[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3000":3,"related-3000":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":35,"created_at":44},3000,"Plant-GeoAT：几何感知3D植物点云器官身份解析用于器官级表型分析","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1809","四川农业大学吴俊杰等提出Plant-GeoAT几何感知解析网络，在RGB融合前编码局部相对-XYZ邻域并将空间邻域与特征空间关系耦合用于密集点预测。在自建油菜数据集、基于图像的大豆数据集、激光扫描Pheno4D玉米和番茄数据集上评估，5个随机种子下mIoU分别达92.26±0.28%、82.50±0.24%、99.74±0.05%、94.75±0.15%，玉米Stem IoU达99.57±0.09%。",null,"MDPI Agronomy 16(18):1809","2026-09-15T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,23,13,6,1,"方法新颖、多作物多数据集验证且精度数据扎实，属器官级表型分析细分领域的重要技术进展，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","油菜","高通量表型","三维点云",[31,32],"四川农业大学 植物点云 器官识别","Plant-GeoAT 表型分析","四川农业大学植物点云器官识别-3000",0,{"doi":8,"openalex_id":8,"authors":36,"venue":8,"cited_by_count":34,"oa_url":8,"card":37,"direction":41,"ingested_from":43},[],{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"提出几何感知网络Plant-GeoAT，实现3D植物点云器官身份解析与器官级表型分析。","编码局部相对XYZ邻域并耦合空间与特征空间关系，在油菜、大豆、玉米、番茄点云数据","五个数据集mIoU最高达99.74%，玉米茎IoU达99.57%，验证了几何感知对器官分割的有效性。","农业遥感与作物表型","可探索跨物种、跨传感器的轻量化几何感知模型，并推动器官级表型与基因型关联分析。","agent","2026-09-20T00:03:08.094512Z",{"total":19,"page":20,"page_size":19,"items":46},[47,105,132,186,231,276],{"id":48,"title":49,"url":50,"summary":51,"summary_zh":52,"content":8,"source_name":53,"source_url":50,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":61,"tags":63,"search_phrases":66,"slug":69,"view_count":34,"doi":70,"paper":71,"created_at":104},2783,"Toward scalable organ-level 3D plant segmentation: A systematic and quantitative review through the lens of the data-algorithm-computing triangle","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isprsjprs.2026.09.003","The precise characterization of plant morphology provides valuable insights into plant-environment interactions and genetic evolution. A key technology for extracting this information is 3D segmentation, which delineates individual plant organs from complex point clouds. Despite significant progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges: (i) the scarcity of large-scale annotated datasets, (ii) technical difficulties in adapting advanced deep neural networks to plant point clouds, and (iii) the lack of standardized benchmarks and evaluation protocols tailored to plant science. This review systematically addresses these barriers by: (i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, (ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, (iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and (iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning strategies. Our findings highlight the efficacy of sparse-convolution and serialization-based backbones, as well as transformer-based instance segmentation networks, while also emphasizing the complementary role of modeling-based and augmentation-based synthetic data generation for sim-to-real learning in reducing annotation demands. Overall, this study bridges the gap between algorithmic advances and practical deployment, providing immediate tools for researchers and a roadmap for developing data-efficient and generalizable deep learning solutions in 3D plant phenotyping. Data and code are available at: https:\u002F\u002Fgithub.com\u002Fperrydoremi\u002FPlantSegStudio .","植物形态的精确表征为理解植物-环境相互作用和遗传演化提供了宝贵见解。提取此类信息的一项关键技术是三维分割，它能够从复杂点云中勾勒出植物各器官。尽管通用三维计算机视觉领域已取得显著进展，三维分割在植物表型分析中的应用仍受限于三大挑战：(i) 大规模标注数据集的稀缺，(ii) 将先进深度神经网络适配于植物点云的技术困难，以及(iii) 缺乏面向植物科学量身定制的标准化基准与评估协议。本综述通过以下方式系统性地应对这些障碍：(i) 在通用三维分割领域的背景下概述现有三维植物数据集，(ii) 系统总结基于深度学习的点云语义与实例分割方法，(iii) 介绍植物分割工作室（Plant Segmentation Studio, PSS）——一个用于可重复基准测试的开源框架，以及(iv) 开展大量定量实验以评估代表性网络和仿真到现实（sim-to-real）学习策略。我们的研究结果凸显了稀疏卷积与基于序列化的主干网络以及基于Transformer的实例分割网络的有效性，同时强调了基于建模和基于增强的合成数据生成在仿真到现实学习中对降低标注需求的互补作用。总体而言，本研究弥合了算法进展与实际部署之间的差距，为研究人员提供了即时可用的工具，并为在三维植物表型分析中开发数据高效且具有泛化能力的深度学习解决方案提供了路线图。数据与代码见：https:\u002F\u002Fgithub.com\u002Fperrydoremi\u002FPlantSegStudio 。","ISPRS Journal of Photogrammetry and Remote Sensing","2026-09-16T00:00:00Z",81,{"impact":16,"substance":57,"depth":16,"authority":58,"freshness":59,"relevant":20,"comment":60},22,14,9,"ISPRS顶刊综述，系统梳理器官级3D植物分割的数据-算法-算力瓶颈，并开源Plant Segmentation Studio基准框架，对作物表型与智慧育种有直接工具价值。",[62],{"name":53,"url":50},[25,26,64,65,29],"开源工具","植物表型",[67,68],"农业人工智能 三维点云 开源工具 智慧农业","农业人工智能 三维点云","农业人工智能三维点云开源工具智慧农业-2783","10.1016\u002Fj.isprsjprs.2026.09.003",{"doi":70,"openalex_id":72,"authors":73,"venue":53,"cited_by_count":34,"oa_url":50,"card":98,"direction":41,"ingested_from":103},"W7213280479",[74,76,78,80,83,86,88,90,92,95],{"name":75,"orcid":8},"Ruiming Du",{"name":77,"orcid":8},"Guangxun Zhai",{"name":79,"orcid":8},"Tian Qiu",{"name":81,"orcid":82},"Shichao Jin","https:\u002F\u002Forcid.org\u002F0000-0003-1150-336X",{"name":84,"orcid":85},"Dawei Li","https:\u002F\u002Forcid.org\u002F0000-0002-9702-8848",{"name":87,"orcid":8},"Zhihong Ma",{"name":89,"orcid":8},"Yongliang Qiao",{"name":91,"orcid":8},"Junfeng Gao",{"name":93,"orcid":94},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",{"name":96,"orcid":97},"Yu Jiang","https:\u002F\u002Forcid.org\u002F0000-0003-4495-3033",{"tldr":99,"method":100,"finding":101,"direction":41,"opportunity":102},"系统综述植物器官级3D分割，提出数据-算法-算力三角框架并开源基准平台PSS。","综述3D植物数据集与深度学习分割方法，构建PSS开源基准并做定量实验。","稀疏卷积与序列化骨干及Transformer实例分割有效，合成数据可降低标注需求。","可探索数据高效、跨物种泛化的3D植物分割模型与标准化评测协议。","openalex","2026-09-17T23:30:30.425366Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":8,"content":8,"source_name":110,"source_url":8,"published_at":111,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":112,"score_detail":113,"sources":115,"tags":117,"search_phrases":120,"slug":123,"view_count":34,"doi":8,"paper":124,"created_at":131},2611,"无人机遥感在水稻高通量表型分析中的研究进展 系统综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260911171723051.htm","发表于Smart Agricultural Technology。依据PRISMA 2020规范系统检索文献最终纳入199项研究(2014–2026年)。研究发现：先进传感、特征集成和建模技术日益支持氮素和叶绿素估算及产量预测；轻量级模型和边缘计算系统在倒伏和病害监测任务中显示出实时部署的可行性；跨区域泛化受环境背景干扰以及地点品种偏倚制约；199项研究中有6项(3.0%)将UAV衍生性状与遗传关联分析联系起来。","Smart Agricultural Technology","2026-09-11T01:00:00Z",77,{"impact":16,"substance":57,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":114},"基于PRISMA规范纳入199项研究的系统综述，方法严谨、数据规模大，对水稻表型与智慧育种有实质参考价值，但属细分领域学术进展，公共影响有限。",[116],{"name":110,"url":108},[25,26,118,119,28],"水稻","无人机遥感",[121,122],"农业人工智能 无人机遥感 高通量表型 智慧农业","农业人工智能 无人机遥感","农业人工智能无人机遥感高通量表型智慧农业-2611",{"doi":8,"openalex_id":8,"authors":125,"venue":8,"cited_by_count":34,"oa_url":8,"card":126,"direction":41,"ingested_from":43},[],{"tldr":127,"method":128,"finding":129,"direction":41,"opportunity":130},"系统综述199项研究，梳理无人机遥感在水稻高通量表型分析中的应用进展与瓶颈。","依据PRISMA 2020系统检索2014–2026年199项研究并归纳分析。","传感与建模支撑氮素、产量预测，跨区域泛化受环境与品种偏倚制约，基因关联研究仅占3%。","UAV表型与遗传关联分析严重不足，可探索跨区域泛化建模及表型-基因型融合方向。","2026-09-16T00:03:52.470505Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":8,"source_name":138,"source_url":135,"published_at":139,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":140,"score_detail":141,"sources":147,"tags":149,"search_phrases":152,"slug":155,"view_count":34,"doi":156,"paper":157,"created_at":185},2523,"Deep learning-based detection and counting of wheat seeds: Comparative benchmarking of YOLO models","https:\u002F\u002Fdoi.org\u002F10.56612\u002Fijaaeb.v6i1.249","Automated wheat-seed detection and counting play important roles in high-throughput plant phenotyping, seed characterization, and agricultural research. Conventional manual counting is labor-intensive, time-consuming, and susceptible to human error when processing large numbers of seed samples. Recent advances in deep learning and object detection provide opportunities to automate these tasks using conventional RGB images. This study focused on a deep learning-based framework for wheat-seed detection and detection-based counting using a custom red-green-blue (RGB) image dataset. A dataset comprising 832 RGB images containing 3,649 manually annotated wheat-seed instances was developed, with images representing one to ten detached wheat seeds per image. All seed instances were manually annotated using bounding boxes and formulated as a single-class object detection problem. Two lightweight object detection models, YOLOv8n and YOLO11n, were trained and evaluated under identical experimental conditions. The model performance was assessed using precision, recall, mean Average Precision at an Intersection over Union (IoU) threshold of 0.5 (mAP@0.5), mean Average Precision averaged across IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95), training loss curves, confidence-based performance curves, confusion matrices, and qualitative detection outputs. Both models achieved excellent detection performance on the custom wheat seed dataset. YOLOv8n achieved a precision of 0.9947, recall of 0.9963, mAP@0.5 of 0.9940, and mAP@0.5:0.95 of 0.5284. YOLO11n produced slightly higher performance, achieving a precision of 0.9988, recall of 0.9984, mAP@0.5 of 0.9950, and mAP@0.5:0.95 of 0.5385. Overall, the performance of the two models was similar at mAP@0.5, but at mAP@0.5:0.95 stricter criterion, YOLO11n consistently demonstrated the strongest overall performance. The findings show that lightweight YOLO models combined with RGB imaging provide an effective and easy way for automated localization and counting of wheat seeds. ​The framework provides a manually annotated RGB wheat-seed dataset and a reproducible benchmark to compare lightweight YOLO models for detection. This study provides a practical foundation for future research on automated seed phenotyping, with future work focusing on external validation using more diverse datasets, multi-class seed-quality assessment, and quantitative evaluation of counting performance.","自动化小麦种子检测与计数在高通量植物表型分析、种子表征和农业研究中发挥着重要作用。传统的人工计数在处理大量种子样本时劳动强度大、耗时长且易受人为误差影响。近年来深度学习和目标检测的进展为利用常规RGB图像实现这些任务的自动化提供了机遇。本研究聚焦于基于深度学习的框架，利用自定义红绿蓝（RGB）图像数据集进行小麦种子检测及基于检测的计数。构建了一个包含832张RGB图像、3，649个手动标注小麦种子实例的数据集，图像中每张包含一到十粒脱离的小麦种子。所有种子实例均使用边界框进行手动标注，并形式化为单类目标检测问题。在相同实验条件下训练和评估了两种轻量级目标检测模型YOLOv8n和YOLO11n。采用精确率、召回率、交并比（IoU）阈值为0.5时的平均精度均值（mAP@0.5）、IoU阈值从0.5到0.95的平均精度均值（mAP@0.5:0.95）、训练损失曲线、基于置信度的性能曲线、混淆矩阵以及定性检测输出对模型性能进行了评估。两种模型在自定义小麦种子数据集上均取得了优异的检测性能。YOLOv8n的精确率为0.9947，召回率为0.9963，mAP@0.5为0.9940，mAP@0.5:0.95为0.5284。YOLO11n的性能略高，精确率为0.9988，召回率为0.9984，mAP@0.5为0.9950，mAP@0.5:0.95为0.5385。总体而言，两种模型在mAP@0.5上的性能相似，但在mAP@0.5:0.95这一更严格的指标下，YOLO11n始终展现出最强的整体性能。研究结果表明，轻量级YOLO模型结合RGB成像为小麦种子的自动化定位和计数提供了一种有效且简便的方法。该框架提供了一个手动标注的RGB小麦种子数据集和一个可重复的基准，用于比较轻量级YOLO模型的检测性能。本研究为未来自动化种子表型分析研究提供了实用基础，未来工作将侧重于使用更多样化数据集进行外部验证、多类种子质量评估以及计数性能的定量评价。","International Journal of Applied and Experimental Biology","2026-09-14T00:00:00Z",73,{"impact":142,"substance":143,"depth":144,"authority":18,"freshness":145,"relevant":20,"comment":146},15,20,17,8,"基于自建RGB数据集对YOLOv8n与YOLO11n进行小麦种子检测计数对比，方法清晰、指标完整，属细分领域可复现基准研究，对自动化种子表型有实用参考价值。",[148],{"name":138,"url":135},[25,26,150,28,151],"小麦","种子检测",[153,154],"农业人工智能 高通量表型 智慧农业 种子检测","农业人工智能 高通量表型","农业人工智能高通量表型智慧农业种子检测-2523","10.56612\u002Fijaaeb.v6i1.249",{"doi":156,"openalex_id":158,"authors":159,"venue":138,"cited_by_count":34,"oa_url":135,"card":180,"direction":41,"ingested_from":103},"W7212562005",[160,163,165,168,170,172,174,176,178],{"name":161,"orcid":162},"Faisal Shahzad","https:\u002F\u002Forcid.org\u002F0009-0003-8413-2456",{"name":164,"orcid":8},"Hafiza Ayesha Arshad",{"name":166,"orcid":167},"Habib‐ur‐Rehman Athar","https:\u002F\u002Forcid.org\u002F0000-0002-8733-3865",{"name":169,"orcid":8},"Israr Hanif",{"name":171,"orcid":8},"Iqra Shokat",{"name":173,"orcid":8},"Ayesha Maryam",{"name":175,"orcid":8},"Laiba Urooj",{"name":177,"orcid":8},"Jaweria Maqbool",{"name":179,"orcid":8},"Aleena Akram",{"tldr":181,"method":182,"finding":183,"direction":41,"opportunity":184},"用自建RGB小麦种子数据集对比YOLOv8n与YOLO11n的检测计数性能。","832张RGB图像、3649个标注框，训练YOLOv8n与YOLO11n并多指标","两模型mAP@0.5均超0.994，YOLO11n在严格指标mAP@0.5:0.95上更优。","可扩展多品种、多类别种子质量评估，并引入更复杂背景与计数精度量化验证。","2026-09-15T23:30:17.202179Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":192,"source_url":189,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":194,"score_detail":195,"sources":197,"tags":199,"search_phrases":201,"slug":203,"view_count":34,"doi":204,"paper":205,"created_at":230},2316,"Vision Transformers Enable Advanced Plant Phenotyping in Controlled Environments","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.04.748299","Reliable plant segmentation in high-throughput phenotyping must transfer across species and imaging conditions without repeated model tuning or extensive reannotation. We compare three segmentation strategies using images from Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory: (i) fixed color-based thresholding, (ii) supervised U-Nets trained from scratch, and (iii) pretrained vision transformers fine-tuned for binary segmentation. Models were evaluated on a held-out test set and a generalization set that comprised unseen species. On the held-out test set, thresholding, the best U-Net, and the best vision transformer achieved mean Dice scores of 58.3, 96.6, and 97.3, respectively. On the generalization set, the corresponding Dice scores were 56.5, 86.2, and 95.7. Thresholding remained effective on some datasets but failed when plant appearance changed. Supervised U-Net training resolved within-distribution errors but failed to generalize to novel species and backgrounds. Pretrained vision transformers consistently produced high-accuracy segmentations across the evaluated species, views, soil backgrounds, and tray types. These results benchmark the practical progression from fixed rules to task-specific supervision and pretrained visual representations for controlled-environment plant phenotyping.","在高通量表型分析中，可靠的植物分割必须能够在不同物种和成像条件之间迁移，而无需重复调整模型或进行大量重新标注。我们利用橡树岭国家实验室先进植物表型实验室的图像，比较了三种分割策略：（i）基于颜色的固定阈值法，（ii）从零开始训练的有监督U-Net，以及（iii）针对二值分割进行微调的预训练视觉Transformer。模型在留出测试集和包含未见物种的泛化集上进行了评估。在留出测试集上，阈值法、最佳U-Net和最佳视觉Transformer的平均Dice分数分别为58.3、96.6和97.3。在泛化集上，相应的Dice分数分别为56.5、86.2和95.7。阈值法在某些数据集上仍然有效，但当植物外观发生变化时则失效。有监督U-Net训练解决了分布内误差，但未能泛化到新物种和背景。预训练视觉Transformer在所评估的物种、视角、土壤背景和托盘类型上始终产生高精度分割。这些结果基准了受控环境植物表型分析中从固定规则到任务特定监督再到预训练视觉表示的实际进展。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-10T00:00:00Z",79,{"impact":16,"substance":57,"depth":16,"authority":18,"freshness":145,"relevant":20,"comment":196},"预训练视觉Transformer在跨物种植物分割上显著优于U-Net与阈值法，为受控环境高通量表型提供可复用基准，方法新颖、数据扎实，值得入选。",[198],{"name":192,"url":189},[25,26,28,65,200],"图像分割",[202,154],"农业人工智能 高通量表型 图像分割 智慧农业","农业人工智能高通量表型图像分割智慧农业-2316","10.64898\u002F2026.09.04.748299",{"doi":204,"openalex_id":206,"authors":207,"venue":192,"cited_by_count":34,"oa_url":8,"card":225,"direction":41,"ingested_from":103},"W7212309344",[208,210,213,216,219,222],{"name":209,"orcid":8},"Janou Milligan",{"name":211,"orcid":212},"Anand Seethepalli","https:\u002F\u002Forcid.org\u002F0000-0003-0937-9128",{"name":214,"orcid":215},"Aristeidis Tsaris","https:\u002F\u002Forcid.org\u002F0000-0002-7734-3349",{"name":217,"orcid":218},"Xiao Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6545-1943",{"name":220,"orcid":221},"Larry M. York","https:\u002F\u002Forcid.org\u002F0000-0002-1995-9479",{"name":223,"orcid":224},"John Lagergren","https:\u002F\u002Forcid.org\u002F0000-0002-8092-7433",{"tldr":226,"method":227,"finding":228,"direction":41,"opportunity":229},"比较阈值法、U-Net和预训练ViT在植物分割中的跨物种泛化能力。","使用ORNL表型实验室图像，对比阈值法、U-Net和微调ViT的分割性能。","预训练ViT在未见物种上Dice达95.7，显著优于U-Net的86.2和阈值法的56.5。","可探索预训练ViT在田间复杂场景的跨物种泛化，并降低微调数据需求。","2026-09-13T23:30:21.474320Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":8,"source_name":110,"source_url":234,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":237,"score_detail":238,"sources":240,"tags":242,"search_phrases":245,"slug":247,"view_count":34,"doi":248,"paper":249,"created_at":275},2289,"High-throughput phenotyping platform for facility crops based on optical sensing technology: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102545","High-throughput acquisition of crop phenotypic information is one of the key technologies for achieving intelligent facility agriculture and precision breeding. Traditional phenotypic data collection methods suffer from low efficiency and strong subjectivity, making it difficult to achieve multi-scale continuous monitoring and meet the demands of modern research and production. This paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops. First, starting from optical sensing technologies, a comparative analysis highlights the advantages and limitations of RGB, multi-\u002Fhyperspectral, thermal infrared, and LiDAR sensors in phenotypic perception. Second, the characteristics and applicable scenarios of stationary, rail-mounted, mobile robot, and unmanned aerial vehicle (UAV) platform architectures are summarized. Furthermore, the evolution of phenotypic data processing methods is examined, focusing on the shift from traditional feature engineering to deep learning-driven approaches. Finally, key challenges such as multimodal data fusion, system cost, and real-time performance are discussed, along with the future direction of phenotypic platforms toward intelligent closed-loop decision-making systems. This article systematically reviews the facility agriculture phenotyping platforms driven by optical sensing technology, and also incorporates representative research progress in field phenotyping studies. These advances provide transferable sensing technologies, methodological frameworks, and platform design concepts that can facilitate the development of phenotyping platforms for controlled-environment agriculture.","高通量获取作物表型信息是实现智能设施农业和精准育种的关键技术之一。传统表型数据采集方式效率低、主观性强，难以实现多尺度连续监测，无法满足现代研究与生产需求。本文系统综述了光学传感技术驱动的设施作物表型平台的技术框架与发展脉络。首先，从光学传感技术出发，对比分析了RGB、多光谱\u002F高光谱、热红外和激光雷达传感器在表型感知中的优势与局限。其次，总结了固定式、轨道式、移动机器人和无人机平台架构的特点与适用场景。进而，梳理了表型数据处理方法的演进，重点分析了从传统特征工程到深度学习驱动方法的转变。最后，讨论了多模态数据融合、系统成本和实时性等关键挑战，并展望了表型平台向智能闭环决策系统发展的未来方向。本文系统综述了光学传感技术驱动的设施农业表型平台，同时纳入了田间表型研究中的代表性进展。这些进展提供了可迁移的传感技术、方法框架和平台设计理念，有助于推动受控环境农业表型平台的发展。",74,{"impact":16,"substance":143,"depth":144,"authority":18,"freshness":19,"relevant":20,"comment":239},"系统综述光学传感驱动的设施作物高通量表型平台，涵盖传感器对比、平台架构与深度学习数据处理演进，方法框架清晰、可迁移性强，对智慧设施农业与精准育种有参考价值。",[241],{"name":110,"url":234},[25,26,243,28,244],"设施农业","光学传感",[246,154],"农业人工智能 高通量表型 光学传感 智慧农业","农业人工智能高通量表型光学传感智慧农业-2289","10.1016\u002Fj.atech.2026.102545",{"doi":248,"openalex_id":250,"authors":251,"venue":110,"cited_by_count":34,"oa_url":269,"card":270,"direction":41,"ingested_from":103},"W7212130452",[252,254,256,258,260,262,265,267],{"name":253,"orcid":8},"Xiaodong Zhang",{"name":255,"orcid":8},"Zhaowei Li",{"name":257,"orcid":8},"Yi Zhang",{"name":259,"orcid":8},"Chuandong Guo",{"name":261,"orcid":8},"Zonghua Leng",{"name":263,"orcid":264},"Xiangyu Han","https:\u002F\u002Forcid.org\u002F0000-0003-0412-9859",{"name":266,"orcid":8},"Hanping Mao",{"name":268,"orcid":8},"Yixue Zhang","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007707\u002Fpdf",{"tldr":271,"method":272,"finding":273,"direction":41,"opportunity":274},"综述光学传感驱动的设施作物高通量表型平台，涵盖传感器、平台架构与数据处理。","综述RGB、多\u002F高光谱、热红外、LiDAR及固定\u002F轨道\u002F机器人\u002F无人机平台与深度","表型平台正从传统特征工程转向深度学习，并迈向智能闭环决策系统。","多模态数据融合、低成本实时表型平台及闭环决策在设施农业中仍待突破。","2026-09-13T23:30:04.259059Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":8,"content":8,"source_name":281,"source_url":8,"published_at":139,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":282,"score_detail":283,"sources":287,"tags":289,"search_phrases":293,"slug":296,"view_count":34,"doi":8,"paper":297,"created_at":305},3002,"改进生物神经网络的农业播种机全覆盖路径规划","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1968","江苏大学魏军等提出一种考虑播种与非播种运动模式切换机制的改进生物神经网络（BNN）方法，基于周围环境条件将下一节点状态分类为播种、封闭或转移节点。在BNN景观引导下机器沿平行直线路径继续播种操作；检测到封闭节点时切换至非播种模式并使用深度优先搜索算法搜索潜在封闭区域；检测到转移节点时同样切换非播种模式搜索合理的新目标节点。仿真表明该方法实现播种操作的完全覆盖同时避免重复遍历已播种区域。","MDPI Agriculture 16(18):1968",69,{"impact":284,"substance":285,"depth":144,"authority":18,"freshness":19,"relevant":20,"comment":286},12,21,"提出改进生物神经网络的全覆盖路径规划方法，方法新颖、结论可靠，但属细分领域学术进展，公共影响有限。",[288],{"name":281,"url":279},[25,26,290,291,292],"智能农机","路径规划","播种机",[294,295],"江苏大学 播种机 全覆盖路径规划","生物神经网络 播种机 路径规划","江苏大学播种机全覆盖路径规划-3002",{"doi":8,"openalex_id":8,"authors":298,"venue":8,"cited_by_count":34,"oa_url":8,"card":299,"direction":303,"ingested_from":43},[],{"tldr":300,"method":301,"finding":302,"direction":303,"opportunity":304},"提出改进生物神经网络，实现农业播种机全覆盖路径规划并避免重复播种。","改进BNN结合节点分类与深度优先搜索，仿真验证。","方法实现播种完全覆盖，同时避免重复遍历已播种区域。","农业人工智能与决策模型","可结合真实农田地形与多机协同，验证动态环境下的路径规划鲁棒性。","2026-09-20T00:03:08.288198Z"]