C) and phosphorus (P). Here, we developed a new approach to quantitatively estimate C and P fluxes in the AM symbiosis from comprehensive network morphology quantification, achieved by robotic imaging and machine learning based on roughly 100 million hyphal shape measurements. We found that rates of C transfer from the plant and P transfer from the fungus were, on average, related proportionally to one another. This ratio was nearly invariant across AM fungal strains despite contrasting growth phenotypes, but was strongly affected by plant host genotype. Fungal phenotype distributions were bounded by a Pareto front with a shape favoring specialization in an exploration-exploitation trade-off. This means AM fungi can be fast range expanders or fast resource extractors, but not both. Manipulating the C/P exchange rate by swapping the plant host genotype shifted this Pareto front, indicating that the exchange rate constrains possible AM fungal growth strategies. We show by mathematical modeling how AM fungal growth at fixed exchange rate leads to qualitatively different symbiotic outcomes depending on fungal traits and nutrient availability.

Why it matches plant phenotyping methodsAM菌ネットワークの形態をロボット撮像と機械学習で定量化し、C/Pフラックスを推定する手法が研究の中心であるため、植物・共生系の表現型計測手法として採用。

abstractHere, we developed a new approach to quantitatively estimate C and P fluxes in the AM symbiosis from comprehensive network morphology quantification, achieved by robotic imaging and machine learning based on roughly 100 million hyphal shape measurements.

Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse ↗

As the agricultural workforce declines and labor costs rise, robotic yield estimation has become increasingly important. While unmanned ground vehicles (UGVs) are commonly used for indoor farm monitoring, their deployment in greenhouses is often constrained by infrastructure limitations, sensor placement challenges, and operational inefficiencies. To address these issues, we develop a lightweight unmanned aerial vehicle (UAV) equipped with an RGB-D camera, a 3D LiDAR, and an IMU sensor. The UAV employs a LiDAR-inertial odometry algorithm for precise navigation in GNSS-denied environments and utilizes a 3D multi-object tracking algorithm to estimate the count and weight of cherry tomatoes. We evaluate the system using two dataset: one from a harvesting row and another from a growing row. In the harvesting-row dataset, the proposed system achieves 94.4\% counting accuracy and 87.5\% weight estimation accuracy within a 13.2-meter flight completed in 10.5 seconds. For the growing-row dataset, which consists of occluded unripened fruits, we qualitatively analyze tracking performance and highlight future research directions for improving perception in greenhouse with strong occlusions. Our findings demonstrate the potential of UAVs for efficient robotic yield estimation in commercial greenhouses.

Why it matches plant phenotyping methodsUAV上のRGB-D・LiDAR・追跡アルゴリズムにより、トマト果実数と重量を推定する手法を開発・評価しており、植物形質取得が研究の中心です。

abstractwe develop a lightweight unmanned aerial vehicle (UAV) equipped with an RGB-D camera, a 3D LiDAR, and an IMU sensor.

Yield Estimation in Banana Orchards Based on DeepSORT and RGB-Depth Images ↗

Orchard yield estimation is one of the key indicators of precision agriculture. The traditional random sampling yield estimation method has strict requirements for the laborer experience and scale of orchards. Intelligent orchard management enables growers to use resources more effectively and make wiser decisions to optimize orchard inputs. This study proposes a banana bunch counting and yield estimation method based on the DeepSORT tracking algorithm. This method involves obtaining RGB-D images and calculating the weight of an individual bunch of bananas, which was promoted in our previous work. Building on this, the DeepSORT was used to solve the repeated counting based on the Hungarian algorithm and Kalman filtering. Three constraints were set to improve the statistical accuracy, and a yield estimation system was designed for orchard management monitoring. This system provides managers with bunch weight predictions and statistical plant information to achieve real-time yield estimations for banana orchards. The experimental results showed that the accuracy of the yield estimations reached 97.25% and that banana bunch counting had a success rate of 96.82%. This demonstrates that the effective integration of RGB-D technology and the DeepSORT algorithm can be successfully applied to the intelligent management and harvesting of banana orchards.

Why it matches plant phenotyping methodsRGB-D画像とDeepSORTによるバナナ房の計数・重量推定という、植物の収量形質を抽出する画像ベース手法が研究の中心であり、精度評価も実施しているため。

abstractThis study proposes a banana bunch counting and yield estimation method based on the DeepSORT tracking algorithm.

ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping ↗

The analysis of plant developmental plasticity, including root system architecture, is fundamental to understanding plant adaptability and development, particularly in the context of climate change and agricultural sustainability. While significant advances have been made in plant phenotyping technologies, comprehensive temporal analysis of root development remainschallenging, with most existing solutions providing either limited throughput or restricted structural analysis capabilities. Here, we present ChronoRoot 2.0, an integrated open-source platform that combines affordable hardware with advanced artificial intelligence to enable sophisticated temporal plant phenotyping. The system introduces several major advances, offering an integral perspective of seedling development: (i) simultaneous multi-organ tracking of six distinct plant structures, (ii) quality control through real-time validation, (iii) comprehensive architectural measurements including novel gravitropic response parameters, and (iv) dual specialized user 1 interfaces for both architectural analysis and high-throughput screening. We demonstrate the systems capabilities through three use cases for Arabidopsis thaliana: characterization of circadian growth patterns under different light conditions, detailed analysis of gravitropic responses in transgenic plants, and high-throughput screening of etiolation responses across multiple genotypes. ChronoRoot 2.0 maintains its predecessors advantages of low cost and modularity while significantly expanding its capabilities, making sophisticated temporal phenotyping more accessible to the broader plant science community. The systems open-source nature, combined with extensive documentation and containerized deployment options, ensures reproducibility and enables community-driven development of new analytical capabilities.

Why it matches plant phenotyping methods植物の時間的表現型を取得・解析するオープンプラットフォームの開発であり、構造計測、品質管理、AI解析、再現可能な運用が中心的な技術貢献である。

abstractHere, we present ChronoRoot 2.0, an integrated open-source platform that combines affordable hardware with advanced artificial intelligence to enable sophisticated temporal plant phenotyping.

A Hybrid Deep Learning Approach for Rice Plant Disease Detection ↗

The agricultural sector necessitates automated identification and analysis of rice diseases to conserve financial and other resources, mitigate yield loss, enhance processing efficiency, and secure healthy crop harvests. Rice is an essential commodity for global food security; however, it is very vulnerable to numerous illnesses that can markedly diminish productivity. Timely identification and precise forecasting of these diseases are crucial for reducing losses. Conventional image-based disease detection techniques frequently utilise Convolutional Neural Networks (CNNs) to extract spatial information; however, they inadequately account for the temporal evolution of diseases, which is essential for efficient monitoring and diagnosis. This research proposes a hybrid model that integrates a Hierarchical Convolutional Recurrent Neural Network (HCRNN) with Long Short-Term Memory (LSTM) networks for the prediction of rice plant illnesses. The HCRNN extracts multi-scale spatial characteristics from rice plant pictures, whilst the LSTM network models temporal relationships in disease progression, hence augmenting predicting capabilities. This integrated methodology enhances performance by integrating spatial and temporal information. We assessed the model using a dataset of rice plant leaf pictures impacted by multiple diseases, including bacterial leaf blight, blast, and sheath blight. The proposed model exhibited enhanced performance, with an accuracy of 98.5%, above that of conventional CNN-based models. This method also resolves the challenge of limited datasets by accurately tracking disease development across time. The findings indicate that the integration of HCRNN with LSTM establishes a resilient framework for predicting rice diseases. The suggested approach is adaptable to additional crops and disease categories, providing a scalable solution for precision agriculture and disease management. Subsequent efforts will concentrate on incorporating environmental variables, including soil and meteorological data, to augment predictive accuracy.

Why it matches plant phenotyping methodsイネ葉画像から病徴・病害状態を推定する画像解析手法を開発・評価しており、植物病害表現型の取得が中心である。

abstractThis research proposes a hybrid model that integrates a Hierarchical Convolutional Recurrent Neural Network (HCRNN) with Long Short-Term Memory (LSTM) networks for the prediction of rice plant illnesses.

Robust Flower Cluster Matching Using The Unscented Transform ↗

Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and occlusions from growth and camera angles. Plants flower in a manner that produces distinct clusters on branches. This paper presents a method for matching flower clusters using descriptors generated from RGB-D data and considers allowing for spatial uncertainty within the cluster. The proposed approach leverages the Unscented Transform to efficiently estimate plant descriptor uncertainty tolerances, enabling a robust image-registration process despite temporal changes. The Unscented Transform is used to handle the nonlinear transformations by propagating the uncertainty of flower positions to determine the variations in the descriptor domain. A Monte Carlo simulation is used to validate the Unscented Transform results, confirming our method's effectiveness for flower cluster matching. Therefore, it can facilitate improved robotics pollination in dynamic environments.

Why it matches plant phenotyping methodsRGB-D画像から花房を時系列追跡・マッチングする画像登録手法が研究の中心で、植物成長の観測に直接用いられるため、植物フェノタイピング手法として含める。

abstractThis paper presents a method for matching flower clusters using descriptors generated from RGB-D data and considers allowing for spatial uncertainty within the cluster.

Whole-Plant Physiological Identification and Quantification of Disease Progression ↗

Visual estimates of plant symptoms are traditionally used to quantify disease severity. Yet, the methodologies used to assess these phenotypes are often subjective and do not allow tracking of disease progression from very early stages. Here, we hypothesized that quantitative analysis of whole-plant physiological vital functions can be used to objectively determine plant health, providing a more sensitive way to detect disease. We studied the tomato wilt that is caused by Fusarium oxysporum f. sp. lycopersici. Physiological performance of infected and non-infected tomato plants was compared using a whole-plant pot-based lysimeter functional-phenotyping system in a semi-environmentally controlled greenhouse. Water-balance traits of the plants were measured continuously and simultaneously in a quantitative manner. Infected plants exhibited early reductions in transpiration and biomass gain, which preceded visual disease symptoms. These changes in transpiration proved to be effective quantitative indicators for assessing both plant susceptibility to infection and virulence of the fungus. Physiological changes linked to fungal outgrowth and toxin release contributed to reduced hydraulic conductance during initial infection stages. The functional-phenotyping method objectively captures early-stage disease progression, advancing plant disease research and management. This approach emphasizes the potential of quantitative whole-plant physiological analysis over traditional visual estimates for understanding and detecting plant diseases.

Why it matches plant phenotyping methods全植物の生理機能を連続測定する機能的フェノタイピング手法を用いて、植物病害の早期進行を定量化しており、表現型取得法が研究の中心である。

abstractwhole-plant pot-based lysimeter functional-phenotyping system

Time-resolved tracking of cellulose biosynthesis and assembly during cell wall regeneration in live Arabidopsis protoplasts. ↗

Cellulose, the most abundant polysaccharide on earth composing plant cell walls, is synthesized by coordinated action of multiple enzymes in cellulose synthase complexes embedded within the plasma membrane. Multiple chains of cellulose fibrils form intertwined extracellular matrix networks. It remains largely unknown how newly synthesized cellulose is assembled into an intricate fibril network on cell surfaces. Here, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly on Arabidopsis thaliana protoplast surfaces as the primary cell wall regenerates. Our observations provide the basis for a model of cellulose fibril network development in protoplasts driven by an interplay of multiscale dynamics that includes rapid diffusion and coalescence of nascent cellulose fibrils, processive elongation of single fibrils, and cellulose fibrillar network rearrangement during maturation. This study provides fresh insights into the dynamic and mechanistic aspects of cell wall synthesis at the single-cell level.

Why it matches plant phenotyping methods生きた植物細胞でセルロース合成と繊維ネットワークを連続可視化する画像プラットフォームの確立が研究の中心であり、植物の細胞壁状態を抽出する手法に該当する。

abstractHere, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly on Arabidopsis thaliana protoplast surfaces as the primary cell wall regenerates.

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset ↗

Computer vision is increasingly used in farmers’ fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimetre ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today’s AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of wheat organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90%. However, the precision for stems with 54% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.

Why it matches plant phenotyping methodsコムギ器官の画素単位セグメンテーションデータセットを構築し、モデル性能を評価する研究であり、植物形態・健全性の定量化に用いる表現型取得手法が中心です。

abstractThe labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.

A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction ↗

Accurate tomato yield estimation and ripeness monitoring are critical for optimizing greenhouse management. While manual counting remains labor-intensive and error-prone, this study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments. The proposed method integrates YOLOv8-based detection, depth filtering, and an inter-frame prediction algorithm to address key challenges such as background interference, occlusion, and double-counting. Our approach achieves 97.09% accuracy in tomato cluster detection, with mature and immature single-fruit recognition accuracies of 92.03% and 91.79%, respectively. The multi-target tracking algorithm demonstrates a MOTA (Multiple Object Tracking Accuracy) of 0.954, outperforming conventional methods like YOLOv8+DeepSORT. By fusing odometry data from an inspection robot, this lightweight solution enables real-time yield estimation and maturity classification, offering practical value for precision agriculture.

Why it matches plant phenotyping methods温室ロボット向けの画像解析フレームワークを中心に、トマト果実の計数、成熟度分類、収量推定という植物器官・状態の定量手法を開発・評価しているため。

abstractthis study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments.

Novel method for crop growth tracking with deep learning model on an Edge Rail Camera ↗

Yield prediction is an essential part of farm management and has been investigated with various kinds of data and technologies in the last decades. With the advent of deep learning technology, recent studies are focusing on crop growth analysis with image processing. Instead of measuring crops in a destructive way, image analysis enables crop measurement without manipulation of the crop itself. Counting crops using tracker algorithms such as DeepSORT is one of the famous approaches for yield prediction and analysis. However, to enable crop growth monitoring and analysis, it needs consideration of temporal analysis along with spatial analysis. It should be able to compare the previous status of the target crop to the current status to analyze the growth, for example, from bud to flower to strawberry. This paper proposes a novel method for monitoring crop growth with crop clustering. Instead of counting the crops from the images, the proposed methods recognized a crop cluster from the image and measured how it changed during its lifespan. Further, the proposed method is implemented in an edge device for a greenhouse that is able to collect and measure. The proposed method has been validated on a strawberry greenhouse for around a year, which shows MoTA score from 0.57 to 0.86, with respect to the dataset.

Why it matches plant phenotyping methods画像から作物クラスターの成長変化を追跡・測定する手法を開発し、エッジデバイスに実装してイチゴ温室で約1年間検証しており、植物表現型の取得が中心です。

abstractThis paper proposes a novel method for monitoring crop growth with crop clustering.

Review on Plant Parasitic Nematode (PPN) Infections in Sugarcane Cultivation Using AI Algorithms ↗

Sugarcane farming plays a vital role in India's economy, society, and culture, as the country is among the top producers and users of sugarcane globally. Plant parasitic nematodes (PPNs) is a major global threat to sugarcane crops, resulting in yield reductions and financial hardship for farmers. In order to minimize crop damage and implement efficient management strategies, the early detection of nematode infestations is imperative. Artificial Intelligence (AI) presents a viable approach for the early identification, tracking, and prevention of damage caused by nematodes through the implementation of cutting-edge machine learning algorithms, remote sensing technologies, and data analytics. This review focuses on the use of AI in sugarcane crop nematode infection detection and management. By integrating AI technologies in a complementary way with conventional agricultural practices, it is feasible to enhance the productivity and resistance of sugarcane crops to nematode infections.

Why it matches plant phenotyping methodsサトウキビの線虫感染という植物状態の検出を対象に、AI、リモートセンシング、データ解析による検出手法を中心に扱うレビューであり、方法論的役割が明確です。

abstractThis review focuses on the use of AI in sugarcane crop nematode infection detection and management.

PlantGaussian: Exploring 3D Gaussian splatting for cross-time, cross-scene, and realistic 3D plant visualization and beyond ↗

Observing plants across time and diverse scenes is critical in uncovering plant growth patterns. Classic methods often struggle to observe or measure plants against complex backgrounds and at different growth stages. This highlights the need for a universal approach capable of providing realistic plant visualizations across time and scene. Here, we introduce PlantGaussian, an approach for generating realistic three-dimensional (3D) visualization for plants across time and scenes. It marks one of the first applications of 3D Gaussian splatting techniques in plant science, achieving high-quality visualization across species and growth stages. By integrating the Segment Anything Model (SAM) and tracking algorithms, PlantGaussian overcomes the limitations of classic Gaussian reconstruction techniques in complex planting environments. A new mesh partitioning technique is employed to convert Gaussian rendering results into measurable plant meshes, offering a methodology for accurate 3D plant morphology phenotyping. To support this approach, PlantGaussian dataset is developed, which includes images of four crop species captured under multiple conditions and growth stages. Using only plant image sequences as input, it computes high-fidelity plant visualization models and 3D meshes for 3D plant morphological phenotyping. Visualization results indicate that most plant models achieve a Peak Signal-to-Noise Ratio (PSNR) exceeding 25, outperforming all models including the original 3D Gaussian Splatting and enhanced NeRF. The mesh results indicate an average relative error of 4% between the calculated values and the true measurements. As a generic 3D digital plant model, PlantGaussian will support expansion of plant phenotype databases, ecological research, and remote expert consultations.

Why it matches plant phenotyping methods3D Gaussian splattingと画像解析を統合し、植物画像から測定可能な3Dメッシュと形態形質を抽出する手法を開発・検証しており、データセットも構築しているため。

abstractA new mesh partitioning technique is employed to convert Gaussian rendering results into measurable plant meshes, offering a methodology for accurate 3D plant morphology phenotyping.

Virtual staining from bright-field microscopy for label-free quantitative analysis of plant cell structures. ↗

Abstract The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.

Why it matches plant phenotyping methods植物細胞構造の仮想染色を用いた画像ベースの形態計測法を開発・検証しており、細胞面積や形状などの表現型抽出が研究の中心である。

abstractThe applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated.

Actomyosin and the Arp2/3 Complex Are Involved in the Internalization of Cellulose Synthase Complexes ↗

The coupling of exo- and endocytic trafficking of Cellulose Synthase Complexes (CSCs) has been proposed to be important for maintaining the population of active CSCs at the plasma membrane (PM) and thus appropriate levels of cell wall assembly. Although actin and myosin are known to participate in the late stages of exocytosis of CSCs, their exact role during CSC internalization events remains controversial. We constructed a functional, photoconvertible fluorescent mEOS2-CESA6 reporter and developed single-particle live-cell imaging approaches to visualize and quantify the dynamic behavior of CSCs at the PM during internalization. Using the small molecule inhibitor of clathrin, Endosidin 9-17 or ES9-17, we confirmed that clathrin-mediated endocytosis is a major pathway for CSC internalization. We also found that the actin cytoskeleton is involved in CSC internalization. Genetic or chemical inhibition of actin, myosin, or the Arp2/3 complex significantly reduced the frequency of CSC internalization events and prolonged the CSC pause time prior to internalization. Additionally, we found that the Arp2/3 complex contributes to the late stage of exocytosis of CSCs into the PM. These results reveal a role for actomyosin and the Arp2/3 complex in both CSC secretion as well as internalization that was previously undescribed in plant cells. One sentence summaryDirect visualization of individual CSC internalization events reveals that actomyosin participates in CSC internalization and the Arp2/3 complex contributes to both exocytosis and internalization of CSCs through regulating the dynamic homeostasis of the cortical actin cytoskeleton.

Why it matches plant phenotyping methods植物細胞内のセルロース合成複合体を対象に、機能的蛍光レポーターと単一粒子ライブセル画像解析を開発し、内在化動態を定量化しているため、画像ベースの植物状態計測が中心である。

abstractWe constructed a functional, photoconvertible fluorescent mEOS2-CESA6 reporter and developed single-particle live-cell imaging approaches to visualize and quantify the dynamic behavior of CSCs at the PM during internalization.

Machine learning segmentation tool trained on synthetic data for tracking cytoskeleton polymerisation and depolymerisation ↗

The cytoskeleton is important in controlling the growth and morphology of plant cells, so tracking its morphological changes is essential. Here, we develop a new machine learning based segmentation tool for microtubules (MTs), which can distinguish between polymerised and depolymerised fibres. To circumvent the low abundance of data, we trained on synthetic images of microtubules from a computational micro-tubule model, pre-processed to reproduce microscope effects and partial depolymerisation. We used this tool to investigate how the MT network in an Arabidopsis thaliana root hair cell repolymerises after depolymerisation under Oryzalin (OZ) drug treatments. Specifically, we show the network initially repolymerises from the shank region. This work demonstrates the viability of using synthetic data to train machine learning systems handling cytoskeletal image data.

Why it matches plant phenotyping methods植物細胞の微小管画像から重合・脱重合状態を抽出する機械学習セグメンテーション手法の開発が中心であり、植物細胞の形態・状態の表現型計測に該当する。

abstractwe develop a new machine learning based segmentation tool for microtubules (MTs), which can distinguish between polymerised and depolymerised fibres.

CottonSense: A High-Throughput Field Phenotyping System for Cotton Fruit Segmentation and Enumeration aon Edge Devices ↗

High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton (Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R² value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R² value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense.

Why it matches plant phenotyping methods綿花の果実を画像から分割・計数する高スループット表現型計測システムを開発し、専門家および手作業計数で検証しているため、方法が研究の中心である。

abstractThis study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll.

DEGAST3D: Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images. ↗

Tracking plant cells in three-dimensional (3D) tissue captured through light microscopy presents significant challenges due to the large number of densely packed cells, non-uniform growth patterns, and variations in cell division planes across different cell layers. In addition, images of deeper tissue layers are often noisy, and systemic imaging errors further exacerbate the complexity of the task. In this paper, we propose a novel learning-based method DEGAST3D: Learning Deformable 3D GrAph Similarity to Track Plant Cells in Unregistered Time Lapse Images exploits the tightly packed 3D cell structure of plant cells to create a three-dimensional graph for accurate cell tracking. We also propose a novel algorithm for cell division detection and an effective three-dimensional registration, improving state-of-the-art algorithms. On a public dataset, our novel cell pair matching method outperforms the baseline by $6.83 \%$, $5.96 \%$, $6.40 \%$ in precision, recall, and F-1 score, respectively. On the same dataset, our proposed novel cell division technique improves the results of the baseline method by $15.38 \%$ and $14.78 \%$ in terms of recall and F1-score, respectively.

Why it matches plant phenotyping methods植物細胞の3D画像から細胞追跡・分裂検出・画像登録を行う手法を開発し、公開データセットで性能評価しているため、植物フェノタイピング手法が中心です。

abstractIn this paper, we propose a novel learning-based method DEGAST3D: Learning Deformable 3D GrAph Similarity to Track Plant Cells in Unregistered Time Lapse Images

LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping ↗

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

Why it matches plant phenotyping methods葉追跡の深層学習フレームワークを開発する研究であり、植物フェノタイピングにおける画像解析手法が中心と明示されている。

titleLeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping

Spatial Information from Biospeckle Imaging for Temporal Tracking and Monitoring of Plants: Application on Sunflowers Leaves under Water Stress ↗

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

Why it matches plant phenotyping methods植物葉の水ストレス状態を対象に、バイオスペックル画像を用いた時間追跡・モニタリング手法を扱っており、植物フェノタイピング手法の適用が中心と判断した。

titleSpatial Information from Biospeckle Imaging for Temporal Tracking and Monitoring of Plants: Application on Sunflowers Leaves under Water Stress

AI Techniques for Plant Disease Detection ↗

Plant diseases affect agricultural production, food security, and economic stability, making them a major concern for global agriculture. To reduce losses and guarantee sustainable farming methods, these diseases must be identified early and managed effectively. Manual inspections, which are labour-intensive, unreliable, and unscalable for large-scale agricultural applications, are frequently the basis of traditional disease monitoring techniques. Innovative approaches to plant disease tracking have been made possible by the quick development of artificial intelligence (AI), which offers improved scalability, accuracy, and efficiency. This study provides a comprehensive assessment of AI-based plant disease tracking systems, concentrating on techniques that integrate machine learning, deep learning, computer vision, and data-driven models. Key uses include integrating satellite imagery and IoT-enabled devices for real-time monitoring, predicting disease outbreaks using environmental data, and detecting diseases using picture processing. Additionally examined is the function of mobile applications in providing farmers with easily accessible diagnostic tools. The study also discusses important issues like model generalisation, data scarcity, computational constraints, and socioeconomic obstacles to AI adoption in agriculture. This review highlights the revolutionary potential of AI in building resilient agricultural systems, ultimately promoting global food security and sustainable development, by combining recent developments and pointing out research needs

Why it matches plant phenotyping methods植物病害の画像処理・コンピュータビジョン等による病徴・病害状態の推定手法を包括的にレビューしており、方法論が中心です。

abstractThis study provides a comprehensive assessment of AI-based plant disease tracking systems, concentrating on techniques that integrate machine learning, deep learning, computer vision, and data-driven models.

Real-Time Multi-View Flower Counting with a Ground Mobile Robot ↗

Although season-long cotton flower counts have value to breeders and growers, a manual data collection process is too laborious to be practical in most cases. In recent years, several fully automated flower counting approaches have been proposed. However, such approaches are typically designed to run offline and require a significant amount of computation. Furthermore, little thought has gone into developing convenient interfaces and integrations so that a layperson can use such systems without extensive training. The goal of this study is to develop a lightweight flower tracking system that is deployable on a ground robot and can operate in real-time. We modify a previous GCNNMatch++ approach to increase the inference speed. Additionally, we fuse data from multiple cameras in order to avoid canopy occlusions, and extract three-dimensional flower locations by integrating GPS data from the robot. We show that our approach significantly outperforms UAV-based counting and single-camera counting while running at above 40 FPS on an edge device, achieving a counting error of 15% and an average localization error of 19 cm. This level of performance is enough to observe significant differences in flowering behavior between genotypes. Overall, we believe that our highly-integrated, automated, and simplified flower counting solution makes significant strides towards a practical commercial cotton phenotyping platform.

Why it matches plant phenotyping methodsリアルタイムの花数・三次元位置推定を行う画像解析・ロボット統合手法を開発し、精度と処理速度を評価した綿花フェノタイピング研究であり、方法が中心である。

abstractThe goal of this study is to develop a lightweight flower tracking system that is deployable on a ground robot and can operate in real-time.

Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models ↗

Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation. Using aerial and ground imaging over a time series, we develop a framework for characterizing the ripening process of cranberry crops, a crucial component for precision agriculture tasks such as comparing crop breeds (high-throughput phenotyping) and detecting disease. Using drone imaging, we capture images from 20 waypoints across multiple bogs, and using ground-based imaging (hand-held camera), we image same bog patch using fixed fiducial markers. Both imaging methods are repeated to gather a multi-week time series spanning the entire growing season. Aerial imaging provides multiple samples to compute a distribution of albedo values. Ground imaging enables tracking of individual berries for a detailed view of berry appearance changes. Using vision transformers (ViT) for feature detection after segmentation, we extract a high dimensional feature descriptor of berry appearance. Interpretability of appearance is critical for plant biologists and cranberry growers to support crop breeding decisions (e.g.\ comparison of berry varieties from breeding programs). For interpretability, we create a 2D manifold of cranberry appearance by using a UMAP dimensionality reduction on ViT features. This projection enables quantification of ripening paths and a useful metric of ripening rate. We demonstrate the comparison of four cranberry varieties based on our ripening assessments. This work is the first of its kind and has future impact for cranberries and for other crops including wine grapes, olives, blueberries, and maize. Aerial and ground datasets are made publicly available.

Why it matches plant phenotyping methods画像取得、ViT特徴抽出、UMAPによる外観・成熟経路・成熟速度の定量化を中核とする、植物表現型解析フレームワークである。

abstractwe develop a framework for characterizing the ripening process of cranberry crops

Near Real Time Crop Health Status Monitoring and Mapping using Sentinel Satellite Image in Menjar Shenkora District, Ethiopia ↗

Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.

Why it matches plant phenotyping methodsSentinel-2衛星画像を用いて作物の健康状態と生育フェノロジーを圃場・区画レベルで推定し、現地データで検証する監視手法が研究の中心であるため、植物状態のリモートセンシング型フェノタイピングとして含める。

abstractremote sensing and GIS play a crucial role for monitoring crop health

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

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

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

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

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

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

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

abstractdeveloped a plasma membrane fluorescent dye

StraTracker: A dynamic counting method for growing strawberries based on multi-target tracking ↗

Accurately counting fruit in orchards is a critical step for effective digital farming management. However, the variability in fruit size, overlapping shadows, and light interference present significant challenges to applying computer vision during the strawberry growth phase. To address these challenges, we propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages. StraTracker transforms the counting task into a frame-by-frame tracking problem, integrating both motion and appearance features. The algorithm is composed of three key components: a strawberry detector based on YOLOv8n, a feature association module, and a dual-area counting (DC) module. First, the strawberry detector accurately recognizes five growth stages, achieving an average accuracy of 91.93 % at 38.3 FPS. Next, the feature association module, incorporating the Feature Slicing Attention (FSA) and Adaptive Kalman Filtering (AKF) modules, mitigates issues such as light interference, impractical tracking frames, and ID switching (IDs). As a result, StraTracker achieves a Multi-Object Tracking Accuracy (MOTA) of 83.28 % and a Higher-Order Tracking Accuracy (HOTA) of 77.26 %, with only 259 IDs, outperforming existing baseline models. Finally, the DC module categorizes fruit counts based on the unique IDs assigned during tracking. The algorithm’s coefficient of determination (R2 = 0.91) and GEH of 2.33 indicate a strong correlation between predicted and actual counts. In conclusion, StraTracker offers a promising solution for farmers to optimize planting strategies and develop more precise harvesting plans.

Why it matches plant phenotyping methodsイチゴ果実数という植物器官の形質を、画像検出・多対象追跡・成長段階認識で自動抽出する手法を開発し、精度検証しているため、方法中心の植物フェノタイピング研究である。

abstractwe propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages.

Fast and High-Resolution Imaging of Pollinated Stigmatic Cells by Tabletop Scanning Electron Microscopy. ↗

In plants, the first interaction between the pollen grain and the epidermal cells of the stigma is crucial for successful reproduction. When the pollen is accepted, it germinates, producing a tube that transports the two sperm cells to the ovules for fertilization. Confocal microscopy has been used to characterize the behavior of stigmatic cells post-pollination [1], but it is time-consuming since it requires the development of a range of fluorescent marker lines. Here, we propose a quick, high-resolution imaging protocol using tabletop scanning electron microscopy. This technique does not require prior sample fixation or fluorescent marker lines. It effectively captures pollen grain behavior from early hydration (a few minutes after pollination) to pollen tube growth within the stigma (1 h after pollination) and is particularly efficient for tracking pollen tube paths. Key features • Analysis of the pollen behavior in stigmatic cells of Arabidopsis thaliana but can be broadly used for other species. • Rapid and high-resolution imaging method. • Allows testing pollen grain hydration states, pollen tube paths on stigmatic cells from various genetic backgrounds, and also pollen tube phenotypes.

Why it matches plant phenotyping methods卓上走査電子顕微鏡を用いて花粉挙動、花粉管経路、花粉管表現型を取得する高速・高解像度イメージングプロトコルが研究の中心であり、植物表現型計測法の開発に該当する。

abstractHere, we propose a quick, high-resolution imaging protocol using tabletop scanning electron microscopy.

Automatic Apple Detection and Counting with AD-YOLO and MR-SORT. ↗

In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model's ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.

Why it matches plant phenotyping methodsリンゴ果実の検出・追跡・計数手法を開発し、精度や計数誤差を検証しており、果実数という植物形質の取得が中心である。

abstracta video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed

PhenoVision: A framework for automating and delivering research-ready plant phenology data from field images ↗

Plant phenology plays a fundamental role in shaping ecosystems, and global change-induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high-quality observations of when plants undergo seasonal transitions such as leaf-out, flowering, and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global scale research-grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist – data spanning all angiosperms and drawn from across the globe – to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy for flower (98.5%) and fruit presence (95%). Key to producing research-ready phenology data is post-calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources, and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, e.g., leaf phenology, and further integration with other resources to form a global central database integrating all in-situ plant phenology resources.

Why it matches plant phenotyping methods植物の野外画像から開花・結実というフェノロジー形質を推定する画像解析モデルを開発し、校正・検証・品質指標も扱っており、フェノタイピング手法が中心である。

abstractto train a computer vision model (PhenoVision) to detect the presence of fruits and flowers

Improved Multi-Size, Multi-Target and 3D Position Detection Network for Flowering Chinese Cabbage Based on YOLOv8 ↗

Accurately detecting the maturity and 3D position of flowering Chinese cabbage ( Brassica rapa var. chinensis) in natural environments is vital for autonomous robot harvesting in unstructured farms. The challenge lies in dense planting, small flower buds, similar colors and occlusions. This study proposes a YOLOv8-Improved network integrated with the ByteTrack tracking algorithm to achieve multi-object detection and 3D positioning of flowering Chinese cabbage plants in fields. In this study, C2F-MLCA is created by adding a lightweight Mixed Local Channel Attention (MLCA) with spatial awareness capability to the C2F module of YOLOv8, which improves the extraction of spatial feature information in the backbone network. In addition, a P2 detection layer is added to the neck network, and BiFPN is used instead of PAN to enhance multi-scale feature fusion and small target detection. Wise-IoU in combination with Inner-IoU is adopted as a new loss function to optimize the network for different quality samples and different size bounding boxes. Lastly, ByteTrack is integrated for video tracking, and RGB-D camera depth data are used to estimate cabbage positions. The experimental results show that YOLOv8-Improve achieves a precision ( P ) of 86.5% and a recall ( R ) of 86.0% in detecting the maturity of flowering Chinese cabbage. Among them, mAP50 and mAP75 reach 91.8% and 61.6%, respectively, representing an improvement of 2.9% and 4.7% over the original network. Additionally, the number of parameters is reduced by 25.43%. In summary, the improved YOLOv8 algorithm demonstrates high robustness and real-time detection performance, thereby providing strong technical support for automated harvesting management.

Why it matches plant phenotyping methods開花中国白菜の成熟度という植物状態を画像から検出・推定する改良YOLOv8と3D位置推定手法が研究の中心であり、収穫対象の単なる位置検出を超える。

abstractThis study proposes a YOLOv8-Improved network integrated with the ByteTrack tracking algorithm to achieve multi-object detection and 3D positioning of flowering Chinese cabbage plants in fields.

Three-view cotton flower counting through multi-object tracking and RGB-D imagery ↗

Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.

Why it matches plant phenotyping methodsRGB-Dカメラ、ロボットプラットフォーム、物体追跡、深度投影、重複除去を統合してワタ花数を自動計測する手法を開発・検証しており、植物表現型取得が中心である。

abstractTo count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform.

Twice matched fruit counting system: An automatic fruit counting pipeline in modern apple orchard using mutual and secondary matches ↗

Fruit counting, as one of the essential parts of yield estimation, is an important factor in production process planning. In the case of apple crops, it is useful in orchard management and as guidance for farmers, showing a decisive role in product market strategies and cultivation practices. Although some machine vision based studies have exhibited notable fruit counting ability, they still need to be improved for clustered fruit. This study proposes an automatic fruit counting pipeline called twice matched fruit counting system to overcome this limitation. The twice matched fruit counting system consists of three sub-algorithms: i) object detection model based on You Only Look Once Version 4-tiny; ii) fruit tracking with mutual match; iii) and fruit counting with ID assignment. The object detection model was developed based on You Only Look Once Version 4-tiny, which quickly and accurately detect fruit and trunks with mean average precision of 96.4% and detection speed of 16 ms. The fruit tracking with mutual match was designed to alleviate match errors associated with the clustered fruit, which achieved superior performance with average ID Switch Rate of 3.9%, Multiple Object Tracking Accuracy of 89.9% and Multiple Object Tracking Precision of 93.5%. The fruit counting was implemented by ID assignment, where each fruit was assigned with a unique ID based on fruit tracking results and direction of camera motion. The root mean squared error and coefficient of determination were 16.3 fruit per video and 0.93, respectively, which indicate a high correlation between fruit count results from the proposed approach and ground truth counting results. The twice matched fruit counting system was implemented on Central Processing Unit at 3–5 frames per second. These results demonstrate a potential of the twice matched fruit counting system for estimating fruit yield in modern apple orchards, which could provide technical support for orchard management.

Why it matches plant phenotyping methodsリンゴ果実数（収量関連形質）を画像から自動抽出・推定する計数パイプラインの開発と技術評価が中心であり、植物フェノタイピング手法に該当する。

abstractThis study proposes an automatic fruit counting pipeline called twice matched fruit counting system to overcome this limitation.

Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset ↗

Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines. We publicly release a multimodal dataset covering five fruit species across their growth seasons at https://4d-metric-semantic-mapping.org/

Why it matches plant phenotyping methods果実の成長追跡・計数・サイズ推定という植物器官形質を対象に、LiDAR-RGB融合と4D対応付け手法を開発・評価し、データセットも公開しているため、フェノタイピング手法が中心です。

abstractWe present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time.

TrackPlant3D: 3D organ growth tracking framework for organ-level dynamic phenotyping ↗

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

Why it matches plant phenotyping methods植物器官の3D成長追跡と動的表現型解析を目的とするフレームワークであり、表現型取得・推定手法が中心です。

titleTrackPlant3D: 3D organ growth tracking framework for organ-level dynamic phenotyping

Tracking Darwin's footprints but with LiDAR for booting up the 3D and even beyond-3D understanding of plant intelligence ↗

As an emerging subject of the implication on revolutionizing many fields from botany to life science, plant intelligence (PI) has been actively studied but also trapped in debate. Inspired by those earlier botanists such as Darwin conceiving this concept when observing plants outdoors, we propose to track Darwin's footprints – go again to the wild where plants show higher-fold adapting performance than in labs for arousing a re-cognition of PI. However, this plan must face a basic challenge on in-situ plant phenotyping, especially in structure, which serves as the three-dimensional (3D) phenomenological display of varying PI behaviors. Aiming at this core bottleneck, we suggest to go but with 3D remote/proximal sensing (R/PS) devices such as Light Detection and Ranging (LiDAR) – a state-of-the-art technology of fully but fine mapping plants, for starting a 3D cognition of PI. Further, to decode the mechanism of PI occurring, we preview the next-generation (e.g., hyperspectral, fluorescence, and polarization) LiDAR with the latent capacity on all-round phenotyping of plants. Their derived 3D biochemical, physiological, and biophysical functional traits can arouse a beyond-3D cognition of PI. Overall, this theoretical prospect, with the available R/PS technology traced for upgrading PI from conceptual debating to mechanistic understanding, can advance the PI field into its 3D and even beyond-3D times and bring the PI and PI-relevant sciences such as sustainability cognition to breathe new life.

Why it matches plant phenotyping methodsLiDARや次世代リモートセンシングによる植物の3D・機能形質計測を中心に論じる方法論的展望であり、植物フェノタイピングが中核です。

abstractwe suggest to go but with 3D remote/proximal sensing (R/PS) devices such as Light Detection and Ranging (LiDAR) – a state-of-the-art technology of fully but fine mapping plants, for starting a 3D cognition of PI.

Running on empty: Mitochondria without mtDNA exhibit differential motility and connectivity ↗

Plant mitochondria are in continuous motion. While providing ATP to other cellular processes, they also constantly consume ATP to move rapidly within the cell. This movement is in part related to taking up, converting and delivering metabolites and energy to and from different parts of the cell. Plant mitochondria have varying amounts of DNA even within a single cell, from none to the full mitochondrial genome. Because mitochondrial dynamics are altered in an Arabidopsis mutant with disrupted DNA maintenance, we hypothesised that exchanging DNA templates for repair is one of the functions of their movement and interactions. Here, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana . In addition to staining mitochondrial DNA with SYBR Green, we have developed and implemented a fluorescent mitochondrial DNA binding protein that will also enable future understanding of mitochondrial dynamics, genome maintenance and replication. We demonstrate that mitochondria without mtDNA have altered physical behaviour and have a lower immediate connectivity to the rest of the population, further supporting a link between the physical and genetic dynamics of these complex organelles.

Why it matches plant phenotyping methods植物ミトコンドリアのDNA可視化と位置追跡を中心に、蛍光DNA結合タンパク質を開発・実装し、ミトコンドリアの挙動と接続性という細胞内植物状態を定量化しているため。

abstractHere, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana .

Graph Neural Networks for lightweight plant organ tracking ↗

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

Why it matches plant phenotyping methods植物器官の追跡を目的とするグラフニューラルネットワーク手法であり、植物フェノタイピングの計算手法開発が中心と判断されます。

titleGraph Neural Networks for lightweight plant organ tracking

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

Fluorescent carbon dot embedded polystyrene: an alternative for micro/nanoplastic translocation study in leguminous plants. ↗

Micro/nanoplastics are widespread in terrestrial ecosystem. Even though many studies have been reported on the effects of these in marine environment, studies concerning their accumulation and impact on terrestrial ecosystem have been scanty. The current study was designed to determine how terrestrial plants, especially legumes, interact with micro/nanoplastics to gain insights into their uptake and translocation. The paper describes the synthesis of fluorescent carbon dot embedded polystyrene (CDPS) followed by its characterization. Translocation studies at different concentrations from 2 to 100% (v/v) for tracking the movement and accumulation of microplastics in Vigna radiata and Vigna angularis were performed. The optical properties of the synthesized CDPS were investigated, and their translocation within the plants was visualized using fluorescence microscopy. These findings were further validated by scanning electron microscopy (SEM) imaging of the plant sections. The results showed that concentrations higher than 6% (v/v) displayed noticeable fluorescence in the vascular region and on the cell walls, while concentrations below this threshold did not. The study highlights the potential of utilizing fluorescent CDPS as markers for investigating the ecological consequences and biological absorption of microplastics in agricultural systems. This method offers a unique technique for monitoring and analyzing the routes of microplastic accumulation in edible plants, with significant implications for both food safety and environmental health.

Why it matches plant phenotyping methods蛍光標識粒子と蛍光顕微鏡・SEMを用いて植物体内のマイクロプラスチック蓄積・移行を可視化する手法が研究の中心であり、植物の生理状態（吸収・転流）を測定する方法として該当する。

abstractThe paper describes the synthesis of fluorescent carbon dot embedded polystyrene (CDPS) followed by its characterization.

Spectral algal fingerprinting and long sequencing in synthetic algal-microbial communities ↗

O_LISynthetic biology has made progress in creating artificial microbial and algal communities, but technical and evolutionary complexities still pose significant challenges. C_LIO_LITraditional methods for studying microbial and algal communities, such as microscopy and pigment analysis, are limited in throughput and resolution. In contrast, advancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints, offering more precise and comprehensive analysis than conventional flow cytometry. C_LIO_LIThis study demonstrates the use of full-spectrum cytometry for analyzing synthetic algal-microbial communities, facilitating rapid species identification and enumeration. The workflow involves recording individual spectral signatures from monocultures, utilizing autofluorescence to distinguish them from noise, and subsequent creation of a spectral library for further analysis. The obtained library is used then to analyze mixtures of unicellular cyanobacteria and synthetic phytoplankton communities, revealing differences in spectral signatures. The synthetic consortium experiment monitored algal growth, comparing results from different instruments and highlighting the advantages of the spectral virtual filter system for precise population separation and abundance tracking. This approach demonstrated higher flexibility and accuracy in analyzing multi-component algal-microbial assemblages and tracking temporal changes in community composition. C_LIO_LIBy capturing the complete emission spectrum of each cell, this method enhances the understanding of algal-microbial community dynamics and responses to environmental stressors. With development of standardized spectral libraries, our work demonstrates an improved characterization of algal communities, advancing research in synthetic biology and phytoplankton ecology. C_LI

Why it matches plant phenotyping methods藻類の個体スペクトル計測とスペクトルライブラリを用いて、群集の構成・個体数・増殖を高スループットに測定する技術が研究の中心であり、植物状態の取得法として実質的です。

abstractadvancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints

Green pepper fruits counting based on improved DeepSort and optimized Yolov5s. ↗

Introduction Green pepper yield estimation is crucial for establishing harvest and storage strategies. Method This paper proposes an automatic counting method for green pepper fruits based on object detection and multi-object tracking algorithm. Green pepper fruits have colors similar to leaves and are often occluded by each other, posing challenges for detection. Based on the YOLOv5s, the CS_YOLOv5s model is specifically designed for green pepper fruit detection. In the CS_YOLOv5s model, a Slim-Nick combined with GSConv structure is utilized in the Neck to reduce model parameters while enhancing detection speed. Additionally, the CBAM attention mechanism is integrated into the Neck to enhance the feature perception of green peppers at various locations and enhance the feature extraction capabilities of the model. Result According to the test results, the CS_YOLOv5s model of mAP, Precision and Recall, and Detection time of a single image are 98.96%, 95%, 97.3%, and 6.3 ms respectively. Compared to the YOLOv5s model, the Detection time of a single image is reduced by 34.4%, while Recall and mAP values are improved. Additionally, for green pepper fruit tracking, this paper combines appearance matching algorithms and track optimization algorithms from SportsTrack to optimize the DeepSort algorithm. Considering three different scenarios of tracking, the MOTA and MOTP are stable, but the ID switch is reduced by 29.41%. Based on the CS_YOLOv5s model, the counting performance before and after DeepSort optimization is compared. For green pepper counting in videos, the optimized DeepSort algorithm achieves ACP (Average Counting Precision), MAE (Mean Absolute Error), and RMSE (Root Mean Squared Error) values of 95.33%, 3.33, and 3.74, respectively. Compared to the original algorithm, ACP increases by 7.2%, while MAE and RMSE decrease by 6.67 and 6.94, respectively. Additionally, Based on the optimized DeepSort, the fruit counting results using YOLOv5s model and CS_YOLOv5s model were compared, and the results show that using the better object detector CS_YOLOv5s has better counting accuracy and robustness.

Why it matches plant phenotyping methods緑 pepper果実の検出・追跡・計数という植物器官形質の画像ベース取得法を開発し、精度と追跡性能を検証しているため、中心的なフェノタイピング手法研究である。

abstractThis paper proposes an automatic counting method for green pepper fruits based on object detection and multi-object tracking algorithm.

TSP-yolo-based deep learning method for monitoring cabbage seedling emergence ↗

Real-time monitoring of seedling emergence is vital for vegetable crop management and yield estimation. Traditionally, crop seedling emergence monitoring relies on low-efficient and time-consuming manual counting. To address this issue, this research proposed an efficient, fast, and real-time cabbage seedling counting method (combining the improved YOLOv8n, tracking algorithm, and image processing) to accurately track cabbage seedlings in the field and implement counting with an unmanned aerial vehicle (UAV). The improved YOLOv8n replaced the C2f Block in the YOLO backbone with a Swin-conv block and incorporated ParNet attention modules in both the backbone and neck parts. This enhancement enables the YOLOv8n to surpass the base model's performance, achieving a mAP50–95 of 90.3 %, representing a 14.5 % improvement. The experiments demonstrated the superior capabilities of the counting method in terms of speed and accuracy. In field experiments, the proposed Tracking algorithms-Swin-conv blocks-ParNet attention-YOLOv8n (TSP-yolo) counting method demonstrated consistent and reliable accuracy in counting cabbage seedlings while demanding only one-seventh of the time needed compared to the manual counting method. In summary, based on TSP-yolo and implemented through an UAV, the developed seedling emergence counting method demonstrated an excellent capability of counting cabbage seedlings, resulting in significant savings in human resources for crop management.

Why it matches plant phenotyping methodsUAV画像と改良YOLOを用いてキャベツ苗の出芽数を自動推定する手法を開発・評価しており、植物状態の取得方法が研究の中心である。

abstractthis research proposed an efficient, fast, and real-time cabbage seedling counting method (combining the improved YOLOv8n, tracking algorithm, and image processing)

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming. ↗

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R 2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Why it matches plant phenotyping methods小麦穂の画像検出・追跡による計数手法を開発し、精度・相関・リアルタイム性能を評価しており、植物形質（穂数）の取得が研究の中心である。

abstractIn order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x.

Persistent Luminescence Nanoplatform for Autofluorescence-Free Tracking of Submicrometer Plastic Particles in Plant. ↗

The uptake of plastic particles by plants and their transport through the food chain make great risks to biota and human health. Therefore, it is important to trace plastic particles in the plant. Traditional fluorescence imaging in plants usually suffers significant autofluorescence background. Here, we report a persistent luminescence nanoplatform for autofluorescence-free imaging and quantitation of submicrometer plastic particles in plant. The nanoplatform was fabricated by doping persistent luminescence nanoparticles (PLNPs) onto polystyrene (PS) nanoparticles. Cr 3+ -doped zinc gallate PLNP was employed as the dopant for autofluorescence-free imaging due to its persistent luminescence nature. In addition, the Ga element in PLNP was used as a proxy to quantify the PS in the plant by inductively coupled plasma mass spectrometry (ICP-MS). Thus, the developed nanoplatform allows not only dual-mode autofluorescence-free imaging (persistent luminescence and laser-ablation ICP-MS) but also ICP-MS quantitation for tracking PS in plant. Application of this nanoplatform in a typical plant model Arabidopsis thaliana revealed that PS mainly distributed in the root (>99.45%) and translocated very limited (<0.55%) to the shoot. The developed nanoplatform has great potential for quantitative tracing of submicrometer plastic particles to investigate the environmental process and impact of plastic particles.

Why it matches plant phenotyping methods植物体内の微小プラスチックを対象に、蛍光・レーザーアブレーションICP-MSによる画像化と定量追跡手法を開発しており、植物状態の取得が研究の中心です。

abstractwe report a persistent luminescence nanoplatform for autofluorescence-free imaging and quantitation of submicrometer plastic particles in plant.

Going cresyl for plant cell imaging ↗

The advent of fluorescent probes and the characterization of their photochemical properties in the past years allowed significant advances in the studies of spatiotemporal cellular processes within complex and crowded systems. Dyes are indeed extremely useful tools for the visualization of cellular and subcellular structures present in living cells, as well as to study their dynamic and molecular composition or physiological changes. There are some areas of plant cell biology that have been more challenging to explore due to the physiology and organization of certain endomembrane compartments. In this study we characterize the labeling properties of cresyl violet as quick and inexpensive imaging agent for tracking endosomes, vacuole compartments which are usually very differentiated and categorized as more acidic as well as acidic plant compartments. Its photobleaching, labelling and cytotoxic properties are compared with other well-known and currently most used synthetic and molecular probes.

Why it matches plant phenotyping methods植物細胞内区画の可視化・追跡用蛍光プローブを開発・特性評価し、既存プローブと比較しているため、植物状態の画像取得法が中心です。

abstractwe characterize the labeling properties of cresyl violet as quick and inexpensive imaging agent for tracking endosomes, vacuole compartments which are usually very differentiated and categorized as more acidic as well as acidic plant compartments.

Automated Pruning and Irrigation of Polyculture Plants ↗

Polyculture farming has environmental advantages but requires substantially more labor than monoculture farming. We present novel hardware and algorithms for automated pruning and irrigation. Using an overhead camera to collect data from physical$1.5~m^{2}$garden testbeds, the autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day. From this garden state, AlphaGardenSim selects plants to autonomously prune. A trained neural network detects and targets specific prune points on the plant. Two custom-designed pruning tools, compatible with a FarmBot commercial gantry system, are experimentally evaluated. Irrigation is automated using soil moisture sensors. We present results for four 60-day garden cycles. Results suggest the system can autonomously achieve 94% normalized plant diversity with pruning shears while maintaining an average canopy coverage of 84% by the end of the cycles. For code, videos, and datasets, see https://sites.google.com/berkeley.edu/pruningpolyculturej/home.Note to Practitioners—While polyculture farming is closer to how plants grow in nature, it is considered more labor intensive that monoculture farming. In this paper we present approaches and custom hardware for automation of pruning and irrigation. Physical experiments suggest that automation can yield both high coverage and diversity.

Why it matches plant phenotyping methods植物の分布・庭の状態・キャノピー被覆をカメラ画像と学習モデルで推定する手法が、自動剪定システムの中核として記述されているため、植物フェノタイピングのプラットフォーム応用に該当する。

abstractthe autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day.

Label-free structural imaging of plant roots and microbes using third- harmonic generation microscopy ↗

ABSTRACT Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF fluorescence signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.

Why it matches plant phenotyping methodsTHG・3PFによる生根の構造と細胞内特徴を、ラベルフリーかつ生体内で可視化するイメージング手法が研究の中心であり、植物形態・状態の表現型取得に直接関与する。

abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution

Molecular engineering of fluorescent dyes for long-term specific visualization of the plasma membrane based on alkyl-chain-regulated cell permeability. ↗

Long-term visualization of changes in plasma membrane dynamics during important physiological processes can provide intuitive and reliable information in a 4D mode. However, molecular tools that can visualize plasma membranes over extended periods are lacking due to the absence of effective design rules that can specifically track plasma membrane fluorescent dye molecules over time. Using plant plasma membranes as a model, we systematically investigated the effects of different alkyl chain lengths of FMR dye molecules on their performance in imaging plasma membranes. Our findings indicate that alkyl chain length can effectively regulate the permeability of dye molecules across plasma membranes. The study confirms that introducing medium-length alkyl chains improves the ability of dye molecules to target and anchor to plasma membranes, allowing for long-term imaging of plasma membranes. This provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes. Using the amphiphilic amino-styryl-pyridine fluorescent skeleton, we discovered that the inclusion of short alkyl chains facilitated rapid crossing of the plasma membrane by the dye molecules, resulting in staining of the cell nucleus and indicating improved cell permeability. Conversely, the inclusion of long alkyl chains hindered the crossing of the cell wall by the dye molecules, preventing staining of the cell membrane and demonstrating membrane impermeability to plant cells. The FMR dyes with medium-length alkyl chains rapidly crossed the cell wall, uniformly stained the cell membrane, and anchored to it for a long period without being transmembrane. This allowed for visualization and tracking of the morphological dynamics of the cell plasma membrane during water loss in a 4D mode. This suggests that the introduction of medium-length alkyl chains into amphiphilic fluorescent dyes can transform them from membrane-permeable fluorescent dyes to membrane-staining fluorescent dyes suitable for long-term imaging of the plasma membrane. In addition, we have successfully converted a membrane-impermeable fluorescent dye molecule into a membrane-staining fluorescent dye by introducing medium-length alkyl chains into the molecule. This molecular engineering of dye molecules with alkyl chains to regulate cell permeability provides a simple and effective design rule for long-term visualization of the plasma membrane, and a convenient and feasible means of chemical modification for efficient transmembrane transport of small molecule drugs.

Why it matches plant phenotyping methods植物細胞膜の長期蛍光イメージング用色素を分子設計・評価し、水分喪失時の膜形態動態を可視化する方法開発が中心である。

abstractThis provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes.

Photochromic reversion enables long-term tracking of single molecules in living plants. ↗

Single-molecule imaging enables the observation of individual molecules in living cells (DEste et al., 2024; Kusumi et al., 2014; Lelek et al., 2021; Nguyen et al., 2023). In plants, however, the tracking of single molecules is typically limited to a few hundred milliseconds (Bayle et al., 2021; Gronnier et al., 2017; Hosy et al., 2015), precluding the observation of dynamic cellular processes at molecular resolution. Here, we describe photochromic reversion, an imaging modality that enables long-term single-molecule tracking of genetically encoded translational fusions. Using this approach, we achieve minute-long tracking of individual cell-surface receptors and reveal previously inaccessible dynamic spatial arrest events of single plasma membrane proteins. We further developed and benchmarked computational analysis of spatial arrests (CASTA), a machine learning-based tool that automatically detects and analyses spatial, temporal, and diffusional properties of these events, thereby enabling precise nanoscale kinetic measurements. Together, these advances provide a powerful framework for deciphering the principles governing membrane dynamics and function.

Why it matches plant phenotyping methods植物細胞表面受容体の動態を長時間単一分子イメージングで取得し、空間停止イベントを解析するCASTAも開発・ベンチマークしており、植物状態の測定法が中心である。

abstractHere, we describe photochromic reversion, an imaging modality that enables long-term single-molecule tracking of genetically encoded translational fusions.

Pollinator‐assisted plant phenotyping, selection, and breeding for crop resilience to abiotic stresses ↗

Food security is threatened by climate change, with heat and drought being the main stresses affecting crop physiology and ecosystem services, such as plant-pollinator interactions. We hypothesize that tracking and ranking pollinators' preferences for flowers under environmental pressure could be used as a marker of plant quality for agricultural breeding to increase crop stress tolerance. Despite increasing relevance of flowers as the most stress sensitive organs, phenotyping platforms aim at identifying traits of resilience by assessing the plant physiological status through remote sensing-assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype-to-phenotype prediction. However, as the transport of photoassimilates from leaves (sources) to flowers (sinks) is reduced in low-resilient plants, flowers are better indicators than leaves of plant well-being. Indeed, the chemical composition and amount of pollen and nectar that flowers produce, which ultimately serve as food resources for pollinators, change in response to environmental cues. Therefore, pollinators' preferences could be used as a measure of functional source-to-sink relationships for breeding decisions. To achieve this challenging goal, we propose to develop a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo-positioning system. Pollinator-assisted selection can be validated by metabolic, transcriptomic, and ionomic traits, and mapping of candidate genes, linking floral and leaf traits, pollinator preferences, plant resilience, and crop productivity. This radical new approach can change the current paradigm of plant phenotyping and find new paths for crop redomestication and breeding assisted by ecological decisions.

Why it matches plant phenotyping methods受粉者の行動を用いて植物の花形質・耐性を定量化する新規フェノタイピング基盤の開発を中心課題としている。

abstractwe propose to develop a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo-positioning system.

Tomato cluster detection and counting using improved YOLOv5 based on RGB-D fusion ↗

Accurate estimation of tomato cluster yields is critical to the advancement of intelligent and unmanned greenhouses, guiding horticultural management and adjusting sales and marketing strategies. However, due to the complex natural environment and tracking stability, there are still considerable challenges for automated yield estimation to be deployed in practice. Therefore, this paper presents an improved tomato cluster counting method that combines object detection, multiple object tracking, and specific tracking region counting. To reduce background tomato misidentification, we proposed the YOLOv5-4D that fuses RGB images and depth images as input. Next, we adopted ByteTrack to track tomato clusters in continuous frames and designed a specific tracking region counting method to overcome the problem of tracked tomato cluster ID shift. In the test set, the improved YOLOv5-4D had a detection accuracy of 97.9 % and a mAP@0.5:0.95 of 0.748. Field experiments showed that the counting method achieved a statistical average counting accuracy of 95.1 % and the integrated algorithm ran at more than 40 FPS, enabling stable real-time yield estimation.

Why it matches plant phenotyping methodsRGB-D画像、物体検出、追跡、計数を統合してトマト房数・収量を推定する手法が研究の中心であり、植物器官の表現型を直接定量している。

abstractthis paper presents an improved tomato cluster counting method that combines object detection, multiple object tracking, and specific tracking region counting.

Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification ↗

This research paper explores a comprehensive approach to advancing capsicum harvesting by integrating cutting-edge technologies. The study addresses key objectives, including capsicum detection using various YOLO algorithms, peduncle detection through YOLO segmentation models in a proposed robotic harvester, laboratory testing of cutting target point coordinates using the Real Sense D455 RGB-D camera, growth stage determination, and capsicum counting/tracking with a supervision algorithm. The investigation highlights the YOLOv8s model as the most successful for capsicum detection, achieving a remarkable mean Average Precision (mAP) of 0.967 at a 0.5 Intersection over Union (IOU) threshold. As part of the growth stage determination task, YOLOv8s achieved a satisfactory mAP of 0.614 at the same IOU threshold. Additionally, the YOLOv8s-seg model demonstrated superior performance in peduncle detection, attaining a box mAP of 0.790 and a mask mAP of 0.771. The YOLOv8s-seg model excels in peduncle detection with a box mAP of 0.790 and a mask mAP of 0.771. Laboratory experiments using the Real Sense D455 RGB-D camera showcased its capability to localize the target point with a maximum error of 8 mm longitudinally, 9 mm vertically, and 12 mm laterally. The developed tracking and counting algorithm achieve a notable counting accuracy of 94.1 % during the third harvesting cycle in the greenhouse. The Android application developed demonstrated robust performance, achieving high accuracy (Mean IoU: 0.92), precise localization (Mean Euclidean Distance: 5 pixels), responsive user interface (Touch Response Time: 150 ms), and broad compatibility across Android versions and device types, with effective error handling (Success Rate: 95 %). The study not only advances capsicum harvesting techniques but also presents practical insights for the integration of advanced technologies, paving the way for efficient robotic harvesting systems in agriculture.

Why it matches plant phenotyping methodsYOLO画像解析、成長段階分類、果実カウント・追跡を統合した植物状態・器官形質の取得手法を開発し、精度を評価しているため、収穫ロボットの単なる対象定位を超える中心的なフェノタイピング研究である。

abstractThe study addresses key objectives, including capsicum detection using various YOLO algorithms, peduncle detection through YOLO segmentation models in a proposed robotic harvester, laboratory testing of cutting target point coordinates using the Real Sense D455 RGB-D camera, growth stage determination, and capsicum counting/tracking with a supervision algorithm.

Simultaneous and Dynamic Super-Resolution Imaging of Two Proteins in Arabidopsis thaliana using dual-color sptPALM ↗

Super-resolution microscopy techniques have revolutionized cell biology by providing insights into single-molecule dynamics and nanoscale organization within living cells. However, the application of dynamic live-cell methods in plants remains limited by the availability of suitable fluorophores for simultaneous visualization of multiple proteins. To address this challenge, we implemented a dual-color single-particle tracking photoactivated localization microscopy (sptPALM) approach based on codon-optimized photoactivatable fluorescent proteins PA-GFP and PATagRFP. Recently, we demonstrated their individual performance in single-color experiments in Nicotiana benthamiana and Arabidopsis thaliana cells. Here, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell. This approach provides a framework to investigate the coordinated dynamics, interactions, and spatial organization of multiple proteins in living plant cells.

Why it matches plant phenotyping methods植物細胞内の2種類のタンパク質を同時追跡するデュアルカラーsptPALM法を開発・確立した研究であり、生細胞の動態・空間配置という植物状態の取得法が中心です。

abstractHere, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell.

Target-activated multicolor fluorescent dyes for 3D imaging of plasma membranes and tracking of apoptosis. ↗

Real-time tracking of dynamic changes in the three-dimensional morphology of the cell plasma membrane is of great importance for a deeper understanding of physiological processes related to the cell plasma membrane. However, there is a lack of imaging dyes that can specifically be used for a long term labelling of plasma membranes, especially for plant cells. Here, we have used molecular engineering strategies to develop a series of target-activated multicolour fluorescent dyes that can be used for long-term and three-dimensional imaging of plant cell plasma membranes. By combining different electron acceptors and donors, four molecular backbones with different emission colours from green to NIR have been obtained. In the designed styrene-based dyes, referred to as the SD dyes, several functional groups were introduced into the backbones to achieve the properties of target-activated fluorescence, rapid and wash-free staining, high plasma membrane targeting ability and long-term imaging function. Using onion epidermal cells as a platform, these dye molecules can provide high-quality imaging of the plasma membrane for up to 6 hours, providing a powerful tool for long-term monitoring of plasma membrane-related biological events. Calcium-mediated apoptosis of plant cells has been tracked for the first time by monitoring the morphological changes of the plasma membrane in real time using SD dyes. These dyes also exhibit excellent 3D imaging performance of the plasma membrane and were further used to track in real time the 3D morphological changes of the plasma membrane during plasmolysis of plant cells, providing a powerful imaging tool for three-dimensional (3D) biology. This work provides a set of multi-colour dye tools for long-term and three-dimensional imaging of plant cell plasma membranes, and also provides molecular design principles for guiding the transmembrane transport of small molecules.

Why it matches plant phenotyping methods植物細胞膜の3D形態を長時間・リアルタイムに取得する蛍光色素とイメージング手法を開発しており、植物の形態・アポトーシス・原形質分離状態の測定が中心である。

abstractHere, we have used molecular engineering strategies to develop a series of target-activated multicolour fluorescent dyes that can be used for long-term and three-dimensional imaging of plant cell plasma membranes.

The Development of a Stereo Vision System to Study the Nutation Movement of Climbing Plants. ↗

Climbing plants, such as common beans ( Phaseolus vulgaris L.), exhibit complex motion patterns that have long captivated researchers. In this study, we introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision. Our approach involves two synchronized cameras, one lateral to the plant and the other overhead, enabling the simultaneous 2D position tracking of the plant tip. These data are then leveraged to reconstruct the 3D position of the tip. Furthermore, we investigate the impact of external factors, particularly the presence of support structures, on plant movement dynamics. The proposed method is able to extract the position of the tip in 86-98% of cases, achieving an average reprojection error below 4 px, which means an approximate error in the 3D localization of about 0.5 cm. Our method makes it possible to analyze how the plant nutation responds to its environment, offering insights into the interplay between climbing plants and their surroundings.

Why it matches plant phenotyping methodsクライミング植物の先端位置とナテーション運動を抽出するステレオビジョン手法を開発し、抽出率と3D定位誤差を評価しており、植物フェノタイピング手法が研究の中心である。

abstractwe introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision.

Plant Cell Wall Enzymatic Deconstruction: Bridging the Gap Between Micro and Nano Scales ↗

Understanding and overcoming the resistance of plant cell wall to enzymatic deconstruction is crucial to achieve a sustainable and economical conversion of plant biomass to bio-based products as alternatives to petroleum-based products. Despite the significant scientific advances over the past decades, the plant cell wall deconstruction at cell and tissue scales has remained under-investigated. In this study, to quantitatively characterize plant cell wall deconstruction, we set up an original imaging pipeline by combining time-lapse 4D (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify cell wall deconstruction at cell and tissue scales offering a digital representation of cell wall deconstruction. Using this pipeline on poplar wood sections, we computed dynamics of several cellular parameters (e.g. cell wall volume, surface area, and number of cell neighbors) while measuring cellulose conversion. The results showed that the effect of enzymatic deconstruction at the cell scale is predominantly noticeable in terms of cell wall volume reduction rather than a significant decrease in surface area and accessible surface area. The results also revealed a negative correlation between pre-hydrolysis 3D cell wall compactness measures and volumetric cell wall deconstruction. The strength of this correlation was modulated by enzymatic activity. Combining cell wall compactness with the number of neighboring cells as a tissue-scale parameter yielded a stronger correlation. Our results also revealed a strong positive correlation between average volumetric cell wall deconstruction and cellulose conversion, thus establishing a link between key parameters and bridging the gap between nano and micro scales.

Why it matches plant phenotyping methods植物細胞壁の分解状態を定量化する4D蛍光画像パイプラインと計算ツールの開発が研究の中心であり、細胞壁体積・表面積・細胞隣接数などの植物組織形質を抽出している。

abstractwe set up an original imaging pipeline by combining time-lapse 4D (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify cell wall deconstruction at cell and tissue scales

山岳地のブナ樹冠変化追跡のためのRTK非搭載UAVで撮影した写真による3次元表面点群作成 : 撮影方法と標定点設置の組み合わせにおける視認性と精度評価 ↗

To monitor the condition of beech canopies in a reproducible, recordable, and labour-efficient manner, we investigated flight planning, aerial photography, and structure-from-motion (SfM) processing techniques to generate orthoimages with accurate positioning using non-real-time kinematic (RTK) unmanned aerial vehicles (UAVs) in mountainous regions where Internet access is restricted. The automated UAV flight maintained a consistent altitude. The data encompassed a 20-ha region on the summits of Mt. Tanzawa and Mt. Hirugatake in the Tanzawa Mountains, and were acquired during July and August of 2021 and 2022, with a ground resolution of 2 cm/pixel. The directly downward photos had a parallel perspective with overlap and side-wrap percentages of 80% and 60%, respectively. The oblique-perspective images had a parallel view, with the camera lens tilted 20° or 30° forward, accompanied by overlap and side-wrap percentages of approximately 40% and 30%, respectively. Ground control points (GCPs) and verification points were established using the precise point positioning (PPP)-RTK method with a dual-frequency global navigation satellite system (GNSS). A virtual reference station received correction signals via the quasi-zenith Michibiki satellite. The GCPs were located in areas with an unobstructed view of the sky, while the validation points were placed at terrestrial features, such as on a staircase or bench along a mountain trail. The study involved manipulating aerial photographs and using GCPs for SfM processing, with variable SfM processing quality. The objective was to compare the clarity and positional accuracy of the orthoimages and a three-dimensional (3D) surface-point cloud obtained through SfM processing. High-quality SfM processing and GCP correction using both nadir and oblique imagery produced colour orthoimages and a 3D surface-point cloud with spatial coordinate accuracy within 0.3 m. The resulting images had satisfactorily clear views of exposed branches and missing canopy material, ensuring dependable canopy identification and tracking. This method proved effective for monitoring and comparing individual trees over time, although some issues remain, such as lens calibration and identifying the optimal GCP locations.

Why it matches plant phenotyping methodsUAV画像とSfMによる3次元点群・オルソ画像生成、GCP配置、精度評価を中心に、ブナ樹冠の状態・欠損を再現可能に取得する手法を開発・検証しているため。

abstractwe investigated flight planning, aerial photography, and structure-from-motion (SfM) processing techniques to generate orthoimages with accurate positioning

CottonSense: A high-throughput field phenotyping system for cotton fruit segmentation and enumeration on edge devices ↗

High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton ( Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R 2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R 2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense .

Why it matches plant phenotyping methods綿花の果実を画像から分割・列挙し、専門家および手作業カウントで検証する高スループット表現型解析システムの開発が中心である。

abstractThis study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll.

Dancing with the Stars: Using Image Analysis to Study the Choreography of the Endoplasmic Reticulum and Its Partners and of Movement Within Its Tubules. ↗

In this chapter, approaches to the image analysis of the choreography of the plant endoplasmic reticulum (ER) labeled with fluorescent fusion proteins ("stars," if you wish) are presented. The approaches include the analyses of those parts of the ER that are attached through membrane contact sites to moving or non-moving partners (other "stars"). Image analysis is also used to understand the nature of the tubular polygonal network, the hallmark of this organelle, and how the polygons change over time due to tubule sliding or motion. Furthermore, the remodeling polygons of the ER interact with regions of fundamentally different topologies, the ER cisternae, and image analysis can be used to separate the tubules from the cisternae. ER cisternae, like polygons and tubules, can be motile or stationary. To study which parts are attached to non-moving partners, such as domains of the ER that form membrane contact sites with the plasma membrane/cell wall, an image analysis approach called persistency mapping has been used. To study the domains of the ER that move rapidly and stream through the cell, image analysis of optic flow has been used. However, optic flow approaches confuse the movement of the ER itself with the movement of proteins within the ER. As an overall measure of ER dynamics, optic flow approaches are of value, but their limitation as to what exactly is "flowing" needs to be specified. Finally, there are important imaging approaches that directly address the movement of fluorescent proteins within the ER lumen or in the membrane of the ER. Of these, fluorescence recovery after photobleaching (FRAP), inverse FRAP (iFRAP), and single particle tracking approaches are described.

Why it matches plant phenotyping methods植物ERの形態・動態を画像解析で抽出する手法を中心に扱う方法論的レビューであり、植物状態の画像ベース計測が主題である。

abstractIn this chapter, approaches to the image analysis of the choreography of the plant endoplasmic reticulum (ER) labeled with fluorescent fusion proteins ("stars," if you wish) are presented.

Multiparameter-based photosynthetic state transitions of single phytoplankton cells ↗

Phytoplankton are a major source of primary production. Their photosynthetic fluorescence uniquely reports on their type, physiological state and response to environmental conditions. Changes in phytoplankton photophysiology are commonly monitored by bulk fluorescence spectroscopy, where gradual changes are reported in response to different perturbations such as light intensity changes. What is the meaning of such trends in bulk parameters if their values report ensemble averages of multiple unsynchronized cells? To answer this, we developed an experimental scheme that enables acquiring multiple fluorescence parameters, from multiple excitation sources and spectral bands. This enables tracking fluorescence intensities, brightnesses and their ratios, as well as mean photon nanotimes equivalent to mean fluorescence lifetimes, one cell at a time. We monitored three different phytoplankton species during diurnal cycles and in response to an abrupt increase in light intensity. Our results show that we can define specific subpopulations of fluorescence parameters for each of the phytoplankton species and in response to varying light conditions. Importantly, we identify the cells undergo well-defined transitions between these subpopulations that characterize the different light behaviors. The approach shown in this work will be useful in the exact characterization of phytoplankton cell states and parameter signatures in response to different changes these cells experience in marine environments, which will be useful in monitoring marine-related effects of global warming. Significance StatementUsing three representatives of red-linage phytoplankton we demonstrate distinct photophysiological behaviors at the single cell level. The results indicate cell wide coordination into discrete cell states. We test cell state transitions as a function of light acclimation during diurnal cycle and in response to large intensity increases, which stimulate distinct photoprotective response mechanisms. The analysis was made possible through the development of flow-based confocal detection at multiple excitation and emission wavelengths monitoring both pigment composition and photosynthetic performance. Our findings show that with enough simultaneously recorded parameters per each cell, the detection of multiple phytoplankton species at their distinct cell states is possible. This approach will be useful in examining the response of complex natural marine populations to environmental perturbations.

Why it matches plant phenotyping methods単一植物プランクトン細胞の光合成状態・生理状態を多波長蛍光で取得するフロー型共焦点検出法を開発し、環境応答の細胞状態を解析しており、表現型取得法が研究の中心です。

abstractwe developed an experimental scheme that enables acquiring multiple fluorescence parameters, from multiple excitation sources and spectral bands.

Graph Neural Networks for Lightweight Plant Organ Tracking ↗

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

Why it matches plant phenotyping methods植物器官の追跡を目的とするグラフニューラルネットワーク手法であり、植物フェノタイピング手法の開発が中心と判断できる。

titleGraph Neural Networks for Lightweight Plant Organ Tracking

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.

Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images. ↗

BACKGROUND: Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS: First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. CONCLUSIONS: We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.

Why it matches plant phenotyping methodsブドウ果実の画像から個々のベリーを検出・セグメント化・追跡し、成長および色彩という植物形質を自動抽出する手法の開発と検証が中心である。

abstractwe propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches.

Leveraging data from plant monitoring into crop models ↗

Researchers using crop models have been devising new roles for data and crop modeling based on the former’s increased availability and the new techniques developed for the latter. From the various available techniques, modeling may be tackled by data-driven methods or through a process-based approach. Process-based or mechanistic models may nonetheless take advantage of real-time observations through data assimilation. And while this approach has been widely used for field crops, this is not the case for crops grown in protected environments. We present a case study of data assimilation in a protected environment, capturing tomato growth data from different sources. We updated growth estimates of the Reduced State TOMGRO model, by assimilating observational data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras, using the Unscented Kalman Filter and the Ensemble Kalman Filter. Since these techniques had not been used yet in the protected cultivation of tomatoes, it was necessary to develop the observation models as well, establishing the relationship between the observed variables and the ones estimated by the process-based model. The employed measurements, i.e., area of organs observed in pictures and plant-water mass, seemed suitable for tracking plant growth and for obtaining good approximations of the state variables estimated by the model. However, the quality of observations and of observation models was crucial for good performance of the assimilation techniques. As with other crops, it was not the case that assimilating one observation was useful for improving the value of others, including yield. We also observed that the assimilation performed better than calibrated models when there was a need to adjust the estimates to growth disturbances and that when filters lead to better yield estimates, continuous observations may not be required. There are then several steps and decisions that should be considered when bringing the idea from its application in field crops to protected environments and more studies are required to better determine the best approach.

Why it matches plant phenotyping methodsトマトの成長を連続的な植物体重測定と画像からの器官面積測定で取得し、観測モデルとデータ同化手法を開発・適用している。植物形質の取得と推定が研究の中心である。

abstractWe updated growth estimates of the Reduced State TOMGRO model, by assimilating observational data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras, using the Unscented Kalman Filter and the Ensemble Kalman Filter.

Development of a crop monitoring system using computer vision and machine learning techniques ↗

The growing global population demands increased agricultural production, necessitating the implementation of smart farming practices. The development of an automated crop monitoring system using computer vision and machine learning techniques can help to reduce the manual labor involved in crop management and enhance crop yield. This paper proposes a crop monitoring system that utilizes a camera mounted on a mobile robotic platform to capture images of crops at regular intervals. The images are analyzed using computer vision algorithms to detect and track plant growth, pest infestations, and nutrient deficiencies. Machine learning techniques are then applied to the data to predict crop yield. The system is designed to be scalable and can be deployed on a variety of crops, making it suitable for use in large-scale agricultural operations. Preliminary results demonstrate the system's effectiveness in detecting plant growth with an overall accuracy rate of 95%. The proposed system has the potential to significantly improve crop management practices and increase crop yield, thereby contributing to sustainable agriculture development.

Why it matches plant phenotyping methodsカメラ搭載ロボットと画像解析・機械学習によって植物の生長を検出・追跡する作物モニタリング手法が研究の中心であり、植物形質の取得性能も評価している。

abstractThe development of an automated crop monitoring system using computer vision and machine learning techniques

Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development ↗

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σVV0, σVH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σVH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σVH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σVV0, σVH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture.

Why it matches plant phenotyping methodsSARセンサー由来指標と時系列フィルタリングを比較・評価し、作物の生育段階や草丈などの植物形質追跡への有用性を検証しており、フェノタイピング手法の評価が中心である。

abstractthis work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato.

Tracking succession by means of 3D scans of plant communities in a glacier forefield to infer assembly processes ↗

In primary successions, assembling plant communities are key for ecosystem functioning and stability. Often, plant successions are described on a taxonomic, functional and/or phylogenetic level, where species' identities, traits or evolutionary histories are considered. In this study, we exploited community features characterizing whole plant assemblages to capture emerging properties only available at the community level. Next to features aggregating over multiple plant species, we used a customized multispectral 3D plant scanner extracting digital community features as proxies for the frequency distribution of traits, productivity, prevalence of plant competition, and plant strategies and functions. Using these community features, we provide a comprehensive description of plant successions along glacier forefield. Additionally, we used a community feature‐based framework assessing the covariance of community feature dissimilarities and environmental dissimilarity to explore the mechanisms underlying plant community assemblies. Our data indicate a shift from fast‐growing and acquisitive, to slow‐growing and conservative plant strategies; increased productivity; higher competition between plant species; and an increasing contribution to the biogeomorphic stability along the successional gradient. Our results further indicate that stochastic processes dominate in early succession, whereas communities are mainly shaped by deterministic processes including environmental filtering and species interactions at late succession. We conclude that assessments of plant community features, facilitated by the use of field‐ready 3D scanners, provide complementary information to trait‐based approaches on the species level with implications for ecological processes.

Why it matches plant phenotyping methodsカスタマイズしたマルチスペクトル3D植物スキャナーで群集形質・生産性・競争などの植物状態を抽出し、遷移解析に中核的に利用しているため、植物フェノタイピング手法の実質的な応用に該当する。

abstractwe used a customized multispectral 3D plant scanner extracting digital community features as proxies for the frequency distribution of traits, productivity, prevalence of plant competition, and plant strategies and functions.

StomataTracker: Revealing circadian rhythms of wheat stomata with in-situ video and deep learning ↗

Plant stomata are essential channels for gas exchange between plants and the environment. The infrared gas-exchange system has greatly accelerated the studies of stomatal conductance (gₛ). Nevertheless, due to the lack of in-situ monitoring techniques, the behavior of stomata themselves remains poorly understood, especially in nocturnal environmental conditions. Here, a deep-learning-based stoma tracking pipeline (StomataTracker) was first proposed to continuously monitor stoma traits from unprecedentedly long-term, continuous, and non-destructive video data. Compared to the semi-automatic method (ImageJ), the open-source StomataTracker could greatly improve the extraction efficiency from 207 s to 1.47 s of stomatal traits, including stomatal area, perimeter, length, and width. The R² adjusted of the four stomatal traits ranged from 0.620 to 0.752. In addition, the rhythm of wheat stomata opening in a completely dark environment was first reported from long-term video data. The closed time of stoma at night was negatively correlated with stomatal traits, and the R ranged from −0.583 to −0.855. The heterogeneity of stomatal behavior also highlighted that smaller stomata have the rhythm pattern of longer closure time at night. Overall, our study provides a novel perspective for stomatal study, and it is conducive to accelerating the application of stomatal circadian rhythm in wheat breeding.

Why it matches plant phenotyping methods深層学習による気孔追跡パイプラインを開発し、動画から気孔形質を抽出・既存法と比較検証しているため、植物フェノタイピング手法が中心である。

abstractHere, a deep-learning-based stoma tracking pipeline (StomataTracker) was first proposed to continuously monitor stoma traits from unprecedentedly long-term, continuous, and non-destructive video data.

(Invited) Nanosensor Coupling to Human and Plant Interfaces for Real Time Chemical Information Transfer ↗

Our laboratory at MIT has been interested over the past few years in new techniques to facilitate the transfer of chemical information from living organisms, specifically plants, animals and humans, for applications ranging from precision agriculture to precision medicine. This presentation will discuss recent advances on this topic. As tool towards this end, fluorescent nanosensors hold the potential to revolutionize life sciences and medicine. However, their adaptation and translation into the in vivo environment is fundamentally hampered by unfavourable tissue scattering and intrinsic autofluorescence. Here we develop wavelength-induced frequency filtering (WIFF) whereby the fluorescence excitation wavelength is modulated across the absorption peak of a nanosensor, allowing the emission signal to be separated from the autofluorescence background, increasing the desired signal relative to noise, and internally referencing it to protect against artefacts. Using highly scattering phantom tissues, an SKH1-E mouse model and other complex tissue types, we show that WIFF improves the nanosensor signal-to-noise ratio across the visible and near-infrared spectra up to 52-fold. This improvement enables the ability to track fluorescent carbon nanotube sensor responses to riboflavin, ascorbic acid, hydrogen peroxide and a chemotherapeutic drug metabolite for depths up to 5.5 ± 0.1 cm when excited at 730 nm and emitting between 1,100 and 1,300 nm, even allowing the monitoring of riboflavin diffusion in thick tissue. As an application, nanosensors aided by WIFF detect the chemotherapeutic activity of temozolomide transcranially at 2.4 ± 0.1 cm through the porcine brain without the use of fibre optic or cranial window insertion. The ability of nanosensors to monitor previously inaccessible in vivo environments will be important for life-sciences research, therapeutics and medical diagnostics. Also towards this overall objective, our laboratory at MIT has been interested in exploring the relatively new interface between living plants and non-biological nanostructures to impart the former with new and enhanced functions, which we call Plant Nanobionics. We have developed a theory of subcellular uptake and kinetic trapping of a wide range of nanoparticles, validated in-vivo in living plants. Confocal visible and near infrared fluorescent microscopy and single particle tracking of Gold-Cystein-AF405 (GNP-Cys-AF405), Streptavidin-Quantum Dot (SA-QD), Dextran and Poly(acrylic acid) nanoceria, and various polymer-wrapped SWCNT, including lipid-PEG-SWCNT, chitosan-SWCNT and (AT)15-SWCNT, were used to demonstrate that particle size and the magnitude, but not the sign, of the zeta potential are key in determining whether a particle is spontaneously and kinetically trapped within chloroplasts or the cytosol. We develop a mathematical model of this Lipid Exchange Envelope Penetration (LEEP) mechanism, which agrees well with observations of this size and zeta potential dependence. As an application, we rationally designed a chitosan-complexed single-walled carbon nanotube (SWNT) as nanocarriers to selectively deliver plasmid DNA (pDNA) to chloroplasts of different plant species without external biolistic or chemical aid. We demonstrate chloroplast-targeted transgene delivery and expression in living mature arugula (Eruca sativa) and watercress (Nasturitium officinale) plants in planta and in isolated Arabidopsis thaliana mesophyll protoplasts. Another application of nanoparticles and nanotechnology to plant sciences is in the form of biochemical sensors that operate in planta and across diverse species. Using non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species: lettuce (Lactuca sativa), arugula (Eruca sativa), spinach (Spinacia oleracea), strawberry blite (Blitum capitatum), sorrel (Rumex acetosa), and Arabidopsis thaliana, ranked in order of wave speed from 0.44 to 3.10 cm/min. The H2O2 wave tracks the concomitant surface potential wave measured electrochemically for the series of plants. We show that the plant NADPH oxidase RbohD, glutamate receptor-like channels (GLR3.3 and GLR3.6) are all critical to the propagation of the H2O2 waveform upon wounding. Our findings highlight the utility of a new type of nanosensor probe that is species-independent and capable of real-time, spatial and temporal biochemical measurements in planta.

Why it matches plant phenotyping methods植物体内の化学状態を非破壊・リアルタイムに測定するナノセンサーと信号処理法を開発し、創傷後のH2O2波の空間・時間特性を植物で実証しており、植物フェノタイピング手法が中心である。

abstractUsing non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species

A low-cost and open-source imaging platform reveals spatiotemporal insight into Arabidopsis leaf elongation and movement ↗

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality, as well as molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity and their interactions. Tracking both elongation and angle of petiole, lamina and entire leaf revealed insight into R:FR sensitivities of leaf growth and movement dynamics, and its interactions with background light intensity. We also identified spatial separation in hyponastic response regulation for the petiole and the lamina of the leaf, depending on the light conditions.

Why it matches plant phenotyping methods低コスト撮像プラットフォームとオープンソース画像解析パイプラインを開発し、葉の伸長・角度・運動を高時間分解能で定量することが中心である。

abstractHere, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution.

Seed Kernel Counting using Domain Randomization and Object Tracking Neural Networks ↗

High-throughput phenotyping (HTP) of seeds, also known as seed phenotyping, is the comprehensive assessment of complex seed traits such as growth, development, tolerance, resistance, ecology, yield, and the measurement of parameters that form more complex traits. One of the key aspects of seed phenotyping is cereal yield estimation that the seed production industry relies upon to conduct their business. While mechanized seed kernel counters are available in the market currently, they are often priced high and sometimes outside the range of small scale seed production firms' affordability. The development of object tracking neural network models such as You Only Look Once (YOLO) enables computer scientists to design algorithms that can estimate cereal yield inexpensively. The key bottleneck with neural network models is that they require a plethora of labelled training data before they can be put to task. We demonstrate that the use of synthetic imagery serves as a feasible substitute to train neural networks for object tracking that includes the tasks of object classification and detection. Furthermore, we propose a seed kernel counter that uses a low-cost mechanical hopper, trained YOLOv8 neural network model, and object tracking algorithms on StrongSORT and ByteTrack to estimate cereal yield from videos. The experiment yields a seed kernel count with an accuracy of 95.2\% and 93.2\% for Soy and Wheat respectively using the StrongSORT algorithm, and an accuray of 96.8\% and 92.4\% for Soy and Wheat respectively using the ByteTrack algorithm.

Why it matches plant phenotyping methods種子カーネル数という植物形質を対象に、合成画像、YOLOv8、物体追跡を組み合わせた低コスト計数システムを開発・精度評価しており、表現型取得手法が研究の中心である。

abstractWe demonstrate that the use of synthetic imagery serves as a feasible substitute to train neural networks for object tracking that includes the tasks of object classification and detection.

Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits ↗

Grapevine phenotyping is the process of determining the physical properties (e.g., size, shape, and number) of grape bunches and berries. Grapevine phenotyping information provides valuable characteristics to monitor the sanitary status of the vine. Knowing the number and dimensions of bunches and berries at an early stage of development provides relevant information to the winegrowers about the yield to be harvested. However, the process of counting and measuring is usually done manually, which is laborious and time-consuming. Previous studies have attempted to implement bunch detection on red bunches in vineyards with leaf removal and surveys have been done using ground vehicles and handled cameras. However, Unmanned Aerial Vehicles (UAV) mounted with RGB cameras, along with computer vision techniques offer a cheap, robust, and timesaving alternative. Therefore, Multi-object tracking and segmentation (MOTS) is utilized in this study to determine the traits of individual white grape bunches and berries from RGB videos obtained from a UAV acquired over a commercial vineyard with a high density of leaves. To achieve this goal two datasets with labelled images and phenotyping measurements were created and made available in a public repository. PointTrack algorithm was used for detecting and tracking the grape bunches, and two instance segmentation algorithms - YOLACT and Spatial Embeddings - have been compared for finding the most suitable approach to detect berries. It was found that the detection performs adequately for cluster detection with a MODSA of 93.85. For tracking, the results were not sufficient when trained with 679 frames.This study provides an automated pipeline for the extraction of several grape phenotyping traits described by the International Organization of Vine and Wine (OIV) descriptors. The selected OIV descriptors are the bunch length, width, and shape (codes 202, 203, and 208, respectively) and the berry length, width, and shape (codes 220, 221, and 223, respectively). Lastly, the comparison regarding the number of detected berries per bunch indicated that Spatial Embeddings assessed berry counting more accurately (79.5%) than YOLACT (44.6%).

Why it matches plant phenotyping methodsUAV RGB動画と画像解析によりブドウ房・果粒の形状や寸法を自動抽出するパイプラインを開発・比較し、データセットも公開しており、植物表現型取得が中心である。

abstractMulti-object tracking and segmentation (MOTS) is utilized in this study to determine the traits of individual white grape bunches and berries from RGB videos obtained from a UAV

NDMFCS: An automatic fruit counting system in modern apple orchard using abatement of abnormal fruit detection ↗

Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fruit counting systems, abnormal fruit detection has often been caused by these algorithms detecting non-target fruits that have similar growth characteristics to target fruits. For abnormal fruit detection, detected fruits in the back row of the tree were defined as DFBRT, while detected fruits on the ground were defined as DFG. Both of them would result in a higher number of fruits counting than the ground truth. This study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard. NDMFCS consists of three sub-systems, i.e. object detection based on You Only Look Once Version 4-tiny (YOLOv4-tiny), abatement of abnormal fruit detection based on threshold, and fruit counting based on trunk tracking and identity document (ID) assignment. YOLOv4-tiny was selected to implement detection of fruits and trunks, whose output is confidence and pixel coordinates of detected object. The DFBRT and DFG were abated by thresholds to improve detection performance of fruit. This meant that detected fruits were removed when their distance from camera is further than a distance threshold or the confidence of fruit detection is less than a confidence threshold. Finally, fruit counting was implemented by trunk tracking and ID assignment, where each fruit was assigned a unique tracking ID. Results on 10 sets of original videos indicated that average fruit detection precision was improved from 89.1% to 93.3% after abatement of abnormal fruit detection. Also, Multiple Object Tracking Accuracy and Multiple Object Tracking Precision were improved on average by 4.2% and 3.3%, respectively, while average ID Switch Rate was decreased on average by 1.1%. And average fruit counting accuracy was improved to 95.0% by 4.2%. Coefficient of determination (R²) was 0.97, which indicated the number of fruits counted by NDMFCS was near to the ground truth. These results demonstrate that the abatement of abnormal fruit detection can improve performance of apple counting, which has the potential to provide a technical support for estimating fruit yield in modern apple orchards.

Why it matches plant phenotyping methodsリンゴ果実数という植物器官の形質を、物体検出・追跡・閾値処理で自動抽出・計数する手法を開発し、精度を検証しているため、植物フェノタイピング手法が中心である。

abstractThis study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard.

GFP labeling of a Bradyrhizobium strain and an attempt to track the crack entry process during symbiosis with peanuts ↗

Compared to the well-studied model legumes, where symbiosis is established via root hair entry, the peanut is infected by Bradyrhizobium through the crack entry, which is less common and not fully understood. Crack entry is, however, considered a primitive symbiotic infection pathway, which could be potentially utilized for engineering non-legume species with nitrogen fixation ability. We utilized a fluorescence-labeled Bradyrhizobium strain to help in understanding the crack entry process at the cellular level. A modified plasmid pRJPaph-bjGFP, harboring the codon-optimized GFP gene and tetracycline resistance gene, was created and conjugated into Bradyrhizobium strain Lb8, an isolate from peanut nodules, through tri-parental mating. Microscopic observation and peanut inoculation assays confirmed the successful GFP tagging of Lb8, which is capable of generating root nodules. A marking system for peanut root potential infection sites and an optimized sample preparation protocol for cryostat sectioning was developed. The feasibility of using the GFP-tagged Lb8 for observing crack entry was examined. GFP signal was detected at the nodule primordial stage and the following nodule developmental stages with robust GFP signals observed in infected cells in the mature nodules. Spherical bacteroids in the root tissue were visualized at the nodules' inner cortex under higher magnification, reflecting the trace along the rhizobial infection path. The GFP labeled Lb8 can serve as an essential tool for plant-microbe studies between the cultivated peanut and Bradyrhizobium, which could facilitate further study of the crack entry process during the legume-rhizobia symbiosis.

Why it matches plant phenotyping methodsGFP標識菌の蛍光顕微鏡観察に加え、感染部位のマーキングと凍結切片作製プロトコルを開発し、植物根・根粒内の感染状態を可視化する方法が中心である。

abstractA marking system for peanut root potential infection sites and an optimized sample preparation protocol for cryostat sectioning was developed.

Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images ↗

Abstract Background Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, as well as its impacts on both vine yield and wine quality, remain nearly unknown. To address these limitations, we propose to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. Result First, a deep-learning approach is used to detect berries with at least 50±10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. Conclusions We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one genotype, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.

Why it matches plant phenotyping methodsブドウ果粒の検出・セグメンテーション・時系列追跡を開発し、ロボット型高スループット表現型解析基盤で検証して、個別果粒の成長・着色形質を抽出する方法が中心である。

abstractwe propose to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches

VA-TIRFM-based SM kymograph analysis for dwell time and colocalization of plasma membrane protein in plant cells ↗

Background The plasma membrane (PM) proteins function in a highly dynamic state, including protein trafficking and protein homeostasis, to regulate various biological processes. The dwell time and colocalization of PM proteins are considered to be two important dynamic features determining endocytosis and protein interactions, respectively. Dwell-time and colocalization detected using traditional fluorescence microscope techniques are often misestimated due to bulk measurement. In particular, analyzing these two features of PM proteins at the single-molecule level with spatiotemporal continuity in plant cells remains greatly challenging. Results We developed a single molecular (SM) kymograph method, which is based on variable angle-total internal reflection fluorescence microscopy (VA-TIRFM) observation and single-particle (co-)tracking (SPT) analysis, to accurately analyze the dwell time and colocalization of PM proteins in a spatial and temporal manner. Furthermore, we selected two PM proteins with distinct dynamic behaviors, including AtRGS1 (Arabidopsis regulator of G protein signaling 1) and AtREM1.3 (Arabidopsis remorin 1.3), to analyze their dwell time and colocalization upon jasmonate (JA) treatment by SM kymography. First, we established new 3D (2D+t) images to view all trajectories of the interest protein by rotating these images, and then we chose the appropriate point without changing the trajectory for further analysis. Upon JA treatment, the path lines of AtRGS1-YFP appeared curved and short, while the horizontal lines of mCherry-AtREM1.3 demonstrated limited changes, indicating that JA might initiate the endocytosis of AtRGS1. Analysis of transgenic seedlings coexpressing AtRGS1-YFP/mCherry-AtREM1.3 revealed that JA induces a change in the trajectory of AtRGS1-YFP, which then merges into the kymography line of mCherry-AtREM1.3, implying that JA increases the colocalization degree between AtRGS1 and AtREM1.3 on the PM. These results illustrate that different types of PM proteins exhibit specific dynamic features in line with their corresponding functions. Conclusions The SM-kymograph method provides new insight into quantitively analyzing the dwell time and correlation degree of PM proteins at the single-molecule level in living plant cells.

Why it matches plant phenotyping methods植物細胞膜タンパク質の滞在時間・共局在という状態を、VA-TIRFMと単一粒子追跡に基づくSM-kymograph法で定量する手法を開発しており、表現型取得・解析が研究の中心である。

abstractWe developed a single molecular (SM) kymograph method, which is based on variable angle-total internal reflection fluorescence microscopy (VA-TIRFM) observation and single-particle (co-)tracking (SPT) analysis, to accurately analyze the dwell time and colocalization of PM proteins in a spatial and temporal manner.

Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking ↗

The ability to accurately represent and localise relevant objects is essential for robots to carry out tasks effectively. Traditional approaches, where robots simply capture an image, process that image to take an action, and then forget the information, have proven to struggle in the presence of occlusions. Methods using multi-view perception, which have the potential to address some of these problems, require a world model that guides the collection, integration and extraction of information from multiple viewpoints. Furthermore, constructing a generic representation that can be applied in various environments and tasks is a difficult challenge. In this paper, a novel approach for building generic representations in occluded agro-food environments using multi-view perception and 3D multi-object tracking is introduced. The method is based on a detection algorithm that generates partial point clouds for each detected object, followed by a 3D multi-object tracking algorithm that updates the representation over time. The accuracy of the representation was evaluated in a real-world environment, where successful representation and localisation of tomatoes in tomato plants were achieved, despite high levels of occlusion, with the total count of tomatoes estimated with a maximum error of 5.08% and the tomatoes tracked with an accuracy up to 71.47%. Novel tracking metrics were introduced, demonstrating that valuable insight into the errors in localising and representing the fruits can be provided by their use. This approach presents a novel solution for building representations in occluded agro-food environments, demonstrating potential to enable robots to perform tasks effectively in these challenging environments.

Why it matches plant phenotyping methodsトマト果実の3D再構成・追跡と総数推定を中心に、多視点画像処理手法を開発・実環境で評価しており、果実という植物器官の形態・数量 phenotyping に該当する。

abstracta novel approach for building generic representations in occluded agro-food environments using multi-view perception and 3D multi-object tracking is introduced

Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking. ↗

Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation. In recent studies, phenotyping of rice panicles during the heading-flowering stage still lacks comprehensive analysis, especially of panicle development under different nitrogen treatments. In this work, we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5, ResNet50, and DeepSORT models. Combined with field observation data, the proposed method was used to test whether it has an ability to identify subtle differences in panicle developments under different nitrogen treatments. The result shows that panicle counting throughout the heading-flowering stage achieved high accuracy ( R 2 = 0.96 and RMSE = 1.73), and heading date was estimated with an absolute error of 0.25 days. In addition, by identical panicle tracking based on the time-series images, we analyzed detailed flowering phenotypic changes of a single panicle, such as flowering duration and individual panicle flowering time. For rice population, with an increase in the nitrogen application: panicle number increased, heading date changed little, but the duration was slightly extended; cumulative flowering panicle number increased, rice flowering initiation date arrived earlier while the ending date was later; thus, the flowering duration became longer. For a single panicle, identical panicle tracking revealed that higher nitrogen application led to earlier flowering initiation date, significantly longer flowering days, and significantly longer total duration from vigorous flowering beginning to the end (total DBE). However, the vigorous flowering beginning time showed no significant differences and there was a slight decrease in daily DBE.

Why it matches plant phenotyping methods時系列画像とYOLO v5、ResNet50、DeepSORTを組み合わせ、イネ穂の検出・追跡から開花関連形質を自動取得するパイプラインが研究の中心であり、窒素処理への応用と精度評価も行っているため。

abstractwe proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5, ResNet50, and DeepSORT models.

A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard ↗

Accurate count of fruits is important for producers to make adequate decisions in production management. Although some algorithms based on machine vision have been developed to count fruits which were all implemented by tracking fruits themselves, those algorithms often make mismatches or even lose targets during the tracking process due to the large number of highly similar fruits in appearance. This study aims to develop an automated video processing method for improving the counting accuracy of apple fruits in orchard environment with modern vertical fruiting-wall architecture. As the trunk is normally larger than fruits and appears clearly in the video, the trunk is thus selected as a single-object tracking target to reach a higher accuracy and higher speed tracking than the commonly used method of fruit-based multi-object tracking. This method was trained using a YOLOv4-tiny network integrated with a CSR-DCF (channel spatial reliability-discriminative correlation filter) algorithm. Reference displacement between consecutive frames was calculated according to the frame motion trajectory for predicting possible fruit locations in terms of previously detected positions. The minimum Euclidean distance of detected fruit position and the predicted fruit position was calculated to match the same fruits between consecutive video frames. Finally, a unique ID was assigned to each fruit for counting. Results showed that mean average precision of 99.35% for fruit and trunk detection was achieved in this study, which could provide a good basis for fruit accurate counting. A counting accuracy of 91.49% and a correlation coefficient R² of 0.9875 with counting performed by manual counting were reached in orchard videos. Besides, proposed counting method can be implemented on CPU at 2 ∼ 5 frames per second (fps). These promising results demonstrate the potential of this method to provide yield data for apple fruits or even other types of fruits.

Why it matches plant phenotyping methodsリンゴ果実の検出・追跡・計数という植物器官および収量関連形質の取得手法を、深層学習と動画処理で開発し、精度検証しているため、植物フェノタイピング手法が研究の中心である。

abstractThis study aims to develop an automated video processing method for improving the counting accuracy of apple fruits in orchard environment with modern vertical fruiting-wall architecture.

A bench-top Dark-Root device built with LEGO® bricks enables a non-invasive plant root development analysis in soil conditions mirroring nature ↗

Roots are the hidden parts of plants, anchoring their above-ground counterparts in the soil. They are responsible for water and nutrient uptake and for interacting with biotic and abiotic factors in the soil. The root system architecture (RSA) and its plasticity are crucial for resource acquisition and consequently correlate with plant performance while being highly dependent on the surrounding environment, such as soil properties and therefore environmental conditions. Thus, especially for crop plants and regarding agricultural challenges, it is essential to perform molecular and phenotypic analyses of the root system under conditions as near as possible to nature (#asnearaspossibletonature). To prevent root illumination during experimental procedures, which would heavily affect root development, Dark-Root (D-Root) devices (DRDs) have been developed. In this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box). The DRD-BIBLOX consists of one or more 3D-printed rhizoboxes, which can be filled with soil while still providing root visibility. The rhizoboxes sit in a scaffold of secondhand LEGO® bricks, which allows root development in the dark and non-invasive root tracking with an infrared (IR) camera and an IR light-emitting diode (LED) cluster. Proteomic analyses confirmed significant effects of root illumination on barley root and shoot proteomes. Additionally, we confirmed the significant effect of root illumination on barley root and shoot phenotypes. Our data therefore reinforces the importance of the application of field conditions in the lab and the value of our novel device, the DRD-BIBLOX. We further provide a DRD-BIBLOX application spectrum, spanning from investigating a variety of plant species and soil conditions and simulating different environmental conditions and stresses, to proteomic and phenotypic analyses, including early root tracking in the dark.

Why it matches plant phenotyping methodsLEGO製の暗所ルートボックスと赤外線カメラによる非侵襲的な根系発達・表現型追跡手法を開発し、その応用範囲を示した研究であり、植物フェノタイピング手法が中心である。

abstractwe describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box).

Plant Leaf Disease Detection using Machine Learning ↗

Abstract: A crucial component of describing plants for tracking plant growth is plant phenotyping. In this research, an effective method for identifying healthy, damaged, or infected leaves utilising image processing and machine learning approaches is presented. Many illnesses deplete the chlorophyll of brown or black markings appear on the leaf area of the leaves. They can be found out utilising machine learning methods for classification, feature extraction, picture preprocessing, and image segmentation. Grey Level Co-occurrence Matrix (GLCM) is used for feature extraction. One of themachine learning techniques used for classification is called the Support Vector Machine (SVM). When compared to the SVM method, the Convolutional Neural Network (CNN) produced better recognition accuracy. Finding disease on crops is a crucial responsibility in agricultural techniques.

Why it matches plant phenotyping methods葉画像から健全・損傷・感染状態を画像処理と機械学習で判定する方法が研究の中心であり、植物病害状態の表現型推定に該当する。

abstractan effective method for identifying healthy, damaged, or infected leaves utilising image processing and machine learning approaches is presented

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort. ↗

The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7-DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video.

Why it matches plant phenotyping methodsUAV動画から小麦穂数という植物形態・収量関連形質を自動抽出する検出・追跡手法を開発し、精度を検証しているため、植物フェノタイピング手法が中心である。

abstractthis paper proposes a counting method based directly on a UAV video multi-objective tracking method

System of counting green oranges directly from trees using artificial intelligence ↗

Abstract Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming increasingly viable in solving agricultural problems. Thus, this work aims to count green oranges directly from the trees through video footage filmed in line along a row of orange trees on the plantation. For the video image processing flow, a solution was proposed integrating the YOLOv4 network with object tracking algorithms. In order to compare the performance of the counting algorithm using the YOLOv4 network, an optimal object detector was simulated in which frame-by-frame corrected detections were used in which all oranges in all video frames were detected, and there were no erroneous detections. The use of YOLOv4 together with object detectors managed to reduce the number of double counting error and obtained a count close to the actual number of oranges visible in the video. The study also resulted in a database with an amount of 644 images with 43109 annotated oranges that can be used in future works.

Why it matches plant phenotyping methods樹上の果実数という植物器官の形質を、動画・YOLOv4・追跡アルゴリズムで自動抽出する手法を開発・評価しており、画像データセットも作成しているため、方法が中心的である。

abstractthis work aims to count green oranges directly from the trees through video footage

A mobile-based system for maize plant leaf disease detection and classification using deep learning ↗

Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

Why it matches plant phenotyping methodsトウモロコシ葉の病害領域を画像から検出・分類し、病斑をセグメンテーションして追跡する深層学習・モバイル手法が研究の中心であり、植物病害状態の表現型推定に該当する。

abstractprovides an application for the detection and classification of diseases in maize crop using deep learning models

Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring ↗

Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.

Why it matches plant phenotyping methods植物の生理状態・病害状態を連続取得するウェアラブル多モーダルセンサープラットフォームの開発が中心であり、機械学習による病害の定量検出も含むため。

abstractWe report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment.

Protocol for real-time imaging, polar protein quantification, and targeted laser ablation of regenerating shoot progenitors in Arabidopsis. ↗

Here, we provide a protocol for real-time tracking of regenerating shoot progenitors, combined with polar protein quantification and targeted laser ablation of callus cells in Arabidopsis. Using Arabidopsis strains expressing GFP-labeled polar auxin efflux carrier, PINFORMED 1 (PIN1) protein, we detail steps to prepare the callus for time-lapse confocal imaging and track the progenitors expressing PIN1-GFP, followed by mapping and quantifying PIN1 polarity using Fiji/ImageJ. We then describe targeted laser ablation of cells and subsequent time-lapse imaging to study regeneration. For complete details on the use and execution of this protocol, please refer to Varapparambath et al. (2022). 1 .

Why it matches plant phenotyping methods再生組織のタイムラプス画像取得、細胞追跡、PIN1極性の画像定量を中心とする実験・解析プロトコルであり、植物状態の取得・抽出法が実質的な主題である。

abstractwe provide a protocol for real-time tracking of regenerating shoot progenitors, combined with polar protein quantification

Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies ↗

Maintenance of sufficient healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semi-synthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time-series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (i) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (ii) investigation of the elusive interaction between biotic stress and physiological senescence. The potentially high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

Why it matches plant phenotyping methods深層学習による画像分割と時系列解析で、コムギ群落の葉・穂の黄化および壊死を定量化する方法を開発・適用し、スコアリングとの相関で検証している。植物病害・老化という状態の取得が研究の中心である。

abstractHere, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation.

Real-time tracking and counting of grape clusters in the field based on channel pruning with YOLOv5s ↗

Accurate fruit counting helps grape wine industry make better logistics and decisions before harvest, and therefore produce higher quality wine. In view of poor real-time performance of the existing fruit tracking and counting methods, and a lack of effective counting methods for cluster-like fruits due to their huge shape variabilities. In this study, an end-to-end lightweight counting pipeline is developed to automate the processing of video data for real-time tracking and counting of grape clusters in field conditions. First, based on channel pruning algorithm, a more lightweight YOLOv5s cluster detection model is obtained, where number of model parameters, model size and floating-point operations (FLOPs) are reduced by 79 %, 76 %, and 58 %, respectively, and the pruned model size is only 3.4 MB. Secondly, the soft non-maximum suppression is introduced in prediction stage to improve detection performance for clusters with overlapping grapes. Test results show that mAP reaches 82.3 % and average inference time is 6.1 ms per image, which effectively reduces model parameters and complexity while ensuring detection accuracy. Finally, online multiple object tracking of clusters is implemented by integrating the detection results and SORT algorithm, where two counting modes are set by introducing counting lines. Test results on 8 videos indicated that the average counting accuracy of the proposed method reached 84.9 %, correlation coefficient with manual counting reached 0.9905, and speed of video processing reached up to 50.4 frames per second (FPS), meeting field real-time requirements. This study provides a timely technical reference for the development of orchard robots to achieve real-time automated yield estimation and accurate crop management decisions.

Why it matches plant phenotyping methodsブドウ房という植物器官の検出・追跡・計数による収量推定手法を開発し、精度・速度を検証しており、表現型取得が研究の中心である。

abstractan end-to-end lightweight counting pipeline is developed to automate the processing of video data for real-time tracking and counting of grape clusters in field conditions

A bench-top dark-root device built with LEGO bricks enables a non-invasive plant root development analysis in soil conditions mirroring nature ↗

Roots are the hidden parts of plants, anchoring their above ground counterparts in the soil. They are responsible for water and nutrient uptake, as well as for interacting with biotic and abiotic factors in the soil. The root system architecture (RSA) and its plasticity are crucial for resource acquisition and consequently correlate with plant performance, while being highly dependent on the surrounding environment, such as soil properties and therefore environmental conditions. Thus, especially for crop plants and regarding agricultural challenges, it is essential to perform molecular and phenotypic analyses of the root system under conditions as near as possible to nature (#asnearaspossibletonature). To prevent root illumination during experimental procedures, which would heavily affect root development, dark-root (D-Root) devices (DRDs) have been developed. In this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box). The DRD-BIBLOX consists of one or more 3D-printed rhizoboxes which can be filled with soil, while still providing root visibility. The rhizoboxes sit in a scaffold of secondhand LEGO® bricks, which allows root development in the dark as well as non-invasive root-tracking with an infrared (IR) camera and an IR light emitting diode (LED) cluster. Proteomic analyses confirmed significant effects of root illumination on barley root and shoot proteome. Additionally, we confirmed the significant effect of root illumination on barley root and shoot phenotypes. Our data therefore reinforces the importance of the application of field conditions in the lab and the value of our novel device, the DRD-BIBLOX. We further provide a DRD-BIBLOX application spectrum, spanning from investigating a variety of plant species and soil conditions as well as simulating different environmental conditions and stresses, to proteomic and phenotypic analyses, including early root tracking in the dark.

Why it matches plant phenotyping methods根系を暗所で非侵襲的に追跡・解析するオープンハードウェア装置を開発し、その構成と応用を示しており、植物表現型取得法が研究の中心である。

abstractIn this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box).

Towards a bionic IoT: Environmental monitoring using smartphone interrogated plant sensors ↗

The utilisation of plants directly as quantifiable natural sensors is proposed. A case study measuring surface wettability of Aucuba japonica, or Japanese Laurel, plants using a novel smartphone field interrogator is demonstrated. This plant has been naturalised globally from Asia. Top-down contact angle measurements map wettability on-site and characterise a range of properties impacting plant health, such as aging, solar and UV exposure, and pollution. Leaves at an early age or in the shadow of trees are found to be hydrophobic with contact angle θ ~ 99°, while more mature leaves under sunlight are hydrophilic with θ ~ 79°. Direct UVA irradiation at λ = 365 nm is shown to accelerate aging, changing contact angle of one leaf from slightly hydrophobic at θ ~ 91° to be hydrophilic with θ ~ 87° after 30 min. Leaves growing beside a road with heavy traffic are observed to be substantially hydrophilic, as low as θ ~ 47°, arising from increased wettability with particulate accumulation on the leaf surface. Away from the road, the contact angle increases as high as θ ~ 96°. The results demonstrate that contact angle measurements using a portable diagnostic IoT edge device can be taken into the field for environmental detection, pollution assessment and more. Using an Internet connected smartphone combined with a plant sensor allows multiple measurements at multiple locations together in real-time, potentially enabling tracking of parameter change anywhere where plants are present or introduced. This hybrid integration of widely distributed living organic systems with the Internet marks the beginning of a new bionic Internet-of-things (b-IoT).

Why it matches plant phenotyping methodsスマートフォン連携の携帯型診断装置を開発・実証し、葉の表面濡れ性（接触角）を植物状態の指標として測定する方法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractmeasuring surface wettability of Aucuba japonica, or Japanese Laurel, plants using a novel smartphone field interrogator is demonstrated.

Phenomics for Komatsuna plant growth tracking using deep learning approach ↗

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

Why it matches plant phenotyping methodsタイトルから、コマツナの成長追跡を深層学習で行うフェノミクス手法が研究の中心と判断できる。

titlePhenomics for Komatsuna plant growth tracking using deep learning approach

Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm. ↗

Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately. In order to address the problems of the low efficiency of the manual counting method and the stability problem of traditional image processing methods in the field complex background environment, a deep-learning-based method for counting maize plants was proposed. Image datasets of the maize field were collected by a low-altitude UAV with a camera onboard firstly. Then a real-time detection model of maize plants was trained based on the object detection model YOLOV5. Finally, the tracking and counting method of maize plants was realized through Hungarian matching and Kalman filtering algorithms. The detection model developed in this study had an average precision mAP@0.5 of 90.66% on the test dataset, demonstrating the effectiveness of the SE-YOLOV5m model for maize plant detection. Application of the model to maize plant count trials showed that maize plant count results from test videos collected at multiple locations were highly correlated with manual count results ( R 2 = 0.92), illustrating the accuracy and validity of the counting method. Therefore, the maize plant identification and counting method proposed in this study can better achieve the detection and counting of maize plants in complex backgrounds and provides a research basis and theoretical basis for the rapid acquisition of maize plant population density.

Why it matches plant phenotyping methodsUAV画像とYOLOV5・追跡アルゴリズムにより、複雑な圃場背景でトウモロコシ個体数（群落密度）を自動推定する方法を開発・検証しており、表現型取得が研究の中心です。

abstracta deep-learning-based method for counting maize plants was proposed

A phenotyping system quantifies pollen populations during heat stress using high- throughput microscopy and computer vision ↗

Plant reproduction is sensitive to heat stress. Pollen tube growth can be accelerated or arrested by high temperatures, leading to unstable tubes, failed sperm cell delivery, and ultimately crop yield loss. Pollen growth dynamics have historically been observed on the scale of individual pollen grains, but there are only a few studies surveying pollen populations across genotypes and environmental conditions. Here we describe a phenotyping system that quantifies tomato pollen characteristics on a large scale and under varied heat stress conditions. In this system, we combined high-throughput bright-field microscopy with automated object detection and tracking to investigate the lives of growing pollen tubes. We used this method to survey pollen from a diverse panel of 220 tomato and close wild relative accessions under different temperatures. This method can be readily adapted to pollen from difference species, providing a rapid way to characterize heat stress responses and molecular functions in flowering plants.

Why it matches plant phenotyping methods高スループット顕微鏡とコンピュータビジョンにより、花粉管の特徴・成長動態を大規模に定量するフェノタイピングシステムを開発・適用しており、表現型取得法が研究の中心です。

abstractHere we describe a phenotyping system that quantifies tomato pollen characteristics on a large scale and under varied heat stress conditions.

Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum ↗

SUMMARY In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index (NDVI) values from the chilling nested association mapping population detected CT QTL that colocialized with manual phenotyping CT QTL. Two of the four first-generation KASP molecular markers, generated using the peak QTL SNPs, failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic F ST analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from two independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13–24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

Why it matches plant phenotyping methodsUASを用いた高スループット表現型計測のスケーラビリティ検証、手動計測との比較、NDVIによる耐寒性QTL検出が研究の主要な技術的要素であるため。

abstractUncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping.

A Deep Local Patch Matching Network for Cell Tracking in Microscopy Image Sequences Without Registration. ↗

Cell tracking is critical for the modeling of plant cell growth patterns. A local graph matching algorithm is proposed to track cells by exploiting the tight spatial topology of cells. However, the local graph matching approach lacks robustness in the unregistered images because the feature descriptors are handcrafted. In this paper, we propose a Deep Local Patch Matching Network (DLPM-Net) to track cells robustly, by exploiting local patches' deep similarity information and cells' spatial-temporal contextual information. Furthermore, to reduce the time consumption during the matching process and enhance tracking accuracy, we take two steps to realize the tracking of non-division cells and the detection of cell divisions. In the first step, the DLPM-Net is employed to match the non-division cells by exploiting the cell pair candidates' local patch contextual information, then the non-matched cells are recorded as the cell division candidates. In the second step, the DLPM-Net is used to detect cell divisions from these non-matched cells, by exploiting the local patch contextual similarity between the mother cell's local patch and daughter cells' local patch. Compared with the existing local graph matching method, the experimental results show that the proposed method gains 29.1% improvement in the tracking accuracy.

Why it matches plant phenotyping methods植物細胞の成長パターン解析を目的に、顕微鏡画像から細胞追跡と細胞分裂検出を行う深層学習手法を開発しており、表現型取得・抽出法が研究の中心である。

abstractCell tracking is critical for the modeling of plant cell growth patterns.

Research on Plant Growth Tracking Based on Point Cloud Segmentation and Registration ↗

Plant phenotypic analysis is of great importance to the development of agricultural engineering, and is one of the core issues in crop science and plant breeding. Since plant growth is spatio-temporal and synchronous, understanding the growth and development of individual plants can help to reveal the growth potential of the whole plot and thus improve planting methods. In recent years, the technical means to analyze the growth situation using 3D point cloud data has received extensive attention. The plant point cloud obtained by scanning plants with LiDAR has the characteristics of high resolution, high precision, etc. Periodic scanning of the same plant for spatio-temporal point cloud data sets allows monitoring of growth through subtle changes of plant organs. Organ tracking of growing plants remains challenging due to the lightward nature of growth, the potential for topological changes and the unpredictability of plant growth over time, with the possibility of new leaf growth and leaf death. This paper designs a plant organ growth tracking method based on point cloud. First of all, for growing plants, we have established a crop point cloud spatio-temporal dataset based on two publicly available point cloud datasets. The data set includes four species, tomato, tobacco, sorghum and maize, each species contains complete organ instance labels, and each organ of the same plant has a unique label, which means that the labels of the same organ of an individual at different scan dates correspond one-to-one. Second, this paper proposes a point cloud data-based plant organ growth tracking method, which uses a cost correlation matrix to automatically track growing plant organs. Finally, based on the set of quantitative evaluation metrics, our algorithm achieves a matching accuracy of 82.89% on the plant spatio-temporal dataset and good growth tracking results in the qualitative analysis.

Why it matches plant phenotyping methods植物器官の成長を3D点群から追跡・定量化する手法を開発し、ラベル付き時系列データセットと評価指標で検証しており、植物フェノタイピング手法が中心である。

abstractThis paper designs a plant organ growth tracking method based on point cloud.

Integrated PET and confocal imaging informs a functional timeline for the dynamic process of vascular reconnection during grafting. ↗

Grafting is a widely used agricultural technique that involves the physical joining of separate plant parts so they form a unified vascular system, enabling beneficial traits from independent genotypes to be captured in a single plant. This simple, yet powerful tool has been used for thousands of years to improve abiotic and biotic stress tolerance, enhance yield, and alter plant architecture in diverse crop systems. Despite the global importance and ancient history of grafting, our understanding of the fundamental biological processes that make this technique successful remains limited, making it difficult to efficiently expand on new genotypic graft combinations. One of the key determinants of successful grafting is the formation of the graft junction, an anatomically unique region where xylem and phloem strands connect between newly joined plant parts to form a unified vascular system. Here, we use an integrated imaging approach to establish a spatiotemporal framework for graft junction formation in the model crop Solanum lycopersicum (tomato), a plant that is commonly grafted worldwide to boost yield and improve abiotic and biotic stress resistance. By combining Positron Emission Tomography (PET), a technique that enables the spatio-temporal tracking of radiolabeled molecules, with high-resolution laser scanning confocal microscopy (LSCM), we are able to merge detailed, anatomical differentiation of the graft junction with a quantitative timeline for when xylem and phloem connections are functionally re-established. In this timeline, we identify a 72-hour window when anatomically connected xylem and phloem strands regain functional capacity, with phloem restoration typically preceding xylem restoration by about 24-hours. Furthermore, we identify heterogeneity in this developmental and physiological timeline that corresponds with microvariability in the physical contact between newly joined rootstock-scion tissues. Our integration of PET and confocal imaging technologies provides a spatio-temporal timeline that will enable future investigations into cellular and tissue patterning events that underlie successful versus failed vascular restoration across the graft junction.

Why it matches plant phenotyping methodsPETと共焦点顕微鏡を統合し、植物の接ぎ木接合部における血管再連結の時空間・機能状態を定量化する手法が研究の中心であるため、植物フェノタイピング手法として収録する。

abstractHere, we use an integrated imaging approach to establish a spatiotemporal framework for graft junction formation in the model crop Solanum lycopersicum (tomato)

Development of a new cold hardiness prediction model for grapevine using phased integration of acclimation and deacclimation responses ↗

Cold injury limits distribution of perennial agricultural crops, though replacement of plants and other management practices may allow for some damage tolerance. However, winter damage to crops such as grapevines (Vitis spp.) can result in losses in yield the following year if buds are damaged, but over many years when vines must be replaced and reach maturity before fruiting. Despite risks, grapevines are cultivated at the edge of permissible climate and rely on cold hardiness monitoring programs to determine when cold damage mitigation and management practices are required. These monitoring programs represent a critical, but laborious process for tracking cold hardiness. To reduce the need for continuous monitoring, a model (WAUS.2) using cold hardiness data collected over many years from Washington state, USA, growers was published in 2014. Although the WAUS.2 model works well regionally, it underperforms in other regions. Therefore, the objective of this work was to develop a new model (NYUS.1) that incorporates recent knowledge of cold hardiness dynamics for better prediction outcomes. Cold hardiness data from V. labruscana Concord, and V. vinifera Cabernet Sauvignon and Riesling from Geneva, NY, USA were used. Data were separated in calibration (~2/3) and validation (~1/3) datasets. The proposed model uses three functions to describe acclimation, and two functions to describe deacclimation, with a total of nine optimized parameters. A shared chill response between acclimation and deacclimation provides a phased integration where acclimation responses decrease over the course of winter and are overcome by deacclimation. The NYUS.1 model outperforms the WAUS.2 model, reducing RMSE by up to 37% depending on cultivar. The NYUS.1 model also tends to be more conservative in its prediction, slightly underpredicting cold hardiness, as opposed to the overprediction from the WAUS.2 model. Some optimized parameters were shared between cultivars, suggesting conserved physiology was captured by the new model. Highlights- Multi-year cold hardiness data from three grapevine cultivars were used for modeling - Cold hardiness was modeled based on daily temperature and accumulated chill - Phased acclimation and deacclimation processes result in cold hardiness predictions - The new model was compared to the currently available model for grapevines - The model proposed here outperforms the currently available model

Why it matches plant phenotyping methodsブドウの耐寒性という植物状態を予測するモデルを開発し、独立データで検証・既存モデルと比較しており、表現型推定手法が研究の中心である。

abstractTherefore, the objective of this work was to develop a new model (NYUS.1) that incorporates recent knowledge of cold hardiness dynamics for better prediction outcomes.

Cascade-SORT: A robust fruit counting approach using multiple features cascade matching ↗

Estimation of fruit yield is of great importance to agricultural management and production decision-making. Fruit counting based on computer vision is faced with many challenges, particularly dense occlusion and difficult detection. To address the problems that exist in agricultural scenarios, we propose a fruit counting pipeline based on multiple features matching. Fruit counting is regarded as a multiple object tracking problem based on tracking-by-detection framework. The proposed method combines object detection with deep learning, Kalman filter, and cascade matching, which integrated motion and appearance features for frame-by-frame data association. Using the detection results of YOLO-v3, cascade matching is leveraged to associate detection bounding boxes with tracks. In cascade matching, the appearance features of fruit, Mahalanobis distance, and intersection over union metric were fused to match objects frame-by-frame. Mahalanobis distance is used to screen detection bounding boxes initially. Furthermore, the vector of locally aggregated descriptors image retrieval method is used to calculate the similarity of appearance between the two objects. In the final step of cascade matching, residual unmatched tracks and detection candidates are matched using intersection over union metric. Moreover, the Kalman filter is optimized for predicting the trajectories of undetectable objects to enhance the accuracy and robustness of fruit counting. In the experiments, the results of predicted fruit counting for camellia is 44 while the ground truth is 38 for a video. For apple counting, the total predicted number of fruits for three videos is 310 while the actual number is 292. And compared to the method of SORT, our method is better in counting accuracy, reduced the number of ID switches, and had more robustness when the detector performance degenerated. All the above mentioned metrics indicate that the proposed method is well performance in fruit counting regardless of whether the fruit is sparsely or densely grown.

Why it matches plant phenotyping methods果実数という植物器官の形質を画像から推定する手法を開発し、既存手法との比較および実データで精度・頑健性を検証しており、フェノタイピング手法が研究の中心である。

abstractwe propose a fruit counting pipeline based on multiple features matching.

Automated Pruning of Polyculture Plants ↗

Polyculture farming has environmental advantages but requires substantially more pruning than monoculture farming. We present novel hardware and algorithms for automated pruning. Using an overhead camera to collect data from a physical scale garden testbed, the autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day. From this garden state, AlphaGardenSim selects plants to autonomously prune. A trained neural network detects and targets specific prune points on the plant. Two custom-designed pruning tools, compatible with a FarmBot gantry system, are experimentally evaluated and execute autonomous cuts through controlled algorithms. We present results for four 60-day garden cycles. Results suggest the system can autonomously achieve 0.94 normalized plant diversity with pruning shears while maintaining an average canopy coverage of 0.84 by the end of the cycles. For code, videos, and datasets, see https://sites.google.com/berkeley.edu/pruningpolyculture.

Why it matches plant phenotyping methods画像と学習アルゴリズムで個体分布・庭の状態・キャノピー被覆を推定し、自律剪定に利用する技術が研究の中心であるため、植物フェノタイピング基盤の開発・応用として適格。

abstractthe autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day.

Simple Phenotypic Sensor for Visibly Tracking H 2 O 2 Fluctuation to Detect Plant Health Status ↗

Hydrogen peroxide (H 2 O 2 ), as a main component of reactive oxygen species (ROS), serves as a key signaling molecule relevant to plant stress response and health status. Many strategies have been developed for detecting or quantifying H 2 O 2 concentration. However, reports on simply, visibly tracking H 2 O 2 fluctuation in vivo are limited. Here, for visibly tracking the plant H 2 O 2 wave, a green fluorescent phenotypic probe was designed by merging a H 2 O 2 -sensitive tertiary amine moiety with the core fluorophore tetraphenylethene skeleton. The green fluorescence emission is quenched up to 52% by H 2 O 2 with good sensitivity, selectivity, and reversibility within the plant physiological range of 10-100 μM H 2 O 2 . In response to various abiotic stresses, including mechanical damage, high salt, strong light and drought, fluorescence fluctuations, response to H 2 O 2 concentration alterations in vivo was visible to the naked eye under irradiation of commercially available UV light (365 nm) after simple injection of this H 2 O 2 probe solution into seedling leaves. This phenotypic fluorescent H 2 O 2 probe illustrates great potential as early sensors of plant health under stress without the aid of skillful operation and specialized equipment.

Why it matches plant phenotyping methods植物体内のH₂O₂変動を可視化してストレス・健康状態を評価する蛍光プローブを開発した研究であり、表現型取得法が中心的です。

abstractHere, for visibly tracking the plant H 2 O 2 wave, a green fluorescent phenotypic probe was designed

A new technique for stain-marking of seeds with safranine to track seed dispersal and seed bank dynamics. ↗

Accurate tracking of seed dispersal is critical for understanding gene flow and seed bank dynamics, and for predicting population distributions and spread. Available seed-tracking techniques are limited due to environmental and safety issues or requirements for expensive and specialized equipment. Furthermore, few techniques can be applied to studies of water-dispersed seeds. Here we introduce a new seed-tracking method using safranine to stain seeds/fruits by immersing in ( ex situ ) or spraying with ( in situ ) staining solution. The hue difference value between pre- and post-stained seeds/fruits was compared using the HSV color model to assess the effect of staining. A total of 181 kinds of seeds/fruits out of 233 tested species of farmland weeds, invasive alien herbaceous plants and trees could be effectively stained magenta to red in hue (320-360°) from generally yellowish appearance (30-70°), in which the other 39 ineffectively-stained species were distinguishable by the naked eye from pre-stained seeds. The most effectively stained seeds/fruits were those with fluffy pericarps, episperm, or appendages. Safranine staining was not found to affect seed weight or germination ability regardless of whether seeds were stained ex situ or in situ . For 44 of 48 buried species, the magenta color of stained seeds clearly remained recognizable for more than 5 months after seeds were buried in soil. Tracking experiments using four species ( Beckmannia syzigachne , Oryza sativa f. spontanea, Solidago Canadensis , and Acer buergerianum ), representing two noxious agricultural weeds, an alien invasive plant, and a tree, respectively, showed that the safranine staining technique can be widely applied for studying plant seed dispersal. Identifying and counting the stained seeds/fruits can be executed by specially complied Python-based program, based on OpenCV library for image processing and Numpy for data handling. From the above results, we conclude that staining with safranine is a cheap, reliable, easily recognized, automatically counted, persistent, environmentally safe, and user-friendly tracking-seed method. This technique may be widely applied to staining most of the seed plant species and the study of seed dispersal in arable land and in disturbed and natural terrestrial or hydrophytic ecological systems.

Why it matches plant phenotyping methods種子を染色して追跡・自動計数する測定法を開発し、染色効果、発芽への影響、土中での持続性、複数種での適用性を検証しており、植物種子の状態・分布を取得する方法が研究の中心である。

abstractHere we introduce a new seed-tracking method using safranine to stain seeds/fruits by immersing in ( ex situ ) or spraying with ( in situ ) staining solution.

Spatio-temporal imaging of cell fate dynamics in single plant cells using luminescence microscope. ↗

Stem cell fates are spatio-temporally regulated during plant development. Time-lapse imaging of fluorescence reporters is the most widely used method for spatio-temporal analysis of biological processes. However, excitation light for imaging fluorescence reporters causes autofluorescence and photobleaching. Unlike fluorescence reporters, luminescence proteins do not require excitation light, and therefore offer an alternative reporter for long-term and quantitative spatio-temporal analysis. We established an imaging system for luciferase, which enabled monitoring cell fate marker dynamics during vascular development in a vascular cell induction system called VISUAL. Single cells expressing the cambium marker, proAtHB8:ELUC , had sharp luminescence peaks at different time points. Furthermore, dual-color luminescence imaging revealed spatio-temporal relationships between cells that differentiated into xylem or phloem, and cells that transitioned from procambium to cambium. This imaging system enables not only the detection of temporal gene expression, but also facilitates monitoring of spatio-temporal dynamics of cell identity transitions at the single cell level.

Why it matches plant phenotyping methods植物細胞の運命・アイデンティティ状態を単一細胞レベルで取得するルシフェラーゼ画像化システムを構築しており、表現型取得法が研究の中心である。

abstractWe established an imaging system for luciferase, which enabled monitoring cell fate marker dynamics during vascular development

High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings ↗

Abstract Background High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. Results We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D+t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ( R 2 = 0.996 and 0, 923 for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ( R 2 = 0. 938 for lateral root growth). Conclusions We designed a novel method of root tracking that accurately and automatically measures both static and dynamic RSA parameters from a novel high-throughput root phenotyping platform. It has been used to characterize developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.

Why it matches plant phenotyping methods根系の静的・動的形質を画像取得と自動解析で抽出する高スループット手法の設計・精度検証が研究の中心であるため。

abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking

Spatial-aware SAR-optical time-series deep integration for crop phenology tracking ↗

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

Why it matches plant phenotyping methodsSARと光学時系列を深層統合して作物の生育フェノロジーを追跡する手法が題名上の中心であり、植物状態の推定に該当する。

titleSpatial-aware SAR-optical time-series deep integration for crop phenology tracking

A new device for continuous non-invasive measurements of leaf water content using NIR-transmission allowing dynamic tracking of water budgets ↗

Leaf water content (LWC) permanently fluctuates under variable transpiration rate and sap flow and influences e.g. stomatal responses and osmotic adjustment of plant cells. Continuous recordings of LWC are therefore central for the investigation of the regulatory networks stabilizing leaf hydration. Available measurement methods, however, either influence local hydration, interfere with the local leaf micro-environment or cannot easily be combined with other techniques. To overcome these limitations a non-invasive sensor was developed which uses light transmission in the NIR range for precise continuous recordings of LWC. For LWC measurements the transmission ratio of two NIR wavelengths was recorded using a leaf-specific calibration. Pulsed measurement beams enabled measurements under ambient light conditions. The contact-free sensor allows miniaturization and can be integrated into many different experimental settings. Example measurements of LWC during disturbances and recoveries of leaf water balance show the high precision and temporal resolution of the LWC sensor and demonstrate possible method combinations. Simultaneous measurements of LWC and transpiration allows to calculate petiole influx informing about the dynamic leaf water balance. With simultaneous measurements of stomatal apertures the relevant stomatal and hydraulic processes are covered, allowing insights into dynamic properties of the involved positive and negative feed-back loops.

Why it matches plant phenotyping methodsNIR透過を用いて葉の含水量を連続・非侵襲測定するセンサーを開発し、精度と時間分解能を実証しており、植物生理形質の取得法が研究の中心である。

abstracta non-invasive sensor was developed which uses light transmission in the NIR range for precise continuous recordings of LWC

Digital whole-community phenotyping to assess morphological and physiological features of plant communities in the field ↗

Summary Traits link observable patterns in plants to ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use manual low-throughput methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, greenhouse or lab-based studies, mostly in agriculture, employ high-throughput phenotyping for plant individuals to track their growth or fertilizer and water demand. We customized an automated plant phenotyping system (PlantEye F500, Phenospex, Heerlen, The Netherlands) for its mobile application in the field for digital whole-community phenotyping (DWCP). By scanning whole plant communities, we gather, within seconds and non-invasively, multispectral and physiological information while simultaneously capturing the 3-dimensional structure of the vegetation. We demonstrated the potential of DWCP by tracking plant community responses to experimental land-use treatments over two years. DWCP captured short- and long-term changes in morphological and physiological plant community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. Thus, DWCP proved to be an efficient method to measure morphological and physiological characteristics of plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.

Why it matches plant phenotyping methods野外植物群落向けに自動フェノタイピングシステムを改良・適用し、形態・生理特性の取得性能を実証しているため、方法が研究の中心である。

abstractWe customized an automated plant phenotyping system (PlantEye F500, Phenospex, Heerlen, The Netherlands) for its mobile application in the field for digital whole-community phenotyping (DWCP).

Towards a bionic IoT: environmental monitoring using smartphone interrogated plant sensors ↗

The utilisation of plants directly as quantifiable natural sensors is proposed. A case study measuring surface wettability of Aucuba japonica, or Japanese Laurel, plants using a novel smartphone field interrogator is demonstrated. This plant has been naturalised globally from Asia. Top-down contact angle measurements map wettability on-site and characterise a range of properties impacting plant health, such as aging, solar and UV exposure, and pollution. Leaves at an early age or in the shadow of trees are found to be hydrophobic with contact angle θ ~ 99°, while more mature leaves under sunlight are hydrophilic with θ ~ 79°. Direct UVA irradiation at λ = 365 nm is shown to accelerate aging, changing contact angle of one leaf from slightly hydrophobic at θ ~ 91° to be hydrophilic with θ ~ 87 ° after 30 min. Leaves growing beside a road with heavy traffic are observed to be substantially hydrophilic, as low as θ ~ 47°, arising from increased wettability with particulate accumulation on the leaf surface. Away from the road, the contact angle increases as high as θ ~ 96°. The results demonstrate that contact angle measurements using a portable diagnostic IoT edge device can be taken into the field for environmental detection, pollution assessment and more. Using an internet connected smartphone combined with a plant sensor allows multiple measurements at multiple locations together in real-time, potentially enabling tracking of parameter change anywhere where plants are present or introduced. This hybrid integration of widely distributed living organic systems with the internet marks the beginning of a new bionic internet-of-things ( b -IoT).

Why it matches plant phenotyping methodsスマートフォン連携の携帯型デバイスで葉の表面濡れ性を定量測定する手法とフィールド利用を中心に扱っており、植物の状態・環境影響を測るフェノタイピング手法に該当する。

abstractcontact angle measurements using a portable diagnostic IoT edge device can be taken into the field for environmental detection, pollution assessment and more.

A Novel Fluoro-Pyrazine-Bridged Donor-Accepter-Donor Fluorescent Probe for Lipid Droplet-Specific Imaging in Diverse Cells and Superoxide Anion Generation. ↗

Purpose Lipid droplets (LDs) are dynamic organelles which associated with many metabolic processes. Reliable long-term imaging of LD is of great importance in LD-based therapy and research. Conventional fluorescent probes suffer from poor photostability and difficulty of preparation, which compromise their LD imaging ability. In this study, we aim to provide a novel and universal fluorescent probe for LD-specific imaging in both eukaryotic and prokaryotic cells. The versatile and potential applications of the probe were also evaluated. Methods We used one-step Suzuki coupling reaction to synthesize a fluoro-pyrazine-bridged donor-acceptor-donor fluorescent probe (T-FP-T). The fluorescent properties and stability of T-FP-T were detected. Then, LD-specific imaging and dynamic movement tracking capabilities of T-FP-T were studied in fungus, bacteria, plant and animal tissues. The biosafety and photodynamic toxicity of the probe under different light irradiation were characterized. Results T-FP-T showed large Stokes shift, superior brightness, excellent photostability, low toxicity. T-FP-T exhibited significant overlaps with adipophilin antibody or the commercial LD probe (LipidSpot™) in the cytoplasm, but not with Mitotracker red, Lysotracker red and Peroxisome Labeling dye. Moreover, T-FP-T also showed efficient superoxide anion generation capability under white LED light irradiation. The viability of Hela cells co-treated with T-FP-T and 1-h white LED light irradiation decreased to 62%. Conclusions All these outstanding capabilities make T-FP-T a new efficient LD-specific imaging probe. The generated superoxide anion from T-FP-T under white LED light irradiation could cause obvious cell death, which will inspire broad study in LD-targeted photodynamic therapy.

Why it matches plant phenotyping methods植物を含む細胞・組織で脂肪滴を特異的に可視化・追跡する蛍光プローブを開発し、性能を評価している。植物での適用も明記され、画像取得法が中心である。

abstractwe aim to provide a novel and universal fluorescent probe for LD-specific imaging in both eukaryotic and prokaryotic cells

Kinematic analysis and visualization of Tetraselmis microalgae 3D motility by digital holography ↗

A study on locomotion in a 3D environment of Tetraselmis microalgae by digital holographic microscopy is reported. In particular, a fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume. Digital holography (DH) in a microscope off-axis configuration is exploited as a useful method to enable fast autofocusing and recognition of objects in the field of view, thus coupling DH with appropriate numerical algorithms. Through the proposed method we measure, simultaneously, the tri-dimensional paths followed by the flagellate microorganism and the full set of the kinematic parameters that describe the swimming behavior of the analyzed microorganisms by means of a polynomial fitting and segmentation. Furthermore, the method is capable to furnish the accurate morphology of the microorganisms at any instant of time along its 3D trajectory. This work launches a promising trend having as the main objective the combined use of DH and motility microorganism analysis as a label-free and non-invasive environmental monitoring tool, employable also for in situ measurements. Finally, we show that the locomotion can be visualized intriguingly by different modalities to furnish marine biologists with a clear 3D representation of all the parameters of the kinematic set in order to better understand the behavior of the microorganism under investigation.

Why it matches plant phenotyping methodsデジタルホログラフィーと数値アルゴリズムを用いて、Tetraselmis微細藻類の3D運動軌跡、運動学的形質、形態を自動・半自動で抽出する方法が研究の中心である。

abstracta fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume.

Automated Time-Lapse Imaging and Manipulation of Cell Divisions in Arabidopsis Roots by Vertical-Stage Confocal Microscopy. ↗

The analysis of dynamic cellular processes such as plant cytokinesis stands and falls with live-cell time-lapse confocal imaging. Conventional approaches to time-lapse imaging of cell division in Arabidopsis root tips are tedious and have low throughput. Here, we describe a protocol for long-term time-lapse simultaneous imaging of multiple root tips on a vertical-stage confocal microscope with automated root tracking. We also provide modifications of the basic protocol to implement this imaging method in the analysis of genetic, pharmacological or laser ablation wounding-mediated experimental manipulations. Our method dramatically improves the efficiency of cell division time-lapse imaging by increasing the throughput, while reducing the person-hour requirements of such experiments.

Why it matches plant phenotyping methodsArabidopsis根の細胞分裂を対象に、複数根端の長時間タイムラプス共焦点撮像と自動根追跡を高スループット化する手法・プロトコルが中心である。

abstractHere, we describe a protocol for long-term time-lapse simultaneous imaging of multiple root tips on a vertical-stage confocal microscope with automated root tracking.

Analysis of Virus Spread Around the Cell Death Zone at Spatiotemporal Resolution Using Confocal Microscopy. ↗