[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3490":3,"related-3490":84},{"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":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":83},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。",null,"Frontiers in Plant Science","2026-09-24T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","水稻","作物表型","三维重建",[33,34],"Metatlas V1 水稻 三维重建","CBAM-UNet 水稻 图像分割","MetatlasV1水稻三维重建-3490",0,"10.3389\u002Ffpls.2026.1882534",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":76,"direction":80,"ingested_from":82},"W7214231705",[41,43,46,48,51,53,55,58,61,64,66,68,71,74],{"name":42,"orcid":9},"Haoyang Zhou",{"name":44,"orcid":45},"Rongjie Chen","https:\u002F\u002Forcid.org\u002F0009-0004-8078-2301",{"name":47,"orcid":9},"Hao Wang",{"name":49,"orcid":50},"Minglu Li","https:\u002F\u002Forcid.org\u002F0000-0003-1751-9418",{"name":52,"orcid":9},"Yongkang Teng",{"name":54,"orcid":9},"Shenghao Ye",{"name":56,"orcid":57},"Menglei Wei","https:\u002F\u002Forcid.org\u002F0009-0004-3070-8023",{"name":59,"orcid":60},"Kun Yu","https:\u002F\u002Forcid.org\u002F0000-0002-0190-8702",{"name":62,"orcid":63},"Pingping Fang","https:\u002F\u002Forcid.org\u002F0000-0002-9911-2963",{"name":65,"orcid":9},"Jing Cao",{"name":67,"orcid":9},"Fanglin Zhu",{"name":69,"orcid":70},"Wenyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3322-9736",{"name":72,"orcid":73},"Ting Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9387-4852",{"name":75,"orcid":9},"Min Jiang",{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"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","农业遥感与作物表型","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。","openalex","2026-09-25T23:30:25.303016Z",{"total":85,"page":22,"page_size":85,"items":86},6,[87,124,167,221,266,287],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":93,"source_url":90,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":98,"tags":100,"search_phrases":102,"slug":105,"view_count":36,"doi":106,"paper":107,"created_at":123},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture","2026-09-21T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":97},"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[99],{"name":93,"url":90},[27,28,29,101,30],"遥感",[103,104],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":106,"openalex_id":108,"authors":109,"venue":93,"cited_by_count":36,"oa_url":90,"card":118,"direction":80,"ingested_from":82},"W7213887432",[110,113,115],{"name":111,"orcid":112},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":114,"orcid":9},"Noboru Noguchi",{"name":116,"orcid":117},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":119,"method":120,"finding":121,"direction":80,"opportunity":122},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":131,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":139,"tags":141,"search_phrases":142,"slug":145,"view_count":36,"doi":146,"paper":147,"created_at":166},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":17,"substance":135,"depth":136,"authority":20,"freshness":137,"relevant":22,"comment":138},20,17,8,"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。",[140],{"name":130,"url":131},[27,28,101,30,31],[143,144],"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","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":146,"openalex_id":148,"authors":149,"venue":130,"cited_by_count":36,"oa_url":160,"card":161,"direction":80,"ingested_from":82},"W7213397688",[150,153,155,158],{"name":151,"orcid":152},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":154,"orcid":9},"Theo Morales",{"name":156,"orcid":157},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":159,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":162,"method":163,"finding":164,"direction":80,"opportunity":165},"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":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":10,"source_url":170,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"search_phrases":182,"slug":185,"view_count":36,"doi":186,"paper":187,"created_at":220},3165,"Explainable growth stage classification of cacao (Theobroma cacao L.) leaves and key feature visualization using vision transformer and transfer learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1885906","Accurate and objective plant phenotyping is crucial for optimizing agricultural practices, understanding plant development, and enabling rapid responses to environmental changes. Traditional methods, often relying on visual observation, can be subjective, time-consuming, and may overlook subtle but important differences. This study demonstrates the power of combining digital imaging with deep learning to classify plant material with high accuracy, even when visual differences are minimal. We focused on differentiating between stage D (light green) and stage E (dark green) leaves of cacao ( Theobroma cacao L. ), which are visually very similar in size and overall structure. Using cleared and stained leaves of the SCA 6 genotype to highlight the venation network, we trained a Vision Transformer (ViT) model, a deep learning architecture, on image patches. At the patch level, the model achieved an overall accuracy of 97.03% on an independent test set, with a recall of 96.0% for stage D and 98.3% for stage E. At the whole-leaf level, majority voting correctly classified 14 of 15 independent test leaves (93.3%). Attention maps indicated that image regions containing the midrib and primary lateral veins contributed strongly to classification. These attention maps identify discriminative image regions, but they do not by themselves determine the biological mechanism underlying the signal. The major-vein signal may reflect developmental differences in vein-associated structure, stage-associated differences in Safranin O uptake or optical density, tissue thickness, or a combination of these factors. Because vascular anatomy, lignification, hydraulic conductance, phloem loading, and source–sink status were not directly measured, these mechanisms are treated as hypotheses requiring future anatomical, histochemical, and physiological validation. Thus, this study provides a proof-of-concept for interpretable image-based classification of stage D and stage E leaves within greenhouse-grown SCA 6 cacao. Extension to other cacao genotypes, field-grown plants, independent seasons, staining batches, stress detection, species identification, genotype discrimination, or precision-agriculture deployment will require external validation.","准确、客观的植物表型分析对于优化农业实践、理解植物发育以及快速响应环境变化至关重要。传统方法通常依赖视觉观察，可能具有主观性、耗时，并且可能忽略细微但重要的差异。本研究展示了将数字成像与深度学习相结合，即使在视觉差异极小的情况下，也能以高精度对植物材料进行分类。我们聚焦于区分可可（Theobroma cacao L.）的D期（浅绿色）和E期（深绿色）叶片，这些叶片在大小和整体结构上视觉上非常相似。利用SCA 6基因型的透明染色叶片以突出脉序网络，我们在图像块上训练了Vision Transformer（ViT）模型，一种深度学习架构。在图像块水平上，该模型在独立测试集上达到了97.03%的总体准确率，D期的召回率为96.0%，E期为98.3%。在整叶水平上，多数投票正确分类了15片独立测试叶片中的14片（93.3%）。注意力图表明，包含中脉和初级侧脉的图像区域对分类贡献显著。这些注意力图识别了具有判别力的图像区域，但它们本身并不能确定信号背后的生物学机制。主脉信号可能反映了脉相关结构的发育差异、番红O摄取或光密度的阶段相关差异、组织厚度，或这些因素的组合。由于未直接测量维管解剖结构、木质化、水力导度、韧皮部装载和源–库状态，这些机制被视为假设，需要未来的解剖学、组织化学和生理学验证。因此，本研究为温室种植的SCA 6可可中D期和E期叶片的可解释图像分类提供了概念验证。扩展到其他可可基因型、田间种植植株、独立季节、染色批次、胁迫检测、物种鉴定、基因型区分或精准农业部署将需要外部验证。",71,{"impact":175,"substance":135,"depth":136,"authority":20,"freshness":21,"relevant":22,"comment":176},12,"方法新颖、数据可靠的可解释作物表型概念验证研究，但属实验室小样本，产业影响有限。",[178],{"name":10,"url":170},[27,28,180,181,30],"深度学习","可可",[183,184],"可可 叶片 生长阶段 分类","Vision Transformer 作物表型","可可叶片生长阶段分类-3165","10.3389\u002Ffpls.2026.1885906",{"doi":186,"openalex_id":188,"authors":189,"venue":10,"cited_by_count":36,"oa_url":170,"card":215,"direction":80,"ingested_from":82},"W7213901640",[190,192,195,197,199,201,204,207,210,212],{"name":191,"orcid":9},"Ezekiel Ahn",{"name":193,"orcid":194},"Eun-Sung Park","https:\u002F\u002Forcid.org\u002F0000-0001-6826-2865",{"name":196,"orcid":9},"Moon S. Kim",{"name":198,"orcid":9},"Hangi Kim",{"name":200,"orcid":9},"Lalit M. Kandpal",{"name":202,"orcid":203},"Sunchung Park","https:\u002F\u002Forcid.org\u002F0000-0002-7398-9476",{"name":205,"orcid":206},"Seunghyun Lim","https:\u002F\u002Forcid.org\u002F0000-0003-3023-4863",{"name":208,"orcid":209},"Lyndel W. Meinhardt","https:\u002F\u002Forcid.org\u002F0000-0001-8299-2629",{"name":211,"orcid":9},"Byoung-Kwan Cho",{"name":213,"orcid":214},"Insuck Baek","https:\u002F\u002Forcid.org\u002F0000-0003-1044-349X",{"tldr":216,"method":217,"finding":218,"direction":80,"opportunity":219},"用ViT和迁移学习对可可叶D、E期进行可解释分类，准确率达97%。","透明染色叶片图像块训练ViT，注意力图可视化关键区域。","模型准确区分D\u002FE期，中脉和主侧脉区域贡献最大。","可扩展到多基因型、田间、胁迫检测，并验证脉信号生物学机制。","2026-09-22T23:30:19.972949Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":234,"tags":236,"search_phrases":239,"slug":242,"view_count":36,"doi":243,"paper":244,"created_at":265},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。","Computers and Electronics in Agriculture","2026-09-22T00:00:00Z",83,{"impact":19,"substance":231,"depth":19,"authority":232,"freshness":13,"relevant":22,"comment":233},23,14,"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。",[235],{"name":227,"url":224},[27,28,237,31,238],"表型分析","杨树",[240,241],"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","10.1016\u002Fj.compag.2026.112454",{"doi":243,"openalex_id":245,"authors":246,"venue":227,"cited_by_count":36,"oa_url":224,"card":260,"direction":80,"ingested_from":82},"W7213933678",[247,249,252,255,257],{"name":248,"orcid":9},"Zhencan Wang",{"name":250,"orcid":251},"Huichun Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6479-0641",{"name":253,"orcid":254},"Liming Bian","https:\u002F\u002Forcid.org\u002F0000-0003-4739-0918",{"name":256,"orcid":9},"Lei Zhou",{"name":258,"orcid":259},"Yufeng Ge","https:\u002F\u002Forcid.org\u002F0000-0002-6460-0780",{"tldr":261,"method":262,"finding":263,"direction":80,"opportunity":264},"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 ","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。","2026-09-22T23:30:01.467174Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":9,"content":271,"source_name":272,"source_url":9,"published_at":273,"category":274,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"search_phrases":282,"slug":285,"view_count":36,"doi":9,"paper":9,"created_at":286},3103,"山东省农科院举办人工智能专题舜耕论坛暨培训交流会——浙江大学数字农业农村研究中心主任何勇教授作\"作物表型多源多尺度智能感知技术与装备\"专题报告","http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml","9-16 山东省农业科学院举办人工智能专题舜耕论坛暨培训交流会，落实院党委\"人工智能驱动科技创新智慧引领高质量发展\"专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞\u002F组织器官\u002F表型获取装备三个层级系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻\u002F草莓\u002F茶叶等作物的示范应用。院长李向东要求各创新团队推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合。会议设主会场和视频分会场，全院科研人员代表 500 余人参会。","![Image 2](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Flogo02.png)\n\n*   [首页](http:\u002F\u002Fwww.saas.ac.cn\u002F)![Image 3](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fxm.png)\n*   [全院概况](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch17267\u002F)![Image 4](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fxm.png)![Image 5](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fch_img01.jpg)  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\n\n![Image 18](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fny_banner02.jpg)\n\nYour browser does not support the HTML5 canvas tag.Your browser does not support the HTML5 canvas tag.Your browser does not support the HTML5 canvas tag.\n\n新闻中心![Image 19](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Flm_icon01png)\n\n*   [图片新闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10910\u002F)\n*   [农科要闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10717\u002F)\n*   [综合新闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10719\u002F)\n*   [媒体聚焦](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10723\u002F)\n*   [媒体聚焦(图片)](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch17317\u002F)\n*   [公开公示](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10800\u002F)\n*   [通知公告](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10798\u002F)\n\n![Image 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智慧引领高质量发展”专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合，激活科研创新内生动力。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，院党委副书记、院长李向东主持会议并讲话。\n\n报告题为《作物表型多源多尺度智能感知技术与装备》，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞、组织器官、表型获取装备三个层级，系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻、草莓、茶叶等作物的示范应用，解读无人机、作物智慧管理装备关键技术及应用路径，提出农业科技创新要加速人工智能深度融入，以多技术交叉融合助推智慧农业高质量发展，为我院农业人工智能科研布局提供重要参考。\n\n李向东指出，报告紧扣智慧农业发展前沿，兼具理论深度和实践价值，对我院科研迭代升级具有重要指导意义。他强调，要提高政治站位，把握战略导向。深入学习贯彻习近平总书记关于人工智能创新发展的重要指示精神，把智慧农业摆在全院科技创新突出位置，强化机遇意识，开辟农业科研新赛道。要聚焦主责主业，精准靶向攻坚。各创新团队依托现有科研基础，推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合，坚持问题导向，紧扣产业瓶颈凝练攻关方向，推动智能技术赋能科研实践。要压实闭环管理，推动落地见效。细化攻关任务清单，强化项目、平台、人才、经费要素保障，健全调度考核机制，将人工智能攻关及成果产出纳入评价体系，力争产出高水平科研成果、实用技术与智能装备，形成可复制推广的农业人工智能应用模式。全院科研人员要以此次论坛为契机，拓宽科研视野，聚力攻关，推动我院智慧农业科技创新再上新台阶。\n\n会议设主会场和视频分会场，院属各单位主要负责人、科研分管负责人，拟组建创新团队首席、副首席及45岁以下青年科研人员代表500余人参加会议。\n\n（撰写：陈英凯 核稿：张文君）\n\n分享\n\n分享到\n\n[微信](http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml 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25](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F3fcb1043-85f2-4dca-958d-b207876c780b.png)\n\n![Image 26](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F219d5bb3-cb2b-4115-a146-fc799b429998.png)","山东省农业科学院","2026-09-16T00:00:00Z","报道",55,{"impact":175,"substance":20,"depth":175,"authority":175,"freshness":85,"relevant":22,"comment":277},"省级农科院举办的AI专题学术交流活动，内容聚焦作物表型智能感知，有一定专业价值但属会议报道，信息增量有限。",[279],{"name":272,"url":269},[27,28,30,281],"学术交流",[283,284],"山东省农科院 舜耕论坛 人工智能","何勇 作物表型 智能感知","山东省农科院舜耕论坛人工智能-3103","2026-09-22T00:05:35.424987Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":9,"content":292,"source_name":293,"source_url":9,"published_at":294,"category":274,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":298,"tags":300,"search_phrases":303,"slug":306,"view_count":36,"doi":9,"paper":9,"created_at":307},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。","## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z",76,{"impact":18,"substance":18,"depth":136,"authority":21,"freshness":85,"relevant":22,"comment":297},"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[299],{"name":293,"url":290},[27,28,29,301,302],"精准农业","无人機巡田",[304,305],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101","2026-09-22T00:05:33.996455Z"]