Background Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits. Results In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut. Conclusion The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.
Why it matches plant phenotyping methodsオオムギ病害の抵抗性スクリーニングプロトコルを評価・改良し、検出力と遺伝子型間の識別性能を検証しているため、植物病害表現型の取得法が研究の中心です。
abstractThe goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes.
Reproduction assets foundThe paper's disease-screening and agronomic-trait measurement data are publicly deposited on Zenodo, as stated in the Availability of data and materials section. No author analysis code or trained models are explicitly deposited.Dataset · publicThe data used and/or analyzed in the current study are available through the Zenodo, which is available at Gopinathan, G. (2025). Optimization of a protocol for covered smut in barley [Dataset]. Zenodo. [ 47 ] (https:/doi.org/ https://doi.org/10.5281/zenodo.17906264 ).Open asset ↗Zenodo · 10.5281/zenodo.17906264lines:190-223Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Eggplant (Solanum melongena) is a major cash crop, yet field detection of its pests and diseases remains difficult because disease evidence is simultaneously occluded by foliage, blurred at lesion boundaries, and highly variable in scale. Fruit rot is especially challenging: the waxy epidermis and purple anthocyanin-rich surface reduce chromatic contrast, while infection spreads gradually from water-soaked tissue to necrotic tissue, producing diffuse borders between diseased and healthy regions. In this work, we reframe eggplant disease detection through a context-boundary-scale coupling principle, which states that accurate field detection should jointly model incomplete contextual cues, ambiguous lesion boundaries, and scale-varying symptom morphology rather than optimize these cues independently. ISA-YOLO is proposed as an implementation of this principle on top of YOLOv13 through coordinated context modeling, boundary-aware aggregation, and progressive multi-scale fusion. Experiments on two public datasets show that ISA-YOLO achieves 78.1 and 77.7% mAP at 30.66 and 31.74 FPS, outperforming mainstream detectors in overall trade-off between accuracy and speed. After pruning and quantization, inference speed increases to about 75 FPS while maintaining strong accuracy. These results indicate that the proposed principle provides an effective pathway for accurate and deployable eggplant pest and disease detection in smart agriculture.
Why it matches plant phenotyping methodsナスの病害・害虫を画像から検出するISA-YOLOモデルの開発と性能評価が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
titleA high-performance detection model ISA-YOLO for eggplant pests and diseases.
Reproduction assets foundThe paper uses four public Roboflow image datasets (BISU, UTM, Papaya, Tomato) and states that supporting data and code are publicly available on Zenodo, all with explicit URLs in the Data availability section.Dataset · publicwas supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No newOpen asset ↗eggplant-disease-detectionlines:1509-1560Dataset · publicty
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No new data collection was performed for this research. The authors confirm that the use of these datasets in thOpen asset ↗eggplant-disease-detection-5fuqvlines:1509-1560Dataset · publicof the outcomes of the Provincial Undergraduate Training Program on Innovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these publicOpen asset ↗papaya-eswlk-r2ydylines:1509-1560Dataset · publicnovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and internatOpen asset ↗tomato-rottenlines:1509-1560Code · publicarch. The authors confirm that the use of these datasets in this study is fully compliant with their original licenses and ethical guidelines. The final images presented in the article accurately reflect the original data and meet community standards. The data and code supporting the conclusions of this article are available at https://zenodo.org/records/19425300 .
Declarations
Ethics approval and consent to participateOpen asset ↗Zenodo · 19425300lines:1509-1560Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Background Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow. Results Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40–100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia’s user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F 1 -scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. The platform reliably quantified epidermal traits despite variations in leaf morphology and image quality, supporting the integrated interpretation of stomatal and pavement-cell responses under stress. Conclusions We developed Stomatalia as a robust, user-friendly deep learning platform for automated, high-throughput analysis of bright-field leaf epidermal images across varying magnifications and resolutions. Stomatalia facilitates rapid, reproducible, and coordinated phenotyping of stomatal and pavement cells by integrating methodological standardisation, computational automation, and multi-trait extraction in a single analytical workflow. Its strongest current application is the analysis of high-quality dicot leaf-print images, particularly in species and imaging conditions similar to those used for model training and validation. Cross-species and benchmark analyses further define its current scope: stomatal detection can be transferred to some additional epidermal architectures, whereas robust pavement-cell segmentation in monocots or highly divergent species will require further annotation and model retraining. Within these defined boundaries, Stomatalia provides a flexible and extensible framework for studying stomatal and pavement cell morphology and environmental plasticity, while also supporting broader efforts to dissect and optimise plant responses to abiotic stress.
Why it matches plant phenotyping methods気孔・舗装細胞の形態形質を画像から自動抽出する深層学習プラットフォームを開発し、独立データで性能評価・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。
abstractwe developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits.
Reproduction assets foundThe authors publicly deposited the paper's test image datasets (raw/input leaf-print images, detection outputs, manual ground-truth counts, exported datasets) and the model file on Figshare, and provide a public Google Colab demo for running the Stomatalia algorithm. Both are paper-specific, public, and actionable.Dataset · publicThe test datasets and model file used in this work are available through the following link:
https://doi.org/10.6084/m9.figshare.32532672. The test_sets.zip archive contains the test image datasets
(cross-species and benchmark analysis), including raw/input images, detection output images, manual
ground-truth counts and exported datasets.Open asset ↗Figshare · 10.6084/m9.figshare.32532672pdf-page:25 lines:1-75Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
RiceRootTissueMorphology / geometry measurementSegmentationRoot system architecture
Abstract Background Quantification of rice root anatomical traits such as cortical aerenchyma lacunae is key to understanding rice adaptation to diverse water regimes and to support climate-smart breeding. Aerenchyma lacunae contributes to rice internal gas transport and influences methane emissions from flooded systems and can also limit rice water conductivity. It could be an interesting anatomical trait for breeding, however, large-scale anatomical phenotyping remains limited because manual analysis of root cross-sections is labor-intensive, subjective, and difficult to scale across heterogeneous imaging conditions. Existing pipelines often require parameter tuning and do not generalize well across environments. Results We developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae. The model was trained on 1,760 annotated images collected across multiple countries, growth stages, cultivation systems, and experimental contexts, using a collaboratively defined annotation protocol. The final model achieved high segmentation accuracy, with mean intersection over union values exceeding 0.92 for cortical tissues and lacunae. Quantification of the lacuna-to-cortex ratio showed strong agreement with manual annotations, with a coefficient of determination of 0.98 on an independent test set. An independent expert review indicated that model predictions were at least as consistent as manual annotations and reduced large annotation inconsistencies. The pipeline is released as open-source software and includes an interactive online demonstrator, and is accompanied by an online test dataset to support testing and reproducibility. Application across six experimental use cases revealed reproducible differences in aerenchyma lacunae across genotypes, water regimes, environments, and developmental stages. Conclusions This work provides a robust, scalable, and transferable tool for automated root anatomical phenotyping under heterogeneous experimental conditions. Transformer-based segmentation enables consistent and high-throughput quantification of lacunae, facilitating integration of these anatomical traits into breeding, physiological studies, and climate-smart crop improvement programs.
Why it matches plant phenotyping methodsイネ根の通気組織空隙を画像から自動セグメンテーション・定量するTransformerベースの表現型解析パイプラインを開発し、独立データで精度検証、ソフトウェアとテストデータセットを公開しているため、植物フェノタイピング手法が中心である。
abstractWe developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae.
Reproduction assets foundThe paper releases its authors' phenotyping pipeline (preprocessing/training code archived on Zenodo and an interactive Hugging Face Space demonstrator with a test dataset subset) as public assets. The full multi-environment training image dataset is only available upon reasonable request, so it is not a public asset.Code · publicall code used for preprocessing and
training is released under an open-source licence on GitHub, tagged v1.0.2, and
archived with a Zenodo DOI (Atef, 2025).Open asset ↗Zenodopdf-page:46 lines:1-65Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Stomatal trait analysis is essential for optimizing crop photosynthesis and transpiration, yet deep learning studies have focused mainly on monocotyledons, leaving dicotyledonous crops such as melon (Cucumis melo L.) understudied. To bridge this gap, we established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images. On this basis, we developed an improved Mask R-CNN framework using Vision Transformer (ViT) as the backbone. Specifically, standard Multi-Head Attention (MHA) was replaced with Dynamically Composable Multi-Head Attention (DCMHA), which enhances information exchange across attention heads and alleviates the low-rank limitation of conventional attention. In addition, a modified effective Squeeze-and-Excitation (eSE) module was incorporated into the Feature Pyramid Network (FPN) to strengthen channel dependency modeling and multi-scale feature representation. On the melon dataset, the proposed model achieved a mean average precision (mAP) of 72.40 ± 0.09%, with AP50 and AP75 of 91.93 ± 0.14% and 84.59 ± 0.19%, respectively. Repeated-run statistical analyses showed that eSE significantly and consistently improved the main detection metrics across backbones, whereas DCMHA provided a more moderate gain within the ViT-based setting, with clearer support for AP50 than for mAP or AP75 under the baseline FPN setting. Overall, the combined configuration remained among the top-performing models for stomatal instance segmentation. Ellipse fitting further enabled automated quantification of stomatal length, width, count, area, and circumference, showing strong agreement with manual measurements (Pearson r = 0.978). The model also showed preliminary transferability to cucumber, watermelon, pumpkin, and loofah, with an average species-specific R² of 0.86, although each species was evaluated on a limited sample set.
Why it matches plant phenotyping methodsメロンの気孔形質を画像から自動抽出するデータセット、改良Mask R-CNN、インスタンスセグメンテーション、楕円フィッティングを開発・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ある。
abstractwe established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images.
Reproduction assets foundThe paper's authors explicitly state that the source code for model training and inference (including the key modules: DCMHA, eSE-enhanced FPN, Mask R-CNN/ViT pipeline) is publicly available at a GitHub repository, which matches an allowed URL. The melon stomatal image dataset (8,154 images) is described in detail but,Code · publicCode Availability
The source code for model training and inference, including the implementation of the
key modules, is publicly available at:
https://github.com/huangyao110/qk_maskrcnn_trsv2.gitOpen asset ↗huangyao110/qk_maskrcnn_trsv2pdf-page:22 lines:1-322Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態を3D画像取得と計算解析で定量化するパイプラインを開発し、形状記述子の抽出と遺伝子型間での検証を行う、植物フェノタイピング手法の中心的研究である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
RootMorphology / geometry measurementSegmentationRoot system architecture
BACKGROUND: Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. Accurate segmentation is a prerequisite for extracting root traits relevant to plant physiology, breeding, and agronomy. While U-Net and other convolutional neural network (ConvNet) architectures have been applied to root segmentation, no systematic comparison of multiple Transformer and ConvNet architectures has been conducted across diverse root imaging conditions. RESULTS: We evaluated 21 segmentation architectures across nine diverse root image datasets, training 1511 models to assess all combinations of architecture, dataset, pre-training strategy, and learning rate, producing over 3 million segmentations for evaluation. Transformer-based models significantly outperformed ConvNets for Dice (mean Dice 0.679 vs 0.659; [Formula: see text]). Root-diameter and root-length correlation were also higher for Transformers, but the differences were not statistically significant ([Formula: see text] and [Formula: see text] respectively). Pre-training significantly improved mean Dice from 0.623 to 0.666 ([Formula: see text]), with Transformers benefiting more from pre-training than ConvNets (Dice improvement + 0.072 vs + 0.021; [Formula: see text]), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. MobileSAM achieved the highest Dice score (0.693) while maintaining computational efficiency. Both architecture families underestimated thin root length compared to manual annotations. Dataset choice explained 70.9% of performance variance, far exceeding model architecture (6.7%). PURPOSE: Transformer architectures significantly outperform ConvNets for root segmentation accuracy, and pre-training significantly improves performance, particularly for Transformers. Pre-trained MobileSAM offers the best accuracy at competitive computational cost. Dataset choice dominates performance variance, suggesting practitioners should prioritize data curation over architecture selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を複数データセットで体系的に比較・検証し、根長・根径などの形質抽出性能も評価しているため、植物フェノタイピング手法が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's root image datasets (DeepRootLab, Grassland, Chicory, PRMI) are publicly available, and the authors' training code and modified RhizoVision Explorer trait-extraction fork are on GitHub with explicit availability statements.Dataset · publicImages are available from https://zenodo.org/records/15213661 .Open asset ↗Zenodo · 15213661lines:872-982Dataset · publicImages are available from https://figshare.com/ndownloader/articles/20440497/versions/2 .Open asset ↗Figshare · 20440497lines:872-982Dataset · publicImages are available from https://zenodo.org/records/3527713 .Open asset ↗Zenodo · 3527713lines:872-982Dataset · publicImages are available from https://gatorsense.github.io/PRMI/ .Open asset ↗lines:872-982Code · publicTraining code is available at https://github.com/sotlampr/seg .Open asset ↗GitHub · sotlampr/seglines:1183-1225Code · publicAll nine root image datasets used in this study are publicly available. DeepRootLab images are available from Zenodo (https://zenodo.org/records/15213661). Grassland images are available from Figshare (https://figshare.com/ndownloader/articles/20440497/versions/2). Chicory images are available from Zenodo (https://zenodo.org/records/3527713). The six PRMI datasets (Papaya, Peanut, Sesame, Sunflower, Cotton, Switchgrass) are available from https://gatorsense.github.io/PRMI/. Training code is available at https://github.com/sotlampr/seg. The modified RhizoVision Explorer fork used for trait extraction is available at https://github.com/sotlampr/RhizoVisionExplorer.Open asset ↗GitHub · sotlampr/RhizoVisionExplorerlines:1294-1347Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Accurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping, as breeding programs rely on reliable severity estimates to identify genotypes with improved resistance. However, most deep learning approaches have been developed as pathogen-specific models, which limits scalability in field-grown barley where multiple diseases naturally co-occur and exhibit substantial visual similarity. Results We evaluated whether a multiclass segmentation model can simultaneously detect and distinguish two fungal diseases of barley, Puccinia hordei and Ramularia collo-cygni, and compared its performance with two disease-specific binary models. Using 336 high-resolution leaf scans collected in the field with naturally occurring co-infections, the multiclass model achieved higher Dice scores for brown rust (0.59 vs 0.40; +47.5% relative improvement) and ramularia (0.60 vs 0.53; +13.2% relative improvement). It also captured a greater proportion of individual lesions across both classes. At the genotype level, the model-predicted disease area percentages were highly consistent with those from ground truth annotations ([Formula: see text]). Conclusions A unified multiclass framework can more effectively segment visually similar foliar diseases than separate binary models, while simplifying the computational workflow. This provides a scalable basis for automated resistance assessment within breeding pipelines. Code and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.
Why it matches plant phenotyping methods圃場画像から葉面病害面積をセグメンテーションし、遺伝子型レベルの病害重症度を推定する手法を開発・比較・検証しており、植物フェノタイピングが中心です。
abstractAccurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping
Reproduction assets foundThe paper's annotated barley leaf disease segmentation dataset (Mendeley Data DOI 10.17632/4ny92p2r8f.1) and the authors' analysis/segmentation code (GitHub grimmlab/BarleyDiseaseSegmentation) are explicitly declared publicly available, directly reproducing this paper's phenotyping measurements and computational modelsDataset · publicThe annotated dataset and the code implementing our machine learning–based model are publicly available on Mendeley Data (https://doi.org/10.17632/4ny92p2r8f.1) and GitHub (https://github.com/grimmlab/BarleyDiseaseSegmentation).Open asset ↗Mendeley Data · 10.17632/4ny92p2r8f.1lines:133-140Code · publicCode and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.Open asset ↗GitHub · grimmlab/BarleyDiseaseSegmentationlines:1-70Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Crop diseases significantly threaten global food security by directly affecting the crop yield and quality. The traditional diagnostic methods are labour intensive and human error prone. However, the existing deep learning solutions suffer with poor generalization due to the sharp loss landscapes. The proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM). This method minimizes both the training loss and loss landscape sharpness and enables the model to converge to a flatter-minima with improved generalization. The proposed work is evaluated on 60,000 corn leaf image samples for four classes with 15,000 balanced samples per class after augmentation. The optimized CNN model has achieved 99.66% test accuracy at 0.33% classification error rate and outperforms the conventional optimizers like Adam (98.44% accuracy) and the Stochastic Gradient Descent (SGD). The state-of-the-art analysis presents a 99% average precision rate along with 99.66% F1-score and 0.0013% mean squared error (MSE). The quantized model achieves an inference latency of 22.7 ms/image (≈44 FPS) on a Raspberry Pi 4 and reduces model overfitting and enhances feature discriminability. These results underscore the potential of SAM-based optimization in precision agriculture by driving a scalable automation of disease management. This work bridges the gap between theoretical advances in deep learning optimization and practical deployment in resource-constrained farming environments.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病害を分類するCNNの最適化と性能評価が中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractThe proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM).
Reproduction assets foundThe paper's Data availability statement lists the public corn leaf image datasets used for its disease-classification experiments (Kaggle corn/maize leaf disease dataset, New Bangladeshi crop disease dataset, New Plant Diseases Dataset, and the MahindiNet maize leaf disease dataset on Science Data Bank). No author codeDataset · publicg.; Gireesh Kumar: Formal Analysis, Visualization, Writing – Review & Editing, Resources.
Funding
Open access funding provided by Manipal University Jaipur. Open access funding provided by Manipal University Jaipur, Jaipur. No external funding was received for this research.
Data availability
Corn or Maize Leaf Disease Dataset, https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset [ 33 ] New Bangladeshi crop disease dataset. https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease [ 38 ] New Plant Diseases Dataset, https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset [ 46 ] Mohammed Abo-Zahhad et al. (2023). MahindiNet: Maize Leaf DOpen asset ↗Kagglelines:2568-2620Dataset · publicby Manipal University Jaipur. Open access funding provided by Manipal University Jaipur, Jaipur. No external funding was received for this research.
Data availability
Corn or Maize Leaf Disease Dataset, https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset [ 33 ] New Bangladeshi crop disease dataset. https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease [ 38 ] New Plant Diseases Dataset, https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset [ 46 ] Mohammed Abo-Zahhad et al. (2023). MahindiNet: Maize Leaf Disease Dataset[DS/OL]. V1. Science Data Bank. https://cstr.cn/31253.11.sciencedb.12556 . CSTR:31,253.11.sciencedb.12556 [ 55 ] Open asset ↗Kagglelines:2568-2620Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Leaf morphology in tea plants (Camellia sinensis L.) profoundly influences tea quality and agronomic value, yet its genetic basis remains elusive due to labor-intensive phenotyping, foliage architecture, and ecological sensitivity of traits. Moreover, traditional methods forfeit quantitative color gradients and population-level morphological complexity. To address this challenge, we developed coleaf, an open-source image recognition-based software that demonstrated 97.6% accuracy over conventional ImageJ measurements, while offering higher efficiency and color hues quantification. We then estimated 7 key morphological traits focusing on leaves from a collection of ~ 4,200 mature leaves and ~ 5,000 bud-leaf samples across 167 genetically diverse tea accessions by coleaf. While classical understanding suggests leaf shape differentiation between two varieties in genus sinensis assamica (CSA) and sinensis (CSS), our phenotypic clustering revealed incomplete congruence with phylogenetic relationships, suggesting the presence of additional genetic or environmental modulators beyond population divergence. Furthermore, we integrated phenotypic data with whole-genome resequencing for multi-model genome-wide association studies (GWAS). Candidate genes associated with leaf architecture were involved in plant development (e.g., CsFAS2), cell division and elongation (e.g., CsFIP1), and cellular morphogenesis (e.g., CsRLK), whereas those associated with leaf color, regulated pigment accumulation (e.g., ABC transporters, CsMYB113). In conclusion, this study establishes a standardized computational framework validating automated image recognition for plant leaf phenomics. The end-to-end framework from high-throughput phenotyping to gene discovery provides critical genetic targets for tea breeding, demonstrating transformative potential in accelerating the genetic improvement of tea plants.
Why it matches plant phenotyping methods茶葉形態の画像認識ソフトウェアを開発・検証し、高スループットな形質抽出フレームワークとして適用しており、植物フェノタイピング手法が研究の中心である。
abstractwe developed coleaf, an open-source image recognition-based software that demonstrated 97.6% accuracy over conventional ImageJ measurements
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 codes and tools used in this study are described in Methods, coleaf is available on github (https://github.com/mengmeng-jiang/coleaf).Open asset ↗mengmeng-jiang/coleafhtml-lines:390-460Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length ( R 2 = 0.972), ear diameter ( R 2 = 0.905), ear volume ( R 2 = 0.976), ear weight ( R 2 = 0.878), kernel number ( R 2 = 0.98), kernel row number ( R 2 = 0.888), kernel number per row ( R 2 = 0.852), kernel thickness ( R 2 = 0.705), kernel width ( R 2 = 0.515), and thousand kernel weight ( R 2 = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との一致を検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline.
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 codes and the manual of command line interface and GUI can be found at the GitHub repository: https://github.com/Chimaco37/OpenEar.Open asset ↗Chimaco37/OpenEarhtml-lines:294-325Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、シミュレーションおよび実画像で既存指標と比較・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。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI)
Reproduction assets foundThe paper deposits its benchmark confocal image dataset in the EMBL-EBI BioImage Archive (S-BIAD2226) and its TTI analysis software (ImageJ macro and Jupyter/Python scripts) on GitHub, both with explicit public availability statements.Dataset · publicImage data generated and analyzed in the current study are available in the EMBL-EBI BioImage Archive repository, accession number S-BIAD2226Open asset ↗EMBL-EBI BioImage Archive · S-BIAD2226lines:141-163Code · publicArchive copy, additional sample data and possible future updates of the software tool generated here are also available at [ https://github.com/GeorgeCaldarescu/TTI-Tonoplast-Topology-Index ] .Open asset ↗GitHub · GeorgeCaldarescu/TTI-Tonoplast-Topology-Indexlines:141-163Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Modern agriculture demands precise genomic prediction to accelerate elite crop breeding, yet traditional genomic prediction approaches, such as genomic best linear unbiased prediction (GBLUP) and Bayesian methods, focus primarily on the cumulative effect of individual SNPs, thus neglecting the concerted influence that the surrounding sequence context has on the phenotype. To overcome these limitations, we propose two novel feature embedding modes (SNP-context and whole-genome) based on DNABERT-2, a cross-species genomic foundation model that uses self-attention mechanisms and transfer learning to automatically identify conserved sequence features across diverse evolutionary lineages without prior biological assumptions. The whole-genome feature embedding aggregates genomic information at a global scale by pooling vectors from chunked sequences processed by DNABERT-2, whereas the context feature embedding captures local information by directly encoding variable-length (500–3000 bp) sequences centered on target SNPs. To reduce noise in the high-dimensional feature embeddings, we employed principal component analysis (PCA) and partial least squares (PLS) to project the features into a lower-dimensional space. We generated two kinds of feature embedding for three crop datasets (rice413, rice395, and maize301), investigated the impact of 500–3000 bp flanking SNP contexts on phenotypic prediction, and compared prediction accuracy variations across algorithms at 4–768 feature dimensions among the PCA, PLS, and no dimensionality reduction strategies. The results demonstrate that machine learning (ML) algorithms operating under the SNP-context embedding mode achieve greater accuracy and lower mean absolute errors (MAEs) than traditional SNP features, with performance peaking at optimal context lengths that proved to be trait-dependent (e.g., 1000 bp to 3000 bp), particularly for traits with low-to-moderate heritability (H 2 ∈ (0.2, 0.7]). In contrast, using whole-genome embeddings as input for ML can further improve the prediction accuracy for highly heritable traits (H 2 ∈ (0.7, 1.0]), even outperforming state-of-the-art deep learning models (such as DNNGP and ResGS) that rely on SNP markers. The proposed feature embedding methods, which leverage DNABERT-2 to capture the contextual features of SNPs, effectively overcome the limitations of traditional prediction models. This study demonstrates that the SNP-context mode is superior for traits with low-to-moderate heritability, while the whole-genome embedding mode excels for highly heritable ones. Our work provides plant breeders with a flexible and powerful analytical framework, enabling them to select the most suitable phenotypic prediction method based on the complexity of the target trait, thereby accelerating genetic gain in the breeding of elite crop varieties.
Why it matches plant phenotyping methodsDNABERT-2を用いた遺伝情報から作物形質を予測する新規特徴埋め込み・機械学習手法の開発と比較評価が中心であり、植物形質推定の計算手法に該当する。
titleCrop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their crop phenotype prediction method (SNP-context and whole-genome DNABERT-2 embedding pipeline) is publicly available on GitHub. Other URLs in the text (samtools, bcftools, pysam, pyfaidx, PyCaret) are generic third-party libraries, not paper-ownCode · publicThe Python implementation of our method is publicly available and downloadable from the GitHub repository: https://github.com/oliveSpring/Crop_DNA_Embedding.git.Open asset ↗oliveSpring/Crop_DNA_Embeddinglines:513-602Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.
Why it matches plant phenotyping methods小麦葉の感染状態をハイパースペクトル画像と深層学習で分類する手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractHyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' training/deployment/testing code for the hyperspectral wheat disease classification models in a public GitHub repository. The 1447-image hyperspectral dataset itself has no stated public deposit, so it is not included as an asset.Code · publicCode for training, deploying and testing the models can be found at https://github.com/mc2295/hyperspectralplants .Open asset ↗mc2295/hyperspectralplantslines:140-218Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cotton diseases and pests pose significant threats to cotton production, necessitating accurate and efficient classification methods. Despite existing advanced methods, there is a research gap in utilizing both local feature extraction and global context capture for enhanced classification accuracy. Hence, this study developed and evaluated three advanced models for cotton disease and pest classification: a convolutional neural network (CNN)-based model, a Vision Transformer (ViT)-based model, and a hybrid CNN-ViT model. These models were trained on a dataset comprising eight classes of cotton diseases and pests, namely aphids, armyworm, bacterial blight, cotton boll rot, green cotton boll, healthy, powdery mildew, and target spot. The results demonstrated that the hybrid CNN-ViT model achieved the highest overall performance with an average test accuracy of 98.5%. The CNN model showed strong performance with an average accuracy of 97.9%. The ViT models, while having self-attention mechanisms to capture context and dependencies, exhibited improved performance with increased depth. The ViT model having four transformer layers outperformed the two-layer variant, achieving an average accuracy of 97.2% compared to 96.3%. The hybrid model effectively combined the strengths of CNN's local feature extraction and ViT's global feature capture, resulting in superior classification accuracy across most classes. Future research should focus on expanding the dataset to include more diverse diseases and pests and integrating the models with autonomous platforms for spraying the chemicals, thus facilitating real-world adoption and application in agricultural settings.
Why it matches plant phenotyping methods綿花の病害・害虫を画像から分類するCNN、ViT、ハイブリッド手法を開発・評価しており、植物の病害状態推定が研究の中心である。
abstractthis study developed and evaluated three advanced models for cotton disease and pest classification
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 dataset for this study was downloaded from Kaggle ( https://www.kaggle.com/datasets/saeedazfar/customized-cotton-disease-dataset ) and comprises images of various cotton diseases and pests.Open asset ↗Kaggle · saeedazfar/customized-cotton-disease-datasetlines:78-87Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Wheat diseases significantly impair production efficiency and grain quality in the wheat industry. In recent research, deep learning techniques have been widely applied to plant disease detection. However, wheat disease images collected in field conditions often face complex backgrounds and diverse lesion shapes, making accurate disease classification difficult. In real-world applications, agricultural disease recognition systems must also deal with limited computational resources and edge device constraints, emphasizing the need for lightweight methods. Results To solve these challenges, this paper introduces a lightweight dual-stream learning (LDSL) framework for wheat disease detection. The framework adopts a unique global-local dual-stream architecture that combines global semantic understanding with local discriminative analysis. The global learning stream extracts comprehensive semantic features and generates saliency maps to highlight key regions, while the local learning stream performs fine-grained inspection of these regions using a novel dynamic-static dual attention (DSDA) mechanism. Additionally, a Kullback-Leibler (KL) divergence perturbation strategy is implemented during training to boost the LDSL framework's robustness in noisy and complex settings. Experimental results show that the proposed LDSL framework achieves an accuracy of 94.44%, a precision of 94.47%, a recall of 94.44%, and an F1-score of 94.45%, outperforming several mainstream classification models in wheat disease recognition, such as ConvNeXt-T (92.66% accuracy, 92.69% precision, 92.66% recall, and 92.63% F1). The proposed LDSL framework is lightweight, using only 4.41 M parameters and 1.71G FLOPs. On the NVIDIA Jetson Orin Nano, it requires just 15.99 MB of storage, 39.49 MB of peak memory, and achieves an inference latency of 234.76 ms/image, demonstrating good potential for real-world deployment. Conclusions This study provides a novel detection framework for wheat disease research, which significantly improves various classification metrics. With low parameter and computation costs, the framework demonstrates good potential for practical deployment.
Why it matches plant phenotyping methodsコムギ葉の画像から病害状態を推定する軽量な画像解析フレームワークを開発・評価しており、植物病害表現型の取得手法が中心である。
abstractthis paper introduces a lightweight dual-stream learning (LDSL) framework for wheat disease detection.
Reproduction assets foundThe paper's wheat disease image dataset (five classes: healthy, powdery mildew, smut, leaf rust, sharp eyespot) is explicitly stated to be publicly available via a Google Drive link, which matches an allowed URL. No code or model checkpoints are shared.Dataset · publicThe dataset used in this study originates from the “Smart Agriculture” Platform of Jilin Agricultural Science and Technology University. To facilitate further research, it has been made publicly available at: https://drive.google.com/file/d/1xK3NX7d2bccBDMQMp0qXp-2pG-Jb_kmx/view?usp=drive_linkOpen asset ↗lines:275-333Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Fusarium head blight (FHB), caused by the Fusarium species complex, significantly endangers wheat yield and safety. Accurate and timely assessment of FHB epidemic level in the field is crucial for effective disease management. However, the complex environment and indistinct edges of diseased areas present substantial challenges in distinguishing between healthy and diseased ears, thereby impacting the accuracy of FHB epidemic level detection. This study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level. The Focal-Edge Selection Module (FSM) within the backbone replaces original C2f module to enhance edge feature representation and facilitate multi-scale feature extraction. Furthermore, the Dual Spatial-Connection Feature Pyramid Network (DSCFPN), integrating Global-to-Local Spatial Aggregation (GLSA) with bidirectional pyramid interaction, balances global and local feature acquisition while optimizing the feature fusion mechanism. This design enables the model to effectively handle occlusions, scale variations, and complex environments. Experimental results demonstrate substantial improvements over eight comparative models in detecting healthy and diseased wheat ears, achieving mean Average Precision (mAP) of 86.1% and 82.9%, respectively. Notably, the model achieved a mean accuracy of 94.7% in detecting FHB epidemic levels through rigorous spatiotemporal validation using datasets collected from independent fields across different years, underscoring its robust generalization capability. Characterized by its low complexity and lightweight design, EBS-YOLO features a parameter count of 2.05 M, 7.4 GFLOPs, and a model size of 5.0 MB, making it an efficient approach for real-time FHB epidemic level detection.
Why it matches plant phenotyping methods小麦穂の健全・罹病状態と赤かび病の流行レベルを圃場画像から推定する深層学習手法を開発し、独立圃場・異なる年のデータで検証しているため、植物病害フェノタイピング手法が中心である。
abstractThis study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level.
Reproduction assets foundThe paper's wheat FHB image dataset (1152 field images used for EBS-YOLO training/evaluation) is explicitly stated as publicly available on the authors' GitHub repository.Dataset · publicam Development Project [2025QCY-KXJ-070]; the Science and Technology Partnership Program, Ministry of Science and Technology of China [KY202002018] and the National Natural Science Foundation of China [32081330501].
Data availability
The datasets supporting the conclusions of this article are available in the GitHub repository, https://github.com/yuanYuan8686/wheat-FHB-dataset.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
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 affiliationOpen asset ↗yuanYuan8686/wheat-FHB-datasetlines:369-441Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Agriculture plays a pivotal role in global food security and socio-economic stability, yet crop productivity remains threatened by plant diseases that incur substantial economic losses. Pomegranate is an important fruit for both nutrition and business, but it is easily infected by pathogens that can lower yields by 20 to 40 percent. Traditional methods of finding these pathogens by hand are time-consuming, subjective, and not very effective, while existing deep learning models struggle with field noise, lighting variations, and computational inefficiency. To address these challenges, this study proposes an automated framework integrating a modified ResNet101 architecture with a Hybrid Genetic Algorithm-Particle Swarm Optimization (HGA-PSO) method. The approach employs dual-stream processing of original and noise-augmented images (Gaussian, salt-and-pepper, speckle) to enhance robustness. Results The framework achieved exceptional performance on a dataset of 5,000 images across five classes (four diseases, one healthy). Feature fusion from dual streams and HGA-PSO optimization reduced dimensionality by 50-70% while preserving discriminative power. Under rigorous 5-fold cross-validation, the Multi-Layer Perceptron (MLP) classifier attained 99.10% accuracy, a perfect ROC-AUC score (1.00), and high precision-recall metrics. Confusion matrices revealed near-zero misclassification, and real-world tests (single/batch images) confirmed strong generalization. Grad-CAM + + visualizations validated precise localization of disease regions. The model outperformed existing techniques (e.g., PSO-YOLOv8: 98.86%, Transformer models: 93.13%) in accuracy, precision, recall, and F1-score CONCLUSIONS: This research presents an optimized model for pomegranate disease detection by combining deep learning with nature inspired optimization. The dual-stream feature fusion and HGA-PSO significantly improves robustness again environment variability while reducing computation overhead. This framework offers a scalable solution for precision agriculture, enabling early disease intervention to mitigate economic losses. Future research could improve scalability and usefulness by looking into lightweight optimization methods, model interpretability, and how they can be used in limited-resource agricultural settings.
Why it matches plant phenotyping methods画像から植物病害領域・病害状態を推定する深層学習手法の開発と検証が研究の中心であり、植物フェノタイピング手法として採用する。
abstractGrad-CAM + + visualizations validated precise localization of disease regions.
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 datasets can be accessed through the Kaggle link: https://www.kaggle.com/datasets/anilsandhii/balanced-pomegranate-dataset.Open asset ↗Kaggle · anilsandhii/balanced-pomegranate-datasethtml-lines:683-713Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Stomatal morphology plays a critical role in regulating plant gas exchange influencing water use efficiency and ecological adaptability. While traditional methods for analyzing stomatal traits rely on labor-intensive manual measurements, machine learning (ML) tools offer a promising alternative. In this study, we evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping. The approach enables non-ML experts to efficiently segment stomatal structures across diverse datasets, including images from different plant species, magnifications, and imprint methods. We trained a single model based on images from five datasets and tested its performance on unseen data, achieving high accuracy for stomatal density (R 2 = 0.98) and size (R 2 = 0.90). Thresholding approaches applied to the U-Net segmentations further improved accuracy, particularly for density measurements. Despite significant variability between datasets, our findings demonstrate the feasibility of training a single segmentation model to analyze diverse stomatal data sets. Validation approaches showed that a semi-automatic approach involving correcting segmentations was five times faster than manual annotation while maintaining comparable accuracy. Our results also illustrate that ML metrics, such as the F1 score, correlate with accuracy in the statistical analysis of trait measurements with improvements diminishing after 2:30 h model training. The final model achieved high precision, allowing the detection of highly significant biological differences in stomatal morphology within plant, between genotypes and across growing environments. This study highlights interactive ML with corrective annotation as a robust and accessible tool for accelerating phenotyping in plant sciences, reducing technical barriers and promoting high-throughput analysis.
Why it matches plant phenotyping methods気孔形態を対象としたU-Net分割と対話的補正による表現型取得手法を開発・検証しており、精度、速度、汎用性を評価しているため。
abstractwe evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping.
Reproduction assets foundThe paper's stomata image datasets (Datasets 1 and 3), validation and training sets, and all intermediate CNN segmentation models are explicitly deposited publicly on Zenodo (DOI 10.5281/zenodo.15316123). The Colab notebook link is for the generic RootPainter tool, not a paper-specific asset.Dataset · publicComplete Dataset 1 and Dataset 3, the validation set and the training dataset, and all intermediate CNN models are publicly available ( https://doi.org/ https://doi.org/10.5281/zenodo.15316123 ).Open asset ↗Zenodo · 10.5281/zenodo.15316123lines:235-281Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Background Fungal diseases are among the most significant threats to global crop production, often leading to substantial yield losses. Early detection of crop infection by fungus is the very first step to deploying a timely and effective treatment. Early and reliable detection is thus key to improving yields, sustainability, and achieving food security. Conventional diagnostic methods are however often destructive, slow, or requiring visible symptoms which appear late in the infection process. To overcome these challenges, we propose using optical coherence tomography (OCT) as an innovative imaging tool to provide cross-sectional and three-dimensional images of the plant internal microstructure non-invasively, in vivo, and in real-time. Results We demonstrate the use of low-cost OCT to monitoring wheat (cultivar AxC 169) when infected by Septoria tritici . We show that OCT analysis can effectively detect signs of infection before any external symptoms appear. Although OCT cannot directly visualize fungal hyphae, OCT reveals apparent morphological changes of the mesophyll where the fungal filaments are expected to develop. This study thus focuses on monitoring and correlating changes within the mesophyll structural organisation with the state of infection. It results in distinct statistical difference between intact and infected wheat plants two days only after infection. We then demonstrate the use of machine learning (ML) for high throughput segmentation of OCT scans, providing a foundation for future automated fungus-detection analysis. Conclusions This work highlights the potential of OCT, combined with ML tools, to enable rapid, non-invasive, and early diagnosis of crop fungal infections, opening new avenues for precision agriculture and sustainable disease management.
Why it matches plant phenotyping methodsOCTによる植物内部構造の非侵襲的画像化と、機械学習によるセグメンテーションを用いて、感染植物の形態変化・感染状態を推定する手法が研究の中心である。
abstractwe propose using optical coherence tomography (OCT) as an innovative imaging tool to provide cross-sectional and three-dimensional images of the plant internal microstructure non-invasively, in vivo, and in real-time.
Reproduction assets foundThe paper's authors publicly released their bespoke ML-based OCT segmentation software (PyQt5 GUI with U-Net model for segmenting mesophyll gaps in wheat OCT scans) via a Google Drive link, stated in both the Methods and Data availability sections. Raw OCT B-scans are only available upon request, so no public phenotypeCode · publicuses OpenCV, TensorFlow, NumPy, and Pandas for image processing and ML-based analysis. After training, the U-Net model (unet_masking3.keras) is used for generating segmentation masks via MaskThread class. The code is provided in supplementary information (SI), and the software is made available for download following this link:
https://drive.google.com/drive/folders/1DJm3OZHfK-P-XSRXGMtpxgSx51WnVNsF?usp=sharing
In both the manual and the automated procedure, the analysis focuses on the thickness of these apparent gaps between the second and third upper layers of the mesophyll.
ResultsOpen asset ↗lines:42-51Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Plant phenotyping has become increasingly important for advancing plant science, agriculture, and biotechnology. Classic manual methods are labor-intensive and time-consuming, while existing computational tools often require advanced coding skills, high-performance hardware, or PC-based environments, making them inaccessible to non-experts, to resource-constrained users, and to field technicians. RESULTS: To respond to these challenges, we introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping. The platform is designed for ease of use, enabling users to phenotype plant traits quickly and efficiently with only a smartphone at hand. We currently instantiate the use of the platform with tools such as SeedPheno, WheatHeadPheno, LeafAnglePheno, SpikeletPheno, CanopyPheno, TomatoPheno, and CornPheno; each offering specific functionalities such as seed size and count analysis, wheat head detection, leaf angle measurement, spikelet counting, canopy structure analysis, and tomato fruit measurement. In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. CONCLUSIONS: By leveraging cloud computing and a widely accessible interface, OpenPheno democratizes plant phenotyping, making advanced tools available to a broader audience, including plant scientists, breeders, and even amateurs. It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. Its integration with smartphones also positions OpenPheno as a powerful tool in the growing field of mobile-based agricultural technologies, paving the way for more efficient, scalable, and accessible agricultural research and breeding.
Why it matches plant phenotyping methodsスマートフォンで植物形質を取得・解析するソフトウェアプラットフォームの開発が中心であり、複数の具体的な表現型解析ツールを提供している。
abstractwe introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping.
Reproduction assets foundThe paper's authors publicly release the OpenPheno platform code (GitHub repository) and the evaluation sample data used for algorithm validation and demonstration (dataset subdirectory). Both are paper-specific, public, and actionable.Dataset · publicEvaluation sample data used for algorithm validation and demonstration has been made publicly available at out GitHub repository: https://github.com/openpheno/OpenPheno/tree/main/dataset .Open asset ↗openpheno/OpenPheno · tree/main/datasetlines:171-191Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Despite the significant progress in deep learning-based object detection, existing models struggle to perform optimally in complex agricultural environments. To address these challenges, this study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease and pest detection, overcoming three key obstacles: small target recognition, multi-scale feature extraction under occlusion, and real-time processing demands. Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. Evaluated on a diverse dataset of 8046 annotated images, YOLO-Pepper achieves state-of-the-art performance, with 94.26% mAP@0.5 at 115.26 FPS, marking an 11.88 percentage point improvement over YOLOv10n (82.38% mAP@0.5) while maintaining a lightweight structure (2.51 M parameters, 5.15 MB model size) optimized for edge deployment. Comparative experiments highlight YOLO-Pepper's superiority over nine benchmark models, particularly in detecting small and occluded targets. By addressing computational inefficiencies and refining small object detection capabilities, YOLO-Pepper provides robust technical support for intelligent agricultural monitoring systems, making it a highly effective tool for early disease detection and integrated pest management in commercial greenhouse operations.
Why it matches plant phenotyping methodsコショウの病害を画像から検出する深層学習手法を開発し、注釈画像データセットと複数モデルで性能比較・検証しており、植物の病害状態の取得方法が中心です。害虫検出も含みますが、病害検出の技術的貢献が明確なため採用します。
abstractthis study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease and pest detection
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 data can be accessed at https://data.mendeley.com/datasets/ disease? Adv Multimed. 2018;2018:6710865.Open asset ↗pdf-page:17 lines:1-54Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R 2 ) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.
Why it matches plant phenotyping methods3D点群の分割・個体抽出ワークフローを開発し、綿花の果実数と枝・果実の空間分布という植物形質を定量化・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants.
Reproduction assets foundThe authors explicitly state that the code, data, and trained PointNet++ weights for cotton boll and branch mapping are publicly available in their GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicThe code, data, and training weights for cotton boll and branch mapping are available at https://github.com/UGA-BSAIL/cotton_organ_mapping.git .Open asset ↗UGA-BSAIL/cotton_organ_mappinglines:101-109Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The real-time monitoring and counting of maize seed germination at seedling stage is of great significance for seed quality detection, field management and yield estimation. Traditional manual monitoring and counting is very time-consuming, cumbersome and error-prone. In order to quickly and accurately identify and count maize seedlings in a complex field environment, this study proposes an end-to-end maize seedling plant detection model H-RT-DETR (Hierarchical-Real-Time DEtection TRansformer) based on hierarchical feature extraction and RT-DETR (Real-Time DEtection TRansformer). H-RT-DETR uses Hierarchical Feature Representation and Efficient Self-Attention as the backbone network for feature extraction, thereby improving the network's ability to extract features of maize seedling stage in UAV remote sensing images. Through experiments on the UAV remote sensing data set of maize seedling stage, the mean Average Precision mAP0.5-0.95, mAP0.5 and mAP0.75 of the improved H-RT-DETR model reached 51.2%, 94.7% and 48.1%, respectively, and the Average Recall (AR) reached 68.5%. In order to verify the efficiency of the proposed method, H-RT-DETR is compared with the widely used and advanced target recognition methods. The results show that the detection accuracy of H-RT-DETR is better than that of the comparison methods. In terms of detection speed, the H-RT-DETR model does not require Non-Maximum Suppression (NMS) post-processing operations, the Frames Per Second (FPS) on the test dataset reaches 84f/s, which is 19,12,11 and 21 higher than that of YOLOv5, YOLOv7, YOLOv8 and YOLOX, respectively, under the same hardware environment. This model can provide technical support for real-time detection of maize seedlings under UAV remote sensing images in terms of both detection accuracy and speed (see https://github.com/wylSUGAR/H-RT-DETR for model implementation and results).
Why it matches plant phenotyping methodsUAV画像からトウモロコシ幼苗を検出・計数する深層学習手法を開発し、既存手法と精度・速度を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。
abstractIn order to quickly and accurately identify and count maize seedlings in a complex field environment, this study proposes an end-to-end maize seedling plant detection model H-RT-DETR
Reproduction assets foundThe authors provide a public GitHub repository containing the H-RT-DETR model implementation and results for maize seedling detection in UAV images. The UAV image dataset itself is not stated as publicly available, and other linked repositories (labelme, YOLOv5, ultralytics) are generic third-party tools, not paper-Code · publicthe test dataset reaches 84f/s, which is 19,12,11 and 21 higher than that of YOLOv5, YOLOv7, YOLOv8 and YOLOX, respectively, under the same hardware environment. This model can provide technical support for real-time detection of maize seedlings under UAV remote sensing images in terms of both detection accuracy and speed (see https://github.com/wylSUGAR/H-RT-DETR for model implementation and results).
Keywords
Maize seedling stage UAV remote sensing RT-DETR Target recognition Real-time detection Plant counting the National Key Research and Development Program of China 2023YFD1900704 2023YFD1900704 pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmOpen asset ↗wylSUGAR/H-RT-DETRlines:1-26Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Plant diseases adversely affect the agricultural sector by substantially affecting food security and limiting production. We introduce PlantCareNet, a novel, automated, end-to-end diagnostic system for plant diseases that can also offer interactive guidance to users. The system utilizes a dual mode strategy that integrates advanced deep learning algorithms for precise disease diagnosis with a knowledge-based framework guided by experts for preventive measures. The proposed architecture utilizes a convolutional neural network (CNN) to examine images of plant leaves, with the final block flattened and subsequently forwarded to Dense-100 and ultimately Dense-35 for the precise classification of various plant diseases. Subsequently, PlantCareNet promptly offers two types of recommendations: automated suggestions based on identified symptoms and expert-guided advice for personalized treatment. Both categories of recommendations are accessible immediately. The experimental findings indicate that PlantCareNet can accurately diagnose diseases in five well-known datasets, with an accuracy between 82% and 97%, outperforming notable models like Inception and ResNet in most cases. The overall approach demonstrates advancement by surpassing lightweight CNN models with 97% precision and an average inference time of 0.0021 s, hence offering farmers precise and quick actions for remedy. This study emphasises a novel blend of artificial intelligence-driven recognition and expert consultation, which contributes to the advancement of sustainable agriculture practices.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNNベースの診断手法を開発・評価しており、植物表現型（病徴・病害状態）の取得が中心的な貢献です。
abstractThe experimental findings indicate that PlantCareNet can accurately diagnose diseases in five well-known datasets, with an accuracy between 82% and 97%, outperforming notable models like Inception and ResNet in most cases.
Reproduction assets foundThe paper's Data availability statement explicitly links a public GitHub repository containing the custom plant disease image dataset, model training/evaluation source code, and the mobile application.Code · publicThe custom dataset, source code for model training and evaluation, and the developed mobile application for plant disease identification and recommendation are available in online repositories. The repository names and access links are provided here: https://github.com/Muhaimin008/Plant-disease-classification-and-recommendations.Open asset ↗Muhaimin008/Plant-disease-classification-and-recommendationslines:230-261Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Posture is a critical phenotypic trait that reflects crop growth and serves as an essential indicator for both agricultural production and scientific research. Accurate pose estimation enables real-time tracking of crop growth processes, but in field environments, challenges such as variable backgrounds, dense planting, occlusions, and morphological changes hinder precise posture analysis. To address these challenges, we propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data from a"keypoint-line"annotated phenotypic database which could mitigate the effects of uneven manual annotations and biases. PFLO also incorporates advanced architectural enhancements to optimize feature extraction and selection, enabling robust performance in complex conditions such as dense arrangements and severe occlusions. On a fivefold validation set of 1,862 images, PFLO achieved 72.2% pose estimation mean average precision (mAP50) and 91.6% object detection mean average precision (mAP50), outperforming current state-of-the-art models. The model demonstrates improved detection of occluded, edge, and small targets, accurately reconstructing skeletal poses of maize crops. PFLO provides a powerful tool for real-time phenotypic analysis, advancing automated crop monitoring in precision agriculture.
Why it matches plant phenotyping methodsトウモロコシの姿勢という植物表現型を、圃場画像から推定するモデルとデータ処理法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data
Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model's ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The AP0.5 reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall mAP0.5 improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.
Why it matches plant phenotyping methodsUAV画像からハダンオオムギの穂を検出し密度評価に用いる軽量化画像解析モデルを開発・比較しており、植物形質状態の取得手法が中心である。
abstractthis study proposes an improved lightweight YOLOv5 model for highland barley spike detection
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' highland barley UAV spike-detection dataset on ModelScope and their analysis code on GitHub, both paper-specific and publicly actionable.Dataset · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗Cai121/highland_barleylines:182-211Code · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗trangle666ddd/YOLOv5-highland-barley-detectionlines:182-211Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Monitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras.
Why it matches plant phenotyping methodsRGB-D画像からレタスの構造・体積・葉密度を抽出し、生体重推定を検証する手法開発が研究の中心であり、データセットも構築している。
abstractIn this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce.
Reproduction assets foundThe paper's own lettuce 3D point cloud dataset (Pii, 402 point clouds with fresh weight references) is deposited on Zenodo, and the vacuum-package surface reconstruction code plus data processing scripts are publicly available on the authors' GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicData used in this study and developed models are available on Zenodo storage service https://zenodo.org/records/8410252 .Open asset ↗Zenodo · 8410252lines:158-220Code · publicThe code used at this study is available at https://github.com/VicB18/LettuceFW (accessed on 1 November 2024).Open asset ↗GitHub · VicB18/LettuceFWlines:158-220Code · publicThe code for the vacuum package method, along with the data processing scripts used in this study, are available in the Supplementary Information.Open asset ↗lines:98-114Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings.
Why it matches plant phenotyping methods植物の病変を画像から検出・分類する深層学習モデルの開発とベンチマーク比較が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractThis study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops.
Reproduction assets foundThe paper uses public leaf-image datasets (PlantVillage via TensorFlow, Kaggle rice leaf and wheat leaf datasets) as phenotyping inputs for its Slender-CNN disease classification. No author analysis code, trained model checkpoints, or deposited supplements are stated in the supplied blocks. The Kaggle rice and wheat-dsDataset · public6. Yang Y, Liu Z, Huang M, Zhu Q, Zhao X. Automatic detection of multi-type 28. Rice Leafs. Available: https://www.kaggle.com/datasets/shayanriyaz/RicelOpen asset ↗kagglepdf-page:21 lines:1-49Dataset · publicmodel. J Food Eng. 2023;336: 111213. 29. Wheat Leaf Dataset. Available: https://www.kaggle.com/datasets/olyadOpen asset ↗kagglepdf-page:21 lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.
Why it matches plant phenotyping methods植物の出芽速度を対象とする大規模RGB深度画像・アノテーションデータセットを提供し、深層学習および高スループット表現型解析への利用性を実証しており、表現型取得基盤が中心である。
abstracta unique annotated dataset of seedling emergence kinetics is proposed
Reproduction assets foundThis is a data paper whose core contribution is a public annotated RGB-depth seedling dataset (~70,000 frames, >700,000 annotations) deposited in DATA INRAE with DOI 10.57745/AMFJTK, explicitly stated as publicly accessible. Other allowed URLs (license, Intel datasheet, Jülich record) are not paper-specific assets.Dataset · publicSynthesis of the full time-lapse and RGB-Depth full frame quantity per species
Species
Pots time-lapse
Labelled pots time-lapse
RGB-depth full frame
Rapeseed
1 760
336
15 218
Tomatoes
1 960
480
33 283
Beans
2 320
400
21 445
Total
6 040
1 216
69 946
The dataset is publicly accessible in the DATA INRAE repository, DOI: https://doi.org/10.57745/AMFJTK . The file tree structure is illustrated in Fig. 4 . The dataset is organized into 11 compressed .zip files, each corresponding to a distinct trial. Within these files, images are sorted chronologically by acquisition start date, then by camera, and stored in .png format within dedicated color and depth folders. Labels are alsoOpen asset ↗DATA INRAE · 10.57745/AMFJTKlines:105-195Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Genomic Selection (GS) predicts traits using genome-wide markers, speeding up genetic progress and enhancing breeding efficiency. Recent emphasis has been placed on deep learning models to enhance prediction accuracy. However, current deep learning models focus on learning specific phenotypes for the given task, overlooking the inter-correlations among different phenotypes. In response, we introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space. Extensive experiments reveal that MtCro outperforms mainstream models, including DNNGP and SoyDNGP, with performance gains of 1-9% on the Wheat2000 dataset, 1-8% on Wheat599, and 1-3% on Maize8652. Furthermore, comparative analysis shows a consistent 2-3% improvement in multi-phenotype predictions, emphasizing the impact of inter-phenotype correlations on accuracy. By leveraging multi-task learning, MtCro efficiently captures diverse plant phenotypes, enhancing both model training efficiency and prediction accuracy, ultimately accelerating the progress of plant genetic breeding. Our code is available on https://github.com/chaodian12/mtcro .
Why it matches plant phenotyping methods複数の作物表現型を予測するマルチタスク深層学習手法を開発し、複数データセットおよび既存モデルと比較検証しており、表現型推定手法が研究の中心である。
abstractwe introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space.
Reproduction assets foundThe paper's authors explicitly state that the MtCro analysis code is publicly available on GitHub, matching an allowed URL. No separate phenotype dataset deposit by the authors is stated (Wheat2000/Wheat599 data were provided by DNNGP; Maize8652 is cited prior work), so only the authors' code qualifies as a paper-assetCode · publicOur code is available on https://github.com/chaodian12/mtcro .Open asset ↗chaodian12/mtcrolines:1-67Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage (Brassica rapa L. ssp. pekinensis) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments.
Why it matches plant phenotyping methodsUAV画像から中国白菜の萎黄病状態を推定する検出モデルを開発・評価しており、植物病徴の取得・抽出手法が研究の中心である。
abstractHere, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV).
Reproduction assets foundThe paper's authors publicly release their analysis/detection code (DSConv-GAN model and monitoring software) via a GitHub repository, while the underlying UAV image dataset is only available upon request.Code · publicData will be made available upon request. The code could be downloaded from https://github.com/919449869coder/disease-detection-DSConv.git .Open asset ↗919449869coder/disease-detection-DSConvlines:151-175Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations ( Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.
Why it matches plant phenotyping methods植物果実の色・サイズ・形状・均一性を画像から抽出するオープンソースの表現型解析ソフトウェアを開発しており、植物フェノタイピング手法が研究の中心である。
abstractBerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity.
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 · publicHere we present BerryPortraits: Phenotyping Of Ripening Traits [‘with Rapid Automated Imaging Tools and Software’, for those disinclined towards brevity]) ( https://github.com/Breeding-Insight/BerryPortraits/ )Open asset ↗Breeding-Insight/BerryPortraitslines:100-105Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multispectral / hyperspectralRootClassificationRoot system architecture
Background Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.
Why it matches plant phenotyping methods根系のハイパースペクトル画像取得・分類パイプラインを開発し、取得条件、分類法、機械学習手法を比較検証しているため、植物フェノタイピング手法が中心である。
abstractThis study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale
Reproduction assets foundThe authors explicitly state that the Python scripts for the paper's HSI root-soil classification pipeline are publicly available on GitHub. No phenotype dataset or image deposit is stated.Code · publicThe scripts for data analysis are available from https://github.com/corinef/Automated-root-classification .Open asset ↗corinef/Automated-root-classificationlines:156-180Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での植物病徴を画像から取得・追跡・定量する撮像および画像解析手法を開発・検証しており、植物表現型測定が中心である。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is publicly available on the authors' GitHub repository, and that a sample dataset plus the trained reference mark detection model are downloadable from the ETH Research Collection. Both are paper-specific, public, and actionable.Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:98-107Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:98-107Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation. RESULTS: This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH. CONCLUSION: The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.
Why it matches plant phenotyping methodsフォックステールミレットの生育形質を画像から抽出する高スループット画像システムと画像処理手法を開発・評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality.
Reproduction assets foundThe paper's authors explicitly state that the source code of their foxtail millet image processing program (used to extract phenotypic i-traits such as PH, CHA, SPA, compactness indices, and colour pixel features) is publicly available on GitHub. The MDSi database URL is a general transcriptomic resource, not a paper-Code · publicmation about plant health and physiological responses.
The program was created using the OpenCV library within the Microsoft Visual Studio (C++) environment. The extracted phenotypic data were converted to CSV file format for further analysis and storage. The source code of the program is available for download and reference at https://github.com/ScreenPlant/Foxtail-Millet-Image-Processing .
Analysis of i-traits throughout the entire growth periodOpen asset ↗ScreenPlant/Foxtail-Millet-Image-Processinglines:50-61Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. RESULTS: We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. CONCLUSION: GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Why it matches plant phenotyping methods植物器官画像から形状・寸法・色などの形質を抽出するソフトウェア手法の開発が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods.
Reproduction assets foundThe paper's authors publicly release the GRABSEEDS software (the computational phenotyping tool used for all measurements in this paper) along with the example images and datasets generated, at the GitHub wiki URL stated in the abstract, availability section, and data availability statement.Code · publicures including label recognition and color correction in a batch setting.
Conclusion
GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Keywords: Image analysis, Phenotype, Seed traits, High throughput, QTL mapping
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 May 21; Accepted 2024 Sep 6; Collection date 2024.
IntroductionOpen asset ↗github.com/tanghaibao/jcvi · GRABSEEDSlines:1-28Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.
Why it matches plant phenotyping methods植物画像を生成し、そこから植物個体別形質を推定する二段階の画像ベース表現型解析フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained.
Reproduction assets foundThe paper's authors explicitly state that source code and links to the phenotyping datasets (Arabidopsis, GrowliFlower, MixedCrop) are publicly available in their GitHub repository, which implements the multi-conditional CWGAN crop growth simulation and growth estimation framework.Code · publicSource code and links to the datasets are publicly available at https://github.com/luked12/crop-growth-cgan .Open asset ↗luked12/crop-growth-cganlines:216-253Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software ( https://github.com/MolPlantPathology/ScAnalyzer ) has the potential to standardize the analysis of disease assays between different groups.
Why it matches plant phenotyping methods植物葉の病徴と病原体拡散を画像処理で定量化するツールおよび解析パイプラインが研究の中心であり、植物の病害状態を直接推定するため。
abstractimage processing provides a more accurate and objective quantification of plant disease symptoms.
Reproduction assets foundThe paper's ScAnalyzer Python/R analysis pipeline and the raw leaf/luminescence images are publicly available on the authors' GitHub repository, along with the printable leaf-grid sheet used for phenotyping.Code · publicThe code is available on GitHub ( https://github.com/MolPlantPathology/ScAnalyzer ).Open asset ↗MolPlantPathology/ScAnalyzerlines:124-131Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Fruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective. Results Here we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit. Conclusions The FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.
Why it matches plant phenotyping methodsリンゴ果実の画像取得・コンピュータ解析・重量測定を統合した装置を開発し、測定速度、再現性、誤差、適切なサンプルサイズを評価しており、果実形態形質の取得法が研究の中心である。
abstractHere we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit.
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw fruit images, extracted datasets, and R scripts in the ETH Research Collection (doi:10.3929/ethz-b-000590509), and the Matlab/R image-analysis scripts (apple fruit feature extractor, affe) on SourceForge. Both are paper-specific, public, and actionable.Dataset · publicSupplementary files for this article, which include raw images in tif format, datasets extracted from the images and R scripts used in this study are available from the ETH Research collection under following doi: https://doi.org/10.3929/ethz-b-000590509Open asset ↗ETH Research collection · 10.3929/ethz-b-000590509lines:125-170Code · publicThe Matlab- and R-scripts are available via sourceforge, project apple fruit feature extractor (affe), https://sourceforge.net/projects/affeOpen asset ↗sourceforge · affelines:125-170Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
RiceMesh / voxelX-ray / CTRootObject detectionSegmentationRoot system architecture
Abstract Background X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice ( Oryza sativa ) root segments in CT volumes. Results VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. Conclusions A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.
Why it matches plant phenotyping methods植物根のCT画像から根をセグメンテーションし、根成長の定量化に用いる手法の開発・技術評価が中心であるため、植物フェノタイピング方法論に該当する。
abstractX-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil.
Reproduction assets foundThe paper's own training/prediction scripts for the 3D semantic segmentation model (SStrainer3D) are publicly available on GitHub with explicit availability language. The CT volume datasets are only available upon request. RSAvis3D and RSAtrace3D are cited prior-work tools, not paper-specific assets.Code · publicThe scripts for the training and prediction of the 3D semantic segmentation are available at the GitHub repository ( https://github.com/st707311g/SStrainer3D/ , branch 1.0).Open asset ↗st707311g/SStrainer3Dlines:156-186Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. Results In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. Conclustion Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.
Why it matches plant phenotyping methodsリンゴ果肉の赤色形質をRGB画像と色解析で定量する手法を開発し、遺伝子型間の識別に実質的に適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images
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 · publicScript designed for image analysis is public and can be found at: https://github.com/pibouillon/colour_val/blob/main/colour_val.pyOpen asset ↗https://github.com/pibouillon/colour_val · colour_val.pylines:166-220Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR's self-attention mechanism to craft a backbone feature extraction network, enhancing the model's capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network's ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.
Why it matches plant phenotyping methodsトマト葉の病斑・病害を画像から検出する新規深層学習手法を開発し、データセット上で精度と速度を評価しており、植物病害状態の表現型取得が中心である。
abstractThis study focuses on tomato disease images within intricate settings
Reproduction assets foundThe paper's Data availability statement explicitly deposits part of the self-gathered tomato disease image dataset (used for TomatoDet training/evaluation) on the authors' GitHub repository, with the complete dataset and code available only by emailing the corresponding author. The public partial dataset qualifies as aDataset · publicThe data utilized in this paper is obtained through self-gathering and is made publicly available (a part of it) to make the study reproducible. It can be accessed at https://github.com/tyuiouio/plant-disease-detection-in-real-field . If you want to request the complete dataset and code, please email the corresponding author.Open asset ↗tyuiouio/plant-disease-detection-in-real-fieldlines:250-271Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background Despite major efforts over the last decades, the rising demands of the growing global population makes it of paramount importance to increase crop yields and reduce losses caused by plant pathogens. One way to tackle this is to screen novel resistant genotypes and immunity-inducing agents, which must be conducted in a high-throughput manner. Results Colour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves. Pixel colour values are often used to distinguish infected from healthy tissues. Some programs employ colour models, such as RGB, HSV or L*a*b*. Colour-analyzer uses two colour models, utilizing both HSV ( Hue, Saturation, Value ) and L*a*b* values. We found that the a* b* values of the L*a*b* colour model provided the clearest distinction between infected and healthy tissue, while the H and S channels were best to distinguish the leaf area from the background. Conclusion By combining the a* and b* channels to determine the lesion area, while using the H and S channels to determine the leaf area, Colour-analyzer provides highly accurate information on the size of the lesion as well as the percentage of infected tissue in a high throughput manner and can accelerate the plant immunity research field.
Why it matches plant phenotyping methods植物病斑面積と感染組織割合を画像から定量するウェブツールの開発であり、植物病害状態の表現型取得が中心的な貢献である。
abstractColour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves.
Reproduction assets foundThe paper's authors publicly released both the Colour-analyzer web tool/code on GitHub and the datasets generated in the study, hosted in a publications subfolder of the same repository. Both are paper-specific, public, and actionable.Dataset · publicThe datasets generated are available at https://github.com/VittorioAccomazzi/LeafSize/tree/main/publications/A_new_dual_colour_model-based_imaging_tool .Open asset ↗VittorioAccomazzi/LeafSizelines:129-216Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS). Results Trichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images. Conclusions HairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.
Why it matches plant phenotyping methods綿花葉の毛状突起という植物形質を画像から定量抽出する深層学習モデルを開発し、複数データセットで検証しており、フェノタイピング手法が研究の中心である。
abstractwe introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS).
Reproduction assets foundThe paper's Availability of data and materials section explicitly deposits the four paper-specific image/annotation datasets (AnnCoT, CotLeaf-1, CotLeaf-2, CotLeaf-X) with public CSIRO DOIs, all present in allowed_urls. No code or model checkpoint deposit is stated.Dataset · publicThe CotLeaf-1 image dataset is available at https://doi.org/10.25919/9vqw-7453 .Open asset ↗10.25919/9vqw-7453lines:256-289Dataset · publicThe CotLeaf-2 image dataset is available at https://doi.org/10.25919/v0qb-er50 .Open asset ↗10.25919/v0qb-er50lines:256-289Dataset · publicThe CotLeaf-X image dataset is available at https://doi.org/10.25919/eqhx-1x73 .Open asset ↗10.25919/eqhx-1x73lines:256-289Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS: Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS: AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
Why it matches plant phenotyping methods植物病徴を画像から定量化するソフトウェアの開発が研究の中心であり、苗の色・形態パラメータを自動抽出して病害症状を評価する。
abstractIn this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.
Reproduction assets foundThe paper's authors publicly released the AraDQ software package (system code, pretrained deep learning models, installation manual) and the datasets generated and analyzed in the study, including the case-study image files, in their GitHub repository.Code · publicThe portable software, system code, and installation manual are available at https://github.com/kist-smartfarm/AraDQ .Open asset ↗kist-smartfarm/AraDQlines:94-101Dataset · publicThe image files used in this case study are provided in the released dataset on GitHub.Open asset ↗lines:137-212Dataset · publicThe AraDQ software package, including the installation manual, and the datasets generated and analyzed during the current study are available in the GitHub repository at https://github.com/kist-smartfarm/AraDQ .Open asset ↗kist-smartfarm/AraDQlines:137-212Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
BACKGROUND: The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic. RESULTS: We tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background. CONCLUSION: The method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.
Why it matches plant phenotyping methodsCNNによる葉のセグメンテーション・追跡手法を開発し、検出・追跡精度を検証した植物フェノタイピング研究である。
abstractHere, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants.
Reproduction assets foundThe paper's authors explicitly state that the full project library (leaf detection/tracking code and dataset) is available as a public GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicof the PyTorch-Vision GitHub repository was used for the model training, specifically the reference scripts found in the folder detection. These scripts are included in the project GitHub under the modelTraining folder without relevant modifications. The full library of this project is available as a public repository at GitHub https://github.com/Fedjurrui/Leaf-Tracking .
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Ethics approval and consent to participate
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Consent for publication
Not applicable.
Competing interests
No competing interests declared.
References
1.Open asset ↗Fedjurrui/Leaf-Trackinglines:174-269Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
Abstract Background Plant defense activators offer advantages over pesticides by avoiding the emergence of drug-resistant pathogens. However, only a limited number of compounds have been reported. Reactive oxygen species (ROS) act as not only antimicrobial agents but also signaling molecules that trigger immune responses. They also affect various cellular processes, highlighting the potential ROS modulators as plant defense activators. Establishing a high-throughput screening system for ROS modulators holds great promise for identifying lead chemical compounds with novel modes of action (MoAs). Results We established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables. Our screening strategy comprised four phases: (1) development of a ROS inference system based on a deep neural network that combines ROS production data in plant cells and multidimensional chemical features of chemical compounds; (2) in silico extensive-scale screening of seven million commercially available compounds using the ROS inference model; (3) secondary screening by visualization of the chemical space of compounds using the generative topographic mapping; and (4) confirmation and validation of the identified compounds as potential ROS modulators within plant cells. We further characterized the effects of selected chemical compounds on plant cells using molecular biology methods, including pathogenic signal-triggered enzymatic ROS induction and programmed cell death as immune responses. Our results indicate that deep learning-based screening systems can rapidly and effectively identify potential immune signal-inducible ROS modulators with distinct chemical characteristics compared with the actual ROS measurement system in plant cells. Conclusions We developed a model system capable of inferring a diverse range of ROS activity control agents that activate immune responses through the assimilation of chemical features of candidate pesticide compounds. By employing this system in the prescreening phase of actual ROS measurement in plant cells, we anticipate enhanced efficiency and reduced pesticide discovery costs. The in-silico screening methods for identifying plant ROS modulators hold the potential to facilitate the development of diverse plant defense activators with novel MoAs.
Why it matches plant phenotyping methods植物細胞のROS蓄積という生理状態を推定する深層学習モデルと、実測による検証を組み合わせたスクリーニング手法の開発が中心である。
abstractWe established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe learning algorithm codes used during the current study are available on the GitHub address ( https://github.com/ma1206ko/in_silico_screening ).Open asset ↗ma1206ko/in_silico_screeninglines:168-249Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS: The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION: Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング環境で、熱画像・ハイパースペクトル画像による干ばつストレス、水利用、蒸散速度の推定手法を評価・モデル比較しており、フェノタイピング手法が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe article's Availability of data and materials statement deposits the datasets generated and analyzed in this study (thermal/hyperspectral phenotyping data and analyses) in three Zenodo repositories with public DOIs. These are paper-specific, publicly accessible assets. No author analysis code with an explicit publicDataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.7807989lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8164473lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8033640lines:198-347Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code. RESULTS: We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content. CONCLUSION: A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves.
Why it matches plant phenotyping methods植物画像からサイズ・色を抽出し、果実リコピンや葉クロロフィルを推定するオープンソース手法と撮像系を開発・検証しており、表現型取得が研究の中心である。
abstractWe provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart.
Reproduction assets foundThe authors provide public, paper-specific assets: the PlantSizeClr Python scripts on GitHub, a snapshot of all scripts and data on OSF, and all data generated for the manuscript on the University of Sheffield data repository.Code · publicThe example lightbox contains LED lighting; this could be further improved by using bulbs that are closer to standard illuminants (D65 for sRGB).
An object of known size (coins work well).
Software: Python 3.8.
Python packages: List of packages and their versions used available in Additional file 1 : S0.
Custom Python Scripts: https://github.com/HarryCWright/PlantSizeClr
Snapshot of all scripts and data is available on Open Science Framework: www.doi.org/10.17605/OSF.IO/QAYMU
Optional for extraction of lycopene: acetone, high purity ethanol, hexane deionised water and a UV/vis spectrophotometerOpen asset ↗HarryCWright/PlantSizeClrlines:34-50Code · publicSnapshot of all scripts and data is available on Open Science Framework: www.doi.org/10.17605/OSF.IO/QAYMUOpen asset ↗OSF.IO/QAYMU · 10.17605/OSF.IO/QAYMUlines:34-50Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. Results Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE 2 = 0.96), and number of spikelets (R 2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R 2 = 0.94), rachis weight (R 2 = 0.83), and seed shattering (R 2 = 0.85). Conclusions SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.
Why it matches plant phenotyping methodsイネ科花序の形態を画像から定量化するPythonベースの表現型解析パイプラインを開発・検証しており、植物表現型の取得・抽出が研究の中心である。
abstractHere, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences
Reproduction assets foundThe paper's SpykProps Python pipeline is openly available on GitHub, and the original/processed spike images, data files, and analysis code are deposited in the University of Minnesota DRUM repository. Both are paper-specific, public, and actionable.Code · publicSpykProps is an open-source program that can be accessed from https://github.com/joanmanbar/SpykProps along with detailed instructions to analyze single spikes using a Python integrated development environment, or to automate it on a set of images using Bash and the SpykBatch.py function.Open asset ↗joanmanbar/SpykPropslines:69-75Dataset · publicAll the original and processed images, along with the data files and code to analyze them, can be accessed through the Data Repository for University of Minnesota (DRUM) at https://hdl.handle.net/11299/256105 .Open asset ↗Data Repository for University of Minnesota (DRUM) · 11299/256105lines:134-257Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
BACKGROUND: Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS: In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS: This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の形態・構造形質を定量化する高スループット表現型計測法を開発し、手動測定で検証しているため。
abstractwe developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet).
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the authors' repository, which is an allowed URL.Code · publicAll code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_barley_spike_detection .Open asset ↗zerosky010/CT_barley_spike_detectionlines:160-268Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata : wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて植物の遺伝的変異を評価する測定法を、異なる環境・測定条件で検証・評価しており、植物フェノタイピング手法が中心です。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurements and analysis code are publicly available: processed spectral data, metadata, and code are on the authors' GitHub repository, and the raw spectral measurement dataset is published in SPECCHIO.Code · publicAll processed spectral data, metadata and code are provided at the GitHub repository: https://github.com/licheng1221/How-leaves-reflect-genetic-variation .Open asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationlines:218-235Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.
Why it matches plant phenotyping methodsアヒルウキクサの画像ベース成長アッセイを含む、培養・実験管理ソフトウェアとデータ解析ワークフローの開発が中心であり、植物表現型取得を支援する方法論的貢献である。
abstractcomputational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis.
Reproduction assets foundThe paper's duckweed growth-assay experimental data and Python analysis scripts are publicly available in the authors' GitHub repository, explicitly stated in the availability statement and results sections. The Aquarium platform itself is a generic pre-existing tool, not a paper-specific asset.Code · publicAll code used in this study is available on Github.Open asset ↗lines:119-135Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Github repository, https://github.com/mtscott321/duckweed_data_analysis .Open asset ↗mtscott321/duckweed_data_analysislines:119-135Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. Results A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. Conclusions A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.
Why it matches plant phenotyping methodsエンドツーエンドのRGB画像解析パイプラインを開発・最適化・検証し、エンドウ葉のさび病症状と病勢進展を定量化する方法が研究の中心である。
abstractThis study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection.
Reproduction assets foundThe authors deposited the R analysis script and the 600 pea leaflet images used in this rust phenotyping study in a public Zenodo repository, explicitly cited in the Data Availability statement and reference list.Dataset · publicThe datasets generated during and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7991462 [ 93 ].Open asset ↗Zenodo · 10.5281/zenodo.7991462lines:147-229Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致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。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Background The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Why it matches plant phenotyping methods植物組織培養の形質を自動・非破壊・連続測定するマルチセンサーフェノタイピングシステムを開発し、画像分割や深度計測を検証しており、方法が研究の中心である。
abstractAn automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe dataset supporting the conclusions of this article (Hard- and Software of “Phenomenon” phenotyping system) are available in an open-access Github repository, https://github.com/halube/Phenomenon .Open asset ↗halube/Phenomenonlines:224-282Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The flowering period is a critical time for the growth of rape plants. Counting rape flower clusters can help farmers to predict the yield information of the corresponding rape fields. However, counting in-field is a time-consuming and labor-intensive task. To address this, we explored a deep learning counting method based on unmanned aircraft vehicle (UAV). The proposed method developed the in-field counting of rape flower clusters as a density estimation problem. It is different from the object detection method of counting the bounding boxes. The crucial step of the density map estimation using deep learning is to train a deep neural network that maps from an input image to the corresponding annotated density map. Results We explored a rape flower cluster counting network series: RapeNet and RapeNet+. A rectangular box labeling-based rape flower clusters dataset (RFRB) and a centroid labeling-based rape flower clusters dataset (RFCP) were used for network model training. To verify the performance of RapeNet series, the paper compares the counting result with the real values of manual annotation. The average accuracy (Acc), relative root mean square error (rrMSE) and [Formula: see text] of the metrics are up to 0.9062, 12.03 and 0.9635 on the dataset RFRB, and 0.9538, 5.61 and 0.9826 on the dataset RFCP, respectively. The resolution has little influence for the proposed model. In addition, the visualization results have some interpretability. Conclusions Extensive experimental results demonstrate that the RapeNet series outperforms other state-of-the-art counting approaches. The proposed method provides an important technical support for the crop counting statistics of rape flower clusters in field.
Why it matches plant phenotyping methodsUAV-RGB画像から菜種の花房数という植物器官形質を推定する深層学習手法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe explored a deep learning counting method based on unmanned aircraft vehicle (UAV).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes, datasets RFRB and RFCP used in the study are available online at: https://github.com/CV-Wang/RapeNet .Open asset ↗CV-Wang/RapeNetlines:216-232Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits. Results The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R 2 value of more than 0.8 and mean absolute percentage error of less than 10% were attained. Conclusion This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available at https://github.com/UGA-BSAIL/plant_3d_deep_learning .
Why it matches plant phenotyping methods3D深層学習による綿花の器官分割と建築形質抽出ワークフローを開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractThe goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits.
Reproduction assets foundThe paper's plant part segmentation code is explicitly stated as publicly available in the authors' GitHub repository. The underlying datasets are only available on request, so they are noted as request-only.Code · publicThe plant part segmentation code is available at https://github.com/UGA-BSAIL/plant_3d_deep_learning .Open asset ↗UGA-BSAIL/plant_3d_deep_learninglines:1-72Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Non-invasive reporter systems are powerful tools to query physiological and transcriptional responses in organisms. For example, fluorescent and bioluminescent reporters have revolutionized cellular and organismal assays and have been used to study plant responses to abiotic and biotic stressors. Integrated, cooled charge-coupled device (CCD) camera systems have been developed to image bioluminescent and fluorescent signals in a variety of organisms; however, these integrated long-term imaging systems are expensive. Results We have developed self-assembled systems for both growing and monitoring plant fluorescence and bioluminescence for long-term experiments under controlled environmental conditions. This system combines environmental growth chambers with high-sensitivity CCD cameras, multi-wavelength LEDs, open-source software, and several options for coordinating lights with imaging. This easy-to-assemble system can be used for short and long-term imaging of bioluminescent reporters, acute light-response, circadian rhythms, delayed fluorescence, and fluorescent-protein-based assays in vivo. Conclusions We have developed two self-assembled imaging systems that will be useful to researchers interested in continuously monitoring in vivo reporter systems in various plant species.
Why it matches plant phenotyping methods植物の蛍光・生物発光を長期的に画像取得する自作システムを開発しており、植物の生理状態・レポーター応答の非侵襲的測定が中心的な方法論的貢献である。
abstractWe have developed self-assembled systems for both growing and monitoring plant fluorescence and bioluminescence for long-term experiments under controlled environmental conditions.
Reproduction assets foundThe paper's authors explicitly state code availability for two public GitHub repositories: the LightsCameraAction µManager plug-in for coordinating lights with imaging (System 1) and the GreenhamLab/CCD_Imaging repository containing Python scripts for time-series acquisition and light scheduling plus sample data for biCode · publicd in µManager 2.0.0 to take a series of evenly-spaced images between two dates/times. Multiple time series can be acquired simultaneously by simply running multiple copies of the Python script with settings for each individual series. Documentation for installing and running the script is included in its README file on github ( https://github.com/GreenhamLab/CCD_Imaging ).
Heliospectra light scheduling for late-model lights
Starting in firmware version 3.0.0, Heliospectra removed their publicly-documented protocol for controlling their ELIXIA, DYNA, and EOS lights remotely. This renders inoperable the “LightsCameraAction” µManager plug-in used in System 1. As such, the only method available Open asset ↗GreenhamLab/CCD_Imaginglines:149-158Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Live imaging is the gold standard for determining how cells give rise to organs. However, tracking many cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. Results We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphoGraphX software for segmenting, tracking lineages, and measuring a suite of cellular properties. We also provide MorphoGraphX image processing scripts we developed to automate analysis of segmented images and data presentation. Conclusions Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is approachable and easy to use for leaf development live imaging.
Why it matches plant phenotyping methodsArabidopsis葉の生細胞イメージング、画像処理、細胞追跡・形質測定を統合した再利用可能な表現型解析パイプラインの開発が中心である。
abstractIn this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development.
Reproduction assets foundThe paper publicly deposits its live-imaging datasets (confocal imaging data for the figures) on OSF under CC-BY 4.0, and its MorphoGraphX/R analysis scripts on the authors' GitHub repositories, all explicitly linked in the Availability of data and materials section.Dataset · publicData for Figs. 1 , 2 , 3 , 4 , 5 A, B is available at https://doi.org/10.17605/OSF.IO/V2TKWOpen asset ↗OSF · 10.17605/OSF.IO/V2TKWlines:139-172Dataset · publicData for Figs. 5 C, 6 and 7 is available at https://doi.org/10.17605/OSF.IO/D7X3YOpen asset ↗OSF · 10.17605/OSF.IO/D7X3Ylines:139-172Code · publicavailable at https://github.com/kateharline/live_img_paper , https://github.com/kateharline/roeder_lab_projects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paperOpen asset ↗github.com/kateharline/roeder_lab_projectslines:139-172Code · publicavailable at https://github.com/kateharline/live_img_paper , https://github.com/kateharline/roeder_lab_projects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paperOpen asset ↗github.com/kateharline/jawd-paperlines:139-172Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Virtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information. Results This study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online. Conclusion In conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.
Why it matches plant phenotyping methods与えられた表現型情報から作物画像を生成するGANベースの手法とソフトウェアを開発しており、植物表現型の可視化・再現が研究の中心である。
abstractThis study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information.
Reproduction assets foundThe paper explicitly states that supplementary files including datasets, trained models, software, and source code are publicly available at the authors' HZAU plant phenotyping download site and a GitHub repository. These directly reproduce the paper's phenotyping datasets and CropPainter analysis.Code · publicSupplementary files for this article, which include datasets, trained models, software as well as the source codes used in this study, are available on website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download and https://github.com/zhwang-hzau/CropPainter-master .Open asset ↗zhwang-hzau/CropPainter-masterlines:157-211Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Frequency is essential in signal transmission, especially in convolutional neural networks. It is vital to maintain the signal frequency in the neural network to maintain the performance of a convolutional neural network. Due to destructive signal transmission in convolutional neural network, signal frequency downconversion in channels results into incomplete spatial information. In communication theory, the number of Fourier series coefficients determines the integrity of the information transmitted in channels. Consequently, the number of Fourier series coefficients of the signals can be replenished to reduce the information transmission loss. To achieve this, the ArsenicNetPlus neural network was proposed for signal transmission modulation in detecting cassava diseases. First, multiattention was used to maintain the long-term dependency of the features of cassava diseases. Afterward, depthwise convolution was implemented to remove aliasing signals and downconvert before the sampling operation. Instance batch normalization algorithm was utilized to keep features in an appropriate form in the convolutional neural network channels. Finally, the ArsenicPlus block was implemented to generate pseudo high-frequency in the residual structure. The proposed method was tested on the Cassava Datasets and compared with the V2-ResNet-101, EfficientNet-B5, RepVGG-B3g4 and AlexNet. The results showed that the proposed method performed [Formula: see text] in terms of accuracy, 1.2440 in terms of loss, and [Formula: see text] in terms of the F1-score, outperforming the comparison algorithms.
Why it matches plant phenotyping methodsカッサバ病害の画像検出を対象に、独自CNN（ArsenicNetPlus）を提案し、既存モデルとの比較評価まで行っており、植物の病害状態を推定する方法が中心である。
abstractTo achieve this, the ArsenicNetPlus neural network was proposed for signal transmission modulation in detecting cassava diseases.
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 cassava dataset used in this study can be found at the following link: https://pan.baidu.com/s/1thrIr_0uB3gzYSPT317gtg (Password: abcd).Open asset ↗lines:218-288Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
Why it matches plant phenotyping methods根系感染像の取得プラットフォームと画像解析ソフトウェアを開発し、線虫数・サイズや感染前根面積を測定する手法が研究の中心であるため。
abstractwe describe an open-source, easy to adopt, imaging hardware and trait analysis software method
Reproduction assets foundThe paper's authors publicly release the imaging tower hardware design (STL files) and all ImageJ/Python analysis scripts used for nematode counting, root area quantification, and colour normalization in a GitHub repository, explicitly stated in the Availability of data and materials section.Code · publicAll scripts used in this experiment are available under the following github repository: https://github.com/OlafKranse/A_low_cost_imaging_tower .Open asset ↗OlafKranse/A_low_cost_imaging_towerlines:141-197Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D時系列形態を抽出・追跡する新規パイプラインを開発し、大規模データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize.
Reproduction assets foundThe paper's own PhenoTrack3D pipeline (source code and examples) is publicly available on GitHub under an open-source licence. Phenomenal (GitHub/Zenodo) is cited prior work used as an input pipeline, not a paper-specific asset; no public phenotype dataset or trained model deposit is stated.Code · publicThe source code and examples are available on Github ( https://github.com/openalea/phenotrack3d ) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dlines:189-246Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS: We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS: We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
Why it matches plant phenotyping methods根系の静的・動的形質を画像から自動抽出する高スループット手法と解析パイプラインを開発・検証しており、表現型取得法が研究の中心である。
abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking
Reproduction assets foundThe paper's root reconstruction/phenotyping pipeline (RootSystemTracker) is released as open-source code on GitHub with an ImageJ plugin documentation page; an example time-lapse movie of the reconstruction is also available on YouTube. No public dataset of the 1000 time-lapse images or RSML outputs is stated in thesupCode · publicThe architecture reconstruction pipeline is supplied as an ImageJ plugin with online documentation (Plugin page: https://imagej.net/plugins/rootsystemtracker [ 9 ]) and as open-source code on GitHub ( https://github.com/Rocsg/RootSystemTracker ).Open asset ↗Rocsg/RootSystemTrackerlines:208-277Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed. Results We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS-Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose-Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose-MorphoLibJ-LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios. Conclusions The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops.
Why it matches plant phenotyping methodsアブラナの種皮・胚・長角果壁の細胞形質を画像から自動定量する手法を開発・検証しており、フェノタイピング手法が研究の中心です。
abstractWe developed three methods to automatically process multicellular images of various tissues in rapeseed.
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 · publicA cell image of 30 DAF silique wall acquired under 100 × optical microscope.Open asset ↗lines:547-634Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. Results For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. Conclusion NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
Why it matches plant phenotyping methodsブドウのスペクトルを用いたフェノミック予測法を開発・評価し、ゲノム予測との比較や予測能力の検証を行っており、植物形質推定手法が研究の中心である。
abstractThis study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour.
Reproduction assets foundThe paper explicitly deposits its grapevine phenotypic/genotypic data and its NIRS spectra, R analysis scripts, and result tables in the INRAE data portal under two DOIs, both listed in allowed_urls. These are paper-specific, publicly actionable assets directly reproducing the phenotyping measurements and computationalDataset · publicGenotypic values and genotypic data for half-diallel and diversity panel populations are available at https://doi.org/10.15454/PNQQUQOpen asset ↗10.15454/PNQQUQlines:204-268Dataset · publicSpectra, R scripts and result tables have been deposited in the INRAE data portal: https://doi.org/10.15454/BICRFXOpen asset ↗INRAE data portal · 10.15454/BICRFXlines:204-268Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.
Why it matches plant phenotyping methodsトウモロコシ雌穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との比較で各形質の信頼性を検証しており、植物フェノタイピング手法が研究の中心です。
abstractWe developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears.
Reproduction assets foundThe authors' full analysis pipeline (MATLAB GUI plus Python deep-learning code for grain segmentation and phenotypic trait extraction) is explicitly stated to be fully available on a public GitHub repository. The phenotype datasets themselves are only available on request, so they do not qualify as public assets.Code · publicThe code for the analysis using a Graphical User Interface in MATLAB is fully available on a public repository ( https://github.com/Phymea-Systems/Earbox ). It is used in combination with a Python code applying the trained neural network to extract the DL2 images, also available in the same public repository.Open asset ↗Phymea-Systems/Earboxlines:104-113Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. Results In this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. Conclusions Both image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
Why it matches plant phenotyping methodsカシャバの病斑を画像から分割・定量する2手法を開発し、処理間の病害症状測定能力を比較検証しており、植物表現型取得法が中心である。
abstractIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem.
Reproduction assets foundThe paper deposits its datasets and custom R scripts on figshare and points to the authors' public PhenotyperCV machine learning workflow on GitHub, both directly supporting this paper's cassava bacterial blight image analysis.Dataset · publicThe datasets and custom R scripts generated and/or analyzed in this study are available in the figshare repository, https://doi.org/10.6084/m9.figshare.17334407 .Open asset ↗figshare · 10.6084/m9.figshare.17334407lines:120-146Code · publicThe machine learning workflow and software download instructions are available on GitHub.
( https://github.com/jberry47/ddpsc_phenotypercv/wiki/Machine-Learning-Workflow ).Open asset ↗github.com/jberry47/ddpsc_phenotypercvlines:120-146Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. Results A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at zeitgeber time 5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 h, because it maximizes germination rates while minimizing contamination. The pollen germination medium's pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. Conclusions We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.
Why it matches plant phenotyping methodsキノア花粉の生存性を測定するin vitro発芽法を開発・最適化し、PlantCVによる発芽花粉の半自動画像計数も開発しており、植物形質取得法が中心である。
abstractA semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community.
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2件を確認しました。Code · publicThis workflow is available at https://github.com/danforthcenter/quinoa-pollen-germination and a tutorial is availabe at https://github.com/danforthcenter/plantcv-tutorial-interactive-pollent-count .Open asset ↗GitHub · danforthcenter/quinoa-pollen-germinationlines:134-159Code · publicThis workflow is available at https://github.com/danforthcenter/quinoa-pollen-germination and a tutorial is availabe at https://github.com/danforthcenter/plantcv-tutorial-interactive-pollent-count .Open asset ↗GitHub · danforthcenter/plantcv-tutorial-interactive-pollent-countlines:134-159Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions. RESULTS: High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis (AtBAG4). CONCLUSIONS: AtBAG4-expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.
Why it matches plant phenotyping methods植物プロトプラストの成長・増殖特性を大量画像から抽出する自動顕微鏡解析と画像処理パイプラインを開発・適用しており、表現型取得手法が研究の中心である。
abstractHigh-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts
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 scripts and codes used in the analysis pipeline, additional downloaded plugins used in processing the images as well as sample data can be found in our Github page https://github.com/jodawson/cell_seg_tracking_analysis .Open asset ↗jodawson/cell_seg_tracking_analysislines:143-159Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated, thanks to the automated image processing systems, MGW estimations have been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which were harvested from a 2-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images including more than 72,000 grains). Results It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area × Circularity, Perimeter × Circularity, and Area/Perimeter indices. Furthermore, it was found that (i) grain width and the Area/Perimeter ratio were the common factors in the structure of the superior predictive indices; and (ii) the superior indices had the highest correlation with grain width, rather than with their mathematical components. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.
Why it matches plant phenotyping methods画像から穀粒形状指標を抽出し、平均穀粒重を推定する手法を開発・検証しており、表現型取得・推定が研究の中心である。
abstractthe present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations.
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 image archive used in this research (with the original resolution) along with the mean values of extracted quantities have been shared on Figshare, at [ 23 ]: https://figshare.com/articles/dataset/Images_of_wheat_grains/18480722Open asset ↗figsharelines:141-154Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
BACKGROUND: Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. RESULTS: In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. CONCLUSIONS: This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Why it matches plant phenotyping methodsイネ葉のハイパースペクトル画像と深層学習により病斑割合・病害重症度を推定し、抵抗性品種選抜へ応用する手法が研究の中心である。
abstractinformative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their LPnet deep learning analysis code on GitHub, explicitly stated in both the Software tools and Availability sections. The hyperspectral phenotype data itself is only available on request, so it is listed separately as request_only.Code · publicRelevant algorithm code is available on the GitHub address ( https://github.com/jinnuozhang/LPnet ).Open asset ↗jinnuozhang/LPnetlines:121-133Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceAnnotation / quality controlCalibration / preprocessingVisualization / data management
Background Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. Results We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline . Conclusion The analysis workflow and document presented are not limited to IRRI's RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI's RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.
Why it matches plant phenotyping methods作物育種における表現型データの前処理・品質管理・統計解析・可視化を一貫して行う、再現可能なオープンソース解析パイプラインが中心である。
abstractHere, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline
Reproduction assets foundThe paper's authors publicly release their phenotypic data analysis pipeline source codes, sample HTML workflow documents, and demo phenotypic dataset on GitHub, directly reproducing this paper's computational analysis.Code · publicThe analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:1-75Dataset · publicAll the instructions, R source codes, examples, and the data sets are freely available in the GitHub repository at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:80-91Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
BACKGROUND: Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. RESULTS: We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. CONCLUSIONS: We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.
Why it matches plant phenotyping methodsCNNとGrad-CAMにより米粒画像から白未熟粒（chalkiness）を検出・定量する高スループット表現型計測手法の開発であり、方法が研究の中心です。
abstractlack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness
Reproduction assets foundThe authors explicitly state that the datasets (rice grain images) generated and analyzed in this study, as well as the trained models, are publicly available on GitHub at the authors' repository. This is a paper-specific, public, actionable asset directly reproducing the paper's phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during the current study are available on GitHub, https://github.com/cwang16/Phenotyping-of-Chalkiness-in-Rice .Open asset ↗cwang16/Phenotyping-of-Chalkiness-in-Ricelines:212-226Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
BACKGROUND: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy. RESULTS: A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model. CONCLUSIONS: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.
Why it matches plant phenotyping methods綿花葉の毛茸という植物形質を対象に、画像取得法と深層学習モデルHairNetを開発・精度評価しており、表現型取得手法が研究の中心である。
abstractCurrently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy.
Reproduction assets foundThe paper publicly releases its de-identified cotton leaf hairiness image dataset (~13,600 leaf images, 27 genotypes) via the CSIRO data access portal and the HairNet analysis code via a public Bitbucket repository, both with explicit availability statements and URLs.Dataset · publicThe HairNet image dataset is available at https://doi.org/10.25919/9vqw-7453 .Open asset ↗10.25919/9vqw-7453lines:242-283Code · publicThe HairNet code is available at https://bitbucket.csiro.au/scm/sth/hairnet.git .Open asset ↗bitbucket.csiro.au/scm/sth/hairnetlines:242-283Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Background Stalk lodging (mechanical failure of plant stems during windstorms) leads to global yield losses in cereal crops estimated to range from 5% to 25% annually. The cross-sectional morphology of plant stalks is a key determinant of stalk lodging resistance. However, previously developed techniques for quantifying cross-sectional morphology of plant stalks are relatively low-throughput, expensive and often require specialized equipment and expertise. There is need for a simple and cost-effective technique to quantify plant traits related to stalk lodging resistance in a high-throughput manner. Results A new phenotyping methodology was developed and applied to a range of plant samples including, maize ( Zea mays ), sorghum ( Sorghum bicolor ), wheat ( Triticum aestivum ), poison hemlock ( Conium maculatum ), and Arabidopsis (Arabis thaliana). The major diameter, minor diameter, rind thickness and number of vascular bundles were quantified for each of these plant types. Linear correlation analyses demonstrated strong agreement between the newly developed method and more time-consuming manual techniques (R 2 > 0.9). In addition, the new method was used to generate several specimen-specific finite element models of plant stalks. All the models compiled without issue and were successfully imported into finite element software for analysis. All the models demonstrated reasonable and stable solutions when subjected to realistic applied loads. Conclusions A rapid, low-cost, and user-friendly phenotyping methodology was developed to quantify two-dimensional plant cross-sections. The methodology offers reduced sample preparation time and cost as compared to previously developed techniques. The new methodology employs a stereoscope and a semi-automated image processing algorithm. The algorithm can be used to produce specimen-specific, dimensionally accurate computational models (including finite element models) of plant stalks.
Why it matches plant phenotyping methods植物茎の横断面形態を高スループットに定量する画像ベースの表現型計測法を開発し、手作業法との一致性検証と有限要素モデルへの応用を行っており、方法が研究の中心である。
abstractA new phenotyping methodology was developed and applied to a range of plant samples
Reproduction assets foundThe paper's MATLAB image-processing algorithm (authors' analysis code) and sample cross-sectional images are publicly available as supplementary files (Additional files 2 and 3) attached to this open-access article, along with standard operating protocols (Additional file 1). These directly reproduce the paper's phenotCode · publicThe code for the image-processing algorithm is also provided as Additional file 2 . Sample images and instructions are provided as Additional file 3 .Open asset ↗lines:110-119Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methods3D画像からトウモロコシ根系の階層別形態形質を抽出する計算手法を開発し、既存法と精度比較・検証しており、植物表現型取得が中心です。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems.
Reproduction assets foundThe paper's authors publicly distribute the TopoRoot analysis software (C++ pipeline with GUI) together with the 45 X-ray CT scans of maize root crowns, per-image threshold values, and hand-measured nodal root counts in a GitHub repository. The synthetic OpenSimRoot images and ground-truth traits are only available on.Code · publicto a Euclidean distance field (e.g., using [ 29 ]). Fig. 12
Hierarchies of sorghum roots computed by TopoRoot, showing one tiller ( A ), two tillers ( B ), and four tillers ( C ). Hierarchy levels 0, 1, 2, 3 and 4 are colored dark blue, light blue, green, orange, and red.
Software availability
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot .
Included in the page are instructions to run the software, and details on the formats of the input and output files. Currently, the accepted inputs are either image slices (suffixed with.png) or.raw files, with a.dat accompanying the.raw file to specify the dimensions. The output consists of a skeleton, a hierarchy annotationOpen asset ↗https://github.com/danzeng8/TopoRootlines:2051-2060Dataset · public\usepackage{amsfonts}
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\begin{document}$$t_{low} ,t_{mid} ,t_{high}$$\end{document} t low , t mid , t high ) and hand measurements of nodal roots for each sample, are available in the TopoRoot Github repository: https://github.com/danzeng8/TopoRoot . The synthetic images of simulated roots and associated ground truth trait measurements are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing inOpen asset ↗https://github.com/danzeng8/TopoRootlines:2061-2116Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs. RESULTS: We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the fluorescent levels in the cell walls. Micrographs are composed of pixels, which have information on position and intensity. From the pixel information we calculated three types of features (spatial, intensity and Haralick) in ER bodies corresponding to segmented cells. The spatial features include basic information on shape, e.g., surface area and perimeter. The intensity features include information on mean, standard deviation and quantile of fluorescence intensities within an ER body. Haralick features describe the texture features, which can be calculated mathematically from the interrelationship between the pixel information. Together these parameters were subjected to multivariate analysis to estimate the morphological diversity. Additionally, we calculated the displacement of the ER bodies using the positional information in time-lapse images. We captured similar morphological diversity and movement within ER body phenotypes in several microscopy experiments performed in different settings and scanned under different objectives. We then described differences in morphology and movement of ER bodies between A. thaliana wild type and mutants deficient in ER body-related genes. CONCLUSIONS: The findings unexpectedly revealed multiple genetic factors that are involved in the shape and size of ER bodies in A. thaliana. This is the first report showing morphological characteristics in addition to the movement of cellular components and it quantitatively summarises plant phenotypic differences even in plants that show similar cellular components. The estimation of morphological diversity was independent of the cell staining method and the objective lens used in the microscopy. Hence, our study enables a robust estimation of plant phenotypes by recognizing small differences in complex cell organelle shapes and their movement, which is beneficial in a comprehensive analysis of the molecular mechanism for cell organelle formation that is independent of technical variations.
Why it matches plant phenotyping methods植物細胞小器官の形態・移動を画像から抽出する特徴量計算法を開発し、異なる撮像条件で頑健性を検証しているため、表現型取得法が研究の中心です。
abstractHere we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe z-stack images were merged using specific criteria for the MaxContrastProjection package ( https://github.com/arpankbasak/ERB_DynaMo ).Open asset ↗arpankbasak/ERB_DynaMolines:96-99Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
Why it matches plant phenotyping methods小麦のハイパースペクトル反射から生理・光合成形質を推定する深層学習モデルを開発・比較し、精度を検証した研究であり、表現型取得・推定法が中心である。
abstractHere we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets.
Reproduction assets foundThe paper publicly releases its authors' model code (GitHub) and hosts the training data and pre-trained models via the Wheat Physiology Predictor web server. The SAMS repository is a generic third-party tool and is excluded.Code · publicThe full code of these models is located at https://github.com/ashwhall/hyperspec-trait-prediction .Open asset ↗ashwhall/hyperspec-trait-predictionlines:132-148Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
Abstract Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
Why it matches plant phenotyping methodsヒヨコマメ個体の3D形態を取得する低コスト撮像システムとオープンソース解析パイプラインを開発し、草丈・表面積を基準値で検証しており、フェノタイピング手法が中心である。
abstractIn this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants.
Reproduction assets foundThe authors deposited the paper's 3D point clouds and meshed chickpea models in an open-access Zenodo repository (DOI 10.5281/zenodo.4018242). Processing scripts are only included as article additional files, and the source images are available only on request from the corresponding author.Dataset · publicThe dataset supporting the conclusions of this article (3D point clouds and meshed models) are available in an open-access Zenodo repository, https://doi.org/10.5281/zenodo.4018242 .Open asset ↗Zenodo · 10.5281/zenodo.4018242lines:149-193Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. RESULTS: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ([Formula: see text]). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. CONCLUSIONS: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の画像セグメンテーションと8形質の自動定量を目的とする手法開発・比較検証および高スループット解析パイプラインの構築が中心である。
abstractWe aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and 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 · publicDeep learning model and manuals with codes for custom detections and model updating: https://gitlab.com/kjschmidlab/deepcob .Open asset ↗gitlab · kjschmidlab/deepcoblines:182-229Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation. Results Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H 2 ) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach. Conclusions A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H 2 estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.
Why it matches plant phenotyping methodsµXRF画像の植物組織分割と、金属蓄積空間パターンを定量化する指標を開発・検証しており、植物表現型の取得・抽出手法が中心である。
abstractWe developed an image-processing method to segment different plant substructures in the µXRF images.
Reproduction assets foundThe authors explicitly state that the analysis code created for this study (µXRF image segmentation and CQ/heritability analysis) is publicly available in their GitHub repository. The phenotype datasets themselves are only available on request from the corresponding author, so they are noted as a request-only asset. NoCode · publicCode is available at https://github.com/LucasYEAST/noccaea .Open asset ↗LucasYEAST/noccaealines:211-272Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Background Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping. Results This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes. Conclusions This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
Why it matches plant phenotyping methodsジャガイモの水ストレス表現型を取得するための非侵襲的イメージング・生理計測手法と装置構成が研究の中心であり、単なる生物学的測定ではない。
abstractThere is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Reproduction assets foundThe paper's MRI phenotyping data (3D images of potato tubers in pots under water deficit) are openly deposited in Data INRAE with an explicit DOI, as stated in the Availability of data and materials section. No author analysis code or trained models are reported.Dataset · publicThe MRI data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ ) repository at: https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/SFAXAA ).Open asset ↗Data INRAE · doi:10.15454/SFAXAAlines:160-172Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates' properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi 1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study.
Why it matches plant phenotyping methodsトウモロコシの光遮断係数kとfPARiを推定する統計手法を比較・評価し、植物キャノピー形質の再現可能な推定ワークフローとして推奨手法を提示しているため、方法論が中心である。
abstractThe parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution.
Reproduction assets foundThe paper's authors explicitly state that the R code implementing the k-estimation analysis (LSE, LogTLM, MLE, Bayesian estimation) is freely available in a public GitHub repository. The underlying phenotype datasets (fPARi/LAI measurements for seven maize genotypes) are only available from the corresponding author on,Code · publicR code is freely available at https://github.com/jlacasa/k-estimation/blob/main/k_estimation_02182021.Rmd .Open asset ↗https://github.com/jlacasa/k-estimation · k_estimation_02182021.Rmdlines:172-210Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. RESULTS: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. CONCLUSION: The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出するソフトウェアを開発・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ある。
abstractThis paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype
Reproduction assets foundThe authors' Maize-IAS analysis software (the paper's phenotyping analysis code) is publicly available on GitHub. The maize image datasets are explicitly not public and require request.Code · publicl development prospects of visual phenotype detection using deep learning methods. The methods and workflow provided in this article can also be easily applied to other crops.
Availability and requirements
Project name: A Maize Image Analysis Software using Deep Learning for High-throughput Plant Phenotyping.
Project home page: https://github.com/surefyyq/Maize-IAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None.
Supplementary information
Additional file 1. Installation and debug guidelines.
Publisher’s Note
Springer Nature remains neutral with regard to juOpen asset ↗surefyyq/Maize-IASlines:403-475Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.
Why it matches plant phenotyping methods植物の気孔を顕微鏡画像から自動検出・個別境界推定し、形態解析に利用する画像ベース表現型解析手法を開発・検証しているため。
abstractIn this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries
Reproduction assets foundThe paper's authors publicly release their complete Mask R-CNN stomata segmentation analysis code at the Smart-Robotic-Viticulture/MaskStomata GitHub repository, with explicit availability statements in multiple blocks. The microscope image datasets themselves are only available from the corresponding author on requestCode · publicThe complete code for the project can be accessed at: https://github.com/Smart-Robotic-Viticulture/MaskStomata .Open asset ↗Smart-Robotic-Viticulture/MaskStomatalines:136-157Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Why it matches plant phenotyping methods種子の発芽状態を画像と機械学習で自動抽出し、手動計数と性能比較しているため、植物表現型取得法が中心です。
abstractWe here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.
Reproduction assets foundThe paper's authors publicly released the labeled germination image dataset (~24,000 annotated images of 2449 seeds) on Mendeley Data and their machine learning analysis code on GitHub, both explicitly stated in the Availability of data and materials section.Dataset · publicThe generated and labeled training data is freely available on Mendeley Data: http://dx.doi.org/10.17632/4wkt6thgp6.2 .Open asset ↗Mendeley Data · 10.17632/4wkt6thgp6.2lines:164-248Code · publicThe code for our proposed machine learning–based model can be found on GitHub: https://github.com/grimmlab/GerminationPrediction .Open asset ↗GitHub · grimmlab/GerminationPredictionlines:164-248Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. RESULTS: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. CONCLUSION: Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.
Why it matches plant phenotyping methods屋外HTPのロードセルデータから蒸散応答の特徴量とVPDブレークポイントを自動抽出し、遺伝型を識別する解析手法が研究の中心であるため。
abstractoutdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes.
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 · publicInterested readers can find the R scripts on the open-source GitHub platform, https://github.com/KSoumya/EZTr .Open asset ↗KSoumya/EZTrlines:185-203Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
BACKGROUND: (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. RESULTS: The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. CONCLUSIONS: DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しているため、方法開発が中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe authors explicitly state their DAPD normalization and segmentation code is publicly available on GitHub.Code · publicOur code and a Python notebook come with a friendly user manual detailing how to use it, and they are available at https://github.com/diloc/DAPD_Normalization.git .Open asset ↗diloc/DAPD_Normalizationlines:73-78Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
BACKGROUND: The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. RESULTS: Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels-TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture. CONCLUSIONS: error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC.
Why it matches plant phenotyping methodsトウモロコシ雄穂の検出・計数という植物形質の取得を対象に、複数の画像認識手法をベンチマークし、専用データセットも構築しているため、表現型計測手法が中心である。
abstractWe create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset
Reproduction assets foundThe paper's MTDC dataset (MTC maize tassel images with new bounding box annotations, 13,562 boxes) is explicitly released publicly at the authors' URL https://git.io/MTDC, stated in both the abstract and Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe MTDC dataset and other supporting materials are made available at https://git.io/MTDC .Open asset ↗lines:201-255Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by creating four categories of image labels: non-wheat ear, one wheat ear, two wheat ears, and three wheat ears, which was then was sent into the convolution neural network (CNN) model for training and testing to reduce the complexity of the model. Results The recognition accuracy of non-wheat, one wheat, two wheat ears, and three wheat ears were 99.8, 97.5, 98.07, and 98.5%, respectively. The model R 2 reached 0.96, the root mean square error (RMSE) was 10.84 ears, the macro F1-score and micro F1-score both achieved 98.47%, and the best performance was observed during late grain-filling stage ( R 2 = 0.99, RMSE = 3.24 ears). The model could also be applied to the UAV platform ( R 2 = 0.97, RMSE = 9.47 ears). Conclusions The classification of segmented images as opposed to target recognition not only reduces the workload of manual annotation but also improves significantly the efficiency and accuracy of wheat ear counting, thus meeting the requirements of wheat yield estimation in the field environment.
Why it matches plant phenotyping methods小麦穂数という植物形態・収量関連形質を、画像セグメンテーションとCNNで自動推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractTo realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices.
Reproduction assets foundThe authors publicly released their analysis code (K-means segmentation + CNN wheat ear counting pipeline) on GitHub. The image/phenotype datasets are only available on request from the corresponding author, so they do not qualify as public assets.Code · publicThe code can be found at https://github.com/xuxin468/earcouting .Open asset ↗xuxin468/earcoutinglines:190-213Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
ArabidopsisGrowth chamberLeafRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / development / phenologyLeaf traitsRoot system architecture
Background Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform, GrowScreen - Agar , for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium. Results The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78 Arabidopsis accessions from the 1001 genomes project. The chosen concept 'plant-to-sensor' is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow Arabidopsis plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately. Conclusions Because the GrowScreen - Agar system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.
Why it matches plant phenotyping methods自動画像計測による根・シュート形質の非破壊取得プラットフォームを開発・検証しており、表現型取得法が研究の中心である。
abstractThe phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min.
Reproduction assets foundThe paper's phenotypic datasets (root/shoot trait measurements of 78 Arabidopsis accessions and experiments 1-2) are publicly deposited in the e!DAL research data publication system. The analysis software is only available upon request from the corresponding author, so it is not a public asset. AraPheno and cited worksDataset · publicThe datasets generated and analysed during the current study are available in the e!DAL research data publication system, https://doi.org/10.25622/FZJ/2020/0 .Open asset ↗e!DAL research data publication system · 10.25622/FZJ/2020/0lines:157-166Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.
Why it matches plant phenotyping methods葉の病斑・損傷面積を染色と自動画像解析で定量する手法自体が中心的に開発・実証されており、植物病害・損傷状態の表現型計測に該当する。
abstractWe stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale.
Reproduction assets foundThe paper's LiMu image analysis pipeline (used for lesion quantification) is publicly available on PyPI. The leaf image datasets are only available from the corresponding author on request, and no public URL for the image data or supplements is present in the allowed list.Code · publicThe original LiMu code is made freely available in the Python Package Index (PyPI), and can be downloaded from https://pypi.org/project/limu/ .Open asset ↗limulines:184-227Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Laboratory / benchtopSeed / grainCountingGrowth / development / phenology
Background Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite's life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite ( Striga seed germination. Results Here, we introduce DiSCount ( Di gital S triga Count er): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. Conclusions DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956.
Why it matches plant phenotyping methodsStriga種子の発芽状態を画像から自動定量するコンピュータビジョン手法の開発・検証が研究の中心であり、植物状態の表現型取得に該当する。
abstractwe introduce DiSCount ( Di gital S triga Count er): a computer vision tool for automated quantification of total and germinated Striga seed numbers
Reproduction assets foundThe paper's DiSCount software (code, manual, trained YOLOv3 model) is hosted on GitLab and archived on Zenodo; the manually counted test image dataset and the installation-test input dataset are also publicly available on Zenodo. The pytorch-yolo-v3 repository is a third-party code base, not a paper-specific asset.Code · publicThe complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI https://doi.org/10.5281/zenodo.3627138 .Open asset ↗lodewijk-track32/discount_paperlines:1-79Code · publicThe DiSCount software is available at https://doi.org/10.5281/zenodo.3627138 along with detailed training settings and a complete installation and user manual.Open asset ↗10.5281/zenodo.3627138lines:84-90Dataset · publicThe dataset analysed to assess the performance of the software is publically available at https://doi.org/10.5281/zenodo.3403956 .Open asset ↗10.5281/zenodo.3403956lines:122-173Dataset · publicThe input dataset used to test the installation of the software is publically available at https://doi.org/10.5281/zenodo.3404131 .Open asset ↗10.5281/zenodo.3404131lines:122-173Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を画像から自動取得・定量化する撮像システムと解析ソフトウェアを開発しており、植物フェノタイピング手法が研究の中心である。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper explicitly states that the phenoSEED analysis script is publicly available on GitLab and that the BELT-captured seed image datasets are available on the first author's Figshare page. Both are paper-specific, public, and actionable.Code · publicAt the time of publication, a version of the processing script is available at https://gitlab.com/usask-speclab/phenoseed .Open asset ↗usask-speclab/phenoseedlines:145-153Dataset · publicThe image datasets captured by BELT analysed for the sample study are available from https://figshare.com/authors/Keith_Halcro/8363580 . The phenoSEED script is available from https://gitlab.com/usask-speclab/phenoseed .Open asset ↗lines:173-192Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
BACKGROUND: Non-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are common. However, often the variation in the data is inappropriately modelled, in part because the required models are complicated and difficult to fit. We provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods. RESULTS: The SET-based and longitudinal analyses were applied to a tomato experiment to investigate the effects on plant growth of zinc (Zn) addition and growing plants in soil inoculated with arbuscular mycorrhizal fungi (AMF). Conclusions from the SET-based and longitudinal analyses are similar, although the former analysis results in more significant differences. They showed that added Zn had little effect on plants grown in inoculated soils, but that growth depended on the amount of added Zn for plants grown in uninoculated soils. The longitudinal analysis of the unsmoothed data fitted a mixed model that involved both fixed and random regression modelling with splines, as well as allowing for unequal variances and autocorrelation between time points. CONCLUSIONS: A SET-based analysis can be used in any situation in which a traditional longitudinal analysis might be applied, especially when there are many observed time points. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. Also discussed are the statistical models that need to be considered for traditional longitudinal analyses and it is demonstrated that the oft-omitted unequal variances and autocorrelation may be required for a valid longitudinal analysis. With respect to the separate issue of the subjective choice of mathematical growth functions or splines to characterize growth, it is recommended that, for both SET-based and longitudinal analyses, an evidence-based procedure is adopted.
Why it matches plant phenotyping methods植物の非破壊ハイスループット表現型データから成長形質を抽出するSET法を開発し、従来の縦断解析と比較・検証しているため、表現型取得・解析手法が研究の中心である。
abstractWe provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods.
Reproduction assets foundThe paper's availability statement describes R scripts and the tomato phenotype dataset (tomato.dat.csv) used for the SET and longitudinal analyses, noting the data is also distributed with the authors' growthPheno R package, which is publicly available on CRAN.Dataset · publicR scripts and data for preparing the tomato data and carrying out the reported analyses. The data is provided in the file tomato.dat.csv , but in R is also available with the growthPheno package.Open asset ↗growthPheno · tomato.dat.csvlines:437-510Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Background Seed size and number are important plant traits from an ecological and horticultural/agronomic perspective. However, in small-seeded species such as Arabidopsis thaliana, research on seed size and number is limited by the absence of suitable high throughput phenotyping methods. Results We report on the development of a high throughput method for counting seeds and measuring individual seed sizes. The method uses a large-particle flow cytometer to count individual seeds and sort them according to size, allowing an average of 12,000 seeds/hour to be processed. To achieve this high throughput, post harvested seeds are first separated from remaining plant material (dust and chaff) using a rapid sedimentation-based method. Then, classification algorithms are used to refine the separation process in silico. Accurate identification of all seeds in the samples was achieved, with relative errors below 2%. Conclusion The tests performed reveal that there is no single classification algorithm that performs best for all samples, so the recommended strategy is to train and use multiple algorithms and use the median predictions of seed size and number across all algorithms. To facilitate the use of this method, an R package (SeedSorter) that implements the methodology has been developed and made freely available. The method was validated with seed samples from several natural accessions of Arabidopsis thaliana, but our analysis pipeline is applicable to any species with seed sizes smaller than 1.5 mm.
Why it matches plant phenotyping methods種子数と種子サイズという植物形質を高スループットに取得するフローサイトメトリー法を開発・検証し、解析用Rパッケージも提供しているため、植物フェノタイピング手法が研究の中心である。
abstractWe report on the development of a high throughput method for counting seeds and measuring individual seed sizes.
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2件を確認しました。Code · publicTo facilitate the use of this method, all necessary computations have been implemented into an R package ( SeedSorter ) that is freely available online at https://github.com/aleMorales/SeedSorter .Open asset ↗aleMorales/SeedSorterlines:76-82Code · publicThe R scripts and data required to reproduce these results can be obtained at https://github.com/aleMorales/SeedSorterPaper .Open asset ↗aleMorales/SeedSorterPaperlines:120-135Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. Results Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. Conclusions Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.
Why it matches plant phenotyping methodsイネ穂の画像セグメンテーション手法を開発し、UAV画像で性能評価しているため、植物形質取得法が研究の中心である。
abstractan unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV)
Reproduction assets foundThe paper's UAV rice panicle image dataset and the authors' MATLAB analysis code are both explicitly stated as publicly available with direct URLs in the Availability of data and materials section.Dataset · publicThe dataset analyzed during the current study are available at https://wuj.hosted.uark.edu/research/datasets/panicle/UBLRPSUI.zip .Open asset ↗lines:221-277Code · publicAlso, the MATLAB code and related materials can be downloaded from https://github.com/i2pt/UBLRPSUI .Open asset ↗i2pt/UBLRPSUIlines:221-277Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeGrowth / time-series analysisGrowth / development / phenology
Background Plants, like all living organisms, metamorphose their bodies during their lifetime. All the developmental and growth events in a plant's life are connected to specific points in time, be it seed germination, seedling emergence, the appearance of the first leaf, heading, flowering, fruit ripening, wilting, or death. The onset of automated phenotyping methods has brought an explosion of such time-to-event data. Unfortunately, it has not been matched by an explosion of adequate data analysis methods. Results and discussion In this paper, we introduce the Bayesian approach towards time-to-event data in plant biology. As a model example, we use seedling emergence data of maize under control and stress conditions but the Bayesian approach is suitable for any time-to-event data (see the examples above). In the proposed framework, we are able to answer key questions regarding plant emergence such as these: (1) Do seedlings treated with compound A emerge earlier than the control seedlings? (2) What is the probability of compound A increasing seedling emergence by at least 5 percent? Conclusion Proper data analysis is a fundamental task of general interest in life sciences. Here, we present a novel method for the analysis of time-to-event data which is applicable to many plant developmental parameters measured in field or in laboratory conditions. In contrast to recent and classical approaches, our Bayesian computational method properly handles uncertainty in time-to-event data and it is capable to reliably answer questions that are difficult to address by classical methods.
Why it matches plant phenotyping methods植物の発芽・生育などの時系列表現型データを解析するベイズ計算法を中心に開発しており、植物フェノタイピング手法の方法開発に該当する。
abstractThe onset of automated phenotyping methods has brought an explosion of such time-to-event data.
Reproduction assets foundThe paper's maize seedling-emergence time-to-event data and Bayesian analysis are accessible via the authors' free public web application at bayes4plants.com, which hosts the simulation software and sample data from the study. The full analysis code is stated to be 'freely available here', but no explicit public URL/reCode · publicThe datasets during and/or analyzed during the current study available from the corresponding author on reasonable request. As we present here data analysis method, the sample data are free available to test in web http://www.bayes4plants.com . The code for the analysis is freely available here .Open asset ↗lines:121-155Code / 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 confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Background Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the P anicle I maging Plat form (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.
Why it matches plant phenotyping methodsイネ穂の非破壊3D画像再構成とデジタル形質抽出を行うPI-Platを開発・比較評価しており、植物フェノタイピング手法が研究の中心である。
abstractThese images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity.
Reproduction assets foundThe paper publicly shares (1) a partial raw image dataset on a UNL Box repository and (2) the authors' PI-Plat Panicle-3D-Reconstruction workflow scripts at wrchr.org. Full raw images and the manual phenotyping dataset are only available on request, so those portions would be request_only, but the two public assets areDataset · publicRaw image data is large and hence only part of them is shared for user testing on a UNL Box repository ( https://unl.box.com/s/g0bof1mpfp33hn66b2qabrk9kiwmhbzv ).Open asset ↗unl.box.comlines:117-127Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
Why it matches plant phenotyping methods低コストの生育光環境、IMAGING-PAMのスループット向上、植物画像の半自動セグメンテーションとデータ解析を開発・検証しており、植物表現型取得手法が中心である。
abstractWe have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets.
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 scripts described in the text can be downloaded from https://github.com/CougPhenomics/ImagingPAMProcessing and the accompanying 11 day dataset can be downloaded from https://doi.org/10.17605/OSF.IO/P32AY .Open asset ↗OSF · 10.17605/OSF.IO/P32AYlines:160-190Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., s p i k e n u m b e r m - 2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predict accurate counts when spikes partially present. Results In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manually-annotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ( 91.01 % counting accuracy) but also is more than an order of magnitude faster than TasselNet (13.82 fps on 912 × 1216 images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. Conclusions This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity , and improving efficiency without sacrificing accuracy . It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy. In practice, one can trade off the performance and efficiency according to certain application scenarios. Code and models are made available at: https://tinyurl.com/TasselNetv2.
Why it matches plant phenotyping methodsコムギ穂数という植物形態形質を画像から自動計数する手法を開発し、精度・速度を評価するとともに大規模データセットを構築しており、植物フェノタイピング手法が中心である。
abstractWe thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2.
Reproduction assets foundThe paper explicitly states that the WSC dataset (1764 images, 675,322 annotated wheat spikes) and code/models are made available online at the authors' public URL https://tinyurl.com/TasselNetv2.Dataset · publicThe WSC dataset and other supporting materials are made available online at: https://tinyurl.com/TasselNetv2 .Open asset ↗lines:205-231Code · publicCode and models are made available at: https://tinyurl.com/TasselNetv2 .Open asset ↗lines:1-72Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Plant population density is an important factor for agricultural production systems due to its substantial influence on crop yield and quality. Traditionally, plant population density is estimated by using either field assessment or a germination-test-based approach. These approaches can be laborious and inaccurate. Recent advances in deep learning provide new tools to solve challenging computer vision tasks such as object detection, which can be used for detecting and counting plant seedlings in the field. The goal of this study was to develop a deep-learning-based approach to count plant seedlings in the field. Results Overall, the final detection model achieved F1 scores of 0.727 (at $$IOU_{all}$$ I O U all ) and 0.969 (at $$IOU_{0.5}$$ I O U 0.5 ) on the $$Seedling_{All}$$ S e e d l i n g All testing set in which images had large variations, indicating the efficacy of the Faster RCNN model with the Inception ResNet v2 feature extractor for seedling detection. Ablation experiments showed that training data complexity substantially affected model generalizability, transfer learning efficiency, and detection performance improvements due to increased training sample size. Generally, the seedling counts by the developed method were highly correlated ( $$R^2$$ R 2 = 0.98) with that found through human field assessment for 75 test videos collected in multiple locations during multiple years, indicating the accuracy of the developed approach. Further experiments showed that the counting accuracy was largely affected by the detection accuracy: the developed approach provided good counting performance for unknown datasets as long as detection models were well generalized to those datasets. Conclusion The developed deep-learning-based approach can accurately count plant seedlings in the field. Seedling detection models trained in this study and the annotated images can be used by the research community and the cotton industry to further the development of solutions for seedling detection and counting.
Why it matches plant phenotyping methods圃場画像から植物個体数（苗立ち密度）を検出・計数する深層学習手法を開発し、人手評価および複数年・地点のデータで検証しており、植物表現型取得が中心である。
abstractThe goal of this study was to develop a deep-learning-based approach to count plant seedlings in the field.
Reproduction assets foundThe paper's availability statement explicitly archives original images and annotations, source code, and testing videos in a public GitHub repository, along with pretrained models for seedling detection and counting — directly reproducing this paper's phenotyping measurements and analysis.Code · publicOriginal images and annotations, source code, and testing videos are archived in a GitHub repository ( https://github.com/UGA-BSAIL/deepseedling ) along with the instruction to run pretrained models for seedling detection and counting.Open asset ↗UGA-BSAIL/deepseedlinglines:190-267Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 9 Sept 2026
BACKGROUND: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. RESULTS: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via "eye-in-hand" movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. CONCLUSIONS: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ-in that the rover takes sensors to the plants rather than moving plants to the sensors.
Why it matches plant phenotyping methodsロボット rover、複数センサー、コンピュータビジョン、葉面プロービングを統合した植物フェノタイピング基盤の開発・実証が中心である。
abstractThe Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover.
Reproduction assets foundThe paper describes the Enviratron phenotyping facility and explicitly states that a Git repository containing the code developed to operate and support the Enviratron is publicly available on GitLab. This is an authors' public code asset directly tied to this paper's phenotyping system. No phenotype datasets or image/Code · publicA git repository containing code developed to operate and support the Enviratron is available online at https://gitlab.com/dill_picl/enviratron .Open asset ↗dill_picl/enviratronlines:165-207Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Epicormic branches arise from dormant buds patterned during the growth of previous years. Dormant epicormic buds remain just below the surface of trees, pushed outward from the pith during secondary growth, but maintain vascular connections. Epicormic buds can be activated to elongate into a new shoot, either through natural processes or horticultural intervention, to potentially rejuvenate orchards and restructure tree architecture. Because epicormic structures are embedded within secondary growth, tomographic approaches are a useful method to study them and understand their development. Results We apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree. Techniques include the flood fill algorithm to find the pith of the tree, edge detection to approximate the radius, and a conversion to polar coordinates to threshold and segment phyllotactic features. Intensity values from magnetic resonance imaging (MRI) of the trunk are projected onto the surface of a perfect cylinder to find the locations of traces in the "boundary image". Mathematical phyllotaxy provides a means to capture the patterns in the boundary image by modeling phyllotactic parameters. Our cherry tree specimen has the conspicuous parastichy pair (2,3), phyllotactic fraction 2/5, and divergence angle of approximately 143°. Conclusions The methods described provide a framework not only for studying phyllotaxy, but also for processing of volumetric image data in plants. Our results have practical implications for orchard rejuvenation and directed approaches to influence tree architecture. The study of epicormic structures, which are hidden within secondary growth, using tomographic methods also opens the possibility of studying genetic and environmental influences such structures.
Why it matches plant phenotyping methodsMRI画像と画像処理を組み合わせ、樹幹内部の維管束痕と葉序パターンを抽出・定量する方法が研究の中心であり、植物形態のフェノタイピング手法に該当する。
abstractWe apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree.
Reproduction assets foundThe paper's availability statement explicitly provides authors' analysis code on GitHub and the raw MRI data on figshare, both paper-specific and publicly actionable.Code · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗eithun/cherry-phyllotaxylines:174-195Dataset · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗10.6084/m9.figshare.7409843lines:174-195Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background As yields of major crops such as wheat ( T. aestivum ) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield. Photosynthesis encompasses a range of steady state and dynamic traits that are key targets for raising Radiation Use Efficiency (RUE), biomass production and grain yield in crops. Traditional methodologies to assess the full range of responses of photosynthesis, such a leaf gas exchange, are slow and limited to one leaf (or part of a leaf) per instrument. Due to constraints imposed by time, equipment and plant size, photosynthetic data is often collected at one or two phenological stages and in response to limited environmental conditions. Results Here we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions. When measured throughout the day, no significant differences ( P > 0.081) were observed between the responses of excised and intact leaves. Using excised leaves, the response of three cultivars of T. aestivum to a user-defined dynamic lighting regime was examined. Cultivar specific differences were observed for maximum PSII efficiency ( F v '/ F m '- P F q '/ F m '- P = 0.04) under both low and high light. In addition, the rate of induction and relaxation of non-photochemical quenching (NPQ) was also cultivar specific. A specialised imaging chamber was designed and built in-house to maintain gaseous conditions around excised leaf sections. The purpose of this is to manipulate electron sinks such as photorespiration. The stability of carbon dioxide (CO 2 ) and oxygen (O 2 ) was monitored inside the chambers and found to be within ± 4.5% and ± 1% of the mean respectively. To test the chamber, T. aestivum 'Pavon76' leaf sections were measured under at 20 and 200 mmol mol -1 O 2 and ambient [CO 2 ] during a light response curve. The F v '/ F m 'was significantly higher ( P 2 ] for the majority of light intensities while values of NPQ and the proportion of open PSII reaction centers (qP) were significantly lower under > 130 μmol m -2 s -1 photosynthetic photon flux density (PPFD). Conclusions Here we demonstrate the development of a high-throughput (> 500 samples day -1 ) method for phenotyping photosynthetic and photo-protective parameters in a dynamic light environment. The technique exploits chlorophyll fluorescence imaging in a specifically designed chamber, enabling controlled gaseous environment around leaf sections. In addition, we have demonstrated that leaf sections do not different from intact plant material even > 3 h after sampling, thus enabling transportation of material of interest from the field to this laboratory based platform. The methodologies described here allow rapid, custom screening of field material for variation in photosynthetic processes.
Why it matches plant phenotyping methods葉緑素蛍光イメージングと専用チャンバーを用いた高スループットな光合成・光防御形質の取得法を開発し、葉およびチャンバー条件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。
abstractHere we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions.
Reproduction assets foundThe paper's phenotyping pipeline (chlorophyll fluorescence imaging of excised wheat leaves in custom gas-controlled chambers) is supported by a paper-specific public asset: Additional file 2, a ZIP supplement containing the CAD files, printer settings, and construction notes for the custom imaging chambers, explicitly'Supplement · publicThe CAD files for the final chamber design are fully available with this manuscript (Additional file 2 ) including printer settings and additional notes, so that users can either print their own, outsource the printing or modify the designs.Open asset ↗lines:94-102Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
RootMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
Background The root is an important organ for water and nutrient uptake, and soil anchorage. It is equipped with root hairs (RHs) which are elongated structures increasing the exchange surface with the soil. RHs are also studied as a model for plant cellular development, as they represent a single cell with specific and highly regulated polarized elongation. For these reasons, it is useful to be able to accurately quantify RH length employing standardized procedures. Methods commonly employed rely on manual steps and are therefore time consuming and prone to errors, restricting analysis to a short segment of the root tip. Few partially automated methods have been reported to increase measurement efficiency. However, none of the reported methods allow an accurate and standardized definition of the position along the root for RH length measurement, making data comparison difficult. Results We developed an image analysis algorithm that semi-automatically detects RHs and measures their length along the whole differentiation zone of roots. This method, implemented as a simple automated script in ImageJ/ Fiji software that we termed Root Hair Sizer, slides a rectangular window along a binarized and straightened image of root tips to estimate the maximal RH length in a given measuring interval. This measure is not affected by heavily bent RHs and any bald spots. RH length data along the root are then modelled with a sigmoidal curve, generating several biologically significant parameters such as RH length, positioning of the root differentiation zone and, under certain conditions, RH growth rate. Conclusions Image analysis with Root Hair Sizer and subsequent sigmoidal modelling of RH length data provide a simple and efficient way to characterize RH growth in different conditions, equally suitable to small and large scale phenotyping experiments.
Why it matches plant phenotyping methods根毛長などの植物形質を画像から半自動抽出・モデル化するアルゴリズムを開発し、大規模フェノタイピングへの適用を明示しているため。
abstractWe developed an image analysis algorithm that semi-automatically detects RHs and measures their length along the whole differentiation zone of roots.
Reproduction assets foundThe paper's Root Hair Sizer analysis scripts (ImageJ/Fiji macros for Medicago, Brachypodium, and Arabidopsis) are published as open-access supplementary files (Additional files 1, 3, 4, 6) of this article, along with a demonstration movie and example root images. No standalone repository URL is given in the supplied; 1Code · publicThe image processing steps are implemented as an automated procedure in the Root Hair Sizer (RHS) script for ImageJ , available as Additional file 1 : Script 1.Open asset ↗lines:86-98Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Characterization and quantification of visual plant traits is often limited to the use of tools and software that were developed to address a specific context, making them unsuitable for other applications. CoverageTool is flexible multi-purpose software capable of area calculation in cm 2 , as well as coverage area in percentages, suitable for a wide range of applications. Results Here we present a novel, semi-automated and robust tool for detailed characterization of visual plant traits. We demonstrate and discuss the application of this tool to quantify a broad spectrum of plant phenotypes/traits such as: tissue culture parameters, ground surface covered by annual plant canopy, root and leaf projected surface area, and leaf senescence area ratio. The CoverageTool software provides easy to use functions to analyze images. While use of CoverageTool involves subjective operator color selections, applying them uniformly to full sets of samples makes it possible to provide quantitative comparison between test subjects. Conclusion The tool is simple and straightforward, yet suitable for the quantification of biological and environmental effects on a wide variety of visual plant traits. This tool has been very useful in quantifying different plant phenotypes in several recently published studies, and may be useful for many applications.
Why it matches plant phenotyping methods植物画像から面積や被覆率などの形質を定量化する半自動ソフトウェアが論文の中心であり、植物フェノタイピング手法に該当する。
abstractThe CoverageTool software provides easy to use functions to analyze images.
Reproduction assets foundThe paper's own phenotyping software CoverageTool is publicly released on GitHub with an explicit project home page, license, and availability statement. Supplementary image datasets (Additional files 5-8, 10-11) are described but only available via the article's supplementary material, not via an allowed URL.Code · publicsigned the phenotyping protocol and the tissue culture experiment. All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
CoverageTool software and it’s additional files are in Additional files 1 , 2 , 3 , 4 , 5 , 6 , 7 and 8 .
Project name: CoverageTool
Project home page: https://github.com/lianneovnat/CoverageTool.git
Operating system(s): MS Windows: XP, Win7, Win10 etc.
Programming language: “C” with WIN32 (Visual Studio 2008 Express Edition)
Other requirements: Visual Studio 2008 Redistributal (or above)
License: GNU.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interesOpen asset ↗lianneovnat/CoverageToollines:346-425Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
With the introduction of high-throughput multisensory imaging platforms, the automatization of multimodal image analysis has become the focus of quantitative plant research. Due to a number of natural and technical reasons (e.g., inhomogeneous scene illumination, shadows, and reflections), unsupervised identification of relevant plant structures (i.e., image segmentation) represents a nontrivial task that often requires extensive human-machine interaction. Registration of multimodal plant images enables the automatized segmentation of 'difficult' image modalities such as visible light or near-infrared images using the segmentation results of image modalities that exhibit higher contrast between plant and background regions (such as fluorescent images). Furthermore, registration of different image modalities is essential for assessment of a consistent multiparametric plant phenotype, where, for example, chlorophyll and water content as well as disease- and/or stress-related pigmentation can simultaneously be studied at a local scale. To automatically register thousands of images, efficient algorithmic solutions for the unsupervised alignment of two structurally similar but, in general, nonidentical images are required. For establishment of image correspondences, different algorithmic approaches based on different image features have been proposed. The particularity of plant image analysis consists, however, of a large variability of shapes and colors of different plants measured at different developmental stages from different views. While adult plant shoots typically have a unique structure, young shoots may have a nonspecific shape that can often be hardly distinguished from the background structures. Consequently, it is not clear a priori what image features and registration techniques are suitable for the alignment of various multimodal plant images. Furthermore, dynamically measured plants may exhibit nonuniform movements that require application of nonrigid registration techniques. Here, we investigate three common techniques for registration of visible light and fluorescence images that rely on finding correspondences between (i) feature-points, (ii) frequency domain features, and (iii) image intensity information. The performance of registration methods is validated in terms of robustness and accuracy measured by a direct comparison with manually segmented images of different plants. Our experimental results show that all three techniques are sensitive to structural image distortions and require additional preprocessing steps including structural enhancement and characteristic scale selection. To overcome the limitations of conventional approaches, we develop an iterative algorithmic scheme, which allows it to perform both rigid and slightly nonrigid registration of high-throughput plant images in a fully automated manner.
Why it matches plant phenotyping methods植物のマルチモーダル画像登録アルゴリズムを比較・検証し、高スループット画像から一貫した表現型解析を可能にする手法を開発しているため。
abstractHere, we investigate three common techniques for registration of visible light and fluorescence images
Reproduction assets foundThe authors provide a public GUI software tool (mPIR) implementing the paper's FP/PC/INT multimodal plant image registration algorithms, together with example FLU/VIS/NIR plant images from the study, downloadable from their homepage.Code · publica GUI software tool with examples of plant images is provided for direct download from our homepage; Footnote 1 a screen shot is shown in Fig. 10Open asset ↗lines:138-172Dataset · publicExamples of FLU, VIS and NIR plant images are included in our online file repository.Open asset ↗lines:138-172Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Recent advances in high throughput phenotyping have made it possible to collect large datasets following plant growth and development over time, and those in machine learning have made inferring phenotypic plant traits from such datasets possible. However, there remains a dirth of datasets following plant growth under stress conditions along with methods for inferring them using only remotely sensed data, especially under a combination of multiple stress factors such as drought, weeds and nutrient deficiency. Such stress factors and their combinations are commonly encountered during crop production and being able to accurately detect and treat such stress conditions in an automated and timely manner can provide a major boost to farm yields with minimal resource input. We present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data following sugarbeet crop growth under optimal, drought, low and surplus nitrogen fertilization, and weed stress conditions, along with a machine learning based methodology for systematically inferring these stress conditions from the remotely measured data. The dataset contains biweekly color images, infra-red stereo image pairs and hyperspectral camera images along with applied treatment parameters and environmental factors like temperature and humidity, collected over two months. We present a plant agnostic methodology for deriving plant trait indicators such as canopy cover, height, hyperspectral reflectance and vegetation indices along with a spectral 3D reconstruction of the plants from the raw data to serve as a benchmark. Additionally, we provide fresh and dry weight measurements for both the above (canopy) and below (beet) ground biomass at the end of the growing period to serve as indicators of expected yield. We further describe a data driven, machine learning based method to infer water, Nitrogen and weed stress using the derived plant trait indicators. We use the plant trait indicators to evaluate 8 different classification approaches from which the best classifier achieved a mean cross validation accuracy of $$\approx$$ 93, 76 and 83% for drought, nitrogen and weed stress severity classification respectively. We also show that our multi-modal approach significantly improves classifier performance over using any single modality. The presented framework and dataset can serve as a valuable reference for creating and comparing processing pipelines which extract plant trait indicators and infer prevalent stress factors from remote sensing data under a variety of environments and cropping conditions. These techniques can then be deployed on farm machinery or robots enabling automated, precise and timely corrective interventions for maximising yield.
Why it matches plant phenotyping methods植物ストレス表現型を推定するデータセット、マルチモーダル画像・分光計測、形質抽出、機械学習推定を一体化した汎用フレームワークであり、表現型取得・解析手法が研究の中心である。
abstractWe present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data
Reproduction assets foundThe paper releases its own plant stress phenotyping dataset (RGB, stereo IR, hyperspectral imagery, reference measurements) and accompanying pre-processing/classification software, both publicly available at author-provided URLs.Dataset · publicThe images and reference data that support the findings of this study are available from ETH Zürich ASL Datasets Repository, “ https://projects.asl.ethz.ch/datasets/doku.php?id=2018plantstressphenotyping ”.Open asset ↗ETH Zürich ASL Datasets Repository · 2018plantstressphenotypinglines:367-481Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana . Results A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. Conclusions HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.
Why it matches plant phenotyping methods植物の表現型取得を目的とする低コスト・オープンソースのハイパースペクトル画像システムを開発し、植物ストレス実験で信頼性と精度を検証しており、手法が研究の中心である。
abstractwe have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner.
Reproduction assets foundThe paper's supporting datasets (growth-environment and hyperspectral reflectance data from the Arabidopsis drought/salt stress experiments) are publicly available in the authors' Cyverse repository; the authors' Ardupy control/analysis software is public on GitHub and archived on Zenodo; and the 3D model files are on Dataset · publicThe datasets supporting the conclusions of this article are available in the Cyverse repository ( https://de.cyverse.org/de/?type=data&folder=/iplant/home/elytas/experiment_repository ).Open asset ↗Cyverselines:379-449Code · publicThese tools, named Ardupy, have been made publicly available on the University of Wisconsin EnSpec organization’s Github page ( https://github.com/EnSpec/Plant_CNC_Controller ) as well as on Zenodo ( https://doi.org/10.5281/zenodo.1406721 )Open asset ↗GitHub · EnSpec/Plant_CNC_Controllerlines:138-145Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus . The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance. The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation. The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.
Why it matches plant phenotyping methods植物向けマルチスペクトル撮像システムと画像解析法を開発・実証し、病徴前の病害検出、重症度・分布、植物構造を定量化しているため、フェノタイピング手法が中心である。
abstractThis study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases
Reproduction assets foundThe paper's Availability of data and materials statement deposits the raw MSI and photometric stereo datasets from both trials (canopy and detached leaf assays) on Mendeley Data, a public repository with a DOI, directly reproducing this paper's phenotyping measurements.Dataset · publicThe MSI and PS datasets for both trials undertaken are available in RAW format, compatible with all ENVI enabled software packages, from Mendeley Data ( https://doi.org/10.17632/ydmtggnzbw.1 ).Open asset ↗Mendeley Data · 10.17632/ydmtggnzbw.1lines:358-484Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background The development of leaf area is one of the fundamental variables to quantify plant growth and physiological function and is therefore widely used to characterize genotypes and their interaction with the environment. To date, analysis of leaf area often requires elaborate and destructive measurements or imaging-based methods accompanied by automation that may result in costly solutions. Consequently in recent years there is an increasing trend towards simple and affordable sensor solutions and methodologies. A major focus is currently on harnessing the potential of applications developed for smartphones that provide access to analysis tools to a wide user basis. However, most existing applications entail significant manual effort during data acquisition and analysis. Results With the development of Plant Screen Mobile we provide a suitable smartphone solution for estimating digital proxies of leaf area and biomass in various imaging scenarios in the lab, greenhouse and in the field. To distinguish between plant tissue and background the core of the application comprises different classification approaches that can be parametrized by users delivering results on-the-fly. We demonstrate the practical applications of computing projected leaf area based on two case studies with Eragrostis and Musa plants. These studies showed highly significant correlations with destructive measurements of leaf area and biomass from both ground truth measurements and estimations from well-established screening systems. Conclusions We show that a smartphone together with our analysis tool Plant Screen Mobile is a suitable platform for rapid quantification of leaf and shoot development of various plant architectures. Beyond the estimation of projected leaf area the app can also be used to quantify color and shape parameters of other plant material including seeds and flowers.
Why it matches plant phenotyping methodsスマートフォン画像と分類アルゴリズムによる葉面積・バイオマス推定アプリを開発し、破壊測定および既存システムと検証しており、植物表現型取得が中心である。
abstractWith the development of Plant Screen Mobile we provide a suitable smartphone solution for estimating digital proxies of leaf area and biomass in various imaging scenarios in the lab, greenhouse and in the field.
Reproduction assets foundThe authors deposited the plant image data and corresponding ground truth measurements from the banana and Eragrostis case studies in the e!DAL research data publication system (DOI 10.25622/FZJ/2018/1), a paper-specific public asset. The project homepage (fz-juelich.de/ibg/ibg-2/psm) hosts the app and manual but is a Dataset · publichave no competing interests.
Availability of data and materials
The app is accompanied by a detailed manual and checkerboard images for calibration, which can also be downloaded from the project homepage. The datasets generated and/or analyzed during the current study are available in the e!DAL research data publication system, http://dx.doi.org/10.25622/FZJ/2018/1 [ 30 ].
Availability and requirements
Project name: Plant Screen Mobile. Project home page: https://fz-juelich.de/ibg/ibg-2/psm . Operating system(s): Android OS 4.0 (Ice Cream Sandwich) or higher. Programming language: Java. Other requirements: OpenCV manager (will be installed during Plant Screen Mobile setup). License: GNU GOpen asset ↗e!DAL research data publication system · 10.25622/FZJ/2018/1lines:127-198Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .
Why it matches plant phenotyping methods種子形態と放出粘液を画像から同時測定する高スループット手法およびImageJツールを開発・適用しており、植物表現型取得が研究の中心である。
abstractHere, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters.
Reproduction assets foundThe paper's MuSeeQ ImageJ macro (the authors' phenotyping analysis code) is explicitly stated to be freely available, with video tutorials, at the authors' dedicated public website http://MuSeeQ.free.fr, which appears in the allowed URLs.Code · publicThe script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .Open asset ↗MuSeeQ.free.frlines:1-73Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Driven by a huge improvement in automation, unmanned areal systems (UAS) are increasingly used for field observations and high-throughput phenotyping. Today, the bottleneck does not lie in the ability to fly a drone anymore, but rather in the appropriate flight planning to capture images with sufficient quality. Proper flight preparation for photography with digital frame cameras should include relevant concepts such as view, sharpness and exposure calculations. Additionally, if mapping areas with UASs, one has to consider concepts related to ground control points (GCPs), viewing geometry and way-point flights. Unfortunately, non of the available flight planning tools covers all these aspects. We give an overview of concepts related to flight preparation, present the newly developed open source software PhenoFly Planning Tool, and evaluate other recent flight planning tools. We find that current flight planning and mapping tools strongly focus on vendor-specific solutions and mostly ignore basic photographic properties—our comparison shows, for example, that only two out of thirteen evaluated tools consider motion blur restrictions, and none of them depth of field limits. In contrast, PhenoFly Planning Tool enhances recent sophisticated UAS and autopilot systems with an optical remote sensing workflow that respects photographic concepts. The tool can assist in selecting the right equipment for your needs, experimenting with different flight settings to test the performance of the resulting imagery, preparing the field and GCP setup, and generating a flight path that can be exported as waypoints to be uploaded to an UAS. By considering the introduced concepts, uncertainty in UAS-based remote sensing and high-throughput phenotyping may be considerably reduced. The presented software PhenoFly Planning Tool ( https://shiny.usys.ethz.ch/PhenoFlyPlanningTool ) helps users to comprehend and apply these concepts.
Why it matches plant phenotyping methods植物表現型取得に用いるUAS画像撮影の計画・品質確保を中心に扱い、ソフトウェア開発と既存ツール比較評価を行っているため含める。
abstractWe give an overview of concepts related to flight preparation, present the newly developed open source software PhenoFly Planning Tool, and evaluate other recent flight planning tools.
Reproduction assets foundThe paper's authors publicly released the PhenoFly Planning Tool source code (GitLab repository, archived version DOI 10.5905/ethz-b-000269865... actually 10.5905/ethz-1007-136) and two sample UAS phenotyping datasets (Experiments 1-3) deposited in the ETH Zurich research collection. These are paper-specific, publicly,Code · publicta repository ( https://www.research-collection.ethz.ch ):
Experiment 1 and 2: 10.3929/ethz-b-000269865 [ 65 ]
Experiment 3: 10.3929/ethz-b-000269882 [ 66 ].
The characteristics of the presented software are:
Project name: PhenoFly Planning Tool
Project home page: http://phenofly.net/PhenoFlyPlanningTool
Development repository: https://gitlab.ethz.ch/crop_phenotyping/PhenoFlyPlanningTool
Archived version: 10.5905/ethz-1007-136 [ 67 ]
Operating system(s): platform independent
Programming language: R
License: GNU General Public License, version 3 (GPL-3.0).
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Funding
This Project received funding fOpen asset ↗gitlab.ethz.ch/crop_phenotyping/PhenoFlyPlanningToollines:2169-2239Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. Methods In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. Conclusion The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.
Why it matches plant phenotyping methods植物の時系列画像から成長・動態という表現型を抽出するCNN-LSTM手法と、ベンチマーク用データセットの開発が中心であるため。
abstractwe propose a CNN-LSTM framework for plant classification of various genotypes.
Reproduction assets foundThe paper's authors publicly released their Arabidopsis time-series image dataset used for the CNN-LSTM accession classification, with explicit availability statements and two public URLs (figshare and ANU PhenoCam).Dataset · publicData is publicly available at https://figshare.com/s/e18a978267675059578f or http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zipOpen asset ↗figsharelines:403-478Dataset · publicData is publicly available at https://figshare.com/s/e18a978267675059578f or http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zipOpen asset ↗lines:403-478Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera. We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H 2 < 0.93), as well as estimated fruit number ( H 2 = 0.68). In addition, we validated the method for estimating fruit number with rev5 , a mutant with increased flower abortion. The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.
Why it matches plant phenotyping methods画像から植物乾物重・果実数・成長動態を推定する手法を開発し、交差検証と変異体で検証しており、表現型取得・推定法が研究の中心である。
abstractHere, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.
Reproduction assets foundThe paper's availability statement explicitly links authors' code on GitHub and the phenotypic dataset on Dryad; supplementary files also contain the ImageJ macros and R code used for the phenotyping analysis.Code · publicCodes are available on Github ( https://github.com/fvasseur ), and phenotypic data are available on the Dryad repository ( https://doi.org/10.5061/dryad.343bd84 ) [ 43 ].Open asset ↗github.com/fvasseurlines:312-394Dataset · publicphenotypic data are available on the Dryad repository ( https://doi.org/10.5061/dryad.343bd84 ) [ 43 ].Open asset ↗Dryad · 10.5061/dryad.343bd84lines:312-394Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1-23, 2017a. 10.1186/s13007-016-0154-2; Plant Methods 13(62):1-29, 2017b. 10.1186/s13007-017-0212-4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. RESULTS: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. CONCLUSIONS: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves.
Why it matches plant phenotyping methods高速表現型データの解析に用いるベイズ関数回帰を体系的に検討し、モデル比較、実装時間評価、GFRパッケージによる実装方法を提示しており、表現型解析手法が中心である。
abstractThe purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image 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 · publicThe phenotypic, genotypic, HTP data and other materials used in this study can be downloaded from the link: https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm . The data used in this study can also be downloaded from the Additional file with the Genomic Functional Regression (GFR) R Package.Open asset ↗lines:236-324Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. RESULTS: A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. CONCLUSION: The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.
Why it matches plant phenotyping methods画像系列から葉・茎を自動検出し、葉数・葉長などの表現型を抽出する手法の開発が中心で、ベンチマークデータセットも提供しているため。
abstractThe central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD.
Reproduction assets foundThe paper releases UNL-CPPD, a maize component plant phenotyping dataset with original images, ground truth, and annotated images, publicly downloadable from the authors' site. The paper's holistic phenotyping analysis also uses Panicoid Phenomap-1, available from the same authors' site. No author analysis code is madeDataset · publicmanuscript.
Acknowledgements
Authors appreciate Dipal Bhandari for his contributions in preparing the ground truth of the dataset.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
UNL-CPPD public dataset is released with the paper and may be freely downloaded from http://plantvision.unl.edu/dataset . The dataset contains original images, ground truth and annotated images.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Funding
The authors would like to thank the Agricultural Research Division in the Institute of Agriculture and Natural Resources of the UNL, USA, for proviOpen asset ↗plantvision.unl.edu · UNL-CPPDlines:1781-1890Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The formation of crossovers during meiosis is pivotal for the redistribution of traits among the progeny of sexually reproducing organisms. In plants the molecular mechanisms underlying the formation of crossovers have been well established, but relatively little is known about the factors that determine the exact location and the frequency of crossover events in the genome. In the model plant species Arabidopsis , research on these factors has been greatly facilitated by reporter lines containing linked fluorescence marker genes under control of promoters active in seeds or pollen, allowing for the visualization of crossover events by fluorescence microscopy. However, the usefulness of these reporter lines to screen for novel modulators of crossover frequency in a high throughput manner relies on the availability of programs that can accurately count fluorescent seeds. Such a program was previously not available in scientific literature. Results Here we present MeioSeed, a novel CellProfiler-based program that accurately counts GFP and RFP fluorescent Arabidopsis seeds with adjustable detection thresholds for fluorescence intensity, making use of a robust seed classifier which was trained by machine learning in Ilastik. Using the previously published reporter line Col3-4/20 as an example, we explain the use of MeioSeed and the steps taken to optimize the thresholding settings of the program to fit the published model for recombination frequency and transgene segregation. The use of MeioSeed is illustrated by investigating salt stress as a novel abiotic trigger for changes in crossover frequency in Col3-4/20 (♂) × Ler-0 (♀) F 1 hybrids. Salt stress was found to trigger increases in crossover frequency between the marker genes of up to 70% compared to the control treatment without salt stress. Genotyping of control and salt treated populations revealed that the changes in crossover frequency were not limited to the region between the marker genes, but that fluctuations in crossover frequency are likely to occur genome-wide after treatment with high salt concentrations. Conclusions MeioSeed allows for the high throughput recognition and counting of fluorescent Arabidopsis seeds and can facilitate the screening for novel abiotic and biotic modulators of crossover frequency using reporter lines in Arabidopsis .
Why it matches plant phenotyping methods蛍光種子を機械学習・画像解析で高スループットに認識・計数し、交差頻度という植物の遺伝的状態を推定するソフトウェア開発が中心であるため。
abstractHere we present MeioSeed, a novel CellProfiler-based program that accurately counts GFP and RFP fluorescent Arabidopsis seeds with adjustable detection thresholds for fluorescence intensity, making use of a robust seed classifier which was trained by machine learning in Ilastik.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe MeioSeed package is available at http://cellprofiler.org/examples/published_pipelines .Open asset ↗lines:44-51Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 10 Sept 2026
BACKGROUND: Image-based plant phenotyping has become a powerful tool in unravelling genotype-environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a 'gold-standard' and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists. Here we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count. RESULTS: to measure intra- and inter-observer variability in a controlled study using specially designed annotation tools but also citizens using a distributed citizen-powered web-based platform. In the controlled study observers counted leaves by looking at top-view images, which were taken with low and high resolution optics. We assessed whether the utilization of tools specifically designed for this task can help to reduce such variability. We found that the presence of tools helps to reduce intra-observer variability, and that although intra- and inter-observer variability is present it does not have any effect on longitudinal leaf count trend statistical assessments. We compared the variability of citizen provided annotations (from the web-based platform) and found that plain citizens can provide statistically accurate leaf counts. We also compared a recent machine-learning based leaf counting algorithm and found that while close in performance it is still not within inter-observer variability. CONCLUSIONS: While expertise of the observer plays a role, if sufficient statistical power is present, a collection of non-experienced users and even citizens can be included in image-based phenotyping annotation tasks as long they are suitably designed. We hope with these findings that we can re-evaluate the expectations that we have from automated algorithms: as long as they perform within observer variability they can be considered a suitable alternative. In addition, we hope to invigorate an interest in introducing suitably designed tasks on citizen powered platforms not only to obtain useful information (for research) but to help engage the public in this societal important problem.
Why it matches plant phenotyping methods画像ベース植物フェノタイピングにおける葉数アノテーションの観察者間・内変動を、専用ツール、市民参加型基盤、機械学習アルゴリズムと比較検証しており、測定手法の技術的妥当性評価が中心である。
abstractHere we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count.
Reproduction assets foundThe article states that the Arabidopsis image dataset used for the leaf-counting observer-variability study is publicly available at the plant-phenotyping.org datasets page, and the citizen-science annotations were collected via the authors' public Zooniverse 'Leaf Targeting' project. No author analysis code or trainedDataset · publicAvailability of data and materials
The image dataset used in this article is available at http://www.plant-phenotyping.org/datasets .Open asset ↗plant-phenotyping.orglines:307-379Dataset · publicThe A data (RPi) were included as part of a larger citizen-powered study (“Leaf Targeting”, available at https://www.zooniverse.org/projects/venchen/leaf-targeting ) built on ZooniverseOpen asset ↗Zooniverse · venchen/leaf-targetinglines:132-149Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 10 Sept 2026
Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.
Why it matches plant phenotyping methods合成植物画像によるデータセット拡張と深層学習を用いたロゼット植物の葉数推定手法が中心であり、植物表現型取得・推定の技術開発に該当する。
abstractDeep learning presents many opportunities for image-based plant phenotyping.
Reproduction assets foundThe paper publicly releases its generated/analysed leaf-counting datasets (including synthetic rosette images and real Ara2012/Ara2013-Canon subsets) via a figshare deposit with an explicit availability statement, and it uses the public IPPN PRL dataset as its real-plant phenotyping input. The L-system model code is inDataset · publicThe datasets generated and analysed during the current study are available for download at the following url: https://figshare.com/articles/SATLC-28-09-17_zip/5450080 .Open asset ↗figshare · SATLC-28-09-17_zip/5450080lines:221-323Dataset · publicwe use a publicly available plant phenotyping dataset from the International Plant Phenotyping Network (IPPN), Footnote 1 referred to by its authors as the PRL datasetOpen asset ↗International Plant Phenotyping Network (IPPN)lines:75-84Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Seed / grainClassificationGrowth / development / phenology
Background Miscanthus is a leading second generation bio-energy crop. It is mostly rhizome propagated; however, the increasing use of seed is resulting in a greater need to investigate germination. Miscanthus seed are small, germination is often poor and carried out without sterilisation; therefore, automated methods applied to germination detection must be able to cope with, for example, thresholding of small objects, low germination frequency and the presence or absence of mould. Results Machine learning using k -NN improved the scoring of different phenotypes encountered in Miscanthus seed. The k -NN-based algorithm was effective in scoring the germination of seed images when compared with human scores of the same images. The trueness of the k -NN result was 0.69-0.7, as measured using the area under a ROC curve. When the k -NN classifier was tested on an optimised image subset of seed an area under the ROC curve of 0.89 was achieved. The method compared favourably to an established technique. Conclusions With non-ideal seed images that included mould and broken seed the k -NN classifier was less consistent with human assessments. The most accurate assessment of germination with which to train classifiers is difficult to determine but the k -NN classifier provided an impartial consistent measurement of this important trait. It was more reproducible than the existing human scoring methods and was demonstrated to give a high degree of trueness to the human score.
Why it matches plant phenotyping methods画像から種子発芽形質を自動抽出するk-NN手法を開発・検証しており、発芽判定が研究の中心です。
abstractMachine learning using k -NN improved the scoring of different phenotypes encountered in Miscanthus seed.
Reproduction assets foundThe paper's Miscanthus sinensis seed germination image dataset (the ~5000 seeds / 16,896 seed images used for k-NN phenotyping) is publicly deposited on OSF, with explicit availability language and URL in the article.Dataset · publicAvailability of data and materials.
The dataset analysed in this study is available at https://osf.io/aud9n/ [ 31 ].Open asset ↗osf.io · aud9nlines:144-226Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Results Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way. Conclusions Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.
Why it matches plant phenotyping methods植物の画像から複数の成長形質を自動抽出するソフトウェアの開発・評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractwe present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application
Reproduction assets foundThe authors explicitly state that the Leaf-GP software package, source code, executables, the 4.3 GB raw image datasets, processed images, and phenotypic CSV trait measurements for the Arabidopsis and wheat case studies are freely available at the public GitHub releases URL.Dataset · publicAll the 4.3 GB image datasets as well as The Leaf-GP software package and source code are freely available from our online repository https://github.com/Crop-Phenomics-Group/Leaf-GP/releasesOpen asset ↗Crop-Phenomics-Group/Leaf-GPlines:367-436Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field's complex background, rice panicle segmentation in the field is a very large challenge. Results In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online. Conclusions In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.
Why it matches plant phenotyping methodsイネ穂の画像ベース表現型解析に用いるセグメンテーション手法を開発・比較検証し、ソフトウェアとデータセットも提供しているため、方法が中心的である。
abstractrice panicle segmentation is the first and key step for image-based panicle phenotyping
Reproduction assets foundThe paper's 48 testing rice panicle images and the Panicle-SEG segmentation software (CPU/GPU installers) are explicitly stated to be publicly available online at the authors' Huazhong Agricultural University plant phenomics site, with access credentials provided in the article.Dataset · publicThe whole testing images and the Panicle-SEG installation file in CPU/GPU mode are available online at: http://plantphenomics.hzau.edu.cn/checkiflogin_en.action (username: UserPP; password: 20170108pp)Open asset ↗plantphenomics.hzau.edu.cnlines:45-48Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
BarleyLaboratory / benchtopMRI / PETRootMorphology / geometry measurementRoot system architecture
Background Root systems are highly plastic and adapt according to their soil environment. Studying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates. Non-invasive 3D root images in soil can be obtained using magnetic resonance imaging (MRI). Not all substrates, however, are suitable for MRI. Using barley as a model plant we investigated the achievable image quality and the suitability for root phenotyping of six commercially available natural soil substrates of commonly occurring soil textures. The results are compared with two artificially composed substrates previously documented for MRI root imaging. Results In five out of the eight tested substrates, barley lateral roots with diameters below 300 µm could still be resolved. In two other soils, only the thicker barley seminal roots were detectable. For these two substrates the minimal detectable root diameter was between 400 and 500 µm. Only one soil did not allow imaging of the roots with MRI. In the artificially composed substrates, soil moisture above 70% of the maximal water holding capacity (WHC max ) impeded root imaging. For the natural soil substrates, soil moisture had no effect on MRI root image quality in the investigated range of 50-80% WHC max . Conclusions Almost all tested natural soil substrates allowed for root imaging using MRI. Half of these substrates resulted in root images comparable to our current lab standard substrate, allowing root detection down to a diameter of 300 µm. These soils were used as supplied by the vendor and, in particular, removal of ferromagnetic particles was not necessary. With the characterization of different soils, investigations such as trait stability across substrates are now possible using noninvasive MRI.
Why it matches plant phenotyping methodsMRIによる土壌中の根の非侵襲的画像化について、異なる土壌基質への適用性と画像品質を評価し、根径の検出性能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。
abstractStudying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates.
Reproduction assets foundThe authors state that the 3D MRI root images and excavated root images from this study are publicly available under a DOI (IPK repository), directly reproducing the paper's root phenotyping measurements.Dataset · public3D root images and excavated root images are available at: http://dx.doi.org/10.5447/IPK/2017/10 .Open asset ↗IPK · 10.5447/IPK/2017/10lines:196-230Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Background In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. Results We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. Conclusions The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.
Why it matches plant phenotyping methodsトウモロコシの穂と絹糸の成長形質を高スループットに取得する画像・ロボティクス・機械学習パイプラインを開発し、精度検証と遺伝子型・水分欠 deficitへの適用を行っているため、植物表現型計測法が中心である。
abstractWe have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks.
Reproduction assets foundThe paper's ear/silk phenotyping pipeline code (eartrack) is publicly available on GitHub with documentation, and the authors deposited subsets of the whole-plant images and ear images with the Ilastik project/outputs on Zenodo. All are paper-specific, public, and actionable.Code · publicalie Luchaire, Benoît Suard, Thomas Laisné, Luciana Galizia, Alexandra Manset-Sarcos, Awaz Mohamed and Adel Meziane for their help in conducting the experiment.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The source code and examples are available on Github ( https://github.com/openalea/eartrack ) under an Open Source license (CeCILL-C). It has been integrated as a reusable package in the OpenAlea platform [ 54 , 55 ]. User and developer documentation is also available at http://eartrack.readthedocs.io . A subset of whole plant images is available at https://zenodo.org/record/1002675 and a subset of ear images, IlastikOpen asset ↗openalea/eartracklines:170-209Code · publico competing interests.
Availability of data and materials
The source code and examples are available on Github ( https://github.com/openalea/eartrack ) under an Open Source license (CeCILL-C). It has been integrated as a reusable package in the OpenAlea platform [ 54 , 55 ]. User and developer documentation is also available at http://eartrack.readthedocs.io . A subset of whole plant images is available at https://zenodo.org/record/1002675 and a subset of ear images, Ilastik project and outputs are available at https://zenodo.org/record/1002173 . It requires Python 2.7 and OpenCV libraries.
Consent for publication
All the authors have approved the manuscript and have made all requiOpen asset ↗lines:170-209Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Plant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition. Several procedures based on image analysis were developed to monitor leaf growth as a major phenotyping target. However, in most proposals, a time-consuming parameterization of the analysis pipeline is required to handle variable conditions between images, particularly in the field due to unstable light and interferences with soil surface or weeds. To cope with these difficulties, we developed a low-cost, 2D imaging method, hereafter called PYM. The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area. Results The new PYM device proved to be efficient and accurate for screening leaf area of various species in wide ranges of environments. In the most challenging conditions that we tested, error on plant leaf area was reduced to 5% using PYM compared to 100% when using a recently published method. A high-throughput phenotyping cart, holding 6 chained PYM devices, was designed to capture up to 2000 pictures of field-grown lettuce plants in less than 2 h. Automated analysis of image stacks of individual plants over their growth cycles revealed unexpected differences in leaf expansion rate between lettuces rows depending on their position below or between the photovoltaic panels. Conclusions The imaging device described here has several benefits, such as affordability, low cost, reliability and flexibility for online analysis and storage. It should be easily appropriated and customized to meet the needs of various users.
Why it matches plant phenotyping methods植物の葉面積を画像から抽出する低コスト手法と装置を開発し、精度検証および圃場でのハイスループット適用まで行っており、フェノタイピング手法が研究の中心である。
abstractwe developed a low-cost, 2D imaging method, hereafter called PYM.
Reproduction assets foundThe authors explicitly state that the original pictures and PYM analysis code are publicly available in a GitHub repository. The lettuce agrivoltaic phenotype datasets are not public (confidentiality agreement; available on request), so only the code qualifies as a public paper-specific asset.Code · publicThe original pictures and code for PYM method used in the current study are available in the repository https://github.com/bevalle/pym .Open asset ↗bevalle/pymlines:174-250Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. Results This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting ( MTC ) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting performance, with a mean absolute error of 6.6 and a mean squared error of 9.6 averaged over 8 test sequences. Conclusions TasselNet can achieve robust in-field counting of maize tassels with a relatively high degree of accuracy. Our experimental evaluations also suggest several good practices for practitioners working on maize-tassel-like counting problems. It is worth noting that, though the counting errors have been greatly reduced by TasselNet, in-field counting of maize tassels remains an open and unsolved problem.
Why it matches plant phenotyping methodsトウモロコシ雄穂数という植物形態形質を、圃場画像から推定する深層学習手法を開発・評価し、データセットも作成しているため、フェノタイピング手法が中心である。
abstractIn the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype.
Reproduction assets foundThe paper's MTC dataset (361 field images with manually-labelled dotted tassel annotations) is explicitly stated to be publicly released online at the authors' site.Dataset · publicThe MTC dataset and other supporting materials are available online at: https://sites.google.com/site/poppinace/ .Open asset ↗lines:1796-1869Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. Results In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Conclusions Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.
Why it matches plant phenotyping methodsX線マイクロCT画像からコムギの穂・粒形態形質を自動抽出・測定する画像解析パイプラインの開発が研究の中心であり、実データへの適用も行っている。
abstractwe describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT).
Reproduction assets foundThe paper's μCT wheat grain phenotyping pipeline is publicly available: author analysis code (microCT_grain_analyser, ISQ-Reader on GitHub) and the reconstructed 3D volumes/segmented images and trait datasets in the Aberystwyth University research data catalogue.Code · publicAll the source code as well as user instructions are available from https://github.com/NPPC-UK/microCT_grain_analyser .Open asset ↗NPPC-UK/microCT_grain_analyserlines:49-62Dataset · publicAll reconstructed 3D volumes and segmented images can be accessed at https://www.aber.ac.uk/en/research/data-catalogue/a11df174-d73d-4443-a7fd-ab5b7039df79/ [ 30 ].Open asset ↗lines:49-62Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount of each tissue fraction. There is therefore a need for high-throughput phenotyping systems that quantify the histology of plant sections. Results We developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners. The procedure results in an automated segmentation of the input images into distinct tissue regions. The size and the fraction area of each tissue region can be quantified, as well as the average coloration within each region. The measured features can discriminate contrasted genotypes and identify changes in histology induced by environmental factors such as water deficit. Conclusions The simplicity and the availability of the software will facilitate the elucidation of the relationships between the chemical composition of the tissues and changes in plant histology. The tool is expected to be useful for the study of large genetic populations, and to better understand the impact of environmental factors on plant histology.
Why it matches plant phenotyping methodsトウモロコシ茎切片の組織形態を画像処理で自動分割・定量する手法を開発しており、植物表現型の取得・抽出が研究の中心である。
abstractWe developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners.
Reproduction assets foundThe paper's image segmentation/quantification workflow is publicly released as an ImageJ/Fiji plugin (QuantifFasga) on GitHub, and the authors' in-house Matlab statistical analysis library (MatStats) is also publicly available on GitHub. The phenotype measurement data themselves are only available upon request.Code · publicThe code for the segmentation of tissue regions and the quantification of histology is freely available on the Internet through the GitHub platform at http://github.com/ijpb/fasga-quantif/releases (last accessed: August 8, 2017).Open asset ↗ijpb/fasga-quantiflines:651-651Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential. Results We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper. Conclusions Our highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.
Why it matches plant phenotyping methods小麦の穂数・穂面積を画像から抽出するニューラルネットワーク手法を開発・評価しており、植物表現型取得が研究の中心です。
abstractWe have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe Matlab programs and sample data are available from https://sourceforge.net/projects/spike-detection .Open asset ↗spike-detectionlines:189-225Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. Results In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. Conclusions We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.
Why it matches plant phenotyping methodsハイパースペクトル画像から小麦の穀粒収量を推定するベイズ機能回帰モデルを開発・比較しており、植物形質の取得・推定手法が研究の中心である。
abstractwe propose Bayesian functional regression models that take into account all available bands
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 phenotypic, genotypic, HTP data and other materials used in this study can be downloaded from the link: https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm .Open asset ↗lines:3867-3999Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
RootMorphology / geometry measurementCalibration / preprocessingRoot system architecture
Background There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Results Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Conclusions Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.
Why it matches plant phenotyping methods根系表現型取得パイプラインを開発し、カスタムソフトウェア、画像からの形質抽出、処理時間と精度のベンチマークを中心に評価しているため、方法論が中心である。
abstractHere we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.
Reproduction assets foundThe paper's ArchiPhen software, analysis scripts, and supporting root phenotyping data are publicly available at archiroot.org.uk and mirrored on the authors' GitHub repository (linked to Zenodo).Code · publicSoftware files are also stored on Github repository https://github.com/LionelDupuy/ARCHI_PHEN and linked to Zenodo (DOI: 10.5281/zenodo.399222).Open asset ↗https://github.com/LionelDupuy/ARCHI_PHEN · ARCHI_PHENlines:126-268Code · publicThe source code is freely available at www.archiroot.org.uk .Open asset ↗lines:99-104Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 10 Sept 2026
Background Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose. Results We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences. Conclusions The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.
Why it matches plant phenotyping methods圃場フェノタイピング用に、照明条件の分類、画像セグメンテーション、品質管理を統合した画像解析パイプラインを開発し、コムギ群落被覆率の時系列を定量化しているため、フェノタイピング手法が中心である。
abstractWe proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series.
Reproduction assets foundThe paper's segmentation reference/original wheat images are publicly deposited on figshare, and the image analysis pipeline code is publicly available on GitHub, both with explicit availability statements and URLs.Dataset · publicAll of the segmentation reference images and the corresponding original images used in this study are publicly available in the ‘figshare’ repository, https://dx.doi.org/10.6084/m9.figshare.4176573 (see [ 30 ]), which can be used for evaluation of image segmentation methods.Open asset ↗figshare · 10.6084/m9.figshare.4176573lines:117-181Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Background Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. Results Here we present rosettR , a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. Conclusions Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments.
Why it matches plant phenotyping methods植物ロゼット面積と成長率を画像から高スループットに測定するプロトコルおよびRソフトウェアの開発が研究の中心であり、再現性・精度も実証している。
abstractHere we present rosettR , a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe rosettR R-package along with example data is available from our github repository http://github.com/hredestig/rosettROpen asset ↗hredestig/rosettRlines:130-159Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
RootMorphology / geometry measurementRoot system architecture
Background Computer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction is not desirable in high throughput approaches. Here, we present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits. To do this we introduce a new set of proxy traits. Results We test the system on a new, automated image capture system, the Microphenotron, which is able to image many 1000s of roots/h. A simple experiment is presented, treating the plants with differing chemical conditions to produce different phenotypes. The automated imaging setup and the new software tool was used to measure proxy traits in each well. A correlation matrix was calculated across automated and manual measures, as a validation. Some particular proxy measures are very highly correlated with the manual measures (e.g. proxy length to manual length, r 2 > 0.9). This suggests that while the automated measures are not directly equivalent to classic manual measures, they can be used to indicate phenotypic differences (hence the term, proxy ). In addition, the raw discriminative power of the new proxy traits was examined. Principal component analysis was calculated across all proxy measures over two phenotypically-different groups of plants. Many of the proxy traits can be used to separate the data in the two conditions. Conclusion The new proxy traits proposed tend to correlate well with equivalent manual measures, where these exist. Additionally, the new measures display strong discriminative power. It is suggested that for particular phenotypic differences, different traits will be relevant, and not all will have meaningful manual equivalent measures. However, approaches such as PCA can be used to interrogate the resulting data to identify differences between datasets. Select images can then be carefully manually inspected if the nature of the precise differences is required. We suggest such flexible measurement approaches are necessary for fully automated, high throughput systems such as the Microphenotron.
Why it matches plant phenotyping methods植物画像から根形質を完全自動抽出するソフトウェアと撮像プラットフォームを開発・検証した研究であり、フェノタイピング手法が中心です。
abstractwe present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe AutoRoot software is open source and available from http://dx.doi.org/10.5281/zenodo.60433 .Open asset ↗zenodo · 10.5281/zenodo.60433lines:250-369Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Background Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling development in the context of a high-throughput chemical screen. We have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored under conditions where the phenotypic effects of large numbers of small molecules can be assessed. Results The 'Microphenotron' platform uses 96-well microtitre plates to deliver chemical treatments to seedlings of Arabidopsis thaliana L. and is based around four components: (a) the 'Phytostrip', a novel seedling growth device that enables chemical treatments to be combined with the automated capture of images of developing roots and shoots; (b) an illuminated robotic platform that uses a commercially available robotic manipulator to capture images of developing shoots and roots; (c) software to control the sequence of robotic movements and integrate these with the image capture process; (d) purpose-made image analysis software for automated extraction of quantitative phenotypic data. Imaging of each plate (representing 80 separate assays) takes 4 min and can easily be performed daily for time-course studies. As currently configured, the Microphenotron has a capacity of 54 microtitre plates in a growth room footprint of 2.1 m 2 , giving a potential throughput of up to 4320 chemical treatments in a typical 10 days experiment. The Microphenotron has been validated by using it to screen a collection of 800 natural compounds for qualitative effects on root development and to perform a quantitative analysis of the effects of a range of concentrations of nitrate and ammonium on seedling development. Conclusions The Microphenotron is an automated screening platform that for the first time is able to combine large numbers of individual chemical treatments with a detailed analysis of whole-seedling development, and particularly root system development. The Microphenotron should provide a powerful new tool for chemical genetics and for wider chemical biology applications, including the development of natural and synthetic chemical products for improved agricultural sustainability.
Why it matches plant phenotyping methodsロボット撮像、画像解析、定量的形質抽出を統合した植物表現型解析プラットフォームの開発・検証が中心である。
abstractWe have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored
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 · publicAutoRoot, the software for automated analysis of the images [ 19 ], is Open Source and can be downloaded from https://zenodo.org/ , and the Phytostrips are available to purchase by contacting the corresponding author.Open asset ↗zenodolines:107-110Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
ArabidopsisLeafGrowth / time-series analysisGrowth / development / phenology
Background The analysis of circadian leaf movement rhythms is a simple yet effective method to study effects of treatments or gene mutations on the circadian clock of plants. Currently, leaf movements are analysed using time lapse photography and subsequent bioinformatics analyses of leaf movements. Programs that are used for this purpose either are able to perform one function (i.e. leaf tip detection or rhythm analysis) or their function is limited to specific computational environments. We developed a leaf movement analysis tool-PALMA-that works in command line and combines image extraction with rhythm analysis using Fast Fourier transformation and non-linear least squares fitting. Results We validated PALMA in both simulated time series and in experiments using the known short period mutant sensitivity to red light reduced 1 ( srr1 - 1 ). We compared PALMA with two established leaf movement analysis tools and found it to perform equally well. Finally, we tested the effect of reduced iron conditions on the leaf movement rhythms of wild type plants. Here, we found that PALMA successfully detected period lengthening under reduced iron conditions. Conclusions PALMA correctly estimated the period of both simulated and real-life leaf movement experiments. As a platform-independent console-program that unites both functions needed for the analysis of circadian leaf movements it is a valid alternative to existing leaf movement analysis tools.
Why it matches plant phenotyping methodsPALMAは、植物の葉運動という表現型を画像から抽出し、周期を解析するソフトウェアとして開発・検証されており、方法論が研究の中心です。
abstractWe developed a leaf movement analysis tool-PALMA-that works in command line and combines image extraction with rhythm analysis using Fast Fourier transformation and non-linear least squares fitting.
Reproduction assets foundThe paper's authors publicly deposited the PALMA leaf movement analysis code (Additional file 2) on SourceForge; the URL matches an allowed entry. No phenotype datasets or images were separately deposited.Code · publicThe PALMA code has been provided as a supplemental file (Additional file 2 ) and can be found on Sourceforge.net ( https://sourceforge.net/projects/palma-leafmov/ ).Open asset ↗Sourceforge.net · palma-leafmovlines:144-160Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. Results This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. Conclusions We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
Why it matches plant phenotyping methodsキャノピーのハイパースペクトル反射を用いて穀粒収量を推定するモデルを提案し、複数の手法および植生指数と予測精度を比較しており、表現型取得・推定手法が中心である。
titlePredicting grain yield using canopy hyperspectral reflectance in wheat breeding 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 · publicThe data and materials used in this study can be downloaded from the link: http://hdl.handle.net/11529/10693 . The links contains file corresponding to the phenotypic and bands data for each environments, Drought.Phe_and_Bands.RData, EarlyHeat.Phe_and_Bands.RData, Irrigated.Phe_and_Bands.RData, Irrigated.Phe_and_Bands.RData.Open asset ↗hdl.handle.net · 11529/10693lines:4711-4843