[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3126":3,"related-3126":60},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":59},3126,"Poplar-Studio: skeleton-guided reconstruction of editable 3D poplar seedling models from multi-view point clouds","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112454","Accurate, topologically consistent 3D plant models are important for quantitative phenotyping and for initializing functional–structural plant models (FSPMs). Structure-from-motion and multi-view stereo (SfM–MVS) reconstruction yields dense plant point clouds at low cost, but these clouds lack explicit organ connectivity, structural roles, and editable geometry. We developed Poplar-Studio, an integrated topology-preserving modelling framework that converts multi-view-derived poplar seedling point clouds into editable, organ-resolved structural models in standard Blender\u002FOBJ formats. Three design properties define this framework. First, a two-class segmentation separates leaves from the aggregated stem system, which is contracted into a skeleton graph by Laplacian contraction and minimum-spanning-tree construction. Main-stem, petiole, junction, and terminal roles are recovered from this graph without point-level main-stem\u002Fpetiole labels and converted into differentiated parametric geometry. Second, each lamina is reconstructed directly from its own point cloud as a boundary-constrained open surface in a PCA-aligned local frame, rather than instantiated from generic leaf templates. Third, the two branches are integrated into a common metric coordinate system in which the main stem, individual petioles, and individual leaves remain as separately editable organ-level objects, so that the framework terminates in a complete digital plant model rather than an intermediate structural representation. Across 240 fully expanded leaves, reconstructed leaf area agreed closely with independent measurements (R 2 = 0.943, relative RMSE = 10.3%, bias = 0.65 cm 2 ), with all leaves reconstructed as complete, hole-free laminae and the proposed method providing the best overall balance of accuracy, completeness, and efficiency among four reconstruction methods. Across four Populus genotypes, mean stem-system Chamfer distance to the source point clouds ranged from 0.143 to 0.227 cm. In a paired comparison on 40 plants at the 1-cm petiole-matching threshold, the graph-guided workflow showed higher mean petiole-branch precision than a geometry-first segment-then-fit baseline (78.4% vs 74.2%; Holm-adjusted p = 0.015). Recall and F1 did not differ significantly, whereas surface agreement with the source point clouds favoured the baseline (mean Chamfer distance 0.14 vs 0.20 cm). Starting from prepared point clouds, the proposed reconstruction workflow required less than 2.5 min per plant, corresponding to at least 24 plants h⁻ 1 on a single workstation. Poplar-Studio therefore provides a quantitatively characterized route from unordered plant point clouds to physically scaled, editable, topology-resolved models for phenotyping, structural analysis, and FSPM-oriented applications.","准确且拓扑一致的三维植物模型对于定量表型分析和功能-结构植物模型（FSPMs）的初始化具有重要意义。运动恢复结构与多视角立体（SfM–MVS）重建能够以低成本生成密集的植物点云，但这些点云缺乏明确的器官连接关系、结构角色和可编辑几何。我们开发了Poplar-Studio，一个集成的拓扑保持建模框架，可将多视角衍生的杨树幼苗点云转换为标准Blender\u002FOBJ格式的可编辑、器官解析的结构模型。该框架由三个设计特性定义。第一，通过两类分割将叶片与聚合茎系统分离，聚合茎系统经拉普拉斯收缩和最小生成树构建被压缩为骨架图。主茎、叶柄、连接点和末端角色从该图中恢复，无需点级别的主茎\u002F叶柄标签，并转换为差异化的参数化几何。第二，每个叶片直接从其自身点云重建，在PCA对齐的局部坐标系中作为边界约束的开放曲面，而非从通用叶片模板实例化。第三，两个分支被集成到统一的度量坐标系中，其中主茎、各叶柄和各叶片保持为可单独编辑的器官级对象，使框架最终输出完整的数字植物模型，而非中间结构表示。在240片完全展开的叶片中，重建叶面积与独立测量结果高度一致（R² = 0.943，相对RMSE = 10.3%，偏差 = 0.65 cm²），所有叶片均重建为完整、无孔洞的叶片，且在四种重建方法中，所提方法在精度、完整性和效率方面提供了最佳的整体平衡。在四种杨属基因型中，茎系统与源点云的平均Chamfer距离范围为0.143至0.227 cm。在40株植物上以1 cm叶柄匹配阈值进行的配对比较中，图引导工作流显示出比几何优先的分割后拟合基线更高的平均叶柄-分支精确率（78.4% vs 74.2%；Holm校正p = 0.015）。召回率和F1无显著差异，而与源点云的表面一致性则有利于基线（平均Chamfer距离0.14 vs 0.20 cm）。从准备好的点云出发，所提出的重建工作流每株所需时间少于2.5分钟。",null,"Computers and Electronics in Agriculture","2026-09-22T00:00:00Z","论文",10,false,83,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,23,14,1,"提出从多视角点云重建可编辑杨树幼苗三维结构模型的框架，方法新颖、数据扎实，对作物表型与功能-结构模型研究有实用价值。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","表型分析","三维重建","杨树",[31,32],"Poplar-Studio 杨树 三维重建","多视角点云 苗木 表型","Poplar-Studio杨树三维重建-3126",0,"10.1016\u002Fj.compag.2026.112454",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":6,"card":52,"direction":56,"ingested_from":58},"W7213933678",[39,41,44,47,49],{"name":40,"orcid":9},"Zhencan Wang",{"name":42,"orcid":43},"Huichun Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6479-0641",{"name":45,"orcid":46},"Liming Bian","https:\u002F\u002Forcid.org\u002F0000-0003-4739-0918",{"name":48,"orcid":9},"Lei Zhou",{"name":50,"orcid":51},"Yufeng Ge","https:\u002F\u002Forcid.org\u002F0000-0002-6460-0780",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"提出Poplar-Studio框架，从多视角点云重建可编辑的杨树幼苗三维结构模型。","SfM-MVS点云、两类分割、拉普拉斯收缩骨架图、PCA局部叶面重建，Blend","叶面积R²=0.943，叶柄分支精度78.4%优于基线，茎Chamfer距离0.143–0.227 ","农业遥感与作物表型","可扩展至更多物种与生长阶段，并将可编辑模型接入FSPM实现表型-功能联动模拟。","openalex","2026-09-22T23:30:01.467174Z",{"total":61,"page":20,"page_size":61,"items":62},6,[63,93,136,180,219,259],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":9,"content":9,"source_name":68,"source_url":9,"published_at":69,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":70,"score_detail":71,"sources":74,"tags":76,"search_phrases":79,"slug":82,"view_count":34,"doi":83,"paper":84,"created_at":92},2851,"F2DMAS:基于智能手机视频的复杂背景盆栽植物三维表型工作流——Frontiers in Plant Science","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1885579\u002Ffull","F2DMAS将消费级智能手机视频、频域清晰度筛选、基于视觉基础模型的FSAM3植物前景提取、SfM位姿估计、2D Gaussian Splatting(2DGS)、TSDF显式网格、尺度恢复与表型测量串成一条连续流程。在复杂背景下,FSAM3取得F1-score 98.3%、mIoU 97.9%,HD95由SEEM的281.9 px降至41.4 px。F2DMAS达到PSNR 31.09 dB、SSIM 0.9711、LPIPS 0.0365;训练时间下降60.94%,网格提取时间下降65.17%。植株高度、冠幅、叶长、叶宽的R²分别约0.99、0.99、0.97、0.90;RMSE为1.21、0.99、0.64、0.64 cm。来自中国农业科学院农业信息研究所等单位。","Frontiers in Plant Science","2026-09-17T00:00:00Z",87,{"impact":72,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":73},22,"中国农科院信息所提出基于手机视频的盆栽植物三维表型工作流，精度与效率显著提升，方法新颖、数据扎实，值得进入每日精选。",[75],{"name":68,"url":66},[25,26,77,28,78],"植物表型","智能手机",[80,81],"农业人工智能 三维重建 智慧农业 智能手机","农业人工智能 三维重建","农业人工智能三维重建智慧农业智能手机-2851","10.3389\u002Ffpls.2026.1885579\u002Ffull",{"doi":83,"openalex_id":9,"authors":85,"venue":9,"cited_by_count":34,"oa_url":9,"card":86,"direction":56,"ingested_from":91},[],{"tldr":87,"method":88,"finding":89,"direction":56,"opportunity":90},"提出基于智能手机视频的盆栽植物三维表型工作流F2DMAS，实现复杂背景下高效高精度重建与测量。","智能手机视频、频域清晰度筛选、FSAM3前景提取、SfM、2DGS、TSDF网格","前景提取F1达98.3%，重建PSNR 31.09 dB，株高冠幅R²约0.99，训练与网格提取时间","可探索将该工作流迁移至田间自然场景、多物种及动态生长监测，并融合时序表型分析。","agent","2026-09-18T00:03:30.495696Z",{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":69,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":106,"tags":108,"search_phrases":111,"slug":114,"view_count":34,"doi":115,"paper":116,"created_at":135},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports",73,{"impact":102,"substance":103,"depth":104,"authority":19,"freshness":13,"relevant":20,"comment":105},12,20,17,"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[107],{"name":99,"url":96},[25,26,109,110,27],"机器学习","精准农业",[112,113],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器学习精准农业-2802","10.1038\u002Fs41598-026-71390-9",{"doi":115,"openalex_id":117,"authors":118,"venue":99,"cited_by_count":34,"oa_url":96,"card":129,"direction":134,"ingested_from":58},"W7213449017",[119,122,124,126],{"name":120,"orcid":121},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":123,"orcid":9},"Hossein Sabouri",{"name":125,"orcid":9},"Ali Tanhaei",{"name":127,"orcid":128},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":130,"method":131,"finding":132,"direction":56,"opportunity":133},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","农业人工智能与决策模型","2026-09-17T23:30:59.169744Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":141,"content":9,"source_name":142,"source_url":143,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":145,"score_detail":146,"sources":151,"tags":153,"search_phrases":156,"slug":158,"view_count":34,"doi":159,"paper":160,"created_at":179},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。","arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926","2026-09-15T00:00:00Z",74,{"impact":147,"substance":103,"depth":104,"authority":148,"freshness":149,"relevant":20,"comment":150},16,13,8,"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[152],{"name":142,"url":143},[25,26,154,155,28],"遥感","作物表型",[157,81],"农业人工智能 三维重建 作物表型 智慧农业","农业人工智能三维重建作物表型智慧农业-2781","10.48550\u002Farxiv.2609.16926",{"doi":159,"openalex_id":161,"authors":162,"venue":142,"cited_by_count":34,"oa_url":173,"card":174,"direction":56,"ingested_from":58},"W7213397688",[163,166,168,171],{"name":164,"orcid":165},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":167,"orcid":9},"Theo Morales",{"name":169,"orcid":170},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":172,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":175,"method":176,"finding":177,"direction":56,"opportunity":178},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","2026-09-17T23:30:27.096274Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":10,"source_url":183,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":193,"search_phrases":196,"slug":198,"view_count":34,"doi":199,"paper":200,"created_at":218},2282,"Mushroom cap phenotype extraction based on YOLOv11-SMD","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112402","Mushroom cap phenotype extraction based on YOLOv11-SMD。Computers and Electronics in Agriculture","基于YOLOv11-SMD的蘑菇菌盖表型提取。《农业计算机与电子》","2026-09-11T00:00:00Z",75,{"impact":189,"substance":103,"depth":17,"authority":19,"freshness":149,"relevant":20,"comment":190},15,"核心期刊论文，提出YOLOv11-SMD用于蘑菇菌盖表型提取，方法有创新且面向食用菌智能化生产，但属细分技术进展，影响力有限。",[192],{"name":10,"url":183},[25,26,194,195,27],"目标检测","食用菌",[197,113],"农业人工智能 智慧农业 目标检测 表型分析","农业人工智能智慧农业目标检测表型分析-2282","10.1016\u002Fj.compag.2026.112402",{"doi":199,"openalex_id":201,"authors":202,"venue":10,"cited_by_count":34,"oa_url":9,"card":213,"direction":56,"ingested_from":58},"W7212266027",[203,206,208,210],{"name":204,"orcid":205},"Yang Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-0100-2652",{"name":207,"orcid":9},"Quanlong Miao",{"name":209,"orcid":9},"Yuefeng Chen",{"name":211,"orcid":212},"Jiajia Yu","https:\u002F\u002Forcid.org\u002F0000-0002-8764-8429",{"tldr":214,"method":215,"finding":216,"direction":56,"opportunity":217},"提出YOLOv11-SMD模型，用于蘑菇菌盖表型自动提取。","基于YOLOv11改进的SMD模型，结合图像数据提取菌盖表型。","YOLOv11-SMD能有效提取蘑菇菌盖表型，提升检测精度。","可探索多品种、多生长阶段菌盖表型动态监测与三维重建。","2026-09-13T23:30:01.859046Z",{"id":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":9,"source_name":225,"source_url":222,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":231,"tags":233,"search_phrases":236,"slug":239,"view_count":34,"doi":240,"paper":241,"created_at":258},1730,"PAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjxb\u002Ferag436","Abstract Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole genome sequencing and mapping candidate mutation. This screen led to the identification of a point mutation in CAP-D2 ( capd2-2 ), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery. Highlight We combined AI-powered image analysis with an easy-to-use desktop app to automate plant pollen counting, then used it to identify a new genetic suppressor of meiotic defects.","定量花粉活力分析是植物生殖生物学中一个关键但劳动密集的步骤。现有的深度学习分割一切模型（SAM）在亚历山大染色花药中无法可靠地分割有活力的花粉。为解决此问题，我们对现有的Cellpose-SAM模型进行了微调，用于花粉分割。我们将其整合到PAT（花粉分析工具）中，这是一个跨平台的桌面应用程序。PAT具备实例分割及交互式质量控制、应用内模型再训练模块以及可直接用于发表的统计输出功能。我们在半不育拟南芥smg7-6突变体的EMS抑制子筛选中部署了PAT，从而能够高效地对候选突变体进行优先级排序，以进行全基因组测序和候选突变定位。该筛选鉴定出CAP-D2（capd2-2）中的一个点突变，该基因编码凝缩蛋白I的一个亚基，该突变能够挽救smg7-6的减数分裂表型。值得注意的是，凝缩蛋白II亚基（CAP-D3和CAP-H2）的突变并未赋予挽救效果。进一步的特征分析表明，capd2-2等位基因属于亚效等位基因，在营养生长、染色质中心压缩或转座子沉默方面未显示任何缺陷。总体而言，我们证明了易于使用的人工智能工具具有弥合植物表型分析差距并加速生物学发现的潜力。亮点：我们将人工智能驱动的图像分析与易于使用的桌面应用相结合，实现了植物花粉计数的自动化，并利用该方法鉴定了一个新的减数分裂缺陷遗传抑制子。","Journal of Experimental Botany","2026-09-04T00:00:00Z",79,{"impact":17,"substance":72,"depth":17,"authority":19,"freshness":229,"relevant":20,"comment":230},7,"AI工具用于花粉活力自动分析，加速遗传筛选，对育种和表型分析有实用价值。",[232],{"name":225,"url":222},[25,26,234,27,235],"育种","图像识别",[237,238],"农业人工智能 图像识别 智慧农业 表型分析","农业人工智能 图像识别","农业人工智能图像识别智慧农业表型分析-1730","10.1093\u002Fjxb\u002Ferag436",{"doi":240,"openalex_id":242,"authors":243,"venue":225,"cited_by_count":34,"oa_url":222,"card":253,"direction":56,"ingested_from":58},"W7160664980",[244,247,250],{"name":245,"orcid":246},"Darya Volkava","https:\u002F\u002Forcid.org\u002F0009-0003-3578-6592",{"name":248,"orcid":249},"Karel Říha","https:\u002F\u002Forcid.org\u002F0000-0002-6124-0118",{"name":251,"orcid":252},"Vivek K. Raxwal","https:\u002F\u002Forcid.org\u002F0000-0002-5182-6377",{"tldr":254,"method":255,"finding":256,"direction":134,"opportunity":257},"开发了花粉分析工具PAT，自动化评估花粉活力，并用于发现新的减数分裂抑制因子。","微调Cellpose-SAM模型，集成到跨平台桌面应用PAT，支持交互式质量控制","PAT高效筛选拟南芥突变体，发现CAP-D2点突变可挽救smg7-6表型，而Condensin II","将AI图像分析工具扩展到其他作物表型，如种子活力、果实发育，结合交互式平台加速遗传筛选。","2026-09-05T23:30:19.596351Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":9,"content":9,"source_name":264,"source_url":262,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":145,"score_detail":266,"sources":269,"tags":271,"search_phrases":273,"slug":275,"view_count":267,"doi":276,"paper":277,"created_at":338},1275,"Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.08.28.747774","Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments - including different growth irradiances, heat treatment and drought stress - with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.","bioRxiv (Cold Spring Harbor Laboratory)","2026-08-28T00:00:00Z",{"impact":17,"substance":72,"depth":17,"authority":102,"freshness":267,"relevant":20,"comment":268},4,"研究融合3D多光谱与高光谱成像及机器学习，实现非破坏性高通量植物表型分析，对精准农业有参考价值。",[270],{"name":264,"url":262},[25,26,27,154,272],"环境胁迫",[274,113],"农业人工智能 智慧农业 环境胁迫 表型分析","农业人工智能智慧农业环境胁迫表型分析-1275","10.64898\u002F2026.08.28.747774",{"doi":276,"openalex_id":278,"authors":279,"venue":264,"cited_by_count":34,"oa_url":331,"card":332,"direction":337,"ingested_from":58},"W7204524092",[280,283,286,288,290,292,295,298,301,304,306,308,310,313,316,318,320,323,326,328],{"name":281,"orcid":282},"Frederike Stock","https:\u002F\u002Forcid.org\u002F0000-0001-8923-4273",{"name":284,"orcid":285},"Saswat Panda","https:\u002F\u002Forcid.org\u002F0000-0002-7635-2753",{"name":287,"orcid":9},"Richard Poire",{"name":289,"orcid":9},"Timothy Brown",{"name":291,"orcid":9},"Ayesha Akram",{"name":293,"orcid":294},"Liang Zheng","https:\u002F\u002Forcid.org\u002F0000-0002-1464-9500",{"name":296,"orcid":297},"Huan Lei","https:\u002F\u002Forcid.org\u002F0000-0002-2945-8934",{"name":299,"orcid":300},"Ruyi Zha","https:\u002F\u002Forcid.org\u002F0009-0005-0410-1807",{"name":302,"orcid":303},"Mingrui Zhao","https:\u002F\u002Forcid.org\u002F0009-0006-3882-8526",{"name":305,"orcid":9},"Sébastien Isabelle",{"name":307,"orcid":9},"Michèle Martel",{"name":309,"orcid":9},"Marc‐André Comeau",{"name":311,"orcid":312},"Louis‐Philippe Hamel","https:\u002F\u002Forcid.org\u002F0000-0001-8289-4498",{"name":314,"orcid":315},"Pierre‐Olivier Lavoie","https:\u002F\u002Forcid.org\u002F0000-0001-9994-2417",{"name":317,"orcid":9},"Marc Andre D'Aoust",{"name":319,"orcid":9},"Hannah Reithinger",{"name":321,"orcid":322},"Pooja Saxena","https:\u002F\u002Forcid.org\u002F0000-0002-0977-5721",{"name":324,"orcid":325},"Eric A. Stone","https:\u002F\u002Forcid.org\u002F0000-0002-2725-4209",{"name":327,"orcid":9},"Hongdong Li",{"name":329,"orcid":330},"Danielle A. Way","https:\u002F\u002Forcid.org\u002F0000-0003-4801-5319","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F08\u002F28\u002F2026.08.28.747774.full.pdf",{"tldr":333,"method":334,"finding":335,"direction":134,"opportunity":336},"结合3D多光谱和2D高光谱成像及机器学习，识别植物生长期间的环境胁迫处理。","融合3D多光谱扫描仪和VNIR高光谱相机数据，提取形态和光谱特征，训练ML模型。","融合3D+HSI模型优于单一模态，能准确区分不同环境胁迫处理。","可探索在田间复杂环境下融合多模态成像与深度学习，实现作物胁迫的实时诊断与预警。","智慧农业 \u002F 农业物联网","2026-09-01T04:03:15.198826Z"]