Abstract Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology. Using soybean ( Glycine max (L.) Merr.), we investigated whether development-associated temporal variation in VOC emissions could delineate and predict developmental phases. Methods We collected VOCs daily under controlled environmental conditions from 16 to 43 days after sowing, spanning the transition from vegetative to reproductive stages, using an automated sampling system coupled with thermal desorption-gas chromatograph-mass spectrometer (TD- GC-MS). To characterise temporal changes in VOC profiles associated with phenological progression, we analysed the daily VOC data using a multi-step pipeline combining statistical filtering and similarity-based network analysis. We defined VOC-derived developmental phases from similarity patterns in the VOC profiles, then developed and evaluated machine-learning models to predict these phases. Key results Seven VOCs exhibited distinct phase-dependent dynamics, including green leaf volatiles and monoterpenes showing characteristic temporal changes during phenological progression. Network-based clustering of VOC profiles resolved five developmental phases closely aligned with conventional developmental stages. A machine-learning model predicted these phases from the VOC profiles with high predictive accuracy on independent test data, demonstrating that phenological progression could be quantitatively inferred from VOC emission patterns. Conclusions Our findings support VOC profiling as a reliable and non-invasive approach for assessing phenological progression in soybean. By extracting temporally structured VOC signals, this framework captures developmental information that may be difficult to obtain through visual observation alone, particularly after canopy closure. VOC profiling offers a practical tool for monitoring crop developmental dynamics and has broader potential for plant phenotyping and precision crop management.
Why it matches plant phenotyping methods自動VOCサンプリング、時系列VOCプロファイリング、機械学習を統合し、VOCから植物の発育段階を非破壊推定する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe peak area matrix obtained from the MS- DIAL analysis (Supplementary Dataset S1) was filtered to remove unreliable features.Open asset ↗lines:66-69Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Technological advances have expanded the adoption of digital technologies in agriculture, helping to reduce labour effort, increase profitability, improve crop efficiency and productivity, enhance product quality, mitigate environmental impacts, and promote human health. This context also extends to soybean farming, a sector of major economic importance in Brazil. Most importantly, Brazil has the global leadership in soybean production with biological nitrogen fixation (BNF) replacing chemical fertilisers. The research and evaluation of BNF is limited by manual counting of nodules, a time-consuming procedure. This study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images. The dataset includes 1,701 images acquired under controlled conditions: 1,662 images of soybean roots with nodules and 39 images of isolated nodules without roots. A total of 49,210 nodule instances are manually annotated with bounding boxes. SoyNodules was designed to promote reuse and interoperability in alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable) and to support the development, training, and evaluation of computer vision and deep learning methods for precision agriculture.
Why it matches plant phenotyping methods大豆根粒を自動識別する画像データセットであり、手作業計数の代替となる植物器官形質の抽出・評価を支援する方法論的データセット。
abstractThis study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images.
Reproduction assets foundThe paper is a data descriptor for SoyNodules, an annotated dataset of 1,701 soybean root/nodule images with 49,210 bounding-box annotations, publicly deposited on Zenodo with a DOI. The same repository also hosts the authors' annotation-format conversion script (AnyLabeling to Pascal VOC/COCO), per the Code AvailabilDataset · publicThe SoyNodules dataset, released as version 1.0, is publicly available on Zenodo [28]
at https://doi.org/10.5281/zenodo.22081914.Open asset ↗Zenodo · 10.5281/zenodo.22081914pdf-page:9 lines:1-43Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs. Sixteen soybean genotypes were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using six YOLO (You Only Look Once) object-detection architectures (YOLOv7–YOLOv12) to identify the best-performing model for automated analysis. Among the tested object-detection architectures, YOLOv9 achieved the best overall performance for detecting germinated and non-germinated pollen grains in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P < 0.05), with a significant genotype × growth temperature interaction. Invitro incubation temperatures ranging from 10 °C to 45 °C produced a clear thermal response; however, no significant genotype × incubation temperature interaction was detected within either growth temperature regime. Although photosynthetic and physiological traits were measured exploring their relationship with pollen germination, their transient and complex response limited their reliability for predicting reproductive performance. The automated pipeline substantially reduced the time required to evaluate pollen germination. The pipeline processed nearly 5,000 images in approximately one hour, substantially increasing throughput and reducing reliance on manual counting. The findings demonstrate that pollen germination is a promising proxy trait for screening reproductive heat tolerance in soybean. Combining controlled environment phenotyping with YOLO-based object detection enabled efficient, accurate, and scalable pollen analysis, and represents the central methodological advance of this study. YOLOv9 performed best among the tested architectures, although discrepancies from manual counts in some images indicate that additional validation is needed. The weak associations with vegetative physiological traits further support the value of direct pollen-based phenotyping.
Why it matches plant phenotyping methods深層学習による花粉画像解析を中心に、花粉発芽という生殖形質を高速・自動測定するハイスループット表現型解析フレームワークを開発・比較・検証している。
abstractThis study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs.
Reproduction assets foundThe authors state that all data supporting the study, including annotated pollen germination images, computational and statistical codes, and analysis tools, were deposited in Zenodo with a public DOI. This is a paper-specific, publicly actionable asset. LabelMe and Ultralytics YOLO are generic third-party tools, not作者Dataset · publicCommission.
Data availability
All data supporting the findings of this study, including annotated images, computational and
statistical codes, and analysis tools, have been deposited in the Zenodo data repository. Additional
data will be made available upon reasonable request following acceptance of the manuscript.
Repository: https://doi.org/10.5281/zenodo.21685593
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing Interests
Authors declared no competing interests
References
1. FAOSTAT: Crops and livestock products: soybean production data. https://www.fao.org/faostat/
(2022). Accessed 15 Feb 2026.
2. Patel D, Franklin KA. TemperaturOpen asset ↗Zenodo · 10.5281/zenodo.21685593pdf-raw-page:28 lines:1-34Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D reconstruction methods perform strongly on generic benchmarks, but rendered appearance may not translate into metrically and agronomically useful geometry in crop fields. We introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys. It contains 88,830 RGB images at $5280 \times 3956$ pixels, with a ground sampling distance of 3.6-5.8 mm, from 91 scenes spanning corn, soybean, wheat, and oat. Track A evaluates seven scene-optimized methods -- Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) variants -- on held-out views, photogrammetry-referenced depth, and canopy-height recovery. Track B tests four pretrained feed-forward models on zero-shot camera-pose and geometry estimation. The scene-optimized methods rank differently across the three targets: Splatfacto-big leads appearance, whereas Scaffold-GS leads depth and is statistically tied with Splatfacto for canopy height. Among feed-forward models, MapAnything leads on seven of the eight metrics, while the remaining models vary more across crops and fail severely on absolute scale in a way that alignment conceals. Repeated acquisitions reveal further sensitivities that differ by output type and by model, associated with position within the acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/
Why it matches plant phenotyping methods植物キャノピー高さという明示的な形質を対象に、UAV 3D再構成手法をベンチマークし、公開データセットとして提供しているため、フェノタイピング手法が中心である。
abstractWe introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys.
Reproduction assets foundThe paper introduces UAV3DCrop, a public benchmark of repeated multi-angle UAV crop surveys (88,830 RGB images, 91 scenes, four crops) with refined poses, photogrammetric depth references, and linked canopy-height and effective-LAI field measurements. The dataset is explicitly stated to be publicly available under CC BDataset · publiche acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/ .
Keywords:
UAV imagery; agricultural datasets; crop-field reconstruction; neural radiance fields;
Gaussian splatting; feed-forward geometry.
1 IntroductionOpen asset ↗UAV3DCroplines:1-90Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings. Here, we propose Lite U-net FiLM (LUF-net), a lightweight U-net framework integrated with feature-wise linear modulation (FiLM) layers, which incorporates both multispectral-derived irradiance and camera exposure parameters as auxiliary inputs. This metadata-aware design enables dynamic modulation of intermediate image features, allowing the network to disentangle unified canopy color from environmental artifacts. Experiments conducted across diverse crop types (soybean, rice, maize), flight altitudes (6m, 12m, 25m, 40m), and illumination conditions (overcast skies, cloudy, sunny, early morning) demonstrate that the LUF-net model substantially reduces the mean absolute percentage error to below 5.5%, outperforming conventional Gray-world, U-net, AlexNet-MLP, and SIDBlock-MLP methods. Ablation experiments further show that both irradiance and exposure metadata provide complementary information, while FiLM-based conditioning effectively integrates these acquisition parameters into feature learning, jointly contributing to improved color reconstruction performance. Moreover, correlation analysis indicates that the model's color reconstruction errors are weakly dependent on irradiance and exposure settings, confirming that LUF-net reduces sensitivity to external imaging conditions while maintaining physically meaningful and robust corrections.
Why it matches plant phenotyping methodsUAV画像の色校正手法を開発し、複数作物・撮影条件で性能検証しており、作物フェノタイピングの画像取得・補正が中心である。
abstractAccurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings.
Reproduction assets foundThe paper's data and code availability statement explicitly points to a public GitHub repository containing training/evaluation/inference code, pretrained model weights, and example data for the LUF-net color calibration method.Code · publicThe data and source code for model training, evaluation, and inference, together with pretrained model weights, example data, and detailed usage instructions, is publicly available at: https://github.com/wangchufeng3652/color-correction.Open asset ↗wangchufeng3652/color-correctionhtml-lines:278-299Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
With climate change and global population growth, accelerating the breeding of superior crop varieties is essential for food security. Genomic prediction, which uses genome-wide genetic markers to predict crop traits, plays an important role in intelligent crop breeding. However, existing methods often lack stable and accurate performance across crops and traits. Here, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm. Compared with the best-performing single base learners, GEG2P improves prediction accuracy by 4.02% on average across maize, wheat, rice, chickpea, and soybean. We use SHAP to quantify the contribution of SNPs to phenotype prediction and find that SNPs with large effects captured by different base learners are functionally complementary. This study provides a robust and accurate genomic prediction method for crop breeding.
Why it matches plant phenotyping methods作物形質の遺伝子型から表現型を予測するアンサンブル計算法を開発し、複数作物で精度比較・検証しており、表現型推定手法が研究の中心である。
abstractHere, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm.
Reproduction assets foundThe paper provides public author code (GitHub GEG2P repository and Docker Hub image), a Zenodo deposit of significant SNP interaction pairs generated in this study, and a Figshare link with the wheat genotypic and phenotypic data used in the analyses. These are paper-specific, publicly available, and actionable.Code · publicScripts used in this study are available at GitHub [ https://github.com/Deep-Breeding/GEG2P ] 89 .Open asset ↗GitHub · Deep-Breeding/GEG2Plines:236-266Dataset · publicThe genotypic and phenotypic data of wheat are available at Figshare [ https://figshare.com/s/287c2c7f1623008487a5 ] 68 .Open asset ↗Figsharelines:236-266Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.
Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。
abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure
Reproduction assets foundThe paper explicitly states that the analysis code is publicly available in the authors' GitHub repository (jianglong26/soybean-insect-resistance), which directly reproduces this paper's phenotyping pipeline (orthomosaic processing, VI/DINOv3 feature extraction, similarity analysis, genotype ranking). The paper also声明sCode · public540 The code used for analysis is available at https://github.com/jianglong26/Open asset ↗pdf-page:16 lines:1-45Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Spatio-Temporal Fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, covering the entire soybean growing season from late June to early November. Near-daily Planet SuperDove imagery with 3 m resolution was used to temporally enhance UAV images, which were acquired at 0.05 m resolution but only at weekly to monthly intervals. Through the downscaling process, the UAV data were converted into a daily dataset with a target spatial resolution of 0.5 m. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms- Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (Fit-FC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)-within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) trajectories, from which the NDVI-based Vegetation Growth Metrics (VGM)85 and the EVI-based VGMmax were derived. The validation results indicated that ESTARFM achieved the highest NDVI performance among the evaluated algorithms, with a Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697. CACAO post-processing further improved these results, with CA-ESTARFM achieving an RMSE of 0.108 and a UIQI of 0.740, corresponding to a 4.4% reduction in RMSE and a 6.2% improvement in UIQI relative to the baseline ESTARFM. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using NDVI-based VGM85 and EVI-based VGMmax showed that CA-ESTARFM remained consistent with simple linear interpolation of UAV observations while retaining finer spatial structure and reducing localized noise in the derived growth metrics. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methodsUAV・衛星画像の時空間融合とCACAO処理により、NDVI/EVIおよび植生成長指標を抽出するワークフローを開発・比較検証しており、植物状態の取得手法が中心である。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Reproduction assets foundThe paper's STF/CACAO analysis code is openly available on Zenodo. The underlying Planet/UAV imagery data are only available from the corresponding author upon request, so they qualify as request_only.Code · publicThe code supporting this study is openly available at Zenodo
(https://doi.org/10.5281/zenodo.20923823).Open asset ↗Zenodo · 10.5281/zenodo.20923823pdf-page:20 lines:1-70Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Crop leaf diseases cause 10–40% annual yield losses, yet timely field diagnosis remains difficult. Vision-language models (VLMs) lift recognition accuracy with rich textual descriptions, but multimodal pipelines are too slow for real-time field use because they require text processing at inference. We present MTL-AWL, a framework built on a training–inference asymmetry: VLM text serves as privileged training-time supervision, and two coupled mechanisms—one retaining VLM semantics in the image encoder and one exploiting them—enable image-only deployment at multimodal accuracy. A modal-dropout strategy (p=0.6) intermittently masks the VLM text sequence during training, forcing the image encoder to retain cross-modal representations independently. An adaptive multi-task loss jointly optimizes InfoNCE contrastive alignment, attention diversity, and modality consistency under learnable softmax weights, consistently converging to a dominant contrastive weight (55% on soybean, 68% on PlantDoc)—identifying cross-modal alignment as the primary mechanism of VLM knowledge transfer. At inference, the model reaches 818 FPS (3.7× faster than multimodal methods) at only 0.41% accuracy cost, attaining 99.30%/98.89% (multimodal/image-only) on soybean and 72.65%/68.80% on PlantDoc—compact enough for real-time, offline field screening.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する画像ベース手法を開発し、複数データセットで精度・速度を評価しており、フェノタイピング手法が中心である。
abstractWe present MTL-AWL, a framework built on a training–inference asymmetry: VLM text serves as privileged training-time supervision, and two coupled mechanisms—one retaining VLM semantics in the image encoder and one exploiting them—enable image-only deployment at multimodal accuracy.
Reproduction assets foundThe paper's Data Availability Statement links a public Dryad DOI for the soybean leaf disease image dataset used in the study's phenotyping/recognition experiments. No author code or model release is stated.Dataset · publicThe datasets utilized in this study are openly accessible. The soybean dataset is available at https://doi.org/10.5061/dryad.41ns1rnj3 .Open asset ↗Dryad · 10.5061/dryad.41ns1rnj3lines:441-459Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean production is significantly affected by crop diseases and improper pesticide use, which hinder effective disease management and reduce yield. In this study, we propose an efficient multi-task convolutional neural network (CNN) framework for the simultaneous detection of soybean seed diseases and pesticide presence from seed images. The model leverages a shared feature extraction backbone with task-specific output heads to learn complementary features for both disease classification and pesticide detection. A dataset of 429 soybean leaf images was preprocessed using normalization and augmentation techniques and split into training, validation, and testing sets. We evaluated three backbone architectures VGG19, MobileNetV3, and ConvNeXt within the multi-task framework. Experimental results demonstrate that the approach maintains computational efficiency suitable for real-world deployment while achieving high performance, with accuracies of 95%, 96%, and 97% for MobileNetV3, VGG19, and ConvNeXt, respectively. Additionally, explainable AI methods, such as Grad-CAM, highlight regions of focus for both tasks, making the model's decision-making process interpretable. This framework provides a practical tool for informed crop management and agricultural monitoring.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNN手法の開発・評価が研究の中心であり、病害表現型の画像ベース推定に該当する。
abstractwe propose an efficient multi-task convolutional neural network (CNN) framework for the simultaneous detection of soybean seed diseases and pesticide presence from seed images.
Reproduction assets foundThe paper's authors publicly released their custom analysis code (preprocessing, training, evaluation) on GitHub, matching an allowed URL. The enriched Kaggle image/annotation dataset is also public but its URL is not among the allowed URLs, so it is not listed.Code · publicThe custom code developed for this study is publicly available on GitHub at https://github.com/fikaduberie/Soybean-Disease-and-Pest (version v1.0).Open asset ↗fikaduberie/Soybean-Disease-and-Pesthtml-lines:1281-1329Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate detection of such diseases is crucial for improving both crop yield and quality. While numerous deep learning approaches rely solely on image data for disease identification, they often overlook the complementary value of textual information in enhancing visual analysis. To address this limitation and effectively fuse features from different modalities, we propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition. Our approach utilizes the Zhipu.ai multi-modal model to generate comprehensive textual descriptions of diseased crop leaves, including global description, local lesion description, and color-texture description. These textual descriptions are then encoded into feature embeddings, while visual features are extracted using the ShuffleNet-v2 model as the image encoder. Subsequently, a cross-attention module aligns and fuses the two modalities, and a gated fusion module enables dynamic feature selection during the fusion process. Extensive evaluations on the Soybean Disease and PlantVillage datasets demonstrate that our method outperforms existing image-based models in terms of accuracy. Specifically, our model achieves recognition accuracies of 99.04% and 99.12% on the respective datasets, surpassing the ShuffleNet-V2 model by 1.09% and 2.53%, respectively. These results highlight the effectiveness of Cross-Model learning in integrating visual and textual cues for accurate and efficient disease recognition, offering a scalable solution for crop disease diagnosis.
Why it matches plant phenotyping methods植物葉の病徴を画像と言語情報から認識する融合フレームワークを開発し、複数データセットで既存手法と比較評価しているため、植物フェノタイピング手法が中心である。
abstractwe propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition.
Reproduction assets foundThe paper's crop disease recognition experiments use two openly available image datasets, both with explicit public availability statements in the Data Availability section: the Soybean Disease dataset (Dryad DOI) and the PlantVillage dataset (Kaggle). No author analysis code, trained models, or generated text-annotaitDataset · publicThe datasets utilized in this study are openly accessible. The soybean dataset is available at https://doi.org/10.5061/dryad.41ns1rnj3.Open asset ↗Dryad · 10.5061/dryad.41ns1rnj3html-lines:403-424Dataset · publicThe plantvillage dataset is available at https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.Open asset ↗Kaggle · plantvillage-datasethtml-lines:403-424Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Rapid population growth and climate change have intensified the need for sustainable agricultural productivity. Plant leaf diseases significantly impact the crop yield, quality, and food safety, necessitating accurate and automated detection methods. Methods This study proposes a deep learning (DL)-based framework for automated detection and classification of tomato and soybean leaf diseases. The proposed framework is trained and evaluated over a large-scale datasets comprising 16,012 tomato leaf images and 6,410 soybean leaf images. Multiple convolutional neural network (CNN) models, including DenseNet121, MobileNetV2, and InceptionV3, are employed for classification. Object detection is performed using YOLOv12. To enhance interpretability, Gradient-Weighted Class Activation Mapping (Grad-CAM) is integrated. Furthermore, a novel Hybrid Attention-Based Stacking Ensemble Model is developed using ResNet152V2, VGG19, and EfficientNetB0, combined with Convolution Block Attention Module (CBAM) and spatial attention mechanisms. Results The CNN models achieved classification accuracies of 97% for DenseNet121, 98% for MobileNetV2, and 99.94% for InceptionV3. YOLOv12 attained a mean average precision (mAP) of 99.5%. The proposed hybrid ensemble model achieved an accuracy of 99.18%, demonstrating improved feature learning through combined channel and spatial attention. Grad-CAM visualizations confirmed that the model effectively identifies the disease-relevant regions. Discussion The results indicate that the proposed framework has attained a high accuracy, robustness, and interpretability for plant disease detection. The integration of attention mechanisms and explainable AI enhances model reliability and transparency. This framework shows a strong potential for the real-time agricultural monitoring, although further validation across diverse crops and real-world field conditions is required.
Why it matches plant phenotyping methods植物葉の病害状態を画像から自動検出・分類する深層学習フレームワークの開発であり、病害表現型の取得・推定が研究の中心。
abstractThis study proposes a deep learning (DL)-based framework for automated detection and classification of tomato and soybean leaf diseases.
Reproduction assets foundThe paper's leaf-disease classification/detection experiments are built on two public Kaggle image datasets cited by the authors as the study's data sources: a soybean leaf dataset (Patil 2024) and a tomato leaf disease dataset (Rex 2019). No authors' analysis code, trained model checkpoints, or paper-specific phenotypDataset · publicPatil A. ( 2024 ). Soyabean-Latest Dataset (
Kaggle ). Available online at: https://www.kaggle.com/datasets/adityapatil1205/soyabean-latestOpen asset ↗Kagglelines:1523-1646Dataset · publicRex E. ( 2019 ). Plant Disease Dataset (Tomato Leaf Diseases) (
Kaggle ). Available online at: https://www.kaggle.com/datasets/emmarex/plantdisease197Open asset ↗Kagglelines:1523-1646Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
3D crop phenotyping technology provides critical support for screening morphology-related plant genes and identification of germplasm resource. Organ segmentation or recognition is the first key step in 3D crop phenotyping, where inductive deep learning currently dominates as the mainstream methodology. However, the high requirement for data annotation in inductive learning paradigm has transformed the manual data labeling into a labor-intensive task, thereby in turn restricting the progress of inductive learning. This problem has inspired us to leverage Graph Neural Networks (GNNs) as the transductive learning tool to directly segment organs on sparsely annotated crop point clouds. We propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds that have featureless point features. Different from existing graph-based networks, DBGCN not only carries out the static-feature-space graph convolutions that are good at mining and aggregating on local information on the point cloud, but also incorporates dynamic graph convolutions that captures the potential changes of the graph manifold in deep feature space. Extensive experiments prove that the fusion of two types of graph feature convolutions brings a high node (point) classification accuracy, outperforming mainstream GNNs and even several popular inductive deep architectures. On the PlantNet sub-dataset, DBGCN achieves an mAcc (mean accuracy of node classification) of 93.00% under 1.95% manual annotation ratio. On the Soybean-MVS sub-dataset, DBGCN achieves an mAcc of 91.05% under 4.88% manual annotation ratio. Furthermore, our DBGCN not only works well on crop 3D data but can also serve other applications such as the segmentation of point cloud data for large-scale street view. Our dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.
Why it matches plant phenotyping methods3D植物点群から器官を分割・推論する深層学習手法を開発し、植物フェノタイピングデータ上で精度検証しているため、表現型取得・抽出法が中心である。
abstractWe propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds
Reproduction assets foundThe authors explicitly state that their dataset (plant point clouds) and DBGCN code are publicly available on GitHub.Code · publicOur data and code are available at: https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:414-455Dataset · publicOur dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:88-94Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published5 Mar 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 2 · OpenAlex ↗
Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognized as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omics-omics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilizes this "proxy" to enhance the accuracy of the phenotype prediction. We compared a linear mixed model (Best Linear Unbiased Prediction, BLUP) and a nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.
Why it matches plant phenotyping methods植物の表現型予測を目的とする非線形マルチオミクス計算フレームワークが研究の中心であり、単なるオミクス測定や生物学的実験ではない。
abstractwe have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omics-omics nonlinearities.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub, and the metabolome data are publicly available via the RIKEN DropMet website (IDs DM0071, DM0072). Phenotype and other multi-omics data are only available from the corresponding author upon request.Code · publicAll source codes are available from the repository in GitHub: https://github.com/Yoska393/Twostep .Open asset ↗Yoska393/Twosteplines:306-317Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高スループット画像フェノタイピングによる大規模データセットを提示し、画像取得基盤と作物成長動態の解析を中心に扱っているため、方法論文として適格です。
titleFIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper's canopy cover analysis code is publicly available on the authors' ETH GitLab repository. The FIP 1.0 soybean image/trait dataset itself is deposited in the ETH Research Collection and Hugging Face, but those URLs are not among the allowed URLs, so only the code asset qualifies.Code · publicCode availability
The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover. Users with similar data can use the implemented workflow to get canopy cover from their experiments.Open asset ↗gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycoverhtml-lines:207-226Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean ( Glycine max L. ) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.
Why it matches plant phenotyping methods大豆種子画像を標準化して公開するデータセット研究であり、種子形質の自動画像解析・セグメンテーションを支援する方法論的資源が中心です。
titleAn open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications.
Reproduction assets foundThe paper is a data descriptor for a public Mendeley Data repository containing the authors' own soybean seed image dataset (raw scans and segmented seed images) used for seed phenotyping, with an explicit direct URL and DOI.Dataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c733bjz4m3.3
Direct URL to data: https://data.mendeley.com/datasets/c733bjz4m3/3Open asset ↗Mendeley Data · 10.17632/c733bjz4m3.3html-lines:115-142Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stem-leaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong early-stage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.
Why it matches plant phenotyping methods大豆の3D点群から器官レベル形態を抽出する分割フレームワークを開発・評価しており、植物表現型取得手法が研究の中心である。
abstractA critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation.
Reproduction assets foundThe authors state that the dataset (Soybean-MVS point clouds) and program code used in this study are publicly available at their GitHub repository, which is an allowed URL.Code · publicThe dataset and program code used in this study can be found at the link below: https://github.com/NiuJiarui718/SOY3DSEG .Open asset ↗NiuJiarui718/SOY3DSEGlines:306-323Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular pathways. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome → microbiome, genome → metabolome, metabolome → microbiome, and microbiome → metabolome) to estimate the predictability of individual omics features. We evaluated whether subsets of features with high cross-omics predictability improved phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling the prioritization of variables that capture coherent biological signals enriched with phenotype-relevant information. Consequently, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environmentally dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and achieved prediction accuracies comparable to those of the full-feature model. Under drought conditions, the phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that the environment-responsive omics layers captured phenotypic variations that were not explained by additive genetic effects. Our results highlight the hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking the genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding.
Why it matches plant phenotyping methods植物形質予測のための新しい多層オミクス統合フレームワークを提案し、予測モデル比較と性能評価を行っているため、計算的フェノタイピング手法が中心である。
abstractwe present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits all source codes and data in a public GitHub repository (Yoska393/ReciprocalBLUP), which contains the authors' analysis code and data for the soybean multi-omics phenotype prediction study. The NARO Genebank URL is only the source of plant accessions, not a phCode · publicAll source codes and data are available from the repository in GitHub: https://github.com/Yoska393/ReciprocalBLUP (accessed on 20 November 2025).Open asset ↗Yoska393/ReciprocalBLUPlines:285-308Code / dataset availability confirmedCrossref · OpenAlex · checked 6 Sept 2026
SoybeanLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementRoot system architecture
Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods (p 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration.
Why it matches plant phenotyping methods根系観察用ハードウェアと画像セグメンテーション手法を開発し、根形質抽出性能を検証した、中心的な植物フェノタイピング研究である。
abstractThis manuscript developed a cost-effective hybrid imaging system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's soybean root image data (the time-series NRMS dataset and scanner validation dataset used for phenotyping) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code availability is stated, so the资产Dataset · publicData Availability Statement: The data presented in this study are openly available in [GitHub] at
[https://github.com/xusiyue/RootPO_DBSCAN/tree/master/project_rootSystem/data (accessed
on 31 October 2025)].Open asset ↗GitHub · xusiyue/RootPO_DBSCANpdf-page:18 lines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
SoybeanLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Accurate phenotyping of root traits is essential for understanding how plants respond to varying soil water treatment conditions, yet traditional phenotyping methods are often destructive and limited in capturing the full three‐dimensional (3D) complexity of root systems. Existing two‐dimensional imaging techniques and advanced 3D methods for performing root phenotyping, like magnetic resonance imaging or computed tomography, either compromise on resolution, are cost‐prohibitive, or lack scalability. To address these limitations, this study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping. Using FPP, two architectural root traits were extracted: the number of root tips and the volumetric occupancy of the root system. These traits, difficult to obtain through manual phenotyping or conventional imaging, were automatically derived from the FPP 3D point clouds and validated against expert‐assigned fibrosity scores serving as the biological reference. The study involved 36 soybean ( Glycine max (L.) Merr.) plants from six genotypes, pre‐classified as either stress‐treated or grown under rain‐fed conditions. Results showed strong alignment between FPP‐derived traits and expert evaluations. Stress‐ treated plants consistently exhibited more root tips and greater volumetric occupancy, confirming the biological relevance of these metrics. While this study does not attempt to classify drought tolerance directly, the structural variations observed under drought stress may serve as a foundation for identifying stress‐responsive phenotypes in future work. Overall, the findings demonstrate that FPP provides a fast, scalable, and accurate tool for 3D root phenotyping under variable water conditions.
Why it matches plant phenotyping methodsFPPによる根系の3次元形質取得・自動抽出を開発し、専門家評価と検証した研究であり、フェノタイピング手法が中心です。
abstractthis study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the datasets generated and/or analyzed in this soybean root FPP phenotyping study, which is an allowed URL. No author analysis code is explicitly deposited.Dataset · publicying and Overcoming Weaknesses via Breed-
ing, Genomics, Phenomics and Physiology).
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated and/or analyzed dur-
ing the current research are available at Google
Drive link: https://drive.google.com/file/d/1BJ4yq8QEWY3E5qQIQmYcOXHhEYn1zTE-
/view?usp=sharing
O RC I D
JiaqiongLi https://orcid.org/0009-0006-2247-425X
ZengluLi https://orcid.org/0000-0003-4114-9509
BeiwenLi https://orcid.org/0000-0001-8130-7730
R E F E R E N C E S
Balasubramaniam, B., Li, J., Liu, L., & Li, B. (2023). 3D imaging with
fringe projection for food and agriculturalOpen asset ↗pdf-raw-page:17 lines:1-91Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Why it matches plant phenotyping methods植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data sourceCode · publicresearch received no external funding
Institutional Review Board Statement: Not applicable. This study did not involve humans or animals.
Informed Consent Statement: Not applicable. This study did not involve humans.
Data Availability Statement: Complete source code, documentation, and example datasets are publicly available
at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally
or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained
from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available.
Acknowledgments: The authors thank the iCEV IOpen asset ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensingpdf-layout-page:11 lines:1-70Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Soybean is one of the world's major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats to its yield and quality during soybean cultivation. Rapid identification of soybean leaf diseases could provide a better solution for efficient control and subsequent precision application. In this study, a lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification. In MFEF-DCNet, a multiscale feature extraction fusion (MFEF) module for soybean leaves was constructed by utilizing a convolutional attention module and depth-separable convolution to improve the model feature extraction capability. Multiscale features are fused by using dense connections (DC) in the backbone network to improve the model generalization capability. Experiments were implemented on eight distinct disease and deficiency classes of soybean images (including bacterial blight, cercospora leaf blight, downy mildew, frogeye leaf spot, healthy, potassium deficiency, soybean rust, and target spot) using the proposed network. The results showed that the MFEF-DCNet had an accuracy of 0.9470, an average precision of 0.9510, an average recall of 0.9480, and an F1-score of 0.9490 for soybean leaf disease identification. And MFEF-DCNet had certain performance advantages in terms of classification accuracy, convergence speed and other effects compared with VGG16, ResNet50, DenseNet201, EfficientNetB0, Xception and MobileNetV3_small models. In addition, the accuracy of the MFEF-DCNet model in recognizing soybean diseases in local data was 0.9024, which indicated that the MFEF-DCNet model had favorable application in practical applications. The proposed model and experience in this study could provide useful inspiration for automated disease identification in soybean and other crops.
Why it matches plant phenotyping methods大豆葉の病害状態を画像から認識するCNNを開発・評価しており、植物病害フェノタイピング手法が研究の中心である。
abstracta lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification.
Reproduction assets foundThe paper's phenotyping analysis is built on a public plant-image dataset: the Auburn Soybean Disease Image Dataset (ASDID), explicitly described as publicly available and cited with a Dryad DOI. No author analysis code, trained model checkpoints, or supplementary code/model deposit is stated in the supplied blocks. NoDataset · publicThe dataset used for the study was the Auburn Soybean Disease Image Dataset (ASDID), which is publicly available and has been extensively studied and validated ( Bevers et al., 2022 ).Open asset ↗lines:324-376Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Deep understanding of slow-wilting is essential for developing drought-tolerant crops. Existing approaches to measure transpiration rates are difficult to apply to large populations due to their high cost and low throughput. To overcome these challenges, we developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device. The system tracked the transpiration rate in real time by measuring changes in the pot weight in 224 recombinant inbred lines of Taekwangkong (fast-wilting) x SS2-2 (slow-wilting) under water-restricted conditions. Among five transpiration features we determined, stress recognition time point (SRTP) and decrease in transpiration rate by stress (DTrs) are informative parameters, that are interconnected and independently affect slow-wilting as well. Quantitative trait loci (QTL) for SRTP and DTrs were identified at the same location as the major QTL for slow wilting, qSW_Gm10 , identified in the previous study. Notably, we found a novel major QTL for DTrs, qDTrs_Gm04 , with a LOD value of 42 and PVE of 47 %. As a candidate gene for qDTrs_Gm04 , GmWRKY58 was selected with differential expression between the parental lines under drought conditions as well as upstream sequence variation. Our high-throughput system is of help not only to biological research but breeding programs of drought-tolerant lines.
Why it matches plant phenotyping methods高スループットなセンサー基盤を開発し、ポット重量変化からダイズの蒸散率・乾燥ストレス応答をリアルタイム抽出することが研究の中心であるため。
abstractwe developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device.
Reproduction assets foundThe paper's data availability statement explicitly deposits the processed phenotypic data (transpiration features from the RIL drought experiment) and trained Random Forest/XGBoost model objects on Figshare, which is a paper-specific, publicly accessible asset.Dataset · publicThe processed phenotypic data, along with the trained Random Forest and XGBoost machine learning model objects (.rds files), are publicly available on Figshare at https://doi.org/10.6084/m9.figshare.c.7951601.v1.Open asset ↗Figshare · 10.6084/m9.figshare.c.7951601.v1html-lines:276-299Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland. Top of canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高解像度RGB画像による圃場フェノタイピングデータセットを提示し、作物生育動態とG×E解析を可能にする方法・データ基盤が中心である。
titleFIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper is a data note whose core contribution is a public soybean phenotyping dataset (raw FIP images, segmentation masks, canopy cover data, BLUEs, weather, reference traits) deposited at ETH Research Collection, plus the authors' canopy cover extraction workflow code on GitLab. Both are paper-specific, public, andDataset · publicason, therefore,
from 2020 to 2022, photosynthetic photon fluence rate (PPFR) was taken from a LI-COR sensor placed next to the
field. The factor to convert radiation in MJ m−
2 to PPFR was 2.04 according to [26].
4.1 Data Files and Structure
The dataset presented in this study is available at ETH Research Collection under DOI: https://doi.org/10.3929/ethz-b-000742401. The dataset is structured into directories that align with the described data processing
pipeline used for extracting and analyzing canopy cover traits from field images. All files are provided in interoperable
and widely-used ‘.csv‘ and ‘.png‘ format.
• data/Design 2015 2022 Eschikon.csv: Experimental design file, including pOpen asset ↗ETH Research Collection · 10.3929/ethz-b-000742401pdf-raw-page:6 lines:1-47Code · publicCollection (https://doi.org/10.3929/ethz-b-000742401) and as Hugging Face data set card (doi.org/10.57967/hf/6052) allowing
interoperability and standardization with other datasets.
9 Code availability
Users with similar data can use the implemented workflow to get canopy cover from their experiments. The code
is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover
10 Author contributions
BK: Developed algorithm, analyzed data and drafted manuscript; NK, LR, AH, AM: FIP development, BK, NK,
CO, LK, LR, OZ, SC, FL, HA, NS, FT, HZ, CAB, CB, AH: Collected and prepared data; Experimental design:
BK, LK, LR, AH; all authors improved and approved the manuscript
5Open asset ↗gitlab.ethz.ch · crop_phenotyping/fip-soybean-canopycoverpdf-raw-page:7 lines:1-46Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition.
Why it matches plant phenotyping methods作物葉の病徴を画像・テキストから認識するマルチモーダル手法の開発と評価が研究の中心であり、植物の病害状態を直接推定している。
abstractwe proposed a cross-modal data fusion via a vision-language model for crop disease recognition.
Reproduction assets foundThe paper's Data Availability Statement explicitly links the public image datasets used for its crop disease recognition experiments: the Soybean Disease dataset (Dryad DOI) and the PlantVillage dataset (Kaggle). These are the phenotyping image inputs directly used in this study. The AI Challenge 2018 dataset is also公开Dataset · publicThe soybean
dataset is available at https://doi.org/10.5061/dryad.41ns1rnj3 (accessed on 1 April 2025).Open asset ↗Dryad · 10.5061/dryad.41ns1rnj3pdf-page:12 lines:1-58Dataset · publicplantvillage dataset is available at https://www.kaggle.com/datasets/abdallahalidev/plantvillage-Open asset ↗Kagglepdf-page:12 lines:1-58Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Genomic selection (GS) and phenotypic selection (PS) are widely used for accelerating plant breeding. However, the accuracy, robustness, and transferability of these two selection methods are underexplored, especially when addressing complex traits. In this study, we introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion. The GPS framework was rigorously tested using an extensive suite of models, including statistical approaches (GBLUP and BayesB), machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), a deep learning method (DNNGP), and a recent phenotype-assisted prediction model (MAK). These models were applied to large datasets from four crop species, maize, soybean, rice, and wheat, demonstrating the versatility and robustness of the framework. Our results indicated that: (1) data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies. The top-performing data fusion model (Lasso_D) improved the selection accuracy by 53.4% compared to the best GS model (LightGBM) and by 18.7% compared to the best PS model (Lasso). (2) Lasso_D exhibited exceptional robustness, achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism (SNP) density variations, underscoring its adaptability to diverse data conditions. Moreover, the model's accuracy improved with the number of auxiliary traits and their correlation strength with target traits, further highlighting its adaptability to complex trait prediction. (3) Lasso_D demonstrated broad transferability, with substantial improvements in predictive accuracy when incorporating multi-environmental data. This enhancement resulted in only a 0.3% reduction in accuracy compared to predictions generated using data from the same environment, affirming the model's reliability in cross-environmental scenarios. This study provides groundbreaking insights, pushing the boundaries of predictive accuracy, robustness, and transferability in trait prediction. These findings represent a significant contribution to plant science, plant breeding, and the broader interdisciplinary fields of statistics and artificial intelligence.
Why it matches plant phenotyping methods植物の形質予測を目的とするGPSデータ融合フレームワークを開発し、複数作物・モデルで精度、頑健性、環境間移 transferability を評価しており、形質推定手法が研究の中心である。
abstractwe introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion.
Reproduction assets foundThe paper's authors publicly released their GPS analysis scripts on GitHub, and the study used public genomic+phenotypic datasets (rice, maize, wheat, SoyNAM) with explicit URLs. DNNGP and MAK repositories are cited third-party tools, not paper-specific assets.Code · publicThe GPS scripts are available in the release package on GitHub ( https://github.com/Jinlab-AiPhenomics/BioGPS ). All public datasets used in this study are listed in the main text ( Table 1 ).Open asset ↗Jinlab-AiPhenomics/BioGPSlines:244-249Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisSoybeanCell / cellular structureRootMorphology / geometry measurementRoot system architecture
Abstract Root system architecture (RSA), the three-dimensional arrangement of roots in soil, is a critical determinant of plant productivity, resource use efficiency, and resilience to environmental stress. Despite its agronomic importance, RSA remains a largely untapped breeding target due to historical technical barriers in root phenotyping. We present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems. Applying RADICYL to a genetically diverse panel of 371 soybean accessions, we combined 3D phenotyping with genome-wide association studies (GWAS), single-nucleus RNA sequencing (snRNA-seq), and gene co-expression network (GCN) analysis to identify RCE1 and NPR3 as central regulators of RSA, suggesting auxin and salicylic acid-mediated signaling impacts RSA in specific root tissues. Functional validation in Arabidopsis mutants revealed conserved effects on root width and lateral root development. Our findings position the endodermis and metaphloem as key regulatory cell types and demonstrate how multi-omic frameworks can accelerate the discovery of functional genes underlying complex traits. This study establishes a foundation for cell-type-targeted genome editing and climate-smart crop engineering, offering actionable genetic targets to optimize root systems for improved nutrient acquisition, drought resilience, and deep carbon sequestration. By bridging genotype, cellular context, and phenotype, this work redefines RSA as a tractable and transformative trait for the future of crop improvement.
Why it matches plant phenotyping methodsRADICYLという根系構造を定量化する高スループット3Dフェノタイピング基盤の開発・適用が研究の中心であり、15形質を測定している。
abstractWe present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems.
Reproduction assets foundThe paper's Data and code availability section names public repositories containing the authors' analysis code: a GitLab repo for WGCNA/single-cell network analysis, a GitHub repo for the RADICYL root image segmentation/phenotyping pipeline, and PyGNA2 on PyPI/GitLab. These are paper-specific, publicly actionable code/Code · publicn every 5°, resulting in 72 images per plant per timepoint for subsequent 3D root
1103 reconstruction. Phenotypic traits were quantified using the same automated pipeline described
1104 above for soybean.
1105
1106 Data and code availability
1107 The code to analyze the WGCNA network and single-cell data can be found here:
1108 https://gitlab.com/salk-tm/soybean-root-gwas/. RADYCL Segmentation pipeline for image
1109 analysis can be found here: https://github.com/Salk-Harnessing-Plants-Initiative/SSRAPC-Soy-
1110 Segmentation-Root-Architecture-Phenotyping-for-Cylinder.git. PyGNA2 is available on PyPI
1111 (https://pypi.org/project/pygna2/) and GitLab (https://gitlab.com/salk-tm/pygna2).
1112Open asset ↗salk-tm/soybean-root-gwaspdf-layout-page:30 lines:1-54Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Soybean is an important oilseed crop, rich in protein and oil, often referred to as a ``cash crop'' or ``gold bean'' by Indian farmers. In Maharashtra, soybean cultivation spans over approximately 3.8 million hectares, producing 3.07 million tons, placing the state second in India for overall soybean production. However, despite of its significance, several issues such as weeds, diseases, and pests hamper the overall productivity of soybean. Addressing these challenges faced by soybean growers it is essential to enhance yield and improve the crop's overall potential Currently, the farming sector is transitioning towards Agriculture 5.0, also known as digital farming. This approach utilizes data-driven technologies, such as artificial intelligence and computer vision, to transform the agriculture sector. These technologies enable the automation of several farming tasks. To develop accurate and robust machine learning/deep learning models high quality datasets are needed. With this aim, we have created a comprehensive dataset of soybean crop images affected by diseases and pest attacks from original fields of Maharashtra region located in India. Data acquisition was conducted across two seasons through aerial as well as ground-based approaches. The dataset is enriched with 4 types of diseases and 1 pest attack. The proposed dataset will serve as a valuable resource for training and testing machine learning and deep learning models ,enabling accurate detection and classification of diseases and pests attack damage.
Why it matches plant phenotyping methods大豆の病害・害虫被害を対象とした航空・地上画像データセットの構築が中心で、植物の病害状態を画像から評価する再利用可能な資源であるため。
abstractwe have created a comprehensive dataset of soybean crop images affected by diseases and pest attacks from original fields of Maharashtra region located in India.
Reproduction assets foundThe paper's own soybean UAV and leaf image dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, matching an allowed URL.Dataset · publicarashtra, India
Banawadi: Longitude 74.1943023 Latitude:17.3179823
Goware: Longitude 74.208238 Latitude:17.286995
Data accessibility
Repository name: Mendeley Data
MH-SoyaHealthVision: An Indian UAV and Leaf Image Dataset for Integrated Crop Health Assessment
Data identification number: 10.17632/hkbgh5s3b7.1
Direct URL to data: https://data.mendeley.com/datasets/hkbgh5s3b7/1
1
Value of the Data
•
The dataset uniquely combines UAV-based aerial images, offering high resolution and a broad spectrum, with ground-level close-up images. UAV imaging is effective for macro level field variability while ground-based images provide micro level finer details of symptoms such as leaf spots, lesions, andOpen asset ↗Mendeley Data · 10.17632/hkbgh5s3b7.1lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Noninvasive analysis of pod phenotypic traits under field conditions is crucial for soybean breeding research. However, previous pod phenotyping studies focused on postharvest materials or were limited to indoor scenarios, failing to generalize to real-field environments. To address these issues, this paper employs an instance segmentation approach for the precise extraction of the pod area from multiplant RGB images in preharvest soybean fields. We first introduce a cost-effective workflow for constructing datasets of densely planted crop images with a uniform backdrop. Starting with video recording, high-quality static frames are collected by automatic selection. Then, a large vision model is explored to facilitate dense annotation and build a large-scale soybean dataset comprising 20k pod masks. Second, the pod instance segmentation model PodNet is developed based on the YOLOv8 architecture. We propose a novel hierarchical prototype aggregation strategy to fuse multiscale semantic features and a U-EMA prototype generation network to improve the model's perception performance for small objects. Comprehensive experiments suggest that lightweight PodNet achieves a superior mean average accuracy of 0.786 in the custom pod segmentation dataset. PodNet also performs competitively on in-field images without a backdrop and enables real-time inference on the edge computing platform. To the best of our knowledge, PodNet is the first pod instance segmentation model for preharvest fields. The low-cost and high-precision extraction of pods is not only a prerequisite for phenotypic analysis of the pod organs but also constitutes an important foundation in conducting cross-scale phenotyping from whole-plant to seed levels.
Why it matches plant phenotyping methods大豆莢の表現型抽出を目的に、データセット構築、インスタンスセグメンテーションモデル、実環境での性能評価を中心的に開発しているため。
abstractNoninvasive analysis of pod phenotypic traits under field conditions is crucial for soybean breeding research.
Reproduction assets foundThe authors open-source the field soybean pod instance segmentation dataset (488 images, 20k pod masks) and PodNet-related resources at their public GitHub repository, explicitly stated in the data availability statement.Dataset · publicefficiency of manual annotation. The average pod number per image is more than 56, and the total number of pod objects is greater than 20k. Fig. 7 (c) shows that most of the pods are located in the upper center region of the image. The field soybean pod instance segmentation dataset is open sourced for the research community at https://github.com/Boatsure/PodNet .
3.2.
Implementation and experiments of PodNet
Considering that instance segmentation is a computationally intensive task, this study selected the lightweight architecture YOLOv8-nano (v8n) as the baseline model for the development of PodNet. Model v8n has simplified module connections and competitive perception accuracy whileOpen asset ↗Boatsure/PodNetlines:96-104Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments' findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development.
Why it matches plant phenotyping methods大豆種子表面の欠陥という植物器官の状態を、画像処理と改良YOLOv9で抽出・検出する手法開発が研究の中心であり、性能評価も行っている。
abstractwe propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc.
Reproduction assets foundThe paper's soybean seed defect detection study uses a public Kaggle dataset of 4,388 soybean seed images (intact, broken, skin-damaged, spotted), explicitly stated in the Data Availability statement. No author analysis code or trained model checkpoints are deposited; makesense.ai is a generic annotation tool, not a deDataset · publicuthors reviewed and approved the final manuscript.
Funding
This work was supported in part by the Special Support Plan for High level Talents in Zhejiang Province (2021R52019), and the Education Department of Hainan Province (Hnky2024-18).
Data availability
The data used in this article can be downloaded from the following link https://www.kaggle.com/datasets/warcoder/soyabean-seeds .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Xia Yu, Email: 100170@hainnu.edu.cn.
Qi Dai, Email: daiqi@zstu.edu.cn.
ReferenOpen asset ↗Kaggle · warcoder/soyabean-seedslines:388-411Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 kg ha -1 ) and an improvement in F1-score for five-class lodging discrimination (0.06) at S7 growth stage. The SoyNet-Res model employing multi-task learning exhibits better accuracy in both yield estimation (RMSE: 349.45 kg ha -1 ) when compared to the H2O-AutoML. Furthermore, our findings indicate that multi-task deep learning outperforms single-task learning in lodging discrimination, achieving an accuracy top-2 of 0.87 and accuracy top-3 of 0.97 for five-class. In conclusion, the point cloud deep learning method exhibits tremendous potential in learning multi-phenotype tasks, laying the foundation for optimizing soybean breeding programs.
Why it matches plant phenotyping methodsUAV・SfM-MVSによるダイズ群落の3D構造再構成と、収量推定・倒伏判別のための専用深層学習モデル開発が研究の中心であり、再利用可能な表現型取得・推定手法に該当する。
abstractIn this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies.
Reproduction assets foundThe article's Data availability statement explicitly says the code and data used in the study (soybean UAV point cloud phenotyping, SoyNet/SoyNet-Res models, yield/lodging analysis) are publicly downloadable from the authors' GitLab repository.Code · publicData availability
The code and data mentioned in the article can be downloaded from https://gitlab.com/zlyzly28/plant-phenomics .Open asset ↗gitlab.com/zlyzly28/plant-phenomicslines:588-659Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
Recent years have seen significant technological advancements in precision farming and plant phenotyping. Remote sensing along with deep learning (DL) techniques can increase phenotyping efficiency and help on-farm decision making with rapid stress detection. In this work, we use these techniques to evaluate drought stress in soybean plants, a crop whose yield is significantly affected by water availability. Images were taken from a high vantage in the field at various times throughout the day. Each image is given a wilting score ranging from 0 to 4 by expert scorers. We implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image. Given the significant overlap between adjacent classes in our dataset, we were able to achieve an overall classification accuracy of 64% and a one-off accuracy of 96% on our holdout test set. Our model outperformed DenseNet121 in most metrics, and provided comparable performance to a vision transformer (ViT) while having fewer parameters overall, less complexity (useful for edge implementations), and some interpretability. Furthermore, we were able to show that our model outperformed expert human annotators by predicting more consistent and accurate wilt levels when considering single-image re-annotation. The results show that our proposed methodology can be a useful approach in detecting drought stress in soybean fields to facilitate efficient crop management and aid selection of drought-resilient varieties.
Why it matches plant phenotyping methods画像からダイズ葉の萎凋・干ばつストレスを推定するMIL手法の開発と性能比較が中心であり、植物状態の表現型推定に該当する。
abstractWe implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image.
Reproduction assets foundThe paper's soybean leaf-wilt image dataset (1788 field images with expert wilt scores) is openly available on Zenodo, and the authors' MIL classification/analysis code is publicly available on GitHub, both explicitly stated in the Data Availability Statement.Dataset · publicThe original data presented in the study are openly available on the
data sharing platform Zenodo, accessed on 6 September 2023 https://zenodo.org/records/8256382
with DOI 10.5281/zenodo.8256382.Open asset ↗Zenodo · 10.5281/zenodo.8256382pdf-page:16 lines:1-58Code · publicWe have also made our code available on github and can be accessed
at https://github.com/ARoS-NCSU/Soybean-Leaf-Wilt-Classification, accessed on 4 March 2025.Open asset ↗GitHubpdf-page:16 lines:1-58Code / dataset availability confirmedCrossref · checked 6 Sept 2026
The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R2) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios.
Why it matches plant phenotyping methods植物病害画像から病斑割合と病害重症度を推定する画像セグメンテーション手法を開発し、野外データセット、精度、速度を評価しており、植物表現型取得法が中心である。
abstractThe segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity.
Reproduction assets foundThe paper's field-scenario plant disease image dataset (SBB, WSR, CAR with three-color pixel labels) is publicly released on Kaggle via DOI, as stated in the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicData Availability Statement: The original data presented in this study are openly available in Kaggle
at https://doi.org/10.34740/kaggle/ds/6620728, accessed on 9 March 2025.Open asset ↗Kaggle · 10.34740/kaggle/ds/6620728pdf-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
CowpeaSoybeanRootMorphology / geometry measurementSegmentationRoot system architecture
A simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes (adzuki bean, mung bean, cowpea, and soybean) based on two-dimensional images. Four different thresholding methods; Otsu, Gaussian adaptive, mean adaptive and triangle threshold were used to know the effect of thresholding in root trait estimation and to optimize the accuracy of root trait estimation. The results generated by the algorithm applied to 400 legume root images were compared with those generated by two separate software (WinRHIZO and RhizoVision), and the algorithm was validated using ground truth data. Distance transform method was used for estimating SA, AD, and RV and ConnectedComponentsWithStat function for TRL estimation. Among the thresholding methods, Otsu thresholding worked well for distance transform, while triangle threshold was effective for TRL. All the traits showed a high correlation with an R² ≥0.98 (p < 0.001) with the ground truth data. The root mean square error (RMSE) and mean bias error (MBE) were also minimal when comparing the algorithm-derived values to the ground truth values, with RMSE and MBE both < 10 for TRL, < 6 for SA, and < 0.5 for AD and RV. This lower value of error metrics indicates smaller differences between the algorithm-derived values and software-derived values. Although the observed error metrics were minimal for both software, the algorithm-derived root traits were closely aligned with those derived from WinRHIZO. We provided a simple Python algorithm for easy estimation of legume root traits where the images can be analyzed without any incurring expenses, and being open source; it can be modified by an expert based on their requirements.
Why it matches plant phenotyping methods根の二次元画像から主要形質を抽出するPythonアルゴリズムを開発し、既存ソフトウェアおよびグラウンドトゥルースで検証しており、植物フェノタイピング手法が研究の中心です。
abstractA simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes
Reproduction assets foundThe authors publicly release their Python root-trait analysis source code together with the 400 legume root images and validation images on GitHub, as stated in the article text and Data availability statement. The Zenodo DOI cited for ground-truth images is a third-party dataset from Rose and Lobet (2018), i.e., citedCode · publicThe source code along with the root images and the validation images can be downloaded from ( https://github.com/AG9843/Legume-Root-Analysis.git ).Open asset ↗AG9843/Legume-Root-Analysislines:65-75Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published27 Feb 2025Journal of Information Systems Engineering and ManagementCited by 0 · OpenAlex ↗
The rapid advancement of artificial intelligence has paved the way for innovative solutions in agriculture, particularly in crop disease detection. Diagnosing plant diseases often rely on manual inspection and expert knowledge that time-consuming and prone to errors. As agriculture faces increasing challenges from pests, diseases and climate change, there is a pressing require for efficient, automated systems to monitor crop health. In this manuscript, Development of an AI-Based Pyramid Convolutional Neural Network Model with ResNet and Coati Optimization for Multi-Crop, Multi-Disease Identification in Leaf Images is (PCNN-ResNet-COA) proposed. Initially the data is collected from Plant Disease Classification Merged Dataset. This dataset includes both healthy and diseased leaves for various crop types. The collected images are organized into categories based on the crop type such as crop, grape and soybean and disease condition healthy or various types of diseases. Then the categorized images are fed to Pyramid Convolutional Neural Network with Residual Network (PCNN-ResNet), for identifying and classifying the Leaf Images as Corn_healthy, Corn_northern_leaf_blight, Corn_gray_leaf_spot, Grape_healthy, Grape_leaf_blight, Grape_black_rot, Grape_black_measles, corn_common_rust, soybean_bacterial_blight, Soybean_downy_mildew, Soybean_mosaic_virus, Soybean_powdery_mildew, Soybean_healty, Soybean_rust, and Soybean_southern_blight. In general, PCNN-ResNet does not express any adaption of optimization methods for determining optimal parameters to assure precise detection and classification of Leaf Images. Coati Optimization Algorithm (COA) is proposed for improving the weight parameter of PCNN-ResNet classifier that accurately predicts crop yield. The proposed PCNN-ResNet-COA method is implemented and analyzed with help of performance metrics like accuracy, precision, F1-score, computational time is evaluated. The proposed ResNet-COA approach attains 18.97%, 24.57% and 32.68% higher accuracy and 19.84%, 24.93% and 31.62% lower computational time with existing method respectively.
Why it matches plant phenotyping methods葉画像から植物病害状態を自動分類する新規CNN手法を開発・評価しており、植物フェノタイピング手法が中心です。
titleDevelopment of an AI-Based Pyramid Convolutional Neural Network Model with ResNet and Coati Optimization for Multi-Crop, Multi-Disease Identification in Leaf Images
Reproduction assets foundThe paper's leaf-image disease classification experiments are built entirely on the public Kaggle 'Plant Disease Classification Merged Dataset' (88 classes, >76,000 images), which is explicitly cited with its Kaggle URL in the references. No author code, models, or checkpoints are reported as available.Dataset · publicThe input data are obtained from Plant Disease Classification Merged Dataset [17]. A huge number of images, at
least one healthy plant and one disease per plant, the most prevalent diseases, annotated images, laboratory and
field photographs, significant staple foods and the plant species with the highest worldwide production were the
self-imposed conditions for the dataset.Open asset ↗Plant Disease Classification Merged Datasetpdf-layout-page:4 lines:1-64Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant's root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.
Why it matches plant phenotyping methodsダイズ根上の線虫シスト数とサイズという植物病害抵抗性関連形質を、画像から自動検出・計測するソフトウェアを開発しており、表現型取得手法が研究の中心です。
abstractwe have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously.
Reproduction assets foundThe paper's SCN cyst phenotyping assets are publicly available: the authors' Nemacounter analysis software on GitHub, two annotated cyst image datasets on Roboflow (bounding-box and segmentation/area annotations), and the authors' trained YOLOv5-xl model (cystmodel.pt) on Iowa State's Box. The SAM weights and ultralyptCode · publicThe Nemacounter software can be downloaded here: https://github.com/DjampaKozlowski/NemaCounter and we provide an installation manual and utilization manual as supplementary data.Open asset ↗DjampaKozlowski/NemaCounterlines:65-70Dataset · publicThe complete dataset is accessible on the Roboflow website at: https://universe.roboflow.com/iowa-state-university-cwvqa/cystnewboundingboxv2Open asset ↗lines:118-138Dataset · publicAll training datasets are available on Roboflow website at : https://universe.roboflow.com/iowa-state-university-cwvqa/cystnewboundingboxv2 and https://universe.roboflow.com/iowa-state-university-cwvqa/cyst-detectors-area.Open asset ↗lines:139-197Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
SoybeanX-ray / CTRootMorphology / geometry measurementRoot system architecture
Typically, root system architecture (RSA) is not visible, and realistically, high-throughput methods for RSA trait phenotyping should capture key features of developing root systems in solid substrates in 3D. In a published 2-D study using thin rhizoboxes, vermiculite as a growing medium, and photography for imaging, triplicates of 137 soybean cultivars were phenotyped for their RSA. In the transition to 3-D work using X-ray computed tomography (CT) scanning and mineral soil, two research questions are addressed: (1) how different is the soybean RSA characterization between the two phenotyping systems; and (2) is a direct comparison of the results reliable? Prior to a full-scale study in 3D, we grew, in pots filled with sand, triplicates of the Casino and OAC Woodstock cultivars that had shown the most contrasting RSAs in the 2-D study, and CT scanned them at the V1 vegetative stage of development of the shoots. Differences between soybean cultivars in RSA traits, such as total root length and fractal dimension (FD), observed in 2D, can change in 3D. In particular, in 2D, the mean FD values are 1.48 ± 0.16 (OAC Woodstock) vs. 1.31 ± 0.16 (Casino), whereas in 3D, they are 1.52 ± 0.14 (OAC Woodstock) vs. 1.24 ± 0.13 (Casino), indicating variations in RSA complexity.
Why it matches plant phenotyping methods2D写真法と3D X線CT法という根系表現型計測システムを比較・評価し、RSA形質の測定結果の信頼性を検討しているため、フェノタイピング手法が中心である。
abstracthigh-throughput methods for RSA trait phenotyping should capture key features of developing root systems in solid substrates in 3D
Reproduction assets foundThe paper's own supplement (MDPI S1) contains two videos produced in MATLAB from skeletal 3-D images of the root systems reconstructed from this study's CT scanning data — paper-specific phenotyping outputs made publicly available. The figshare links are explicitly described as 'soybean genomic data' from the prior GWASupplement · publicTwo videos (.AVI files), one per soybean cultivar, were produced in MATLAB (MathWorks, Natick, MA, USA) from skeletal 3-D images of the root systems, and are made available as a supplement to the graphical results presented for the 3-D phenotyping system in Figure 2 in the manuscript.Open asset ↗lines:100-116Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The increase in the global population is leading to a doubling of the demand for protein. Soybean ( Glycine max ), a key contributor to global plant-based protein supplies, requires ongoing yield enhancements to keep pace with increasing demand. Precise, on-plant seed counting and localization may catalyze breeding selection of shoot architectures and seed localization patterns related to superior performance in high planting density and contribute to increased yield. Traditional manual counting and localization methods are labor-intensive and prone to error, necessitating more efficient approaches for yield prediction and seed distribution analysis. To solve this, we propose MSANet: a novel deep learning framework tailored for counting and localization of soybean seeds on mature field-grown soy plants. A multi-scale attention map mechanism was applied to maximize model performance in seed counting and localization in soybean breeding fields. We compared our model with a previous state-of-the-art model using the benchmark dataset and an enlarged dataset, including various soybean genotypes. Our model outperforms previous state-of-the-art methods on all datasets across various soybean genotypes on both counting and localization tasks. Furthermore, our model also performed well on in-canopy 360° video, dramatically increasing data collection efficiency. We also propose a technique that enables previously inaccessible insights into the phenotypic and genetic diversity of single plant vertical seed distribution, which may accelerate the breeding process. To accelerate further research in this domain, we have made our dataset and software publicly available: https://github.com/UTokyo-FieldPhenomics-Lab/MSANet.
Why it matches plant phenotyping methods大豆種子の計数・位置推定と垂直分布という植物形質を対象に、深層学習手法を開発・比較検証し、データセットとソフトウェアも公開しているため、フェノタイピング手法が中心である。
abstractwe propose MSANet: a novel deep learning framework tailored for counting and localization of soybean seeds on mature field-grown soy plants.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicwe have made our dataset and software publicly available: https://github.com/UTokyo-FieldPhenomics-Lab/MSANet .Open asset ↗UTokyo-FieldPhenomics-Lab/MSANetlines:1-25Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2003 to 2015 improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art techniques, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R 2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.
Why it matches plant phenotyping methods作物収量という植物形質を遠隔センシング画像から推定する前処理法を開発し、CNN・LSTM・ViTとの比較で性能を検証しており、フェノタイピング手法が中心です。
abstractThis paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers.
Reproduction assets foundThe paper's declarations section explicitly states that the code for data collection, processing, and analysis is openly available on GitHub, and that a sample dataset used in the study is available via a Google Drive link. Both are paper-specific, public, and actionable.Code · publicThe code used in this study is openly available on GitHub at
https://github.com/mananthakkar24/RemoteSensingBlobDetection. This reposi-
tory contains all the necessary code for data collection, processing, and analysis as
described in this paper.Open asset ↗mananthakkar24/RemoteSensingBlobDetectionpdf-page:3 lines:1-48Dataset · publicA sample dataset used in this study is available at:
https://drive.google.com/drive/folders/18Z3hcqRf0nnE5vjqDat99qh3o-Open asset ↗pdf-page:3 lines:1-48Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.
Why it matches plant phenotyping methods衛星スペクトル指数と解析手法によりダイズの生育ステージを推定し、空間解像度や分類性能を評価しており、植物フェノタイピング手法の検証・適用が研究の中心です。
abstractThe aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops.
Reproduction assets foundThe authors state all relevant data (soybean phenology/vegetation index measurements from Sentinel-2 and Amazonia-1) are publicly available in their GitHub repository.Dataset · publicat the difference in spatial resolution of the two sensors evaluated, 10 meters per pixel of Sentinel-2 and 65 meters per pixel of Amazônia-1, is not a determining factor in the correct identification of soybean phenological stages.
Data Availability
All relevant data is available in the GitHub repository at the following link: https://github.com/FSilva-826/DADOS---AMAZONIA1-CENTINEL2 .
Funding Statement
This study was funded by the College of Food and Agriculture Sciences, King Saud University, RSPD2024R678 (to Mohamed A. El-Tayeb). This study was also funded by a scholarship from CAPES, Coordination for the Improvement of Higher Education Personnel, 88887.677482/2022-00 (to Airton Andrade Open asset ↗FSilva-826/DADOS---AMAZONIA1-CENTINEL2lines:240-262Code / dataset availability confirmedOpenAlex · checked 7 Sept 2026
Soybean pod count is a crucial aspect of soybean plant phenotyping, offering valuable reference information for breeding and planting management. Traditional manual counting methods are not only costly but also prone to errors. Existing detection-based soybean pod counting methods face challenges due to the crowded and uneven distribution of soybean pods on the plants. To tackle this issue, we propose a Soybean Pod Counting Network (SPCN) for accurate soybean pod counting. SPCN is a density map-based architecture based on Hybrid Dilated Convolution (HDC) strategy and attention mechanism for feature extraction, using the Unbalanced Optimal Transport (UOT) loss function for supervising density map generation. Additionally, we introduce a new diverse dataset, BeanCount-1500, comprising of 24,684 images of 316 soybean varieties with various backgrounds and lighting conditions. Extensive experiments on BeanCount-1500 demonstrate the advantages of SPCN in soybean pod counting with an Mean Absolute Error(MAE) and an Mean Squared Error(MSE) of 4.37 and 6.45, respectively, significantly outperforming the current competing method by a substantial margin. Its excellent performance on the Renshou2021 dataset further confirms its outstanding generalization potential. Overall, the proposed method can provide technical support for intelligent breeding and planting management of soybean, promoting the digital and precise management of agriculture in general.
Why it matches plant phenotyping methods大豆莢数という植物形質を画像から自動推定する手法を開発し、データセット上で性能評価しているため、植物フェノタイピング手法が研究の中心です。
abstractSoybean pod count is a crucial aspect of soybean plant phenotyping
Reproduction assets foundThe paper's SPCN analysis code is stated to be publicly available on GitHub. The BeanCount-1500 dataset itself has no public deposit or availability statement (authors must be contacted), so it does not qualify as a public asset.Code · publicThe source code is available at https://github.com/johnhamtom/
soybean_counting_SPCN (accessed on 10 August 2024).Open asset ↗pdf-page:2 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN 15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Why it matches plant phenotyping methodsラマン分光によるダイズの生物的窒素固定活性の非侵襲的定量法を開発し、IRMSで検証しているため、植物生理状態の取得手法が中心である。
abstractThis study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the original Raman spectra/BNF measurement data on FigShare, a public, paper-specific asset directly reproducing the study's phenotyping measurements.Dataset · publicData Availability Statement: The original data presented in the study are openly available in FigShare
at https://figshare.com/articles/dataset/dx_doi_org_10_6084_m9_figshare_25909780/25909780 (ac-
cessed on 28 May 2024).Open asset ↗FigSharepdf-page:13 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy ( F-score = 0.77) and branch angle calculation accuracy ( r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine ( F-score = 0.53) and density-based methods ( F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.
Why it matches plant phenotyping methodsLiDAR点群からダイズの枝を検出し枝角度などの形態形質を抽出するアルゴリズムを開発・検証しており、植物表現型取得手法が研究の中心である。
abstractwe developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data
Reproduction assets foundThe paper's Soybean3D point cloud dataset, source code, and software are explicitly stated as openly available on the authors' public GitHub repository, directly supporting the paper's soybean branch angle phenotyping analysis.Code · publicThe Soybean3D datasets, source code, software, and other supporting data are openly available on GitHub ( https://github.com/Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor ).Open asset ↗Jinlab-9AiPhenomics/SCAG_PlantAngleExtractorlines:178-220Dataset · publicThe Soybean3D datasets, source code, software, and other supporting data are openly available on GitHub ( https://github.com/Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor ).Open asset ↗Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor · Soybean3Dlines:291-296Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.
Why it matches plant phenotyping methods植物成長を対象に、遺伝・環境効果を分離し、品種別の成長曲線を推定するNLMEM/SAEM手法を中心的に提案・適用しているため、成長形質の計算的推定に該当する。
abstractWe propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth.
Reproduction assets foundThe authors state that all materials, including the real soybean UAV-derived plant-height phenotype data, are publicly available in their GitHub repository, which also contains the analysis code for the NLMEM/SAEM approach.Dataset · publics proposed in this study could genetically model the growth patterns of plants and animals, and help to understand, control, and predict their inheritance. The method proposed in this study is scalable to larger data sets owing to its computational speed. The codes for the method and the data used in this study are available at https://github.com/madelattre/Scripts-soybean-paper and can be extended according to the conditions of the application in each research project.
Supporting information
S1 File
The section entitled “Algorithmic details” describes the SAEM algorithm key distributions and the algorithm steps for both parameter estimation (Algorithm A) and genetic effects prediction (AlgoOpen asset ↗madelattre/Scripts-soybean-paperlines:207-227Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.
Why it matches plant phenotyping methods大豆の莢・種子数を高スループットに推定する画像解析モデルを開発し、既存モデルと比較検証しているため、植物表現型取得手法が研究の中心です。
abstractthe present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping
Reproduction assets foundThe paper's DEKR-SPrior analysis code is publicly available on GitHub with an explicit availability statement and URL. The homemade soybean pod/seed image datasets are not publicly deposited and require contacting the corresponding author.Code · publicThe source code is publicly available. It can be accessed at the following GitHub repository: https://github.com/Cyncihe/DEKR-SPrior.gitOpen asset ↗https://github.com/Cyncihe/DEKR-SPrior.gitlines:300-403Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularization strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Why it matches plant phenotyping methodsラマン分光と回帰モデルを用いてダイズの生物的窒素固定活性を定量する手法を開発・検証しており、植物の生理状態の取得が研究の中心である。
abstractThis study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans.
Reproduction assets foundThe authors state that the original data (Raman spectra and BNF/IRMS measurements) are openly available on FigShare under DOI 10.6084/m9.figshare.25909780. This is a paper-specific, publicly deposited dataset directly reproducing the study's phenotyping measurements. No author analysis code or trained model is reportedDataset · publicData Availability Statement: The original data presented in the study are openly available in FigShare at
10.6084/m9.figshare.25909780FigShare · 10.6084/m9.figshare.25909780pdf-raw-page:13 lines:1-55Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improving model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-of-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, with the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, with the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets related to this study are available at Github.
Why it matches plant phenotyping methods植物画像向け超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子の計数性能を評価しており、表現型取得・抽出法が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper's authors publicly released their analysis code (PlantSR super-resolution model and experiments) on GitHub, and also deposited the PlantSR dataset and HR soybean images on figshare; however, only the GitHub URL is among the allowed URLs, so only the code asset is reported.Code · publicThe source code is available at https://github.com/SkyCol/PlantSR (accessed on 28 November 2023).Open asset ↗SkyCol/PlantSRlines:94-293Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications.
Why it matches plant phenotyping methods3D点群と特徴抽出・分類モデルを用いてダイズ個体の鉄欠乏症ストレス重症度を推定し、2D画像手法と比較検証しており、植物表現型取得・推定法が中心である。
abstractThis study explores the potential of leveraging 3D point cloud data for improved plant stress assessment.
Reproduction assets foundThe authors publicly release the paper's soybean IDC 3D point cloud dataset and analysis scripts (including the 2D projection generation script) via their GitHub repository, explicitly stated in the Data Availability Statement and Methods sections.Code · publicThe script for generating these 2D
images from the 3D voxelized point cloud is accessible on our GitHub repository: https:Open asset ↗pdf-page:5 lines:1-54Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.
Why it matches plant phenotyping methods植物画像の超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子のカウント性能を改善する手法を検証しており、植物器官数の画像ベース推定が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the PlantSR plant image dataset (figshare), and the high-resolution soybean seed images used for transfer learning (figshare). The P2PNet-Soy repository is cited prior work, not a paper-specific asset.Code · publicThe source codes and associated
datasets are available at https://github.com/SkyCol/PlantSR.Open asset ↗SkyCol/PlantSRpdf-page:2 lines:1-63Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p 0.22, p
Why it matches plant phenotyping methods作物を対象に、塔載観測によるPRI・SIFを用いたGPP推定関係の改善と検証を主題としており、植物の生理状態推定手法が中心である。
titleAssessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean
Reproduction assets foundThe paper's tower-based SIF, PRI, and GPP measurements for corn and soybean at US-Ne2/US-Ne3 derive from an openly downloadable ORNL DAAC dataset (ds_id=2136), explicitly cited in the Data Availability Statement. No author analysis code or models are disclosed.Dataset · publicResearch and Development Program of
China, grant number 2021YFC2600501; the National Natural Science Foundation of China, grant
number SKLNBC2023-01.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset used in this study can be downloaded at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2136 (accessed on 1 January 2024).
Acknowledgments: We appreciate the open-access dataset supported by Wu from the Agroecosystem
Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at
Urbana-Champaign, Urbana, IL, USA.
Conflicts of Interest: The authors declare no conflictsOpen asset ↗daac.ornl.gov · ds_id=2136pdf-raw-page:17 lines:1-54Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce economic losses. Hyperspectral imaging, combined with deep learning, has already been proved as a powerful tool to detect crop diseases. However, current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels, leading to the fact that the detection accuracy of current models remains further improvement. In this study, we proposed a deformable convolution and dilated convolution neural network (DC 2 Net) for the ASR detection. The deformable convolution module was used to extract the spatial features, while the dilated convolution module was applied to extract features from the spectral dimension. We also adopted the Shapley value and the channel attention methods to evaluate the importance of each wavelength during decision-making, thereby identifying the most contributing ones. The proposed DC 2 Net can realize early asymptomatic detection of ASR even when visual symptoms have not appeared. The results of the experiment showed that the detection performance of DC 2 Net dominated state-of-the-art methods, reaching an overall accuracy at 96.73%. Meanwhile, the experimental result suggested that the Shapley Additive exPlanations method was able to extract feature wavelengths correctly, thereby helping DC 2 Net achieve reasonable performance with less input data. The research result of this study could provide early warning of ASR outbreak in advance, even at the asymptomatic period.
Why it matches plant phenotyping methodsハイパースペクトル画像から植物病害状態を抽出する深層学習手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractIn this study, we proposed a deformable convolution and dilated convolution neural network (DC 2 Net) for the ASR detection.
Reproduction assets foundThe paper's Data Availability statement provides an explicit public GitHub link to the authors' DC 2 Net analysis code. The hyperspectral dataset itself is only available upon request, so it is not a public asset.Code · publicThe code of our work can be found via the following link: https://github.com/NJAUJerry/DC2Net .Open asset ↗NJAUJerry/DC2Netlines:310-335Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. Results This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO 2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO 2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. Conclusion In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.
Why it matches plant phenotyping methods低コスト2D画像フェノタイピングを用いて、シュート構造やキャノピー被覆を定量化し、圃場での植物形態評価法としての有用性を扱っているため、フェノタイピング手法が実質的に中心である。
abstractIn addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Material 4: Table S3: (download XLSX )
Reliability estimates, BLUPs calculated for the traits measured in this study and data associated with each trait measured in the study.Open asset ↗lines:429-492Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.
Why it matches plant phenotyping methodsダイズ葉の健全・病害状態を画像データセットとして構築し、機械学習による植物病害の識別・分類に利用可能にする研究であり、表現型取得用データセットが中心である。
abstractThis research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy.
Reproduction assets foundThe paper is a Data in Brief article describing a public soyabean leaf disease image dataset (3363 images, six classes) deposited on Mendeley Data with an explicit DOI and direct URL. This is a paper-specific, publicly available plant phenotyping asset (raw and preprocessed leaf images). No separate analysis code assetDataset · publicor unhealthy.
Data source location
Goudgaon Village farm of (Sub. Major Gulab Alam Kotwal), Tal: Barshi, Dist: Solapur, Maharashtra, India.413406.
18.2157727 Latitude and 75.6680118 Longitude.
Data accessibility
Repository name: An India soyabean leaf dataset
Data identification number: 10.17632/bshkvgbzpt.1
Direct URL to data: https://data.mendeley.com/datasets/bshkvgbzpt/1
Instructions for accessing these data:
Datasets consist of Single leaf and multi-leaf folder.
Related research article
Case study:
Author: Mr.Jameer Kotwal, Dr.Ramgopal Kashyap, Dr.Shafi Pathan
Paper: https://link.springer.com/article/10.1007/s11042-023-16882
Journal: Multimedia Tools and Application [ 1 ].
1
Value of thOpen asset ↗10.17632/bshkvgbzpt.1lines:1-71Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF 760 ) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.
Why it matches plant phenotyping methods作物キャノピーの光合成状態を推定する地上SIFについて、取得データセットと処理・校正・フットプリント拡大プロトコルが中心的に提示されており、植物フェノタイピング手法およびデータセットに該当する。
abstractWe introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint.
Reproduction assets foundThe paper's ground far-red SIF760 and vegetation indices dataset (15 site-years, US Corn Belt) is explicitly deposited in a public repository (ORNL DAAC) with a DOI matching an allowed URL.Dataset · publicThe processed half-hourly
SIF760 and VIs are available at the on Oak Ridge National Laboratory Distributed Active Archive Center (ORNL
DAAC) data repository https://doi.org/10.3334/ORNLDAAC/213653.Open asset ↗10.3334/ORNLDAAC/213653pdf-page:4 lines:1-47Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops.
Why it matches plant phenotyping methods大豆植物の3D形態取得を目的に、画像セグメンテーション前処理を開発・比較し、再構成精度と処理効率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。
abstractThe investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition.
Reproduction assets foundThe paper publicly releases its semantic segmentation dataset of 500 annotated soybean plant images, the 3D reconstruction model data from both preprocessing methods, and the authors' four segmentation network implementations (DeepLabv3+, Unet, PSPnet, HRnet) via Baidu pan links and GitHub repositories.Dataset · publicent research, LabelMe was used to annotate 500 images of soybean plants during
the vegetative period. The soybean plants and the calibration pad were labeled as a whole
and marked as “soybean”. The training set and testing set were divided in an 8:2 ratio. A
dataset was created for semantic segmentation, and the dataset link is
https://pan.baidu.com/s/13qpZsOl3bgmAgua2D441UQ (accessed on 4 August 2023). Ex-
tract code: dr2v.
Four deep-learning-based semantic segmentation models were selected as follows:
DeepLabv3+ [19], Unet [20], PSPnet [21] and HRnet [22]. These models were used to sep-
arate the soybean plants and the calibration pad from the background. Figure 3 shows the
network architeOpen asset ↗pdf-raw-page:5 lines:1-36Dataset · publichis study is pub-
licly available. These data can be found at: https://pan.baidu.com/s/13qpZsOl3bgmAgua2D441UQ
(accessed on 4 August 2023). Extract code: dr2v. Meanwhile, 3D reconstruction of soybean plant
images obtained using two image preprocessing methods was conducted, and the constructed
model data were linked as follows: https://pan.baidu.com/s/1UIBAts1dbjIiLvBv6YVpPA (accessed
on 4 August 2023). Extract code: 65xf.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
(a) (b)
(c) (d)
Figure A1. The confusion matrix diagram of the true value and the predicted value of the training
set. (a) DeepLabv3+; (b) Unet; (c) PSPnet; (d) HRNet.
(a)Open asset ↗pdf-layout-page:25 lines:1-32Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R 2= 0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs
Why it matches plant phenotyping methodsRGB画像から種子形態形質を抽出し、機械学習で種子重量を予測する画像ベース表現型解析が研究の中心である。
abstractThe present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean.
Reproduction assets foundThe paper's image-based seed architectural trait (i-trait) measurements and hundred-seed weight data for 164 soybean accessions are stated to be included in the article's Supplementary Material (e.g., Supplementary Table 1 of genotypes and trait data), publicly available at the Frontiers supplementary-material URL. No专Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1206357/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
References
Abdelhakim L. O. A., Rosenqvist E., Wollenweber B., Spyroglou I., Ottosen C. O., Panzarová K. (2021). Investigating combined drought-and heat stress effects in wheat under controlled conditions Open asset ↗lines:434-465Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plant breeding is gaining importance as a sustainable tool to address the challenges posed by a growing global population and enhance food security. Advanced high-throughput omics technologies are utilized to accelerate crop improvement and develop resilient varieties with higher yield performance. These technologies generate vast genetic data, which can be exploited to manipulate key plant characteristics for crop improvement. The integration of big data and AI in plant breeding has the potential to revolutionize the field and increase food security. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN regression, ANN regression, Autoencoders regression, and MLP regression) and seven machine learning models (e.g., SVR, XGBoost regression, Random Forest regression, LightGBM regression, GPS regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, ANN, and Autoencoder outperformed other models and could obtain a better prediction accuracy if they were used for phenotype prediction. To support the evaluation of deep learning methods, feature importance and GO enrichment analyses were conducted. After comprehensively comparing four feature importance algorithms, there was no significant difference among the feature importance ranking score among these four algorithms, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The genes identified by the SVR model plus SHAP importance combination clearly grouped into three clusters on the soybean whole genome. Our GO enrichment results also confirmed the prediction accuracy of this methods combination. The results of this study offer valuable insights for AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.
Why it matches plant phenotyping methodsダイズ分枝形質の予測を対象に、11種類の非線形回帰モデルを比較・評価し、SHAPによる特徴量選択も行っており、植物表現型推定手法が研究の中心である。
abstractAfter being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error)
Reproduction assets foundThe paper states that the datasets generated and/or analyzed (soybean branching phenotype and SNP genotype data used for the 11 AI models) are publicly available in a ScienceDB repository, with an explicit authors' URL matching an allowed URL. This is a paper-specific, publicly actionable data asset. No author analysisDataset · publicThe datasets generated and/or analyzed during the current study are available in the ScienceDB repository,
https://www.scidb.cn/en/anonymous/MkFWdklm.Open asset ↗ScienceDBpdf-page:14 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.
Why it matches plant phenotyping methods作物形質を予測する深層学習モデルとウェブツールの開発・比較検証が研究の中心であり、遺伝子型から植物形質を推定する再利用可能な計算手法に該当する。
abstractwe have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction.
Reproduction assets foundThe paper's genomic-prediction analysis code, pre-built SoyDNGP models, and standalone network structure are publicly available in the authors' GitHub repositories, and the manuscript source code is deposited on Figshare with a DOI. These are paper-specific, public, and actionable assets.Code · publicFUNDING
The National Key Research and Development Program of China
(grant 2022YFD1201502).
DATA AVAILABILITY
Complete data sets can be found within the main text, supple-
mentary materials and referenced studies, as well as in public
databases. The code, pre-built models and the standalone net-
work structure are accessible at https://github.com/IndigoFloyd/SoybeanWebsite and https://github.com/IndigoFloyd/Soybean
Website. In addition to this, the source code detailed in our
manuscript has been deposited in Figshare and can be accessed
using the following DOI: https://doi.org/10.6084/m9.figshare.23537067.v2. We have packaged our code and uploaded it to PyPi
for easier access. The packageOpen asset ↗IndigoFloyd/Soybeanpdf-raw-page:10 lines:1-88Code · publicFUNDING
The National Key Research and Development Program of China
(grant 2022YFD1201502).
DATA AVAILABILITY
Complete data sets can be found within the main text, supple-
mentary materials and referenced studies, as well as in public
databases. The code, pre-built models and the standalone net-
work structure are accessible at https://github.com/IndigoFloyd/SoybeanWebsite and https://github.com/IndigoFloyd/Soybean
Website. In addition to this, the source code detailed in our
manuscript has been deposited in Figshare and can be accessed
using the following DOI: https://doi.org/10.6084/m9.figshare.23537067.v2. We have packaged our code and uploaded it to PyPi
for easier access. The package canOpen asset ↗IndigoFloyd/SoybeanWebsitepdf-raw-page:10 lines:1-88Code · publicabases. The code, pre-built models and the standalone net-
work structure are accessible at https://github.com/IndigoFloyd/SoybeanWebsite and https://github.com/IndigoFloyd/Soybean
Website. In addition to this, the source code detailed in our
manuscript has been deposited in Figshare and can be accessed
using the following DOI: https://doi.org/10.6084/m9.figshare.23537067.v2. We have packaged our code and uploaded it to PyPi
for easier access. The package can be installed by running ’pip
install SoyDNGPNext’ in the terminal.
REFERENCES
1. FAO, IFAD, UNICEF, WFP and WHO. The State of Food Security andOpen asset ↗10.6084/m9.figshare.23537067.v2pdf-raw-page:10 lines:1-88Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.
Why it matches plant phenotyping methods高スループット表現型解析のための低コスト灌水・水利用測定システムを開発・実証しており、植物の水利用と乾燥ストレス関連形質の取得が中心的な方法論的貢献である。
abstractThis study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping.
Reproduction assets foundThe article's Data availability statement places the study's original contributions (phenotype measurements and supplementary experiment data) in the article/Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, models, or standalone phenotypeSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1201102/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
References
Araya Y. N.
Gowing D. J.
Dise N.
( 2010 ).
A controlled water-table depth system toOpen asset ↗lines:288-364Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Soy is the main product of Brazilian agriculture and the fourth most cultivated bean globally. Since soy cultivation tends to increase and due to this large market, the guarantee of product quality is an indispensable factor for enterprises to stay competitive. Industries perform vigor tests to acquire information and evaluate the quality of soy planting. The tetrazolium test, for example, provides information about moisture damage, bedbugs, or mechanical damage. However, the verification of the damage reason and its severity are done by an analyst, one by one. Since this is massive and exhausting work, it is susceptible to mistakes. Proposals involving different supervised learning approaches, including active learning strategies, have already been used, and have brought significant results. Therefore, this paper analyzes the performance of non-supervised techniques for classifying soybeans. An extensive experimental evaluation was performed, considering (9) different clustering algorithms (partitional, hierarchical, and density-based) applied to 5 image datasets of soybean seeds submitted to the tetrazolium test, including different damages and/or their levels. To describe those images, we considered 18 extractors of traditional features. We also considered four metrics (accuracy, FOWLKES, DAVIES, and CALINSKI) and two-dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbor embedding) for validation. Results show that this paper presents essential contributions since it makes it possible to identify descriptors and clustering algorithms that shall be used as preprocessing in other learning processes, accelerating and improving the classification process of key agricultural problems.
Why it matches plant phenotyping methods大豆種子画像から損傷とその程度を分類するため、複数のクラスタリング手法・特徴量・評価指標を比較検証しており、種子状態の表現・抽出手法が中心である。
titleAssessment of clustering techniques to support the analyses of soybean seed vigor.
Reproduction assets foundThe paper's Data Availability statement points to the authors' public GitHub repository containing the soybean seed image datasets and feature files used in the clustering experiments. JFeatureLib is a generic third-party library, not a paper-specific asset.Dataset · publiclf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All files are available from https://github.com/BioinfoCP/visual-features-soybean-vigor .
Data Availability
All files are available from https://github.com/BioinfoCP/visual-features-soybean-vigor .
1 Introduction
Soy is the fourth most cultivated bean globally and the main product in Brazilian agriculture. In 2021/22, Brazil estimates a production record of 142,009 million tons of soybeans. This Open asset ↗BioinfoCP/visual-features-soybean-vigorlines:48-65Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In order to address the challenges related to the classification and recognition of soybean disease and healthy leaf identification, it is essential to have access to high-quality images. A meticulously curated dataset named "SoyNet" has been created to provide a clean and comprehensive dataset for research purposes. The dataset comprises over 9000 high-quality soybean images, encompassing healthy and diseased leaves. These images have been captured from various angles and directly sourced from soybean agriculture fields; The soybean leaves images are organized into two sub-folders: SoyNet Raw Data and SoyNet Pre-processing Data. Within the SoyNet Raw Data folder are separate folders for healthy and diseased images captured using a digital camera. The SoyNet Pre-processing Data folder comprises resized images of 256*256 pixels and the grayscale versions of disease and healthy images, following a similar organizational structure. We captured the images using the Nikon digital camera and the Motorola mobile phone camera, utilizing different angles, lighting conditions, and backgrounds. They were taken in different lighting conditions and backgrounds at soybean cultivation fields to represent the real-world scenario accurately. The proposed dataset is valuable for testing, training, and validating soybean leaf disease classification.
Why it matches plant phenotyping methods大規模なダイズ葉画像データセットを構築し、健全・罹病状態の分類検証用データとして提供することが中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractA meticulously curated dataset named "SoyNet" has been created to provide a clean and comprehensive dataset for research purposes.
Reproduction assets foundThe paper is a Data in Brief article describing SoyNet, a public soybean leaf image dataset (9,338 raw and preprocessed images of healthy and diseased leaves) deposited on Mendeley Data with an explicit direct URL and DOI, matching an allowed URL. This is a paper-specific, publicly available plant image dataset used inDataset · publicpur, Madhya Pradesh. India
• Latitude and longitude 23°12′36.9″N 79°56′47.7″E
Data accessibility
Repository name : Mendeley Data [7]
SoyNet: Indian Soybean Image dataset with quality images captured from the agriculture field (healthy and disease Images)
Data identification number(doi): 10.17632/w2r855hpx8.2
Direct URL to data: https://data.mendeley.com/datasets/w2r855hpx8/2Open asset ↗Mendeley Data · 10.17632/w2r855hpx8.2lines:1-51Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Improving soybean ( Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model's performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7's pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model's performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.
Why it matches plant phenotyping methods深度カメラと物体検出モデルを用いてダイズ莢数という植物形質を非破壊推定する手法を開発・比較・評価しており、表現型取得が中心的である。
abstractwe explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model.
Reproduction assets foundThe paper's Data Availability Statement points to an authors' public GitHub repository containing the datasets generated and analyzed (soybean depth-camera images and pod-count segmentation data). Other URLs (labelImg, scikit-learn, CC license) are generic tools/licenses, not paper-specific assets.Dataset · publicThe datasets generated and analyzed for this study can be found in the Github repository Soybean pod count depth segmentation project 2022 accessible at https://github.com/jithin8mathew/soybean_pod_count_Depth_segmentation_project (accessed on 28 June 2023).Open asset ↗jithin8mathew/soybean_pod_count_Depth_segmentation_projectlines:183-198Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans.
Why it matches plant phenotyping methods大豆全生育期の3D再構成、器官ラベル付き点群データセットの構築と検証が中心で、植物表現型自動取得を支援する再利用可能な基盤である。
abstractThe study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition.
Reproduction assets foundThe paper's own 3D soybean phenotyping assets are publicly available: the original reconstructed 3D models and the annotated Soybean-MVS point cloud dataset are on Kaggle, and the authors' analysis code (BAAF-Net and RandLA-Net segmentation implementations used to train/test the dataset) is on GitHub with explicit dataDataset · publicNatural Science Foundation of Heilongjiang Province of
China (LH2021C021).
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Original models are available in a publicly accessible repository: The
original contributions presented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authorsOpen asset ↗soberguo/soybean-original-modelpdf-raw-page:15 lines:1-47Dataset · publicesented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Image collection quantity of soybean plants of different varieties in different stages.
V1 V2 V3 V4 V5 R1 R2 R3 R4 R5 R6 R7 R8
2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 20Open asset ↗soberguo/soybeanmvspdf-raw-page:15 lines:1-47Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This paper presents a dataset with 5513 images of individual soybean seeds, which encompass five categories : (Ⅰ) Intact, (Ⅱ) Immature, (Ⅲ) Skin-damaged, (Ⅳ) Spotted, and (Ⅴ) Broken . Furthermore, there are over 1000 images of soybean seeds in each category. Those images of individual soybeans were classified into five categories based on the Standard of Soybean Classification ( GB1352-2009 ) [1]. The soybean images with the seeds in physical touch were captured by an industrial camera. Subsequently, individual soybean images (227×227 pixels) were divided from the soybean images (3072×2048 pixels) using an image-processing algorithm with a segmentation accuracy of over 98%. The dataset can serve to study the classification or quality assessment of soybean seeds.
Why it matches plant phenotyping methods大豆種子の画像データセットを構築し、画像分割アルゴリズムと品質カテゴリ分類を提示しており、植物器官の状態・品質を画像から抽出する方法が中心です。
abstractThis paper presents a dataset with 5513 images of individual soybean seeds
Reproduction assets foundThe paper is a data descriptor for a public soybean seed image dataset (5513 images, five quality classes) deposited in Mendeley Data, with explicit direct URL and DOI, directly reproducing the paper's phenotyping measurements.Dataset · public7 × 227 pixels) from the soybean images (3072×2048 pixels). Finally, the individual soybean images were saved in JPG format.
Data source location
Nanjing Agricultural University, Nanjing, China
Data accessibility
Repository name: Soybean Seeds
Data identification number:
https://doi.org/10.17632/v6vzvfszj6.6
Direct URL to data:
https://data.mendeley.com/datasets/v6vzvfszj6
Instructions for accessing these data: Download the data from Soybean Seeds repository in ZIP formats.
Value of the Data
•
The soybean image dataset can meet the practical requirement of assessing soybean quality. Because those individual soybean images in our dataset were classified based on the Standard of Soybean ClassiOpen asset ↗10.17632/v6vzvfszj6.6lines:1-64Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
SoybeanField / plotRootSeed / grainCountingGrowth / development / phenologyRoot system architecture
The symbiotic relationship between soybean [ Glycine max L. (Merr.)] roots and bacteria ( Bradyrhizobium japonicum ) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N 2 ) is fixed into bio-available ammonia (NH 3 ) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant- Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.
Why it matches plant phenotyping methodsSNAPという機械学習ベースの表現型取得・解析パイプラインを用いて、根粒数・サイズ・面積を定量化することが研究の中心的手法として明示されている。
titleUsing machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean
Reproduction assets foundThe paper states that all data and code will be shared through the authors' Singh group GitHub. This is the only paper-specific availability statement; it points to a lab-level GitHub organization rather than a specific repository, and uses future tense ('will be shared'), so the exact deposit for this paper's nod phenCode · publicAll data and code will be shared through the Singh group GitHub https://github.com/SoylabSingh .Open asset ↗SoylabSinghlines:1171-1263Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
Why it matches plant phenotyping methods複数の表現型予測モデルを植物種の実データとシミュレーションで系統比較・評価しており、計算による植物形質推定が研究の中心である。
abstractour aim is to systematically compare 12 different phenotype prediction models
Reproduction assets foundThe paper's authors publicly release their analysis code (easyPheno framework and the phenotype_prediction repository containing simulated phenotypes, hyperparameter optimization results, GWAS results, and figure-generation code), plus the Arabidopsis SNP matrix (figshare) and four AraPheno phenotype datasets used in aCode · publicAll simulated phenotypes, detailed results of the whole hyperparameter optimization, precomputed permutation-based GWAS results, and the code for conducting the simulations and generating all figures can be freely downloaded from our GitHub repository: https://github.com/grimmlab/phenotype_prediction .Open asset ↗grimmlab/phenotype_predictionlines:619-631Dataset · publicThe fully imputed SNP matrix data for Arabidopsis thaliana is publicly available and can be downloaded from https://doi.org/10.6084/m9.figshare.11346893.v1 .Open asset ↗10.6084/m9.figshare.11346893.v1lines:619-631Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング枠組みと、大規模画像から生存性・細胞分裂・細胞同一性を解析する高スループット画像解析パイプラインを開発しており、表現型取得・解析が研究の中心的な技術的貢献である。
abstractIn this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their high-throughput confocal z-stack cell quantification pipeline (Python scripts wrapped in an R Shiny GUI), used to analyze the paper's bioprinted plant cell imaging datasets, on GitHub with a Zenodo deposit (10.5281/zenodo.7012765). The Zenodo record 5537065 in allowed_urls isCode · publicScripts for our high-throughput and automatic image analysis are available at https://github.com/LisaVdB/Confocal-z-stack-cell-detection and 10.5281/zenodo.7012765 .Open asset ↗LisaVdB/Confocal-z-stack-cell-detection · 10.5281/zenodo.7012765lines:247-261Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.
Why it matches plant phenotyping methods大豆の地上部バイオマスを推定するための時系列マルチスペクトル画像と植生指数を中心に、予測精度および撮像時期を評価しているため、植物表現型計測手法の実質的な適用・検証に該当する。
abstractMultispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the datasets generated and analyzed (phenotype/vegetation-index data and analysis materials) for this soybean multispectral imaging study. Supplemental files are only docx summaries, not datasets themselves.Dataset · publicThe datasets generated and analyzed in the present study are available from the ‘Sakuraikengo/TSMS_supple’ repository in the GitHub, https://github.com/Sakuraikengo/TSMS_supple.Open asset ↗Sakuraikengo/TSMS_supplehtml-lines:718-899Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determination R 2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.
Why it matches plant phenotyping methodsダイズの花・莢数という植物形質を画像から自動認識・計数する深層学習手法を比較、改良、検証しており、表現型取得法が研究の中心である。
abstractthe use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study
Reproduction assets foundThe authors publicly deposited the soybean flower and pod image datasets (the phenotyping inputs used for detection/counting) in an online repository via a Baidu Netdisk link with password, stated in the Data availability statement. No author analysis code or trained model checkpoints are explicitly shared; LabelImg isDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://pan.baidu.com/s/1j ZE6BHlpVjGay_JqVmOew:password: ate8 .Open asset ↗pan.baidu.com · ZE6BHlpVjGay_JqVmOewlines:751-822Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5 (Sub-tropical 23° LS) × RMG 9.4 (Tropical 0° LS) and two populations with a narrow variability obtained between genitors RMG 7.3 (Tropical 20° LS) × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher's linear and Anderson's quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding.
Why it matches plant phenotyping methodsANNを用いたダイズ遺伝型の相対成熟期グループ分類を開発・評価し、他の判別法と性能比較しているため、植物表現型の抽出・分類手法が中心である。
abstractthis study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group
Reproduction assets foundThe paper's supplementary material is a public dataset of the soybean progeny phenotypic measurements (NDF, NDM, CYCLE, AIV, APM, Ac, VA, PG across three agricultural years) used directly in the ANN and discriminant analyses. No author analysis code or trained model is deposited; the Data Availability Statement only onDataset · publicedgments
We thank the State University “Júlio de Mesquita Filho”—Unesp/FCAV Jaboticabal Campus and the Graduate Agronomy Program (Genetics and Plant Breeding) and the Coordination for the Improvement of Higher Education Personnel (CAPES).
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.814046/full#supplementary-material
Supplementary Table
Dataset collected from soybean progenies belonging to different relative maturity groups, during three agricultural years of evaluation.
Click here for additional data file.
References
Alliprandini L. F., Abatti C., Bertagnolli P. F., Cavassim J. E., Gabe H. LOpen asset ↗lines:442-477Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The stem-related phenotype of mature stage soybean is important in soybean material selection. How to improve on traditional manual methods and obtain the stem-related phenotype of soybean more quickly and accurately is a problem faced by producers. With the development of smart agriculture, many scientists have explored soybean phenotypes and proposed new acquisition methods, but soybean mature stem-related phenotype studies are relatively scarce. In this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm. We subsequently obtained the pitch number, internodal length, branch number, branching angle, plant type spatial conformation, plant height, main stem length, and new phenotype-stem curvature. After 300 epochs, we compared the recognition results of various detection algorithms to select the best. Among them, YOLOX had a maximum average accuracy (mAP) of 94.36% for soybean stem nodes and scale markers. Through comparison of the phenotypic information extracted by the directed search algorithm with the manual measurement results, we obtained the Pearson correlation coefficients, R, of plant height, pitch number, internodal length, main stem length, stem curvature, and branching angle, which were 0.9904, 0.9853, 0.9861, 0.9925, 0.9084, and 0.9391, respectively. These results show that our algorithm can be used for robust measurements and counting of soybean phenotype information, which can reduce labor intensity, improve efficiency, and accelerate soybean breeding.
Why it matches plant phenotyping methods深層学習と指向性探索アルゴリズムを開発し、成熟ダイズの茎関連形質を画像から抽出・手測定と検証しており、植物フェノタイピング手法が研究の中心です。
abstractIn this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm.
Reproduction assets foundThe paper's mature soybean stem-node image dataset (1,523 original images, augmented to 6,092) is publicly deposited on Kaggle by the authors. No author analysis code or trained model checkpoints are explicitly released; supplementary material contains only figures/tables, not datasets or code.Dataset · publicThe dataset is available at https://www.kaggle.com/datasets/soberguo/soybeannode .Open asset ↗Kaggle · soberguo/soybeannodelines:331-343Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
Why it matches plant phenotyping methods葉面ハイパースペクトル反射から光合成・葉形質を推定するモデルを構築し、精度評価と集団への適用を行っており、表現型取得・推定手法が研究の中心である。
abstractwe used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits
Reproduction assets foundThe paper's leaf reflectance processing code (FieldSpec R Package, Zenodo DOI 10.5281/zenodo.6248237) and its paper-specific phenotype/reflectance data, PLSR model coefficients, and complete genetic mapping dataset are publicly available via the Genetics figshare supplemental repository (DOI 10.25386/genetics.19394693)Dataset · publicgenetic mapping and
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analyses can be found in File S18. The majority of lines and accessions used in this manuscript
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are available via GRIN (https://www.ars-grin.gov/) or by request from Soybase.org for the NAM
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lines (https://soybase.org/SoyNAM/SoyNAM_RIL_request.htm). Supplemental Material
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available at figshare: https://doi.org/10.25386/genetics.19394693
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Acknowledgements
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We thank Troy Cary, Chris Moller, and Noah Mitchell for help in setting up and maintaining the
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experimental plots, collecting data, and processing samples.
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Funding
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This work was supported by soybean checkoff funding from the United Soybean Board. ASS
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was supported by a post-doctoraOpen asset ↗figshare · 10.25386/genetics.19394693pdf-raw-page:39 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Capturing cell-to-cell and cell-to-environment signals in a defined 3 dimensional (3D) microenvironment is key to study cellular functions, including cellular reprogramming towards tissue regeneration. A major challenge in current culturing methods is that these methods cannot accurately capture this multicellular 3D microenvironment. In this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis root cells and soybean meristematic cells. To analyze the large image datasets generated during these long-term viability studies, we developed an open source high-throughput image analysis pipeline. Furthermore, we showed the cell cycle re-entry of the isolated Arabidopsis and soybean cells leading to the formation of microcalli. Finally, we showed that the identity of isolated cells of Arabidopsis roots expressing endodermal markers maintained longer periods of time. The framework established in this study paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle re-entry towards tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング系と、長期画像データから細胞生存性・分裂・同一性を抽出するオープンソース解析パイプラインを開発しており、植物状態の取得・解析手法が中心である。
abstractIn this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their in-house confocal z-stack cell-counting image analysis pipeline (Python scripts wrapped in an R Shiny GUI) on GitHub, which directly reproduces the paper's computational analysis of bioprinted plant cell images. Generic dependencies (pyimageJ, OpenCV, ComDet, R shiny) and theCode · publicpipeline
contained in Python and further developed into an R Shiny application (44) can be easily accessed, along
with the usage instructions, from the Github repository at https://github.com/LisaVdB/Confocal-z-stack-
cell-detection.
Data availability
Scripts for our high-throughput and automatic image analysis are available at
https://github.com/LisaVdB/Confocal-z-stack-cell-detection.Open asset ↗LisaVdB/Confocal-z-stack-cell-detectionpdf-layout-page:10 lines:1-49Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.
Why it matches plant phenotyping methodsSARデータとGPRモデルにより作物のPAI・VWC・湿重量バイオマスを推定する手法を開発・評価しており、植物形質取得が研究の中心である。
abstractThe purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the authors' GPR analysis code for crop biophysical parameter retrieval from RADARSAT-2 data. The in situ SMAPVEX16-MB measurements and RADARSAT-2 imagery themselves are not stated as publicly released by the authors.Code · publicData Availability Statement: The code for the present work is available at: https://github.com/
Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2, accessed 15 February 2022.Open asset ↗Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2pdf-page:24 lines:1-60Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Soybean is most often grown under rainfed conditions and negatively impacted by drought stress in the upper mid-south of the United States. Therefore, identification of drought-tolerance traits and their corresponding genetic components are required to minimize drought impacts on productivity. Limited transpiration (TR lim ) under high vapor pressure deficit (VPD) is one trait that can help conserve soybean water-use during late-season drought. The main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO 3 , for the TR lim trait. A secondary objective was to undertake a genetic marker/quantitative trait locus (QTL) genetic analysis using the AgNO 3 phenotyping results. A set of 122 soybean genotypes (120-RILs and parents) were grown in controlled environments (32/25-d/n °C). The transpiration rate (TR) responses of derooted soybean shoots before and after application of AgNO 3 were measured under 37°C and >3.0 kPa VPD. Then, the decrease in transpiration rate (DTR) for each genotype was determined. Based on DTR rate, a diverse group (slow, moderate, and high wilting) of 26 RILs were selected and tested for the whole plant TRs under varying levels of VPD (0.0-4.0 kPa) at 32 and 37°C. The phenotyping results showed that 88% of slow, 50% of moderate, and 11% of high wilting genotypes expressed the TR lim trait at 32°C and 43, 10, and 0% at 37°C, respectively. Genetic mapping with the phenotypic data we collected revealed three QTL across two chromosomes, two associated with TR lim traits and one associated with leaf temperature. Analysis of Gene Ontologies of genes within QTL regions identified several intriguing candidate genes, including one gene that when overexpressed had previously been shown to confer enhanced tolerance to abiotic stress. Collectively these results will inform and guide ongoing efforts to understand how to deploy genetic tolerance for drought stress.
Why it matches plant phenotyping methodsAgNO3を用いた高スループット測定法でダイズの蒸散制限形質を取得し、表現型データをQTL解析に用いており、フェノタイピング手法の適用が研究の中心です。
abstractThe main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO 3 , for the TR lim trait.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary File 3
R/QTL package file containing genotypic and phenotypic data used for genetic mapping.Open asset ↗lines:1432-1530Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から画像特徴を抽出して二次作物形質を構築し、育種選抜性能を検証することが研究の中心であるため、植物フェノタイピング手法研究に該当する。
abstractA UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons.
Reproduction assets foundThe paper explicitly states that the LASSO model code and the UAV imagery datasets are publicly available in the authors' GitHub repository. The raw data availability statement only offers data on request, but the code/imagery asset has an explicit public URL.Code · publicThe code for the LASSO model and the imagery datasets can be found at: https://github.com/Heyphil/Soybean-variety-selection.git .Open asset ↗https://github.com/Heyphil/Soybean-variety-selection.gitlines:320-328Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Deep learning (DL) methods have transformed the way we extract plant traits—both under laboratory as well as field conditions. Evidence suggests that “well‐trained” DL models can significantly simplify and accelerate trait extraction as well as expand the suite of extractable traits. Training a DL model typically requires the availability of copious amounts of annotated data; however, creating large‐scale annotated dataset requires nontrivial efforts, time, and resources. This limitation has become a major bottleneck in deploying DL tools in practice. Self‐supervised learning (SSL) methods give exciting solution to this problem, as these methods use unlabeled data to produce pretrained models for subsequent fine‐tuning on labeled data and have demonstrated superior transfer learning performance on down‐stream classification tasks. We investigated the application of SSL methods for plant stress classification using few labels. We select a plant stress classification problem to test the effectiveness of SSL, as it is a fundamentally challenging problem due to (a) disease classification which depends on the abnormalities in a small number of pixels, (b) high data imbalance across different classes, and (c) fewer annotated and available plant stress images than in other domains. We compared seven SSL approaches spanning four broad classes of SSL methods on soybean [ Glycine max L. (Merr.)] plant stress dataset and report that pretraining on unlabeled plant stress images significantly outperforms transfer learning methods using random initialization for plant stress classification. In summary, SSL‐based model initialization and data curation improves annotation efficiency for plant stress classification tasks and will circumvent data annotation challenges associated with DL methods.
Why it matches plant phenotyping methods植物ストレス画像から形質・状態を抽出する深層学習手法を比較検証し、自己教師あり学習によるアノテーション効率向上を評価しており、表現型抽出法が研究の中心である。
abstractDeep learning (DL) methods have transformed the way we extract plant traits—both under laboratory as well as field conditions.
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository containing the authors' data and code for the soybean stress SSL phenotyping analysis.Code · publicifferent SSL loss functions, (c) updating pre-
trained SSL models with unlabeled data from new classes, and
(d) develop new SSL-based foundational models for annota-
tion efficient image classification, segmentation, and object
detection applications.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data and code are available at https://github.com/koushik-n/SSL_soy.AC K N OW L E D G M E N T S
This work was supported by AI Institute for Resilient Agri-
culture (USDA-NIFA #2021-67021-35329), COALESCE:
25782703,
2022,
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CondiOpen asset ↗SSL_soy.ACpdf-raw-page:9 lines:1-106Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation-a 1:1 ( v / v ) mixture of 95% ethanol and NaOCl (6-14%)-produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.
Why it matches plant phenotyping methodsダイズ葉の気孔画像取得法と、気孔を自動検出・解析する深層学習手法を開発・評価しており、植物形質取得が研究の中心である。
abstractWe present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning.
Reproduction assets foundThe paper's soybean stomatal phenotype dataset (Table S2, manual vs. automatic stomatal density for 386 accessions) is publicly available via the MDPI supplement, and the trained YOLOv5 stomata-detection model is publicly served through the authors' web application. The 183-image training dataset and analysis code haveSupplement · publicnowledge the personnel from the Plant Genetics and Breeding lab at the Kyungpook National University for their time and work at the greenhouse.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10122714/s1 , Table S1: The representative methods for detecting stomata in different species, Table S2: Comparison stomata density between counting by manual and develped program.
Click here for additional data file.
Author Contributions
Conceptualization, J.-D.L. and Y.J.K.; methodology, S.N.S.; formal analysis, H.P., S.H.COpen asset ↗lines:69-162Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.
Why it matches plant phenotyping methods大豆地上部バイオマスを時系列のRGB・マルチスペクトル画像から予測するハイスループット表現型解析法を開発し、予測精度も評価しているため、表現型取得・推定法が中心的です。
abstractOur main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) previously reported in the literature to correlate with crop biomass ( Babar et al.Open asset ↗lines:305-313Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).
Why it matches plant phenotyping methods葉の画像およびハイパースペクトル反射を用いて、病斑被覆率に基づくダイズ葉の病害状態を分類する手法を開発・評価しており、植物表現型取得と解析が中心である。
abstractimage processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicS4 Data. Hyperspectral data used in this study.Open asset ↗lines:335-377Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online .
Why it matches plant phenotyping methods葉画像から形態・形状・質感・葉脈特徴を抽出して品種を識別するパイプラインを開発・ベンチマークし、ウェブサービス化しており、植物表現型取得・解析手法が中心である。
abstractHere, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN).
Reproduction assets foundThe paper's sweet cherry and soybean leaf image datasets are publicly available at http://mfcis.online/, and the full MFCIS analysis pipeline code (with Docker files and requirements) is publicly available at https://github.com/WeizhenLiuBioinform/mfcis under a BSD-3-Clause license.Code · publicAll the code and Docker files are available at the source code repository.
Code availability
Project name: Multifeature combined plant cultivar identification system
Project home page: https://github.com/WeizhenLiuBioinform/mfcisOpen asset ↗WeizhenLiuBioinform/mfcislines:185-202Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Recent advances in unmanned aerial vehicle (UAV) remote sensing and image analysis provide large amounts of plant canopy data, but there is no method to integrate the large imagery datasets with the much smaller manually collected datasets. A simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets. It has three steps: developing a model for predicting sample values from UAV imagery, field gridding and trait value prediction, and statistical testing of predicted values. A field cultivation experiment was conducted to examine the effectiveness of the GAUSS framework, using a soybean-wheat crop rotation as the model system Fourteen soybean cultivars and subsequently a single wheat cultivar were grown in the same field. The crop rotation benefits of the soybeans for wheat yield were examined using GAUSS. Combining manually sampled data ( n = 143) and pixel-based UAV imagery indices produced a large amount of high-spatial-resolution predicted wheat yields ( n = 8,756). Significant differences were detected among soybean cultivars in their effects on wheat yield, and soybean plant traits were associated with the increases. This is the first reported study that links traits of legume plants with rotational benefits to the subsequent crop. Although some limitations and challenges remain, the GAUSS approach can be applied to many types of field-based plant experimentation, and has potential for extensive use in future studies.
Why it matches plant phenotyping methodsUAV画像と手作業データを統合し、植物形質・収量を高解像度で推定するGAUSS解析フレームワークを開発・実証しており、フェノタイピング手法が研究の中心である。
abstractA simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ) with different plant types and yield potentials were used ( Kaga et al.Open asset ↗lines:317-335Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
SoybeanSugar beetLiDAR / point cloudLeafMorphology / geometry measurement2D/3D reconstructionLeaf traits
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Why it matches plant phenotyping methods3D点群から植物葉面を再構成し、ノイズ耐性と葉面積推定の安定性を従来法と比較検証する手法開発研究であり、植物フェノタイピング手法が中心です。
abstractwe developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their proposed leaf surface reconstruction method is publicly available on GitHub. No public deposit of the 3D point cloud phenotype data (soybean/sugar beet scans) is mentioned, so only the code qualifies as a paper-specific public asset.Code · publicWe implemented the algorithm for the proposed method in Python ( http://www.python.org/ ). The source code is at https://github.com/oceam/LeafSurfaceReconstruction .Open asset ↗oceam/LeafSurfaceReconstructionlines:46-55Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Deep learning models have been successfully deployed for a diverse array of image‐based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant sciences (and most biological) domain due to the inherent complexities. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed to reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods work by adaptively suggesting samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) core‐set, with conventional random sampling‐based annotation for two vastly different image‐based classification datasets. The first image dataset consists of soybean [ Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models using active learning based acquisition strategies is better than random sampling‐based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where resources dedicated to annotations are limited.
Why it matches plant phenotyping methods植物画像フェノタイピングにおけるアクティブラーニングによるアノテーション削減手法を複数手法と比較評価しており、画像分類性能とデータ取得ワークフローが中心である。
titleHow useful is active learning for image‐based plant phenotyping?
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the codes for the active learning approaches described in this work are available for the community at https://github.com/koushik-n/Active-Learning-Plant-Phenotyping.Open asset ↗Active-Learning-Plant-Phenotypinglines:108-129Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.
Why it matches plant phenotyping methods大豆の炭腐病という植物の疾病状態を、生理・形態特徴と機械学習で予測する手法を開発・比較しており、表現型取得・推定が研究の中心です。
abstractIn this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code .Open asset ↗GitHub · Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-codelines:227-299Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface designed and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistacia nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.
Why it matches plant phenotyping methodsCT画像取得法と3D解析ソフトウェアを開発し、種子・果実形態形質の自動抽出をベンチマークしており、フェノタイピング手法が中心である。
abstractHere, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · public3DPheno-Seed&Fruit software and CT image datasets used in this manuscript are free for academic purpose and can be downloaded from http://www.wutbiolab.com/resources/39/info/29 and https://github.com/whut-biolab-liuchang/projectOpen asset ↗lines:304-314Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.
Why it matches plant phenotyping methodsUAV画像とCNNを用いて rapeseed の葉を認識し、植物体数（stand count）を自動推定する手法の開発・性能評価が研究の中心であるため、植物フェノタイピング方法論に該当します。
abstractA CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves.
Reproduction assets foundThe paper's data availability statement explicitly deposits the 'Rapeseed_seedling_counting' data (supporting the UAV imagery-based stand count findings) in a public GitHub repository with an authors' URL, qualifying as a paper-specific public asset.Dataset · publicThe “Rapeseed_seedling_counting” data that support the findings of this study are available in “LARSC-Lab/Rapeseed_seedling_counting” in GitHub, which can be found at https://github.com/LARSC-Lab/Rapeseed_seedling_counting .Open asset ↗LARSC-Lab/Rapeseed_seedling_countinglines:590-664Code / dataset availability confirmedarXiv · checked 14 Sept 2026
Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required.
Why it matches plant phenotyping methods植物画像フェノタイピングにおける能動学習手法を比較評価しており、ラベル付け削減と分類性能が中心的な方法論的貢献である。
titleHow useful is Active Learning for Image-based Plant Phenotyping?
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the codes for the active learning approaches described in this work are available for the community at https://github.com/koushik-n/Active-Learning-Plant-Phenotyping.Open asset ↗koushik-n/Active-Learning-Plant-Phenotypinglines:108-129Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
SoybeanRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract Background Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. Results This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. Conclusions This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.
Why it matches plant phenotyping methods画像取得、機械学習による根のセグメンテーション、形質抽出を統合した高スループット根系フェノタイピング基盤の開発であり、方法が研究の中心です。
abstractWe developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline
Reproduction assets foundThe paper publicly releases ARIA 2.0 phenotyping software on Bitbucket and analysis code on GitHub; raw images and segmented masks are only available upon request.Code · publicAnalysis code is freely available at the address: https://github.com/mighster/ARIA2.0 .Open asset ↗mighster/ARIA2.0lines:176-226Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.
Why it matches plant phenotyping methods大豆種子の形態・色を対象に、画像取得・処理・解析による高スループット表現型計測法を開発・検証しており、方法自体が研究の中心です。
abstractwe propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following are available online at https://www.mdpi.com/1424-8220/20/1/248/s1 , Table S1: Measurement feature of ImageJ used in this study, Table S2: Morphological data of soybean seeds in the 400 lines.Open asset ↗lines:45-107Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of the passing eggs and debris, and the videos were reconstructed and processed by custom software program to obtain egg counts. The performance of the software programs for egg counting was characterized with SCN-infested soil collected from two farms, and the results using these methods were compared with those obtained through manual counting.
Why it matches plant phenotyping methods植物病害状態（線虫卵密度・感染重症度）の定量を目的に、画像取得、深層学習およびレンズレス撮像・画像処理による自動計数法を開発し、手動計数と比較検証しているため、方法が中心である。
abstractcomputerized methods were developed to automate the identification and counting of nematode eggs from the processed samples.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicData Availability: All software files are available through gitHub. ( https://github.com/ukalwa/nematode_egg_counting ).Open asset ↗ukalwa/nematode_egg_countinglines:133-142Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included.
Why it matches plant phenotyping methodsキャノピー画像情報を組み込んだ穀物収量予測モデルを開発しており、画像由来の植物情報を用いる計算手法が研究の中心である。
abstractwe developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield
Reproduction assets foundThe paper's canopy coverage and phenotypic/genotypic measurements derive from the publicly available SoyNAM population dataset hosted at SoyBase, which the authors explicitly identify as the data source for their study. No author analysis code, scripts, or trained models are mentioned with any availability statement,soDataset · publicFor the evaluation of our model, we used data collected from the SoyNAM population ( https://www.soybase.org/SoyNAM/ ), which is a nested association mapping population originally consisting of 5600 F5-derived recombinant inbred lines (RILs). The RILs were derived by crossing a common, high-yielding parent (IA3023) to 40 other parents. Out of the 40 parents, 17 were high-yielding, elite lines from 8 different states from the United States, 15Open asset ↗SoyNAMlines:32-44Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
SoybeanWheatRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a back light and a monochrome machine vision camera to capture root crown silhouettes. RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. RhizoVision Analyzer was physically validated using copper wire and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean ( Glycine max ) root crowns of 187 lines and 1,753 wheat ( Triticum aestivum ) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns, and sets a standard by which open plant phenotyping platforms can be benchmarked.
Why it matches plant phenotyping methods根冠形質を高スループットに取得するハードウェア、画像取得・解析ソフトウェアを開発し、物理的・シミュレーション画像で検証しているため、植物フェノタイピング手法が中心である。
abstractThe RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns.
Reproduction assets foundThe paper's data availability statement deposits the wire and root crown image sets, tabular phenotype data, and R analysis code on Zenodo (10.5281/zenodo.3380473), and the authors' RhizoVision Imager and Analyzer software are publicly available on Zenodo (10.5281/zenodo.2585882 and 10.5281/zenodo.2585892). These are直接Dataset · public6953), the
520 Department of Energy ARPA-E ROOTS program (DE-AR0000822), and the United Soybean
521 Board (1420-532-5613).
522 Competing interests: The authors declare no competing interests.
523 Data availability: The wire and root crown image sets, tabular data, and R code for statistics and
524 graphing are available online: http://doi.org/10.5281/zenodo.3380473. The simulated root images
30Open asset ↗Zenodo · 10.5281/zenodo.3380473pdf-layout-page:30 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Agribusiness has a great relevance in the world׳s economy. It generates a considerable impact in the gross national product of several nations. Hence, it is the major driver of many national economies. Nowadays, from each new planting to harvesting process it is mandatory and crucial to apply some kind of technology to optimize a given singular process, or even the entire cropping chain. For instance, digital image analysis joined with machine learning methods can be applied to obtain and guarantee a higher quality of the harvest, leading to not only a greater profit for producers, but also better products with lower cost to the final consumers. Thus, to provide this possibility this work describes a visual feature dataset from soybean seed images obtained from the tetrazolium test. This is a test capable to define how healthy a given seed is (e.g. how much the plant will produce, or if it is resistant to inclement weather, among others). To answer these questions we proposed this dataset which is the cornerstone to provide an effective classification of the soybean seed vigor (i.e. an extremely tiresome human visual inspection process). Besides, as one of the most prominent international commodity, the soybean production must follow rigid quality control process to be part of world trade. Hence, small mistakes in the seed vigor definition of a given seed lot can lead to huge losses.
Why it matches plant phenotyping methodsテトラゾリウム試験を受けたダイズ種子画像の視覚特徴データセットを構築し、種子活力の分類を支援することが中心であり、植物状態の画像ベース表現型データセットに該当する。
abstractthis work describes a visual feature dataset from soybean seed images obtained from the tetrazolium test.
Reproduction assets foundThe paper describes a public visual-feature dataset from soybean seed tetrazolium test images, explicitly deposited on GitHub by the authors.Dataset · publicratory.
Data source location
The seeds were scanned and annotated in the seed analysis laboratory in Tamarana, Paraná, Brazil. The preprocessing and feature extraction phases occurred at the Federal University of Technology - Paraná, in Cornélio Procópio, Paraná, Brazil.
Data accessibility
Data is publicly available on github ( https://github.com/BioinfoCP/visual-features-soybean-vigor ).
Related Research Article
Pereira et al. [1] . An image analysis framework for effective classification of seed damages. Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC), ACM, 2016, pp. 61–66.
Value of the data
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The first open-access visual feature dataset that describes characteristiOpen asset ↗BioinfoCP/visual-features-soybean-vigorlines:1-53Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p
Why it matches plant phenotyping methodsVIIRS・MODISによる作物フェノロジー（緑化開始、成長段階、最大緑度）の推定手法を提示し、地上観測報告との精度比較で検証しているため、植物フェノタイピング手法が中心である。
abstractwe presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets
Reproduction assets foundThe paper uses two public datasets directly in its phenotyping analysis: USDA NASS Crop Progress field observations (via QuickStats) for evaluation, and the Cropland Data Layer (via CropScape/Cropland release) to identify 'pure' corn and soybean VIIRS pixels. No author code, models, or derived data deposits are stated.Dataset · publicwe used CDL in 2014 and
2015 that were publicly available at https://www.nass.usda.gov/Research_and_Science/Cropland/
Release/index.php or at USDA CropScape (https://nassgeodata.gmu.edu/CropScape/)Open asset ↗pdf-page:6 lines:1-62Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.
Why it matches plant phenotyping methods温室自動フェノタイピング platformで測定する早期形質を選定・評価し、異なる実験と圃場データで検証しており、形質取得と評価フレームワークが研究の中心である。
abstractThe aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1
Phenotypic traits evaluated during the Experiment 1 with GlyPh and four criteria considered for the evaluation of phenotyping efficiency.Open asset ↗lines:584-646Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundRoot crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been largely supplanted by image-based approaches. However, most image-based systems have been limited to one or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to distinguish plant root pixels from background pixels in color images and using hardware acceleration (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine max (L.) Merr.] cultivars released between 1930 and 2005.\n\nResultsRelative to previous reports of imaging throughput, this system provides greater throughput with sustained rates of 1.66 root crowns min-1. The EM-GMM segmentation algorithm with hardware acceleration was able to segment images in 10 s, faster than previous methods, and the output images were consistently better connected with less loss of fine detail. Image-based phenes had similar heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine Radius Frequency. Correlations were also noted, especially among the manual Complexity score and phenes such as number of roots and Total Root Length. Averaging phenes across perspectives generally increased heritability, and no single perspective consistently performed better than others. Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were the most sensitive to perspective with decreased correlations among perspectives.\n\nConclusionThe substantial heritabilities measured for many phenes suggest that they are potentially useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no single perspective consistently performed better than others. Thus, as illustrated here for soybean, multiple perspectives may be beneficial for root crown phenotyping systems. This system can contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security and sustainability.
Why it matches plant phenotyping methods根冠形質を高スループットに取得する多視点画像プラットフォーム、画像セグメンテーション、形質抽出を開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractAn efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP).
Reproduction assets foundThe paper's EM-GMM segmentation and MATLAB phene-extraction software for the M-PIP root crown phenotyping platform is publicly available on GitHub with a Zenodo DOI. Raw images and segmented masks are only available upon request, so they do not qualify as public assets.Code · publicnder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
a CC-BY-NC 4.0 International license.
559 Availability of data and materials
560 Raw images and/or segmented masks are available upon request. Software code is available on github
561 (DOI: 10.5281/zenodo.1213805 | website: https://github.com/GatorSense/MPIP).
562 Competing Interests
563 The authors declare no competing interests.
564 Restrictions or Required Licenses
565 No restrictions on this research are known under local or national laws.
566 Funding
567 The authors gratefully acknowledge partial funding for the research from the United Soybean Board to
568 FBF.
569 Authors' cOpen asset ↗GatorSense/MPIP · 10.5281/zenodo.1213805pdf-layout-page:38 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Legumes are protein sources for billions of humans and livestock. These traits are enabled by symbiotic nitrogen fixation (SNF), whereby root nodule-inhabiting rhizobia bacteria convert atmospheric nitrogen (N) into usable N. Unfortunately, SNF rates in legume crops suffer from undiagnosed incompatible/suboptimal interactions between crop varieties and rhizobia strains. There are opportunities to test much large numbers of rhizobia strains if cost/labor-effective diagnostic tests become available which may especially benefit researchers in developing countries. Inside root nodules, fixed N from rhizobia is assimilated into amino acids including glutamine (Gln) for export to shoots as the major fraction (amide-exporting legumes) or as the minor fraction (ureide-exporting legumes). Here, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity following inoculation of both amide exporting and ureide exporting legumes. The assay is based on measuring Gln output using the GlnLux biosensor, which consists of Escherichia coli cells auxotrophic for Gln and expressing a constitutive lux operon. Subsistence farmer varieties of an amide exporter (lentil) and two ureide exporters (cowpea and soybean) were inoculated with different strains of rhizobia under controlled conditions, then extracts of single leaf punches were incubated with GlnLux cells, and light-output was measured using a 96-well luminometer. In the absence of external N and under controlled conditions, the results from the leaf punch assay correlated with 15 N-based measurements, shoot N percentage, and shoot total fixed N in all three crops. The technology is rapid, inexpensive, high-throughput, requires minimum technical expertise and very little tissue, and hence is relatively non-destructive. We compared and contrasted the benefits and limitations of this novel diagnostic assay to methods.
Why it matches plant phenotyping methods葉片穿孔とGlnLuxバイオセンサーを用いて窒素固定関連状態を迅速・高スループットに測定する新規アッセイを開発し、15N測定等と比較検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity
Reproduction assets foundThe article's Supplementary Material (Figures S1–S4) contains the paper's own phenotyping measurements (GlnLux luminometer outputs, Gln standard curves, nodulation counts, root/shoot morphometric data, and root images) and is publicly available at the Frontiers supplementary-material URL stated in the text. No author分析Supplement · publicewan) and Alice Chang (University of British Columbia) for 15 N analysis.
Footnotes
Funding. This research was supported by CIFSRF grant 107791 to MR from the International Food Development Centre (IDRC, Ottawa) and Global Affairs Canada.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2017.01714/full#supplementary-material
FIGURE S1
Luminescence measurement of Gln standards using the GlnLux 96-well luminometer bioassay to demonstrate the linearity of the assay. Luminescence was measured using a concentration gradient of pure Gln standards (0, 125 × 10 -8 , 25 × 10 -7 , 5 × 10 -6 , and 1 × 10 -5 M) uOpen asset ↗lines:155-178Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Digital imagery can help to quantify seasonal changes in desirable crop phenotypes that can be treated as quantitative traits. Because limitations in precise and functional phenotyping restrain genetic improvement in the postgenomic era, imagery-based phenomics could become the next breakthrough to accelerate genetic gains in field crops. Whereas many phenomic studies focus on exploratory analysis of spectral data without obvious interpretative value, we used field images to directly measure soybean canopy development from phenological stage V2 to R5. Over 3 years, we collected imagery using ground and aerial platforms of a large and diverse nested association panel comprising 5555 lines. Genome-wide association analysis of canopy coverage across sampling dates detected a large quantitative trait locus (QTL) on soybean ( Glycine max , L. Merr.) chromosome 19. This QTL provided an increase in yield of 47.3 kg ha -1 Variance component analysis indicated that a parameter, described as average canopy coverage, is a highly heritable trait ( h 2 = 0.77) with a promising genetic correlation with grain yield (0.87), enabling indirect selection of yield via canopy development parameters. Our findings indicate that fast canopy coverage is an early season trait that is inexpensive to measure and has great potential for application in breeding programs focused on yield improvement. We recommend using the average canopy coverage in multiple trait schemes, especially for the early stages of the breeding pipeline (including progeny rows and preliminary yield trials), in which the large number of field plots makes collection of grain yield data challenging.
Why it matches plant phenotyping methods圃場画像を用いてダイズの季節的なキャノピー被覆率を直接測定し、地上・空撮プラットフォームによる再利用可能な表現型取得を大規模に適用しているため、画像ベース表現型解析が研究の中心的要素です。
abstractwe used field images to directly measure soybean canopy development from phenological stage V2 to R5.
Reproduction assets foundThe paper makes two paper-specific assets publicly available: (1) visual canopy field images of the SoyNAM parents hosted on SoyBase, and (2) the study's phenotype and genotypic data (the 'met' dataset) distributed within the authors' R package NAM on CRAN, which also implements the gwas2 analysis function used in the Dataset · publicPhenotypes and genotypic data are available in the R package NAM (https://CRAN.R-project.org/package=NAM). Load the data using the following command: data(met, package = “NAM”).Open asset ↗NAMhtml-lines:53-114Dataset · publicVisual canopy field images of the SoyNAM parents are available at http://www.soybase.org/SoyNAM/imagebrowser.php.Open asset ↗html-lines:17-31