[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3126":3,"related-3126":80},{"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,"primary_topic":9,"secondary_topics":30,"classification":31,"search_phrases":49,"slug":52,"view_count":53,"doi":54,"paper":55,"created_at":79},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",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":14,"reason":48},[37,43],{"code":38,"score":39,"supported":14,"title":40,"abstract":41,"tags":42},"I2",2,[],[],[28,26],{"code":44,"score":20,"supported":14,"title":45,"abstract":46,"tags":47},"I1",[],[],[25],"农业相关性需要复核",[50,51],"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",4,"10.1016\u002Fj.compag.2026.112454",{"doi":54,"openalex_id":56,"authors":57,"venue":10,"cited_by_count":71,"oa_url":6,"card":72,"direction":76,"ingested_from":78},"W7213933678",[58,60,63,66,68],{"name":59,"orcid":9},"Zhencan Wang",{"name":61,"orcid":62},"Huichun Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6479-0641",{"name":64,"orcid":65},"Liming Bian","https:\u002F\u002Forcid.org\u002F0000-0003-4739-0918",{"name":67,"orcid":9},"Lei Zhou",{"name":69,"orcid":70},"Yufeng Ge","https:\u002F\u002Forcid.org\u002F0000-0002-6460-0780",0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"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":81,"page":20,"page_size":81,"items":82},6,[83,147,243,282,358,419],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":10,"source_url":86,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":96,"tags":98,"primary_topic":9,"secondary_topics":101,"classification":102,"search_phrases":116,"slug":119,"view_count":53,"doi":120,"paper":121,"created_at":146},3967,"GC-3DGS-pheno: geometry-constrained 3d Gaussian splatting for affordable plant point cloud reconstruction and high-precision phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112467","GC-3DGS-pheno: geometry-constrained 3d Gaussian splatting for affordable plant point cloud reconstruction and high-precision phenotyping。Computers and Electronics in Agriculture","GC-3DGS-pheno：几何约束的三维高斯泼溅用于低成本植物点云重建与高精度表型分析。《计算机与电子农业》","2026-10-01T00:00:00Z",81,{"impact":17,"substance":92,"depth":93,"authority":19,"freshness":94,"relevant":20,"comment":95},21,19,9,"提出几何约束3D高斯泼溅方法，实现低成本植物点云重建与高精度表型分析，方法新颖且发表于农业信息领域核心期刊，值得进入每日精选。",[97],{"name":10,"url":86},[25,26,99,28,100],"植物表型","点云",[],{"status":103,"locked":14,"version":33,"method":34,"evidence":104},"unclassified",{"candidates":105,"agricultural_evidence":114,"reason":115},[106,110],{"code":38,"score":39,"supported":14,"title":107,"abstract":108,"tags":109},[],[],[28,26],{"code":44,"score":20,"supported":14,"title":111,"abstract":112,"tags":113},[],[],[25],true,"缺少足够的主题证据",[117,118],"3D Gaussian splatting 植物点云","植物表型 三维重建","3DGaussiansplatting植物点云-3967","10.1016\u002Fj.compag.2026.112467",{"doi":120,"openalex_id":122,"authors":123,"venue":10,"cited_by_count":71,"oa_url":9,"card":141,"direction":76,"ingested_from":78},"W7215060835",[124,126,129,131,133,136,139],{"name":125,"orcid":9},"Yandong Wu",{"name":127,"orcid":128},"Weiliang Wen","https:\u002F\u002Forcid.org\u002F0000-0001-7184-5121",{"name":130,"orcid":9},"Yongji Zhu",{"name":132,"orcid":9},"WU Zhangbin",{"name":134,"orcid":135},"Yinglun Li","https:\u002F\u002Forcid.org\u002F0000-0003-0138-9057",{"name":137,"orcid":138},"Xinyu Guo","https:\u002F\u002Forcid.org\u002F0000-0002-7282-7379",{"name":140,"orcid":9},"Chunjiang Zhao",{"tldr":142,"method":143,"finding":144,"direction":76,"opportunity":145},"提出几何约束3D高斯泼溅方法，低成本重建植物点云并实现高精度表型分析。","几何约束3D高斯泼溅（3DGS），基于多视角图像重建植物点云。","该方法能以低成本获得高精度植物点云，支撑精准表型测量。","可探索将3DGS轻量化并迁移至田间高通量、多物种动态表型监测场景。","2026-10-02T23:30:01.803835Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":161,"tags":163,"primary_topic":166,"secondary_topics":171,"classification":172,"search_phrases":194,"slug":197,"view_count":53,"doi":198,"paper":199,"created_at":242},3490,"Balancing accuracy, completeness, and efficiency for rice 3D reconstruction through CBAM-UNet-based multi-view segmentation and camera configuration optimization","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1882534","Multi-view 3D reconstruction has been widely applied in plant phenotyping, but the complex canopy structure of rice plants poses significant challenges for reconstruction accuracy, completene7ss, and efficiency. In this study, we developed an optimized workflow for 3D reconstruction of rice using the self-developed Metatlas V1 multi-view imaging platform combined with the COLMAP + OpenMVS pipeline. A CBAM-UNet-based image segmentation model was used to extract plant regions from complex backgrounds, outperforming thresholding, U-Net, and U-Net++, and increasing the number of reconstructed points. Camera configuration optimization was performed by systematically combining equidistant and greedy strategies, resulting in an optimal six-camera setup (#2, #3, #4, #6, #7, and #8, corresponding to −30°, −15°, 0°, +30°, +45°, and +60° relative to the horizontal viewpoint), which provided complementary views of the canopy inner structure and stem base. This configuration reduced the average nearest-neighbor distance to 0.037–0.066 cm across different varieties and growth stages compared with the full 11-camera setup, retained 73–76% of the points, and decreased reconstruction duration by approximately 70–80%. The strong agreement between point-cloud-derived and manually measured plant height and canopy width ( R 2 = 0.989 and 0.946, respectively) supported the accuracy of the point-cloud-derived phenotypic measurements. Overall, integrating image segmentation with camera-configuration optimization offers an accurate and efficient solution for high-throughput 3D phenotyping of rice using Metatlas V1, balancing reconstruction accuracy, point-cloud completeness, and computational efficiency, and may provide a methodological reference for camera-configuration design in other rotational multi-view phenotyping platforms.","多视角三维重建已广泛应用于植物表型分析，但水稻复杂的冠层结构对重建精度、完整性和效率构成了重大挑战。本研究利用自主研发的Metatlas V1多视角成像平台，结合COLMAP + OpenMVS流程，开发了一套优化的水稻三维重建工作流。采用基于CBAM-UNet的图像分割模型从复杂背景中提取植株区域，其性能优于阈值分割、U-Net和U-Net++，并增加了重建点数量。通过系统组合等距策略和贪心策略进行相机配置优化，得到了最优的六相机方案（#2、#3、#4、#6、#7和#8，分别对应相对于水平视角的−30°、−15°、0°、+30°、+45°和+60°），该方案提供了冠层内部结构和茎基部的互补视角。与完整的11相机方案相比，该配置将不同品种和生育期的平均最近邻距离降至0.037–0.066 cm，保留了73–76%的点云，并将重建时间缩短了约70–80%。点云提取的株高和冠幅与人工测量结果高度一致（R²分别为0.989和0.946），验证了点云表型测量的准确性。总体而言，将图像分割与相机配置优化相结合，为利用Metatlas V1进行水稻高通量三维表型分析提供了一种准确高效的解决方案，在重建精度、点云完整性和计算效率之间取得了平衡，并可为其他旋转式多视角表型平台的相机配置设计提供方法学参考。","Frontiers in Plant Science","2026-09-24T00:00:00Z",78,{"impact":157,"substance":158,"depth":17,"authority":159,"freshness":94,"relevant":20,"comment":160},16,22,13,"该研究提出结合CBAM-UNet分割与相机配置优化的水稻三维重建流程，方法新颖、数据详实，对高通量作物表型分析有参考价值，但属细分领域进展，时效性高。",[162],{"name":153,"url":150},[25,26,164,165,28],"水稻","作物表型",{"code":167,"slug":168,"name":164,"path":169,"parent_code":170},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":173,"locked":14,"version":33,"method":34,"evidence":174},"classified",{"candidates":175,"agricultural_evidence":114},[176,181,185,189],{"code":167,"score":177,"supported":114,"title":178,"abstract":179,"tags":180},15,[168],[164,168],[164],{"code":38,"score":39,"supported":14,"title":182,"abstract":183,"tags":184},[],[],[28,26],{"code":44,"score":20,"supported":14,"title":186,"abstract":187,"tags":188},[],[],[25],{"code":190,"score":20,"supported":14,"title":191,"abstract":192,"tags":193},"I3",[],[],[165],[195,196],"Metatlas V1 水稻 三维重建","CBAM-UNet 水稻 图像分割","MetatlasV1水稻三维重建-3490","10.3389\u002Ffpls.2026.1882534",{"doi":198,"openalex_id":200,"authors":201,"venue":153,"cited_by_count":71,"oa_url":150,"card":237,"direction":76,"ingested_from":78},"W7214231705",[202,204,207,209,212,214,216,219,222,225,227,229,232,235],{"name":203,"orcid":9},"Haoyang Zhou",{"name":205,"orcid":206},"Rongjie Chen","https:\u002F\u002Forcid.org\u002F0009-0004-8078-2301",{"name":208,"orcid":9},"Hao Wang",{"name":210,"orcid":211},"Minglu Li","https:\u002F\u002Forcid.org\u002F0000-0003-1751-9418",{"name":213,"orcid":9},"Yongkang Teng",{"name":215,"orcid":9},"Shenghao Ye",{"name":217,"orcid":218},"Menglei Wei","https:\u002F\u002Forcid.org\u002F0009-0004-3070-8023",{"name":220,"orcid":221},"Kun Yu","https:\u002F\u002Forcid.org\u002F0000-0002-0190-8702",{"name":223,"orcid":224},"Pingping Fang","https:\u002F\u002Forcid.org\u002F0000-0002-9911-2963",{"name":226,"orcid":9},"Jing Cao",{"name":228,"orcid":9},"Fanglin Zhu",{"name":230,"orcid":231},"Wenyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3322-9736",{"name":233,"orcid":234},"Ting Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9387-4852",{"name":236,"orcid":9},"Min Jiang",{"tldr":238,"method":239,"finding":240,"direction":76,"opportunity":241},"提出CBAM-UNet分割与相机配置优化结合的水稻多视角三维重建流程，兼顾精度、完整性与效率。","Metatlas V1多视角平台、COLMAP+OpenMVS、CBAM-UNe","最优6相机配置将最近邻距离降至0.037–0.066 cm，保留73–76%点云，重建耗时减少约70","可将相机配置优化策略迁移到其他旋转多视角平台，并探索自适应配置与分割模型联合优化。","2026-09-25T23:30:25.303016Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":9,"content":9,"source_name":153,"source_url":9,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"primary_topic":9,"secondary_topics":256,"classification":257,"search_phrases":268,"slug":271,"view_count":39,"doi":272,"paper":273,"created_at":281},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。","2026-09-17T00:00:00Z",87,{"impact":158,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":251},"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。",[253],{"name":153,"url":246},[25,26,99,28,255],"智能手机",[],{"status":103,"locked":14,"version":33,"method":34,"evidence":258},{"candidates":259,"agricultural_evidence":114,"reason":115},[260,264],{"code":38,"score":39,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[28,26],{"code":44,"score":20,"supported":14,"title":265,"abstract":266,"tags":267},[],[],[25],[269,270],"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":272,"openalex_id":9,"authors":274,"venue":9,"cited_by_count":71,"oa_url":9,"card":275,"direction":76,"ingested_from":280},[],{"tldr":276,"method":277,"finding":278,"direction":76,"opportunity":279},"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":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":9,"source_name":288,"source_url":285,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":295,"tags":297,"primary_topic":300,"secondary_topics":305,"classification":306,"search_phrases":333,"slug":336,"view_count":53,"doi":337,"paper":338,"created_at":357},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":291,"substance":292,"depth":293,"authority":19,"freshness":13,"relevant":20,"comment":294},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。",[296],{"name":288,"url":285},[25,26,298,299,27],"机器学习","精准农业",{"code":38,"slug":301,"name":302,"path":303,"parent_code":304},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":173,"locked":14,"version":33,"method":34,"evidence":307},{"candidates":308,"agricultural_evidence":114},[309,314,320,327],{"code":38,"score":157,"supported":114,"title":310,"abstract":312,"tags":313},[311],"machine learning",[298,311],[26,298],{"code":44,"score":315,"supported":114,"title":316,"abstract":317,"tags":319},8,[],[299,318],"precision agriculture",[25,299],{"code":321,"score":322,"supported":114,"title":323,"abstract":324,"tags":326},"B2",3,[],[325],"orchard",[],{"code":328,"score":322,"supported":114,"title":329,"abstract":330,"tags":332},"H7",[],[331],"气候变化",[],[334,335],"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":337,"openalex_id":339,"authors":340,"venue":288,"cited_by_count":71,"oa_url":285,"card":351,"direction":356,"ingested_from":78},"W7213449017",[341,344,346,348],{"name":342,"orcid":343},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":345,"orcid":9},"Hossein Sabouri",{"name":347,"orcid":9},"Ali Tanhaei",{"name":349,"orcid":350},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":352,"method":353,"finding":354,"direction":76,"opportunity":355},"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":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":9,"source_name":364,"source_url":365,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":378,"classification":379,"search_phrases":395,"slug":397,"view_count":53,"doi":398,"paper":399,"created_at":418},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":157,"substance":292,"depth":293,"authority":159,"freshness":315,"relevant":20,"comment":369},"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。",[371],{"name":364,"url":365},[25,26,373,165,28],"遥感",{"code":190,"slug":375,"name":376,"path":377,"parent_code":304},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":173,"locked":14,"version":33,"method":34,"evidence":380},{"candidates":381,"agricultural_evidence":114},[382,387,391],{"code":190,"score":157,"supported":114,"title":383,"abstract":385,"tags":386},[384],"crop phenotyping",[165,384],[165,373],{"code":38,"score":39,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[28,26],{"code":44,"score":20,"supported":14,"title":392,"abstract":393,"tags":394},[],[],[25],[396,270],"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":398,"openalex_id":400,"authors":401,"venue":364,"cited_by_count":71,"oa_url":412,"card":413,"direction":76,"ingested_from":78},"W7213397688",[402,405,407,410],{"name":403,"orcid":404},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":406,"orcid":9},"Theo Morales",{"name":408,"orcid":409},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":411,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":414,"method":415,"finding":416,"direction":76,"opportunity":417},"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":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":9,"source_name":10,"source_url":422,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":427,"sources":429,"tags":431,"primary_topic":434,"secondary_topics":439,"classification":440,"search_phrases":457,"slug":459,"view_count":322,"doi":460,"paper":461,"created_at":479},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":177,"substance":292,"depth":17,"authority":19,"freshness":315,"relevant":20,"comment":428},"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。",[430],{"name":10,"url":422},[25,26,432,433,27],"目标检测","食用菌",{"code":435,"slug":436,"name":433,"path":437,"parent_code":438},"B5","mushrooms","\u002Ftopic\u002Fhorticulture\u002Fmushrooms","B",[],{"status":173,"locked":14,"version":33,"method":34,"evidence":441},{"candidates":442,"agricultural_evidence":114},[443,449,453],{"code":435,"score":177,"supported":114,"title":444,"abstract":446,"tags":448},[445],"mushroom",[447,445],"蘑菇",[433],{"code":38,"score":39,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[26,432],{"code":44,"score":20,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[25],[458,335],"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":460,"openalex_id":462,"authors":463,"venue":10,"cited_by_count":71,"oa_url":9,"card":474,"direction":76,"ingested_from":78},"W7212266027",[464,467,469,471],{"name":465,"orcid":466},"Yang Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-0100-2652",{"name":468,"orcid":9},"Quanlong Miao",{"name":470,"orcid":9},"Yuefeng Chen",{"name":472,"orcid":473},"Jiajia Yu","https:\u002F\u002Forcid.org\u002F0000-0002-8764-8429",{"tldr":475,"method":476,"finding":477,"direction":76,"opportunity":478},"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"]