Fruit quality is a critical determinant of economic returns in pear production, and maintaining an appropriate fruit load (FL) is essential for achieving high yield and quality. As a direct indicator of canopy photosynthetic capacity and assimilate supply, leaf number constitutes the key biological basis of reasonable FL determination under the leaf-to-fruit ratio concept. However, accurate and efficient estimation of leaf number in mature pear trees remains technically challenging, limiting its practical use in precision FL regulation. Here, we propose a data-driven framework for leaf number and reasonable FL estimation by integrating 3D point cloud-derived canopy structure with machine learning. A pipeline for extracting 3D architectural traits was developed and implemented in the software tool FTPCT, enabling rapid and standardized trait acquisition. Through correlation analysis, multicollinearity diagnosis, and variance inflation factor screening, five key traits strongly associated with leaf number were identified and incorporated into five machine learning models optimized using Bayesian optimization. Among them, the optimized random forest regression model achieved the highest and most stable performance, with R 2 of 0.85, RMSE of 239.74, and MAE of 149.26 for test dataset. SHAP analysis identified tree crown volume as the dominant contributor to leaf number estimation. Field validation demonstrated that FL regulation guided by the proposed framework significantly improved fruit weight and size without reducing yield compared with conventional practices. Notably, the proposed approach avoids explicit leaf-level reconstruction and relies on less canopy-scale traits, substantially reducing data requirements and computational cost, and thereby offering strong potential for rapid, field-deployable FL regulation in large-scale orchards.
Why it matches plant phenotyping methods3D点群から樹冠構造形質を抽出し、葉数と適正着果量を推定する手法およびソフトウェアを開発・検証しており、植物表現型取得が中心である。
abstractA pipeline for extracting 3D architectural traits was developed and implemented in the software tool FTPCT, enabling rapid and standardized trait acquisition.
Reproduction assets foundThe paper's phenotyping analysis assets are the authors' publicly released LeafNumPred source code and trained models, and the FTPCT software for 3D trait extraction from pear tree point clouds. Phenotype/point-cloud datasets are only available on request.Code · public. Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
mmc1.docx (1.6MB, docx)
Data availability
Data will be made available on request. Anyone who wants to obtain other public data can contact us at taost@njau.edu.cn. The source codes and models have been made publicly available at https://github.com/Zhang-Fanhang/LeafNumPred, and the FTPCT software has been released at https://github.com/Zhang-Fanhang/FTPCT/tree/Installation-package.
References
1.Tao S., Khanizadeh S., Zhang H., Zhang S. Anatomy, ultrastructure and lignin distribution of stone cells in two Pyrus species. Plant Sci. 2009;176:413–419. [Google Scholar]
2.Zhang F., Wang Q., Yuan K.Open asset ↗Zhang-Fanhang/LeafNumPredhtml-lines:284-315Code · publical variations [34,35]. The method for calculating these traits are shown in the Supplementary information 1.
2.5. Software implementation for 3D trait extraction (FTPCT)
To facilitate efficient and standardized extraction of canopy structural traits from point cloud data, we used a standalone software tool, FTPCT (available at: https://github.com/Zhang-Fanhang/FTPCT/tree/Installation-package), which integrates the trait extraction procedures applied in this study. The software provides a graphical user interface, enabling users to process tree-level point cloud data and extract key 3D structural traits without requiring advanced programming skills.
FTPCT implements a series of predefined proOpen asset ↗Zhang-Fanhang/FTPCThtml-lines:138-149Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Practical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging, and systematic evidence comparing both accuracy and acquisition efficiency under outdoor conditions remains limited. This study presents a field-deployable evaluation framework and implements it in a 2-ha commercial Japanese pear orchard trained under a joint V-trellis system. Using a terrestrial laser scanner (TLS) as the reference, we evaluated two handheld LiDAR systems (a low-cost SLAM-based system and a high-performance system), structure from motion / multi-view stereo (SfM/MVS) reconstructions from three camera platforms (a digital camera, an action camera, and a 360° camera), and 3D Gaussian splatting (3DGS) constructed from action-camera video. Measurements were taken at two spatial scales to capture scale-dependent effects. In the span-scale survey (4 m), location error was derived from TLS-referenced target coordinate differences, and reconstruction error was quantified using cloud-to-mesh distances with cubic targets. In the row-scale survey (one tree row), positional stability during continuous mapping was evaluated as location error. Operational metrics (acquisition time, data volume, and processing effort) were also documented. The results demonstrate clear trade-offs among the methods: LiDAR enables rapid wide-area acquisition but is susceptible to cumulative drift in row-structured environments, whereas SfM/MVS provides superior geometric fidelity at the cost of increased time and data volume. Although 3DGS is less suitable for precise quantitative measurement, it demonstrates strong potential for intuitive visualization of orchard structure and fruit distribution. These findings highlight the need for staged, purpose-specific, and seasonally adaptive strategies for orchard-scale digital twin development.
Why it matches plant phenotyping methods果樹園における複数の3D取得法を比較・評価し、樹体構造や果実分布の定量的取得精度、安定性、運用性を検証しており、植物フェノタイピング手法が中心である。
abstractPractical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging
Low temperature stress severely restricts the cultivation and distribution of pear ( Pyrus L.) germplasms, frequently resulting in frost injury and yield reduction. To accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress. In this study, 122 pear germplasms were classified into high (HR), medium (MR), and low (LR) cold-tolerance categories based on their semi-lethal temperature (LT 50 ). Further analysis of pear germplasms with different levels of cold resistance revealed that, with decreasing temperature, HR germplasms exhibited smaller increases in relative electrolyte conductivity (REC) and malondialdehyde (MDA) content and higher accumulation of proline (Pro), soluble proteins (SP), soluble sugars (SS), and peroxidase activity compared with LR germplasms. In addition, the peak values of these indicators generally occurred at lower temperatures in HR germplasms. A correlation analysis and principal component analysis indicated that physiological indices, including REC, bound water/free water ratio, SS, and MDA, as well as branch anatomical traits related to xylem and cortex proportions, were closely associated with variation in LT 50 . An integrated assessment using membership function analysis produced rankings consistent with LT 50 -based clustering, supporting the reliability of the multivariate evaluation framework. Overall, this study establishes an integrated, indicator-based approach for evaluating cold resistance in pear germplasm by integrating physiological, biochemical, and anatomical characteristics. These results provide a theoretical basis and methodological reference for screening cold resistance germplasms.
Why it matches plant phenotyping methods生理・生化学・解剖学的形質を統合し、LT50と多変量評価によってナシ遺伝資源の耐寒性を分類・スクリーニングする評価フレームワークが研究の中心である。
abstractTo accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress.
Reproduction assets foundThe article's Data Availability statement links a public Zenodo deposit containing the paper's raw phenotyping data (LT50, physiological/biochemical and anatomical measurements for pear germplasms). Supplemental files also contain germplasm characteristics and LT50 comparisons, but the Zenodo raw-data deposit is the明确,Dataset · publicThe data is available at Zenodo: liu186253. (2025). liu186253/Data: raw data (Version V11). Zenodo. https://doi.org/10.5281/zenodo.17524773 .Open asset ↗Zenodo · 10.5281/zenodo.17524773lines:636-710Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Conventional spatial frequency domain imaging (SFDI) based optical property inversion is inefficient, while deep learning methods suffer from heavy reliance on large-scale real datasets. To address this contradiction, a simulation-driven approach for subsurface fruit bruise discrimination was proposed. An SFDI simulation environment was built with Blender to generate 800 paired datasets of diffuse reflectance images and optical transport coefficients, overcoming the high cost and long cycle of real dataset acquisition. We designed the CBAM-GAN-U-Net model and adopted surface profile correction in the prediction method to eliminate curved surface-induced non-planar distortion, with the whole method validated on liquid phantoms, green apples and crown pears. This prediction method achieved high accuracy in predicting the reduced scattering coefficient μ s ', with NMAE of 0.021 ± 0.007 (phantoms), 0.039 ± 0.012 (severely bruised green apples) and 0.044 ± 0.015 (severely bruised crown pears), outperforming U-Net and GANPOP. Based on the predicted μ s ', a discrimination strategy combining coefficient of variation, mean ratio and receiver operating characteristic (ROC) curve analysis was adopted, attaining 100% accuracy for non-bruised/bruised fruit discrimination, with misclassification rates of 6% (green apples) and 8% (crown pears) for mild/severe bruise differentiation. This method enables accurate subsurface fruit bruise detection, providing a reliable technical solution for the fruit and vegetable industry and helping reduce postharvest supply chain losses.
Why it matches plant phenotyping methodsSFDIと深層学習による果実内部の打撲状態・重症度の画像推定手法を開発し、ファントムと果実で検証しており、植物（果実）の状態取得が中心である。
abstracta simulation-driven approach for subsurface fruit bruise discrimination was proposed.
The growing global demand for food production, coupled with the increasing threat of plant diseases, necessitates advanced and automated solutions for crop health monitoring. Among various crops, pome fruits such as apples and pears are widely cultivated yet highly susceptible to multiple diseases that can significantly reduce yield and quality. Existing approaches for disease detection and severity classification are often limited by their dependency on manual inspection and their inability to handle complex real-world imagery, especially when multiple diseases coexist on a single leaf. To address these limitations, this research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves. A fine-tuned MobileNetV2 backbone is employed to extract high-level discriminative features from a specialized pome leaf dataset annotated with multiple disease types and severity levels. The proposed system integrates a lightweight Lite-U-Net for semantic segmentation to isolate diseased regions and an enhanced Lite-YOLACT for instance segmentation using a linear combination of prototype masks and mask coefficients. Moreover, a new multi-disease severity scale is proposed to quantify the impact of multiple coexisting infections on a single leaf, an aspect not addressed in previous studies. To enhance interpretability, an improved Grad-CAM technique generates visual heatmaps highlighting the most influential regions in the model's decision-making process, providing transparency and validation for agricultural experts. Experimental evaluations demonstrate that the proposed framework achieves 95% accuracy in disease severity estimation, effectively identifying and grading multiple infections simultaneously. This study represents a significant step forward in precision agriculture, offering an efficient, interpretable, and scalable deep learning solution for real-world crop health monitoring and management. The source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .
Why it matches plant phenotyping methods果樹葉の病斑領域を画像から分割し、複数病害の重症度を定量推定する深層学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractthis research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves.
Reproduction assets foundThe paper's authors publicly release source code and trained models on GitHub, and the study analyzes two public Kaggle plant-image datasets (DiaMOS Plant and PlantVillage) used directly for the multi-disease severity phenotyping experiments.Code · publicThe source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .Open asset ↗https://github.com/mqasim0787/Multi-Disease-Severity · mqasim0787/Multi-Disease-Severitylines:1-23Dataset · publicThe datasets analyzed during the current study are available publicly in the Kaggle repository, DiaMOS dataset (1) and PlantVillage Dataset (2) 0.1. [https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset]Open asset ↗https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset · diamos-plant-datasetlines:964-977Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
AppleCottonPearField / plotNeRF / 3D Gaussian SplattingFruitCounting2D/3D reconstructionSegmentation
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Why it matches plant phenotyping methodsNeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
Reproduction assets foundThe paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-pDataset · publicthat incorporates crop visibility
and mask consistency, enabling robustness against occlusions and annotation
discrepancies.
•
We release a public infield cotton plant dataset designed for 3D
rendering and cotton boll counting tasks.
The source code, dataset, and multimedia material associated with this project
can be found at
https://robotic-vision-lab.github.io/cropnerf .
II Related Work
II-A Image-Based Techniques
Image-based methods typically employ object detection to identify crops within
images. For example, Chen et al. [ 4 ] utilized multiple
convolutional neural networks (CNNs) to map input images to total fruit counts.
Similarly, Häni et al. [ 5 ] formulated crop counting as a
multOpen asset ↗lines:108-187Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Accurate estimation of canopy geometric and structural characteristics, such as leaf area (LA), is essential for improving resource efficiency in fruit tree crop management. LA is a key biophysical parameter, influencing physiological processes like carbon fixation, evapotranspiration, and light interception, as well as fruit quality and yield. However, its measurement is complex due to the substantial number of leaves and the three-dimensional nature of tree canopies.An alternative approach, the Projected Tree Row Surface (PTRS), has shown a strong correlation with LA and has been recognized by the scientific community. Despite its robustness, the original PTRS method requires time-consuming manual data collection, which limits its practical application in the field.This study introduces a novel automated methodology for calculating the PTRS, validated using high-resolution ground-truth data providing LA values at 0.1-m intervals along the tree rows. When evaluated on almond, pear, and apple trees as well as vineyards, the method achieved remarkably high correlations between PTRS and LA, with coefficients up to r = 0.97 and r = 0.99 at optimal resolutions (0.1 –0.2 m PTRS per 1 m row section). These results demonstrate that the approach delivers consistent and reliable measurements of LA under diverse field conditions, enabling real-time, high-resolution assessment of tree-row canopies.The automated PTRSₙ approach enables fast and efficient LA estimation and can be adapted to any point cloud dataset. It supports flexible resolution to balance accuracy and processing time and can be applied to full rows, individual trees, or canopy segments. This methodology represents a step forward in automating LA assessment and supports the development of real-time applications in precision agriculture.
Why it matches plant phenotyping methods果樹・ブドウ樹冠の葉面積を推定するLiDARベースの自動PTRS手法を開発し、実測LAで検証しており、植物形質取得法が研究の中心である。
abstractThis study introduces a novel automated methodology for calculating the PTRS, validated using high-resolution ground-truth data providing LA values at 0.1-m intervals along the tree rows.
Spatial frequency domain imaging (SFDI) is a non-invasive optical imaging technique widely used for the quantitative determination of fruit tissue optical properties, specifically absorption coefficient (μₐ) and reduced scattering coefficient (μₛ’). However, traditional SFDI methods rely on multiple frequency and phase images, limiting real-time imaging capabilities. To address this issue, we present a novel rapid prediction method based on a two-stage deep neural network architecture, termed FSGOP (Frequency-Spatial Attention UNet and GAN-based two-stage network for optical properties prediction). Compared with conventional three-phase demodulation SFDI, this method reduces the acquisition time by approximately 5/6 and requires only 0.21 s for inference. In the first stage, a UNet network enhanced by Frequency-Spatial Attention (FSA) is employed to effectively decouple the multi-frequency components. In the second stage, a Generative Adversarial Network (GAN) is utilized to predict the optical properties, thereby enabling the simultaneous extraction of μₐ and μₛ’ maps under different frequency conditions from a single multi-frequency mixed fringe image. In experiments on apples, pears, and peaches, the method yielded normalized mean absolute errors of 0.10 (f₁) and 0.09 (f₂) for μₛ’, and 0.07 and 0.06 for μₐ, respectively. The results revealed significant complementary information in the optical property maps at different frequencies, with lower frequencies being more sensitive to subsurface damage and higher frequencies revealing surface texture features more effectively. This method enhances information utilization and real-time performance in multi-frequency imaging, offering a rapid, accurate, and low-cost solution for optical property extraction and quality inspection of agricultural products.
Why it matches plant phenotyping methods果実の光学特性を単一画像から推定する画像・深層学習手法を開発し、取得時間と精度を評価しており、植物器官の状態計測が中心である。
abstractwe present a novel rapid prediction method based on a two-stage deep neural network architecture, termed FSGOP
Accurate and rapid monitoring canopy-scale nitrogen content (CNC) on pear trees is crucial for precise application of nitrogen fertilizer. Unmanned Aerial Vehicle (UAV)-based spectral analysis is becoming a promising solution for fast monitoring plant nutrition. However, complex data collection conditions, e.g., unpredictable local microclimate, in orchards could easily compromise the quality of spectral images, thereby affecting the estimation accuracy of CNC inversion model. This study aimed to enhance the quality of canopy-scale raw spectral images to improve the accuracy of CNC inversion through the fusion of ground-space spectral imagery. Firstly, collected leaf-scale hyperspectral images, i.e., spectral reflectance and color data, were used as reference values to enhance the quality of canopy-scale raw multispectral images through constructing mapping models based on machine learning algorithms. The conversion of spectral reflectance and color data between leaf-scale and canopy-scale were conducted using the 4SAIL model and the CIELAB color space, respectively. Then, according to the accuracy of mapping models from four classic machine learning algorithms, the RF algorithm was the optimal choice for constructing CNC inversion models. Furthermore, 10 Vegetation Indexes (VIs), 6 Color Indexes (CIs), and their combinations were analyzed using fitting models with simulated canopy-scale spectral reflectance and leaf-scale color data. Based on the top three R² and RMSE values in each type of model, CNC inversion models were constructed using single VI, single CI, and combinations of VIs and CIs. Meanwhile, four methods were tested in each inversion model. The experimental results showed that the R² and RMSE values of the models using mapped data were averagely improved 0.066 and 0.006, respectively, compared to those using raw canopy reflectance and color data. Among all the inversion models using the mapped data, the combination 1 (C1) inversion model (7 VIs and 2 CIs) performed the best, with R², RMSE, nRMSE, and MAE values reaching 0.832, 0.155, 8.333%, and 0.152, respectively. Finally, compared to the C1 inversion model, by screening the inversion results from multi model, the R² of CNC inversion model increased 0.089, enhancing to 0.921. Meanwhile, the RMSE, nRMSE, and MAE decreased 0.038, 2.043%, and 0.072. reaching 0.117, 6.290%, and 0.080, respectively. This study effectively improved the accuracy of CNC inversion by the fusion of ground-space spectral imagery and can offer reference for the application of nitrogen fertilizer in pear orchards.
Why it matches plant phenotyping methodsナシ樹冠の窒素含量という植物形質を、地上・空撮スペクトル画像の融合と機械学習で推定する手法の開発・精度検証が研究の中心である。
abstractThis study aimed to enhance the quality of canopy-scale raw spectral images to improve the accuracy of CNC inversion through the fusion of ground-space spectral imagery.
The comprehensive understanding of the dormant pruning patterns in pear trees, along with the accurate identification of shoots suitable for pruning, is essential for implementing automated pruning and fruit production. Due to the complexity of tree architecture, previous descriptions of pruning strategies were qualitative summaries based on experience. In this study, we proposed a high-precision shoot extraction pipeline through point cloud alignment at different times, enabling a quantitative analysis of the pruning patterns. The structural parameters of 126 full bearing period pear trees, encompassing two cultivars and three architectures, were characterized, including the shoot number, single shoot angle and length, as well as shoot length density. The validation results demonstrated that the method attained an R 2 of 0.82, 0.92, and 0.85 for shoot number, single shoot angle and length, respectively, with mean absolute error of 18.72, 6.08°, and 0.13 m. The findings indicate that tree architecture exerts a greater influence on pruning compared to cultivar, particularly in Cuiguan, where significant differences were observed across diverse tree architectures. The characters of the corresponding annual (one-year-old) shoots (AS) and pruned shoots (PS) exhibit similar distribution. The AS, constituted 78.62% of the PS number, and 94.90% of length of AS were pruned, indicating that dormant pruning in full bearing period pear tree primarily targets at the annual shoots, and the pruning of annual shoots is mainly by thinning. This study could help the automatic pruning system make pruning decisions and promotes the development of fine orchard management.
Why it matches plant phenotyping methodsナシ樹のシュート形態を点群アライメントで抽出・定量化する手法を開発し、精度検証まで行っており、植物フェノタイピング手法が中心です。
abstractwe proposed a high-precision shoot extraction pipeline through point cloud alignment at different times, enabling a quantitative analysis of the pruning patterns.
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' source code and point cloud samples at a public GitHub repository, matching an allowed URL.Code · publicThe source code and point clouds samples used in this study are publicly available at: https://github.com/Lixiao-bai/Pear_branch_seg_and_analysis .Open asset ↗Lixiao-bai/Pear_branch_seg_and_analysislines:227-309Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Abstract Purpose Dormant pruning is critical for fruit tree management and maintaining fruit quality. Traditional manual pruning is labor-intensive, driving interest in automated robotic dormant pruning. However, automated robotic dormant pruning meets significant challenges in trunk and branches detection, due to the complexity of the orchard environment and the interlacing of the branches. This paper proposes an automatic method for real-time detection and pruning of pear tree trunks and upright branches using an RGB-D camera. Methods Pear trunk detection was conducted by enhancing the You Only Look Once version 5 Nano (YOLOv5n) model with Squeeze-and-Excitation Networks (SENet) and optimizing the anchor boxes. For branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The length and angle of the branches were calculated from the generated mask images, and the pruning position was determined. A PRUNING_ROS package was developed for real-time orchard applications. Results The evaluation demonstrated 96.7% mean average precision (mAP) for trunk detection and 82.6% mAP for branch segmentation in test dataset. Field test results showed that the mean absolute error (MAE) of trunk distance localization compared to manual measurements was 3.71 cm, with the root mean square error (RMSE) of 3.84 cm, and the frames per second (FPS) of 31.6. The MAE was 2.3 cm (RMSE: 2.6 cm) for pruning points in depth direction and 2.34° (RMSE: 2.71°) for upright branches angle, with the FPS of 36.2. The field pruning experiment showed a pruning success rate of 47.6%. Conclusion This method provides technical support for the operation of fruit tree pruning robots, representing a step toward the full automation of fruit tree management.
Why it matches plant phenotyping methodsRGB-D画像からナシ樹の幹・枝を検出・分割し、枝の長さと角度を算出する手法が中心で、単なる対象位置検出を超えた植物器官形態の計測と技術評価を行っている。
abstractFor branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The length and angle of the branches were calculated from the generated mask images, and the pruning position was determined.
The rapid advancement of technologies such as artificial intelligence (AI), deep learning, and precision agriculture tools is driving the development of efficient, data-driven crop management solutions. These innovations are increasingly critical in modern agriculture, where early and accurate detection of plant diseases plays a vital role in securing crop yields and sustainability. Agronomists, agriculturists, and local farmers continue to face significant economic losses due to delayed diagnosis or misclassification of diseases affecting high-value crops, key contributors to the global market. Failure to identify and manage such diseases in time can severely impact both agricultural productivity and global food supply chains. To achieve the United Nations’ sustainable development goals of zero hunger, climate change, good health, and well-being, early and timely disease detection is critical to ensure increased apple-related production, damage control, and reduced application of inappropriate herbicides that pollute the environment. Despite the availability of various methods for early disease detection and classification, how early signs of green attacks can be identified remains uncertain. Using the Turkey Plant Pests and Diseases (TPPD) dataset with 4,447 images categorized into 15 diverse classes, this research implements ResNet-9 to detect and classify the commonly known pests and diseases of six plants, including Malus pumila, Prunus armeniaca, Prunus padus, Prunus persica L. Batsch., Pyrus communis L., and Juglans regia. A laborious hyperparameter tuning, hyperparameter optimization, and augmentation procedure on the training set was done for some imbalanced dataset classes. Testing results of the proposed model demonstrated accuracy, precision, recall, and F1-score values of 97.4%, 96.4%, 97.09%, And 95.7%, respectively, which is a significant leap in comparison to other existing research. This study further elucidates and enhances the interpretability of the proposed model by making saliency maps available using SHapley Additive exPlanations (SHAP) that efficiently illustrate the rationale behind the model’s prediction capabilities. The study further tested the statistical significance of the model, the Area Under the receiver operating characteristic curve (AUC-ROC), and the confidence interval (CI). Critical observations revealed that the model uses several visual cues for disease detection and classification, including (i) edge contours and shape structures that help define lesion boundaries, (ii) texture and color variations that signal symptom type and severity, and (iii) high-activation regions that indicate areas of strong feature relevance. These cues collectively guide the model in distinguishing between visually similar disease patterns across different plant parts. The application of SHAP saliency maps further enabled interpretation by visually localizing and quantifying the influence of these features on the model’s predictions.
Why it matches plant phenotyping methods植物の病害・害虫状態を画像から分類する深層学習手法を開発・評価しており、病徴の局在化と重症度に関連する視覚特徴も解析しているため、植物フェノタイピング手法が中心である。
abstractthis research implements ResNet-9 to detect and classify the commonly known pests and diseases of six plants
Leaf total phosphorus content (LTP) is a key indicator for assessing fruit nutrition status. As a rapid non-destructive inspection method, Near-infrared spectroscopy technology is susceptible to the influence of changes in plant growth periods and spectral noise on its prediction accuracy. At present, how to synergistically utilize growth period information and Spectral pre - processing methods to optimize the LTP Prediction model remains to be further studied. The study systematically collected Leaf sample and their near-infrared Spectral data during three key growth periods of Korla fragrant pear (fruit-setting period, fruit swelling period, and Maturity period). In the Spectral pre-processing stage, multiple scattering correction, Savitzky-Golay Smooth, First Derivative (FD), Second Derivative (SD) and their combined algorithms were comprehensively applied. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was used for characteristic wavelength selection, and based on this, Growth period specificity BP neural network model and cross-growth period general prediction models were constructed respectively to evaluate the performance of different Modeling strategies. Results The study showed that LTP content exhibited a significant differential distribution across different growing stage. In the characteristic wavelength bands, after processing with Combined pre-processing method (e.g., MSC+ FD), the correlation coefficient between the spectrum and LTP content significantly increased to approximately 0.90. The predictive performance of the Growth-period-specific model was comprehensively superior to that of the general model, with the Validation set coefficient of determination remaining above 0.83. Compared with the general model, the Coefficient of determination (R 2 ) increased by 0.05-0.16, and the root mean square error decreased by 0.0029-0.0079. This study successfully constructed a technical system of "Growth period-Preprocessing-Model". The results indicated that the Modeling strategy considering the characteristics of crop growing stage could significantly improve the predictive ability of near-infrared spectroscopy models. This study provides a reliable technical framework for Precision nutrient management in orchard, and the established methodology can also serve as a reference for nutrient Surveillance of other fruit tree plants.
Why it matches plant phenotyping methods近赤外分光法、前処理、波長選択、ニューラルネットワークを組み合わせ、ナシ葉のリン含量という植物生理形質を非破壊推定する技術体系を構築・検証しており、表現型取得法が研究の中心である。
abstractAs a rapid non-destructive inspection method, Near-infrared spectroscopy technology is susceptible to the influence of changes in plant growth periods and spectral noise on its prediction accuracy.
The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40-44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25-30 °C) from April to July 2023-2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するデータセットが研究の中心であり、植物病害フェノタイピング用の画像データセットとして適格です。
abstractThe PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases.
Reproduction assets foundThe paper is a Data in Brief article describing the PlantCity plant leaf image dataset (10,667 original images, 52 classes, 12 crops, collected in Pakistan). The dataset itself is the paper's core phenotyping asset and is publicly deposited on Mendeley Data with a direct URL provided in the article.Dataset · publicon of diseases, pests, or environmental stress in plant leaves.
Data source location
Charsadda (Village Sarki) chosen for tomato and Chitral (Village Danin) for the other 11 crops, Khyber Pakhtunkhwa, Pakistan
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w8kh2xkspx.2
Direct URL to data: https://data.mendeley.com/datasets/w8kh2xkspx/1
Related research article
None
1
Value of the Data
•
The PlantCity dataset is comprehensive, consisting of 10,667 high-resolution images across 52 classes (41 diseased, 11 healthy) from 12 crop species, collected from Charsadda (tomato disease symptoms) and Danin Chitral (selected for its agro-climatic suitability for fruOpen asset ↗Mendeley Data · 10.17632/w8kh2xkspx.2lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Oct 2025Computers and Electronics in Agriculture.
Variable-rate spraying technology based on plant canopy volume is widely applied during precision agricultural practices. However, field observations indicate that larger canopies typically correlate with healthier trees, exhibiting reduced disease risk and severity. Therefore, the integration of canopy health information into variable-rate pesticide application is imperative. This study developed a variable-rate spraying system that addresses the critical limitations of conventional variable-rate spraying systems by employing a multisensory fusion methodology, which integrates real-time spot detection with plant canopy volumetry. Dual-modality sensing system achieves concurrent canopy volume measurement (4.34% error) and spot detection (82.6% accuracy), enabling adaptive pesticide dosage optimization through integrated decision-making that synergizes disease severity with canopy volumetry. Experimental tests demonstrated a 74.0% reduction in chemical usage compared to conventional spraying, while maintaining the required deposition parameters for low-severity diseases, and a 42.7% reduction for high-severity diseases. When targeting pear rust disease, the system attained 86.53% control efficacy with 66.4% and 22.2% dosage savings over traditional and volume-only variable-rate spraying approaches, respectively. The newly developed variable-rate spraying system can perform as a precise variable-rate spraying system using fused information on fruit-tree canopy volumes and disease, which significantly reduces the use of pesticides without affecting the control effect, further enabling a reduction in pesticide quantity and an increase in efficiency.
Why it matches plant phenotyping methods植物キャノピー体積と病害スポットを同時測定するマルチセンサ計測システムを開発・検証しており、植物の形態・病害状態の取得が散布最適化の中心的技術貢献である。
abstractThis study developed a variable-rate spraying system that addresses the critical limitations of conventional variable-rate spraying systems by employing a multisensory fusion methodology, which integrates real-time spot detection with plant canopy volumetry.
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imaginary. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian Splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear ‘Bartlett,’ which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D Gaussian Splattingを用いて果実の3D再構成と空間的成長形質の非破壊計測パイプラインを開発し、手動記録との精度比較で検証しているため、フェノタイピング手法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics
Reproduction assets foundThe paper's data availability statement deposits a subset of the generated 3DGS fruit reconstruction models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data is request-only. No author analysis code is explicitly deposited.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Sept 2025Computers and Electronics in Agriculture.
This study proposes a real-time fruit volume estimation model based on YOLOv9 (RTFVE-YOLOv9) and binocular stereo vision technology to address the challenges of low automation and insufficient accuracy in fruit volume measurement in complex orchard environments, particularly in scenarios with diverse canopy structures and severe branch-leaf occlusion. The model achieves effective recognition of occluded fruits through the innovative design of a Dual-Scale and Global–Local Sequence (DSGLSeq) module while incorporating a Multi-Head and Multi-Scale Self-Interaction (MHMSI) module to improve the detection performance of small fruit targets. Systematic validation experiments conducted on major economic fruit tree varieties, including apples, pears, pomelos, and kiwifruit, demonstrate that RTFVE-YOLOv9 improved the mean Average Precision (mAP) by 2.1%, 1.6%, 4%, and 3.8% respectively on the four fruit datasets compared to the baseline YOLOv9-c model. The model’s internal working mechanisms were thoroughly revealed through multi-dimensional evaluation, including ablation experiments, Heatmap Analysis, and Effective Receptive Field (ERF) analysis, providing a theoretical foundation for subsequent optimization. The research findings enrich the application theory of computer vision in smart agriculture and provide reliable technical support for achieving precise orchard management.
Why it matches plant phenotyping methods果実の体積という植物器官形質を、YOLOv9と両眼ステレオビジョンで推定する手法を開発・検証しており、画像取得・計算による表現型推定が研究の中心である。
abstractThis study proposes a real-time fruit volume estimation model based on YOLOv9 (RTFVE-YOLOv9) and binocular stereo vision technology
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Summary Orchard fruits such as pear and apple are important for ensuring global food security and agricultural economy as they not only provide essential nutrients, but also support biodiversity and ecosystem services. Breeders, growers and plant researchers constantly study desirable tree morphological features and floral characteristics to ensure fruit production and quality. Still, traditional orchard phenotyping is often laborious, limited in scale and prone‐to‐error, resulting in many attempts to develop reliable and scalable toolkits to address this challenge. Here, we present OrchardQuant‐3D, an analytic pipeline for automating tree‐level analysis of key canopy and floral traits for different types of fruit orchards. We first built a data fusion algorithm to register 3D point clouds collected by both drones (for colour signals) and Light Detection And Ranging (LiDAR, for precise spatial properties), reconstructing high‐quality 3D orchard models at different growth stages. Then, we utilised precise global navigation satellite system signals to position trees in orchards with millimetre‐level accuracy, enabling tree‐level analysis of key canopy (e.g. crown volume and the number or branches) and floral traits (e.g. blossom clusters and volumes) using 3D computer vision, complex graph theory and feature engineering techniques. Equipped with the OrchardQuant‐3D pipeline, we successfully measured varietal differences of four pear cultivars from a small pear orchard in Nanjing China, followed by a scale‐up study that surveyed 3D tree morphologies, key floral and fruit traits from 1104 apple trees in an orchard in East Malling, United Kingdom. To the best of our knowledge, such a multi‐source, comprehensive and expandable methodology has not yet been introduced to this important research domain. Hence, we believe that our work demonstrates a step change in our ability to conduct scalable 3D orchard phenotyping, which is highly valuable to advance orchard breeding, precise tree management and orchard research greatly to sustain fruit tree production in a rapidly changing climate.
Why it matches plant phenotyping methodsドローンとLiDARのデータ融合、3D再構成、コンピュータビジョンによる樹冠・花形質の自動抽出パイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractHere, we present OrchardQuant‐3D, an analytic pipeline for automating tree‐level analysis of key canopy and floral traits for different types of fruit orchards.
Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue, respectively, outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark. The model successfully labeled vascular bundles with a Dice Similarity Coefficient (DSC) of 0.51 in apple tissue and 0.79 in pear tissue, although thin vasculature in apple remained more challenging to segment. The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue. Despite evaluating different methods to enhance segmentation quality, none improved test performance beyond that of the model trained on the standard dataset. The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images, without extensive sample preparation such as contrast labelling. The developed method, if not replaces, drastically accelerates conventional human-in-the-loop analysis of such images.
Why it matches plant phenotyping methods植物組織の3DマイクロCT画像から微細構造を自動セグメンテーションし、形態計測する手法の開発・比較検証が中心であるため。
abstracta 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images.
Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R2) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R2 value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees.
Why it matches plant phenotyping methodsナシ葉のクロロフィル含量という植物形質を、フィールド分光・ウェーブレット変換・PLS回帰で推定する手法の開発と精度評価が研究の中心であるため。
abstractThis study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves.
The pear trees, significant both ecologically and economically, play a crucial role in arid and semi-arid areas such as Xinjiang, which make the study of its growth simulation and water use efficiency vital. Few studies have integrated remote sensing with crop growth models to simulate fruit tree growth and plant water transport at the field level. However, plant growth simulations encounter challenges such as uncertain input parameters and regional variability when they are applied to different regions. To address these challenges, we proposed to assimilate inversion data from satellite remote sensing (Sentinel-1, Sentinel-2, and DEM) into the WOFOST model based on Ensemble Kalman Filter (EnKF) techniques to simulate pear tree growth and evaluate water use efficiency at the field scale. We validated this approach at the regional scale by analyzing leaf area index (LAI), yield, and water use efficiency data from 118 pear orchards across five regions during four key growth periods. The results indicated that the NDVI, NDREI, SAVI, and EVI indices were well correlated with LAI. The RIME-LSSVM algorithm enhanced LAI and soil moisture (SM) inversion models, outperforming traditional regression methods. In the four phenological periods, the R² of LAI inversion model ranged from 0.76 to 0.96, NRMSE ranged from 3% to 6.6%, and SM inversion R² values ranged from 0.27 to 0.41, NRMSE values ranged from 10.9% to 16.6%. The results showed that the joint assimilation of SM and LAI into the calibrated WOFOST model significantly improved the simulation performance of regional yield and water use efficiency compared to univariate assimilation and non-assimilation. Specifically, the yield estimation R² increased from 0.37 to 0.58, and the NRMSE decreased from 8.2% to 6.2%. Similar improvements were achieved in the water use efficiency simulations, with R² rising from 0.52 to 0.70 and NRMSE declining from 9.1% to 7.1%. The proposed assimilation method can simulate growth processes and analyze water transport across four critical periods in pear orchards in various regions, aligning with regional observations. The proposed method facilitated the quantitative simulation of pear growth and water transport, providing a promising method for water management in other orchards in arid and semi-arid regions.
Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・土壌水分の反演と、それらをWOFOSTへ同化する手法を開発・検証し、ナシ樹の生育・収量・水利用効率を推定しているため、植物表現型取得・推定が中心的である。
abstractwe proposed to assimilate inversion data from satellite remote sensing (Sentinel-1, Sentinel-2, and DEM) into the WOFOST model based on Ensemble Kalman Filter (EnKF) techniques to simulate pear tree growth and evaluate water use efficiency at the field scale.
Pear leaf diseases represent one of the major challenges in agriculture, significantly affecting fruit quality and reducing overall yield. With the advancement of precision agriculture, accurate identification and segmentation of diseased areas are critical for targeted disease management and optimizing crop production. To address these issues, this study proposes a novel segmentation model, CMSAF-Net, for pear leaf diseases. CMSAF-Net integrates a Multi-scale Convolutional Attention Module (MBCA), a Self-adaptive Attention-augmented Upsampling Module (SAUP), and a Cross-layer Feature Alignment Module (CGAG) to enhance feature extraction, preserve edge information in complex disease regions, and optimize cross-layer information fusion. Additionally, CMSAF-Net incorporates pre-trained weights to leverage prior knowledge, accelerating convergence and improving segmentation accuracy. On a self-constructed dataset containing three types of pear leaf diseases, experimental results demonstrate that CMSAF-Net achieves 88.65%, 93.36%, and 93.86% in key metrics of MIoU, MPA, and Dice, respectively. Compared with mainstream models such as Unet++, DeepLabv3+, U 2 -Net, and TransUNet, CMSAF-Net exhibits significant performance improvements, with MIoU increases of 2.45%, 3.86%, 2.21%, and 8.28%, respectively. This study highlights CMSAF-Net's potential for large-scale disease monitoring in intelligent agriculture, providing an efficient segmentation solution with substantial theoretical and practical implications.
Why it matches plant phenotyping methodsナシ葉の病斑領域を画像からセグメンテーションし、植物の病害状態・重症度を推定する手法の開発と評価が研究の中心であるため。
abstractthis study proposes a novel segmentation model, CMSAF-Net, for pear leaf diseases.
AppleMangoPeachPearPlumField / plotNeRF / 3D Gaussian SplattingRGB / grayscaleFruitWhole plant / canopy / plot / field
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields We introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards. Our work is based on FruitNeRF [6], which employs a neural semantic field combined with a fruit-specific clusteringapproach. The requirement for adaptation for each fruit type limits the applicability of the method, and makes it difficult to use in practice. To lift this limitation, we design a shape-agnostic multi-fruit counting framework, that complements the RGB and semantic data with instance masks predicted by a vision foundation model. The masks are used to encode the identity of each fruit as instance embeddings into a neural instance field. By volumetrically sampling the neural fields, we extract apoint cloud embedded with the instance features, which can be clustered in a fruit-agnostic manner to obtain the fruit count. We evaluate our approach using a synthetic dataset containing apples, plums, lemons, pears, peaches, and mangoes, as well as a real-world benchmark apple dataset. Our results demonstrate that FruitNeRF++ is easier to control and compares favorably to other state-of-the-art methods.
Why it matches plant phenotyping methods果実を対象とした画像ベースの汎用カウント手法を開発し、合成および実データで評価しているため、植物形質（果実数）の取得・推定が研究の中心です。
abstractWe introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards.
In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral properties of lenticels, pericarp, and combined lenticels and pericarp regions of interest (ROI) were analyzed using two-dimensional correlation spectroscopy method (2D-COS), factor discriminant analysis (FDA) and principal component analysis (PCA). Partial least squares regression (PLSR) was performed to develop calibration models of SSC for each ROI separately. Furthermore, variable selection algorithm and one-dimensional convolutional neural network (1D-CNN) were utilized to simplify and improve the model prediction capability. The results showed that the spectral properties of lenticels and pericarp vary considerably, while PCA could highlight the distribution of lenticels. The spectral measurement location has a significant effect on the SSC prediction accuracy. The models can be kept robust when the data sources for the prediction and calibration sets are the same. Specifically, for apple fruit, the SPA-1D-CNN achieved the best model performance with Rc² of 0.845 and Rₚ² of 0.808, respectively. For pear fruit, the best model is the CARS-1D-CNN model with Rc² of 0.887 and Rp² = 0.762. This study demonstrated that lenticels have a significant effect on model prediction performance and the 1D-CNN could be an alternative to conventional PLSR method.
Why it matches plant phenotyping methodsリンゴとナシの果実SSCという植物形質を、ハイパースペクトル画像と回帰・CNNモデルで推定し、測定領域やモデル性能の影響を検証しているため、形質取得・抽出手法が中心である。
abstractthe effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated
In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral properties of lenticels, pericarp, and combined lenticels and pericarp regions of interest (ROI) were analyzed using two-dimensional correlation spectroscopy method (2D-COS), factor discriminant analysis (FDA) and principal component analysis (PCA). Partial least squares regression (PLSR) was performed to develop calibration models of SSC for each ROI separately. Furthermore, variable selection algorithm and one-dimensional convolutional neural network (1D-CNN) were utilized to simplify and improve the model prediction capability. The results showed that the spectral properties of lenticels and pericarp vary considerably, while PCA could highlight the distribution of lenticels. The spectral measurement location has a significant effect on the SSC prediction accuracy. The models can be kept robust when the data sources for the prediction and calibration sets are the same. Specifically, for apple fruit, the SPA-1D-CNN achieved the best model performance with R c 2 of 0.845 and R p 2 of 0.808, respectively. For pear fruit, the best model is the CARS-1D-CNN model with Rc 2 of 0.887 and Rp 2 = 0.762. This study demonstrated that lenticels have a significant effect on model prediction performance and the 1D-CNN could be an alternative to conventional PLSR method.
Why it matches plant phenotyping methodsハイパースペクトル画像からリンゴ・ナシ果実の可溶性固形分含量を推定するモデルを開発・比較しており、形質取得・推定手法が研究の中心である。
abstractapple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated
The evaluation of fruit genetic resources regarding a resistance to pathogens is an essential basis for subsequent selection in fruit breeding. Both genetic analysis and phenotyping of defined traits are important tools and provide decision data in the evaluation process. However, the phenotyping of plants is often carried out 'by hand' and remains the bottleneck in fruit breeding and fruit growing. The development of a digital and UAV (unmanned aerial vehicle)-based phenotyping method for the assessment of genotype-specific susceptibility or resistance against diseases in orchards would significantly increase the efficiency of plant breeding. In this framework, a workflow for drone-based monitoring of pathogens in orchards was developed using the European pear rust ( Gymnosporangium sabinae ) as model pathogen. Pear rust is widespread in orchards and causes conspicuous, clearly visible, yellow to orange-colored disease symptoms. In this paper, we provide a dataset with expert-annotated high-resolution RGB images with pear rust symptoms. For data collection, ten UAV-flight campaigns were realized between 2021 and 2023 under various weather conditions and with different flight parameters in the experimental orchard of the Julius Kühn-Institute for Breeding Research on Fruit Crops in Dresden-Pillnitz (Germany). 1394 images were captured of different pear genotypes, including varieties, wild species and progeny from breeding. The dataset contains manually labelled images with a size of 768 × 768 pixels of leaves infected with pear rust at different stages of development, labelled as class GYMNSA, as well as background images without symptoms. Each leaf with pear rust symptoms was annotated with the drawing method by two points (bounding boxes) using the Computer Vision Annotation Tool (CVAT, v1.1.0) [1] and presented in YOLO 1.1 file format (.txt files). A total of 584 annotated images and 162 background images, organized into a training and validation set, are included in the GYMNSA dataset. This GYMNSA dataset can be used as a resource for researchers and developers working on drone-based plant disease monitoring systems.
Why it matches plant phenotyping methodsナシさび病の植物症状をUAV画像から検出するための注釈付きデータセットを提供しており、植物病害状態の画像ベース表現型取得・解析ワークフローが中心的です。
abstractThe development of a digital and UAV (unmanned aerial vehicle)-based phenotyping method for the assessment of genotype-specific susceptibility or resistance against diseases in orchards would significantly increase the efficiency of plant breeding.
Reproduction assets foundThe paper's GYMNSA dataset — annotated UAV RGB images of pear rust symptoms with YOLO labels — is publicly deposited on Mendeley Data under DOI 10.17632/44kjgc4gkc.1, directly reproducing the paper's phenotyping measurements.Dataset · publicl orchard of the Julius Kühn-Institute (JKI - Federal Research Centre for Cultivated Plants) at the Institute for Breeding Research on Fruit Crops located in Dresden-Pillnitz (Germany) [51°00ʹ01"N 13°53ʹ12"E].
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/44kjgc4gkc.1
Direct URL to data: https://data.mendeley.com/datasets/44kjgc4gkc/1
1.
Value of the Data
•
These data were collected on an approximately 1.6 ha experimental field with over 1000 different pear genotypes (breeding material and genetic resources of pear varieties and species) and presents a wide spectrum of phenotypic characteristics of pear rust infections at different stages of developmeOpen asset ↗Mendeley Data · 10.17632/44kjgc4gkc.1lines:43-69Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Spring frosts occur in many pome fruit-producing regions globally. They are highly detrimental to floral tissues and yield, making frost tolerance of the reproductive organs one of the major breeding challenges. Currently, frost tolerance of flowers and floral buds is determined by meticulous observations of frost damage symptoms, or by methods that relate the damage with the accumulation of physiologically-relevant biochemicals. These methods are often inaccurate and are only feasible in instances of severe frost. We propose differential scanning calorimetry (DSC) to assess the frost tolerance of pome fruit floral tissues by measuring the heat flow of tissue samples when passing the freezing transition. DSC was applied to floral organs isolated from recently open king flowers of the apple 'Jonagold' (Malus domestica Borkh.) and the European pear 'Conference' (Pyrus communis L.) to simulate frost and determine freezing temperature as a quantitative indicator of frost tolerance. Freezing, crystallization, and melting points were measured by cooling and heating isolated ovules, stamens, and stigmas of mature king flowers from 20 to −40 °C and back to 20 °C. In pear, tissue-specific effects were observed, with ovules showing the highest (−9.6 °C) and stamens showing the lowest average freezing temperature (−13.1 °C), indicating that stamens are less susceptible to frost. In contrast, no significant differences in frost tolerance were detected among apple organs (freezing temperature of −11.1 °C). This study shows that DSC is an efficient method for monitoring and evaluating frost tolerance in pome fruit floral tissues and could be utilized for high-throughput phenotyping in breeding programs.
Why it matches plant phenotyping methodsDSCによる花器官の凍結温度測定を、霜害耐性の定量的な表現型取得法として提案・評価しており、方法開発と検証が中心である。
abstractWe propose differential scanning calorimetry (DSC) to assess the frost tolerance of pome fruit floral tissues by measuring the heat flow of tissue samples when passing the freezing transition.
Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue, respectively, outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark. The model successfully labelled vascular bundles with a Dice Similarity Coefficient (DSC) of 0.51 in apple tissue and 0.79 in pear tissue, although thin vasculature in apple remained more challenging to segment. The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue. Despite evaluating different methods to enhance segmentation quality, none improved test performance beyond that of the model trained on the standard dataset. The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images, without extensive sample preparation such as contrast labelling. The developed method, if not replaces, drastically accelerates conventional human-in-the-loop analysis of such images.
Why it matches plant phenotyping methods植物果実組織の3D微細構造をマイクロCT画像から自動抽出・定量化する深層学習手法を開発し、既存手法およびベンチマークと性能比較しているため、植物フェノタイピング手法が研究の中心です。
abstracta 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images.
Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.
Why it matches plant phenotyping methodsドローン画像、物体検出、写真測量を統合し、ナシ樹のさび病症状を検出・局在化して樹体ごとの感染強度を推定するデジタル表現型解析システムの開発が中心である。
abstracta digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the annotated UAV image dataset on Mendeley Data and the trained model, detection workflow, and Metashape loading script on figshare, both with public URLs matching allowed entries.Dataset · publicsource repository Mendeley Data (https://data.mendeley.com/datasets/44kjgc4gkc/1, accessed on 8Open asset ↗Mendeley Data · 44kjgc4gkc/1pdf-page:15 lines:1-67Code · publicThe model, the detection workflow with instructions and the script for
loading the detections into Agisoft’s Metashape are available in the open-source figshare repository
(https://doi.org/10.6084/m9.figshare.27225312.v2, accessed on 28 October 2024).Open asset ↗figshare · 10.6084/m9.figshare.27225312.v2pdf-page:15 lines:1-67Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Phenotyping of selected traits is an important prerequisite for evaluating genetic resources and providing resistant genotypes for subsequent fruit breeding. In order to establish a high-throughput method for more objective and accurate phenotyping in the future, the aim of our study was to develop a UVA-based digital phenotyping method in the field. European pear rust (Gymnosporangium sabinae) was selected as a model pathogen for this purpose because without pesticide application it is widely distributed in pear orchards and shows conspicuous yellow-orange disease symptoms. In 2021 and 2022, 705 images showing symptoms of European pear rust were taken in the experimental field of the Julius Kühn Institute in Dresden-Pillnitz and the symptoms were labeled using the Computer Vision Annotation Tool (CVAT). Model training was performed based on four pre-trained YOLOv5 algorithms that use an object detector approach and allow unique identification of each symptom in an image. Accurate localization of disease symptoms within the orchard was enabled by a novel photogrammetry approach on georeferenced image data. For subsequent quantification of disease symptoms per genotype, the number of infected leaves was related to the total volume of the tree. In the future, this digital phenotyping system will provide a high-throughput method for evaluating European pear rust in pear genetic resources.
Why it matches plant phenotyping methodsナシさび病の症状を画像から検出・局在化・定量するUAVデジタルフェノタイピング手法の開発が研究の中心であり、植物病害状態を直接測定する。
abstractthe aim of our study was to develop a UVA-based digital phenotyping method in the field
Fire blight is an infectious disease found in apple and pear orchards. While managing the disease is critical to maintaining orchard health, identifying symptoms early is a challenging task which requires trained expert personnel. This paper presents an inspection technique that targets individual symptoms via deep learning and density estimation. We evaluate the effects of including multi-spectral sensors in the model's pipeline. Results show that adding near infrared (NIR) channels can help improve prediction performance and that density estimation can detect possible symptoms when severity is in the mid-high range.
Why it matches plant phenotyping methodsナシ園の火傷病症状を対象に、多チャンネルセンサー、深層学習、密度推定による植物病害状態の検出手法を評価しており、表現型取得・抽出が中心である。
abstractThis paper presents an inspection technique that targets individual symptoms via deep learning and density estimation.
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Vis-NIR spectroscopy coupled with chemometric models is frequently used for pear soluble solid content (SSC) prediction. However, the model robustness is challenged by the variations in pear cultivars. This study explored the feasibility of developing universal models for predicting SSC of multiple pear varieties to improve the model's generalizability. The mature fruits of 6 pear cultivars with green skin (Pyrus pyrifolia Nakai cv. 'Cuiyu', 'Sucui No.1' and 'Cuiguan') and brown skin (Pyrus pyrifolia Nakai cv. 'Hosui','Syusui' and 'Wakahikari') were used to establish single-cultivar models and multi-cultivar universal models using convolutional neural network (CNN), partial least square (PLS), and support vector regression (SVR) approaches. Multi-cultivar universal models were built using full spectra and important variables extracted by gradient-weighted class activation mapping (Grad-CAM), respectively. The universal models based on important variables obtained satisfactory performances with RMSEPs of 0.76, 0.59, 0.80, 1.64, 0.98, and 1.03°Brix on 6 cultivars, respectively.
Why it matches plant phenotyping methodsVis-NIR分光とCNN・PLS・SVR・Grad-CAMを用いて、複数ナシ品種の果実SSCを非破壊推定するモデルの開発・汎化性能評価が研究の中心である。
titleNondestructive detection of SSC in multiple pear (Pyrus pyrifolia Nakai) cultivars using Vis-NIR spectroscopy coupled with the Grad-CAM method.
A large number of countries worldwide depend on the agriculture, as agriculture can assist in reducing poverty, raising the country's income, and improving the food security. However, the plan diseases usually affect food crops and hence play a significant role in the annual yield and economic losses in the agricultural sector. In general, plant diseases have historically been identified by humans using their eyes, where this approach is often inexact, time-consuming, and exhausting. Recently, the employment of machine learning and deep learning approaches have significantly improved the classification and recognition accuracy for several applications. Despite the CNN models offer high accuracy for plant disease detection and classification, however, the limited available data for training the CNN model affects seriously the classification accuracy. Therefore, in this paper, we designed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets. In addition, we developed an efficient plant disease classification approach, where we adopt the CycleGAN architecture in order to enhance the classification accuracy. The obtained results showed an average enhancement of 7% in the classification accuracy.
Why it matches plant phenotyping methods植物病害を画像から分類するCycleGANベースの手法開発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、植物フェノタイピング手法として適格です。
abstractTherefore, in this paper, we designed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets.
Reproduction assets foundThe paper's Data availability statement explicitly points to the DiaMOS pear disease image dataset (the plant image dataset used for all classification and CycleGAN experiments) hosted publicly on Zenodo. No author analysis code, trained models, or generated CycleGAN image dataset is reported as publicly available.Dataset · publicData availability
The dataset that has been used in this study is available in https://zenodo.org/record/5557313.Open asset ↗zenodopdf-page:10 lines:1-64Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 7 Sept 2026
Background High quality 3D information of the microscopic plant tissue morphology-the spatial organization of cells and intercellular spaces in tissues-helps in understanding physiological processes in a wide variety of plants and tissues. X-ray micro-CT is a valuable tool that is becoming increasingly available in plant research to obtain 3D microstructural information of the intercellular pore space and individual pore sizes and shapes of tissues. However, individual cell morphology is difficult to retrieve from micro-CT as cells cannot be segmented properly due to negligible density differences at cell-to-cell interfaces. To address this, deep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from apple and pear fruit with different cell and porosity characteristics. Results The best segmentation model achieved an Aggregated Jaccard Index (AJI) of 0.86 and 0.73 for apple and pear tissue, respectively, which is an improvement over the current benchmark method that achieved AJIs of 0.73 and 0.67. Furthermore, the neural network was able to detect other plant tissue structures such as vascular bundles and stone cell clusters (brachysclereids), of which the latter were shown to strongly influence the spatial organization of pear cells. Based on the AJIs, apple tissue was found to be easier to segment, as the porosity and specific surface area of the pore space are higher and lower, respectively, compared to pear tissue. Moreover, samples with lower pore network connectivity, proved very difficult to segment. Conclusions The proposed method can be used to automatically quantify 3D cell morphology of plant tissue from micro-CT instead of opting for laborious manual annotations or less accurate segmentation approaches. In case fruit tissue porosity or pore network connectivity is too low or the specific surface area of the pore space too high, native X-ray micro-CT is unable to provide proper marker points of cell outlines, and one should rely on more elaborate contrast-enhancing scan protocols.
Why it matches plant phenotyping methodsX線マイクロCT画像から植物組織の個別細胞形態を3D定量化する深層学習セグメンテーション手法を開発・ベンチマークしており、植物表現型取得が研究の中心です。
abstractdeep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from apple and pear fruit
과일의 품질은 색택, 크기, 모양, 결함, 당도 등 여러 요인에 의하여 결정된다. 하지만 과실의 크기, 모양 등 기하학적 정보에 대한 선별은 대부분의 농산물 산지유통센터에서 수작업으로 이루어지고 있다. 과실의 기하학적 정보의 처리를 자동화하기 위하여 2차원 영상 기반의 연구들이 수행되었지만, 한 면에 대한 2차원 영상으로 얻을 수 있는 정보는 제한적이다. 3차원 영상 정보를 농산물에 이용하고자 하는 선행 연구들이 수행되었지만, 복잡한 처리 과정으로 인해 수확 후 선별 라인에서 적용 가능한 실시간의 처리가 불가능하였다. 본 연구에서는 이러한 문제점을 해결하고 과일의 3차원 기하학 정보를 정확히 측정하고자 실시간 3차원 영상 측정 기술을 개발하고, 배 과실에 대하여 부피 계측 정확도를 평가하였다. 여러 개의 RGBD 카메라를 이용하여 여러 면의 영상 촬영이 동시에 이루어지도록 하였으며, 카메라 간의 위치 보정을 사전에 수행하여 특징점 매칭 등 시간 소모적인 작업이 촬영 시마다 수행되지 않도록 하였다. 또한, 영상 및 점군 처리를 통하여 정확한 부피 계측이 가능한 알고리즘을 설계하였다. 그 결과, 결정계수 0.9931, 평균 절대 백분율 오차 0.61%의 성능으로 배 과실의 부피가 계측 가능함을 확인하였다.
Why it matches plant phenotyping methods배 과실의 3차원 기하학적 형질인 부피를 RGB-D 다중 카메라와 점군 처리로 자동 측정하는 기술을 개발하고 정확도를 평가한 방법론 중심 연구이다.
abstract본 연구에서는 이러한 문제점을 해결하고 과일의 3차원 기하학 정보를 정확히 측정하고자 실시간 3차원 영상 측정 기술을 개발하고, 배 과실에 대하여 부피 계측 정확도를 평가하였다.
Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions. .
Why it matches plant phenotyping methodsLiDARとUAV写真測量を用いてブドウ樹冠の高さ・幅・体積を抽出し、両手法を定量比較・評価しており、植物形質取得法が研究の中心である。
abstracttwo systems were compared to characterise a 50 m long vineyard row section
A large number of countries worldwide depend on the agriculture, as agriculture can assist in reducing poverty, raising the country’s income, and improving the food security. However, the plan diseases usually affect food crops and hence play a significant role in the annual yield and economic losses in the agricultural sector. In general, plant diseases have historically been identified by humans using their eyes, where this approach is often inexact, time-consuming, and exhausting. Recently, the employment of machine learning and deep learning approaches have significantly improved the classification and recognition accuracy for several applications. Despite the CNN models offer high accuracy for plant disease detection and classification, however, the limited available data for training the CNN model affects seriously the classification accuracy. Therefore, in this paper, we employed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets. In addition, we developed an efficient plant disease classification approach, where we adopt the CycleGAN architecture in order to enhance the classification accuracy. The obtained results showed an average enhancement of 7% in the classification accuracy.
Why it matches plant phenotyping methods植物病害画像を対象に、CycleGANによるデータ拡張・分類手法を開発し、分類精度を評価しているため、病害状態の画像ベース表現型推定が中心です。
abstractwe employed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets.
Reproduction assets foundThe paper uses the public DiaMOS pear leaf/fruit image dataset as its phenotyping input and explicitly states its availability on Zenodo. No author analysis code, trained models, or generated CycleGAN image dataset is reported as publicly deposited.Dataset · publicData availability: The dataset that has been used in this study is available
in https://zenodo.org/record/5557313.Open asset ↗zenodo · 5557313pdf-page:15 lines:1-40Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.
Why it matches plant phenotyping methodsUAV-DAPとMTLS-LiDARによる果樹キャノピー形状計測を比較・検証し、高スループット表現型解析への適用可能性を評価しているため、植物形質取得法が中心である。
abstractA comparison between both kinds of measurements was performed
Volumetric soil water content is commonly used for irrigation management in fruit trees. By integrating direct information on tree water status into measurements of soil water content, we can improve detection of water stress and irrigation scheduling. Thermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees ( Pyrus communis L.). These indicators are easy, quick, and cost-effective. The soil and tree water status of two cultivars of pear trees 'D'Anjou' and 'Bartlett' submitted to regulated deficit irrigation was measured regularly in a pear orchard in Rock Island, WA (USA) for two seasons, 2021 and 2022. These assessments were compared to the canopy temperature (Tc), the difference between the canopy and air temperature (Tc-Ta) and the crop water stress index (CWSI). Trees under deficit irrigation had lower midday stem water potential and stomatal conductance but higher Tc, Tc-Ta, and CWSI. Tc was not a robust method to assess tree water status since it was strongly related to air temperature (R = 0.99). However, Tc-Ta and CWSI were greater than 0°C or 0.5, respectively, and were less dependent on the environmental conditions when trees were under water deficits (midday stem water potential values -2 s -1 . Soil water content (SWC) was the first indicator in detecting the deficit irrigation applied, however, it was not as strongly related to the tree water status as the thermal-based indicators. Thus, the relation between the indicators studied with the stem water potential followed the order: CWSI > Tc-Ta > SWC = Tc. A multiple regression analysis is proposed that combines both soil water content and thermal-based indices to overcome limitations of individual use of each indicator.
Why it matches plant phenotyping methodsナシ樹の水分状態を熱画像由来の指標で推定し、土壌水分や茎水ポテンシャルと比較・検証することが中心であり、植物生理状態のフェノタイピング手法に該当する。
abstractThermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees
The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch-leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precision sem , mean Recall sem , mean F 1 -score , and mean Intersection over Union ( IoU ) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precision ins , Recall ins , and mean coverage ( mCoV ) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm 2 ), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management.
Why it matches plant phenotyping methodsLiDAR三次元点群とセグメンテーションを用いて、葉の形態形質を自動抽出・推定する手法を開発し、手測定との相関で検証しているため、植物フェノタイピング手法が中心である。
abstractThis paper proposed an automatic branch-leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example.
Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves. First, the shallow features of nutrient-deficient pear leaf images are extracted using manual feature extraction methods, and the depth features are extracted by the deep network model. Second, the shallow features are fused with the depth features using serial fusion. In addition, the fused features are trained using three classification algorithms, F-Net, FC-Net, and FA-Net, proposed in this paper. Finally, we compare the performance of single feature-based and fusion feature-based identification algorithms in the nutrient-deficient pear leaf diagnostic task. The best classification performance is achieved by fusing the depth features output from the ConvNeXt-Base deep network model with shallow features using the proposed FA-Net network, which improved the average accuracy by 15.34 and 10.19 percentage points, respectively, compared with the original ConvNeXt-Base model and the shallow feature-based recognition model. The result can accurately recognize pear leaf deficiency images by providing a theoretical foundation for identifying plant nutrient-deficient leaves.
Why it matches plant phenotyping methodsナシ葉の栄養欠乏症状をRGB画像から分類する画像解析手法を提案・比較しており、植物状態の取得・推定が研究の中心であるため。
abstractThis study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves.
Near-infrared spectroscopy (NIRS) is one of the most promising technique for nondestructive and rapid detection of fruit's soluble solid content (SSC). However, when using NIRS to assess the SSC of fruit, a strategy for individually modeling different fruit cultivars is usually required, while the maintenance and upgrading of the models are time-consuming and laborious. To cope with these problems, this study aimed to explore the feasibility of developing a universal model to predict SSC for thin-skinned fruits with similar physicochemical properties. A progressive hybrid variable selection strategy was used to establish the universal model to decrease the complexity of the modeling and ultimately increase the model accuracy. First, the characteristic wavebands of four cultivars were chosen by synergy interval partial least squares (Si-PLS). Next, the wavelength point selection method was employed to filter the uninformative wavelengths and then mapped to an L1 regularization optimization task with constraints. Finally, the effective variables were further identified by simulated annealing (SA) and genetic algorithm (GA), separating them from the remaining variables. The coefficients of determination of calibration (RC²) and prediction (RP²) were both 0.93, and the root mean square error of calibration (RMSECV) and prediction (RMSEP) were 0.62 °Brix and 0.60 °Brix, respectively, for the Si-L1-UVE-GA model. A new external sample set was used for external prediction, and the RMSEP and Rₚ² for the multi-cultivars model were 0.73 °Brix and 0.90, respectively. The results showed superiority of developing a universal model for predicting SSC of different species by using wavelength-limited portable NIR equipment.
Why it matches plant phenotyping methods果実のSSC（可溶性固形分）を非破壊NIRで推定する汎用モデルの開発と外部検証が研究の中心であり、植物器官の品質形質を取得・推定する方法論に該当する。
abstractthis study aimed to explore the feasibility of developing a universal model to predict SSC for thin-skinned fruits with similar physicochemical properties.
Over the last few years, the impact of climate change has increased rapidly. It is influencing all steps of plant production and forcing farmers to change and adapt their crop management practices using new technologies based on data analytics. This study aims to classify plant diseases based on images collected directly in the field using deep learning. To this end, an ensemble learning paradigm is investigated to build a robust network in order to predict four different pear leaf diseases. Several convolutional neural network architectures, named EfficientNetB0, InceptionV3, MobileNetV2 and VGG19, were compared and ensembled to improve the predictive performance by adopting the bagging strategy and weighted averaging. Quantitative experiments were conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. Data augmentation was adopted to improve the generalization of the model. The results, evaluated with a range of metrics, including accuracy, recall, precison and f1-score, showed that the proposed ensemble convolutional neural network outperformed the single convolutional neural network in classifying diseases in real field-condition with variation in brightness, disease similarity, complex background, and multiple leaves.
Why it matches plant phenotyping methodsナシ葉の画像から病害状態を推定する深層学習分類法の開発・比較が中心であり、植物病害表現型の取得・抽出手法に該当する。
abstractThis study aims to classify plant diseases based on images collected directly in the field using deep learning.
Reproduction assets foundThe paper's authors publicly released both the DiaMOS Plant dataset (field-collected pear leaf images used for all experiments, on Zenodo) and their analysis code as the LeafBox toolbox (website and GitHub repository), with explicit availability statements.Dataset · publicaMOS Plant dataset is available at https://doi.org/10.5281/zenodo.5557313, accessed on 16 JanuaryOpen asset ↗Zenodo · 10.5281/zenodo.5557313pdf-page:10 lines:1-59Code · publicThe source code is available at https://leafbox.francescamalloci.com/,
https://github.com/mallociFrancesca/leaf-disease-toolbox, accessed on 16 January 2023.Open asset ↗GitHub · mallociFrancesca/leaf-disease-toolboxpdf-page:10 lines:1-59Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 7 Sept 2026
Owing to iron chlorosis, pear trees are some of the most severely impacted by iron deficiency, and they suffer significant losses every year. While it is possible to determine the iron content of leaves using laboratory-standard analytical techniques, the sampling and analysis process is time-consuming and labor-intensive, and it does not quickly and accurately identify the physiological state of iron-deficient leaves. Therefore, it is crucial to find a precise and quick visualization approach for metabolites linked to leaf iron to comprehend the mechanism of iron deficiency and create management strategies for pear-tree planting. In this paper, we propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves. According to our findings, iron deficiency significantly decreased the Raman peak intensities of chlorophylls and lipids in leaves. The spatial distributions of chlorophylls and lipids in the leaves changed significantly as the symptoms of iron insufficiency worsened. The technique offers a new, prospective tool for rapid recognition of iron deficiency in pear trees because it is capable of visual detection of plant physiological metabolites induced by iron deficiency.
Why it matches plant phenotyping methodsナシ葉の鉄欠乏状態に関連する代謝物をマイクロラマン分光イメージングで非破壊・迅速に可視化する手法を提案しており、植物の生理状態を取得する方法開発が中心である。
abstractwe propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves.
Abstract In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.
Why it matches plant phenotyping methods植物病徴を画像から分類する深層学習手法の改良と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画像ベース表現型手法に該当する。
abstractThis paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs
The 'Huangguan' pear disease spot detection and grading is the key to fruit processing automation. Due to the variety of individual shapes and disease spot types of 'Huangguan' pear. The traditional computer vision technology and pattern recognition methods have some limitations in the detection of 'Huangguan' pear diseases. In recent years, with the development of deep learning technology and convolutional neural network provides a new solution for the fast and accurate detection of 'Huangguan' pear diseases. To achieve automatic grading of 'Huangguan' pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models. In the first stage, Mask R-CNN and Mask R-CNN with the introduction of the preprocessing module are used to segment 'Huangguan' pears from complex backgrounds. In the second stage, DeepLabV3+, UNet and PSPNet are used to segment the 'Huangguan' pear spots to get the spots, and the ratio of the spot pixel area to the 'Huangguan' pear pixel area is calculated and classified into three grades. In the third stage, the grades of 'Huangguan' pear are obtained using ResNet50, VGG16 and MobileNetV3. The experimental results show that the model proposed in this paper can segment the 'Huangguan' pear and disease spots in complex background in steps, and complete the grading of 'Huangguan' pear fruit disease severity. According to the experimental results. The Mask R-CNN that introduced the CLAHE preprocessing module in the first-stage instance segmentation model is the most accurate. The resulting pixel accuracy (PA) is 97.38% and the Dice coefficient is 68.08%. DeepLabV3+ is the most accurate in the second-stage semantic segmentation model. The pixel accuracy is 94.03% and the Dice coefficient is 67.25%. ResNet50 is the most accurate among the third-stage classification models. The average precision (AP) was 97.41% and the F1 (harmonic average assessment) was 95.43%.In short, it not only provides a new framework for the detection and identification of 'Huangguan' pear fruit diseases in complex backgrounds, but also lays a theoretical foundation for the assessment and grading of 'Huangguan' pear diseases.
Why it matches plant phenotyping methodsナシ果実の病斑を画像分割で抽出し、病斑面積比から病害重症度・外観品質を等級化する画像ベースの植物表現型推定法が研究の中心である。
abstractTo achieve automatic grading of 'Huangguan' pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models.
Russet, one of the fruit skin colors of Asian pears, affects the fruit's commercial value.A precise phenotyping method is required because fruit skin colors occur irregularly in various pear cultivars.Here, we propose a bi-dimensional image analysis to evaluate the russet of pear skin accurately and effectively by comparing the results of image analysis with the Hunter a value.The fruits of 'Whangkeumbae' (non-russet), 'Minibae' (russet), and their F 1 individuals were used for russet evaluation.The Hunter a value was measured using a color-difference meter.The russet coverage (%) was calculated using Photoshop software.In F 1 individuals derived from 'Whangkeumbae' × 'Minibae', the Hunter a value represented normal distribution, and the russet coverage distributed variously.The positive correlation coefficients between the Hunter a value and russet coverage was confirmed (r > 0.7).Although several F 1 individuals showed abundant russet formation, they showed a negative Hunter a value.Therefore, the bi-dimensional image analysis more accurately evaluated russet in pear fruit skin than did the Hunter a value.The bi-dimensional image analysis could be used for high-throughput phenotyping in pears.
Why it matches plant phenotyping methodsナシ果皮のさび（russet）被覆率を画像解析で定量する手法を提案・比較検証しており、植物形質取得法が研究の中心である。
abstractHere, we propose a bi-dimensional image analysis to evaluate the russet of pear skin accurately and effectively by comparing the results of image analysis with the Hunter a value.
Fourier transform near-infrared (FT-NIR) spectroscopy is a nondestructive, rapid, real-time analysis of technical detection methods with an important reference value for producers and consumers. In this study, the feasibility of using FT-NIR spectroscopy for the rapid quantitative analysis and qualitative analysis of 'Zaosu' and 'Dangshansuli' pears is explored. The quantitative model was established by partial least squares (PLS) regression combined with cross-validation based on the spectral data of 340 pear fresh fruits and synchronized with the reference values determined by conventional assays. Furthermore, NIR spectroscopy combined with cluster analysis was used to identify varieties of 'Zaosu' and 'Dangshansuli'. As a result, the model developed using FT-NIR spectroscopy gave the best results for the prediction models of soluble solid content (SSC) and titratable acidity (TA) of 'Dangshansuli' (residual prediction deviation, RPD: 3.272 and 2.239), which were better than those developed for 'Zaosu' SSC and TA modeling (RPD: 1.407 and 1.471). The results also showed that the variety identification of 'Zaosu' and 'Dangshansuli' could be carried out based on FT-NIR spectroscopy, and the discrimination accuracy was 100%. Overall, FT-NIR spectroscopy is a good tool for rapid and nondestructive analysis of the internal quality and variety identification of fresh pears.
Why it matches plant phenotyping methodsFT-NIR分光法を用いて生果実の可溶性固形分・滴定酸度を非破壊推定し、交差検証と精度評価を行っており、植物器官の品質形質取得法が研究の中心である。
abstractthe feasibility of using FT-NIR spectroscopy for the rapid quantitative analysis and qualitative analysis of 'Zaosu' and 'Dangshansuli' pears is explored.
Key message We describe a semi in vivo pollination technique to determine the compatibility relation between different pear cultivars. This assay provides a valuable addition to existing tools in GSI research. The gametophytic self-incompatibility (GSI) system in Pyrus inhibits fertilization by pollen that shares one of the two S-alleles of the style. Depending on their S-locus genotype, two pear cultivars therefore either show a cross-compatible, semi-compatible or incompatible interaction. Because GSI greatly influences seed and fruit set, accurate knowledge of the compatibility type of a cultivar is key for both pear fruit production and breeding. Currently, compatibility relations between different pear cultivars are generally assessed via S-genotyping. However, this approach is restricted to the currently known S-alleles in pear, and does not provide functional assessment of the level of (self-)incompatibility. We here present an optimized semi in vivo pollination assay, that enables quantitative analysis of (self-)incompatibility in pear, and that can also serve useful for more fundamental studies on pollen tube development and pollen-style interactions. This assay involves in vitro incubation of cut pollinated styles followed by microscopic counting of emerging pollen tubes at a specific time interval. The validity and selectivity of this method to determine compatibility interactions in pear is demonstrated in the cultivars "Celina" and "Packham's Triumph." Overall, this technique constitutes a valuable tool for quantitatively determining in vivo pollen tube growth and (cross-)compatibility in pear.
Why it matches plant phenotyping methodsナシの受粉後の花粉管成長と交雑適合性を定量化する半生体測定法を開発・最適化し、品種で妥当性を実証しているため、植物表現型取得法が中心である。
abstractWe here present an optimized semi in vivo pollination assay, that enables quantitative analysis of (self-)incompatibility in pear
Abstract Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi‐view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer‐grade components for capturing, calibrating, and reconstructing the 3D form of small‐to‐moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R 2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground‐truth object and the reconstructed models was >0.97 and root‐mean square error (RMSE) was 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, five grapes, seven peppers, and four Bosc and two red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.
Why it matches plant phenotyping methods果実形状の3D取得・再構成システムを開発し、基準物体との比較で精度検証しているため、植物表現型取得法が中心である。
abstractWe present a complete system constructed of consumer‐grade components for capturing, calibrating, and reconstructing the 3D form of small‐to‐moderate sized fruits and tubers.
The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
Why it matches plant phenotyping methods植物病徴を画像で診断・モニタリングする公開データセットの構築と既存データセット比較が中心であり、植物病害状態の画像ベース表現型解析に該当する。
abstractwe release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant
Reproduction assets foundThe paper releases the DiaMOS Plant dataset (3505 field images of pear leaves/fruits with csv and YOLO annotations) on Zenodo, and the authors' LeafBox analysis code (used for the CNN classification benchmark) on GitHub. Both are paper-specific, public, and directly actionable.Dataset · publicThe dataset is freely available for
academic purposes from a repository at https://doi.org/10.5281/zenodo.5557313 (accessed
on 17 October 2021)Open asset ↗Zenodo · 10.5281/zenodo.5557313pdf-page:4 lines:1-46Code · publicThe source code is available
at https://github.com/mallociFrancesca/leaf-disease-toolbox, accessed on 17 October 2021.Open asset ↗GitHubpdf-page:12 lines:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 15 Sept 2026
Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi-view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R 2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground-truth object and the reconstructed models was > 0.97 and root-mean square error (RMSE) was 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, 5 grapes, 7 peppers, and 4 Bosc and 2 red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.
Why it matches plant phenotyping methods果実・塊茎の3D形状を取得・再構成する低コスト高スループット画像フェノタイピングシステムの開発と精度検証が研究の中心である。
abstractWe present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers.
Background and aims The programmed softening occurring during fruit development requires scission of cell wall polysaccharides, especially pectin. Proposed mechanisms include the action of wall enzymes or hydroxyl radicals. Enzyme activities found in fruit extracts include pectate lyase (PL) and endo-polygalacturonase (EPG), which, in vitro, cleave de-esterified homogalacturonan in mid-chain by β-elimination and hydrolysis, respectively. However, the important biological question of whether PL exhibits action in vivo had not been tested. Methods We developed a method for specifically and sensitively detecting in-vivo PL products, based on Driselase digestion of cell wall polysaccharides and detection of the characteristic unsaturated product of PL action. Key results In model in-vitro experiments, pectic homogalacturonan that had been partially cleaved by commercial PL was digested to completion with Driselase, releasing an unsaturated disaccharide ('ΔUA-GalA'), taken as diagnostic of PL action. ΔUA-GalA was separated from saturated oligogalacturonides (EPG products) by electrophoresis, then subjected to thin-layer chromatography (TLC), resolving ΔUA-GalA from higher homologues. The ΔUA-GalA was confirmed as 4-deoxy-β-l-threo-hex-4-enopyranuronosyl-(1→4)-d-galacturonic acid by NMR spectroscopy. Driselase digestion of cell walls from ripe fruits of date (Phoenix dactylifera), pear (Pyrus communis), rowan (Sorbus aucuparia) and apple (Malus pumila) yielded ΔUA-GalA, demonstrating that PL had been acting in vivo in these fruits prior to harvest. Date-derived ΔUA-GalA was verified by negative-mode mass spectrometry, including collision-induced dissociation (CID) fragmentation. The ΔUA-GalA:GalA ratio from ripe dates was roughly 1:20 (mol mol-1), indicating that approx. 5 % of the bonds in endogenous homogalacturonan had been cleaved by in-vivo PL action. Conclusions The results provide the first demonstration that PL, previously known from studies of fruit gene expression, proteomic studies and in-vitro enzyme activity, exhibits enzyme action in the walls of soft fruits and may thus be proposed to contribute to fruit softening.
Why it matches plant phenotyping methods果実軟化という植物状態に関連する酵素作用を検出する新規分析法を開発し、複数果実で検証しているため、単なる生物学的測定ではなく方法中心の研究と判断した。
abstractWe developed a method for specifically and sensitively detecting in-vivo PL products, based on Driselase digestion of cell wall polysaccharides and detection of the characteristic unsaturated product of PL action.
In fruit production, the number of flowers plays a critical factor in crop management decision in an orchard. This paper proposes an automated apple, peach and pear flower detection method under varied environments. The semantic segmentation network DeepLab-ResNet is fine-tuned using apple flower dataset and used in detection for apple, peach and pear flower datasets. On the assumption that the network can roughly locate the flower object and there is distinct color difference between the flower and the surrounding background, an active contour model is used to refine the coarse segmentation results of the network. Specifically, the result from the network presents a shape constraint in the active contour model. The method is tested on four public available image datasets of apple, peach and pear flowers under different environments. The experimental results reveal that the level set model can improve the segmentation result of the semantic segmentation network, especially when the network is generalized to datasets other than those used in network training. Our method achieves a F₁ score at pixel-level up to 89.6% on one of the apple dataset and an average F₁ score of 80.9% on the peach, pear and another apple datasets, which are 6% and 5% higher than the previous state-of-the-art region growing refinement method on the same datasets, respectively.
Why it matches plant phenotyping methods果実生産管理に用いる花数という植物器官形質を、画像セグメンテーションで自動抽出する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractThis paper proposes an automated apple, peach and pear flower detection method under varied environments.
Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences the consumers and the market price significantly. To solve the problem of feature size extraction in 3D fruit scanning, this paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera. For getting a complete fruit model, not only the surface point cloud is obtained, but also the bottom point cloud is rotated to the same coordinate system, and the whole fruit model is obtained by iterative closest point algorithm. According to the centroid and principal direction of the fruit, the cut plane of the fruit is made in the x-axis, y-axis, and z-axis respectively to obtain the contour line of the fruit. The experiment is divided into two groups, the first group is various sizes of pears to get the morphological parameters; the second group is the various colors, shapes, and textures of many fruits to get the morphological parameters. Comparing the predicted value with the actual value shows that the automatic extraction scheme of the size information is effective and the methods are universal and provide a reference for the development of the related application.
Why it matches plant phenotyping methodsKinect深度カメラと3D再構成を用いて果実形態・サイズ形質を自動抽出する手法を開発し、実測値との比較で検証しているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera.
Over the last few decades, Fire Blight (FB) has recognized as the most dangerous diseases of apple and pear trees in the world. Timely diagnosis is very important for the detection of this disease. Visual assessment and scouting are usually used for FB detection while these methods are time-consuming and labor-intensive. Remote sensing technology can be an alternative method for visual assessment of plant diseases. So, in this research, the capability of multispectral remote sensing was evaluated for FB disease diagnosis of pear orchards in leaf and tree crown levels. Ground multispectral imaging was carried out of healthy leaves (HEL) from healthy trees and non-symptomatic diseased leaves (NSL) and symptomatic diseased leaves (SDL. Aerial multispectral imagery of trees crown was carried out by unmanned aerial vehicle. Then preprocessing and processing of ground and aerial images were performed. Some vegetation indices were calculated to detect infected leaves. Among the studied indices, SIPI, RDVI, MCARI1, MCARI2, TVI, MTVI1, MTVI2, TCARI, PSRI and ARI indices were appropriate for early detection of FB in leaf level. Support vector machine (SVM) method was used for the detection of infected trees. The overall accuracy of the classification obtained 95.0%. Based on the results, it could be concluded that multispectral imaging in leaf and tree crown levels is a reliable method for the detection of FB infected pear trees spatially in the early stage.
Why it matches plant phenotyping methodsナシ樹の火傷病症状をマルチスペクトル画像とSVMで検出・分類する手法を開発・評価しており、植物病害状態の取得が中心である。
abstractRemote sensing technology can be an alternative method for visual assessment of plant diseases.
The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.
Why it matches plant phenotyping methodsUAV画像から個体樹冠を検出・分割し、樹冠面積と直径を推定する画像ベースの植物表現型取得手法を開発・精度評価しており、方法が研究の中心である。
abstractwe developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees
Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms has shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This architecture takes the input image and divides into AxA grid, where A is a configurable hyper-parameter that defines the fineness of the grid. To each grid cell an image detection and localization algorithm is applied. Each of those cells is responsible to predict bounding boxes and confidence score for fruit (apple and pear in the case of this study) detected in that cell. We want this confidence score to be high if a fruit exists in a cell, otherwise to be zero, if no fruit is in the cell. More than 100 images of apple and pear trees were taken. Each tree image with approximately 50 fruits, that at the end resulted on more than 5000 images of apple and pear fruits each. Labeling images for training consisted on manually specifying the bounding boxes for fruits, where (x, y) are the center coordinates of the box and (w, h) are width and height. This architecture showed an accuracy of more than 90% fruit detection. Based on correlation between number of visible fruits, detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Processing speed is higher than 20 FPS which is fast enough for any grasping/harvesting robotic arm or other real-time applications. Highlights Using new convolutional deep learning techniques based on single-shot detectors to detect and count fruits (apple and pear) within the tree canopy.
Why it matches plant phenotyping methods果実の検出・計数を中心に、深層学習による画像ベースの果実数推定手法と精度・処理速度を評価しており、単なる収穫対象の位置検出を超えた植物器官形質の抽出である。
abstractThis paper details the use of deep convolution neural networks architecture based on single-stage detectors.
Ancient tree community surveys have great scientific value to the study of biological resources, plant distribution, environmental change, genetic characteristics of species, and historical and cultural heritage. The largest ancient pear tree communities in China, which are rare, are located in the Daxing District of Beijing. However, the environmental conditions are tough, and the distribution is relatively dispersed. Therefore, a low-cost, high-efficiency, and high-precision measuring system is urgently needed to complete the survey of ancient tree communities. By unmanned aerial vehicle (UAV) photogrammetric program research, ancient tree information extraction method research, and ancient tree diameter at breast height (DBH) and age prediction model research, the proposed method can realize the measurement of tree height, crown width, and prediction of DBH and tree age with low cost, high efficiency, and high precision. Through experiments and analysis, the root mean square error (RMSE) of the tree height measurement was 0.1814 m, the RMSE of the crown width measurement was 0.3292 m, the RMSE of the DBH prediction was 3.0039 cm, and the RMSE of the tree age prediction was 4.3753 years, which could meet the needs of ancient tree survey of the Daxing District Gardening and Greening Bureau. Therefore, a UAV photogrammetric measurement system proved to be capable when applied in the survey of ancient tree communities and even in partial forest inventories.
Why it matches plant phenotyping methodsUAV写真測量による樹高・樹冠幅の測定とDBH・樹齢推定の手法開発および精度評価が研究の中心であり、樹木形態形質を直接推定するため含める。
abstractBy unmanned aerial vehicle (UAV) photogrammetric program research, ancient tree information extraction method research, and ancient tree diameter at breast height (DBH) and age prediction model research
In order to detect the redox states of fruits and vegetables simply, a SERS (surface-enhanced Raman scattering) method was developed based on a redox-sensitive probe and a pH-sensitive probe. The two probes were dropped onto the flesh of the fresh incision of fruits and vegetables, respectively. The SERS spectra of redox-sensitive probes were used to indicate their redox states, and the SERS spectra of pH-sensitive probes were used to indicate their pH values to discount the influence of pH on the redox states. The order of redox states (redox potential) of the detected fruits and vegetables is as follows: carrot < Green delicious apple < Xinjiang kuerle fragrant pear < Chinese royal pear < Fuji apple < crystal pear < Golden marshall apple < potato. Compared with traditional methods based on the detection of extracts, the developed method is simple without any pretreatments and consumption of additional chemicals, which would become a popular evaluation methodology of the redox states of fruits and vegetables during their growth and storage stages.
Why it matches plant phenotyping methods果実・野菜の組織に対するSERSで酸化還元状態とpHを非破壊的に評価する手法を開発しており、植物器官の生理状態取得が研究の中心である。
abstracta SERS (surface-enhanced Raman scattering) method was developed based on a redox-sensitive probe and a pH-sensitive probe.
Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha -1 . The mid-portion leaves on the year's shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets-both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration ( n = 1000) and validation ( n = 420) of this model resulted in high R² values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R² = 0.77) and 2014 (R² = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24-27 g/kg.
Why it matches plant phenotyping methodsナシ葉の窒素濃度という植物形質を、圃場可視/近赤外分光で非破壊推定する測定法の比較・モデル検証が中心であり、植物フェノタイピング手法に該当する。
abstractNon-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards.
Calcium and potassium are essential for cell signaling, ion homeostasis and cell wall strength in plants. Unlike nutrients such as nitrogen and potassium, calcium is immobile in plants. Localized calcium deficiencies result in agricultural losses; particularly for fleshy horticultural crops in which elemental imbalances in fruit contribute to the development of physiological disorders such as bitter pit in apple and cork spot in pear. Currently, elemental analysis of plant tissue is destructive, time consuming and costly. This is a limitation for nutrition studies related to calcium in plants. Handheld portable x-ray fluorescence (XRF) can be used to non-destructively measure elemental concentrations. The main objective was to test if handheld XRF can be used for semi-quantitative calcium and potassium analysis of in-tact apple and pear. Semi-quantitative measurements for individual fruit were compared to results obtained from traditional lab analysis. Here, we observed significant correlations between handheld XRF measurements of calcium and potassium and concentrations determined using MP-AES lab analysis. Pearson correlation coefficients ranged from 0.73 and 0.97. Furthermore, measuring apple and pear using handheld XRF identified spatial variability in calcium and potassium concentrations on the surface of individual fruit. This variability may contribute to the development of localized nutritional imbalances. This highlights the importance of understanding spatial and temporal variability in elemental concentrations in plant tissue. Handheld XRF is a relatively high-throughput approach for measuring calcium and potassium in plant tissue. It can be used in conjunction with traditional lab analysis to better understand spatial and temporal patterns in calcium and potassium uptake and distribution within an organ, plant or across the landscape.
Why it matches plant phenotyping methodsリンゴとナシの果実内元素濃度を非破壊測定する携帯型XRFを開発・検証しており、植物形質取得法が研究の中心です。
abstractThe main objective was to test if handheld XRF can be used for semi-quantitative calcium and potassium analysis of in-tact apple and pear.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
(1) Background: Many disorders and diseases of agricultural produce change the physical features of surfaces of plant organs; in terms of russet, e.g., of apple or pear, affected fruit peel becomes rough and brown in color, which is associated with changes in light reflection; (2) Objective and Methods: The objective of the present project was an interdisciplinary approach between horticultural science and engineering to examine two new innovative technologies as to their suitability for the non-destructive determination of surfaces of plant organs, using russet as an example, and (a) an industrial luster sensor (type CZ-H72, Keyence, Japan) and (b) a new type of a three-dimensional (3D) color microscope (VHX 5000); (3) Results: In the case of russet, i.e., suberinization of the fruit peel, peel roughness increased by ca. 2.5-fold from ca. 20 µm to ca. 50 µm on affected fruit sections when viewed at 200× magnification. Russeted peel showed significantly reduced luster, with smaller variation than russet-devoid peel with larger variation; (4) Conclusion: These results indicate that both sensors are suitable for biological material and their use for non-contact, non-invasive detection of surface disorders on agricultural produce such as russet may be a very powerful tool for many applications in agriculture and beyond in the future.
Why it matches plant phenotyping methods植物器官表面の状態（粗さ・光沢、さび症状）を非破壊・非接触で測定するセンサーおよび3D顕微鏡の適用性を検証しており、表現型取得手法が中心である。
abstractan industrial luster sensor (type CZ-H72, Keyence, Japan) and (b) a new type of a three-dimensional (3D) color microscope (VHX 5000)