Plant phenotyping methods.

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

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

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

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

MoeBi-ViT: Non-destructive shoot-stage phenotyping of lettuce in plant factory via dual-branch mixture-of-experts network and enhanced segmentation →

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

Why it matches plant phenotyping methodsレタスのシュート段階を対象とした非破壊フェノタイピング手法と、強化セグメンテーションを含む画像解析モデルの開発が題名で明示されており、表現型取得・抽出が中心です。

titleMoeBi-ViT: Non-destructive shoot-stage phenotyping of lettuce in plant factory via dual-branch mixture-of-experts network and enhanced segmentation

Three-dimensional organ segmentation and structural phenotyping of salt-stressed coriander seedlings using the optimized point transformer-based model PTV-SegCo →

Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem–leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow–deep feature fusion, and a combined cross-entropy–Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L −1 ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training–validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors—plant height, projected area, voxel occupancy volume, and leaf point ratio—were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R 2 values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.

Why it matches plant phenotyping methods3D点群による器官セグメンテーションと構造形質抽出の手法開発・内部比較・手動検証が研究の中心であり、塩ストレスは適用対象である。

abstractwe developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress.

From LiDAR point clouds to 3D tree morphometrics: New approach to quantitatively evaluate tree shapes →

Abstract Tree crowns are complex, three‐dimensional structures whose morphology varies among species, individuals and environments. Although light detection and ranging (LiDAR) provides high‐resolution, single‐tree point clouds that advance species discrimination and the assessment of intraspecific variation in situ, crown shape is still commonly reduced to low‐dimensional metrics (e.g. crown diameter or crown base height), losing much of its three‐dimensional geometric complexity. We introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels. Pre‐segmented LiDAR single‐tree point clouds of eight temperate forest species were converted into three‐dimensional shape representations using radial bounding volumes (RBVs), which partitioned each crown into a standardized set of vertical layers and radial sectors. Surface points automatically digitized from each RBV formed geospatially aligned, 3D pseudolandmark configurations representing geometric morphometric crown shapes. These configurations served as the input data for multivariate analyses of crown shape variation within and between species. Twelve structural traits, including crown and stem dimensions, were extracted from the same RBVs and integrated into analyses of trait–shape associations. The morphospace of crown shape was structured along different axes of variation in broadleaf species than in conifers. Within these groups, species pairs—such as Fagus versus Quercus and Picea versus Pinus —exhibited contrasting intraspecific morphological gradients, with different structural traits driving shape variation in each. Crown base height and total crown height emerged as the strongest predictors of crown shape. Differences in crown shape among species were primarily captured by symmetric components, with asymmetry providing a negligible signal. Interspecific differentiation was largely driven by architectural variation rather than pure size differences. Morphological differences derived from pseudolandmarks and convolutional neural network features exhibited stronger correlations in conifers than in broadleaf species. We present a reproducible, LiDAR‐native framework for quantifying and comparing 3D crown morphology within and across species. Using the RBV approach, geospatially aligned pseudolandmarks can be derived from any pre‐segmented, single‐tree LiDAR point cloud, enabling scalable, multi‐regional analyses of intraspecific variability. This framework provides a robust foundation for integrating crown shape into ecological, evolutionary, silvicultural and modelling studies, including assessments of environmental effects and architectural constraints.

Why it matches plant phenotyping methodsLiDAR点群から樹冠形状を抽出・定量化する3D幾何形態計測フレームワークを開発し、再現可能な植物形態計測手法として検証・適用しているため。

abstractWe introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels.

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

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

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

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

CITRUS FLOWER, FRUIT, AND SHOOT RECOGNITION BASED ON IMPROVED YOLOv10 →

In the process of agricultural intelligence, precise detection of plant organs serves as the foundation for core tasks such as crop phenotyping analysis and yield prediction. However, in complex field environments, small targets such as citrus flowers and shoots face challenges including scale variation, background interference, and dense occlusion, which severely impact detection accuracy. This study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition. The BAM attention mechanism enhances the model's feature extraction capability for small target organs under complex backgrounds through parallel channel and spatial attention branches; the GIoU loss function improves the localization accuracy of densely occluded targets by optimizing the geometric alignment between predicted and ground-truth boxes. Validation experiments were conducted on a self-constructed dataset. The experimental results show that the improved YOLOv10s achieves significant advantages in comprehensive detection accuracy, with an mAP50 of 89.1%, representing an improvement of 2.9%~9.5% over the original YOLOv10s and other comparative models. In fine-grained category detection, the model achieves mAP50 of 91.2%, 83.6%, and 92.5% for shoots, flowers, and fruits, respectively. Furthermore, while maintaining high detection accuracy, the model achieves a detection speed of 23.6 ms per frame, meeting real-time detection requirements. The research results demonstrate that the improved YOLOv10s model integrating the BAM attention mechanism and GIoU loss function achieves an optimal balance between accuracy and speed in citrus organ detection tasks, providing a preferred solution for field real-time detection systems.

Why it matches plant phenotyping methods柑橘の花・果実・シュートという植物器官を画像から検出する改良モデルを開発し、データセットで精度と速度を検証しており、表現型取得手法が中心である。

abstractThis study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition.

Structural failure in cereal stems under climate extremes: insights from barley head loss. →

Structural failure of cereal stems during late-season climate extremes is a critical determinant of yield stability. In barley, breakage of the stem below the spike, known as head loss, leads to major yield losses, particularly in hot and dry regions where the crop is widely grown. Despite a predicted increase in head loss risk due to global warming, current understanding of the genetic, physiological, anatomical, and environmental factors that control head loss remains limited. Overcoming these knowledge gaps is essential to providing a systems-level strategy for barley breeders to develop climate-ready cultivars that are resilient to stem breakage and suitable for industry adoption. Here, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting, and genetic modification strategies informed by studies on hormonal regulation and cell wall composition. Coupled with genotypic data, these efforts will enable the development of a genomic selection platform to facilitate future breeding programs. The framework and tools discussed here are broadly applicable to improving stem resilience in other cereal crops.

Why it matches plant phenotyping methods茎の強度・柔軟性や穂数を対象とする表現型計測手法をレビューし、機械試験とドローンによる高スループット計測を育種基盤として論じているため、表現型手法が中心的です。

abstractHere, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting

YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments →

Maize diseases affecting leaves, stalks, and ears can substantially reduce yield and quality; therefore, rapid and accurate recognition in complex field environments is important for intelligent agricultural monitoring. To address the large-scale variation, weak fine-grained texture, and strong background interference associated with multi-part maize diseases, this study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n. The method jointly improves spatial position awareness, shallow detail preservation, local-context modeling, key semantic-region enhancement, and lightweight detection-head reconstruction. RFCAConv, C3k2_RFCAConv, and Detect_LSDECD are introduced into the baseline network to strengthen directional texture modeling, multi-scale feature aggregation, and detection-head feature representation. FG-RFCAConv, HGD-C3k2, LCA-C3k2, and GRN-BiAttn are further designed for high-frequency differential gated detail compensation, P3 high-resolution detail enhancement, local-context fusion, and global-response-normalized attention regulation, respectively. Experimental results show that YOLOv11-MPD achieves Precision, Recall, mAP50, and mAP50-95 of 72.3%, 72.8%, 79.5%, and 50.2%, improving YOLOv11n by 2.4, 2.5, 2.9, and 2.4 percentage points, respectively, while reducing parameters from 2.6 M to 2.4 M. These results indicate that, within the scope of the dataset used in this study, YOLOv11-MPD improves multi-part maize disease detection under complex field conditions. However, the current conclusions are limited to the constructed dataset, and further validation using larger multi-region, multi-season, and multi-device datasets is required to evaluate its broader generalization ability.

Why it matches plant phenotyping methodsトウモロコシの葉・茎・穂における病害状態を画像から検出するアルゴリズムを開発し、性能比較・検証しており、植物表現型取得が研究の中心です。

abstractthis study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n.

Integrated semantically guided spatial fragmentation and biomorphological forest segmentation using terrestrial laser scanning data: layer-wise dynamic connectivity features as the key factor in stem structure reconstruction accuracy →

The separation of a forest plot into individual trees and the automatic extraction of their stem structures from terrestrial laser scanning data are complicated by dense stands, occlusions, and the diversity of biomorphological forms. Existing algorithms usually solve scene partitioning, voxel classification, and tree growing as independent tasks, which leads to error accumulation at subsequent processing stages. This paper proposes a unified model for spatial fragmentation and biomorphological forest segmentation comprising three interrelated stages: scene partitioning by estimated stem coordinates using a Voronoi diagram, probabilistic voxel- or point-level classification, and bottom-up tree growing guided by spatial connectivity and stem membership criteria. For the semantic module, tabular, volumetric, and point-based approaches are compared: gradient boosting with layer-by-layer inference, TabNet, a three-dimensional convolutional neural network, PointNet2, and two-stage pipelines in which gradient boosting builds an initial stem mask for subsequent neural segmentation. The experiment was conducted on 567 mixed-species trees. Considering both quality and computational performance, the {CatBoost; CNN3D} pipeline was selected as the preferred solution, achieving AUC = 0.9966 and IoU = 0.9831. The obtained results show that combining interpretable layer-by-layer classification with subsequent spatial analysis improves the quality of stem structure reconstruction, which is important for automatic forest inventory tasks.

Why it matches plant phenotyping methods地上レーザースキャンから個体樹木と幹構造を自動抽出する統合セグメンテーション手法を開発・比較しており、植物形態の取得が研究の中心である。

abstractThe separation of a forest plot into individual trees and the automatic extraction of their stem structures from terrestrial laser scanning data are complicated by dense stands, occlusions, and the diversity of biomorphological forms.

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

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

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

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

An optimised FISH-based approach for tissue and subcellular localisation of apple scar skin viroid in cucumber. →

Fluorescence in situ hybridisation (FISH) is a valuable technique for visualising RNA molecules in their native cellular context. Still, its application in plant tissues is often limited by tissue autofluorescence and the lack of optimised protocols. Here, we developed and validated a simple, reproducible FISH workflow to detect Apple scar skin viroid (ASSVd) in cucumber. Systematic optimisation of probe chemistry, tissue selection, and sampling stage significantly improved assay sensitivity and reproducibility. The AZDye594-labelled antisense riboprobe produced higher signal-to-background ratios and lower background fluorescence than fluorescein-labelled probes, enabling reliable detection of ASSVd in vascular-associated tissues. The optimised workflow consistently detected ASSVd in both leaves and stems. High-resolution confocal imaging further revealed predominant nuclear accumulation of ASSVd RNA in infected cells. Together, this study establishes a sensitive and accessible FISH workflow for localisation of ASSVd in cucumber and provides a practical platform for investigating the spatial distribution of viroid and other plant RNA pathogens.

Why it matches plant phenotyping methods植物組織内の病原体RNAの空間局在を可視化・定量可能にするFISHワークフローを開発・検証しており、植物状態の画像取得法が研究の中心である。

abstractHere, we developed and validated a simple, reproducible FISH workflow to detect Apple scar skin viroid (ASSVd) in cucumber.

Image-Based Phenotyping for Early Assessment of Radiosensitivity of Cowpea (Vigna unguiculata L. Walp.) Seedlings Irradiated with Gamma Rays →

Calibrating the mutagenic dose is the first practical step of any radiation mutation-breeding programme, and it is usually summarised by the median lethal dose (LD50) or the median growth-reduction dose (GR50). We asked whether an accessible, image-based phenotyping pipeline can quantify the early radiation response of cowpea (Vigna unguiculata L. Walp.) seedlings finely enough to estimate GR50 and to rank organ- and pigment-level sensitivities. Seeds of the traditional Paraguayan landrace kumandá pyta’i were exposed to Cobalt-60 gamma rays at 0, 100, 200, 300, 400, 500, 600, and 700 Gy, grown in a greenhouse, and photographed at the early seedling stage. A single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits (total, root, and shoot length, root:shoot ratio, tortuosity, and a two-dimensional biomass proxy) and colorimetric traits (CIE L*a*b*, a normalised greenness index, and colour-class pixel fractions). Because the data departed from normality, dose effects were tested with Kruskal–Wallis, Spearman rank correlation, and Dunn post hoc tests, and GR50 was estimated by regression of each trait expressed as a percentage of the control. Total length, shoot length, and the biomass proxy declined significantly with dose (Spearman ρ = −0.40, −0.51, and −0.47; all p < 0.001), preceded by a low-dose stimulation at 100 Gy. Estimated GR50 values were ≈390 Gy for shoot length, ≈510 Gy for total length, and ≈550 Gy for the biomass proxy, within the range reported for other cowpea genotypes. Shoot elongation was more radiosensitive than root elongation, so the root:shoot ratio did not decline; tortuosity showed no dose response. Among pigment traits, the loss of greenness was the most robust signal (a* increased, ρ = +0.62, p = 5 × 10−10; green pixel fraction fell from 0.32 to near zero by 500 Gy). These results show that single-photograph phenotyping resolves a coherent, statistically supported dose response and yields a GR50 estimate usable for dose calibration. For kumandá pyta’i, doses of roughly 300–400 Gy (below GR50) are the most defensible starting window for mutation induction. The framework is reproducible and low-cost, but it is based on one greenhouse experiment and a single genotype, and should be validated across independent trials and cultivars.

Why it matches plant phenotyping methods画像取得・セグメンテーション・解析による形態および色彩形質の抽出を中心に、放射線応答とGR50を推定する低コスト画像ベース表現型解析法を提示しているため。

abstractA single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits

Multi-scale reconstruction of snow-avalanche frequency and vegetation structure using dendrogeomorphology, satellite imagery, and UAV photogrammetry →

This study presents a multi-scale framework for reconstructing snow avalanche (SA) frequency and assessing vegetation structural responses in data-scarce mountain environments. The approach integrates dendrogeomorphological reconstructions, satellite-based spectral disturbance detection, and UAV-based Structure-from-Motion (SfM) photogrammetry, complemented by field data, and was applied to two avalanche paths in the Piatra Craiului Mountains (Southern Carpathians, Romania). Tree ring analyses allowed reconstruction of spatially explicit minimum avalanche chronologies for the 1980–2025 period. These reconstructions were combined with a DEM-based upslope algorithm to derive spatially variable avalanche return periods, revealing the highest frequencies in release and upper-track sectors and progressively longer return periods toward lower-track zones. Sentinel-2 imagery was used to assess the surface footprint of a reconstructed avalanche event in 2018. Among the tested spectral indices, the Moisture Stress Index (MSI) showed the most spatially coherent response, while the combined MSI-NDMI-NBR approach reduced index-specific noise. UAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states. Vegetation was classified using a machine-learning-based object-oriented approach (Random Forest) integrating spectral, geometric, structural, and textural parameters. The multi-parameter feature set yielded very high classification accuracy (Cohen’s Kappa ≈ 0.95). Across avalanche return-period gradients, both UAV-derived and field-based metrics showed a systematic associations between tree height and avalanche frequency, whereas tree age and stem diameter exhibited more variable, path-dependent responses. The proposed framework provides a transferable basis for linking avalanche disturbance regimes with vegetation structure and surface stability in mountain landscapes lacking long-term observational records.

Why it matches plant phenotyping methodsUAV-SfMと機械学習による植生構造・樹高の高解像度推定が研究枠組みの主要部分であり、分類精度も評価しているため、植物状態の画像ベース表現型計測として含める。

abstractUAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states.

Non-destructive tree volume estimation using mobile laser scanning: Impact of the tree shape on measurement error. →

Accurate tree volume estimation is central to forest management and carbon accounting. Allometric equations are widely used but limited in transferability across species, regions, and environmental conditions. Mobile Laser Scanning (MLS) offers a promising alternative through direct measurement of tree geometry; however, the influence of tree shape on MLS accuracy remains poorly understood. This study evaluated MLS-derived estimates of stem diameters, total tree height, and merchantable stem volume against destructive reference measurements from 176 trees spanning eight species (four hardwood, four softwood) in Wallonia, Belgium. A Zeb Horizon RT scanner was used; tree architectural descriptors extracted from the point cloud were tested for associations with measurement error. Across 7,824 stem diameter measurements, MLS achieved a mean error of 0.46 cm, with precision declining above 15 m. MLS-derived height outperformed Vertex IV clinometer measurements for hardwood species (RMSE% = 6.88 vs. 8.78) but performed slightly less well for softwoods (RMSE% = 7.36 vs. 6.14). QSM-based volume estimates systematically underestimated reference values, while taper-based reconstruction produced nearly unbiased estimates with an RMSE of 15.72%. Correlation analyses and PCA showed that tree architectural variables explained only a small fraction of MLS error variability. Diameter and height errors were largely independent of structural attributes, while volume errors showed moderate associations with tree size and crown density. These findings indicate that tree architecture is not a primary source of MLS measurement uncertainty. Future MLS-based forest inventory efforts should prioritize acquisition and processing optimization, as scanning conditions and forest structure appear more influential than tree shape.

Why it matches plant phenotyping methodsMLSによる樹木形状・直径・樹高・幹材積の非破壊推定を、破壊測定と比較検証し、測定誤差の要因を評価した方法検証研究である。

titleNon-destructive tree volume estimation using mobile laser scanning: Impact of the tree shape on measurement error.

Plant electrophysiological responses to drought and their association with live fuel moisture and flammability in Salvia rosmarinus . →

Live fuel moisture content is a key determinant of live fuel flammability, yet its destructive and discontinuous measurement limits high-temporal-resolution monitoring. This study evaluated whether leaf electrical potential can serve as a non-invasive proxy for LFMC and flammability-related traits under natural drought conditions. From February to July 2025, leaf and trunk electrical potentials were monitored weekly in Salvia rosmarinus individuals from a Mediterranean shrubland, while LFMC, essential oil yield, fatty-acid fraction, and laboratory-based flammability metrics-ignition time, combustion duration, and flame height-were assessed bi-weekly. Leaf electrical potential was strongly associated with LFMC (R 2 = 0.64, p < 0.001), decreasing as plants underwent seasonal drought-induced dehydration. Periods of high temperature and low rainfall reduced both LFMC and electrical potential, coinciding with shorter ignition times, which declined to approximately 20-30 s during the driest period. Based on the observed shifts in ignition time, combustion duration, and flame height, three empirical LFMC response zones were identified, with leaf electrical potential closely tracking transitions in plant hydration and flammability. These results suggest that plant electrophysiology may provide a promising non-invasive indicator of live fuel water status and seasonal flammability dynamics, with potential applications in wildfire risk monitoring when combined with conventional LFMC, meteorological, and remote-sensing approaches.

Why it matches plant phenotyping methods葉の電気的電位をLFMC（水分状態）および可燃性関連形質の非破壊・連続的な指標として評価しており、植物状態の取得方法の検証が研究の中心である。

abstractThis study evaluated whether leaf electrical potential can serve as a non-invasive proxy for LFMC and flammability-related traits under natural drought conditions.

Individual-Tree Stem Volume Modeling Using Handheld LiDAR and UAV-SfM Photogrammetry in a Mediterranean Mixed Forest →

Integrating ground-based and aerial remote sensing for individual tree-level stem volume modeling remains underexplored in Mediterranean mixed forests, despite the growing need for cost-effective, automated forest inventory approaches. This study evaluated the combined use of Handheld Laser Scanning (HLS) and Unmanned Aerial Vehicle (UAV)-based Structure from Motion (SfM) photogrammetry for individual-tree stem volume modeling in a mixed stand in Castilla y León, Spain, dominated by Pinus halepensis, Pinus pinea, Quercus faginea, and Cupressus sempervirens. Two open-source HLS processing tools; the Forest Structural Complexity Tool (FSCT) and 3D Forest Inventory (3DFin), were compared for individual tree attribute extraction, with FSCT outperforming 3DFin across all species. Reference stem volumes were derived by applying species-specific Spanish National Forest Inventory (SNFI) allometric equations to FSCT-extracted diameter and height values. Random Forest models were then built using UAV-SfM crown metrics as predictors, testing two image overlap configurations: 80 × 80 F (80% front and side overlap) and 80 × 60 CF (80% front, 60% side, cross-flight). The 80 × 80 F configuration produced the best-performing model (R2 = 0.730), with 80 × 60 CF achieving comparable accuracy (R2 = 0.688), results confirmed by spatially independent leave-one-plot-out cross-validation (LOPO-CV R2 = 0.627 and 0.613, respectively). These results show that combining HLS and UAV-SfM through a predominantly open-source workflow offers a viable, reproducible approach to stem volume modeling in structurally complex Mediterranean mixed forests.

Why it matches plant phenotyping methodsHLSとUAV-SfMを用いて個体樹の直径・樹高から幹材積を推定する再現可能な計測・解析ワークフローを構築し、複数ツールと飛行条件を比較検証しているため、植物表現型取得が中心である。

abstractIntegrating ground-based and aerial remote sensing for individual tree-level stem volume modeling remains underexplored

Multivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones. →

Tolerance against winter freeze is the main focus of variety development in Louisiana, which represents the northernmost sugarcane-growing region worldwide. Antifreeze metabolites, xylem structure, and fiber content represent interrelated physicochemical properties contributing to freeze tolerance. This study first classified the cold tolerance of sugarcane cultivars using metabolites in juice as predictor variables. The best-fit model (XGBoost discriminant analysis) estimated the higher cold tolerance of the final on-station clone progeny to the stress tolerance-inducing wild germplasm line. Stalks of the tolerant sugarcane genotype contained higher fiber for mechanical support against cellular injury, compared to susceptible varieties. Fluorescence microscopy visualized phospholipids responsible for maintaining membrane fluidity during frost in lignin surrounding the vascular bundle. Thermal imaging is proposed for real-time monitoring of spatiotemporal temperature changes, as stalk injury is initiated by ice formation at sub-freeze temperatures during winter freeze. As additional datasets for independent prediction become available, developed methods could be used to explore the biomarkers for stress resistance in simpler multivariate discriminant analysis and the distribution of specific biomarkers in cellular components by microscopic imaging, and to trace stalk injury hot spots as a function of time and relationships with resistance markers.

Why it matches plant phenotyping methodsサトウキビの耐寒性という植物状態を、XGBoost判別モデルと画像・熱画像によって分類・評価する方法が研究の中心であり、単なる生物学的測定ではない。

titleMultivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones.

Improved YOLOv8n-seg for instance segmentation of mango fruits and peduncles in natural orchard environments →

To improve the instance segmentation accuracy of mango fruits and peduncles in complex mountainous orchard scenes and provide visual decision support for robotic harvesting, this study proposes a model-driven perception and picking-point localization method. Specifically, an RGB-D mango dataset was constructed under natural orchard conditions, covering strong illumination, shadows, backlighting, fruit overlap, branch and leaf occlusion, and peduncle crossing. An improved lightweight instance segmentation model named SHS-YOLOv8n-seg was then developed based on YOLOv8n-seg. StarNet_s1 was introduced as the backbone to enhance feature extraction under complex backgrounds. Furthermore, a high-frequency and spatial perception feature pyramid network was adopted to strengthen multi-scale feature fusion and improve the representation of slender peduncles. The SPPF module was used to expand the receptive field, and the parameter-free SimAM attention mechanism was introduced to enhance target responses while suppressing background interference. In the single-run comparison, the proposed model achieved Precision, Recall, mAP@50, and mAP@50:95 values of 89.62%, 88.17%, 90.19%, and 67.94%, respectively. Compared with the baseline YOLOv8n-seg model, these values increased by 2.42, 2.90, 2.75, and 2.57 percentage points, respectively. Moreover, fruit–peduncle matching, geometric constraints, RGB-D depth information, and PCA-based local direction estimation were combined to infer the picking point and recover its 3D coordinates from the segmentation masks. In the evaluation of 57 RGB-D images, the picking point position accuracy reached 98.2%, while the local peduncle direction accuracy reached 91.2%. Overall, the proposed method can accurately segment mango fruits and peduncles in complex natural environments and convert the segmentation results into picking point positions, 3D coordinates, and local direction information, thereby providing theoretical and technical support for intelligent mango harvesting robots.

Why it matches plant phenotyping methodsマンゴー果実・果梗の画像セグメンテーションと3D形状情報の抽出手法を開発・評価しており、単なる収穫対象の位置検出を超えて、再利用可能な植物器官の形態情報を取得する方法が中心である。

abstractfruit–peduncle matching, geometric constraints, RGB-D depth information, and PCA-based local direction estimation were combined to infer the picking point and recover its 3D coordinates from the segmentation masks

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.

HagPF: Hierarchical-annotation-guided phenotypic framework for stem instance segmentation and length measurement in plant point clouds →

As a critical structural component that connects almost all other types of plant organs, the stem system not only supports the weight of the total plant, but also serves as a vital channel for nutriment transportation. Accurate phenotypic measurement of stem instances is of practical significance for assessing crop growth dynamics and predicting yield. To address current 3D phenotyping challenges of crops such as the difficulty in separating stem segments from the stem system and the low accuracy in stem length measurement, we propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds. Specifically, a hierarchical leaf-stem organ instance annotation strategy is devised to effectively train a PSegNet network for leaf and stem instance segmentation. The segmentation is then followed by a shape-adaptive measurement algorithm to automatically measure the length of stem segments that are morphologically diverse in space. On a 3D dataset comprising four crop species, the proposed framework achieved an Intersection over Union (IoU) of 95.45% for organ semantic segmentation and a Mean Weighted Coverage (mWCov) of 87.87% for instance segmentation (both stem and leaf). Regarding to the stem length measurement, the method obtained an average Root Mean Square Error (RMSE) of 1.044 cm and a relative error of 11.907%, outperforming 7 mainstream methods. The relevant dataset and source code can be found at: https://github.com/Jinx00/stem-length-measurement .

Why it matches plant phenotyping methods植物点群から茎のインスタンスを分割し、茎長を自動測定する3D表現型解析フレームワークの開発・比較検証が中心である。

abstractwe propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds.

Linking plant water status dynamics to yield and fruit cracking in citrus orchards using UAV multi-sensor data and machine learning →

Citrus fruit cracking causes substantial yield and economic losses, yet its relationship with plant water status (PWS) and irrigation management remains insufficiently characterized. Unlike previous UAV-based irrigation studies that focused on water-stress detection or yield estimation, this study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale. UAV-based multispectral, thermal, and LiDAR data, combined with field physiological measurements and machine-learning models, were evaluated in an irrigation management experiment in an ‘Ori’ mandarin orchard (Israel) across three contrasting growing seasons (2023–2025). Several irrigation treatments with different irrigation timings and water inputs were applied during the growing season to evaluate their effects on temporal PWS dynamics and fruit cracking. Trunk growth (TG), stem water potential (SWP), stomatal conductance (SC), and plant area index (PAI) were measured throughout the two seasons and estimated using Random Forest models (R 2 > 0.783). These indicators were subsequently used to predict yield and fruit cracking with high accuracy (yield: R² = 0.896; cracking: R² = 0.845). Cracking was lowest in 2023 (∼3%), with ∼25% lower irrigation, suggesting reduced irrigation may reduce cracking risk. Higher cracking in 2024 (∼14%, vs ∼8% in 2025) coincided with intense heat events. Mid-season SWP and SC were strongly associated with yield formation and cracking patterns. These findings demonstrate that monitoring temporal PWS dynamics can support precision irrigation management by identifying high-risk zones and enabling irrigation strategies that stabilize PWS, reduce the incidence of cracking, and improve yield under variable climatic conditions.

Why it matches plant phenotyping methodsUAVマルチセンサーと機械学習により、樹体水分状態などの植物形質を推定し、収量・果実裂果を予測する技術的枠組みが研究の中心である。

abstractthis study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale.

Machine Learning-Based Crop Growth Diagnosis System Using Spatiotemporal Relative Analysis of Vegetation Indices via a Quartile-Based Method →

Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数、画像分割、クラスタリングを統合した作物生育診断システムの開発・評価が中心であり、作物形質との相関検証や実用インターフェースも扱っている。

abstractWe proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values.

MobileDBH: Estimating Tree Diameter at Breast Height from Smartphone Images Using a Lightweight Diffusion Depth Network for Field Tree Phenotyping →

Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations.

Why it matches plant phenotyping methodsスマートフォン画像と軽量深度推定ネットワークにより樹木DBHを推定する手法を開発・検証しており、植物形質取得が研究の中心である。

abstractwe propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization.

LBD-PointNet++: a point cloud segmentation network for phenotypic trait extraction of broccoli seedlings →

Broccoli is a globally significant vegetable, but climate change and soil salinization increasingly threaten its productivity. Precise seedling phenotyping is essential for selecting salt-tolerant germplasm, yet traditional manual methods are labor-intensive and error-prone. This study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings at the germination and early developmental phase under salt stress. High-fidelity 3D point clouds were reconstructed from a precision three-view imaging system using Structure from Motion (SfM) algorithms. LBD-PointNet++ introduces three core optimizations: (1) a Large Kernel Attention (LKA) mechanism using 3D sparse decomposition to capture long-range global dependencies; (2) a Dual Uncertainty and Shape-Adaptive Sampling (DUSAS) mechanism to preserve high-frequency features of fragile stems and margins; and (3) a joint Boundary-Aware Nested Contrastive and Adaptive Varifocal Joint Loss (BNCV-Loss) to effectively isolate overlapping leaves. Experimental results demonstrate superior performance, achieving an overall mean Intersection over Union (mIoU) of 88.07% across all three categories (Leaf, Stem, and Pot) and a Mean F1-score of 93.48%. Compared to state-of-the-art Transformer architectures like PTv3, LBD-PointNet++ achieves higher accuracy with less than 6% of the parameter volume and over twofold faster inference speed. Furthermore, dynamic monitoring across NaCl gradients (0-250 mmol/L) revealed a potential non-linear threshold effect, identifying 100 mmol/L as a preliminary phenotypic threshold under these conditions. Beyond this threshold, growth inhibition intensified rapidly; At 250 mmol/L, plant height decreased by 54.43% and the 3D entity volume shrank to approximately one-fifth of the control group. In summary, LBD-PointNet++ provides a high-efficiency solution for phenotypic identification and digital breeding of salt-tolerant Brassicaceae crops.

Why it matches plant phenotyping methods3D点群分割ネットワークと三視点SfM撮像を開発し、ブロッコリー幼植物の表現型形質抽出を中心的に評価しているため。

abstractThis study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings

Canonical Ru(ii) tris-polypyridyl complexes as confocal-compatible stains for lignified secondary-wall domains in Arabidopsis thaliana stems. →

Fluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing. Here, we evaluated two canonical Ru(ii) tris-polypyridyl luminophores, Ru1 ([Ru(deeb) 3 ] 2+ ) and Ru2 ([Ru(phen) 3 ] 2+ ), as non-derivatizing stains for fixed Arabidopsis thaliana stem sections. In situ spectral profiling defined practical 405-nm confocal detection windows, and both probes produced reproducible wall-associated photoluminescence enriched in secondary-wall-rich vascular domains, especially xylem vessels and interfascicular fibers. Their anatomical distribution showed qualitative concordance with Wiesner/Mäule lignin histochemistry and condition-validated Safranin O maps, supporting their use as spatial reporters of matrix-associated enrichment within anatomically defined lignified secondary-wall territories. The molecular determinants of this enrichment, including the relative contribution of lignin and other wall polymers, remain to be resolved. Sequential co-staining with Calcofluor White separated broad β-glucan-rich wall architecture from Ru-enriched secondary-wall domains, while spectral-overlap analysis identified far-red Alexa Fluor 647 excitation at 638 nm as the most orthogonal tested third-label configuration. Ligand-comparative DFT descriptors provided structure-property fingerprints summarizing differences in π-surface continuity and electrostatic anisotropy. Overall, these results position canonical Ru(ii) polypyridyl luminophores as confocal-compatible, chemically tractable scaffolds for anatomical imaging of lignified plant-wall territories.

Why it matches plant phenotyping methods植物の木化二次細胞壁を可視化・空間評価する共焦点蛍光染色法の開発と検証が中心であり、単なる生物学的測定ではない。

abstractFluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing.

A Novel Non-Invasive Method for Real-Time Monitoring of Plant Water Status Based on Xylem Electrical Conductivity →

Non-invasive, real-time monitoring of plant water status is critical for precision agriculture and plant physiology. However, existing methods often lack continuous in situ measurement capability or are limited by temporal resolution. This paper proposes a novel non-invasive method based on xylem electrical conductivity, inspired by industrial non-contact fluid measurement. As a ground-based complement to remote sensing, this approach demonstrates the feasibility of online, in situ, and non-invasive monitoring of water stress in grapevine stems under controlled laboratory conditions. The industrial C4D sensing system is adaptively modified into a specialized Plant-C4D sensor with an array-based design for batch signal acquisition. To validate the electrical response to water loss, a gravimetric natural dehydration experiment was conducted, demonstrating a clear correlation between electrical signals and water content changes in detached stem samples. Full-day dynamic experiments are conducted under three conditions: normal water supply, varying water stress, and plant inactivation. Sensitive characteristic parameters are extracted through signal analysis, and a pattern recognition framework is established to eliminate environmental interference and suppress individual differences. Experimental results on 24 plant samples (Shine Muscat) show that the method accurately discriminates viable from inactivated plants with an accuracy of 91.67% (22/24 correct). Furthermore, the Fuzzy C-Means (FCM) clustering algorithm successfully quantifies the severity of water stress in viable plants, yielding results consistent with actual water supply conditions. While these findings demonstrate the capability of Plant-C4D sensor to capture stem water status-related information, the current results do not establish full physiological validation, warranting further exploration with in vivo experiments.

Why it matches plant phenotyping methods植物の水分状態を直接推定する非侵襲センサーと解析手法の開発・検証が研究の中心であり、明確な植物フェノタイプ測定に該当する。

abstractThe industrial C4D sensing system is adaptively modified into a specialized Plant-C4D sensor with an array-based design for batch signal acquisition.

Spatio-Temporal Monitoring of the Invasive Plant Alternanthera philoxeroides in a Narrow River Using Sentinel-2 Time-Series Data →

Alternanthera philoxeroides, an invasive alien species, spreads rapidly in river systems via vegetative propagation from stem fragments, requiring river-system-scale monitoring to understand its expansion dynamics and habitat preferences. This study used multi-temporal Sentinel-2 data to analyze spatio-temporal variations in fractional vegetation cover (FVC) within a 3.5 km river reach. FVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%), enabling reliable monitoring even in narrow (~24 m) channels. Time-series analysis from 2019 to 2024 revealed downstream expansion beginning in 2022. Annual maximum FVC (Cmax) was used to assess relationships with removal records and bank structures, showing that removal effects were temporary and more pronounced in the first year, while steel sheet-pile banks limited vegetation growth compared to concrete revetments. These results demonstrate that Sentinel-2 data can provide an effective and accessible tool for evaluating invasive plant dynamics and management effectiveness in low-flow river systems where A. philoxeroides dominates the floating vegetation community.

Why it matches plant phenotyping methodsSentinel-2時系列から侵入植物の植生被覆率を推定し、航空画像で精度検証しており、植物状態の取得・評価手法が中心です。

abstractFVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%)

The informativeness of vegetation indices as predictors of the yield of two spring wheat varieties in an experiment with a plant protection system against the background of fertilizers →

The use of a combined assessment of the informational significance of vegetation indices for predicting the yield of spring wheat, taking into account varietal specificity and agrotechnical factors, has been studied. The test site was the field experience in the forest-steppe zone of the Novosibirsk Priobye. In the experiment, spring wheat of the Suenga and Novosibirsk 41 varieties was cultivated using intensive agricultural technology. For the analysis, data obtained using the DJI Phantom 4 Multispectral Phantom unmanned aerial vehicle during the crop growing period in 2023–2025 were used. Vegetation index values were calculated using five spectral channels: blue (B, 450 ± 16 nm), green (G, 560 ± 16), red (R, 650 ± 16), red edge (RE, 730 ± 16) and near-infrared (NIR, 840 ± 26 nm). For the analysis of informational importance, the following indices were used as predictors of crop yield: NDVI, NDWI, GNDVI, LAI, CVI, GCI, and ChlRE. For assessing the informativeness of the indices, independent methods were used: the F-statistic of one-way regression (ANOVA F-test), evaluation of mutual information (Mutual Information, MI), and feature importance of the random forest algorithm (Random Forest, RF). Each of the scores was normalized in the range [0; 1] using the min-max normalization method, after which a combined score was calculated as a weighted sum. For the Suenga variety, the stable predictors regardless of the experimental variants were CVI (tillering) and ChlRE (stem elongation and heading), while for Novosibirsk 41, the set of informative predictors significant ly depended on the combination of plant protection and fertilizer systems. It was found that chlorophyll content indices (GCI, ChlRE) increased the predictive relationship with yield under fertilization, while the water status index (NDWI) lost informativeness when fertilizers were applied.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を抽出し、複数の統計・機械学習手法を統合して小麦収量予測における指標の有用性を評価しており、植物形質推定ワークフローが中心です。

abstractThe use of a combined assessment of the informational significance of vegetation indices for predicting the yield of spring wheat

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

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

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

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

Using UAV-LiDAR for stem volume phenotyping in the genetic selection of radiata pine →

Abstract Context : Radiata pine breeding programmes rely on stem volume as a key objective, but phenotyping constraints limit selection intensity. UAV-LiDAR offers a scalable alternative to labour-intensive field measurements. Aims : We evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials and quantified their utility relative to field-measured diameter at breast height (DBH). Methods: LiDAR metrics describing tree height and size were assessed against allometric stem volume (ASV) across 11 genetic trials (~27,000 trees, two trial series) using single-step genomic best linear unbiased prediction (ssGBLUP) with ~9,500 SNPs. Results : The 3D surface area of the individual tree convex hull (convexhull3D_area) had the highest correlation with ASV (up to r = 0.87) and similar heritability to DBH (mean h 2 = 0.26). LiDAR tree height had the highest heritability (mean h 2 = 0.37) and moderate to high genetic correlation with DBH. Selecting the top 100 genotypes by convexhull3D_area recovered 67-86% of potential ASV genetic gain, versus 88-96% for DBH. Including malformed trees in the genetic analyses of LiDAR traits marginally reduced their performance as stem volume proxies. Conclusion UAV-LiDAR-derived tree height and 3D convex hull surface present desirable properties to complement field phenotyping for stem volume selection in radiata pine. Their scalability, repeatability and high heritability support lower phenotyping costs, better early selection and accelerated genetic gain in radiata pine breeding.

Why it matches plant phenotyping methodsUAV-LiDARを用いて樹高・樹体サイズなどの形質を抽出し、茎体積の遺伝選抜プロキシとして相関・遺伝率・選抜効果を検証しており、植物フェノタイピング手法が中心である。

abstractWe evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials

Plant architecture reveals contrasting developmental strategies in two threatened West African timber species (Khaya senegalensisand Pterocarpus erinaceus): implications for conservation and sustainable forest management →

Abstract Architectural analysis provides a powerful analytical framework for understanding the ontogenetic trajectories of tree species and their adaptive responses to environmental constraints. Despite their ecological, economic, and cultural importance in West African agroforestry systems, the architectural development of Khaya senegalensis (Desr.) A. Juss. (Meliaceae) and Pterocarpus erinaceus Poir. (Fabaceae), two overexploited taxa classified as Vulnerable on the IUCN Red List, had never been formally described. This study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities along a south-north bioclimatic gradient in Côte d'Ivoire, covering contrasting vegetation zones ranging from dense humid forest to dry Sudanian savanna. Both species display well-defined ontogenetic trajectories comprising four phases: juvenile establishment, architectural construction, reproductive transition, and crown restructuring associated with ageing. Distinct architectural models were identified: K. senegalensis conforms to Rauh's model, characterized by a monopodial orthotropic trunk with indefinite growth and rhythmic acrotonic branching; P. erinaceus follows Troll's model, in which the orthotropic trunk progressively gives rise to a sympodial plagiotropic system with mixed terminal and lateral flowering. Architectural units, defined as the minimal structural organization enabling a species to reach reproductive maturity, were established at the adult stage: that of K. senegalensis comprises four axis categories and five branching orders, while that of P. erinaceus comprises three axis categories and up to six branching orders in old trees. Significant variation in growth-unit morphology among habitats and localities ( P ) revealed the architectural plasticity of both species in response to ecological gradients. The calculated Favourable Development indices ( FDi ) identified Bouaké and Katiola as optimal zones for K. senegalensis , and Bouaké and Toumodi for P. erinaceus , providing objective spatial criteria for reforestation planning. These findings demonstrate that architectural traits are robust indicators of development, adaptive strategies, and crown functioning. By linking structural organization to productivity, resilience, and regeneration potential, this study provides a scientific basis for integrating architectural analysis into reforestation programmes, sustainable forest management, and the design of agroforestry systems for threatened African tree species facing growing climatic and anthropogenic pressures. Complementary regression analyses further showed that phytomer number, rather than internode elongation, primarily governs growth-unit length in both species, and that growth unit diameter scales positively with growth unit length; a multivariate analysis of variance (MANOVA) confirmed that ontogenetic stage and locality, but not habitat alone, robustly structure growth-unit morphology.

Why it matches plant phenotyping methods樹木の成長段階・分枝構造・成長単位形態を対象に、建築学的および回顧的解析を中心的手法として適用し、植物構造形質と発達状態を定量・比較しているため。

abstractThis study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities

3D Reconstruction of deciduous Trees using low-cost UAV - and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies →

Abstract. Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dßprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Why it matches plant phenotyping methods樹冠全体のシュート伸長という植物形質を取得するための3D再構成手法を開発・評価しており、精度・解像度・完全性の検証も中心的に扱っている。

abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.

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

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

Why it matches plant phenotyping methods果樹の接ぎ木不親和性という植物状態の早期検出法を、画像・生理・分子・AI技術の観点から体系的にレビューしており、フェノタイピング手法が中心である。

abstractThis review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility

Deriving Tree Stem Profile and Volume Using a Close-Range Remote Sensing and Machine Learning Approach →

Abstract. Accurate estimation of tree volume is essential for precision forestry and sustainable forest management. Traditional forest inventory methods rely on manual measurements of tree height and diameter, which are time-consuming and costly to conduct over large areas, and difficult to perform efficiently in dense forest stands. This study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets. While the study relies on harvester production data (Sweden) and field-measured tree stem profiles (Brazil), the framework is designed to support the estimation of tree volume from close-range remote sensing techniques, such as terrestrial photogrammetry using handheld cameras. Three modelling approaches were evaluated, including two machine learning models (XGBoost and Random Forest) using partial tree stem profile measurements as predictors, and one baseline model (XGBoost) using diameter at breast height and tree height as predictors. The models were developed using two independent datasets: harvester production data of Norway spruce (Picea abies (L.) H. Karst.) from Sweden and field-measured tree stem profiles of Slash pine (Pinus elliottii Engelm.) and Loblolly pine (Pinus taeda L.) plantations from Brazil. The results show that tree volume can be predicted with reasonable accuracy using partial tree stem profiles, although models incorporating tree height achieved the lowest prediction errors. The findings demonstrate that partial tree stem profiles provide valuable structural information for machine learning-based tree volume estimation. This framework supports the future integration of close-range remote sensing techniques into modern forest inventory systems.

Why it matches plant phenotyping methods樹幹プロファイルから樹木体積という植物形質を推定する機械学習・近距離リモートセンシング手法が研究の中心であり、複数モデルと独立データセットで評価している。

abstractThis study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets.

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

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

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

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

Pixel-registered multimodal synchrotron XRF and FTIR microscopies reveal salinity stress response mechanisms in pistachio. →

Background Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. Results We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. Conclusions We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity-treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.

Why it matches plant phenotyping methods植物組織の元素・生化学状態を空間的に取得するピクセル登録型マルチモーダル画像パイプラインを開発し、植物ストレス表現型解析への汎用的プラットフォームとして提示しているため。

abstractwe developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1

3D micro–X-ray tomography of lumen space in dried stem, branch, and outer tissues of Moringa oleifera: A structural case study →

Abstract Moringa oleifera is widely used in dry tropical and subtropical regions due to its rapid growth and high nutritional value, yet its internal tissue organization has primarily been described using two-dimensional anatomical approaches. Here, we present a three-dimensional micro–X-ray computed tomography (micro-XCT) characterization of lumen space in stem, branch, and outer tissues (bark region) from a single M. oleifera individual. Samples were oven-dried prior to imaging; therefore, the quantified void fraction represents apparent lumen/void space in dried material and should not be interpreted as in vivo porosity. Micro-XCT datasets were segmented to quantify cross-sectional lumen area distributions and apparent void fraction from pooled reconstructed slices. All data originate from a single individual; reported metrics represent structural descriptors of pooled cross-sections and not replicated biological measurements. The stem dataset exhibited a dense arrangement of small lumen features and a high number of segmented objects, consistent with a compact woody tissue organization in the scanned region. The branch dataset showed a larger proportion of void space and a strongly right-skewed size distribution with a minority of large lumen features. The outer tissue dataset displayed heterogeneous void space organization, which likely reflects a mixture of cell lumens, intercellular spaces, and drying-related cracks, and therefore is reported descriptively without assigning xylem-vessel identity. This study provides a conservative 3D structural dataset and an image-analysis workflow for quantifying lumen space in dried M. oleifera tissues, complementing published anatomical descriptions. The results highlight strong within-plant heterogeneity across tissue types and underscore the importance of sample preparation and histological validation when interpreting micro-XCT measurements in woody plants.

Why it matches plant phenotyping methods乾燥植物組織の3D micro-XCT画像から管腔・空隙の構造形質を抽出する画像解析ワークフローとデータセットが研究の中心であり、単なる生物学的測定ではない。

abstractMicro-XCT datasets were segmented to quantify cross-sectional lumen area distributions and apparent void fraction from pooled reconstructed slices.

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

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

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

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

Efficient Attention Guided Fusion Model for Plant Disease Classification →

Early detection of diseases in plants has been identified as a critical factor for ensuring the maintenance of productivity, preventing economic losses, and promoting sustainable agriculture. Traditional manual approaches for diagnosing diseases are time-consuming, subjective, and inappropriate for large-scale and real-time agriculture. In order to overcome the limitations of traditional approaches, the CNN–CBAM–MobileViTNet has been proposed, an efficient attention-guided network by the fusion of Convolutional Neural Networks (CNNs), Convolutional Block Attention Module (CBAM), and Mobile Vision Transformer (MobileViT) for plant diseases recognition.The CNN component is effective in capturing local visual patterns like lesions, discoloration, and texture. The CBAM component is effective in refining the feature representations by focusing on disease-related spatial areas and useful channels. The MobileViTNet branch is useful in capturing contextual relationships from the leaf areas through lightweight transformer blocks. The CNN–CBAM—MobileViTNet is tested on an enhanced dataset with 38 classes of plant diseases and health conditions, splitting data into 70% training, 15% validation, and 15% testing. Significantly, extensive experimental analysis reveals that the test accuracy is 99%, with high precision, recall, and [Formula: see text]1-score values. Training-validation curves show that the model converges stably with little overfitting, while ROC analysis shows high classwise discrimination ability of the model. Hence, the CNN–CBAM–MobileViTNet model is reliable and may be used for real-world applications in smart agriculture and automatic crop disease monitoring systems.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類するCNN・注意機構・Transformer融合モデルを開発し、38クラスの病害・健全状態で性能評価しており、病害状態の表現型取得・推定が中心である。

abstractthe CNN–CBAM–MobileViTNet has been proposed, an efficient attention-guided network by the fusion of Convolutional Neural Networks (CNNs), Convolutional Block Attention Module (CBAM), and Mobile Vision Transformer (MobileViT) for plant diseases recognition.

Using unoccupied aerial systems (UAS) and photogrammetry to estimate tree height and trunk diameter of urban American elm trees →

Urban forest health may be monitored and supported with the implementation and maintenance of an accurate community tree inventory. The longitudinal recording of a tree’s attributes (i.e. diameter and height) may inform potential inputs and activities related to maintenance, health, and the establishment of a tree protection zone. Urban tree inventories feature barriers to implementation including resources (i.e. labour, time, and finances), competing priorities, and gaps in knowledge. The objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter (1) with oblique RGB imagery, (2) with and without GCP, and (3) during leaf-off and leaf-on conditions. Structure-from-Motion datasets were obtained using Unoccupied Aerial Systems (UAS) – “drones” and related components – to collect oblique aerial imagery, which was processed with photogrammetry software Agisoft Metashape. Tree height measurements using leaf-on imagery (R2 = 0.58, RMSE = 1.34 m) were more accurate when compared to leaf-off imagery with Ground Control Points (GCP) (R2 = 0.47, RMSE = 3.41 m) and leaf-off imagery without GCPs (R2 = 0.43, RMSE = 3.49 m). Tree height measurements during the leaf-off period had no significant difference when comparing imagery with and without GCPs. Trunk diameter measurements using leaf-off imagery were not significantly different with the use of GCPs (R2 = 0.68, RMSE = 6.39 cm) compared to those without (R2 = 0.68, RMSE = 7.85 cm). This case study highlights the applicability and accuracy of Unoccupied Aerial Systems and Structure-from-Motion methods when collecting important urban tree inventory parameters, and presents an accessible, reliable, and replicable workflow for urban forestry practitioners with limited photogrammetry-related experience.

Why it matches plant phenotyping methodsUAS-SfMフォトグラメトリによる樹高・幹径という植物形態形質の推定法を開発・比較検証し、精度と再現可能なワークフローを評価しているため、方法が中心的です。

abstractThe objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter

3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies →

Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Why it matches plant phenotyping methods樹冠全体のシュート伸長を測定するための3D再構成手法を開発・評価し、センサー評価、取得・処理戦略、精度・完全性の検証を中心に扱っているため。

abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.

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

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

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

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

Strengths and Limitations of Handheld Mobile Laser Scanning in Vegetation Assessment in Tropical Mangroves, Wetlands, and Rangelands Ecosystems, Queensland Australia →

Handheld Mobile Laser Scanning (HMLS) is increasingly used for high resolution 3D mapping in construction, mining and natural environments. This study evaluates the strengths and limitations of HMLS for vegetation assessment in diverse tropical eco-systems across north Queensland, Australia, including rangelands, grasslands, man-groves and estuarine wetland forests. We assessed the accuracy of HMLS-derived point clouds against ground-truth measurements and compared performance with UAV SfM–MVS surveying. HMLS achieved centimeter-level accuracy for vegetation metrics, with mean absolute errors of 8.5 cm for Diameter at Breast Height (DBH) in rangeland forests and 6.7 cm for tussock height. The system consistently produced high-density point clouds, enabling detailed characterization of vertical structure, particularly understory vegetation often obscured in aerial surveys. HMLS proved operationally flexible across closed-canopy wetlands, mangroves, rangeland forests and open grasslands. Key limi-tations included restricted horizontal point cloud penetration in dense vegetation, compounded by access constraints and environmental conditions, and point cloud drift in areas with few geometric features, such as grasslands, which introduced uncertainty in vegetation metrics. High computational demands further constrained workflow effi-ciency. Overall, HMLS demonstrates strong potential as an accurate and versatile tool for vegetation mapping and structural analysis in complex tropical ecosystems.

Why it matches plant phenotyping methodsHMLSを用いた植物の3D形態・構造計測手法を開発的に評価し、地上真値およびUAV手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。

abstractThis study evaluates the strengths and limitations of HMLS for vegetation assessment

YOLO-DC: A Crop Detection and Counting Network for UAV-Based Agricultural Scenes →

Crop targets in UAV aerial images are typically characterized by small scale, dense distribution, severe mutual occlusion, and complex backgrounds, which often lead to low detection accuracy and large counting errors for existing deep learning models. To address these issues, this study proposes an improved YOLOv12-based crop detection and counting model, named YOLO-DC. By introducing an attention mechanism (LGCB-AM) and a multi-scale detection head (MS-DH), the proposed model effectively enhances local texture extraction, global modeling, foreground–background contrast, and boundary perception for dense small objects. Subsequently, a series of comparative experiments, ablation studies, and transfer experiments were conducted on the wheat and rice datasets. The results show that YOLO-DC achieves a favorable balance among detection accuracy, counting error, and model efficiency and overall outperforms the other comparison models. Ablation studies further verify the effectiveness of the proposed design, showing that LGCB-AM is the key contributor to the performance improvement, while the boundary branch and repulsion branch play critical roles in dense-target discrimination. In addition, an appropriate module insertion strategy can effectively balance high-level semantic enhancement and feature fusion stability. Transfer experiments demonstrate that pretraining on the wheat dataset and fine-tuning on the rice dataset significantly outperform training from scratch, indicating strong cross-crop transfer potential. Overall, the proposed YOLO-DC provides an effective solution for high-precision crop detection and counting in agricultural scenarios.

Why it matches plant phenotyping methodsUAV画像から作物個体を検出・計数する手法を中心に、モデル開発、比較、アブレーション、転移検証を行っており、植物個体数という観測可能な形態・集団特性を抽出するため、植物フェノタイピング手法として適格です。

abstractthis study proposes an improved YOLOv12-based crop detection and counting model, named YOLO-DC.

Structural Volume Composition of Internodes is a Key Morphological Factor Contributing to Culm Non-structural Carbohydrate Accumulation in Rice. →

Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological factors associated with NSC accumulation remain unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. In addition, plant growth regulator treatments that altered VCR were accompanied by changes in culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving stem carbohydrate storage capacity in rice.

Why it matches plant phenotyping methodsイネ茎の形態からNSC蓄積能力を評価する新規指標VCRを導入し、複数年で再現性と既存形態形質との性能を検証しており、形態表現型の抽出・評価法が中心である。

abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.

Lightweight contour-aware 2D Gaussian splatting under Plant-to-Camera →

The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping

Why it matches plant phenotyping methods植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。

abstractThe objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud.

A Review of Fruit Tree Canopy Branch Feature Extraction and 3D Reconstruction Algorithms →

Accurate perception and 3D reconstruction of fruit tree branch structures are fundamental to smart orchard development, with broad applications in intelligent harvesting, crop phenotyping, and precision management. However, the slender and highly branched morphology, multi-scale distribution, weak surface texture, and severe occlusion inherent to fruit tree branches pose substantial challenges to high-fidelity modeling. This paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies. A structured literature search was conducted using the Web of Science, Scopus, and Google Scholar databases, with search terms including “fruit tree branch”, “point cloud reconstruction”, “3D canopy modeling”, “branch feature extraction”, and “agricultural robotics”. Studies published between 2000 and 2025 were considered, with inclusion criteria requiring relevance to branch structure perception, reconstruction accuracy, or orchard application; non-peer-reviewed sources and studies lacking quantitative evaluation were excluded. We trace the evolution of feature extraction from classical 2D image processing and geometric fitting, through point cloud segmentation and skeleton extraction, to modern deep learning approaches and multimodal perception techniques. For 3D reconstruction, we compare active and passive sensing strategies alongside both explicit and implicit scene representation methods, discussing their respective strengths and applicable scenarios. A five-dimensional evaluation framework is also proposed, encompassing geometric accuracy, structural consistency, feature stability, computational efficiency, and generalization capability. Finally, we identify key bottlenecks in fine-grained structure recovery, occlusion handling, and cross-scene generalization, and highlight future directions in structural prior integration, multimodal collaborative modeling, and lightweight neural representations—offering a structured reference for advancing 3D perception research in smart orchards.

Why it matches plant phenotyping methods果樹の枝構造の特徴抽出と3D再構成を対象とする、植物形態計測・表現型取得手法のレビューであり、方法論が中心です。

abstractThis paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies.

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

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

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

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

Forest biometrics in the 21 century special issue →

Abstract Forest biometrics has evolved from a measurement-driven discipline focused on field efficiency and statistical rigor to a data-rich, technology-enabled science integrating multisensor information and advanced modeling approaches. This special issue, inspired by the Second North American Forest Mensurationists Conference held in 2022, highlights this transformation through nine studies that collectively span scales from individual branches to regional forest dynamics. Together, they emphasize a shift from identifying single optimal models to developing integrated, uncertainty-aware model systems that support operational decision-making. At the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume. At the tree level, extensive benchmarking of height–diameter relationships demonstrates that model form and stand origin strongly influence predictive performance, with generalized additive models often outperforming traditional approaches. Complementary work shows that calibration strategies are not universally transferable across model forms, underscoring the need for careful alignment of function choice and calibration design. Addressing biases in young stands, Bayesian model averaging offers a practical interim solution where traditional volume models trained on mature cohorts fail. At broader scales, studies demonstrate the operational potential of integrating public and low-cost remote sensing data. Freely available USGS 3DEP LiDAR supports highly accurate dominant height and site index estimation, while bias-corrected digital aerial photogrammetry provides a viable alternative in areas lacking LiDAR coverage. Landscape-level analyses using Landsat time series and permanent plots enable mapping of basal area growth, revealing spatial variability and temporal trends driven largely by stand dynamics. Collectively, these studies define a cohesive framework for modern forest biometrics: combining multiple data sources, selecting model families deliberately, applying light but effective calibration, and explicitly quantifying uncertainty. This integrated approach supports scalable, reliable predictions tailored to the needs of forest managers and policymakers. The special issue thus outlines a forward-looking research agenda that prioritizes resilient modeling systems over isolated solutions, enabling forestry to meet contemporary challenges across scales from tree components to landscapes.

Why it matches plant phenotyping methods森林の枝形状、樹高、林分指標などの植物形質を、レーザースキャン、航空写真、LiDAR、時系列衛星データ、統計モデルで推定・検証する方法群を中心に扱う特集概説であり、測定・推定手法が中心である。

abstractAt the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume.

Species-Specific Stem Volume Models for Urban Broad-Leaved Trees in Beijing Using Handheld Photogrammetric Height Measurement and Destructive Validation →

Accurate stem-volume estimation is fundamental for urban tree inventory and management, but equations developed for forest-grown trees may not be directly suitable for open-grown urban trees with altered stem form and height–diameter relationships. This study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China: Quercus mongolica, Sophora japonica, Ginkgo biloba, and Populus davidiana. A total of 2679 standing trees from 535 plots were used for model development and evaluation. The diameter at breast height and ground diameter were field-measured, whereas tree height was obtained as a photogrammetry-derived non-destructive measurement using a handheld tree-measurement superstation. Bivariate DBH–height models, DBH-based linked models, and ground-diameter-based chained models were fitted using weighted nonlinear least squares. Model performance was assessed using validation statistics, 10-fold cross-validation, Monte Carlo uncertainty propagation, and an independent destructive reference dataset of 55 felled trees with section-measured stem volume. Across species, the bivariate models performed best, with mean percent standard errors of 8.68%–16.24%, compared with 9.76%–20.25% for DBH-based linked models and 15.13%–28.56% for ground-diameter-based models. Destructive reference validation showed acceptable agreement within the available validation dataset, with relative RMSE values of 2.30%–5.03% and relative bias values of 0.51%–2.51%. Monte Carlo simulation indicated species-specific propagation of photogrammetric height error, with the lowest average volume fluctuation in Ginkgo biloba. These results suggest that handheld photogrammetry combined with species-specific modelling provides a practical and uncertainty-aware basis for urban stem-volume estimation. This study directly estimates stem volume rather than biomass or carbon stock, and the equations may support future biomass- and carbon-related assessments when combined with appropriate conversion parameters.

Why it matches plant phenotyping methods携帯型フォトグラメトリによる樹高取得と、幹体積推定モデルの開発・交差検証・伐倒木による独立検証が研究の中心であり、樹木の形態形質を定量化する実質的なフェノタイピング手法である。

abstractThis study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China

Non-destructive yield estimation of onion and garlic using UAV-based hyperspectral imaging and hybrid machine learning models. →

Background Accurate pre-harvest yield estimation of underground bulb crops such as onion and garlic is important for precision agriculture, harvest planning, and food-security-oriented decision-making. However, their harvestable organs develop below ground and cannot be directly observed using conventional remote sensing methods. This study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models. Method Field experiments were conducted in Muan-gun, Korea, using onion and garlic as representative underground bulb crops. UAV-based hyperspectral images, crop growth traits, and destructive live bulb weight measurements were collected during the growing period. Hyperspectral images were processed through geometric correction, radiometric correction, and Savitzky-Golay spectral smoothing. Three dimensionality reduction methods, including genetic algorithm (GA), principal component analysis (PCA), and clustering, were used to reduce spectral redundancy. Five prediction models, including random forest (RF), XGBoost, partial least squares regression (PLSR), multilayer perceptron (MLP), and residual network (ResNet), were then evaluated for live bulb weight prediction. Result Significant spectral differences were observed in the 550-680 nm and 730-800 nm bands, which were closely associated with crop yield and below-ground bulb development. GA was the most effective feature selection method for extracting yield-related spectral bands. For onion yield prediction, the GA + RF model achieved the highest predictive accuracy, with an R 2 of 0.9656 and an NRMSE of 18.55%. For garlic yield prediction, PLSR showed the best performance, with an R 2 of 0.9260 and an NRMSE of 27.20%. Conclusion The proposed UAV-based hyperspectral framework enables accurate, real-time, and non-destructive yield estimation for underground bulb crops. This approach reduces reliance on labor-intensive destructive sampling and provides a practical tool for precision crop monitoring and data-driven agricultural management.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習による地下球根の収量（生体球重）推定フレームワークの開発・比較評価が研究の中心であり、植物形質の取得・推定手法に該当する。

abstractThis study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models.

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.

Oil Palm Stem Disease Detection Based on Color Moments and GLCM Texture Features Using Artificial Neural Networks →

Oil palm is an essential commodity for the economy; however, basal stem rot caused by Ganoderma boninense poses a significant threat to plantation productivity and long-term vitality. It highlights the importance of early detection of stem disease to facilitate timely intervention and minimize potential economic losses. This study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework. The dataset contained 525 images of oil palm stems, of which 205 depicted healthy specimens, and 320 depicted diseased ones. These were captured within their natural environment. Color features were derived by analyzing color moments within the HSV color space, while texture features were extracted from the Grey-Level Co-occurrence Matrix (GLCM). The extracted features were classified employing an Artificial Neural Network (ANN) and were subsequently contrasted with classifiers including Decision Tree, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine. Model performance was evaluated using k-fold cross-validation with k = 5 and k = 10 to ensure the consistency and reliability of the assessment. The experimental results demonstrated that the highest accuracy of 97.52% was achieved when the ANN model was used to classify the integrated color and texture features. The innovative aspect of this research resides in demonstrating that handcrafted features integrated with artificial neural networks can attain high detection accuracy in scenarios with limited data, providing a viable alternative to data-intensive deep learning techniques. This method facilitates a dependable, computer vision-driven early detection system for oil palm stem diseases, thereby promoting sustainable plantation management.

Why it matches plant phenotyping methods油ヤシ幹の病徴を画像から色・テクスチャ特徴として抽出し、分類器で病害状態を推定する方法が研究の中心であり、交差検証による性能評価も行っているため、植物表現型計測手法として含める。

abstractThis study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework.

Non-destructive and accurate phenotypic detection method for okra seedlings under salt stress based on dual-view feature fusion and lightweight PointNet+. →

Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.

Why it matches plant phenotyping methodsオクラ幼苗の点群から草丈・茎径・樹冠幅を抽出する計算フェノタイピング手法を開発し、手動測定との比較で精度検証しているため、方法が研究の中心である。

abstractthis study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model.

Automatic measurement of rice tiller angle from unmanned aerial vehicle images →

Abstract Rice ( Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery. Our method leverages keypoint detection models to estimate tiller angles efficiently and accurately. We collected and annotated a dataset of UAV‐captured rice plant images for tiller angle estimation. We demonstrated that our approach provides scalable and precise measurement for keypoints under real‐world field conditions, achieving a mean average precision (mAP)@50 of 0.982 and a mAP@50:95 of 0.859 on the test set. Predicted tiller angle distribution aligns well with human annotations, with a mean absolute error of 5.3° across a range of 10.7°–27.8° and a Pearson's correlation coefficient of 0.64, offering acceptable accuracy for tiller angle measurement in real‐world agricultural settings. Additionally, the predicted plant base width ranges from 2.8 to 5.5 cm, with a mean absolute error of 1.02 cm compared to human annotations, highlighting the model's capability for precise spatial analysis. Significant differences in tiller angle and plant base width were detected among 27 rice genotypes. These results validate the proposed pipeline's potential for accurate and efficient differentiation of plant architecture traits. This research is the first to measure the rice tiller angle directly from UAV images. It lays a foundation for automated phenotyping of plant architecture traits and has the potential for integration into plant phenotyping frameworks to further promote artificial intelligence‐driven rice research and production.

Why it matches plant phenotyping methodsUAV画像と深層学習により、イネの分げつ角度・株元幅という植物形態形質を自動推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。

abstractwe proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery.

Integration of UAV photography, field data and machine learning algorithms for stem volume estimation →

In this study, the diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques. The research was conducted in southwestern Türkiye within the brutian pine stands managed by the Isparta Regional Directorate of Forestry. A total of 175 trees were measured for height and diameter at breast height (d1.3), and these measurements used to estimate volume. The accuracy of these estimations predictions was examined, with volume estimation values serving as dependent variables in various ML algorithms. The performance of nine ML algorithms - AdaBoost Regression, Artificial Neural Network, Deep Neural Network, Decision Tree Regression, Gradient Boosting Regression, Linear Regression, Random Forest Regression, Support Vector Regression, and eXtreme Gradient Boosting Regression - were compared. The results indicated that using only the diameter values (max. correlation 0.984) produced better results than using only the height values (max. correlation 0.932), while combining diameter and height variables (max. correlation 0.987) produced the most accurate results. Among the all algorithms, Random Forest Regression achieved the highest average correlation (0.968), whereas Decision Tree Regression had the lowest (0.906). All algorithms produced correlations exceeding 0.90. These findings demonstrate that ML models can effectively estimate stem volume from UAV-derived diameter and height data under field conditions similar to those in southwestern Türkiye. The integration of remote sensing and ML may therefore offer a viable approach for stem volume estimation in structurally comparable forest environments.

Why it matches plant phenotyping methodsUAV画像から樹木の直径・樹高を取得し、機械学習で幹材積を推定する手法を比較評価しており、植物形質の取得・推定が研究の中心である。

abstractthe diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques.

DDCANet for SiO 2 -mediated drought regulation research: high-precision segmentation and phenotypic detection of cucumber point clouds. →

Cucumber is a core cultivated facility vegetable in China. Drought stress at the seedling stage severely inhibits its growth and development. The regulatory mechanism and optimal application concentration of SiO 2 nanoparticles in alleviating drought stress in cucumber seedlings remain unclear. Moreover, traditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress. To address these issues, this study focused on phenotypic detection under drought stress and analysis of the regulatory effects of SiO 2 nanoparticles. An improved compact and low-redundancy segmentation model, DDCANet, was proposed based on PointNet++-SSG. Combined with 3D point cloud technology and the Euclidean clustering algorithm, it enables automatic extraction of phenotypic parameters from cucumber seedlings treated with SiO 2 nanoparticles under drought stress. In this study, Trailing cucumber seedlings were used as experimental materials. 3D point cloud data of cucumber seedlings were collected under treatments with different concentrations of SiO 2 nanoparticles and PEG-simulated drought stress. A dataset containing 70 valid samples was constructed and labeled into two categories: Stem and Leaf. The core optimizations of the DDCANet model are as follows: Firstly, an Adaptive Density-Aware Feature Enhancement (ADFE) module is embedded to accurately capture point cloud density heterogeneity induced by SiO 2 ;Secondly, a Channel Attention and Normalization-enhanced SA Layer (CANL) is designed to strengthen the coupling of local and global drought-related phenotypic features. Thirdly, a Drought-Aware Hybrid Loss (DHL) function is constructed to alleviate the class imbalance of seedling stem and leaf point clouds under drought stress. Results show that the DDCANet model achieves a mean Intersection over Union (mIoU) of 89.01 ± 0.32% and a Stem IoU of 83.6 ± 0.45%, representing improvements of 6.55% and 9.6% respectively compared with the baseline PointNet++-SSG model, and a 30.5% improvement in stem segmentation accuracy compared with the classic PointNet model. It thus enables high-throughput, non-destructive detection of drought phenotypes in cucumber seedlings under SiO 2 nanoparticle treatment. Ablation experiments verified the positive contributions of the ADFE, CANL, and DHL modules. Furthermore, instance segmentation and phenotype extraction were completed using the Euclidean clustering algorithm to analyze the drought-alleviating effects of SiO 2 nanoparticles under PEG-simulated drought stress. Results indicate that a low concentration of 20 mg/L exhibits a weak alleviating effect, medium concentrations of 40-60 mg/L show bidirectional regulatory characteristics, and a high concentration of 100 mg/L causes negative physiological effects. The optimal application concentration is 80 mg/L, which comprehensively improves key phenotypes such as seedling height and volume under drought stress and exerts a positive regulatory effect on seedling growth under drought conditions. The DDCANet model constructed in this study provides an efficient technical tool for the accurate phenotypic detection of crop seedlings treated with SiO 2 nanoparticles under drought stress. It clarifies the optimal application concentration of SiO 2 nanoparticles, offers a precise concentration threshold and theoretical support for the scientific application of SiO 2 nanoparticles in drought-stressed cultivation of protected cucumber, and establishes a novel methodological reference for the research on phenotypic regulation of crops under drought stress via nano-agricultural technology.

Why it matches plant phenotyping methods3D点群分割モデルを開発・検証し、キュウリ幼苗の茎葉分離と形質抽出を自動化することが研究の中心であるため、植物フェノタイピング手法論文として採用。

abstracttraditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress.

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

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

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

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

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

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

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

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

Developing a sinusoidal-polynomial fitting model for deriving the structural and biochemical circadian rhythms for different parts of Birch from hyperspectral LiDAR data. →

A deeper understanding of circadian rhythms in plants, especially trees, is crucial for uncovering how structural and physiological processes align with daily environmental cycles. However, most studies analyze biochemical changes and positional variations separately, with limited exploration of their coordination within the whole-plant system. Hyperspectral light detection and ranging (HSL) integrates three-dimensional (3D) structural mapping with hyperspectral reflectance, enabling non-destructive assessment of plant biochemistry. Previous work showed that HSL can detect nocturnal vertical canopy displacements with centimeter accuracy, but organ-level rhythmic patterns (e.g., branches vs. leaves) remain poorly studied. Here, we developed a hyperspectral point cloud classification method combining spectral and spatial data to separate branches from leaves and analyze their sleep movements independently. A novel sinusoidal-polynomial fitting model was then proposed to characterize circadian rhythms in both sleep movements and reflectance variations. We applied HSL data from a single birch tree (Betula pendula) collected at 30 distinct HSL measurement times over 24 h to develop a 3D canopy partitioning approach that divides the canopy into nine grids (3 × 3) and ten vertical layers per grid. Results revealed near-24-hour rhythmic patterns (max R² = 0.5841, P < 0.05) and stratified sleep movements: branches exhibited larger amplitudes than the corresponding canopy layers, with the overall maximum movement amplitude occurring shortly before sunrise (04:00-06:30) and recovering after sunrise. The model also effectively characterized diurnal reflectance variations (max R² = 0.5812, P < 0.05). In addition, chlorophyll-related spectral indices exhibited a sinusoidal variation, reaching a minimum around 03:00. These findings highlight the potential of HSL for the joint analysis of structural and biochemical circadian rhythms, providing a non-destructive approach to investigate plant rhythmicity in both structural and physiological domains.

Why it matches plant phenotyping methods hyperspectral LiDARによる植物の構造・生理形質の取得と、葉・枝の分類および概日リズム推定モデルの開発が研究の中心である。

abstractHere, we developed a hyperspectral point cloud classification method combining spectral and spatial data to separate branches from leaves and analyze their sleep movements independently.

Active and passive 3D sensing for forest stem geometry: Comparing MLS, consumer LiDAR, SfM and Gaussian Splatting →

Accurate characterization of tree stem geometry is essential for forest inventories, yet conventional field measurements of diameter at breast height (DBH) are limited to a single cross-section and do not capture vertical variability along the trunk. This study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements. Data were acquired simultaneously within a 2.5 m radial plot. DBH was estimated through RANSAC-based circular fitting, and stem sections were extracted every 20 cm to assess diameter stability along the trunk. All techniques produced similar mean DBH values closely matching field measurements (23 cm), with MLS achieving the lowest RMSE (1.29 cm), followed by SfM (1.52 cm), GS (1.60 cm), and iPad-LiDAR (2.26 cm). However, marked differences were observed in vertical completeness. MLS captured the full vertical profile of the stems, reaching 14.11 m, whereas SfM and GS from iPhone, and iPad-LiDAR were limited to approximately 6 m or less. The results indicate that although low-cost image-based approaches can provide accurate DBH estimates under controlled conditions, MLS remains the most robust solution for comprehensive vertical stem characterization.

Why it matches plant phenotyping methods森林樹幹のDBHと垂直方向の形状を、複数の3Dセンシング手法で推定・比較し、RMSEや垂直完全性を評価している。植物形状計測法の技術比較・検証が中心である。

abstractThis study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements.

Multi-Scale and Global–Local Feature Enhanced Detection for Tobacco Plants in Complex Field Environments →

Accurate detection of tobacco plants in complex field environments is critical for precision agriculture, crop monitoring, and yield estimation. Traditional manual counting methods are time-consuming, labor-intensive, and susceptible to environmental and subjective factors. In this study, we propose an improved YOLO11-based framework for automated tobacco plant detection, specifically designed to address challenges such as scale variation, dense distribution, and background interference. The framework integrates four key modules: the Edge-Enhanced Feature Stem (EEFS) to strengthen low-level feature extraction, the Multi-Scale Kernel Interaction (MSKI) to capture multi-scale contextual information, the Adaptive Weighted Feature Fusion (AWFF) to optimize feature aggregation, and the Global–Local Synergistic Attention (GLSA) to enhance feature discrimination by jointly modeling local details and global context. A comprehensive UAV-based tobacco dataset was constructed, encompassing multiple lighting conditions, collection heights, and observation angles. Experimental results demonstrate that the proposed method significantly outperforms the YOLO11 baseline and achieves superior performance compared to mainstream YOLO variants. Ablation studies and heatmap visualizations confirm the effectiveness of each module. Furthermore, the model exhibits robust performance under multi-dimensional environmental perturbations, including varying illumination, scale, and camera angles. The proposed framework provides a practical and efficient solution for automated tobacco plant counting, offering potential applications in UAV-based precision agriculture and large-scale crop monitoring.

Why it matches plant phenotyping methodsUAV画像からタバコ個体を自動検出・計数する手法を開発し、専用データセット、比較実験、アブレーション、頑健性評価まで行っており、植物個体数の取得方法が中心です。

abstractwe propose an improved YOLO11-based framework for automated tobacco plant detection

Large language model assisted decision support framework for uncertainty aware detection and management of tomato lateral shoots. →

Accurate identification of tomato lateral shoots is essential for automated pruning and plant monitoring in greenhouse production. However, complex illumination, leaf occlusion, and morphological variability often reduce detection reliability in optical vision systems. This study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support. A tomato lateral Shoot image dataset was constructed using RGB imaging in greenhouse environments. A lightweight YOLOv8n instance segmentation model with the Convolutional Block Attention Module (CBAM) was developed to enhance feature representation. Data augmentation strategies were applied to simulate illumination variations and improve model robustness. Model interpretability was analyzed using Principal Component Analysis (PCA) and Gradient weighted Class Activation Mapping (Grad CAM). Experimental results show that the proposed YOLOv8n-seg+CBAM model achieves a mAP 0.5 of 98.1% with only 3.28M parameters and an average inference time of 8.0 ms per image. Monte Carlo Dropout was further introduced to estimate the spatial uncertainty of cutting points. These structured perception features were provided to a large language model (LLM), enabling context aware pruning decision assistance. The proposed framework integrates vision-based shoot detection, uncertainty estimation, and LLM-assisted reasoning into a unified pipeline, enabling more reliable pruning decisions and improving safety and robustness compared with vision-only approaches in greenhouse environments.

Why it matches plant phenotyping methodsトマト側枝をRGB画像から検出・セグメンテーションし、不確実性推定まで行う画像ベースの植物形態計測手法を開発しており、方法論が中心である。

abstractThis study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support.

High-Precision Instance Segmentation of Tree Saplings by Multimodal Mask R-CNN Integrating RGB and Multispectral Image-Derived Indices Through a Field Phenotyping Platform →

The high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture. However, sapling segmentation remains challenging because of blurred boundaries, object adhesion, missed detections, and inaccurate mask delineation in field environments. To improve sapling segmentation performance and address these challenges, this study proposes a multimodal Mask R-CNN framework in which RGB imagery was paired with one multispectral-derived vegetation index at a time to construct separate RGB-VI input combinations, taking ginkgo saplings as a representative case. A dataset of 400 saplings was constructed using a high-throughput field phenotyping platform. The backbone network was extended with an independent vegetation index branch, and three fusion strategies (early, multi-step, and late fusion) were designed within a feature pyramid network to enable multi-scale multimodal feature integration. The results showed that all multimodal models outperformed unimodal baselines in terms of segmentation accuracy and recall. Among them, the multi-step fusion strategy achieved the best performance, while the RGB-EVI multi-step fusion model achieved the highest strict-matching precision (AP@75 = 87.7%) and recall (71.3%), with superior performance in dense sapling delineation and background suppression. These findings indicate that multimodal feature fusion can effectively improve sapling instance segmentation and provide methodological support for high-throughput plant phenotyping.

Why it matches plant phenotyping methodsマルチモーダル画像による樹木苗個体のインスタンスセグメンテーション手法を開発・比較し、高スループット表現型解析を支援することが中心である。

abstractThe high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture.

Data Fusion of Sentinel-2 Spectral and Meteorological Data for Field-Scale Sugarcane Biomass Prediction in Humid Tropical Mexico Using Machine Learning →

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R2 values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce.

Why it matches plant phenotyping methodsSentinel-2と気象データの融合および機械学習により、サトウキビの茎・葉・総バイオマスという植物形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。

abstractThis study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions.

Application of deep learning in crop research: From genomics to phenomics →

Abstract Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures—such as convolutional neural networks, recurrent neural networks, and transformers—across key crop genomic tasks, including gene expression prediction, alternative splicing analysis, cis ‐regulatory element identification, epigenomic profiling, and genome‐based trait prediction. In phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade‐offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning—such as data scarcity, model transparency, and computational demands—and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.

Why it matches plant phenotyping methods作物フェノミクスにおける深層学習による画像からの形質抽出を中心的にレビューしており、フェノタイピング手法の方法論的整理に該当する。

abstractIn phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery

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.

Integration of Aerial Photogrammetry, UAV LiDAR and Terrestrial LiDAR Point Clouds for Individual Tree Measurement and Individual Tree Carbon Storage Estimation →

Urban forest carbon sequestration is vital for environmental health, climate change mitigation, and enhancing the quality of life in urban areas. This paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation. The aerial photos, UAV-based LiDAR and terrestrial LiDAR were observed based on common ground control points and combined using Iterative Closest Point (ICP) algorithm. The combined point cloud was iltered to separate ground points and normalized based on Digital Terrain Model (DTM). The normalized point cloud was used for individual tree segmentation from which individual tree measurements such as, tree height, Diameter at Breast Height (DBH) and crown diameter were estimated. The estimated tree parameters were used for individual carbon estimation. The results show that the individual tree segmentation method signi icantly underestimated the number of trees. The estimation of DBH, tree height, and crown diameter achieved the Root Mean Square Error (RMSE) value of 0.107m, 1.385m and 2.650m respectively. However, in general the estimates experience underestimation as shown by Mean Bias Error (MBE) with 0.003m, -0.636m and 0.001m for DBH, tree height and crown diameter respectively. The estimated values for each individual tree were used for individual tree biomass and carbon storage recording the Root Mean Square Error (RMSE) at 1970.236 kg and 886.606 kgC respectively while attaining the Mean Bias Error (MBE) measure of 140.019 kg and 63.009 kgC each. The proposed framework showed promising results for individual tree carbon estimation. Nonetheless, further attention should be given on individual tree delineation process.

Why it matches plant phenotyping methods航空写真、UAV・地上LiDARを統合し、個体樹木の分離と樹高・DBH・樹冠径を推定して精度評価する手法が中心であり、植物形質計測の技術的検証に該当する。

abstractThis paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation.

Toward smart agriculture: a hybrid mamba-transformer vision framework for plant disease detection →

Plant disease detection under complex field conditions remains a critical challenge for precision agriculture due to varying illumination, scale variations, subtle lesion patterns, and inter-class visual ambiguity. This study proposes MAFusionNet, a disease-aware hybrid vision framework integrating Mamba and Transformer architectures, with components explicitly designed for plant disease-specific challenges. The MAFusion Mixer operates parallel CS-Mamba and self-attention branches to simultaneously capture sequential lesion boundary evolution and global diseasecontext spatial relationships. The CS-Mamba branch employs the SS2D-LS Block with twodimensional selective scanning and Local-Selective enhancement for linear-complexity longrange modeling while preserving 2D lesion morphology. The PConv operator uses asymmetric directional kernels forming cross-shaped receptive fields to capture anisotropic disease patterns such as vein-aligned blights and directional rust streaks. We constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops, with inter-annotator agreement Cohen’s κ = 0.874. Extensive experiments demonstrate that MAFusionNet achieves 94.7% mAP 50 and 81.8% mAP 50:95 on PD40, surpassing 25 state-of-the-art baselines including recent hybrid Mamba-Transformer detectors (CropMamba, HybridMamba, Mamba-DETR), with comprehensive ablation studies validating each component’s non-redundant contribution. Edge deployment analysis on NVIDIA Jetson hardware demonstrates practical feasibility: the compressed MAFusionNet-T-Lite variant (8.7M parameters) achieves 89.3% mAP 50 at 18.4 FPS on Jetson Nano with 8.3W power consumption. The dataset and code are available at PD40-Dataset GitHub Repository.

Why it matches plant phenotyping methods植物病害の症状を画像から検出・分類する視覚モデルを開発し、注釈付き大規模データセットで検証しているため、植物状態の画像ベース表現型計測が中心である。

abstractWe constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops

COMBINED SYSTEM FOR PLANT DISEASE MONITORING USING UAVS, GROUND ROBOTIC PLATFORMS, AND NEURAL NETWORK-BASED IMAGE ANALYSIS →

This paper addresses the problem of automated monitoring of agricultural crop diseases under field conditions using unmanned aerial vehicles. It is shown that most existing solutions are primarily focused on disease detection from individual images. In contrast, issues such as further diagnosis refinement, repeated inspection of problematic areas, and subsequent action determination after disease detection are considered much less frequently. Stem-type diseases, in particular sclerotinia, pose an additional challenge, as top-view imaging alone may not detect early infection signs promptly. On this basis, a proposed automated system combines a UAV for primary inspection, subsequent georeferencing of the point of interest, a ground module for additional follow-up inspection in the case of stem-type diseases, and a central computing module for data processing and decision-making regarding the treatment of infected areas. This approach enables combining rapid aerial inspection of large areas with more accurate follow-up inspection of plants from a side view, which is especially important for diagnosing lesions that are poorly visualized from above. As part of the experimental study, a prototype classifier based on the EfficientNetV2S convolutional neural network and the transfer learning approach was implemented. To improve training quality, image augmentation and a pseudo-negative sample generation method were applied. The obtained results confirmed the potential of convolutional neural networks for automated plant condition classification, as well as the feasibility of combining aerial imaging and ground-based follow-up inspection within a unified monitoring system. The proposed approach can serve as a basis for the further development of an intelligent system for the detection and localized treatment of disease foci under field conditions within the framework of precision agriculture.

Why it matches plant phenotyping methodsUAV・地上ロボット・画像解析を統合し、植物病害の病変・状態を画像から分類するシステムの開発が中心であり、植物の疾病状態を対象とする実質的なフェノタイピング手法である。

abstracta proposed automated system combines a UAV for primary inspection, subsequent georeferencing of the point of interest, a ground module for additional follow-up inspection in the case of stem-type diseases, and a central computing module for data processing and decision-making regarding the treatment of infected areas.

LiDAR-Guided Semantic 3D Gaussian Splatting for Forest Digital Twins →

Forest digital twins play a crucial role in modern precision forestry by supporting biomass estimation and carbon cycle monitoring. However, existing 3D reconstruction methods struggle to simultaneously achieve metric-level structural accuracy and visual realism in complex understory environments. This study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery. First, a multi-modal fusion mechanism is constructed to extract geometric anchors from registered LiDAR data for precise 3DGS spatial initialization, which mitigates rendering artifacts and geometric drift caused by poor initialization in purely visual methods. Second, a semantic regularization optimization strategy is proposed to realize differentiated modeling of tree trunks and canopies, effectively balancing the structural accuracy of rigid trunks and the photorealistic rendering of non-rigid canopies. Experiments conducted on three study plots demonstrate that the proposed approach achieves an average PSNR of 24.94 dB, SSIM of 0.773, and LPIPS of 0.231 across all plots, outperforming standard NeRF and baseline 3DGS, while enabling DBH estimation with R2 = 0.848 and RMSE = 2.705 cm. This method provides a solution for high-fidelity forest digital twin construction in open-canopy forest environments such as urban and campus forests.

Why it matches plant phenotyping methodsLiDAR・UAV画像を統合した3D再構成法を開発し、樹幹・樹冠の構造モデル化とDBH推定を評価しており、植物形質取得が中心的な技術貢献である。

abstractThis study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery.

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

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

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

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

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

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

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

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

3D reconstruction and segmentation of grape bunches for robotic berry thinning →

• End-to-end pipeline from neural reconstruction to physical grape berry manipulation. • Efficient point clouds generation using NeRF and the metric scale derived directly from robot kinematics. • RANSAC sphere fitting achieves 92.1% berry detection precision without annotated training data. • Stem-aligned 6-DoF pose optimization improves end-to-end grip success by 17.2%. Table grape thinning requires selective removal of 20–40% of berries from dense clusters. In practice, workers decide which berries to remove by considering both the approximate berry count and local 3D spatial characteristics such as crowding and relative positioning. Automating this task is challenging because conventional 2D image-based approaches suffer from occlusion-related counting errors and lack explicit 3D spatial information necessary for reliable manipulation. We propose a robot-integrated vision pipeline that reconstructs grape bunch structure from posed multi-view RGB images. Neural Radiance Fields (NeRF) is used to learn a volumetric scene representation, from which a dense, low-noise point cloud is extracted via depth back-projection, and RANSAC-based geometric fitting models individual berries and stems, enabling berry-level segmentation and orientation estimation for manipulation planning. The perception pipeline uses an eye-in-hand RealSense D405 camera mounted on a Fanuc CRX-5iA collaborative robot. Camera poses are derived from the robot kinematic chain, allowing the reconstructed point cloud and detected berry centers to be expressed directly in the metric robot base frame without external scale recovery. In robot-mounted RealSense D405 experiments on 10 grape bunches, RANSAC sphere fitting achieved a counting MAE of 0.50 berries, RMSE of 0.71 berries, and mean center localization error of 2.71 mm. On a 52-bunch benchmark, RANSAC sphere fitting outperforms the learning-based SoftGroup++ method for 3D berry instance segmentation (92.1% vs 82.8% average precision) without requiring annotated training data. In 35 manipulation trials, the system achieved an 85.7% pre-grasp reachability rate and an 83.3% conditional target success rate demonstrating an end-to-end pipeline from neural reconstruction to manipulation-ready berry poses.

Why it matches plant phenotyping methodsブドウ房の3D再構成、ベリー分割・計数・位置推定を中核とするロボット統合型フェノタイピング手法であり、技術性能も定量評価している。

abstractWe propose a robot-integrated vision pipeline that reconstructs grape bunch structure from posed multi-view RGB images.

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

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

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

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

Combinatorial bioassay for fast screening of organic agrivoltaic materials →

ABSTRACT Agriphotovoltaics (APV) combines crop production with solar energy generation to address increasing demands for food and energy while reducing land-use competition. Unlike conventional opaque photovoltaic systems, semitransparent organic photovoltaics (OPVs) selectively absorb light, potentially improving efficiency but also altering both light quantity and spectral quality, key factors affecting plant growth. Here, we developed a rapid bioassay based on hypocotyl elongation to evaluate plant responses to OPV-filtered light using Arabidopsis thaliana and Cardamine hirsuta , two species with contrasting shade strategies. Screening a diverse set of OPV materials revealed that plant growth responses depend more on spectral composition than on total light intensity alone. Certain materials, such as PTB7-Th and D18, produced growth patterns similar to neutral shading, while others promoted elongation. Our analyses identified blue light wavelengths, linked to cryptochrome activity, as more critical than red light wavelengths, linked to phytochrome activity, for maintaining normal development. These findings provide a scalable framework to assess OPV-plant compatibility and demonstrate that optimizing spectral quality alongside light intensity is essential for designing efficient APV systems that sustain crop performance while generating renewable energy.

Why it matches plant phenotyping methods植物の光応答を測定する迅速・スケーラブルな低胚軸伸長バイオアッセイを開発し、OPV材料評価に適用しており、表現型取得法が中心的です。

abstractHere, we developed a rapid bioassay based on hypocotyl elongation to evaluate plant responses to OPV-filtered light

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

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

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

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

A study on a 3D morphological measurement method for tobacco stems based on point cloud data and central skeleton extraction →

Conventional two-dimensional image-based methods are limited in measuring the three-dimensional morphology of tobacco stems, especially thickness and curved geometry. This study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system. The method combines improved centerline skeleton extraction, upper–lower surface registration, and skeleton-guided cross-sectional analysis to estimate length, width, thickness, and fineness. Adaptive neighborhood re-weighting and curvature-constrained regularization are introduced to improve skeleton extraction, and reference-assisted registration is used to support thickness measurement. For a standard gauge block, the proposed method achieved mean absolute errors below 0.009 mm and root mean square errors below 0.011 mm for length, width, and thickness measurements. Validation on 30 tobacco stem samples showed good agreement with the YC image-based method for length and width, with correlation coefficients of 0.998 and 0.997, respectively. The results demonstrate the feasibility of thickness-aware three-dimensional morphological measurement of tobacco stems under the tested conditions.

Why it matches plant phenotyping methodsタバコ茎の長さ・幅・厚さ・細さという植物形態形質を、点群・レーザースキャン・骨格抽出で測定する手法の開発と検証が研究の中心である。

abstractThis study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system.

A Survey on Sugarcane Plant Disease Detection Using Deep Learning With Fusion Method →

ABSTRACT - Sugarcane is one of the most important commercial crops worldwide but its productivity is greatly affected by diseases such as red rot, rust, mosaic, smut and yellow leaf disease. Conventional disease detection techniques are based on manual inspection which is a time-consuming, labor-intensive and error prone process. This paper gives a detailed review of the deep learning methods for the automated detection of sugarcane diseases with a special focus on the fusion methods of stem and leaf features. Different deep learning architectures such as CNN, VGG, ResNet, EfficientNet, DenseNet, MobileNet, and YOLO are analyzed and compared in terms of accuracy, efficiency, and deployment capability. The study also explores multimodal approaches, such as hyperspectral imaging, thermal imaging and environmental data integration, to enhance prediction performance. Reported results show that advanced models like EfficientNet-B7 and DenseNet201 achieve accuracies above 99%, while lightweight models like MobileNet allow for real-time mobile deployment. The review highlights significant research gaps such as small datasets, lack of stem-leaf fusion studies, no severity classification, and real-world deployment issues. Future research directions are related to explainable AI, multimodal fusion, lightweight edge computing models, and precision agriculture applications for sustainable sugarcane cultivation. Key Words: Sugarcane disease detection, Deep learning, CNN, Stem-leaf fusion, Computer vision, Precision agriculture.

Why it matches plant phenotyping methodsサトウキビ病害の画像・深層学習による検出手法を中心にレビューしており、植物の病態を観測・推定するフェノタイピング手法レビューに該当する。

abstractThis paper gives a detailed review of the deep learning methods for the automated detection of sugarcane diseases with a special focus on the fusion methods of stem and leaf features.

Research on Branch Recognition and Pruning Method for Dormant Apple Trees Based on Neural Radiance Fields and PointNeXt →

Abstract To address the problem of fine branch identification and pruning decision for dormant apple trees, this study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network. This method employs the neural radiance field theory to construct a point cloud model of apple trees, achieving fine detail representation and providing a high-precision, high-standard dataset for subsequent branch pruning experiments. First, a panoramic video is captured by circling the fruit tree, and a multi-view image sequence is obtained through frame sampling. Subsequently, the Structure from Motion (SfM) algorithm is employed for sparse reconstruction to recover the pose information of the images. On this basis, a neural radiance field model is trained. Hierarchical sampling is performed using ray casting, and the sampled points, combined with positional encoding, are fed into a multi-layer perceptron (MLP). The radiance field is then generated via volume rendering, from which a high-fidelity 3D point cloud model of the fruit tree is derived. Finally, the point cloud is processed using the PointNeXt semantic segmentation network to achieve the identification and segmentation of branches to be pruned and branches to be retained. To verify the effectiveness of the method, this study reconstructed point cloud models of dormant apple trees and selected 10 of them for experimental analysis. The algorithm achieved an average overall recognition accuracy of 75.15% and an average false negative rate (FNR) of 24.85%. The experimental results demonstrate that the proposed method constructs a 3D point cloud model with multi-scale, multi-modal, and high-precision phenotypic information at a relatively low cost. It not only overcomes the limitations of traditional 3D reconstruction methods, such as insufficient point cloud accuracy and difficulty in accurately identifying thin branches, but also effectively mitigates the high misrecognition rate observed in conventional branch recognition approaches. This provides technical support for unmanned agricultural machinery pruning in orchards and holds significant implications for achieving precision agriculture and sustainable development.

Why it matches plant phenotyping methodsNeRFとPointNeXtを用いてリンゴ樹の3D点群を構築し、剪定対象枝を認識・分割する手法が研究の中心であり、植物の形態・構造状態を直接推定して性能評価している。

abstractthis study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network.

Crop Disease Prediction in Agriculture Using Deep Learning: A Comprehensive Review →

Crop diseases continue to pose a serious danger to agricultural productivity worldwide, resulting in large losses in crop quality, yield, and economic value. For large-scale farming, traditional disease detection techniques, which mostly rely on specialist knowledge and manual examination, are frequently laborious, subjective and ineffective. Deep learning (DL), a branch of artificial intelligence, has become a potent method for automated and precise crop disease prediction because to developments in information technology. With an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction. Convolutional Neural Networks (CNNs), one type of deep learning architecture, have shown exceptional performance in reliably diagnosing different crop illnesses and extracting complicated characteristics from plant photos. The detection accuracy has been further enhanced by advanced versions like ResNet, VGGNet, and EfficientNet, which frequently surpass 95 percentage under controlled circumstances. Precision agriculture techniques have been improved by the real-time monitoring and early disease identification made possible by the integration of deep learning with Internet of Things (IoT) devices, remote sensing technologies, and drone-based imaging systems. Despite these developments, a number of problems still exist, such as the requirement for sizable labelled datasets, high processing demands, overfitting problems, and restricted model generalisation in practical settings. This paper identifies these drawbacks and explores possible remedies, such as explainable deep learning methods, data augmentation, and transfer learning. Future research will focus on integrating intelligent decision-support systems, scalable deployment approaches, and edge computing. All things considered, deep learning-based crop disease prediction systems have enormous potential to revolutionise contemporary agriculture by facilitating early intervention, enhancing crop health management, and encouraging sustainable farming methods.

Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。

abstractWith an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction.

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

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

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

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

Stem Modeling for Savanna Tree Characterization with Close-Range Photogrammetry: Comparison of the Performance of Automatic Algorithms →

Sudanian savannas remain underexplored in terms of utilizing close-range photogrammetry (CRP) for assessing tree characteristics, leaving a gap in ecological research. This study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species. Two labeling methods, a machine learning-based approach (StemML) and a flatness/vertical structure-based method (StemFlat), were used to extract stem points. We applied three diameter estimation techniques: convex-hull line fitting (CHM), least squares circle fitting (LSM), and Ransac circle fitting (RANSAC), comparing their results against field measurements using root mean square error (RMSE), bias and the coefficient of determination R2. The combination of StemML and CHM yielded the best performance, with an RMSE of 2.1 cm (5.8%), R2 of 0.983 and a bias of −0.30 cm, accurately identifying 93% of stem segments. Diameter estimation accuracy varied with height, with optimal alignment between CRP-derived profiles and manual measurements occurring between 0.5 and 2.5 m. These findings demonstrate CRP’s potential for modeling savanna tree stems and highlight the importance of method selection in ensuring reliable measurements.

Why it matches plant phenotyping methods近距離写真測量による樹幹点群の抽出・モデル化と直径推定手法を開発・比較し、野外測定で性能検証しているため、植物形質計測手法が研究の中心である。

abstractThis study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species.

LASH-SegNet: A Lightweight Deep Learning Network for Multi-Trait Segmentation of Early-Stage Soybean Plants →

Accurate segmentation of multiple phenotypic traits in early-stage soybean plants is essential for automated phenotyping and early-stage breeding analysis. However, the morphological diversity and heterogeneous visual characteristics of key traits, including hypocotyls, flowers, pubescence, and leaves, make unified segmentation challenging under complex backgrounds. To address this problem, this study proposes LASH-SegNet, a lightweight deep learning network for multi-trait segmentation of early-stage soybean plants. The network integrates dynamic snake convolution to model elongated and non-rigid structures and incorporates a SegNeXt-Attention module to enhance multi-scale feature representation and boundary awareness. In addition, the WIoUv3 loss function is adopted to improve localization accuracy and boundary alignment, particularly for slender targets. Experimental results show that LASH-SegNet achieves a precision of 88.82%, recall of 89.78%, and an F1-score of 89.30%, with an mAP50 of 91.24%, while maintaining a compact model size of 5.9 M parameters and 11.3 MB. These results demonstrate that LASH-SegNet provides an accurate and efficient solution for high-throughput multi-trait early-stage soybean plant phenotyping.

Why it matches plant phenotyping methods大豆幼苗の複数形質を自動抽出する画像セグメンテーション手法を開発しており、植物フェノタイピング手法が研究の中心である。

abstractthis study proposes LASH-SegNet, a lightweight deep learning network for multi-trait segmentation of early-stage soybean plants.

Phenotypic analysis method for 3D reconstruction of eggplant seedlings fused with background purification — based on the improved EggplantPointNet++ model and DBSCAN clustering →

Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.

Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。

abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings

Construction of a Precision Thinning Framework for Cunninghamia Lanceolata Plantations Driven by Point Cloud Fusion and Multi-Criteria Weighting →

Thinning is a cornerstone of sustainable forest management (SFM), yet decisions are often constrained by subjective experience and a lack of quantitative spatial data. To address this, we developed a precision thinning framework for Cunninghamia lanceolata plantations by fusing terrestrial and aerial LiDAR data. Instead of relying on manual selection, this study established a quantitative mechanism for identifying harvesting targets. We employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85). Furthermore, an objective multi-criteria weighting method (AHP – CRITIC) was constructed to prioritize thinning targets based on stand stability and growth status. Results indicated that the proposed framework achieved an individual tree segmentation F1-score of 89.24%. Simulated thinning based on the calculated weights significantly optimized the stand spatial structure: canopy openness increased by 11.8%, and spatial indices converged toward optimal ranges. Compared with conventional practices, the proposed approach effectively reduced growth dispersion and enhanced population coordination. These findings demonstrate that integrating multi-platform LiDAR with objective weighting algorithms offers a scientifically rigorous pathway for precision forestry, promoting both productivity and the long-term sustainability of plantation ecosystems.

Why it matches plant phenotyping methodsLiDAR融合による個体木セグメンテーションと樹高・DBH・樹冠幅の抽出、および精度評価が枯間伐フレームワークの中心的な技術要素であるため、植物表現型計測手法として採用する。

abstractWe employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85).

Stomatal setpoints and environmental responsiveness are sculpted by developmental trajectories →

Efficient gas and water exchange between plants and their environment largely depends on the number and distribution of stomata, cellular valves in leaf epidermis. Core genetic regulators of stomatal cell identity and pattern along with asymmetric stem-cell like divisions in stomatal precursors are hypothesized to customize stomatal production for optimal leaf performance. How these regulators work in concert and how division dynamics are modified and adjusted in different environments, however, are poorly understood. Here, we leveraged the variation in stomatal patterning in Arabidopsis thaliana accessions from diverse environments to define developmental rules and constraints in the stomatal lineage. The accessions subtle and quantitative variation enables us to identify which cellular parameters are flexible, revealing how developmental plasticity generates phenotypic plasticity. By developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins. Variation in final stomatal numbers is driven by differences in the relative contributions of stomatal initiation, cell size-based fate thresholds, general proliferative capacity, and coordination between sister and neighbor cell behaviors. Overall, diverse accessions converge toward two lineage regimes: one dominated by autonomous decisions with loose cell-cell coordination, the other by extensive cell-cell coordination. Challenging accessions with environmental fluctuations revealed regime-specific flexibility, with plasticity primarily mediated by a single division-related parameter. Our results show how cellular parameters integrate into alternative developmental strategies that shape environmental responsiveness.

Why it matches plant phenotyping methods葉の成長中の細胞挙動を追跡するライブセルイメージングツールを開発し、気孔密度の発生的起源を定量化しており、植物フェノタイピング手法が研究の中心である。

abstractBy developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins.

Parameters calibration and experimentation of a discrete element model for tomato stems →

Abstract The effective optimization of tomato pruning robots was hindered by the lack of accurate simulation models for the shearing process of tomato stems and precise calibration and optimization methods for bonding parameters to predict shearing force. This paper proposed a simulation model along with a bonding parameter calibration method. Taking shearing force as the evaluation metric, a two-level factorial experiment was conducted to screen for significant parameters. A steepest ascent experiment was employed to determine the optimal range of these significant parameters. Then a Box-Behnken design was implemented, and the optimal combination of bonding parameters was derived based on the established regression model. Finally, comparative experiments were conducted to validate the simulation's shearing performance under this optimal parameter set. The results show that the optimal combination for the tomato stem model bonding parameters was a normal stiffness x 3 = 2.05×10⁸ N·m⁻³, tangential stiffness x₈=1.62×10⁸ N·m⁻³, and a bonding radius x₂₁=3.26×10⁻⁴ m. The optimized model reduced the shearing force simulation error by 75.8 and 43.7 percentage points compared to the traditional and pre-optimization models. These results demonstrate that the calibrated parameters of the simulation model are accurate and reliable. It can provide valuable parameters for optimizing the design of a tomato pruning robot.

Why it matches plant phenotyping methodsトマト茎のせん断力を推定する離散要素シミュレーションモデルと結合パラメータ校正法を開発し、実験で性能検証しており、植物器官の測定・推定手法が中心である。

abstractThis paper proposed a simulation model along with a bonding parameter calibration method.

Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using YOLOv8. →

Introduction: Tiller production is a critical determinant of turfgrass canopy density and plant performance, yet manual tiller counting is too labor-intensive for large breeding programs. Methods: To address this limitation, we evaluated 770 plants from an interspecific bentgrass hybrid population and developed three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8. Using a large annotated image dataset, we assessed each method's accuracy, robustness under occlusion, and computational efficiency. Results: Although two-stage detectors are often expected to provide superior precision for complex plant structures, the one-stage YOLOv8 model achieved the highest accuracy (R² = 0.97) and processed images substantially faster than Faster R-CNN, while both the edge-based method and Faster R-CNN showed reduced performance in dense canopies. Discussion: These findings demonstrate that recall-oriented one-stage detection can outperform more complex two-stage models for phenotyping tasks involving fine, highly occluded structures. The resulting workflow provides a reliable, high-throughput solution for generating biologically meaningful tiller counts and offers a transferable framework for integrating image-derived phenotypes into genetic analyses and breeding pipelines across grass species.

Why it matches plant phenotyping methodsイネ科植物の分げつ数を画像から自動抽出する複数手法を開発・比較検証しており、植物表現型取得法が研究の中心である。

abstractdeveloped three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8.

Branch shape parameters for young open-grown Carya laciniosa (F.Michx.) G.Don (Juglandaceae) →

Abstract Branch shape is a relevant tree morphological trait, representative of tree ontogenetic stage, successional status and resource-use strategy. However, branch-level studies have been limited due to tedious, time-consuming or costly measurement procedures. Here we applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation, applied to young open-grown Carya laciniosa , an ecologically valuable but rare large-seeded deciduous tree species. After accounting for branch size and orientation, we fitted different polynomial models to photogrammetric points of 41 major branch axes belonging to four arboretum-grown leaf-off C. laciniosa trees, ranging from 6.1 to 8.7 m in height. Parametric branch shape was identified with great precision by the fourth-order polynomials (R 2 = 0.96 ± 0.07 SD), but also acceptably by the third-order polynomials (R 2 = 0.93 ± 0.13 SD). The shape parameters were weakly related to branch position within the crown (R 2 < 0.40), in contrast to branch size (R 2 = 0.73). The identified S-shaped branch type may be termed plagio-orthotropic, with the proximal part arching plagiotropically and the distal part ascending orthotropically. This type of shape was stable across canopy height strata, but the shape variation and the magnitude of branch curvature clearly decreased towards the upper canopy layers, revealing combined effects of branch age, gravitropism and bending strains induced by the seasonal loads. Our results corroborate the architectural similarity among the mid-successional Carya spp. This study highlights the relevance of branch shape, which can be feasibly recorded in terms of transferable parameters, possibly as a generic functional trait with a potential for quantification of ecosystem services, such as rainfall interception and retention, shading potential, thermal regulation and biodiversity support.

Why it matches plant phenotyping methods枝形状という植物形態形質をフォトグラメトリで定量化する枠組みの開発・適用が研究の中心であり、植物フェノタイピング手法に該当する。

abstractwe applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation

UAV-BASED HIGH-THROUGHPUT PHENOTYPING OF SOYBEAN USING LIGHTWEIGHT POINT DETECTION FOR MULTI-ORGAN TRAIT EXTRACTION →

Accurate soybean field phenotyping is increasingly important for breeding. However, traditional measurement methods are labor-intensive and subjective, while UAV-based approaches are challenged by complex backgrounds and densely distributed small targets. This study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry. A lightweight point-based model, Soy-MOPNet, is then proposed for fast and parallel detection of soybean seeds and stem nodes. The model incorporates the proposed SDConv, optimized hierarchical dilated convolution (HDC) principles, and PBOS to enhance adaptive feature fusion, receptive field design, and multi-branch training stability, respectively. Based on the detected keypoints, six phenotypic traits are extracted in parallel, providing comprehensive support for field phenotyping, breeding selection, and precision agricultural management.

Why it matches plant phenotyping methodsUAV画像の幾何再構成、軽量点検出モデル、複数器官からの6形質抽出を開発しており、植物表現型取得・抽出手法が研究の中心である。

abstractThis study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry.

A collaborative management approach to transmission line forest clearing tasks in the context of grid intelligence transformation →

Tree barriers are the key factors of current transmission line failures, early discovery of tree barriers hidden dangers, the implementation of forest clearing tasks on transmission lines is the current power inspectors need to pay attention to the key issues. The article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data. Then the Mean Shift algorithm is used to preprocess the remote sensing data, and the forest diameter measurement system and transmission line forest clearing program are designed. QDN Power Supply Bureau was selected as a research sample to verify the effectiveness of the application of the above methods. The study showed that the RMSE of the breast diameter monitoring results ranged from 4.30% to 5.05%, and the reduction of forced outage rate of transmission lines of 110kV and above in the power grid could be up to 72.28%, and the overall work efficiency was improved by about 5.14 times. Therefore, actively realizing the optimization of transmission line forest clearing tasks can ensure the stable operation of transmission lines and provide basic support for ensuring the power supply of the grid.

Why it matches plant phenotyping methods傾斜写真測量とMean Shift処理により樹木の胸高直径を推定する測定システムを構築し、RMSEで検証しており、植物形質の取得・評価が実質的な方法貢献として含まれる。

abstractThe article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data.

ParaLeafNet-Severity: A Parallel Deep CNN with Squeezeand-Excitation Attention for Automated Plant Disease Severity Assessment and Agricultural Health Monitoring →

Accurate and timely detection of plant diseases is a critical component of modern agricultural biosecurity, directly impacting crop yield, food safety, and the health of farming communities. Plant diseases cause annual agricultural losses that exceed $220 billion worldwide, yet accurately quantifying how severely a plant is infected remains one of the least-addressed problems in automated crop monitoring. This paper presents ParaLeafNet-Severity, a deep parallel convolutional neural network designed to perform disease identification and four-level severity grading simultaneously within a single forward pass. The architecture draws complementary feature representations from two lightweight backbones — MobileNetV2 and MobileNetV3Small — running in parallel, fuses their outputs through channel-wise Squeeze-and-Excitation (SE) attention, and routes the resulting shared embedding to two task-specific output heads. An optional K-Means clustering branch operates on the shared feature space to discover natural severity groupings without requiring additional expert labels. Experimental evaluation on the adapted PlantVillage dataset demonstrates that the proposed framework achieves strong performance in disease identification and severity classification, outperforming existing baseline approaches while maintaining robustness across multiple classes. The integration of an unsupervised clustering module further confirms that the learned feature representations preserve meaningful severity-related structure consistent with expert annotations. In addition, the model maintains a compact design and efficient inference characteristics, making it suitable for deployment on resource-constrained devices such as smartphones and edge platforms. Interpretability analysis using Grad-CAM indicates that the model focuses on pathologically relevant regions of the leaf, supporting transparent and reliable decision-making. Furthermore, the extended ParaLeafNet framework incorporates deployment-oriented optimization techniques that enhance performance without requiring modifications to the core architecture.

Why it matches plant phenotyping methods葉画像から植物病害の重症度を自動推定するCNN手法が研究の中心であり、植物状態の表現型計測に該当する。

abstractThis paper presents ParaLeafNet-Severity, a deep parallel convolutional neural network designed to perform disease identification and four-level severity grading simultaneously within a single forward pass.

Integrating mobile RGB-D imaging and digital odometry for trunk diameter mapping in tart cherry orchards →

• Depth informed trunk detection performed reliably under field conditions. • Trunk diameter estimates aligned closely with ground truth. • Transmission-based digital odometer provided reliable along-row positioning. • Full-block mapping demonstrated large-scale applicability. Trunk diameter is an important structural trait used to assess tree size, vigor, and long-term growth in orchard systems, but it remains difficult to measure efficiently at orchard scale. This study developed and validated a low-cost mobile imaging system for automated trunk diameter estimation and spatial mapping in tart cherry (Prunus cerasus) orchards. The system integrated RGB-D cameras, deep learning-based trunk detection and segmentation, three-dimensional depth reconstruction, and driveline-based digital odometry for spatial positioning under canopy conditions where GNSS performance was unreliable. Trunks were detected using a YOLO-based model, segmented using a Segment Anything Model (SAM)-based approach, and reconstructed in 3D from depth data to estimate real-world trunk diameter at 40 cm above the trunk base. The system was evaluated in a 15-year-old, 1 ha experimental orchard in Utah, USA, and then applied in a 14-year-old, 9.5 ha commercial orchard. Under unobstructed viewing conditions, trunk diameter estimates showed strong agreement with manual measurements, with a mean absolute error (MAE) of 0.95 cm, a root mean square error (RMSE) of 1.16 cm, and R 2 of 0.79. Across all 462 trees, automated per-tree averaging produced an MAE of 1.40 cm. In the commercial orchard, mapped trunk diameter patterns aligned with UAV-derived canopy height, reflecting underlying zones of tree vigor. These results show that mobile RGB-D imaging combined with driveline-based odometry can provide practical, cost-effective orchard-scale trunk diameter mapping under commercial field conditions.

Why it matches plant phenotyping methodsRGB-D画像、深度再構成、物体検出・セグメンテーション、オドメトリを統合し、樹幹径という植物構造形質を自動推定・空間マッピングする手法を開発・検証しているため。

abstractThis study developed and validated a low-cost mobile imaging system for automated trunk diameter estimation and spatial mapping in tart cherry (Prunus cerasus) orchards.

Evolution of Forest Tree DBH Measurement Technologies: From Contact-Based Traditional Approaches to Remote Sensing Non-Contact Methods →

Diameter at Breast Height (DBH) is a key parameter in forest measurement. However, existing research has mostly focused on improving the accuracy of individual technologies, lacking a systematic synthesis of the evolutionary logic of measurement techniques and a standardized selection framework for forestry applications. To this end, this paper constructs a multi-level classification framework based on measurement platforms and technical principles, establishes for the first time a five-dimensional comprehensive evaluation system (covering accuracy, efficiency, cost, environmental adaptability, and automation) along with a hierarchical technology decision tree, and systematically analyzes the application logic of multi-source fusion technologies across three levels: ground-based, near-ground mobile, and aerial. The review indicates that traditional contact-based measurement has limited efficiency; modern remote sensing technologies (photogrammetry and LiDAR) offer significant advantages in automation and accuracy, but still face challenges such as high equipment costs, complex data processing, and poor environmental adaptability. Multi-source fusion and machine learning are key methods to overcome the limitations of single sensors and improve the robustness of DBH estimation. Finally, it is anticipated that with decreasing sensor costs and the advancement of intelligent algorithms, DBH measurement will continue to evolve toward automation, intelligence, and engineering practicality, providing technical support for large-scale, long-term, and repeatable forest monitoring.

Why it matches plant phenotyping methods森林樹木のDBHという明示的な植物形態形質の測定技術を対象に、測定原理の分類、評価体系、意思決定木、リモートセンシングと機械学習の比較を体系化した方法論レビューであり、フェノタイピング手法が中心である。

abstractDiameter at Breast Height (DBH) is a key parameter in forest measurement.

DSH_YOLO enables factory farming production: a method for detecting and grading the cumulative internode length of tomato seedlings. →

Introduction The industrial cultivation of tomato seedlings requires a high degree ofuniformity and consistency in grading. However, traditional grading methods based on phenotypictraits such as leaf area and canopy width are susceptible to environmental conditions, therebylimiting the accuracy and efficiency of grading. Since cumulative internode length is relativelystable and closely correlated with seedling vigor, this study aims to develop an accurate methodfor measuring and grading the cumulative internode length of tomato seedlings. Methods A tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework. First, deformable convolution was introducedinto the backbone to enhance feature extraction for curved stems and occluded regions. Second,the original loss function was replaced with SIoU to improve the alignment between predictedregions and the actual stem structure. Third, a Haar wavelet downsampling module was embeddedinto the backbone to preserve high-frequency detail information and reduce information loss underocclusion conditions. An intelligent grading system was further developed to verify the practicalapplicability of the proposed method. Results Experimental results showed that DSH_YOLO achieved a Precision of 96.1%, aRecall of 94.3%, and an mAP@0.5 of 92.1%. Compared with the baseline YOLOv8 model, theproposed method substantially improved segmentation performance for cumulative internoderegions in tomato seedlings. In prototype validation, the intelligent grading system achieved anaverage grading success rate of 87.50%, with an average cumulative internode length error of 8.0mm. Discussion The results indicate that DSH_YOLO and the grading system can meet therequirements for large-scale grading and detection of tomato seedlings, demonstrating highdetection accuracy and success rates. This approach can provide insights for grading other types ofseedlings during their growth stages and offer support for seedling production and thedevelopment of intelligent agricultural equipment.

Why it matches plant phenotyping methodsトマト苗の累積節間長という植物形態形質を画像分割・検出し、実用的な等級判定まで検証する手法開発が研究の中心であるため。

abstractA tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework.

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

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

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

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

VGDS-PointNet++ for organ segmentation and phenotypic trait estimation in greenhouse tomato seedlings. →

Introduction To accurately segment point clouds and quickly calculate leaf length and stem diameter, thereby enabling phenotypic analysis and variety selection of greenhouse tomato plants, this paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation. Methods The point clouds of the tomato canopy were acquired using a depth camera. After labeling, point cloud augmentation was performed, and the tomato point cloud dataset (TPCD) containing 1,552 sets of data was rebuilt. Voxel grid downsampling was applied to replace the original sampling strategy of PointNet++. Models of PointNet, PointNet++, VGDS-PointNet++, and Point Transformer were trained with the TPCD and compared on segmentation quality with accuracy and mean Intersection over Union (mIoU). After segmentation, skeletal morphology was fitted for non-occluded leaves by applying a series of surface fitting techniques. The leaf lengths and stem diameters were automatically calculated and compared with the manually measured values. Results The validation results showed that the average runtime of voxel grid downsampling was 0.132 s, which was lower than under the same number of sampled points. Compared to the other three models, the proposed model had higher accuracy and mIoU, reaching up to 96.80% and 88.95%, respectively. The proposal's accuracy and mIoU increased by 3.9% and 4.45% over PointNet++, respectively. The determination coefficient R 2 between the automatic calculation and manual measurement values of leaf length and stem diameter was 0.93 and 0.87, respectively. Discussion This can help extract phenotypic traits of tomatoes using depth cameras.

Why it matches plant phenotyping methods深度カメラ点群のセグメンテーションと葉長・茎径の自動推定手法を開発し、手測定および複数モデルと比較検証しており、表現型取得が研究の中心である。

abstractthis paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation

A terrestrial laser scanning‐based workflow for component‐wise estimation of individual tree above‐ground biomass →

Abstract Accurate estimation of individual tree above‐ground biomass (AGB) and its component‐wise allocation is crucial for advancing ecological research and forest management. However, current biomass estimation methods, such as destructive sampling and allometric equation–based approaches, face limitations in both operational efficiency and cost‐effectiveness, and only destructive sampling can provide component‐wise biomass measurements, which is impractical for large‐scale studies or repeated measurements. In this study, we present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation. A total of 68 trees were scanned to obtain high‐resolution TLS data and subsequently destructively harvested to acquire field reference measurements for validation. The results demonstrate that the workflow achieved high accuracy in predicting AGB at the individual tree level (coefficient of determination/ R 2 = 0.88, root mean squared error/RMSE = 16.83 kg, mean absolute error/MAE = 12.18 kg), significantly outperforming estimates derived from locally calibrated allometric equations ( R 2 = 0.61, RMSE = 29.86 kg, MAE = 24.52 kg). Furthermore, this study provides evidence of the strong capability of TLS in estimating branch‐level biomass, with high accuracy achieved across branch orders ( R 2 ranging from 0.66 to 0.91, RMSE from 3.55 to 380 g and MAE from 2.97 to 290 g). By providing precise, non‐destructive estimates of biomass distribution across branches and leaves, this workflow demonstrates strong potential for improving the accuracy of tree biomass quantification, supporting investigations of resource allocation strategies, and enhancing forest carbon monitoring.

Why it matches plant phenotyping methodsTLSによる樹木の地上部・器官別バイオマスを推定するワークフローを開発し、破壊的実測で精度検証しており、植物形質取得法が研究の中心である。

abstractwe present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation.

Comprehensive Survey of Plant Disease Detection and Classification based on Machine Learning and Deep Learning Algorithms →

Plant diseases are a major problem worldwide, affecting crop yield and quality and impacting food security. Early identification and accurate diagnosis of plant diseases is crucial to minimizing crop damage and maximizing eco-friendly farming practices. Traditional methods of plant disease detection include manual diagnosis by experts, which is time consuming to arrive at the final diagnosis and is prone to human errors. In recent years, Machine Learning (ML) and Deep Learning (DL) algorithms have been used for the automatic detect plant diseases, with no intervention needed by experts. These algorithms are used to detect diseases by analysing images of plant stems, leaves and other parts, differentiating between healthy and diseased plants. Step by step procedures are used to diagnose plant diseases by employing techniques such as image pre-processing, feature extraction, and classification to enhance image quality for accurate disease classification and prediction. This paper details the application of various existing ML and DL algorithms in the detection, classification and prediction of plant diseases, where image-based techniques are also adopted for accurate and swift diagnosis. Performance metrics such as sensitivity, accuracy, and specificity elaborate on the efficacy of ML and DL algorithms, acting as reliable tools for accurate plant disease detection. The outcomes of this study illustrate that these ML and DL models are well suited for the identification and classification of plant diseases.

Why it matches plant phenotyping methods植物画像から病害状態を検出・分類する機械学習手法を体系的に扱うレビューであり、植物フェノタイピング手法が中心です。

abstractThis paper details the application of various existing ML and DL algorithms in the detection, classification and prediction of plant diseases, where image-based techniques are also adopted for accurate and swift diagnosis.

Unseen-Crop Plant Disease Classification via Disentangled Representation Learning →

Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in cross-unseen-crop scenarios. Cross-crop generalization for plant disease recognition requires models to identify known disease categories in crop domains never observed during training. A central challenge is that disease symptoms are strongly coupled with crop-specific appearance cues, which severely degrades generalization. Here, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition. The proposed method employs a dual-branch visual encoder to separately capture disease semantic representations and crop-domain representations, and it leverages a frozen CLIP text encoder to use disease and crop prompts for text-guided semantic anchoring. A semantic-anchor-only contrastive disentanglement strategy is further formulated under a hybrid label space, where crop-branch features are incorporated as stop-gradient hard negatives to suppress semantic–domain information leakage and strengthen the intra-class aggregation of the same disease across crops. Residual domain-discriminative cues are mitigated via domain-adversarial learning. During inference, only the disease branch is retained for classification, improving generalization while reducing deployment overhead. Experiments demonstrate that under the PlantVillage cross-crop setting, the method achieves 98.04% and 74.29% Top-1 accuracy on seen and unseen crop domains, respectively. Moreover, it attains 81.99% on a real-world field dataset of strawberry powdery mildew and 76.31% on a low-illumination degradation set, validating robustness under realistic imaging distribution shifts.

Why it matches plant phenotyping methods植物病害症状を画像から認識する新規深層学習手法を中心に提案し、未知作物・実環境データで性能検証しているため、植物フェノタイピング手法として含める。

abstractHere, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition.

Structural volume composition of internodes determines culm non-structural carbohydrates accumulation in rice →

Abstract Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological determinants of NSC accumulation are unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. Manipulation of internode development using plant growth regulators demonstrated that altering VCR effectively modified culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving NSC accumulation to achieve high and stable yield performance in rice.

Why it matches plant phenotyping methodsイネ茎の形態からNSC蓄積能を評価する新規指標VCRを導入し、再現性と既存形態形質との比較まで行っており、表現型評価法が研究の中心である。

abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.

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

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

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

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

CotSkNet: Semantic segmentation of point clouds and phenotypic trait extraction for fragile organs in densely planted cotton under field conditions →

Accurate segmentation of fine-scale organs from 3D point clouds poses a substantial challenge in high-throughput plant phenotyping (HTP), where existing methods are hindered by the loss of topological features and the resulting low accuracy. To address this, we introduce CotSkNet, a semantic segmentation network based on structured representation learning. This approach is the first to define plant architecture as a knowledge-based intermediate representation that integrates geometric, topological, and hierarchical relationships, thereby incorporating topological information into the segmentation process. CotSkNet features an innovative Topological Geometric Feature Fusion Extractor and a Two-Way Reinforcement Module to efficiently extract and enhance salient features. Furthermore, the proposed Topological Attention Aggregator enables dynamic focus on key branch points. On a dataset of 403 field-grown cotton plants, our method achieved mean intersection over union values of 91.55%, 92.37%, and 98.21% for the main stem, fruiting branches, and leaves, respectively, far surpassing those of mainstream methods. Moreover, automatically extracted phenotypic parameters, such as plant height and fruiting branch length showed excellent consistency with manual measurements (R 2 > 0.91, root mean square error (RMSE) < 0.14). This study confirms that structured representation learning excels at capturing fine-organ phenotypes, providing an innovative analytical pathway for HTP in complex field crops.

Why it matches plant phenotyping methods3D点群の器官セグメンテーションと形質抽出手法を開発し、圃場データで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。

abstractwe introduce CotSkNet, a semantic segmentation network based on structured representation learning.

Investigating phloem transport dynamics in Arabidopsis through compartmental modelling of positron emission tomography data. →

Background Phloem is the long-distance transport tissue of vascular plants in which photoassimilates are distributed from sources (e.g., leaves) to sinks (e.g., roots, fruits). Phloem transport occurs under pressure, making it very sensitive to manipulation and almost experimentally inaccessible. Therefore, functional data on phloem speed and dynamic distribution of photoassimilates along the transport pathway are still scarce, both in trees and herbaceous plants. This study presents a methodological pipeline to image phloem transport in very thin shoots of the model plant Arabidopsis using photosynthetic uptake of 11 CO 2 and state-of-the-art positron emission tomography (PET). Results Successful application of the latest generation preclinical PET scanners allowed in vivo visualization of internal movement of 11 C-labelled photoassimilates inside primary and secondary shoots of 1 to 2 mm diameter every 5 min. Using this data as input in a compartmental model enabled estimation of (i) phloem front speed, and (ii) radial carbon partitioning between leakage-retrieval phloem, carbon storage and respiratory efflux. The methodology shows that the phloem front speed of recently fixed carbon in primary shoots was almost two-fold the speed in secondary shoots (128 vs. 70 µm s -1 ). Furthermore, it was estimated that the fraction of recently fixed 11 CO 2 that was unloaded from the phloem to the surrounding storage cells and retrieved back into the phloem was higher in primary shoots than in secondary shoots, and that allocation to the storage compartment was higher in secondary shoots. Within the primary shoot, the fraction of unloading and retrieval of the 11 C-labelled photosynthates increased towards the inflorescence. Conclusion Here, we demonstrate the synergistic application of high-resolution PET scanning and compartmental modelling as a promising approach to advance our understanding of phloem dynamics in small-dimension plants, such as the model plant Arabidopsis. With this, an opportunity is created to explore the genetic basis of phloem dynamics.

Why it matches plant phenotyping methodsPET撮像とコンパートメントモデルを組み合わせ、植物体内の師部輸送速度や炭素分配という生理形質を推定する方法論が研究の中心であるため。

abstractThis study presents a methodological pipeline to image phloem transport in very thin shoots of the model plant Arabidopsis using photosynthetic uptake of 11 CO 2 and state-of-the-art positron emission tomography (PET).

A Reproducible Methodology for 3D Tree-Structure Mensuration and Risk-Oriented Decision Support: Integrating SfM–MVS, Field Referencing, and Rule-Based TRAQ/ALARP Logic →

This manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration and transparent, rule-based decision support for tree risk management. The workflow integrates (i) Structure-from-Motion/Multi-View Stereo (SfM–MVS) reconstruction from multi-view imagery, (ii) independent referencing to ensure metric scaling and a consistent local frame, and (iii) point cloud analytics to derive branch-level geometric descriptors (e.g., base diameter, length, inclination, slenderness, and projected reach). A clear rule-based layer operationalizes Tree Risk Assessment Qualification (TRAQ)-style risk components and As Low As Reasonably Practicable (ALARP) principles to map geometry and exposure into auditable management recommendations (e.g., monitoring intervals, pruning/weight reduction, supplemental support, and exclusion-zone planning). To provide a real-data example, the demonstration uses the public Fuji-SfM apple orchard dataset, including three neighboring trees with partially overlapping crowns for tree instance extraction and subsequent TRAQ/ALARP scenarios on an outer tree. The proposed decision layer is intentionally based on external geometry and exposure; internal decay indicators and species-specific mechanical properties (e.g., Modulus of Elasticity (MOE), Modulus of Rupture (MOR)) are outside this demonstration and should be incorporated via complementary diagnostics in operational deployments.

Why it matches plant phenotyping methodsSfM–MVSと点群解析による樹木・枝の3D形状形質抽出が中心的な方法論であり、実データでの適用も含むため。

abstractThis manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration

1. S. Mustofa et al., “A Comprehensive Review on Plant Leaf Disease Detection using Deep Learning,” arXiv preprint, 2023. 2. H. Rehana et al., “Plant Disease Detection using Region-Based Convolutional Neural Network,” arXiv preprint, 2023. 3. Y. Zhang et →

Indeed, precise and timely detection of diseases in plants is one of the most vital elements in maximizing crops to uphold global food security. Traditional methods are often time-consuming, subjective, and sometimes require expert knowledge. This project involves the use of a Deep Learning framework for automatic Field Plant Disease Detection and Classification, making use of the advanced YOLOv11 object detection model. Namely, YOLOv11 is utilized because of its superior balance of detection speed and accuracy in comparison with earlier models. This makes it ideal for real-time applications on the field. The plant image dataset is proposed to be collected, preprocessed, and annotated, involving different crops and common diseases in a large dataset. The YOLOv11 architecture is trained to simultaneously locate the disease regions (bounding boxes) on leaves, stems, or fruits and classify the specific type of disease. This may involve techniques for improving model robustness, such as data augmentation and transfer learning, which should enable better generalization across diverse environmental conditions. Such performances will then be checked using metrics such as Mean Average Precision and the speed of inferences: Frames Per Second. The system will be reliable, efficient, and scalable for farmers and agricultural experts. Implementation challenges include model size optimization for mobile deployment and continuous retraining on new disease strains, but the integration of YOLOv11 has great potential to revolutionize precision agriculture and smart farming by providing the ability for instantaneous disease management.

Why it matches plant phenotyping methods植物器官上の病変領域と病害種を画像から自動抽出・分類するYOLOv11手法の開発と性能評価が中心であり、植物病害状態のフェノタイピングに該当する。

abstractThis project involves the use of a Deep Learning framework for automatic Field Plant Disease Detection and Classification, making use of the advanced YOLOv11 object detection model.

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.

HFA-Net: Explainable Multi-Scale Deep Learning Framework for Illumination-Invariant Plant Disease Diagnosis in Precision Agriculture →

Robust plant disease detection in real-world agricultural environments remains challenging due to dynamic environmental conditions. Accurate and reliable disease identification is essential for precision agriculture and effective crop management. Although computer vision and Artificial Intelligence (AI) have shown promising results in controlled settings, their performance often drops under lesion scale variability, inter- and intra-class similarity among diseases, class imbalance, and illumination fluctuations. To overcome these challenges, we propose a Heterogeneous Feature Aggregation Network (HFA-Net) that brings together architectural improvements, illumination-aware preprocessing, and training-level enhancements into a single cohesive framework. To extract richer and more discriminative features from the early layers of the network, HFA-Net introduces a multi-scale, multi-level feature aggregation stem. The Reduction-Expansion (RE) mechanism helps preserve important lesion details while adapting to variations in scale. Considering real agricultural environments, an Illumination-Adaptive Contrast Enhancement (IACE) preprocessing pipeline is designed to address illumination variability in real agricultural environments. Experimental results show that HFA-Net achieves 96.03% accuracy under normal conditions and maintains strong performance under challenging lighting scenarios, achieving 92.95% and 93.07% accuracy in extremely dark and bright environments, respectively. Furthermore, quantitative explainability analysis using perturbation-based metrics demonstrates that the model’s predictions are not only accurate but also faithful to disease-relevant regions. Finally, Grad-CAM-based visual explanations confirm that the model’s predictions are driven by disease-specific regions, enhancing interpretability and practical reliability.

Why it matches plant phenotyping methods植物病害の症状領域を画像から診断する深層学習・前処理フレームワークの開発が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。

abstractwe propose a Heterogeneous Feature Aggregation Network (HFA-Net)

Characterizing plant hydraulic behaviour under drought stress using vegetation modelling →

Droughts have emerged as the primary driver of forest disturbances across Europe in the 21st century, significantly impacting both tree growth dynamics and mortality rates. Tree species are differently affected under drought, and these differences are related to species-specific plant hydraulic traits that govern water storage, hydraulic conductivity, and stomatal regulation. However, quantifying variability in these hydraulic traits across sites, species, and time remains challenging, as site measurements have historically rarely been comprehensive enough to assess the evolution of plant hydraulic behavior under drought stress. New continuous, high temporal resolution observational plant hydraulic data paired with process-based plant hydraulic modelling opens an opportunity to address this gap, by providing a framework to test and quantify theories based on first principles across species and sites.In this study, we apply the terrestrial biosphere model QUINCY, augmented by a recently developed plant hydraulic architecture module, across three eddy covariance sites in Germany covering broadleaved forest species (Aplern, Hainich, and Hartheim). The model is parameterized for three common temperate tree species present at the aforementioned sites. We constrain QUINCY across these species and sites using 30-minute resolution stem water potential measurements collected during the summer and autumn of 2023. Our results show that two groups of model parameters explain most of the simulated plant water potentials: parameters controlling plant water uptake from soil (plant ability to extract water from soil and the root distribution), and parameters regulating stomatal sensitivity to pre-dawn leaf water potential. Across species, we find ash to be more drought resistant than beech and hornbeam, as it closes its stomata earlier than other species under similar levels of drought stress, and it is characterised by a higher hydraulic capacitance per unit stem volume. Our study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters, effectively reducing uncertainty in, and providing robust constraints on, modelled responses to drought.

Why it matches plant phenotyping methods植物の水理状態・水理形質を連続観測と拡張モデルで定量化する手法の適用が研究の中心であり、単なる生物学的実験のルーチン測定ではない。

abstractOur study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters

TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement →

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement.

Why it matches plant phenotyping methods樹木の胸高直径（DBH）という植物形態形質を、航空画像から3D再構成と信頼度重み付き推定で測定する手法を開発・検証しており、フェノタイピング手法が中心である。

abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.

RAM-UNet: an improved U-Net-based semantic segmentation model for the main stem of mature soybean plants. →

As the key structure connecting the vegetative and reproductive organs of soybean plants, the main stem plays a crucial role, and its morphological parameters serve as core phenotypic indicators for evaluating plant growth, lodging resistance, and yield potential. At the mature stage, the main stem exhibits high similarity to pods in color and texture, along with complex curvature and severe occlusion by pods and leaves, making accurate and continuous extraction challenging for conventional segmentation methods. To address this, this study proposes RAM-UNet, a high-precision semantic segmentation model based on an improved U-Net architecture. The model adopts ResNet50 as the backbone and replaces standard convolutions with deformable convolutions to capture curved stem morphology and improve feature extraction for low-contrast edges. In the encoder, the Convolutional Block Attention Module (CBAM) is combined with an improved atrous spatial pyramid pooling (ASPP) module (C-ASPP) with four dilation rates, enhancing multi-scale feature representation compared to the original three-rate design. A multi-scale attention aggregation (MSAA) module in the decoder improves continuity and integrity of stem boundaries. During training, a composite loss function combining Dice loss and cross-entropy loss is employed to mitigate foreground pixel sparsity. Experimental results on a self-constructed dataset show that RAM-UNet achieves a mean Intersection over Union (mIoU) of 90.58%, with Recall and Precision reaching 94.99% and 94.58%, respectively. Compared with U-Net, DeepLabv3+, PSPNet, and SegNet, RAM-UNet improves mIoU by 6.41%, 10.51%, 22.41%, and 17.37%, respectively. Automatically measured stem lengths show high agreement with manual measurements (R² = 0.9746), validating practical applicability. RAM-UNet also generalizes well on the public PASCAL VOC 2012 dataset, achieving an mIoU of 73.14%. The results indicate that the proposed model enables high-precision and continuous segmentation of main stems in mature soybean plants, providing an effective technical solution for automated and non-destructive measurement of crop phenotypic parameters.

Why it matches plant phenotyping methods大豆主茎のセグメンテーションと長さ測定を目的とする画像解析手法を開発し、手動測定との一致性で検証しており、植物表現型取得が中心である。

abstractthis study proposes RAM-UNet, a high-precision semantic segmentation model based on an improved U-Net architecture.

Breeding for resistance to charcoal rot disease: a review of oilseed crops →

Charcoal rot, caused by Macrophomina phaseolina, is a destructive soil-borne disease that threatens several oilseed crops. Its persistence in the soil through microsclerotia, wide host range, and strong association with drought and heat stress makes it a formidable challenge for sustainable production. Breeding for resistance is widely recognized as the most effective and environmentally sound management strategy. This review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions. Field-based techniques such as root and stem severity scoring and colony-forming unit indices remain central to resistance evaluation, although challenges of standardization and reproducibility persist. Advances in molecular and genomic tools, including QTL mapping, genome-wide association studies (GWASs), marker-assisted selection (MAS), and genomic selection (GS), have begun to strengthen the identification and deployment of resistance loci. In addition, speed breeding, high-throughput phenotyping, and gene-editing platforms such as CRISPR/Cas offer novel opportunities to accelerate cultivar development. Integration of these approaches, along with the exploration of wild relatives and pre-breeding materials, is essential for broadening the genetic base of resistance and achieving durable, climate-resilient oilseed production. By linking pathogen biology, screening methods, and advanced genetic strategies, this review provides a comprehensive framework for future breeding programs aimed at mitigating the impact of charcoal rot in oilseed crops.

Why it matches plant phenotyping methods耐病性評価に用いる根・茎の病徴重症度スコアリングやCFU指標などのスクリーニング方法を明示的に扱い、標準化・再現性の課題も論じるレビューであり、植物病害表現型の取得法が主要な内容に含まれる。

abstractThis review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions.

MSA-MVSNet: A Cross-Scale Collaborative Attention-Based Multi-View Reconstruction Network for Orchard Tree 3D Reconstruction with Instance Segmentation for Fruit Counting →

Abstract To address the issues of detail loss and matching difficulties in fruit tree 3D reconstruction caused by complex branch–leaf morphology, fruit occlusion, and illumination variations, this paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting. A multi-scale feature enhancement module is designed to adaptively fuse deep semantic features and shallow fine-grained details through a spatial–channel collaborative attention mechanism, thereby enhancing the network’s capability to represent multi-scale structures such as trunks, branches, and leaves. Multi-branch dilated convolutions are introduced to enlarge the receptive field, and deformable convolutions are incorporated to adaptively capture the irregular geometric shapes of fruits, improving modeling robustness. In addition, a feature matching transformer is introduced to strengthen long-range global contextual correlations within and across images via intra-attention and inter-attention mechanisms, thereby improving matching stability in low-texture and repetitive-texture regions.To validate the effectiveness of the proposed method, experiments are conducted on self-collected real orchard dataset and public benchmark datasets. The results demonstrate that MSA-MVSNet outperforms baseline models by 8.2% in terms of 3D reconstruction quality. Finally, by combining depth filtering with the semantic segmentation results of YOLOv11-Seg, a semantic-guided fruit reconstruction and counting framework is constructed. This framework achieves an overall counting F1-score of 92.8% on the self-collected dataset with varying scene sparsity and 93.5% on the public Fuji-sfm dataset, demonstrating its effectiveness and generalization capability.

Why it matches plant phenotyping methods果樹の3D再構成と果実カウントという植物形質取得を目的に、マルチビュー再構成ネットワークとセグメンテーション統合手法を開発・検証しており、フェノタイピング手法が中心である。

abstractthis paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting.

Quantitative Approach for Simultaneous In Situ Profiling of Lignin, Cellulose, and Hemicellulose Using Confocal Raman Microscopy. →

Label-free confocal Raman microscopy (CRM) is characterized by its high chemical specificity, making it a promising tool for the in situ quantitative analysis of plant cell walls. However, the simultaneous quantification of components in Gramineous species remains challenging. This is due to the complex "lignin-ferulate-carbohydrate" cross-linked network, as well as the amorphous property of hemicellulose, specifically its weak Raman signal and severe spectral overlap with cellulose. To address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS). We acquired CRM mapping images of rice stems pretreated with acidified sodium chlorite (ASC) for varying durations. The CS values between the preprocessed cell wall spectra and reference spectra (milled wood lignin, microcrystalline cellulose, and xylan) were then calculated and used as quantitative indicators. The results showed that CS values allow for accurate profiling, exhibiting significant positive correlations with the contents of lignin, cellulose, and hemicellulose. These correlations follow piecewise linear relationships with high determination coefficients ( R 2 ) of 0.9728 and 0.9809 for lignin, 0.9592 and 0.9810 for cellulose, and 0.9004 and 0.9901 for hemicellulose. The CS-based method consistently outperforms the conventional characteristic peak intensity approach. In particular, it resolves the difficulty of accurately quantifying hemicellulose, a task where single-band methods typically underperform ( R 2 in situ simultaneous quantification of lignin, cellulose, and hemicellulose contents in rice stem cell walls during ASC pretreatment. Thus, the CRM-CS algorithm enables simultaneous in situ quantification in Gramineous cell walls, offering a valuable approach for crop breeding and the high-value utilization of lignocellulosic biomass.

Why it matches plant phenotyping methods植物細胞壁中のリグニン、セルロース、ヘミセルロース含量を定量するCRM-CS手法を開発・検証しており、植物形質の取得方法が研究の中心である。

abstractTo address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS).

Breeding for resistance to charcoal rot disease: a review of oilseed crops →

Charcoal rot, caused by Macrophomina phaseolina, is a destructive soil-borne disease that threatens several oilseed crops. Its persistence in the soil through microsclerotia, wide host range, and strong association with drought and heat stress makes it a formidable challenge for sustainable production. Breeding for resistance is widely recognized as the most effective and environmentally sound management strategy. This review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions. Field-based techniques such as root and stem severity scoring and colony-forming unit indices remain central to resistance evaluation, although challenges of standardization and reproducibility persist. Advances in molecular and genomic tools, including QTL mapping, genome-wide association studies (GWASs), marker-assisted selection (MAS), and genomic selection (GS), have begun to strengthen the identification and deployment of resistance loci. In addition, speed breeding, high-throughput phenotyping, and gene-editing platforms such as CRISPR/Cas offer novel opportunities to accelerate cultivar development. Integration of these approaches, along with the exploration of wild relatives and pre-breeding materials, is essential for broadening the genetic base of resistance and achieving durable, climate-resilient oilseed production. By linking pathogen biology, screening methods, and advanced genetic strategies, this review provides a comprehensive framework for future breeding programs aimed at mitigating the impact of charcoal rot in oilseed crops.

Why it matches plant phenotyping methods油糧作物の炭腐病抵抗性評価に用いる症状重症度スコアやCFU指標などのスクリーニング方法を中心に整理したレビューであり、植物病害状態の表現型測定法が実質的な主題である。

abstractThis review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions.

A new method for measuring the diameter of natural rubber trees using instance segmentation and a monocular RGB camera with non-fixed distances →

The diameter of natural rubber trees serves as a critical crop parameter, not only for determining whether rubber trees meet tapping requirements and assessing their growth status, but also playing a significant role in calculating parameters such as tapping angle, trajectory, depth and yield prediction. To further advance the intelligent production level of natural rubber trees and achieve low-cost, automated diameter measurement methods, this study proposes a new approach that combines YOLO11s instance segmentation algorithms with a monocular RGB camera to enable non-contact and non-fixed distance diameter measurement of natural rubber trees. The YOLO11-seg is used to obtain masks and bounding boxes for the ID, trunk, and tapped area. This method employs image processing techniques such as contour smoothing, trunk skeleton extraction, angle calculation, and localization. With using the tree ID tag as the primary dimensional reference and incorporating the segmentation contours of trunk categories, it achieves the measurement and calculation of rubber tree trunk diameter. The results demonstrated that among the compared instance segmentation models, the highest segmentation accuracy mAP50-95ˢᵉᵍ reached 0.934. The diameter estimation based on this segmentation and geometric correction process achieved a root mean square error (RMSE) of 2.85 cm and a mean absolute percentage error (MAPE) of 12.58 % under the original measurement conditions. After error compensation, the RMSE and MAPE decreased to 2.13 cm and 8.68 %, respectively. The proposed method can accurately measure the diameter of natural rubber trees, significantly reducing the hardware cost. It provides a new approach for measuring the diameter of natural rubber trees, and also provides both theoretical support and practical basis for the intelligent production and precision agriculture in natural rubber cultivation.

Why it matches plant phenotyping methodsゴム樹の幹径という植物形態形質を、RGB画像とインスタンスセグメンテーションで非接触・自動推定する手法を開発し、精度検証まで行っており、フェノタイピング手法が中心である。

abstractthis study proposes a new approach that combines YOLO11s instance segmentation algorithms with a monocular RGB camera to enable non-contact and non-fixed distance diameter measurement of natural rubber trees.

ExoHeat: A continuous heating solution for bi-directional sap flow in small-diameter plant organs demonstrated in tomato peduncles →

Accurate sap flow measurements in small-diameter plant organs are essential for understanding water transport and source-sink dynamics, yet existing methods are limited by their temporal resolution, reduced sensitivity to low or reverse flow, and incompatibility with small organ dimensions. In this study, the ExoHeat sensor, a continuous-heating solution was developed for bidirectional sap flow measurements in small-diameter plant organs. Its performance was validated on tomato truss peduncles (Solanum lycopersicum L.). Zero-flow corrections accounting for ambient temperature and peduncle diameter ensured robust baseline adjustment, while gravimetric calibrations revealed a strong linear relationship between the sensor-measured temperature difference and sap flow rate up to 2 g h⁻¹, corresponding to a sap flux density of 5.8 10⁻³ cm³ cm⁻² s⁻¹. Whole-plant validation further demonstrated close agreement between ExoHeat-derived sap flow and gravimetric transpiration data. Anatomical imaging showed an asymmetrical distribution of xylem vessels in the tomato truss peduncle, underscoring the importance of correct sensor orientation. High-resolution measurements on ripening trusses successfully captured dynamic bidirectional flow patterns. The ExoHeat sensor thus provides a novel, high-temporal-resolution tool for accurate monitoring of sap flow in small-diameter organs, with promising applications in plant physiology, irrigation optimisation and stress detection.

Why it matches plant phenotyping methods小径植物器官の双方向樹液流を測定するセンサーを開発し、重力法による校正・検証を行った、植物生理状態の取得手法が中心の研究。

abstractIn this study, the ExoHeat sensor, a continuous-heating solution was developed for bidirectional sap flow measurements in small-diameter plant organs.

Quantitative assessment and predictive modelling of stem damage during seedling separation in mechanical rice transplanting →

Rice seedling stems are particularly vulnerable to structural damage during the seedling separation phase of mechanical transplanting, especially under non-ideal plant-machine interactions. Owing to its internal and transient nature, such damage is inherently difficult to quantify or predict. This study presents a novel modelling framework for stem damage assessment, which establishes a quantitative relationship between the maximum impact load (Fₘₐₓ) during seedling separation and internal damage severity, quantified by the damaged area ratio (Dₐᵣ). High-speed imaging and triaxial force sensors were employed to measure Fₘₐₓ across seedlings aged 20, 30 and 40 d under varying transplanting speeds. Microscopic cross-sections of stems were analysed to calculate Dₐᵣ. A composite impact force model, incorporating stem bending rigidity, lateral needle–stem offset and contact duration, was developed to support experimental design. A strong positive correlation was observed between Fₘₐₓ and Dₐᵣ across all seedling age groups (ρ > 0.93, p 8 %. Age-specific linear regression models achieved high predictive accuracy and good calibration (cross-validated R² of 0.86–0.91; RMSE of 0.33–0.73 percentage points in Dₐᵣ), while extending these models with a restricted cubic spline further reduced errors in the upper damage tail. This framework offers theoretical insights into age- and speed-dependent stem damage and practical tools for optimising transplanting parameters and supporting real-time, damage-aware control strategies to mitigate mechanical damage risk and improve seedling survival and post-transplant performance.

Why it matches plant phenotyping methods稲苗の茎損傷を画像・力センサー・断面解析で定量化し、予測モデルを開発・検証しており、植物状態の取得・推定方法が研究の中心である。

abstractThis study presents a novel modelling framework for stem damage assessment

High-throughput Verticillium wilt detection in cotton: A comparative study of faster R-CNN and YOLOv11 →

Verticillium wilt (VW), a soil-borne fungal disease of cotton, can lead to significant yield loss and has become a growing problem for global cotton production. From a global perspective, the local biotype has a high pathogenicity. Traditional phenotyping and screening methods for resistance to VW are slow, costly, and prone to human error. However, advancements in object detection models can enable automated, high-throughput screening of resistant varieties, therefore, improving speed, reducing costs, and eliminating operator bias. This study develops and evaluates the effectiveness and generalisation of two widely adopted object detection models: the two-stage Faster R-CNN and the single-stage YOLOv11 for VW in cotton stems across various backbone architectures. Digital cameras were used to collect cotton stem images from several fields. The results showed that the Faster R-CNN with the ResNet-101 model achieved a mean average precision (mAP at intersection over union (IOU) of 0.5) between 5 % and 55 % higher for the most complex YOLOv11-x and simpler YOLOv11-n, respectively, on the test dataset. Further evaluation with an independent dataset confirmed that the Faster R-CNN with ResNet-101 was the most robust and generalisable model, achieving a mAP of 85.68 %, outperforming YOLOv11 models by at least 12 % and up to 82 %. However, this enhanced mAP of the Faster R-CNN model incurred a computational cost approximately 8 % higher than that of YOLOv11-x. Nevertheless, in the context of VW detection for cotton breeding, the value of a higher mAP substantially outweighs the value of a lower computational load.

Why it matches plant phenotyping methods綿花の茎に現れる萎凋病を画像から検出・評価する物体検出手法を開発し、複数モデルの性能と一般化を比較検証しており、植物病害表現型の取得が中心です。

abstractTraditional phenotyping and screening methods for resistance to VW are slow, costly, and prone to human error.

A stem-leaf segmentation method of maize plant point cloud based on region growing and leaf phenotypic parameters measurement →

Maize leaf phenotypic parameters effectively reflect the photosynthesis and growth information of maize plants, which is crucial for breeding superior maize varieties. Current challenges include separating stems and leaves from a single maize plant and accurately measuring the phenotypic parameters of maize leaves. This study proposes a stem-leaf segmentation method based on region growing, incorporating adaptive cuboid region growing and slice region growing, alongside techniques for measuring phenotypic parameters of maize leaves. First, terrestrial laser scanning (TLS) was employed to obtain three-dimensional (3D) point cloud data of maize at the five-leaf (V5) and six-leaf (V6) stages. The point cloud data were then preprocessed to isolate single plant point clouds. Next, the maize point clouds were pre-segmented into three categories-central point clouds, partially expanded leaf point clouds, and unexpanded leaf point clouds-using center-edge segmentation, statistical filtering, and leaf classification. Adaptive cuboid region growing was applied to segment the unexpanded leaf point clouds, while slice region growing was used for partially expanded leaves, with Euclidean clustering optimizing the leaf point clouds, completing the segmentation process. Finally, various methods-including clustering counting, point-to-point distance accumulation, point-to-line distance, vector angle, point cloud triangulation, and triangle area accumulation-were utilized to automatically measure the number of maize leaves, leaf length, leaf width, leaf inclination angle, and leaf area. Compared with other point cloud stem-leaf segmentation methods based on geometric features and common 3D point cloud deep learning models (PointNet++, PointTransformer), the method proposed in this paper performs better. The segmentation results indicated that the Precision (P), Recall (R) and F₁-Score (F₁) for stem-leaf segmentation of all maize plants at the V5 stage exceeded 92.00%, with average values of 96.87%, 97.08%, and 96.97%, respectively. At the V6 stage, P, R, and F₁ exceeded 95.00%, with averages of 97.73%, 97.01%, and 97.67%, respectively. The algorithm accurately measured the number of leaves at the V5 stage, while a small error was noted at the V6 stage, yielding a percentage error (PE) of 0.93%. Measurement accuracy for leaf length, width, and area at both growth stages was greater than 93.80%, 92.80%, and 89.50%, respectively. Measurement accuracy for leaf inclination angle was lower, at 82.00% and 88.02% for the V5 and V6 stages, respectively. The proposed methods for stem-leaf segmentation and measurement of leaf phenotypic parameters are fast and accurate, providing technical support for high-quality breeding and intelligent management of maize. Our point cloud data of maize and source code is available from https://github.com/lmj-cau/stem-leaf-segmentation.git.

Why it matches plant phenotyping methodsトウモロコシの3D点群から茎葉を分割し、葉数・長さ・幅・面積・傾斜角を自動推定する手法を開発・比較検証しており、植物表現型取得が研究の中心である。

abstractThis study proposes a stem-leaf segmentation method based on region growing, incorporating adaptive cuboid region growing and slice region growing, alongside techniques for measuring phenotypic parameters of maize leaves.

DRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds →

Tomatoes are a globally important horticultural crop, and their high-yield, high-quality breeding relies on high-throughput, precise phenotyping. While 3D point cloud technology offers a new avenue for non-destructive plant phenotyping, the inherent complexity of tomato plant organ morphology and growth dynamics poses a significant challenge to existing segmentation methods. To address this, this study employed multi-view RGB image reconstruction to cost-effectively acquire high-quality point cloud data from four growth cycles. Based on the characteristics of our data, we adapted and proposed a hybrid dual-path downsampling method (HDPD) for dataset augmentation, and constructed a dynamic reference point propagation network (DRP-Net) for semantic segmentation. The DRP-Net architecture addresses geometric feature mismatches between organs through a dynamic kernel edge convolution module (DKEC). Furthermore, it utilizes a global–local semantic feature fusion upsampling module (GL-SFFU) to overcome boundary blurring caused by plant growth and enhance detail discrimination. Based on the semantic segmentation results, a clustering algorithm was used to achieve leaf instance segmentation and extract key phenotypic parameters. Experimental results demonstrate that DRP-Net achieves significant performance in the tomato stem and leaf segmentation task, with mean precision, recall, F1 score, and mIoU reaching 94.97%, 93.93%, 94.43%, and 89.34%, respectively. The extracted phenotypic parameters, such as leaf length, leaf width, and leaf area, exhibit strong correlations with manual measurements (R² greater than 0.92 and 0.88, respectively). This study provides an effective technical solution for the precise segmentation of complex plant organs and high-throughput phenotyping analysis for breeding.

Why it matches plant phenotyping methodsトマトの3D点群から茎葉をセグメンテーションし、葉形質を抽出する手法を開発・検証しており、植物フェノタイピングが研究の中心である。

abstractconstructed a dynamic reference point propagation network (DRP-Net) for semantic segmentation

A high-performance biocompatible biomass-based fish gelatin organohydrogel strain sensor for long-term accurate plant growth monitoring →

Organohydrogel-based plant strain sensors hold significant potential for enabling accurate and real-time monitoring of plant growth processes. However, existing strain sensors typically face challenges such as inferior biocompatibility, trade-off between sensing performance and mechanical properties, as well as poor long-term stability, leading to inaccurate monitoring and plant tissue damage and thus hindering their practical applications. Herein, we propose a synergistic metal ion and multiple hydrogen bond dual crosslinking strategy to develop a biomass-based fish gelatin organohydrogel as a strain sensing material. The resultant organohydrogel simultaneously exhibits excellent mechanical properties (Young's modulus of 99.9 kPa and strong adhesiveness of 60 kPa), high sensing performance (GF = 2.13, stable response across a wide temperature range from -80 °C to 25 °C), outstanding plant tissue and human cell biocompatibility, and long-term stability (over 5000 loading-unloading cycles under 100% strain), demonstrating superior overall performance to most existing organohydrogels. To harness these unique material performances, we fabricate a sandwich-structured plant strain sensor for long-term monitoring of plant growth. The fabricated strain sensor enables successful real-time monitoring of the growth dynamics of lotus stems and pomelo fruits with high accuracy and long-term stability up to three weeks. Our novel design strategy of high-performance organohydrogels enables high-fidelity plant growth monitoring, unlocking new potentials for advancing data-driven smart and precision farming practices.

Why it matches plant phenotyping methods植物成長を長期・リアルタイムに測定するひずみセンサーの材料設計、性能評価、植物での検証が研究の中心であり、植物フェノタイピング手法に該当する。

abstractwe propose a synergistic metal ion and multiple hydrogen bond dual crosslinking strategy to develop a biomass-based fish gelatin organohydrogel as a strain sensing material

A stem-leaf segmentation method of maize plant point cloud based on region growing and leaf phenotypic parameters measurement →

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

Why it matches plant phenotyping methodsトウモロコシの点群から茎・葉を分割し、葉の表現型パラメータを測定する手法開発が主題であり、植物フェノタイピング手法が中心です。

titleA stem-leaf segmentation method of maize plant point cloud based on region growing and leaf phenotypic parameters measurement

Multi-trait analysis to identify key factors influencing wheat lodging resistance and validation of an integrative lodging index →

Lodging is a complex trait that limits wheat (Triticum aestivum L.) yield potential, and no single trait can fully capture lodging resistance. Identifying key traits and developing reliable, field-applicable indicators are crucial for breeding lodging-resistant cultivars. In this study, lodging resistance was systematically assessed in 274 wheat varieties across three consecutive growing seasons (2022–2024). Genotype, growing season, growth stage, and their interactions significantly affected lodging-associated traits, with a clear temporal alignment between meteorological conditions and lodging events. Comparative analysis between lodged and non-lodged plants revealed that lodging negatively influenced spike and kernel traits. Multivariate analyses indicated that height-related traits accounted for nearly 50 % of the phenotypic variance related to lodging resistance and showed negative correlations, while traits related to stem weight and fullness explained 24 % and 8 %, respectively. Among these, stem wall thickness (SWT), second basal internode fullness (SBF), single stem elasticity (SSE), and stem strength (SS) emerged as key positive contributors, whereas plant height (PH), center of gravity height (CGH), and basal internode lengths were negatively associated. Stepwise regression and path analyses further identified SWT and SBF as primary determinants of SS, while CGH was the key factor influencing SSE. Structural equation modeling demonstrated that height-related traits exerted significant negative effects on stem anatomical structure, mechanical traits, and lodging index. Furthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed. It exhibited a strong correlation with the comprehensive lodging score (D value) and high consistency with clustering results, providing a practical assessment tool. These findings provide valuable insights for assessing lodging resistance and guiding strong-stem breeding strategies in wheat.

Why it matches plant phenotyping methods小麦の倒伏抵抗性を複数形質から統合的に評価し、新規倒伏指数を提案して既存スコアやクラスタリング結果で検証しているため、形質評価手法の開発・検証が中心である。

abstractFurthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed.

Detached Twig Assay to Evaluate Bacterial Canker on Peaches. →

Pseudomonas syringae pv. syringae ( Pss ) is the causal agent of bacterial canker, a disease that can result in yield losses, aerial tissue damage, and tree mortality in stone fruits worldwide. Peach, one of the major stone fruit crops, experiences significant yield losses and tree mortality attributed to bacterial canker in the United States. As the second-largest peach-producing state, South Carolina faces direct and significant impacts due to Pss . Early evaluations of peach scion responses to Pss infection have relied primarily on circumstantial field observations in rootstock trials. Although laboratory evaluations in peach have been reported, these studies primarily focused on pathogen virulence testing or small accession sets and did not establish a standardized, scalable detached twig protocol for systematic germplasm phenotyping. The absence of a clearly described laboratory assay has limited reproducible and large-scale evaluation of bacterial canker tolerance in peach. To address this gap, a detached dormant twig assay, previously developed for cherry, was adapted and optimized for peach. Dormant shoots from nine peach accessions were cut into 10 cm segments, surface-sterilized, and inoculated with a Pss suspension prepared in 10 mM MgCl 2 buffer or with the buffer alone. After six weeks of incubation, inner bark lesion size was evaluated visually and quantified using ImageJ. A newly developed visual rating scale was established and compared with quantitative lesion measurements. Spearman correlation analysis showed strong positive correlations between visual disease scores and ImageJ-based lesion measurements across two independent replicates (ρ = 0.80-1.00, p < 0.01), while shoot segment diameter showed weak-to-moderate negative correlations with disease severity. This adapted and consolidated dormant twig assay provides a practical, reproducible, and scalable method for phenotyping bacterial canker tolerance in peach and supports future germplasm screening and breeding efforts.

Why it matches plant phenotyping methodsモモの細菌性潰瘍病抵抗性を評価するため、切離枝アッセイを適応・最適化し、病斑測定と視覚評価を検証した中心的な表現型測定法の研究。

abstracta detached dormant twig assay, previously developed for cherry, was adapted and optimized for peach

Integrating SEM-based phenotyping with GWAS reveals the genetic architecture of rice straw secondary cell wall and internode cell features →

Rice stem performs assimilate transport and promises sturdiness due to cell wall structure and composition. However, less is known about the genetic basis of its structural characteristics. In this study, for the first time, the scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits collected from the cross-sections of 147 rice accessions. Genome-wide association studies (GWAS) identified 54 significant single-nucleotide polymorphisms (SNPs; integrated into 28 quantitative trait loci) residing in the genic sequences of rice (promoter and coding DNA sequence), and classified into three groups: 1) cell wall-defining genes, 2) cell size-defining genes, and 3) transcription factors. DUF246 and DUF1218 , galactose oxidase , mitochondrial Rho GTPase , WUSCHEL-related homeobox 5 and scarecrow-like 9 are the novel genes identified among the 21 candidate genes. These genes may play roles in stem development traits, specifically the distance from the vascular bundle to the end of the parenchymal cells (DVBEPC) and the thickness of the straw cell wall in the protruding part (TSCWP). Post-GWAS analyses showed one significant haplotype on chromosome 4 and 25 significant epistatic interactions. Most notably, nine TF families were repeatedly detected among the significant QTL. Os07g0644300 (XPA-binding protein 2), located in the q7-1 genomic segment and associated with DVBEPC, was found to have a missense mutation. Phenotyping via SEM imaging provides precise genome-phenome association in understanding rice stem cell size and cell wall architecture, which ultimately can define biomass and lodging resistance. systematic scheme of the current study • This study pioneers the use of SEM imaging to digitally phenotype rice stem traits, revealing the genetic basis of cell size and wall structure using GWAS. • The GWAS studies identified 28 QTLs and 21 candidate genes, including novel ones linked to stem strength and architecture. • The findings from the study enhance our understanding of rice stem development and provide a foundation for improving biomass and lodging resistance through precise genome-phenome associations.

Why it matches plant phenotyping methodsSEM画像を用いたイネ茎のデジタル表現型取得法の開発が研究の中心で、18形質を定量化しGWASに適用しているため。

abstractthe scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits

Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring →

Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing methods, which make quantitative evaluation difficult. Terrestrial Laser Scanners (TLS), Mobile Laser Scanners (MLS), or traditional photogrammetry approaches poorly reconstruct thin branches, dense foliage, and lack the scale consistency needed for long-term monitoring. Implicit 3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are promising alternatives, but cannot recover the true scale of a scene and lack any means to be accurately geo-localised. In this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings. Our system proposes a three-level representation: (i) coarse Earth-frame localisation using GNSS, (ii) LiDAR-based SLAM for centimetre-accurate localisation and reconstruction, and (iii) NeRF-derived object-centric dense reconstruction of individual saplings. This approach enables repeatable quantitative evaluation and long-term monitoring of sapling traits. Our experiments in forest plots in Wytham Woods (Oxford, UK) and Evo (Finland) show that stem height, branching patterns, and leaf-to-wood ratios can be captured with increased accuracy as compared to TLS. We demonstrate that accurate stem skeletons and leaf distributions can be measured for saplings with heights between 0.5m and 2m in situ, giving ecologists access to richer structural and quantitative data for analysing forest dynamics.

Why it matches plant phenotyping methodsNeRF・LiDAR SLAM・GNSSを融合した幼木の3D再構成・定位パイプラインを開発し、樹高、分枝、葉対木質比などの植物形質をTLSと比較検証しており、表現型取得手法が中心である。

abstractIn this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings.

Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications →

Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch topology. Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous pruning tool positioning. All code and processed data are publicly released to facilitate further UAV forestry research.

Why it matches plant phenotyping methods樹木の枝を対象に、ステレオ深度推定・セグメンテーション・深度最適化による枝単位の3D形状抽出手法を開発・評価しており、植物器官の表現型取得が中心である。

abstractThis paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds.

Imaging the three-dimensional structure of haustoria in host and Cuscuta interactions via laser ablation tomography →

Cuscuta spp. are stem holoparasitic plants that use haustoria to draw water, photosynthates, and nutrients from host plant vascular systems. Cuscuta has served as a model plant for understanding plant-plant interactions and haustoria development of stem parasitic plants; however, studies of the three-dimensional (3D) internal host-parasite interface and interconnections are limited due to their unique structures developed inside host stems. This study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images. LATscan imaging of Cuscuta invading Arabidopsis (Arabidopsis thaliana) and beet (Beta vulgaris) stems yielded 3D renderings and detailed images of the anatomy of Cuscuta-host tissue interactions, including Cuscuta searching hyphae penetrating the host vasculature. Laser-tissue interactions generated color contrast and facilitated easy differentiation between Cuscuta and host tissues in 3D renderings and 2D images, demonstrating that LATscan technology can be an efficient tool to investigate the development and function of host-parasitic plant interactions.

Why it matches plant phenotyping methodsレーザーアブレーション断層撮影による植物組織の3D画像化・再構成が研究の中心であり、宿主—寄生植物組織の形態・構造状態を取得する手法を実証している。

abstractThis study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images.

PTV2-Fr: a point cloud segmentation network for phenotypic trait extraction and gibberellin effect analysis in sorghum seedlings. →

Sorghum is a globally important crop. Under the breeding goals of high yield and stress resistance, the precise selection of elite germplasm is crucial. Phenotypic parameters such as plant height and leaf area at the seedling stage are core indicators for evaluating growth vitality. However, traditional manual measurement is inefficient and error-prone, making it difficult to meet the needs of high-throughput research. To address this, this study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters and explores the regulatory effects of different gibberellin (GA 3 ) concentrations. In this study, videos of sorghum seedlings were collected using the relevant system of Nanjing Agricultural University, and reconstructed into.ply format 3D point cloud files via the open-source software Colmap. The core optimizations of the PTV2-Fr model are as follows: Firstly, it proposes a Multi-Radius Dual-Coordinate Attention (MRDCA) mechanism to address the problems of leaf overlap and uneven point cloud density, thereby enhancing feature discrimination ability; Secondly, it introduces a Point-Graph Invariant Feature Refinement (PG-InvFR) module to improve the sensitivity of the segmentation head to local geometric details; Thirdly, it constructs a composite loss function (EL Loss) combining class-weighted cross-entropy loss and Lovász loss to alleviate class imbalance and boost segmentation accuracy. We selected 50 valid datasets from 112 video groups, annotated into three categories: Stem, Leaf, and Pot. The results show that PTV2-Fr outperforms PTV2 by 2.5% in accuracy, with significant improvements in Recall and mean F1-score (mF1). Ablation experiments confirm the positive effects of MRDCA, PG-InvFR, and EL Loss. Furthermore, PTV2-Fr demonstrates good robustness in analyzing GA concentrations, revealing that 50-100 mg/L GA concentrations promote seedling growth, while concentrations exceeding 200 mg/L inhibit growth. The PTV2-Fr model provides an efficient solution for the automatic determination of sorghum seedling phenotypes, and the revealed GA 3 regulatory mechanism can offer theoretical references for high-quality seedling cultivation and hormone management.

Why it matches plant phenotyping methods3D点群分割ネットワークを開発・検証し、ソルガム幼苗の草丈や葉面積などの表現型形質を自動抽出する方法が研究の中心である。ジベレリン処理の解析は付加的な応用であり、方法論的貢献が明確。

abstractthis study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters

Digital image-based morphometrics and mixed effects inference resolve environment sensitive and stable traits in onion (allium cepa L.). →

Despite their relevance to postharvest engineering and cultivar improvement, the genotype- and environment-dependent variation of the physical and geometrical traits of onion bulb remains poorly characterized in the Republic of Korea. The study evaluated these traits in six commercial onion cultivars grown across two distinct production regions (Muan and Changnyeong), using a randomized complete block design with three replications. A standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions, including polar and equatorial diameters, neck and bulb thickness. Linear mixed models were used to partition genotype (G), location (L), and G x L interaction effects. Most traits exhibited significant G, L, and G×L effects, indicating strong environmental sensitivity alongside genetic control Combined heritability estimates were high for bulb thickness (0.83), bulb weight (0.66), and diameter- and area-related traits (0.54–0.68), while broad-sense heritability across locations was consistently high (0.71–0.99), particularly for single bulb weight and size traits. Spring Breeze, Katamaru, and Healthy Q consistently produced larger bulbs, while Cheonjujeok and Eomji Nara exhibited smaller bulb dimensions. Trait responses varied markedly between environments, with changes ranging from reduction of approximately 80% to increases exceeding 160%, highlighting pronounced genotype × environment interactions. Hierarchical cluster heatmap analysis revealed strong associations among bulb size–related traits and distinct genotype groupings, with clear location-dependent differences in trait expression between Muan and Changnyeong. These findings demonstrate the utility of image-based phenotyping for robust environment-aware assessment of onion bulb geometry and provides a quantitative basis for region-specific cultivar selection, postharvest system design, and future multi-site breeding evaluations.

Why it matches plant phenotyping methods画像取得とImageJによる形質抽出を組み合わせた標準化フェノタイピングワークフローが、タマネギ球の形態形質測定の中心的手法として明示されているため。

abstractA standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions

Root electrical capacitance method for the field monitoring of maize response to elevated carbon dioxide concentration →

Abstract Aims This study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment. Methods A two-year FACE study was conducted with maize grown under ambient and elevated [CO 2 ], and low and high nitrogen supply in three replicate plots. The saturation root electrical capacitance (C R *) was monitored during the plant growth cycle. Aboveground plant parameters were measured in situ at flowering. Results Capacitance measurements revealed a seasonal pattern in root development with a peak at flowering, and the positive effect of higher nitrogen dose and [CO 2 ] enrichment on plant growth. At anthesis, C R * was significantly ( p < 0.001) and linearly correlated with stem basal area (R 2 : 0.51–0.68), aboveground biomass index (basal area × plant height; R 2 : 0.47–0.62) and leaf chlorophyll concentration (R 2 : 0.40–0.56). However, the best correlation (R 2 : 0.73 and 0.74) was found for plant leaf area, which is closely related to root water uptake, suggesting that the applied current signal penetrated the roots, and that the capacitance method directly measured root status in the field. In addition, C R * at flowering was a reasonable early predictor of maize grain yield (R 2 : 0.58 and 0.64) under our experimental conditions. Conclusions The electrical capacitance method proved to be a practical high-throughput tool for phenotyping not only the root but the whole plant in the field. Being noninvasive, it is particularly beneficial in FACE systems, where destructive sampling and soil disturbance should be minimized. It would also provide cost-effective support for breeding stress-tolerant and climate-resilient crops. Graphical Abstract

Why it matches plant phenotyping methods根の電気容量測定を非破壊・高スループットな植物フェノタイピング手法として評価し、圃場での相関および予測性能を検証しているため、方法が研究の中心である。

abstractThis study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment.

An attention-augmented lightweight convolutional framework for fine-grained plant leaf disease classification →

In the recent era, the growth of deep learning is inevitable. Various models such as convolutional neural networks (CNNs) and transformers are used widely in images for high classification accuracy. Since the invention of transformers, researchers have widely used novel approaches using transformers to achieve an impressive accuracy. In spite of this, this paper proposes a novel custom lightweight CNN model called Attentive and Lightweight Network (ALNet). ALNet consists of three major blocks: stem, core, and head. The core part is the novel classifier built as an inspiration from various pre-trained models such as ResNet, SENet (Squeeze and Excitation Network), EfficientNet, SqueezeNet, and ShuffleNet. The main objective is to build a model that has a high classification accuracy while reducing the number of parameters. This reduces the size of the model and hence makes it easy to deploy on cloud platforms and use in edge devices. The model was evaluated using 5-fold cross-validation on three different datasets. The primary dataset was a grapevine dataset with an accuracy of 99.78 percent and 100 percent in multi-class and binary classification respectively. To test the robustness of the model, a multi-class classification using the apple dataset achieved an accuracy of 99.95 percent and a binary classification with the cherry dataset achieved an accuracy of 100 percent. ALNet uses only 0.17 million parameters which is 18 times less parameters than the lightest model (SqueezeNet) and it takes only 14 seconds to train each epoch while pretrained models take 17–31 seconds. ALNet requires only 151.98 MFLOPs with a model size of 677.20 KB, making it approximately 18 times smaller than SqueezeNet. On the whole, ALNet is a highly accurate, lightweight model for plant leaf diseases prediction.

Why it matches plant phenotyping methods葉画像から植物病害状態を分類する軽量CNNを開発・検証しており、病害表現型の画像ベース抽出手法が研究の中心である。

abstractthis paper proposes a novel custom lightweight CNN model called Attentive and Lightweight Network (ALNet).

Auto3DPheno: Automated 3D Maize Seedling Phenotyping via Topologically-Constrained Laplacian Contraction with NeRF →

Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy.

Why it matches plant phenotyping methodsRGBカメラとNeRF・点群処理・骨格解析を統合し、トウモロコシ幼苗の形質を自動抽出する手法を開発・精度評価した研究であり、フェノタイピング手法が中心である。

abstractthis study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras.

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

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

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

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

Diversity of root system architecture and root-shoot biomass allocation in industrial hemp (Cannabis sativa L.). →

Roots are major contributors to nutrient acquisition, water absorption, and plant anchoring and stability. However, little is known about the root system of industrial hemp (Cannabis sativa L.), an increasingly important crop worth $16 billion annually. Hemp is commonly cultivated for grain as an oilseed, stalk biomass for fiber and industrial materials, but has also had growing interest for its carbon sequestration potential due to its reported deep rooting profile. The objectives of this research were to (1) phenotype a panel of 46 industrially-relevant hemp genotypes, (2) quantify the phenotypic differences of shoot and root traits through 2D image analysis, (3) and to investigate genotype grouping strategies and gene targets that could be useful for crop improvement. To phenotype the root system architecture of multiple hemp genotypes representative of production hemp, a large format raised-bed was developed in a greenhouse in which hemp was planted in rows. Root and shoot traits varied across genotypes, with a difference of 175% in total root length between the largest and smallest genotype, and heritability values ranging from 0.51 to 0.88 for key root traits. A strong positive correlation was found between root and shoot biomass (R = 0.93) suggests coordinated resource allocation strategies across genotypes. Of the 46 genotypes studied, two genotypes consistently showed the greatest differences across most of the traits analyzed in the panel. A root-to-shoot quadrant framework was applied to classify hemp ideotypes based on biomass allocation and architectural traits. In addition, comparative genomic analysis identified 74 candidate root architecture genes in hemp that are orthologous to known regulators in maize, rice, and Arabidopsis. These findings highlight substantial phenotypic diversity in hemp root systems and provide a foundation for developing genotype grouping strategies and selecting breeding targets for mapping populations.

Why it matches plant phenotyping methods複数遺伝子型の根系形態を2D画像解析で定量し、温室内の大規模 raised-bed フェノタイピング基盤も開発しているため、植物形質取得が研究の中心である。

abstractTo phenotype the root system architecture of multiple hemp genotypes representative of production hemp, a large format raised-bed was developed in a greenhouse

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

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

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

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

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

Early prediction of coffee production per plant using morphological indices →

Purpose: Coffee farming plays an essential role in the global economy, making accurate productivity prediction methods indispensable for strategic decision-making in the sector. This study aimed to develop models for early prediction of coffee production per plant based on morphological indices.Methods:Two models were proposed using the following attributes: plant height, canopy width, and the number of fruits on the productive internodes of plagiotropic branches. In Model 1, fruit counts were manually conducted at the 4th and 5th productive nodes of the branches, while in Model 2, the average fruit count from the 1st to the 5th productive nodes was obtained automatically through branch image analysis using Detectron2, an open-source object detection library. Both models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior. The research was conducted in three coffee plots in Viçosa, Minas Gerais, Brazil, where 60 plants were selected to evaluate the production prediction model. During harvest, the production of each plant was individually recorded, enabling validation of the predictions. Results: The results revealed a strong correlation between the models and the field-observed production data, especially for the model based on data collected three months before harvest. Model 1 demonstrated a better fit (R² = 0.889; RMSE = 0.923 L/plant; MAE = 0.635 L/plant), while Model 2 had a lower absolute error (R² = 0.747; RMSE = 0.374 L/plant; MAE = 0.460 L/plant). Additionally, productivity maps were generated for each plot, showing good agreement with field-observed productivity data.Conclusions: It was concluded that the proposed models are promising for application in coffee farming, contributing to early production prediction.

Why it matches plant phenotyping methodsコーヒー果実数を枝画像から自動抽出し、個体あたり生産量を早期予測する手法を開発・検証しており、表現型取得と予測ワークフローが研究の中心である。

abstractBoth models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior.

Automatic stem phenotyping in soybean using keypoint detection →

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、ダイズの茎形質をキーポイント検出で自動取得する画像ベースの表現型解析手法が研究の中心と明確に判断できる。

titleAutomatic stem phenotyping in soybean using keypoint detection

Species-specific modeling of tree diameter at breast height using tree height and relative density with implications for remote sensing-based forest inventory →

Accurate estimation of tree diameter at breast height (DBH) is essential for forest monitoring, biomass modeling, and carbon accounting. While DBH is traditionally measured in the field, this approach is labor-intensive and costly, especially at large scales. In contrast, tree height can now be efficiently obtained from remote sensing platforms such as airborne LiDAR and photogrammetry, creating opportunities to estimate DBH indirectly. To address this, we developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD) in the mixed temperate forests of New Brunswick, Canada. Our analysis used 1807 trees from 653 permanent sample plots (1985–2014), representing six dominant species: Abies balsamea, Acer rubrum, Acer saccharum, Picea mariana, Picea rubens, and Picea glauca. Allometric (height-only) models explained part of DBH variation, with R² ranging from 0.15 to 0.35 (broadleaves) and 0.41–0.74 (conifers), but predictive accuracy was notably low for Acer rubrum and Acer saccharum. Incorporating RD as a competition index substantially improved model performance, with R² increasing to 0.85–0.89 (broadleaves) and 0.72–0.88 (conifers). Prediction errors (RMSE and MAE) consistently decreased, with broadleaves showing the greatest improvement compared to conifers, reflecting their stronger sensitivity to stand density. These findings demonstrate that combining tree height with RD provides reliable estimates of DBH across diverse species. The framework bridges ground-based inventory with remote sensing applications, offering a scalable approach for biomass estimation, stand density analysis, and sustainable forest management in temperate mixed-species forests.

Why it matches plant phenotyping methods樹木のDBHという明示的な植物形質を、樹高と林分密度から推定する種別非線形モデルを開発しており、形質推定手法が研究の中心である。

abstractwe developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD)

LED-Net: A lightweight and efficient dual-branch convolutional neural network for high-performance fruit tree branches semantic segmentation on mobile devices →

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

Why it matches plant phenotyping methods果樹枝のセマンティックセグメンテーション手法を開発する研究であり、植物器官の画像取得・抽出が中心的な方法論的貢献である。

titleLED-Net: A lightweight and efficient dual-branch convolutional neural network for high-performance fruit tree branches semantic segmentation on mobile devices

Grapevine-chronology: Annual growth ring analysis for climate adaptation and vineyard management – A review →

The study of annual growth rings in Vitis vinifera has recently emerged as a promising framework to explore long-term vine responses to climate and management. This review synthesizes current knowledge on grapevine wood anatomy, xylem functionality, and isotopic signatures, highlighting their role as archives of environmental and agronomic information. Grapevine growth rings provide high-resolution records of climate signals, including drought and heat stress, and reveal cultivar-specific hydraulic strategies shaped by soil conditions and management practices. Stable isotope analyses further complement anatomical chronologies by integrating physiological responses to water availability. Together, these approaches offer valuable insights into the structural memory and plasticity of grapevine xylem, with direct implications for vineyard sustainability and climate adaptation. We also examine the impact of viticultural practices such as irrigation, pruning, grafting, and rootstock choice on xylem architecture, emphasizing how agronomic decisions leave long-lasting anatomical imprints in wood. Finally, we outline future research perspectives, including the integration of dendrochronological, isotopic, and high-resolution imaging techniques, to fully exploit grapevine chronologies as tools for understanding vine resilience and for guiding varietal selection, breeding, and precision viticulture under changing environmental conditions.

Why it matches plant phenotyping methodsブドウの年輪解剖、安定同位体、画像解析を用いて環境応答や木部機能などの植物形質・状態を評価する方法群をレビューしており、測定・解析手法が中心的である。

abstractGrapevine growth rings provide high-resolution records of climate signals, including drought and heat stress, and reveal cultivar-specific hydraulic strategies shaped by soil conditions and management practices.

Robust plant disease segmentation in complex field environments: an in-depth analysis and validation with STAR-Net →

Introduction Plant disease segmentation in real-world agricultural environments poses significant technical challenges, including complex backgrounds, diverse lesion morphologies, and extreme class imbalance. Methods In this paper, we propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy. The architecture features an innovative Heterogeneous Branch Attention Aggregation (HBAA) module to robustly represent multi-scale and multi-morphology features. The training strategy employs a Dynamic Phase-Weighted Loss (DPW-Loss) to navigate the complexities of imbalanced data. Results Our method achieves a state-of-the-art average mIoU of 93.36% on the NLB dataset. This result demonstrates its superior ability to precisely segment diseases with specific elongated morphologies. Furthermore, the model obtains a competitive average mIoU of 41.13% on the highly challenging PlantSeg dataset. This result validates its robustness in complex 'in-the-wild' scenarios. Discussion Our work presents a powerful, well validated, and synergistic solution for plant disease segmentation. It also paves the way for practical applications in precision agriculture.

Why it matches plant phenotyping methods植物病徴を画像からセグメンテーションする新規ネットワークと学習法を開発し、複数データセットで検証しているため、病害状態の表現型抽出が中心である。

abstractwe propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy.

Seasonal dynamics and sun/shade heterogeneity of leaf gas exchange and VOC emissions inside a tall temperate forest canopy →

Leaf gas exchange is the key driver of forest carbon uptake and directly determines forest carbon sink activity. Additionally, plants release a variety of biogenic volatile organic compounds (VOCs) acting as stress signals of trees. However, continuous hourly resolved measurements of leaf gas exchange and VOC emissions in tall tree canopies are challenging and remain scarce. To this end, we developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest. We additionally measured sap flux density (Js), radial growth and tree water deficit (TWD) to gain a holistic picture of seasonal leaf and stem water and carbon flux dynamics during the summer of 2024. During midsummer, we found a gradual reduction of stomatal conductance (gs) and VOC emissions of sun, but not shade branchlets of P. menziesii in response to moderate atmospheric and edaphic drying. Decreased gs led to a downregulation of transpiration (E), Js, and carbon isotope discrimination accompanied by an increase in TWD and intrinsic water used efficiency. Leaf gas exchange of shade branchlets remained unaffected due to microclimatic buffering effects. Contrarily, sun leaves of F. sylvatica, profited from sunny midsummer conditions and increased leaf gas exchange, whereas shade leaves benefitted from more diffuse light during early summer exhibiting similar carbon assimilation, transpiration and VOC emissions as sun leaves. For both species we found a clear time lag of four to five hours between maximum leaf and stem water fluxes and a delay of up to 20 hours for the recovery of TWD, highlighting the role of stem water reserves. Pronounced seasonal and diurnal differences of leaf gas exchange, stem water fluxes and VOC emissions showed, that continuous data are essential to better understand variability of ecosystem flux dynamics.

Why it matches plant phenotyping methods樹木葉のガス交換を連続測定する24チャンバーのin situ測定システムを開発し、植物の生理形質・状態を取得する方法が研究の中心であるため。

abstractwe developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest.

Structural volume composition of internodes determines culm non-structural carbohydrates accumulation in rice →

Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological determinants of NSC accumulation are unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. Manipulation of internode development using plant growth regulators demonstrated that altering VCR effectively modified culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving NSC accumulation to achieve high and stable yield performance in rice. Highlight This novel internode structural index robustly predicts the culm non-structural carbohydrate accumulation capacity, providing a practical morphological indicator for improving yield stability in rice.

Why it matches plant phenotyping methods稲の節間形態から茎のNSC蓄積能力を評価する新規指標VCRを導入し、複数年・品種で再現性と予測性能を検証しているため、形態表現型の測定・評価法が研究の中心である。

abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.

Characterising Multi-Pathogen Grapevine Dieback in Subtropical Australia: Field Symptomatology and Fungal Morphology Reveal Dominance of Botryosphaeriaceae and Phomopsis Species →

Grapevine trunk diseases in subtropical climates show complex patterns of multi-pathogen co-infection and spatial clustering, while current diagnosis still relies mainly on expert judgement with limited quantification and functional testing. This study investigated an 18-acre vineyard in south-eastern Queensland and used 7,440 vine records from 744 plots to build quantitative indices for symptoms and cross-section necrosis, followed by comprehensive characterisation of 46 fungal isolates through isolation, microscopy, physiological assays and greenhouse pathogenicity tests. Analyses identified three spatial disease regions, with wedge- and semi-ring-shaped necrosis strongly enriched in high-disease plots, and showed that Botryosphaeriaceae and Phomopsis groups dominated the pathogen community and had much higher composite pathogenicity indices than other fungi. Even without molecular data, the integrated pipeline of disease quantification, microscopic and physiological traits, pathogenicity testing and computational analysis allowed robust identification of dominant pathogen combinations in a subtropical vineyard and provided a methodological basis for regional risk assessment and targeted management.

Why it matches plant phenotyping methodsブドウ樹の症状と壊死を定量化する指標および統合解析パイプラインが研究の中心で、植物の病害状態を直接測定・評価しているため。

abstractused 7,440 vine records from 744 plots to build quantitative indices for symptoms and cross-section necrosis

TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement →

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM--MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS's depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement .

Why it matches plant phenotyping methods樹木のDBHという明示的な植物形質を、航空画像と3D Gaussian splattingから推定する新規手法を開発し、実測値およびLiDARと比較検証しているため、方法中心の植物フェノタイピング研究として含める。

abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.

A lightweight fruit branch angle extraction method for cotton plants based on micro-element reconstruction and clustering →

Achieving an optimal plant architecture is a key objective in cotton breeding for enhancing yield potential, and accurate quantification of the fruit branch angle (FBA) is essential for understanding genotype–phenotype relationships and advancing ideotype breeding. However, in-field FBA measurement remains technically challenging due to severe occlusion, variable illumination, and background interference. To overcome these limitations, we propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM). The framework decomposes reconstructed cotton point clouds into linear micro-elements using RANSAC, followed by geometric clustering via K-means to identify and separate the main stem and fruit branches. This process operates without topological priors or large annotated datasets. Model fidelity assessment shows that 80% of point pairs between the LPCRM and the original 3DGS reconstruction exhibit Euclidean distances ≤ 0.5 cm. Phenotypic validation using 268 fruit branches from 25 cultivars demonstrates high measurement accuracy, achieving an R² of 0.874 and an RMSE of 4.01° for FBA extraction. Plant height estimation also shows strong agreement with manual measurements (R² = 0.915, RMSE = 3.858). Overall, this study presents a lightweight and robust solution for extracting 3D structural traits of field-grown cotton. The proposed framework reduces data dependency, adapts well to complex field conditions, and offers an efficient approach for high-throughput phenotyping and cotton ideotype breeding.

Why it matches plant phenotyping methods綿花の果枝角度などの3D植物形質を抽出する画像ベース表現型解析フレームワークを開発し、複数品種・枝で精度検証しており、方法開発と技術検証が研究の中心である。

abstractwe propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM).

Fast and Reliable: Evaluating Smartphone LiDAR App for Stem Diameter Measurement and Tree Mapping →

Tree inventories require rapid, accurate measurements of stem diameter at breast height (DBH) and precise tree locations to support monitoring, planning, and informed decision-making. We evaluated a smartphone-based LiDAR app (SBLA), Forest Scanner, against (i) a diameter tape for DBH and (ii) a Vertex ultrasonic device for spatial coordinates. Across DBH of 725 trees, the LiDAR closely matched diameter tape measurements: discrepancies >5 cm occurred in 10.5% and > 10 cm in 3.5% of trees. Errors were concentrated in trees with smaller DBH, where occasional overestimation by SBLA arose from point-cloud misfitting. For medium and large trees, agreement was consistently high. Tree coordinates from SBLA and the ultrasonic device were broadly comparable at fine scales. Field efficiency was substantially improved: a 1,000 m2 plot with 70-80 trees required [~]2 hours using an ultrasonic device and diameter tape versus [~]20 minutes (one person) with SBLA, an [~]85-90% reduction in person-hours. Current limitations of SBLA are primarily software-related (stability, data handling, low-light performance). Overall, SBLA offers an efficient, auditable, and operationally relevant tool for tree inventories, with utility for rapidly updating DBH and spatial data used in management, planning, and asset databases.

Why it matches plant phenotyping methodsスマートフォンLiDARによる樹木の胸高直径と位置測定法を既存機器と比較検証しており、植物形質取得法が研究の中心である。

abstractWe evaluated a smartphone-based LiDAR app (SBLA), Forest Scanner, against (i) a diameter tape for DBH and (ii) a Vertex ultrasonic device for spatial coordinates.

A 3D stem diameter measurement method for field maize at jointing stage: combining RLRSA-PointNet++ and structural feature fitting →

Introduction In precision agriculture, accurate measurement of maize stem diameter during the jointing stage is crucial for lodging resistance assessment and yield prediction. However, existing methods have certain limitations: manual measurement is time-consuming and highly subjective, while two-dimensional image recognition can only capture local features and fails to reconstruct the true three-dimensional structure of the stem. Therefore, there is a critical need for an accurate and automated three-dimensional stem diameter measurement approach. Methods This study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting, focusing on the position of the second above-ground internode of maize plants. Specifically, multi-view image reconstruction is employed to generate three-dimensional point clouds of maize stems, and Relative Position Encoding, the Local Group Rearrangement Module, and the Local Region Self-Attention mechanism are incorporated into the PointNet++ network to achieve precise segmentation of stems from the ground. On this basis, a structural feature fitting strategy is applied, where principal axis analysis and ellipse fitting are utilized to extract cross-sectional features, thereby obtaining the major axis and minor axis parameters for stem diameter estimation. Results Experimental results demonstrate that the proposed method maintains high accuracy under complex field conditions, achieving a mean absolute error (MAE) of 1.27 mm (R² = 0.87) for major-axis stem diameter and 1.38 mm (R² = 0.82) for minor-axis stem diameter. Discussion The proposed method effectively overcomes the limitations of traditional manual and two-dimensional measurement techniques. It provides a robust and accurate solution for maize stem diameter measurement during the jointing stage. This approach offers technical support for intelligent maize growth monitoring, lodging resistance analysis, and three-dimensional phenotypic trait extraction.

Why it matches plant phenotyping methodsトウモロコシ茎径という植物形態形質を、3D再構成、点群セグメンテーション、構造特徴フィッティングで自動推定する手法が研究の中心であり、精度検証も行っている。

abstractThis study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting

SEAFEC: a spatial–edge adaptive convolution for multi-scale and boundary-aware plant disease and weed imagery →

Introduction Plant diseases and weeds are among the leading biological threats to global crop production. While deep learning has advanced automated analysis, existing approaches often fail under challenges like large multi-scale variations and blurred boundaries. Methods To address this, we propose SEAFEC (Spatial-Edge Adaptive Feature Enhancement Convolution), a novel convolutional module that jointly enhances scale adaptivity and boundary precision. SEAFEC employs a dual-branch design: the SCARF branch dynamically adjusts receptive fields, while the MEFE branch explicitly strengthens edge features. Results Across three representative tasks—plant disease classification, corn leaf disease detection, and sugarcane-weed segmentation—SEAFEC achieved consistent improvements (+1.8% accuracy, +2.5% mAP, +3.4% mIoU), with notable gains in boundary-sensitive cases. Discussion These results highlight SEAFEC as a general-purpose enhancement module, providing a unified solution for tackling scale-boundary challenges in agricultural imagery to support reliable disease diagnosis and precision weed management.

Why it matches plant phenotyping methods植物病害画像を対象に、マルチスケール・境界認識のための新規畳み込みモジュールSEAFECを開発し、病害分類・検出で技術性能を評価しているため、植物状態の画像ベース推定手法が中心である。

abstractwe propose SEAFEC (Spatial-Edge Adaptive Feature Enhancement Convolution), a novel convolutional module that jointly enhances scale adaptivity and boundary precision.

3D Point Cloud Segmentation Algorithm Based on Deep Learning and Its Application in Phenotype Detection →

ABSTRACT Three‐dimensional measurement technology based on point clouds can effectively solve the problem of plant occlusion and is a hot research direction for plant phenotyping methods. Rapid and low‐cost 3D reconstruction and accurate 3D point cloud segmentation are two major challenges in 3D phenotyping technology. Taking watermelon seedlings as an example, we proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation. We performed dynamic downsampling and filtering based on the point cloud scale and designed different phenotypic measurement methods for hypocotyl and leaf point clouds. To overcome the difficulty of measuring the hypocotyl caused by slenderness, curvature and inclination, we proposed a segmented stem 3D point cloud skeleton extraction algorithm. The experimental results show that our method achieved satisfactory measurement results for the seedling phenotypes of four growth stages. The detection accuracy of the number of cotyledon leaves and the number of true leaves both exceed 95% and the coefficient of determination ( R 2 ) of leaf area, hypocotyl length and stem diameter phenotypes are all beyond 0.8. The proposed method provides a novel, efficient and precise 3D plant phenotyping solution, with good application and promotion value.

Why it matches plant phenotyping methods3D再構成、点群セグメンテーション、骨格抽出、形質測定を統合した植物フェノタイピング手法の開発が中心であり、精度評価も実施している。

abstractwe proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation.

Adaptive Shortest Path Tracking for Robust Leaf–Wood Separation in Individual Trees from TLS Point Clouds →

Leaf-wood separation is crucial for single-tree aboveground biomass estimation and 3D reconstruction. Although the non-destructive and efficient acquisition of fine-grained, high-density point cloud data can be performed using terrestrial laser scanning (TLS) technology, existing methods suffer from various drawbacks, including insufficient detection of fine branches, limited robustness to point cloud subsampling, and weak adaptability across different tree species and crown structures. A core issue lies in the over-reliance on prior values for key algorithm parameters. This study proposes an adaptive shortest path tracking for robust leaf–wood separation (ASPTS) in individual trees. First, a graph is constructed, and the shortest path backtracking is employed to extract skeleton points. Second, an improved k-nearest neighbor (KNN) algorithm is proposed to adaptively optimize the number of neighboring points based on the shortest path, thereby obtaining initial wood points. Third, the feature descriptor construction for characterizing trunk and branch structures is optimized using principal component analysis (PCA) by implementing an enhanced adaptive neighborhood radius selection strategy. Finally, final wood points are extracted using a region-growing approach guided by a stepwise feature thresholding scheme. Twenty-two individual trees, which represent different species, heights, and crown structures, are selected as test subjects. The results demonstrate the capability of ASPTS to make a good balance between type I and type II errors. ASPTS consistently exhibits strong fine-branch detection capability and robust performance under varying conditions, including different tree species, crown structures, and point cloud densities. ASPTS demonstrates superior performance compared to four state-of-the-art methods.

Why it matches plant phenotyping methodsTLS点群から個体樹木の葉・木部を分離し、枝構造やバイオマス推定・3D再構成に用いる新規アルゴリズムを開発・比較検証しており、植物形態の取得・抽出が中心である。

abstractASPTS consistently exhibits strong fine-branch detection capability and robust performance under varying conditions, including different tree species, crown structures, and point cloud densities.

3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies →

Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Why it matches plant phenotyping methodsUAVおよびマルチカメラによる3D再構成を開発・評価し、樹冠全体のシュート伸長という植物形質の測定に用いる方法が中心である。

abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.

DEVELOPMENT OF COMPUTER VISION ALGORITHMS FOR DIGITAL PHENOTYPING OF PEAS →

This research aims to overcome key limitations of traditional pea breeding, namely the lengthy variety development cycle and the subjectivity of manual phenotyping, by developing automated image analysis methods. The study compares three computer vision methods applied to peas: YOLO-based detection, semantic segmentation for recognizing plant elements in dry and green samples (using a proprietary digital phenotyping setup), and an original algorithm for detecting stem nodes by analyzing stem width. The detection method demonstrated low accuracy for plant parts. Semantic segmentation achieved 65 % accuracy for dry and 76 % for green plants. The node detection algorithm demonstrated 100 % accuracy. The developed software package enables objective assessment of key pea phenotypic traits. Further development of the system is aimed at integration with neural networks for determining leaf surface area and the number of productive nodes, which creates the basis for accelerated pea breeding.

Why it matches plant phenotyping methodsエンドウの表現型を抽出するコンピュータビジョン手法とソフトウェアを開発・評価しており、方法開発が研究の中心である。

abstractThis research aims to overcome key limitations of traditional pea breeding, namely the lengthy variety development cycle and the subjectivity of manual phenotyping, by developing automated image analysis methods.

A novel three-dimensional canopy photosynthesis model for tea plant: integrating point cloud deep learning and ray tracing to optimize photosynthetic efficiency →

The tea plant (Camellia sinensis) is economically and nutritionally important because of its bioactive compounds. Photosynthesis directly affects tea's growth and productivity, requiring a detailed study of its relationship with cultivation outcomes. We developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction. The ISBNet architecture was optimized for precise leaf–stem segmentation from point cloud data, achieving 0.897 average precision (AP) for leaves and 0.793 AP for stems. We then created a plant leaf morphology-adapted meshing algorithm optimized for plant leaf morphology, achieving an average mesh reduction of approximately 96% while maintaining morphological fidelity compared with conventional meshing methods. We generated multiple tea plant canopies representing distinct planting patterns, and used a ray tracing algorithm to simulate the spatiotemporal distribution of light within these structures. Canopy photosynthesis simulation revealed significant cultivar-specific differences, with 'Yuehuang 1' exhibiting the highest photosynthetic activity. Dense planting (10 cm spacing) significantly enhanced canopy photosynthetic rates compared with wider spacing (20 cm), and a strong linear correlation (r = 0.99) was identified between total leaf area and daily canopy photosynthetic rate across cultivars. This work establishes a methodological foundation for precision agriculture optimization in perennial crops, providing quantitative guidance for maximizing tea plantations' productivity through optimal cultivar selection and spatial configuration.

Why it matches plant phenotyping methods茶樹キャノピーの3D再構築、葉・茎セグメンテーション、形態適応メッシュ化、光線追跡による光合成推定を統合した方法開発が中心であり、植物形態・光合成状態の定量化に直接つながる。

abstractWe developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction.

Integrated assessment of tree health and structural stability using UAV multispectral imaging, 3D photogrammetry, VTA and acoustic tomography: A case study of a lime tree →

Accurate assessment of the physiological and mechanical condition of trees in urban environments represents a key component of risk management and the planning of protection measures. This study presents the integration of four methodological approaches - multispectral UAS (drone) analysis, a photogrammetrically generated 3D model, Visual Tree Assessment (VTA) and acoustic tomography (Arbotom) - applied to an old lime tree (Tilia platyphyllos) located in the courtyard of the Bishop's Palace of the Šabac Eparchy. Multispectral analysis was used to calculate the NDRE index of physiological activity, while the 3D trunk model was employed for precise positioning of the Arbotom sensors. Tomographic measurements performed at heights of 40 cm and 200 cm identified degradation zones with a reduction in load-bearing cross-sectional area of 27-39% (lower section) and 43-52% (upper section). The NDRE index indicated localized areas of reduced physiological activity within the crown, while the VTA method confirmed the presence of fungi of the genus Ganoderma. The integrated results indicate that the tree currently maintains a stable mechanical structure, with localized degradation zones that do not yet affect its static stability. The presented multi-sensor approach is highlighted as an efficient tool for detection, evaluation, and risk management in urban forestry, however as this research was conducted on a single Tillia platyphyllos specimen, the findings should be interpreted as a case study and methodological demonstration rather than as results directly generalizable to a broader population of urban trees.

Why it matches plant phenotyping methods樹木の生理状態と構造安定性を推定するため、UAVマルチスペクトル画像、3Dフォトグラメトリ、音響トモグラフィーなどを統合した測定手法が中心であり、方法論的実証として報告されている。

abstractThis study presents the integration of four methodological approaches - multispectral UAS (drone) analysis, a photogrammetrically generated 3D model, Visual Tree Assessment (VTA) and acoustic tomography (Arbotom) - applied to an old lime tree (Tilia platyphyllos) located in the courtyard of the Bishop's Palace of the Šabac Eparchy.

Infrared Microimaging of Sucrose Distribution in Plant Vascular Tissues. →

Sucrose is the primary transport sugar in plants, serving as an essential energy source and signaling molecule. Detection, visualization, and quantification of sucrose in various plant tissues are essential for understanding the metabolic and physiological processes that sustain plant life. Traditional metabolite-mapping techniques have struggled to visualize the quantitative distribution of sucrose at sufficient resolution to distinguish vascular bundles from surrounding tissues. Here, we present a Fourier-transform infrared (FTIR) imaging approach that can visualize sucrose in plant tissues quantitatively at a microscopic resolution (~12 µm). This IR-based, label-free method can be used with both model plants and agriculturally important crops. The assay has a detection range of 20-1000 mM and can map sucrose distribution within complex organs such as stems, leaves, and seeds. Notably, it enables the precise quantification of sucrose levels in vascular tissues. This is a trait of great interest in many current breeding and plant biotechnology approaches aimed at increasing crop yield.

Why it matches plant phenotyping methods植物組織内のスクロース分布を定量化するFTIR画像法を開発・提示しており、植物の生理状態（糖分布）を取得する方法が研究の中心である。

abstractHere, we present a Fourier-transform infrared (FTIR) imaging approach that can visualize sucrose in plant tissues quantitatively at a microscopic resolution (~12 µm).

DRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds →

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

Why it matches plant phenotyping methodsトマト点群から茎・葉を分割し、表現型形質を抽出する手法の開発がタイトルで明示されており、植物フェノタイピング手法が中心です。

titleDRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds

Corn stem diameter and ear orientation angle measurement method based on D3-YOLOv11 and RGB-D camera →

The operational effect of the reverse ear picking device for fresh corn is affected by stem diameter and ear orientation angle. The existing devices lack the ability to sense these parameters in real-time, making it difficult to dynamically adjust operating parameters, which leads to a high damage rate and harvest loss. To this end, this study focuses on the visual perception aspect and proposes a recognition method based on a depth camera and an improved D3-YOLOv11 segmentation model, which provides reliable visual input for subsequent adaptive regulation. Specifically, this study proposes Dual-Domain Dynamic Gate Conv (D3GConv) to enhance the multi-scale feature extraction ability of the model. In the neck network, a bidirectional weighted pyramid structure with semantic detail injection is designed to improve the segmentation accuracy of small objects. Generalized Focal Loss V2 was used to optimize the detection head to enhance the accuracy of boundary localization in dense stem scenes. Finally, the depth information is fused to realize the real-time measurement of stem diameter and ear orientation angle. Experimental results show that the Mask-mAP50 of the D3-YOLOv11 model reaches 99.3% and 94.6% in stem and ear instance segmentation tasks, respectively. The Mean Absolute Error of stem diameter measurement based on depth information is only 0.16 cm, and the Coefficient of Determination of ear orientation angle reaches 0.95, which verifies the reliability and practicability of this method in the adaptive control of the ear harvesting device. It provides an effective visual perception basis for improving the intelligence level of equipment.

Why it matches plant phenotyping methods深度カメラと改良YOLOによってトウモロコシの茎径・穂の向き角をリアルタイム測定する手法を開発・検証しており、植物形質の取得法が中心である。

abstractproposes a recognition method based on a depth camera and an improved D3-YOLOv11 segmentation model

Predicting individual tree diameter at breast height for genetically diverse Catalpa bungei using nonlinear mixed-effects models and UAV LiDAR data. →

Introduction Diameter at breast height (DBH) is a key parameter for assessing tree growth, carbon storage, and ecological functions. Traditional ground surveys are inefficient, labor-intensive, and terrain-limited, making them unsuitable for large-scale monitoring. Airborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation. However, most existing LiDAR-based models overlook the influence of genotype differences, limiting prediction accuracy. Methods In this study, we used data from 2,899 Catalpa bungei trees of different genotypes to develop a nonlinear mixed-effects (NLME) model that incorporates genotype as a random effect. This approach improved model generalizability by using LiDAR-derived tree height (LH) and LiDAR-derived crown diameter (LCD) as core predictors. Multiple sampling strategies were also evaluated to assess their impact on model performance. Results The results showed that, considering genotype effects, the proposed NLME model outperformed both traditional regression models and dummy-variable models (R 2 = 0.8624, RMSE = 1.1330, TRE = 3.9555), demonstrating the important role of genotype differences in improving model accuracy. Random sampling further improved prediction accuracy while effectively reducing measurement costs. Discussion This research introduces a new framework for integrating genotype variability into DBH prediction models and offers valuable insights for future LiDAR-based studies in genetically heterogeneous plantations. The findings provide technical support for forest management and ecosystem monitoring, as well as a methodological foundation for predicting tree growth under varying site and genetic conditions.

Why it matches plant phenotyping methodsUAV LiDARと非線形混合効果モデルを用いて個体樹木のDBHを推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。

abstractAirborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation.

Multi-Organ Plant Disease Detection Using CNN and Machine Learning: A Root-to-Leaf Approaches →

Detecting crop diseases is critical but labor-intensive task in agriculture, often requiring expert knowledge and manual inspection. This paper describes an efficient technique for automated disease using computer vision and Machine learning. The system analyzes images of plant leaves, stems, and roots to identify symptoms with high accuracy using Otsu's thresh-olding. A structured data acquisition process ensures quality input, while convolutional neural networks (CNNs) enable robust classification. This approach reduces reliance on skilled labor, supports early disease intervention, and improves overall crop health monitoring. The solution is designed for scalability and real-time use, including mobile-based applications for on-field diagnosis.

Why it matches plant phenotyping methods植物の葉・茎・根の画像から病徴を自動検出する画像解析・CNN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。

abstractThis paper describes an efficient technique for automated disease using computer vision and Machine learning.

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

In situ estimation of cotton fourth internode length and height-to-node ratio using UAV-derived vegetation indices and machine learning algorithms. →

This study investigates the potential of utilizing nonparametric, nonlinear machine learning (ML) algorithms, in conjunction with vegetation indices (VIs) derived from unmanned aerial vehicles (UAVs), to estimate the height-to-node ratio and the fourth internode length in cotton plants. The objective was to enhance the monitoring of these traits, thereby providing more accurate guidance on the optimal timing of plant growth regulator (PGR) applications. Data was collected from eight plots in our experimental field, with six plots used for model training and two for testing. During model development, the performance was assessed using nested 5-fold cross-validation, repeated three times with different partitions. For each algorithm, hyperparameters were tuned on the inner folds via Bayesian optimization with a Gaussian process surrogate, and the tuned model was evaluated on the corresponding outer test fold. We evaluated the performance of the ML algorithms using the Friedman test and interpreted their result using the Wilcoxon signed-rank test. The results demonstrate that VIs, combined with ML algorithms, can reliably estimate both the height-to-node ratio and the length of the fourth internode. Additionally, among the tested ML algorithms, Support Vector Regression (SVR) demonstrated superior performance for predicting height-to-node ratio, with an R² value of 0.8257 (95% CI: 0.7404 - 0.9110), RMSE value of 0.0998 (95% CI: 0.0953 - 0.1044), and rRMSE value of 5.51 (95% CI: 5.30 - 5.7). Meanwhile, the CatBoost demonstrated higher performance in estimating the fourth internode length, with an R² value of 0.799 (95% CI: 0.7570 - 0.8415), an RMSE of 0.1788 (95% CI: 0.1631 - 0.1945), and a rRMSE of 10.64 (95% CI: 9.90 - 11.38). Furthermore, using the Shapley Additive exPlanations (SHAP) approach, we revealed the contribution of each of the VI to the model's prediction. Overall, the findings demonstrate that UAV-derived VIs, combined with a machine learning algorithm, can consistently estimate these cotton traits. Additionally, this approach can replace traditional field-based measurements, thereby supporting more efficient monitoring and precise PGR management decisions.

Why it matches plant phenotyping methodsUAV由来の植生指数と機械学習により綿花の節間長・節位比を推定し、交差検証やアルゴリズム比較で性能を評価しており、形質取得手法が研究の中心である。

abstractThis study investigates the potential of utilizing nonparametric, nonlinear machine learning (ML) algorithms, in conjunction with vegetation indices (VIs) derived from unmanned aerial vehicles (UAVs), to estimate the height-to-node ratio and the fourth internode length in cotton plants.

GaussianPlant: Structure-aligned Gaussian Splatting for 3D Reconstruction of Plants →

We present a method for jointly recovering the appearance and internal structure of botanical plants from multi-view images based on 3D Gaussian Splatting (3DGS). While 3DGS exhibits robust reconstruction of scene appearance for novel-view synthesis, it lacks structural representations underlying those appearances (e.g., branching patterns of plants), which limits its applicability to tasks such as plant phenotyping. To achieve both high-fidelity appearance and structural reconstruction, we introduce GaussianPlant, a hierarchical 3DGS representation, which disentangles structure and appearance. Specifically, we employ structure primitives (StPs) to explicitly represent branch and leaf geometry, and appearance primitives (ApPs) to the plants' appearance using 3D Gaussians. StPs represent a simplified structure of the plant, i.e., modeling branches as cylinders and leaves as disks. To accurately distinguish the branches and leaves, StP's attributes (i.e., branches or leaves) are optimized in a self-organized manner. ApPs are bound to each StP to represent the appearance of branches or leaves as in conventional 3DGS. StPs and ApPs are jointly optimized using a re-rendering loss on the input multi-view images, as well as the gradient flow from ApP to StP using the binding correspondence information. We conduct experiments to qualitatively evaluate the reconstruction accuracy of both appearance and structure, as well as real-world experiments to qualitatively validate the practical performance. Experiments show that the GaussianPlant achieves both high-fidelity appearance reconstruction via ApPs and accurate structural reconstruction via StPs, enabling the extraction of branch structure and leaf instances.

Why it matches plant phenotyping methods植物の多視点画像から枝構造と葉インスタンスを再構成・抽出する手法を開発しており、植物フェノタイピングへの適用と形態情報の抽出が中心的な技術貢献である。

abstractWe present a method for jointly recovering the appearance and internal structure of botanical plants from multi-view images based on 3D Gaussian Splatting (3DGS).

An efficient and low-cost 3D phenotyping framework for tomato seedlings via neural radiance fields and PointNet++ →

Accurate and efficient plant phenotyping is essential for modern precision agriculture. as it provides reliable information for seedling quality evaluation, early detection of plant stress, and data support for crop breeding and yield prediction. Traditional three-dimensional (3D) reconstruction and analysis methods are often costly and time-consuming, because they usually depend on expensive laser scanning devices or require many input images. Even with these resources, they often fail to capture fine plant structures such as leaves and branches, which limits their application in seedling monitoring. To address these challenges, we propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings. The proposed framework can be used to reconstruct detailed 3D models at a low computational cost using only ordinary cameras and a limited number of 2D images. We validate the framework on the basis of a tomato seedling dataset and show that our approach outperforms traditional multiview stereo scanners and simple commercial 3D scanners in terms of both detail and efficiency. The accuracy of plant part segmentation reaches 90 %, and the extracted parameters (e.g., leaf area, stem height, branch angle, and internode distance) are highly correlated with the manual measurements (e.g., R 2 = 0.875 for the leaf area). This study provides a low-cost and scalable solution for 3D plant analysis, with direct benefits for automated monitoring of seedling quality in nursery production. Moreover, the proposed framework can be extended to other crops with complex structures, thus supporting wider applications in smart agriculture.

Why it matches plant phenotyping methodsトマト苗の3D再構成、植物部位分割、形態形質抽出を統合した低コスト画像ベース手法を開発・検証しており、植物フェノタイピングが研究の中心である。

abstractwe propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings.

A Multi-Phenotype Acquisition System for Pleurotus eryngii Based on RGB and Depth Imaging →

High-throughput phenotypic acquisition and analysis allow us to accurately quantify trait expressions, which is essential for developing intelligent breeding strategies. However, there is still much potential to explore in the field of high-throughput phenotyping for edible fungi. In this study, we developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras. We developed an innovative Unet-based semantic segmentation model by integrating the ASPP structure with the VGG16 architecture. This allows for precise segmentation of the cap, gills and stem of the fruiting body. By leveraging depth images from RGB-D cameras, we can effectively collect phenotypic information about Pleurotus eryngii. By combining K-means clustering with Lab color space thresholds, we are able to achieve more precise automatic classification of Pleurotus eryngii cap colors. Moreover, AlexNet is utilized to classify the shapes of the fruiting bodies. The Aspp-VGGUnet network demonstrates remarkable performance with a mean Intersection over Union (mIoU) of 96.47% and a mean pixel accuracy (mPA) of 98.53%. These results reflect respective improvements of 3.03% and 2.23% compared to the standard Unet model, respectively. The average error in size phenotype measurement is just 0.15 ± 0.03 cm. The accuracy for cap color classification reaches 91.04%, while fruiting body shape classification achieves 97.90%. The proposed multi-phenotype acquisition system reduces the measurement time per sample from an average of 76 s (manual method) to about 2 s, substantially increasing data acquisition throughput and providing robust support for scalable phenotyping workflows in breeding research.

Why it matches plant phenotyping methodsRGB・深度画像による食用菌の複数形質取得システムを開発し、セグメンテーション、サイズ・色・形状の自動測定性能を検証しており、植物（菌類）の表現型取得が中心である。

abstractwe developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras.

An investigation of three machine learning models using UAV derived photogrammetry for detecting and measuring tree stumps →

Accurately quantifying stump volume on post-harvested sites is required to assess potential volume gains for biomass utilisation. The relatively uniform distribution and shape of stumps across such sites makes them well-suited for detection using machine learning (ML) algorithms. Recent developments in the analysis of Digital Aerial Photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAVs) have enabled the reliable identification of stumps via advanced ML methods. Furthermore, the processed outputs from these algorithms provide estimates of stump diameter and height, facilitating calculations of biomass volume. This integration of UAV-based photogrammetry and ML techniques presents a promising approach for enhancing forest management and biomass assessment. In this study, we trained three different ML model types: Faster Region-based Convolutional Neural Network (R-CNN), Single Shot Multibox Detector (SSD) and You-Only-Look-Once (YOLO). The data for the virtual stump detections came from two Norwegian sites, with stumps of Picea abies (L.) H.Karst., and three South African sites, with stumps of Pinus patula Schiede ex Schltdl. & Cham. We assessed the detection rates of each model and compared metrics by using similarly annotated images. The resultant encapsulating bounding boxes of detected stumps were used to calculate diameters and compared to field measurements. Each bounding box is rectangular in shape, and the average of the height and width was calculated to get an estimated diameter value. Virtual stump heights were determined from the Digital Surface Model (DSM) by subtracting the mean height of the surrounding area from the mean height of the stump. The calculated heights of the stumps can be used to assess potential loss of wood volume due to inefficient harvesting techniques. Similarly, the calculated wood volume can be used to estimate residual biomass, and therefore assist Foresters in deciding how best to utilise these stumps. Visible stumps on post-harvested sites could be detected with high rates of accuracy, with almost perfect precision from some object detection models, albeit at low levels of recall. Overall, all three model types had an F1-score of above 73% with the best model attaining an F1-score of 89%. Stump diameters were generally overestimated and this was not found to be related to stump size. Stump heights were underestimated in most cases.

Why it matches plant phenotyping methodsUAV画像と機械学習を用いて切り株を検出するだけでなく、直径・高さ・体積を推定し、野外測定と比較検証しているため、植物器官形質の取得手法が中心である。

abstractThe resultant encapsulating bounding boxes of detected stumps were used to calculate diameters and compared to field measurements.

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

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

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

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

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

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

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

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

A multispectral feature framework for predicting soybean high temperature resistance grades based on masked autoencoding and supervised contrastive learning with dual-branch pretraining →

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

Why it matches plant phenotyping methodsマルチスペクトル画像からダイズの高温耐性等級を予測する特徴量・学習フレームワークが題名上の中心であり、植物のストレス耐性状態を推定するフェノタイピング手法に該当する。

titleA multispectral feature framework for predicting soybean high temperature resistance grades based on masked autoencoding and supervised contrastive learning with dual-branch pretraining

Multimodal learning on RGB-D image for precise litchi phenotyping and weight estimation →

Accurate measurement of key phenotypic traits, including the horizontal and vertical diameters, the weights of both fruit and pit, is essential for the selection of elite litchi cultivars and the advancement of breeding research. Manual measurement, however, is laborious, inefficient, and subjective, highlighting the urgent need for automated and precise phenotyping tools. Unlike apples, mangoes, and grapes, litchi combines a spiny, highly variable pericarp (heterogeneous areoles/tubercles across cultivars) with diverse seed morphology (including irregular, wrinkled aborted seeds), thereby increasing the difficulty of semantic segmentation and biasing diameters and weight estimation. This study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information. Experiments were conducted on an RGB-D dataset comprising 1,198 image pairs (1280×720) across 10 cultivars, using a stratified train/test split of 958/240 pairs by cultivar. To address inherent semantic and scale inconsistencies between modalities, the framework incorporates the RD-Fusion module for precise cross-modal feature extraction, improving robustness under complex and variable pericarp surfaces. Comparative experiments show that LitchiPhenoNet consistently outperforms leading YOLO-based models, achieving millimeter-level diameter estimation with coefficients of determination approaching 0.98 and mean errors within 2 mm. For weight estimation, gram-level precision is attained across whole fruit, pit, and pulp, with coefficients of determination up to 0.98 and mean errors comparable to repeated manual measurements. By handling fine-scale surface relief and cross-cultivar variability, the framework is readily extensible to other textured fruits and scalable for high-throughput phenotyping in breeding programs. Collectively, these results demonstrate that LitchiPhenoNet provides an efficient, reliable, and accurate solution for quantifying litchi phenotypic traits, substantially advancing the objectivity and efficiency of phenotypic analysis and breeding selection.

Why it matches plant phenotyping methodsRGB-D画像を用いてライチ果実・種子・果肉の径と重量を自動推定する専用フレームワークを開発し、複数品種・比較実験で性能検証しているため、植物表現型取得法が中心である。

abstractThis study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information.

Estimating Plant Physiological Parameters for Vitis vinifera L. Using In Situ Hyperspectral Measurements and Ensemble Machine Learning →

Accurate prediction of photosynthetic parameters is pivotal for precision viticulture, as it enables non-invasive monitoring of plant physiological status and informed management decisions. In this study, spectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (ΦPSII), and electron transport rate (ETR), as well as stem and leaf water potential (Ψstem and Ψleaf), in Vitis vinifera (cv. Müller-Thurgau) grown in an experimental vineyard in Lower Franconia (Germany). Measurements were obtained on 25 July, 7 August, and 12 August 2024 using a LI-COR LI-6800 system and a PSR+ hyperspectral spectroradiometer. Various machine learning models (SVR, Lasso, ElasticNet, Ridge, PLSR, a simple ANN, and Random Forest) were evaluated, both as standalone predictors and as base learners in a stacking ensemble regressor with a Random Forest meta-learner. First derivative reflectance (FDR) preprocessing enhanced predictive performance, particularly for ΦPSII and ETR, with the ensemble approach achieving R2 values up to 0.92 for ΦPSII and 0.85 for A at 1 nm resolution. At coarser spectral resolutions, predictive accuracy declined, though FDR preprocessing provided some mitigation of the performance loss. Diurnal patterns revealed that morning to mid-morning measurements, particularly between 9:00 and 11:00, captured peak photosynthetic activity, making them optimal for assessing vine vigor, while midday water potential declines indicated favorable timing for irrigation scheduling. These findings demonstrate the potential of integrating hyperspectral data with ensemble machine learning and FDR preprocessing for accurate, scalable, and high-throughput monitoring of grapevine physiology, supporting real-time vineyard management and the use of cost-effective sensors under diverse environmental conditions.

Why it matches plant phenotyping methodsハイパースペクトル測定と機械学習によるブドウの光合成・水ポテンシャル推定が研究の中心であり、複数モデルの性能評価と前処理比較も実施しているため。

abstractspectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (ΦPSII), and electron transport rate (ETR), as well as stem and leaf water potential (Ψstem and Ψleaf)

Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone →

Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. In dense forests, relying on the Global Navigation Satellite System (GNSS) for localization is not feasible. In addition, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests. Specifically, the study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera. The autonomous flight capability of the prototype was evaluated through multiple test flights in boreal forests. The tree parameter estimation capability was studied by performing diameter at breast height (DBH) estimation. The prototype successfully carried out flights in selected challenging forest environments, and the experiments showed promising performance in forest 3D modelling with a miniaturized stereoscopic photogrammetric system. The DBH estimation achieved a root mean square error (RMSE) of 3.33 - 3.97 cm (10.69 - 12.98 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 - 2.56 cm (5.74 - 12.47 %). The results provide valuable insights into autonomous under-canopy forest mapping and highlight the critical next steps for advancing lightweight robotic drone systems for mapping complex forest environments.

Why it matches plant phenotyping methods森林内ドローンとステレオ画像による3D計測・DBH推定を開発および性能評価しており、樹木形質の取得方法が研究の中心である。

abstractbuilding a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests

Analyzing maize stem circumference, stem height, and stem circumference-to-height ratio using UAV, UGV, and deep learning →

Maize (Zea mays L.) is a crucial grain and economic crop with extensive applications in food, feed, and industry. Phenotypic traits such as stem circumference (SC), stem height (SH), and the stem circumference-to-height ratio (SCHR) are essential indicators for studying maize development, environmental adaptation, and lodging resistance. Traditional manual measurement methods are inefficient, costly, and unsuitable for large-scale phenotypic monitoring. While unmanned aerial vehicle (UAV)-based approaches have achieved relatively accurate SH estimation, SC estimation remains challenging using UAV technology alone, limiting SCHR estimation. This study combined digital camera sensors on unmanned ground vehicle (UGV) and UAV platforms to capture maize stem and canopy images, enabling the estimation of SC, SH, and SCHR. The primary contributions of this study are as follows: (1) We propose the maize-stem segmentation network for calculating stem diameter and circumference (MSSDCNet) to segment maize stems in images and estimate SC based on the segmentation results. (2) We process UAV-derived digital surface models to extract SH information and employ a linear regression (LR) model for SH estimation. (3) Using the estimated SC and SH, we calculate SCHR and analyze its temporal variations across different growth stages. The results demonstrate that: (1) MSSDCNet accurately segments maize stems from images and facilitates SC estimation (R² = 0.759, RMSE = 0.414 cm, nRMSE = 0.087). Temporal analysis of SC reveals a gradual decrease during the reproductive growth stage, potentially due to the transfer of photosynthetic products to the maize cob and stem water loss. (2) This study accurately estimated SH (R² = 0.941, RMSE = 0.151 m, nRMSE = 0.078). However, SH estimates during the reproductive growth stage tend to be underestimated, likely due to DSM point clouds being more sensitive to sharp features such as tassels. (3) SCHR estimation achieves R² = 0.453, RMSE = 2.287 × 10⁻³, nRMSE = 0.136. Temporal analysis reveals a general decline in SCHR from the kernel blister stage to the dough stage, with some maize materials consistently exhibiting lower SCHR levels during each growth stage. This may be related to genetic traits, planting density, soil fertility, or other environmental factors. By integrating UGV-based maize stem images and UAV-based maize canopy images with MSSDCNet and LR, this study successfully estimates SC, SH, and SCHR for various maize materials. This study provides a novel technique for maize, early lodging risk prediction, and lodging-resistant breeding lines screening, contributing to rapid and efficient maize phenotypic monitoring under field conditions.

Why it matches plant phenotyping methodsUAV・UGV画像、DSM、深層学習によってトウモロコシの茎周囲長・草丈・比率を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す。

abstractThis study combined digital camera sensors on unmanned ground vehicle (UGV) and UAV platforms to capture maize stem and canopy images, enabling the estimation of SC, SH, and SCHR.

Early in-situ detection of tobacco root diseases using a wearable plant sensor →

Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (SₜWC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the SₜWC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm³/cm³) indicated a slower decrease compared to healthy tobacco plants (0.021 cm³/cm³). In accordance with this phenomenon, the daily variation of SₜWC near roots of diseased tobacco plants (0.023 cm³/cm³) was significantly less than that of healthy tobacco plants (0.048 cm³/cm³). Moreover, the abnormal changes of SₜWC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of SₜWC was continuously less than 0.037 cm³/cm³. Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection.

Why it matches plant phenotyping methods根域付近の茎水分量という植物の生理状態を測定し、根病害の早期検出に用いるウェアラブルセンサーを開発・検証しており、表現型取得手法が中心である。

abstractwe developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.

High-precision tobacco phenotype extraction based on 3D point clouds →

As an economically important crop, tobacco requires the precise extraction of phenotypic characterization data, which is crucial for breeding, cultivation practices, physiological research, and industrial applications. However, there is currently a lack of automated algorithms for extracting key basic phenotypic traits such as plant height, leaf number, leaf area, and stem-leaf angle. In this study, we developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data. Specifically, our pipeline includes: (1) preprocessing the 3D point cloud data, involving operations such as downsampling, denoising, normal vector estimation, and coordinate transformation; (2) integrating a graph neural network with a region-growing algorithm to segment leaves, stems, and other organs, and refining the segmentation results to address the challenge of overlapping leaves; and (3) calculating fundamental phenotypic attributes including plant height, leaf count, leaf area, and stem-leaf angle based on the segmentation output. Additionally, to address potential gaps in the scanned point cloud, we implemented perforation detection and repair operations. The effectiveness and accuracy of the proposed algorithm were validated through mathematical model simulations. Distinct from traditional statistical discriminative methods, this approach provides a novel framework for the precise extraction of tobacco phenotypic data.

Why it matches plant phenotyping methods3D点群から植物器官を分割し、草丈・葉数・葉面積・茎葉角を自動抽出する計算手法の開発と検証が中心である。

abstractwe developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data.

Early in-situ detection of tobacco root diseases using a wearable plant sensor →

Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (S t WC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the S t WC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm 3 /cm 3 ) indicated a slower decrease compared to healthy tobacco plants (0.021 cm 3 /cm 3 ). In accordance with this phenomenon, the daily variation of S t WC near roots of diseased tobacco plants (0.023 cm 3 /cm 3 ) was significantly less than that of healthy tobacco plants (0.048 cm 3 /cm 3 ). Moreover, the abnormal changes of S t WC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of S t WC was continuously less than 0.037 cm 3 /cm 3 . Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection. • A wearable plant sensor is developed for early warning of tobacco root diseases. • The sensors were used to monitor diseased and healthy tobacco plants in the field. • The sensor can achieve early in-situ detection of tobacco root diseases.

Why it matches plant phenotyping methods植物茎内水分状態を測定し、根部病害の早期検出へ用いるウェアラブルセンサーを開発・検証しており、植物状態の取得法が中心である。

abstractTherefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.

Geometry-based point cloud fusion of dual-layer UAV photogrammetry and a modified unsupervised generative adversarial network for 3D tree reconstruction in semi-arid forests →

We present the first application of geometry-based relationship constraints for point-cloud registration and unsupervised 3D reconstruction of tree structure in semi-arid forest using unmanned aerial vehicle (UAV) photogrammetry. Accurate three-dimensional (3D) reconstruction of tree structure is essential for a plethora of subsequent tasks like assessing ecosystem health and informing sustainable forest management strategies, in particular over ecologically sensitive arid and semi-arid ecosystems that increasingly face decline due to prevalence of environmental stressors. This highlights the need for high-resolution geospatial monitoring approaches. While UAV-based photogrammetry offers a flexible and cost-effective means of capturing forest structure, conventional top-of-canopy imaging fails to sufficiently represent critical under-canopy features, including stem morphology and lower crown structure. Here, we suggest an integrated 3D reconstruction framework that combines dual-layer UAV photogrammetry, acquiring data from both above and below the canopy, with an innovative geometry-based point cloud registration method. Unlike conventional approaches like Iterative Closest Point (ICP) and Random Sample Consensus (RANSAC), this method leverages spatial relationships among individual trees to robustly align multi-view point clouds acquired under occluded and variable conditions. To further refine the reconstructed tree models, we suggest an updated unsupervised Generative Adversarial Network (Denoise-GAN), enabling both noise reduction and structural completion without reliance on labeled training data. The resulting models were used to extract key phenotypic features with high accuracy compared to reference data (root collar diameter (DRC) R² = 0.93, height R² = 0.97,Crown area R² = 0.99, number of stems R² = 1), providing vital indicators for quantifying forest structure and health. The presented methodology not only enhances the completeness and accuracy of 3D tree reconstruction in semi-arid forest, but also represents a significant advancement toward a scalable, data-driven semi-arid forest monitoring system. This workflow offers substantial potential for ecological applications, particularly in degraded and topographically complex ecosystems.

Why it matches plant phenotyping methodsUAV画像からの3D樹木再構成、点群登録、ノイズ除去・構造補完を開発し、樹木形質の抽出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。

abstractWe present the first application of geometry-based relationship constraints for point-cloud registration and unsupervised 3D reconstruction of tree structure in semi-arid forest using unmanned aerial vehicle (UAV) photogrammetry.

Rice Straw Tissue Preparation for Reproducible Electron Microscopy Imaging and Analysis. →

Common problems in biological sample processing for scanning electron microscopy (SEM) include cell collapse and destruction. To overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples. Dried stems are rehydrated with a glycerol solution and fixed in formalin-acetic-alcohol to avoid cell wall collapse or organ distortion. The protocols detailed here comprise the first published method for preparing SEM images of dried plant tissue. The protocols offer a cost-effective approach to obtaining high-quality micrographs, facilitating the reconstruction of growth processes and the study of plant cell wall features. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Pretreatment and preparation of rice straw samples at the heading stage Basic Protocol 2: Paraffin infiltration and embedding Basic Protocol 3: Preparation of microscopic sections Basic Protocol 4: Transferring, adhering, and expanding sections on slides Support Protocol: Preparation of gelatin slides before sectioning to affix samples Basic Protocol 5: Preparation of samples for SEM imaging Basic Protocol 6: SEM analysis Basic Protocol 7: Processing and analysis of SEM images using ImageJ software.

Why it matches plant phenotyping methods乾燥イネ組織のSEM画像取得・処理・解析プロトコル自体が中心で、植物細胞壁形態などの表現型観察を可能にする方法開発である。

abstractTo overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples.

Research on the Possibilities of Determining the Structural Characteristics of Plant Communities Dominated by Reeds Using High-Resolution Satellite Imagery, Ground Measurements, and Unmanned Aerial Vehicles →

Abstract— Plant communities dominated by reeds (Phragmites altissimus (Benth.) Mabille, Phragmites australis (Cav.) Trin. ex Steud.) are widely distributed in floodplain and delta landscapes. Despite their significant biospheric role and potential for industrial use, insufficient attention has been paid to the mapping and assessment of these communities in Russia. The objective of this study is to explore the possibilities of mapping biomass and vegetation height in reed-dominated communities in the Volga Delta using Sentinel-1/2 satellite data supported by ground measurements and aerial surveys conducted with a drone. Allometric relationships between the heights, stem diameters of reeds, and biomass were established for 92 sample plots within the Astrakhan Nature Reserve in the Volga Delta enabling the use of aerial imagery to obtain reference data through photogrammetric methods. The application of vegetation height calculated photogrammetrically based on aerial imagery across 27 test polygons combined with temporally distinct satellite data and the Random Forest nonparametric regression method yielded a high accuracy in mapping heights (coefficient of determination R2 = 0.80, root mean square error (RMSE) 0.46 m) and biomass (R2 = 0.65, RMSE = 12.6 t/ha) of reed-dominated communities in the Volga Delta. Thus, the approach employed proves to be effective for mapping the biomass of reed communities in the Volga Delta and similar landscapes.

Why it matches plant phenotyping methodsヨシ群落の高さ・バイオマスという植物形質を、衛星画像、ドローン空撮、写真測量、回帰モデルで推定・検証する方法が研究の中心である。

abstractThe objective of this study is to explore the possibilities of mapping biomass and vegetation height in reed-dominated communities in the Volga Delta using Sentinel-1/2 satellite data supported by ground measurements and aerial surveys conducted with a drone.

Estimating plant height, nitrogen uptake and above-ground biomass using UAV multispectral imaging coupled with machine learning in industrial hemp (Cannabis sativa L.) →

Industrial hemp (Cannabis sativa L.) is known for its high fiber production with lower ecological footprint. Nitrogen (N) status and stem biomass (SB) and total above-ground biomass (AGB) of the crop highly influence fiber quantity and quality. Conventional monitoring practices are labour intensive and time consuming. Unmanned Aerial Vehicles (UAVs) with imaging sensors can be a promising tool for mitigating these challenges. This study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB. Field trials were conducted at UF/IFAS West Florida Research and Education Centre, Jay, FL during the years 2021 and 2022 consisting of two cultivars and six N treatments. The PH was estimated through Crop Height Model, yielding an R² of 0.87 at full crop maturity (90 days after planting). Twenty-seven Vegetation Indices (VIs) were extracted and features, including PH and VIs, were selected through Recursive Feature Elimination with adjusted Variance Inflation Factor (VIF<10) to develop machine learning models for the estimation of yield components. The LNU prediction was best with Support Vector Machine model with R², RMSE and nRMSE % value of 0.364, 34.55 kg N ha⁻¹ and 68.48 respectively. Random Forest Regressor predicted the SB and total AGB most accurately with R², RMSE and nRMSE % value of 0.752 and 0.707, 890.70 and 1492.73 kg ha⁻¹, 48.86 and 43.05 respectively. The results demonstrate the potential of UAVs to generate more reliable estimates of PH, SB and total AGB whereas it remained unreliable for LNU.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物高、窒素吸収、茎・地上部バイオマスを推定し、モデル性能を評価しているため、表現型取得・推定法が中心である。

abstractThis study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB.

Multimodal GiaC-phenomic approach for microbiome-tree system profiling →

We constructed a computational methodology to assess health of plant-microbiome system through microbiome structure modelling combined with plant remote sensing. As a test dataset, we selected soil mycobiome and morphometry of Tilia cordata in nursery and forest sites. Our method is also applicable on forest or regional scale. Microbiome part called GiaC ( G u i lds a nd o C currences) combines taxonomic and trophic composition as well as species co-occurrence modelled with advanced graph methods. We complemented state-of-the-art approaches with novel ones for visualisations, species filtering (Flexible99) and graph transformation modelling species clusters (ClusterCollapse). Flexible99 is a method that adjusts the species abundance cut-off to each sample set and removes rare species. ClusterCollapse generalises co-occurrence networks to species clusters by edge contraction and serves as an implicit homogeneity test. To assess biomass of the seedlings we used low-cost and field-adopted morphometric and manual measurements. Top and side tree images, acquired with handheld RGB camera, were analysed using colour segmentation and pixel count based methods. Parameters, such as crown size, shape, area and pigment content, number of leaves, branch length and foliage density, allowed the seedlings to be classified into three different vitality groups. Presented multimodal approach was capable to differentiate and characterize distinct best, suboptimal or critical states of microbiome-host system, both on microbial and plant side. Our results show that more stable fungal co-occurrence patterns should be attributed to the plant set of the best growth. In contrast, more chaotic patterns can be considered non-optimal for plant-mycobiome cooperation.

Why it matches plant phenotyping methods植物の健康・活力状態を推定するマルチモーダル手法の一部として、RGB画像の色分割・画素計数から樹冠形状、葉数、枝長、葉密度などの形質を抽出しており、フェノタイピング手法の適用が実質的に含まれる。

abstractWe constructed a computational methodology to assess health of plant-microbiome system through microbiome structure modelling combined with plant remote sensing.

A Comparative Analysis of Low-Cost Devices for High-Precision Diameter at Breast Height Estimation →

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices.

Why it matches plant phenotyping methods低コストの画像・レーザーセンシング手法を比較検証し、樹木の胸高直径（DBH）という明示的な植物形質を推定することが研究の中心である。

abstractThis study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation.

Digital Image Quantification of Rice Sheath Blight: Optimized Segmentation and Automatic Classification →

Rapid and accurate phenotypic screening of rice germplasms is crucial for identifying potential sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, diseased lesions of rice sheath blight (ShB)-infected plants are time-consuming, labor-intensive, and subject to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a Pixel Color- and Coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing the Mahalanobis distance metric, aimed at accurately segmenting symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC, combined with manual classification of the segmented regions, was evaluated using Lin’s concordance correlation coefficient (ρc) by comparing its results to visual measurements of ShB lesion height (cm) and to lesion/diseased area (cm2) measured using ImageJ. Low bias (Cb) and high precision (r) were observed for absolute lesion height (Cb = 0.93, r = 0.94) and absolute symptomatic area (Cb = 0.98, r = 0.97) studies. Furthermore, to automatically classify the segmented regions produced by the PCC-KMC algorithm, we employed a convolutional neural network (CNN). Unlike conventional CNNs that require fixed-size image inputs, our CNN is designed to take the RGB histogram of each segmented region (a 1000 by 3 representation) as input and determine whether the region corresponds to ShB infection. This design effectively handles the arbitrary sizes and irregular shapes of segmentation regions generated by PCC-KMC. Our CNN was trained based on an 85%:15% composition for the training and testing dataset from a total of 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (Cb = 0.86, r = 0.90) and absolute diseased area (Cb = 0.99, r = 0.97) studies, indicating that PCC-KMC combined with automatic CNN-based classification performs very effectively. These results demonstrate that the potential of our methodology to serve as an alternative to the traditional visual-based ShB disease severity assessment and can be considered to be utilized for lab-scale, high-throughput phenotyping of rice ShB.

Why it matches plant phenotyping methodsイネ紋枯病の病徴面積・病斑高を画像処理とCNNで定量化する手法を開発・検証しており、植物表現型取得が中心である。

abstractwe propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area.

3DWPGS: End-to-end 3D Gaussian splatting for woody-plant modeling and physical simulation →

The 3D reconstruction and physical simulation of plants in natural scenes are of significant research and practical value in fields such as agronomy, forestry, ecology, and remote sensing. However, mainstream 3D reconstruction methods generally focus on geometric detail recovery but lack integration with physics-driven approaches, making it challenging to accurately and efficiently simulate the dynamic changes in plant structures. Although the latest physical Gaussian methods can simulate a variety of nonrigid deformations, there is limited consideration of plant-specific structural features, which affects the accuracy of reconstruction and simulation. To address this challenge, an end-to-end woody-plant Gaussian is proposed, which is a framework of high-precision 3D reconstruction and physical simulation for woody plants. This framework begins by fine-tuning a pre-trained plant instance segmentation model tailored for this purpose to reduce environmental noise interference and improve the accuracy of skeleton extraction from point cloud data. It leverages the extracted topology to guide fine-grained hierarchical classification of branches. By segmenting hierarchical radii and cross-sectional proportions, the Gaussian point distribution is constrained, enabling the Gaussian ellipsoids to better align with branch surfaces, thereby enhancing reconstruction details. In the physical simulation stage, the framework incorporates material property variation rules. Using topological guidance, Gaussian ellipsoids are mapped to branch hierarchies, and a cantilever beam physical model predicts Gaussian distributions and covariance matrix parameters. This approach not only improves rendering quality but also enhances the realism of branch-bending simulations. Finally, we evaluate our framework on the photos of real woody plants we took (3D deformable wood plant) and a public dataset (NeRF-synthetic). Compared to existing plant reconstruction methods, woody-plant Gaussian achieves state-of-the-art performance and significantly improves the visual quality of plant physical simulations.

Why it matches plant phenotyping methods木本植物の3D形状・骨格・枝半径を画像から再構成する計算手法を中心に開発しており、植物の構造形質の取得に直接関係する。物理シミュレーションも含む技術評価が行われている。

abstracta framework of high-precision 3D reconstruction and physical simulation for woody plants

Automated Phenotyping of Maize from 3D Point Clouds Using an Optimized Deep Learning Approach →

Accurate plant organ segmentation and efficient phenotypic parameter acquisition remain major challenges in plant phenomics. This study develops an automated phenotyping framework for maize that integrates deep learning with 3D point cloud analysis to overcome the inefficiency and subjectivity of traditional manual methods. A high-quality 3D maize point cloud dataset was constructed, and a segmentation model named PSCSO was proposed based on the PointNet++ architecture. The model incorporates an SCConv module to reduce feature redundancy and uses the Sophia optimizer to improve convergence efficiency. Experimental results show the model achieved segmentation accuracies of 0.926 on the training set and 0.861 on the testing set, with a MIoU of 0.843, while significantly reducing training time. Based on the segmentation results, the model automatically estimates seven key phenotypic parameters: plant height, crown diameter, stem height, stem diameter, leaf length, leaf width, and leaf area. This is achieved by integrating point cloud algorithms including linear regression, PCA, and Delaunay triangulation. The predictions showed excellent agreement with manual measurements, with all parameters achieving R2 values exceeding 0.91. Overall, this automated framework provides a reliable and high-throughput solution for plant phenotypic analysis.

Why it matches plant phenotyping methods3D点群と深層学習によるトウモロコシ器官セグメンテーションおよび7種類の表現型形質推定を開発・検証した研究であり、フェノタイピング手法が中心である。

abstractThis study develops an automated phenotyping framework for maize that integrates deep learning with 3D point cloud analysis

Limitations of phenomic prediction for evaluating wheat stem sawfly resistance in wheat breeding programs →

Abstract Wheat stem sawfly (WSS, Cephus cinctus Norton) threatens wheat ( Triticum aestivum L.) production in the US Great Plains. Increased stem solidness improves resistance to WSS but developing solid‐stemmed cultivars requires time‐consuming and destructive phenotyping. To expedite development of WSS‐resistant cultivars, a high‐throughput phenotyping method is needed. Therefore, we assessed phenomic prediction using uncrewed aerial systems (UAS) to predict stem solidness, WSS infestation, and yield in wheat. Multispectral and red‐green‐blue UAS data were collected in‐season at two naturally infested locations in western Nebraska from 2023 to 2024. The spectral reflectance indices from the UAS data were compared with agronomic traits (i.e., yield and plant height) and WSS traits (i.e., stem solidness and WSS infestation). Ridge regression, k ‐nearest neighbors (KNN), and random forest (RF) models were then trained to use spectral indices to predict yield, stem solidness, and WSS infestation. Correlations between WSS and spectral traits were temporally and environmentally dependent. The best prediction model depended on the biological trait. RF performed the best for yield ( r = 0.469), KNN for stem solidness ( r = 0.231), and ridge regression for WSS infestation ( r = 0.194). Predicting traits based on spectral data in a new environment was poor for both stem solidness ( r = 0.02–0.38) and WSS infestation ( r = 0.01–0.22). Our ability to predict WSS resistance was low, and UAS‐based phenotyping was not viable with current technology.

Why it matches plant phenotyping methodsUASマルチスペクトル/RGBデータと機械学習によって、茎の充実度、害虫被害、収量を推定し、環境間の性能を評価するフェノタイピング手法の検証が中心である。

abstractwe assessed phenomic prediction using uncrewed aerial systems (UAS) to predict stem solidness, WSS infestation, and yield in wheat

Species-specific modeling of tree diameter at breast height using tree height and relative density with implications for remote sensing-based forest inventory →

Accurate estimation of tree diameter at breast height (DBH) is essential for forest monitoring, biomass modeling, and carbon accounting. While DBH is traditionally measured in the field, this approach is labor-intensive and costly, especially at large scales. In contrast, tree height can now be efficiently obtained from remote sensing platforms such as airborne LiDAR and photogrammetry, creating opportunities to estimate DBH indirectly. To address this, we developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD) in the mixed temperate forests of New Brunswick, Canada. Our analysis used 1807 trees from 653 permanent sample plots (1985–2014), representing six dominant species: Abies balsamea, Acer rubrum, Acer saccharum, Picea mariana, Picea rubens, and Picea glauca. Allometric (height-only) models explained part of DBH variation, with R² ranging from 0.15 to 0.35 (broadleaves) and 0.41–0.74 (conifers), but predictive accuracy was notably low for Acer rubrum and Acer saccharum. Incorporating RD as a competition index substantially improved model performance, with R² increasing to 0.85–0.89 (broadleaves) and 0.72–0.88 (conifers). Prediction errors (RMSE and MAE) consistently decreased, with broadleaves showing the greatest improvement compared to conifers, reflecting their stronger sensitivity to stand density. These findings demonstrate that combining tree height with RD provides reliable estimates of DBH across diverse species. The framework bridges ground-based inventory with remote sensing applications, offering a scalable approach for biomass estimation, stand density analysis, and sustainable forest management in temperate mixed-species forests. • Developed species-specific nonlinear models to predict DBH from tree height and RD. • Used 1807 trees from 653 permanent sample plots across New Brunswick (1985–2014). • Including RD as a competition index improved model accuracy (R² up to 0.89). • Broadleaves exhibited greater DBH response to stand density compared to conifers. • Framework supports scalable DBH estimation in mixed temperate forests.

Why it matches plant phenotyping methods樹高と相対密度から樹木DBHという明示的な植物形質を推定する種別非線形モデルを開発・評価しており、形質推定手法が研究の中心である。

abstractwe developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD)

Stem-FIT and Soil-FIT: Integrated Plant and Soil Nitrogen-Hormone Sensing with Machine Learning-based Forecasting for Next-Generation Precision Agriculture →

Abstract Inefficient fertilizer application in agriculture leads to reduced crop productivity, nutrient losses, and reduced crop resilience, highlighting the urgent need for real-time monitoring of plant–soil nutrient dynamics. This research aims to develop and validate a multiplexed sensing platform for simultaneous, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation. The proposed sensor suite integrates three 3D-printed modules for continuous monitoring of nitrate, ammonium, and pH in both soil and plant sap, along with salicylic acid (SA), indole-3-acetic acid (IAA), methyl jasmonate (MeJA), and ethylene (ET) in plant sap. The sensors, functionalized with non-enzymatic electrode coatings, were deployed in bell pepper plants grown under four treatment combinations of irrigation (full vs. deficit) and nitrogen application (medium vs. high). Data were collected every three hours over the growing period and analyzed using a long short-term memory (LSTM) model for short-term prediction of nutrient and hormone fluctuations. The sensors exhibited high sensitivity and stability, achieving detection limits of 0.218 µM for IAA, 1.07 µM for MeJA, 1.315 µM for SA, 1.08 ppm for nitrate, 1.017 ppm for ammonium, 0.29 ppm for ethylene, and 0.01 pH. The LSTM model demonstrated strong predictive capability (R² = up to 0.86), accurately forecasting short-term variations in plant and soil nutrient–hormone profiles. These findings demonstrate that coupling real-time, multiplexed sensing with machine learning enables early detection and prediction of crop stress, supporting precision nitrogen management and advancing sustainable agricultural practices.

Why it matches plant phenotyping methods植物体内のホルモン・栄養状態を連続測定するセンサープラットフォームの開発と性能検証が中心で、機械学習による予測も含むため、植物フェノタイピング手法として適格です。

abstractThis research aims to develop and validate a multiplexed sensing platform for simultaneous, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation.

Intelligent Sugarcane Plant Disease Detection using Deep Learning →

Abstract: Sugarcane is a vital crop that makes a substantial contribution to the agricultural economy globally, according to this study. Unfortunately, diseases that have a substantial effect on productivity and quality sometimes present a risk to its production. the system processes images of sugarcane leaves and stems. Combining visual processing and Manual inspections are used in most traditional disease detection techniques, which can be labour-intensive, time-consuming, and prone to human mistake. This paper provides a machine learning-based approach for sugarcane disease prediction that increases detection efficiency and accuracy by utilising Convolutional Neural Networks (CNNs) and environmental data. To visually recognise the signs of a disease, predictive modelling, the project aims to create an automated, real-time sickness diagnosis tool. Due to this tool’s ability to offer timely interventions, farmers will be able to lower crop losses and adopt sustainable agricultural practices. The proposed paradigm presents the agricultural community with a readily accessible and scalable alternative that could revolutionise crop health management. Data collection, pre-processing, augmentation, model training, and evaluation are some of the steps in the methodology. OpenCV, NumPy, and TensorFlow/Keras were used to handle image datasets, while Google Colab was used for training with GPU acceleration. The suggested model outperformed alternative CNN designs including VGG19, Xception, and ResNet50, with an accuracy of 91.94%. Gradio was used to create an intuitive user interface that allows users to upload leaf photos and receive immediate diagnostic feedback and confidence scores, enabling real-time illness identification.

Why it matches plant phenotyping methodsサトウキビ葉・茎の画像から病徴を推定するCNN手法の開発・評価が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。

abstractthe system processes images of sugarcane leaves and stems.

Serial section videography (SSV): A low‐cost protocol for generating 3D reconstructions of internal plant structure →

Premise Analyzing structural changes along the length of an organ provides insight into its development. However, traditional histological methods are limited by intensive procedures and size restrictions. Micro-computed tomography (microCT) enables non-destructive internal imaging along the length of an organ, but high cost, technical complexity, and limited accessibility hinder widespread application. Here, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software. Methods and results SSV was applied to four fern rhizomes with varied gross morphology and diverse vascular architectures. Specimens were sectioned using a sliding microtome or a handheld blade, and imaged using either a digital camera or smartphone setup. Images were aligned using Fiji and segmented using 3D Slicer. The SSV method enabled continuous visualization of internal stem anatomy over several centimeters and is adaptable to both laboratory and field settings. Conclusions This protocol offers an alternative to microCT for generating 3D anatomical reconstructions, enabling researchers to examine development and structural variation across organs with minimal equipment and software. This accessible protocol reduces technical and financial barriers and is particularly well-suited for comparative studies of vascular tissues, advancing the study of plant anatomy and development.

Why it matches plant phenotyping methods植物器官内部構造を連続撮像・画像処理して3D形態を再構成する低コスト手法の開発と適用が中心であり、植物形態・解剖状態の取得法として収載対象。

abstractHere, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software.

Alleviating labeled data scarcity: a lightweight semi-supervised network for Moso bamboo age determination →

Accurate determination of Moso bamboo ( Phyllostachys edulis ) age is a critical task for efficient and sustainable bamboo forest management. However, existing methods face significant challenges: traditional manual assessment is subjective and labor-intensive, while advanced technologies like LiDAR are prohibitively expensive for widespread application. Furthermore, high-performance deep learning models, which offer a promising alternative, typically rely on large-scale labeled datasets, a resource that is particularly scarce and costly to acquire in the field of Moso bamboo. To address these limitations, we propose a lightweight, semi-supervised framework, the Dual-Color-Texture Moso Bamboo Age Decoupling Network (DCT-MBADNet). Our framework first leverages the Segment Anything Model (SAM) to isolate the bamboo culm, effectively eliminating complex background interference. A novel dual-stream feature decoupling module is then introduced to independently extract color degradation and texture evolution features, which are biologically significant indicators of bamboo age. A dynamic gating mechanism is employed to adaptively fuse these features. Simultaneously, we integrate an age-dependent dynamic threshold strategy within a Mean Teacher semi-supervised framework to synergistically utilize a small set of labeled data and a large volume of unlabeled data, thereby enhancing pseudo-label quality and model generalization. Experimental results demonstrate that our semi-supervised DCT-MBADNet achieves a test set accuracy of 89.6%, representing a 4.5% improvement over its fully supervised baseline. With a minimal parameter count of just 1.6 M, the proposed model provides a low-cost, robust, and deployable solution for precise Moso bamboo management and offers a novel paradigm for plant phenotyping analysis under data-scarce conditions.

Why it matches plant phenotyping methods竹稈画像から色・テクスチャ特徴を抽出して齢という植物形質を推定する半教師あり手法を開発しており、モデル構築と性能評価が研究の中心である。

abstractwe propose a lightweight, semi-supervised framework, the Dual-Color-Texture Moso Bamboo Age Decoupling Network (DCT-MBADNet).

Real-time Detection and Characterization of Trunks and Upright Branches of Pear Trees for Automatic Dormant Pruning →

Abstract Purpose Dormant pruning is critical for fruit tree management and maintaining fruit quality. Traditional manual pruning is labor-intensive, driving interest in automated robotic dormant pruning. However, automated robotic dormant pruning meets significant challenges in trunk and branches detection, due to the complexity of the orchard environment and the interlacing of the branches. This paper proposes an automatic method for real-time detection and pruning of pear tree trunks and upright branches using an RGB-D camera. Methods Pear trunk detection was conducted by enhancing the You Only Look Once version 5 Nano (YOLOv5n) model with Squeeze-and-Excitation Networks (SENet) and optimizing the anchor boxes. For branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The length and angle of the branches were calculated from the generated mask images, and the pruning position was determined. A PRUNING_ROS package was developed for real-time orchard applications. Results The evaluation demonstrated 96.7% mean average precision (mAP) for trunk detection and 82.6% mAP for branch segmentation in test dataset. Field test results showed that the mean absolute error (MAE) of trunk distance localization compared to manual measurements was 3.71 cm, with the root mean square error (RMSE) of 3.84 cm, and the frames per second (FPS) of 31.6. The MAE was 2.3 cm (RMSE: 2.6 cm) for pruning points in depth direction and 2.34° (RMSE: 2.71°) for upright branches angle, with the FPS of 36.2. The field pruning experiment showed a pruning success rate of 47.6%. Conclusion This method provides technical support for the operation of fruit tree pruning robots, representing a step toward the full automation of fruit tree management.

Why it matches plant phenotyping methodsRGB-D画像からナシ樹の幹・枝を検出・分割し、枝の長さと角度を算出する手法が中心で、単なる対象位置検出を超えた植物器官形態の計測と技術評価を行っている。

abstractFor branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The length and angle of the branches were calculated from the generated mask images, and the pruning position was determined.

Graph Self-Attention Network with Semantic Embedding for Stem-Leaf Separation from 3D Point Clouds →

Abstract. In the context of agricultural modernization, precise 3D organ segmentation has become indispensable for automated extraction of phenotypic traits. In particular, the precise delineation of stem and leaf structures from 3D point clouds is critical for monitoring plant growth and supporting high-throughput breeding programs. However, the intricate structure of crops and the blurred boundaries between stems and leaves present significant challenges, leading to the poor segmentation performance. To tackle these problems, we propose a Semantic Embedding-Guided Graph Self-Attention Network for stem-leaf separation in 3D point clouds, to tackle weak feature representation and low inter-class separability in complex plant structures. During the encoding stage, a multi-scale feature extraction module captures fine-grained local geometries, while a feature fusion module integrating graph convolution and self-attention facilitates deep fusion of local and global semantic information. In the decoding stage, hierarchical upsampling combined with multi-level feature fusion reconstructs high-resolution representations to achieve fine-grained segmentation. Furthermore, we introduce a joint loss function that integrates inter-class discriminative loss with cross-entropy, aiming to optimize intra-class uniformity and reinforce class boundary delineation. Validation experiments on the Plant-3D dataset demonstrate that our methodology attains superior performance, with mean precision, recall, and IoU achieving 96.47%, 96.39%, and 93.50%, respectively. The proposed approach demonstrates high robustness and generalizability across diverse plant species and growth stages, providing an effective solution for high-throughput plant phenotyping.

Why it matches plant phenotyping methods3D点群から茎・葉を分離するセグメンテーション手法を開発し、植物表現型抽出と高スループットフェノタイピングへの有用性をデータセットで検証しているため、方法が中心である。

abstractwe propose a Semantic Embedding-Guided Graph Self-Attention Network for stem-leaf separation in 3D point clouds

CADFFNet: a dual-branch neural network for non-destructive detection of cigar leaf moisture content during air-curing stage →

Introduction The cigar leaves moisture content (CLMC) is a critical parameter for controlling curing barn conditions. Along with the continuous advancement of deep learning (DL) technologies, convolutional neural networks (CNN) have provided a way of thinking for the non-destructive estimation of CLMC during the air-curing process. Nevertheless, relying merely on single-perspective imaging makes it difficult to comprehensively capture the complementary morphological features of the front and back sides of cigar leaves during the air-curing process. Methods This study constructed a dual-view image dataset covering the air-curing process, and proposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images. Firstly, the model utilizes two independent and parallel ResNet as its backbone structure to capture the heterogeneous features of dual-view images. Secondly, the Dual Efficient Channel Attention (DECA) module is introduced to dynamically adjust the channel attention weights of the features, thereby facilitating interaction between the two branches. Lastly, a Multi-scale convolutional feature fusion (MSCFF) module is designed for the deep fusion of features from the front and back images to aggregate multi-scale features for robust regression. Results On five-fold cross-validation, CADFFNet attains R2 of 0.974±0.007 and mean absolute error (MAE) of 3.80±0.37%. On an independent cross-region, cross-variety testing set, it maintains strong generalization (R2=0.899, MAE=5.82%), compared with the classic CNN models ResNet18, GoogLeNet, VGG19Net, DenseNet121, and MobileNetV2, its R2 value has increased by 0.047, 0.041, 0.055, 0.098, and 0.090 respectively. Discussion Generally, the proposed CADFFNet offers an efficient and convenient method for non-destructive detection of CLMC, providing a theoretical basis for automating the air-curing process. It also provides a new perspective for moisture content prediction during the drying process of other crops, such as tea, asparagus, and mushrooms.

Why it matches plant phenotyping methods葉の水分含量という植物状態を、二視点RGB画像と新規深層学習回帰モデルで非破壊推定する手法を開発し、交差検証および独立試験で性能評価しており、植物フェノタイピング手法が中心である。

abstractproposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images.

A Reliable In Planta Inoculation and Antifungal Screening Protocol for Rhizoctonia solani -Induced Sheath Blight in Rice. →

Sheath blight, caused by Rhizoctonia solani , is a major fungal disease of rice that leads to significant yield losses globally. Conventional inoculation methods often fail to achieve consistent and uniform infection, limiting their applicability in antifungal screening studies. This protocol describes a reliable in planta inoculation method for R. solani using mature sclerotia placed at the internodal region of tillering-stage rice seedlings. The procedure includes step-by-step instructions for seed germination, seedling preparation, pathogen culture, artificial inoculation, and post-infection application of antifungal treatments, including botanical compounds such as Ocimum gratissimum essential oil and thymol. Lesion development is monitored and quantified over time, and data are analyzed statistically to evaluate treatment efficacy. The protocol is optimized for reproducibility, scalability, and compatibility with sustainable disease management approaches. It provides a robust platform for evaluating antifungal agents in a biologically relevant and controlled environment. Key features • Establishes a reliable in planta inoculation method for R. solani in rice, overcoming the common challenge of achieving consistent disease development. • Enables post-inoculation screening of botanicals for antifungal efficacy under realistic plant-pathogen interaction conditions. • Integrates sustainable research practices by detailing botanical extraction and their in planta assessment against R. solani infection.

Why it matches plant phenotyping methodsイネ葉鞘枯病の接種法を再現性・拡張性の観点から開発し、病斑の経時的定量によって植物病害状態を評価する中心的なプロトコル研究である。

abstractThis protocol describes a reliable in planta inoculation method for R. solani using mature sclerotia placed at the internodal region of tillering-stage rice seedlings.

Wheat3D PartNet: Annotated dataset for 3D wheat part segmentation →

High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection-facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset's utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest.

Why it matches plant phenotyping methods植物の3D点群を用いた部位セグメンテーション用データセットの構築・検証が中心で、植物形質の定量化を直接支援するため。

abstractTo address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.)

Semantic embedding-guided graph self-attention network for plant stem–leaf separation from 3D point clouds →

With the advancement of agricultural modernization, precise plant phenotyping—such as stem-leaf separation—has gained significant importance in the fields of intelligent plant breeding and phenotypic trait extraction. Although deep learning techniques offer superior solutions in the task of complex plant structure segmentation, challenges remain due to insufficient feature representation and low interclass separability. To address this issue, this paper proposes a semantic embedding-guided graph self-attention network for plant stem–leaf separation from 3D point clouds. Specifically, the proposed method is built on an encoder–decoder architecture. The multiscale features are first extracted as the receptive field progressively increases to learn the local geometric representation. Following this, the proposed method constructs a feature enhancement module that integrates graph convolution and self-attention mechanisms. By leveraging graph convolution and the self-attention mechanism, both local and global sets of information are aggregated across multiple scales, capturing intricate geometric and topological relationships to ensure highly descriptive and distinguishing feature representations. Afterwards, we employ a hierarchical decoding structure that combines upsampling and feature fusion to progressively reconstruct high-resolution point cloud feature representations. Finally, the integration of semantic-aware discriminative loss with cross-entropy loss is designed to increase intraclass compactness, interclass separability, and regularization, thereby further strengthening class distinction and segmentation quality. To validate the effectiveness and reliability of the proposed method, experiments were conducted on publicly available Plant-3D and Pheno4D datasets. The results demonstrate that the proposed method achieves superior performance from both quantitative and qualitative perspectives in terms of stem-leaf separation, demonstrating a trend towards outperforming existing methods on the tested datasets, with improvements of 3.97% in precision, 4.35% in recall, 4.3% in the F1 score, 5.23% in the IoU and 7.64% in the mIoU. Additionally, t-SNE visualization and qualitative comparisons further confirm the model’s superiority in feature clustering and structural boundary recognition. Our code is publicly available at https://github.com/Ahaoyang1/3D-SemSeg-RandlAnet.

Why it matches plant phenotyping methods植物の3D点群から茎葉を分離する深層学習手法を開発し、公開フェノタイピングデータセットで性能検証しているため、植物形態の取得・抽出法が中心である。

abstractTo validate the effectiveness and reliability of the proposed method, experiments were conducted on publicly available Plant-3D and Pheno4D datasets.

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

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

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

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

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

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

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

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

Estimation de la densite des ligneux avec des images drones dans les ecosystemes Saheliens du Senegal →

Les avancées récentes des drones et du traitement des données permettent aujourd’hui de produire des images hautes résolution et des modèles 3D utiles pour évaluer les attributs des arbres. Cette étude a été menée à Widou thiengoly dans la localité du Ferlo, Nord du Sénégal, avec comme objectif général d’appliquer une approche photogrammétrique pour la mesure de la densité des tiges (tiges/ha) avec des images drones. Une méthode de l’approche arbre basée sur un modèle numérique de hauteur d'une zone d'étude de 10 hectares a été mise en œuvre, ce modèle a été construit à partir d'images obtenues par des drones. Au total, 92 arbres de référence ont été comptés dans le cadre de cette étude et l'algorithme a détecté 75 arbres, ce qui donne une précision supérieure à 90 % (score F de 0,93). Dans l'ensemble, l'algorithme a manqué 10 arbres (erreurs d'omission) et a faussement détecté 3 arbres (erreurs de commission), ce qui donne un compte total de 88 arbres. Cette étude suggère que l'algorithme de filtrage des maxima locaux combiné avec des tailles de fenêtre optimale, appliqués sur un Modèle Numérique de Hauteur construit par photogrammétrie est capable d’effectuer des comptages d'arbres avec une précision acceptable (F > 0,90) dans la zone sahélienne. Recent advances in drone technology and data processing now make it possible to generate high-resolution images and 3D models that are useful for assessing tree attributes. This study was conducted in Widou Thiengoly, in the Ferlo area of northern Senegal, with the overall objective of applying a photogrammetric approach to measure stem density (stems/ha) using drone imagery. A tree-based method was implemented on a Digital Height Model covering a 10-hectare study area, constructed from drone images. In total, 92 reference trees were counted during the study, and the algorithm detected 75 trees, resulting in an accuracy above 90% (F-score of 0.93). Overall, the algorithm missed 10 trees (omission errors) and falsely detected 3 trees (commission errors), giving a total count of 88 trees. This study suggests that the local maxima filtering algorithm, combined with optimal window sizes and applied to a photogrammetrically derived Digital Height Model, can perform tree counts with acceptable accuracy (F > 0.90) in the Sahelian zone.

Why it matches plant phenotyping methodsドローン画像とフォトグラメトリによる樹木密度の推定手法を開発・評価し、Fスコアで精度検証しているため、植物形質取得が研究の中心である。

abstractavec comme objectif général d’appliquer une approche photogrammétrique pour la mesure de la densité des tiges (tiges/ha) avec des images drones.

A technique for measuring non-structural carbohydrate reserves in flag leaves of paddy rice using Fourier transform infrared spectroscopy (FTIR). →

The application of Fourier transform infrared (FTIR) spectroscopy for non-structural carbohydrates (NSC) prediction as a tool for pre-breeding screening has immense potential but remains to be unexplored, because of technical challenges associated with these measurements. This study investigated the potential of employing FTIR spectroscopy as a high-throughput tool for forecasting NSC content, including total soluble sugar (TSS) and starch content, of 30 rice accessions from the Rice Diversity Panel 1 (RDP1) germplasm and RiceTec hybrids grown in 2019 (320 genotypes) and 2020 cropping (312 genotypes). Partial Least Squares (PLS) regression analysis was used to construct predictive models to estimate NSC content in flag leaves and stem of rice exposed to elevated and ambient nighttime air temperature during the flowering stage of rice. The TSS model exhibited a coefficient of determination (R 2 ) value of 0.63 and root mean square error of prediction (RMSEP) values of 3.62 mg g - 1 . Notably, the NSC model demonstrated a superior metric performance, with R 2 = 0.66 and RMSEP of 5.58 mg g - 1 . The predictive model created in this research effectively measured the NSC composition present in the flag leaves of rice. Expanding the sample size and incorporating additional principal components may enhance the model's predictive accuracy. The FTIR technique can produce fast accurate results and resolve the high analytical costs. Overall, the use of FTIR in conjunction with PLS regression analysis provides a potential tool to advance our understanding of various rice genotypes, particularly concerning their ability to withstand abiotic stress such as HNT.

Why it matches plant phenotyping methodsFTIRとPLS回帰を用いてイネ葉・茎の非構造性炭水化物含量を高速推定する手法を開発・評価しており、植物形質の取得法が研究の中心である。

abstractThe application of Fourier transform infrared (FTIR) spectroscopy for non-structural carbohydrates (NSC) prediction as a tool for pre-breeding screening

Tree stem diameter estimation using inexpensive UAV photogrammetric data and Monte Carlo methods →

Abstract. Accurate diameter estimation from point cloud data allows for characterizing stem volume and shape without resorting to destructive methods. Typically, circles are fitted at various stem heights using statistical techniques. However, these techniques are susceptible to noise and occlusion in the point cloud, often caused by obstacles or weather phenomena. This susceptibility reduces the feasibility of applying such methods to point clouds captured by low-cost sensors, which tend to be less precise and noisier. Photogrammetry, however, can be used together with consumer-grade cameras and inexpensive UAVs to generate high-quality point clouds from under-canopy data. This study presents MACiF (Morphology-Aware Circle Fit), a novel method to accurately estimate diameters at various heights from noisy point clouds. Our approach uses robust statistical methods and Monte Carlo simulation to filter the point cloud. We also leverage how stems vary gradually to iteratively correct erroneous estimates. This iterative correction enables estimating diameters with an error lower than -3.34 cm, even when data quality limits the use of other methods. These results support the use of undercanopy low-cost photogrammetry as a viable source of data for automatic stem characterization.

Why it matches plant phenotyping methodsUAVフォトグラメトリの点群から樹幹直径を推定する新規手法を開発しており、植物形態形質の取得が研究の中心である。

abstractThis study presents MACiF (Morphology-Aware Circle Fit), a novel method to accurately estimate diameters at various heights from noisy point clouds.

A connectivity‐based algorithm for wood–leaf separation from terrestrial laser scanning data →

Abstract Tree architecture, characterized by the three‐dimensional (3D) arrangement of branches, plays a critical role in regulating key ecological functions such as light interception, resource transport and structural stability. Terrestrial laser scanning (TLS) has emerged as a powerful tool for capturing detailed and accurate structural information of trees in complex forest environments, making it a promising technique for quantifying tree architecture. However, effective wood–leaf separation—a critical prerequisite for reconstructing three‐dimensional tree models from TLS data—remains a significant challenge, limiting the broader application of TLS in large‐scale studies of tree architecture. In this study, we propose a novel algorithm, connectivity‐based wood–leaf separation (CWLS), which integrates geometric classification with connectivity analysis to automatically extract wood points with high accuracy. To evaluate its performance and generalizability, we applied CWLS to TLS data collected from 55 trees representing diverse species and structural forms across six forest sites along a latitudinal gradient in eastern China, ranging from cold temperate to tropical zones. Each TLS point was manually annotated as ground truth. CWLS achieved an average overall accuracy (OA) of 94.97%, precision of 93.34%, recall of 90.87% and F1‐score of 91.97%. Notably, the algorithm maintained OA above 94.31% across all branch orders, demonstrating particularly strong performance in extracting wood points for higher order branches. Furthermore, CWLS outperformed three state‐of‐the‐art wood–leaf separation algorithms—LeWoS, TLSeparation and graph‐based leaf–wood separation—by offering a superior balance between precision and recall, especially for small branches in the upper canopy. The ability of CWLS to substantially reduce noise while maintaining branch continuity makes it especially well suited for accurate and reliable 3D tree modelling from TLS data. Its integration with 3D reconstruction algorithms such as L 1 ‐tree and TreeQSM offers a promising pathway for large‐scale quantification of tree architecture and for advancing our understanding of its adaptability and ecological functions under global climate change.

Why it matches plant phenotyping methodsTLSデータから木部・葉を分離し、樹木の3D構造・建築を定量化するためのアルゴリズムを開発・検証しており、植物表現型取得法が中心である。

abstractwe propose a novel algorithm, connectivity‐based wood–leaf separation (CWLS), which integrates geometric classification with connectivity analysis to automatically extract wood points with high accuracy.

Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats →

Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.

Why it matches plant phenotyping methods植物の遮蔽部位を撮像・セグメンテーションし、3Dデジタルツインとして植物構造を取得するロボット型表現型計測システムの開発が中心である。

abstractwe present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models.

Image-based assessment of morphological responses and biomass allocation in cowpea seedlings: A methodological approach to drought resilience phenotyping →

Water deficit during the early development of cowpea (Vigna unguiculata (L.) Walp.) can compromise seedling establishment and reduce crop uniformity. This study aimed to evaluate morphological responses and biomass allocation in eight cowpea genotypes, including four commercial cultivars and four landraces, under two water conditions (control and deficit). A randomized block design was applied in a 2 × 8 factorial scheme. Morphological traits of roots and shoots, including length, surface area, volume, and diameter, were measured using image-based analysis. Dry biomass and root-to-shoot ratio were determined through gravimetric methods. Significant genotype-by-environment interactions were observed. Commercial cultivars tended to maintain structural attributes such as stem and root diameter, while landraces, particularly “Marronzinha” and “Verdinha”, exhibited greater plasticity in root morphology and biomass accumulation under water restriction. Although the methodology allowed efficient early phenotyping, limitations such as the short stress duration and use of two-dimensional imaging may restrict broader inferences. Future studies should incorporate extended drought periods, field validation, and physiological assessments to enhance the identification of drought-resilient genotypes.

Why it matches plant phenotyping methods画像解析による根・シュート形態形質の抽出を中心に、乾燥耐性フェノタイピングへ適用した研究であり、単なる生物学的測定にとどまらない。

titleImage-based assessment of morphological responses and biomass allocation in cowpea seedlings: A methodological approach to drought resilience phenotyping

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

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

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

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

Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants →

In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained increasing importance in plant phenotyping. Morphological traits reflect the physiological status of a plant and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective and repeatable monitoring of plant development and health, thus supporting data-driven decision-making in agricultural and food research. This study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions. Existing systems often rely on expensive instrumentation and provide limited adaptability, whereas the platform described here combines affordability with high precision and robustness. A key innovation is the use of a robotic arm to control an industrial RGB camera, providing substantial flexibility in image acquisition. This mobility ensures comprehensive coverage of plants of different sizes and architectures while minimising occlusions. Another distinctive feature is the implementation of an optimised parameter tweak in the photogrammetric pipeline, which markedly improves the reconstruction of thin and delicate plant parts such as leaves, petioles, and fine stems. In combination with optimised acquisition parameters, including an exposure time of 50 milliseconds, a tweak value of 0.9, and a camera-to-object distance of 16 centimetres, the system achieves consistent model fidelity across diverse plant structures. Efficiency was further enhanced through automation and an optimised scanning procedure. Comparative testing showed that using a larger number of camera positions with fewer frames per position improved throughput, with the best configuration consisting of three height levels and 40 frames each. These improvements reduced the processing time by 75%, decreasing the average scan duration from 8 min to only 2.7 min per plant, while maintaining accuracy and reliability. Overall, the developed apparatus constitutes a reliable and low-cost solution that integrates robotic-assisted flexibility, improved reconstruction through the parameter tweak, and markedly reduced scanning time. The combination of precision, affordability, and efficiency makes the system competitive with existing approaches and, due to its accessibility and detailed methodological description, provides a distinctive contribution to the phenotyping community.

Why it matches plant phenotyping methods植物形態形質の3D取得を目的とするSfM-MVS撮像・再構成プラットフォームを開発し、精度、処理時間、撮像条件を比較検証しており、フェノタイピング手法が中心である。

abstractThis study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions.

BranchMatch: point cloud registration for individual apple trees with limited overlap based on local structure characteristics →

Point cloud registration is a critical technology for 3D reconstruction and personalized management of fruit trees. While ensuring the accuracy and completeness of 3D point cloud reconstruction, the simplest and most efficient approach is to acquire and register point clouds from two stations separated by 180°. For this, we propose BranchMatch, a low-overlap viewpoints acquisition and registration method tailored for tall-spindle individual apple trees during dormancy. The method requires only two point clouds captured from stations 180° apart. Then, it leverages key branch segments in a single viewpoint, utilizing their spatial and geometric structure features in combination with a dynamically weighted feature discriminant function to perform feature matching and initial rigid-body transformation under low overlap conditions. Subsequently, an iterative closest point algorithm, enhanced with local feature matching optimization based on the tree-specific point cloud, is applied to refine the registration and prevent over-registration. Experiments conducted on multiple individual apple trees with two low-overlap point clouds (180° apart) demonstrate a registration success rate of 90%. Compared to the spherical markers registration method, BranchMatch achieves average rotation and translation errors of 1.93 mrad and 4.33 mm, respectively, with a pointwise error of 2.70 mm. Furthermore, compared to multi-site high-overlap registration methods under similar conditions, BranchMatch significantly reduces computational costs while maintaining registration accuracy and reconstruction completeness, highlighting its efficiency and reliability in individual tree registration.

Why it matches plant phenotyping methods個体リンゴ樹の3D点群取得・登録・再構成を目的とする手法を開発し、複数樹体で成功率と誤差を検証しており、植物形態・樹体構造のフェノタイピング基盤として中心的です。

abstractwe propose BranchMatch, a low-overlap viewpoints acquisition and registration method tailored for tall-spindle individual apple trees during dormancy.

Highly Stretchable and Reliable Graphene-Based Strain Sensor for Plant Health Monitoring and Deep Learning-Assisted Crop Recognition →

Stretchable sensors hold great potential for monitoring plant physiological parameters and enabling crop identification in smart agriculture. However, achieving long-term, stable, reliable monitoring of plants in dynamic environments, as well as improving crop identification accuracy, remains a substantial challenge, primarily due to the limited biocompatibility of conventional stretchable sensors. Here, we present a highly stretchable and reliable strain sensor based on a graphene/Ecoflex composite. This sensor features a mesh structure that combines graphene's high electrical conductivity and strain sensitivity with Ecoflex's excellent stretchability, biocompatibility, and resistance to environmental degradation. By structural optimization, the sensor achieves high sensitivity (gauge factor = 138), a low detection limit (0.1% strain), and high reliability (over 1,500 cycles), along with waterproofing and resistance to both acidic and alkaline conditions. Furthermore, the sensor conforms tightly to various plant leaves and stems without hindering growth, enabling real-time monitoring of plant growth patterns and in situ detection of mechanical damage to predict plant stress. Moreover, assisted by deep learning, it precisely classifies 8 crop types with an accuracy of 95.2%. These demonstrate that stretchable sensors based on mesh graphene/Ecoflex can operate reliably in outdoor agricultural environments even in the face of variable climatic and chemical conditions, providing a practical platform for advancing plant phenomics and smart agricultural robotics.

Why it matches plant phenotyping methods植物の成長パターンと機械的損傷・ストレスを測定する伸縮性センサーを開発・性能評価しており、植物表現型取得プラットフォームが中心である。

abstractHere, we present a highly stretchable and reliable strain sensor based on a graphene/Ecoflex composite.

Plant stem occlusion inpainting with Deep Reinforcement Learning →

Growth monitoring of tomato plants in large greenhouse environments is critical for quality and efficient production. Stem diameter and elongation are key phenotypic traits for plant growth monitoring. Traditional methods, however, rely on manual operations, which are time-consuming and labor-intensive and do not apply to large-scale greenhouses. Currently, automated image-based methods exemplified by three-dimensional (3D) point cloud technology are among the preferred solutions. Nevertheless, the occlusion of plant structures during the information acquisition process is challenging for practical applications. To address this challenge, this study proposes a novel method for plant stem occlusion inpainting using Deep Reinforcement Learning (DRL). Unlike most existing 3D reconstruction approaches that require depth data from multiple viewpoints, our solution captures 3D point cloud data from a single direction. The DRL model is applied to inpaint the incomplete stem for accurate stem reconstruction and phenotypic measurements. Specifically, our approach consists of two parts, structural completion and stem diameter completion. First, we extract the point cloud of incomplete stems from the RGB-D camera data. Second, we obtain the spatial structure of the stems by inpainting the 3D stem centerline with the DRL model. Finally, we add shape features (stem diameters) by inpainting the two edge lines of the stem occlusion part with the DRL model. For stem inpainted 3D point cloud data, we conducted validation experiments by measuring several commonly used stem phenotypic traits in tomato plants, including stem diameter, stem length, and stem inclination. The experimental results show that the Mean Absolute Percentage Error (MAPE) of the occluded main stem diameter is 9.7%, stem length is 5.7%, and tilt angle is 1%. For the occluded branch stem, the MAPE of stem diameter is 23.1%, stem length is 7.9%, and tilt angle is 1.5%. The accuracy of these measurements for occluded stems is acceptable compared to that obtained from 3D point clouds of unoccluded stems. This highlights the significant potential of using DRL to effectively inpaint occluded 3D point cloud data of plants.

Why it matches plant phenotyping methods植物茎の遮蔽部分を3D点群と深層強化学習で補完し、茎径・茎長・傾斜角という表現型形質の測定精度を検証しており、フェノタイピング手法が中心的です。

abstractTo address this challenge, this study proposes a novel method for plant stem occlusion inpainting using Deep Reinforcement Learning (DRL).

Dynamic Recognition and Cutter Positioning Based on Morphological Features of Cane Tip Growth →

Aiming to address the accuracy problem of cane tip recognition in complex natural environments, this paper proposes a cane tip feature annotation method based on the growth characteristics of sugarcane. In the context of the demand for lightweight and fast detection of cane tips, this paper optimizes the Yolov8n-Seg model with lightweight shared convolutional separated batch normalized detection head, model pruning, and knowledge distillation strategies. With these improvements, the accuracy of the optimized model increased by 0.2 percentage points, the number of parameters was reduced by 75.03%, the model size was reduced by 70.15%, the inference time is accelerated by 17.34%, and the GFLOPs were reduced by 40.00%. The lightweight cane tip detection model was deployed on the Jetson Orin NX platform with an average recognition frame rate of 7.42 f/s provides a lightweight hardware deployment solution for real-world applications in sugarcane harvesters. Finally, the depth camera was used for cane tip recognition and height measurement. The experimental results showed that the average relative errors of the camera were 0.189%, 0.675%, and 0.949% when the camera was 50 cm, 75 cm, and 100 cm away from the cane tip, respectively, which were all controlled within 1%, and were able to achieve accurate height measurement. Based on the statistical analysis of sugarcane clusters, this paper further proposes a sugarcane cluster identification method, providing a theoretical basis for saving adjustment time of the tip cutter during the harvesting process. It lays a theoretical and technical foundation for researching feature recognition, cutter height positioning, and real-time control of sugarcane harvester cuttings.

Why it matches plant phenotyping methodsサトウキビ先端の画像認識と深度カメラによる高さ測定を開発・検証しており、植物形態形質の取得が中心である。

abstractthis paper proposes a cane tip feature annotation method based on the growth characteristics of sugarcane.

Early detection and severity classification of verticillium wilt in cotton stems using Raman spectroscopy and machine learning. →

The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating disease. Traditional diagnostic methods, which often rely on manual expertise or destructive sampling, are limited by low efficiency and high subjectivity. In recent years, Raman spectroscopy has emerged as a promising solution due to its rapid, non-destructive, and highly sensitive characteristics for plant disease detection. In this study, we analyzed cotton stems using Raman spectroscopy, applying Savitzky-Golay (SG) smoothing combined with multiple preprocessing methods including Scaling and Shifting (SS), Standard Normal Variate (SNV), inverse first-order differential (1/SG)', and multiplicative scatter correction (MSC). For baseline correction, we employed polynomial fitting (PolyFit) and adaptive iterative weighted penalized least squares (airPLS). Feature selection was performed using principal component analysis (PCA), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS).Three optimized models were developed: support vector machine (SVM) with weighted mean of vectors (INFO) algorithm, random forest (RF) enhanced by particle swarm optimization (PSO), and long short-term memory (LSTM) network optimized via chameleon swarm algorithm (CSA).The results show that the INFO-SVM model with SG-airPLS-(1/SG)' -CARS preprocessing demonstrated superior performance, achieving 97.5% accuracy (0.974 F1-score) on training data and 90.0% accuracy (0.867 F1-score) on validation data, outperforming both PSO-RF and CSA-LSTM models. These results confirm that Raman spectroscopy integrated with optimized machine learning enables accurate VW classification in cotton stems. This method enables early disease detection during infection, facilitating timely fungicide application and reducing yield losses.

Why it matches plant phenotyping methodsランダ分光と機械学習により、ワタ茎の病徴・萎凋病の早期検出および重症度分類を開発・検証しており、植物状態の取得手法が研究の中心である。

titleEarly detection and severity classification of verticillium wilt in cotton stems using Raman spectroscopy and machine learning.

Comparison between different major data assimilation algorithms on region tobacco growth simulation →

While tobacco plays a significant role in the global economy, research on regional tobacco growth simulation remains limited. This study integrates the WOFOST crop model with satellite remote sensing data, focusing on the data assimilation (DA) of leaf area index (LAI) to enhance the accuracy of regional tobacco growth simulations. Field survey data were used for model calibration, providing the foundation for the analysis. The performance of four 4-Dimensional Variational Assimilation algorithms (4DVAs)—Particle Swarm Optimization (PSO), Simulated Annealing (SA), Shuffled Complex Evolution-University of Arizona (SCE-UA), and Gray Wolf Optimization (GWO)—was compared with four sequential DA algorithms (SDAs)—Ensemble Kalman Filter (EnKF), Ensemble Variational (EnVar), Ensemble Square Root Filter (EnSRF), and Particle Filter (PF). The 4DVAs were developed by integrating constraint DA Algorithms (CDAs) into the 4D-Var framework, enhancing their capability to optimize model states over a time window. Additionally, the performance of their coupled DA algorithms was evaluated. The results indicated that the coupled of SA and PF (SA-PF) achieved the best performance in terms of model accuracy. Compared to field survey data for biomass, stem mass and leaf mass, our method achieved the coefficient of determination (R²) values of 0.89, 0.86, and 0.81, respectively, with normalized root mean square error (NRMSE) values of 0.12, 0.10, and 0.09. The SA-PF coupling algorithm also performs better than some new DA algorithms. This study provides a valuable reference for regional tobacco growth simulation and data assimilation, improving the accuracy and applicability of crop growth models.

Why it matches plant phenotyping methods衛星リモートセンシングのLAIを作物モデルへ同化し、バイオマス・茎重・葉重を推定するデータ同化手法を開発・比較・検証しており、植物形質推定が中心である。

abstractThis study integrates the WOFOST crop model with satellite remote sensing data, focusing on the data assimilation (DA) of leaf area index (LAI) to enhance the accuracy of regional tobacco growth simulations.

Single-image estimation of tree volume via pixel-mapped 3D reconstruction: A low-cost solution using deep learning and curvature segmentation →

Although LiDAR is widely used for tree measurement, its high cost and operational complexity remain significant barriers to widespread adoption. With advances in photogrammetry and deep learning, efficient and accurate alternatives have become increasingly important for forest resource surveys. Accordingly, we propose an automated trunk-parameter measurement framework that maps image pixels to physical units. The framework integrates the SegFormer deep-learning model, trunk-skeleton extraction, an adaptive curvature-segmentation algorithm, and segment-wise 3-D reconstruction, thereby enabling image segmentation, curvature analysis, three-dimensional reconstruction, and measurement. To validate its practical value, we collected images of 3013 trees across four species in the Beijing region. Additionally, we acquired point-cloud data and conducted destructive measurements on 141 trees of various species for comparative evaluation. Experimental results indicate that the stem segmentation algorithm effectively extracts trunk regions in images, and the adaptive segmentation method substantially improves trunk volume estimation accuracy. The approach achieves only 2.01 %-7.68 % error in single-tree volume and height measurements-primarily due to segment-height inaccuracies-and offers an approximately 6.9-fold improvement in efficiency compared with the existing HMLS method. In summary, this method provides an efficient, low-cost solution for forestry surveys and shows great potential for monitoring tasks that require high accuracy under resource constraints. This innovative method is expected to further advance forest resource assessment.

Why it matches plant phenotyping methods画像から樹幹を抽出し、3D再構成によって単木の体積・高さを推定する手法を開発・検証しており、植物形質取得が研究の中心です。

abstractwe propose an automated trunk-parameter measurement framework that maps image pixels to physical units.

An automated skeleton extraction method for 3D point-cloud phenotyping of Schima Superba seedlings →

Aiming to address the issues of low efficiency and large errors in the manual measurement process of phenotypic parameters in Schima Superba seedlings, an automated non-destructive method for acquiring phenotypic parameters based on three-dimensional point clouds is proposed, which includes the main steps of alignment, skeleton extraction, and automatic phenotypic calculation. Aiming to overcome the technical challenges of stem and leaf separation in Schima Superba , a density-weighted voxel centroid method is proposed to extract skeleton points, combined with minimum spanning tree (MST) and principal component analysis (PCA) techniques to accurately identify the stem skeleton point cloud, effectively addressing the problem of stem-leaf separation. The separation process encounters difficulties at the stem-leaf junction, resulting in suboptimal separation accuracy. An improved K-means++ algorithm is proposed to initially estimate the number of adhering leaves based on coarse segmentation, followed by fine segmentation to achieve higher precision in leaf segmentation, effectively improving the accuracy and efficiency of the segmentation process. Following the completion of stem and leaf segmentation, a fully automated phenotypic characterization method based on the segmented point cloud is proposed for the first time. The method automatically outputs relevant phenotypic parameters, including plant height, stem length, stem diameter, and leaf area. The predicted correlation coefficients for the experimental phenotypes were 0.994, 0.992, 0.938, and 0.873, meeting the requirements for on-site measurement of phenotypic parameters in Schima Superba and providing strong technical support for plantation management and cultivar improvement.

Why it matches plant phenotyping methods3D点群による茎葉分離、骨格抽出、形質自動計算を開発・検証しており、植物表現型取得手法が研究の中心である。

abstractan automated non-destructive method for acquiring phenotypic parameters based on three-dimensional point clouds is proposed

An improved YOLOv8-seg-based method for key part segmentation of tobacco plants →

Accurate segmentation of key tobacco structures is essential for enabling automated harvesting. However, complex backgrounds, variable lighting conditions, and blurred boundaries between the stem and petiole significantly hinder segmentation accuracy in field environments. To overcome these challenges, we propose an enhanced instance segmentation approach based on YOLOv8-seg, incorporating depth-based background filtering and architectural improvements. Specifically, depth information from RGB-D images is employed to spatially filter non-target background regions, thereby enhancing foreground clarity. In addition, a Hybrid Dilated Residual Attention Block (HDRAB) is integrated into the YOLOv8-seg backbone to improve boundary discrimination between petioles and stems, while a Lightweight Shared Detail-Enhanced Convolution Detection Head (LSDECD) is designed to efficiently capture fine-grained texture features. Experimental results demonstrate that depth filtering increases mAP50 bb and mAP50 seg by 7.9% and 6.3%, respectively, while the architectural enhancements further raise them to 89.5% and 91.1%, surpassing the YOLOv8-seg baseline by 5.2% and 10.0%. Compared with mainstream models such as Mask R-CNN and SOLOv2, the proposed method achieves superior segmentation accuracy with low computational cost, highlighting its potential for practical deployment in automated tobacco harvesting.

Why it matches plant phenotyping methodsタバコ植物の茎・葉柄などの構造をRGB-D画像からセグメンテーションする手法を開発・比較しており、植物器官形態の取得が中心である。

abstractwe propose an enhanced instance segmentation approach based on YOLOv8-seg, incorporating depth-based background filtering and architectural improvements.

Edge-Preserving Multi-Scale Network for Plant Point Cloud Segmentation →

Accurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes, as it provides the foundational data for both trait measurement and structural modeling. Although existing studies have made significant progress, plant semantic segmentation (stems and leaves) across multiple species remains underexplored. To this end, this paper introduces an edge-aware downsampling algorithm and a novel network for segmenting plant point clouds at multiple scales, named MSPlantSegNet. Experimental results on a dataset of tobacco, tomato, and sorghum demonstrate that MSPlantSegNet attained superior performance across all four key metrics-precision (97.13 %), recall (95.63 %), F1-score (96.20 %), and IoU (93.14 %). MSPlantSegNet surpasses a set of leading models, including PointNet++, PointNet, ASIS, DGCNN, PlantNet, PSegNet, and PointNeXt. This research has valuable implications for plant phenotyping, the development of smart agriculture, and ideal type selection.

Why it matches plant phenotyping methods植物点群から茎・葉を分割する新規ネットワークとダウンサンプリング法を開発し、複数種データで性能比較しており、表現型抽出の基盤手法が中心である。

abstractAccurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes

A Method for Sesame ( Sesamum indicum L.) Organ Segmentation and Phenotypic Parameter Extraction Based on CAVF-PointNet+. →

Efficient and non-destructive extraction of organ-level phenotypic parameters of sesame ( Sesamum indicum L.) plants is a key bottleneck in current sesame phenotyping research. To address this issue, this study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering. First, this method constructs a high-precision 3D point cloud using multi-view RGB image sequences. Based on the PointNet++ model, a CAVF-PointNet++ model is designed to perform feature learning on point cloud data and realize the automatic segmentation of stems, petioles, and leaves. Meanwhile, different leaves are segmented using curvature-density clustering technology. Based on the results of segmentation, this study extracted a total of six organ-level phenotypic parameters, including plant height, stem diameter, leaf length, leaf width, leaf angle, and leaf area. The experimental results show that in the segmentation tasks of stems, petioles, and leaves, the overall accuracy of CAVF-PointNet++ reaches 96.93%, and the mean intersection over union is 82.56%, which are 1.72% and 3.64% higher than those of PointNet++, demonstrating excellent segmentation performance. Compared with the results of manual segmentation of different leaves, the proposed clustering method achieves high levels in terms of precision, recall, and F1-score, and the segmentation results are highly consistent. In terms of phenotypic parameter measurement, the coefficients of determination between manual measurement values and algorithmic measurement values are 0.984, 0.926, 0.962, 0.942, 0.914, and 0.984 in sequence, with root-mean-square errors of 5.9 cm, 1.24 mm, 1.9 cm, 1.2 cm, 3.5°, and 6.22 cm 2 , respectively. The measurement results of the proposed method show a strong correlation with the actual values, providing strong technical support for sesame phenotyping research and precision agriculture. It is expected to provide reference and support for the automated 3D phenotypic analysis of other crops in the future.

Why it matches plant phenotyping methods3D画像・点群分割と幾何クラスタリングにより、ゴマの器官分割および6種類の表現型形質抽出法を開発し、手動測定との精度検証も行っているため、方法が中心的である。

abstractthis study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering.

Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery →

Accurate, high-throughput phenotyping is a critical component of modern crop breeding programs, especially for improving traits such as mechanical stability, biomass production, and disease resistance. Stalk diameter is a key structural trait, but traditional measurement methods are labor-intensive, error-prone, and unsuitable for scalable phenotyping. In this paper, we present a geometry-aware computer vision pipeline for estimating stalk diameter from RGB-D imagery. Our method integrates deep learning-based instance segmentation, 3D point cloud reconstruction, and axis-aligned slicing via Principal Component Analysis (PCA) to perform robust diameter estimation. By mitigating the effects of curvature, occlusion, and image noise, this approach offers a scalable and reliable solution to support high-throughput phenotyping in breeding and agronomic research.

Why it matches plant phenotyping methodsRGB-D画像から作物の茎径を推定するコンピュータビジョン手法を開発しており、植物形質の取得・抽出が研究の中心であるため。

abstractIn this paper, we present a geometry-aware computer vision pipeline for estimating stalk diameter from RGB-D imagery.

Non-destructive estimation of rice canopy LAI using NIR/PAR: application to four rice cultivars with diverse leaf characteristics and plant architectures →

Accurate and non-destructive estimation of leaf area index (LAI) is crucial for monitoring rice growth and predicting yield. This study tested the applicability of non-destructive method for estimating rice canopy LAI using the ratio of near-infrared to photosynthetically active radiation (NIR/PAR) transmitted through the rice canopy to four rice cultivars with different leaf characteristics and plant architectures. We further compared the accuracy of the present method with a conventional plant canopy analyzer estimation. The NIR/PAR method accurately estimated LAI across all cultivars regardless of leaf characteristics (nitrogen content, leaf mass per area) or plant architecture (height, stem number, biomass). Furthermore, the NIR/PAR method accurately estimated LAI even in dense canopies (> 8 m 2 m −2 ) where the plant canopy analyzer underestimated LAI. These findings demonstrate the robustness and accuracy of the NIR/PAR method for rice LAI estimation, suggesting its potential for improving growth assessment, yield prediction, and developing smart agriculture technologies.

Why it matches plant phenotyping methodsイネ群落のLAIという植物形質を、NIR/PAR透過光比で非破壊推定する手法の適用性・精度・頑健性を品種間で検証し、従来法とも比較しているため、方法が研究の中心です。