[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5562":3,"related-5562":94},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":57,"slug":60,"view_count":61,"doi":62,"paper":63,"created_at":93},5562,"pxGPT: A schema-constrained multimodal LLM framework for plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtag781","Abstract Summary Deep learning can scale image-based plant phenotyping but typically targets predefined traits, whereas manual phenotypic description is more flexible but difficult to scale. We present Phenotype eXplorer Generative Plant Traits (pxGPT), a Python framework that converts multi-view plant images into structured phenotype records. The workflow combines free-text description, dataset-specific schema generation with human review, and schema-constrained information extraction. We evaluated the extraction stage on three Brassica datasets using 78,973 annotations from ten annotators who had no access to pxGPT predictions. For 38-45 qualitative traits per dataset, pxGPT-human agreement ranged from 0.572 to 0.712, compared with 0.620 to 0.739 agreement among human annotators. All 32,736 required trait fields were present; 93 values were empty, and no qualitative descriptor violations were recorded. For selected morphological structures, pooled false-positive rates were 0.5-8.0%, while sensitivity varied across structure types in one mature-plant dataset (37.5-100%). The curated schemas were applied unchanged with two commercial models and one locally hosted open-weight model, with differences in mean accuracy of 0.03 or less. In the evaluated Brassica datasets, these results support schema-guided extraction of visible plant traits without separate model training for each trait. Human-guided schema development offers a path to extend pxGPT to other crops and imaging conditions, with accuracy requiring validation in each new setting. Availability and Implementation The software is available at https:\u002F\u002Fgithub.com\u002FYaoChengLab\u002Fpxgpt and an archival version of the source code is available on Zenodo (https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22931745). Curated schemas used in this study, annotation guides, phenotype records and human annotations are available at https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22907554. Supplementary information Supplementary data are available at Bioinformatics online.","摘要 深度学习能够扩展基于图像的植物表型分析，但通常针对预定义性状，而人工表型描述更为灵活却难以规模化。我们提出Phenotype eXplorer Generative Plant Traits（pxGPT），一个将多视角植物图像转换为结构化表型记录的Python框架。该工作流程结合了自由文本描述、数据集特定模式生成与人工审核，以及模式约束的信息提取。我们在三个芸薹属（Brassica）数据集上评估了提取阶段，使用了来自十名标注者的78,973条标注，这些标注者无法获取pxGPT的预测结果。对于每个数据集38-45个定性性状，pxGPT与人工标注者之间的一致性范围为0.572至0.712，而人工标注者之间的一致性为0.620至0.739。所有32,736个所需性状字段均已存在；93个值为空，未记录到定性描述符违规。对于选定的形态结构，合并假阳性率为0.5-8.0%，而在一个成熟植株数据集中，灵敏度因结构类型而异（37.5-100%）。经整理的模式在未作修改的情况下应用于两个商业模型和一个本地部署的开放权重模型，平均准确率差异不超过0.03。在所评估的芸薹属数据集中，这些结果支持在无需为每个性状单独训练模型的情况下，对可见植物性状进行模式引导的提取。人工引导的模式开发为将pxGPT扩展至其他作物和成像条件提供了途径，其准确性需在每个新设置中加以验证。可用性与实现 该软件可在https:\u002F\u002Fgithub.com\u002FYaoChengLab\u002Fpxgpt获取，源代码的存档版本可在Zenodo（https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22931745）获取。本研究中使用的经整理模式、标注指南、表型记录和人工标注可在https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22907554获取。补充信息 补充数据可在Bioinformatics在线获取。",null,"Bioinformatics","2026-10-20T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出基于模式约束的多模态大模型植物表型提取框架，在近8万条标注上验证，方法新颖、数据规模可观，对智慧育种与表型自动化有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","植物表型","多模态大模型","芸薹属",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},8,true,[],[49,50],"深度学习","deep learning",[27,29],{"code":53,"score":21,"supported":14,"title":54,"abstract":55,"tags":56},"I1",[],[],[26],[58,59],"pxGPT 植物表型","Brassica 表型 多模态大模型","pxGPT植物表型-5562",0,"10.1093\u002Fbioinformatics\u002Fbtag781",{"doi":62,"openalex_id":64,"authors":65,"venue":10,"cited_by_count":61,"oa_url":84,"card":85,"direction":91,"ingested_from":92},"W7223127960",[66,69,71,74,77,80,82],{"name":67,"orcid":68},"Po‐Xing Zheng","https:\u002F\u002Forcid.org\u002F0000-0001-5827-5865",{"name":70,"orcid":9},"Jheng-Yan Ou",{"name":72,"orcid":73},"Yu-Lin Wu","https:\u002F\u002Forcid.org\u002F0009-0003-8596-9047",{"name":75,"orcid":76},"Ruei-Shiuan Wang","https:\u002F\u002Forcid.org\u002F0009-0007-5159-1129",{"name":78,"orcid":79},"WU Ting-li","https:\u002F\u002Forcid.org\u002F0000-0001-7996-5515",{"name":81,"orcid":9},"Te-Chang Hsu",{"name":83,"orcid":9},"Yao-Cheng Lin","https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtag781\u002F72201337\u002Fbtag781.pdf",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"提出pxGPT框架，用多模态大模型将多视角植物图像转为结构化表型记录。","结合自由文本描述、人工审核的模式生成与模式约束信息抽取，在三个芸薹属数据集上评估","pxGPT与人工标注一致性0.572-0.712，接近人工间一致性，且无需逐性状训练模型。","农业遥感与作物表型","可探索将模式引导抽取迁移至更多作物与复杂成像条件，并研究自动化模式生成以降低人工审核成本。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:22.173561Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,153,216,254,322,386],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":105,"sources":108,"tags":110,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":137,"slug":140,"view_count":61,"doi":141,"paper":142,"created_at":152},5315,"PLANT-GPK v4.0: Multi-Stage Plant Phenotyping with Time-Discovery and Harvest Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23461121","This paper presents a comprehensive extension to the PLANT-GPK framework, demonstrating its robust capability to analyze plants across entirely different developmental stages from single images. By formulating an inverse problem that simultaneously optimizes physiological parameters and the hidden time dimension (t), the model successfully discovered the exact age of two distinct wheat samples: a vegetative (green) stage and a senescent (golden) stage. The framework bridges computer vision, spatial heterogeneity analysis, and ODE-based physics to provide a complete digital twin capable of forecasting the exact day of harvest regardless of the plant's current state.","本文对PLANT-GPK框架进行了全面扩展，展示了其仅凭单张图像即可分析处于完全不同发育阶段植物的稳健能力。通过构建一个同时优化生理参数与隐藏时间维度（t）的反问题，该模型成功推断出两个不同小麦样本的精确年龄：一个处于营养期（绿色阶段），另一个处于衰老期（金色阶段）。该框架融合了计算机视觉、空间异质性分析以及基于常微分方程（ODE）的物理建模，提供了一个完整的数字孪生，无论植物当前处于何种状态，均能预测确切的收获日期。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":106,"freshness":13,"relevant":21,"comment":107},13,"该研究提出从单张图像反演植物生育阶段与收获日的数字孪生框架，方法新颖且具产业应用潜力，值得进入每日精选。",[109],{"name":103,"url":100},[26,27,111,28,112],"数字孪生","收获预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":116},{"candidates":117,"agricultural_evidence":46},[118,123,130],{"code":53,"score":45,"supported":46,"title":119,"abstract":120,"tags":122},[],[111,121],"digital twin",[111,26],{"code":32,"score":124,"supported":46,"title":125,"abstract":126,"tags":129},7,[],[127,128],"计算机视觉","computer vision",[27],{"code":131,"score":95,"supported":46,"title":132,"abstract":133,"tags":136},"A2",[],[134,135],"小麦","wheat",[],[138,139],"PLANT-GPK 植物表型","小麦 收获期 预测","PLANT-GPK植物表型-5315","10.5281\u002Fzenodo.23461121",{"doi":141,"openalex_id":143,"authors":144,"venue":103,"cited_by_count":61,"oa_url":100,"card":147,"direction":89,"ingested_from":92},"W7222875391",[145],{"name":146,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":148,"method":149,"finding":150,"direction":89,"opportunity":151},"扩展PLANT-GPK框架，从单张图像反演植物生理参数与隐藏时间维度，实现跨生育期表型分析与收获期预","基于ODE物理模型与逆问题优化，联合估计生理参数和隐藏时间t，用两个不同生育期小","模型能准确发现两个不同生育期小麦样本的精确年龄，并预测确切收获日期。","可将单图时间反演扩展到多作物多生育期，结合时序遥感构建大尺度数字孪生与收获期预报。","2026-10-19T23:30:19.267920Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":103,"source_url":156,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":165,"tags":167,"primary_topic":170,"secondary_topics":171,"classification":172,"search_phrases":191,"slug":194,"view_count":21,"doi":195,"paper":196,"created_at":215},5194,"A Comprehensive Review on Multi-Modal Large Language Models and Agentic Architectures for Precision Agricultural Decision Support","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23411181","This article reviews recent developments in multi-modal large language models and agentic architectures for agricultural decision support. Forty-five representative studies published between 2023 and 2026 were selected across perception, language, orchestration, soil intelligence and governance, organised under a four-layer taxonomy, and classified against nine functional capabilities using a stated, reproducible procedure. The comparative analysis exposes a pronounced imbalance. Crop and disease perception is covered by 36 per cent of the selected studies, while dose-bearing prescription appears in 7 per cent, profit-aware recommendation in 4 per cent, and persistent per-field memory in a single study. Advisory tools in this literature therefore remain unable to exchange state: image-based scanners are rarely conditioned on measured soil chemistry, and crop-recommendation models optimise agronomic suitability without reference to farm economics. On this basis the article synthesises a conceptual reference architecture from the reviewed literature. It is a literature-derived synthesis rather than an implemented system, and calibrated abstention, evaluation protocols for longitudinal memory, and vernacular voice coverage emerge as the most urgent gaps.","本文综述了面向农业决策支持的多模态大语言模型与智能体架构的最新进展。按照可复现的既定流程，选取2023年至2026年间发表的45项代表性研究，涵盖感知、语言、编排、土壤智能与治理等方向，将其纳入一个四层分类体系，并依据九项功能能力进行分类。比较分析揭示出明显的不均衡：作物与病害感知在所选研究中占36%，而含剂量处方的研究仅占7%，利润感知推荐占4%，持久性田块记忆仅见于一项研究。因此，该领域的咨询工具仍无法交换状态：基于图像的扫描器很少以实测土壤化学状况为条件，作物推荐模型在优化农艺适宜性时也未考虑农场经济性。在此基础上，本文综合所综述文献提出一个概念性参考架构。该架构是文献衍生的综合框架，而非已实现的系统；校准弃权、纵向记忆的评估协议以及方言语音覆盖被列为最紧迫的空白。","2026-10-16T00:00:00Z",63,{"impact":162,"substance":17,"depth":163,"authority":45,"freshness":20,"relevant":21,"comment":164},12,16,"系统性综述揭示处方决策与农场经济维度严重缺位，对农业AI研发方向有参考价值，但属文献综述而非实证突破，影响范围有限。",[166],{"name":103,"url":156},[26,27,168,29,169],"智能体","农业决策支持",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":173},{"candidates":174,"agricultural_evidence":46},[175,180,187],{"code":32,"score":19,"supported":46,"title":176,"abstract":178,"tags":179},[177],"decision support",[177],[27,29,168],{"code":181,"score":95,"supported":46,"title":182,"abstract":183,"tags":186},"H2",[],[184,185],"土壤","soil",[],{"code":53,"score":21,"supported":14,"title":188,"abstract":189,"tags":190},[],[],[26],[192,193],"多模态大模型 农业决策支持","农业人工智能 农业决策支持 多模态大模型 智慧农业","多模态大模型农业决策支持-5194","10.5281\u002Fzenodo.23411181",{"doi":195,"openalex_id":197,"authors":198,"venue":103,"cited_by_count":61,"oa_url":156,"card":209,"direction":213,"ingested_from":92},"W7222605322",[199,201,203,205,207],{"name":200,"orcid":9},"Tanmay Bhamre",{"name":202,"orcid":9},"Yash Barde",{"name":204,"orcid":9},"Devendra Yeole",{"name":206,"orcid":9},"Gajanan Chaudhary",{"name":208,"orcid":9},"Amarsinh Verpe",{"tldr":210,"method":211,"finding":212,"direction":213,"opportunity":214},"综述多模态大模型与智能体架构在精准农业决策支持中的应用与不足。","筛选2023-2026年45篇代表性研究，按四层分类与九项功能能力归类比较。","感知研究占36%，处方仅7%、利润推荐4%、田块记忆仅1篇，系统难以交换状态。","农业人工智能与决策模型","可研究跨模态状态交换、纵向记忆评测协议与方言语音覆盖，填补处方与经济决策空白。","2026-10-17T23:30:55.282694Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":9,"content":9,"source_name":221,"source_url":9,"published_at":222,"category":223,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":225,"sources":229,"tags":231,"primary_topic":234,"secondary_topics":235,"classification":236,"search_phrases":249,"slug":252,"view_count":21,"doi":9,"paper":9,"created_at":253},4933,"中国农科院信息所提出层次知识引导枸杞害虫识别框架","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20261013A07CL800","中国农业科学院农业信息研究所科学数据研究室提出一种层次知识引导的枸杞害虫识别框架,有效解决真实农业场景下样本稀缺与类间混淆的双重挑战。相关研究成果发表在《人工智能的工程应用》(Engineering Applications of Artificial Intelligence)上。该研究依托长文本-图像检索模型,把专家撰写的长文本描述解析为粗、中、细三个语义层次,并与多尺度视觉特征实现精准跨模态对齐,为枸杞等特色作物的虫害智能监测与精准防控提供新的技术路径。","中国农业科学院农业信息研究所 腾讯新闻 2026 年 10 月 13 日","2026-10-13T00:00:00Z","报道",78,{"impact":17,"substance":226,"depth":227,"authority":19,"freshness":45,"relevant":21,"comment":228},21,17,"国家级科研机构在农业AI领域的方法创新，跨模态对齐思路对特色作物虫害监测有推广价值，值得进入每日精选。",[230],{"name":221,"url":219},[26,27,29,232,233],"害虫识别","枸杞",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":237},{"candidates":238,"agricultural_evidence":46},[239,245],{"code":32,"score":240,"supported":46,"title":241,"abstract":242,"tags":244},5,[],[243],"artificial intelligence",[27,29],{"code":53,"score":21,"supported":14,"title":246,"abstract":247,"tags":248},[],[],[26],[250,251],"中国农科院信息所 枸杞害虫识别","枸杞 害虫识别 视觉语言模型","中国农科院信息所枸杞害虫识别-4933","2026-10-15T00:06:01.623759Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":262,"sources":264,"tags":266,"primary_topic":269,"secondary_topics":270,"classification":271,"search_phrases":283,"slug":286,"view_count":287,"doi":288,"paper":289,"created_at":321},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",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":263},"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。",[265],{"name":260,"url":257},[26,27,267,28,268],"开源工具","三维点云",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":272},{"candidates":273,"agricultural_evidence":46},[274,279],{"code":32,"score":275,"supported":46,"title":276,"abstract":277,"tags":278},11,[],[49,127,50,128],[268,27],{"code":53,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[26],[284,285],"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",4,"10.1016\u002Fj.isprsjprs.2026.09.003",{"doi":288,"openalex_id":290,"authors":291,"venue":260,"cited_by_count":61,"oa_url":257,"card":316,"direction":89,"ingested_from":92},"W7213280479",[292,294,296,298,301,304,306,308,310,313],{"name":293,"orcid":9},"Ruiming Du",{"name":295,"orcid":9},"Guangxun Zhai",{"name":297,"orcid":9},"Tian Qiu",{"name":299,"orcid":300},"Shichao Jin","https:\u002F\u002Forcid.org\u002F0000-0003-1150-336X",{"name":302,"orcid":303},"Dawei Li","https:\u002F\u002Forcid.org\u002F0000-0002-9702-8848",{"name":305,"orcid":9},"Zhihong Ma",{"name":307,"orcid":9},"Yongliang Qiao",{"name":309,"orcid":9},"Junfeng Gao",{"name":311,"orcid":312},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",{"name":314,"orcid":315},"Yu Jiang","https:\u002F\u002Forcid.org\u002F0000-0003-4495-3033",{"tldr":317,"method":318,"finding":319,"direction":89,"opportunity":320},"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。","2026-09-17T23:30:30.425366Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":354,"slug":357,"view_count":358,"doi":359,"paper":360,"created_at":385},2742,"LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112417","LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping。Computers and Electronics in Agriculture","LeafTrackNet：一种用于自上而下植物表型分析中稳健叶片追踪的深度学习框架。","Computers and Electronics in Agriculture",70,{"impact":162,"substance":17,"depth":227,"authority":19,"freshness":20,"relevant":21,"comment":331},"核心期刊发表的叶片追踪深度学习框架，方法有创新但属细分技术进展，产业影响有限，可作为农业AI主题聚合素材。",[333],{"name":328,"url":325},[26,27,49,335,28],"作物监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":339},{"candidates":340,"agricultural_evidence":46},[341,345,349],{"code":32,"score":163,"supported":46,"title":342,"abstract":343,"tags":344},[50],[49,50],[27,49],{"code":53,"score":21,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[26],{"code":350,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},"I3",[],[],[335],[355,356],"农业人工智能 作物监测 智慧农业 植物表型","农业人工智能 作物监测","农业人工智能作物监测智慧农业植物表型-2742",2,"10.1016\u002Fj.compag.2026.112417",{"doi":359,"openalex_id":361,"authors":362,"venue":328,"cited_by_count":61,"oa_url":325,"card":380,"direction":89,"ingested_from":92},"W7213430727",[363,366,369,371,373,375,378],{"name":364,"orcid":365},"Shanghua Liu","https:\u002F\u002Forcid.org\u002F0009-0009-9855-9040",{"name":367,"orcid":368},"Majharulislam Babor","https:\u002F\u002Forcid.org\u002F0000-0002-5440-7573",{"name":370,"orcid":9},"Christoph Verduyn",{"name":372,"orcid":9},"Breght Vandenberghe",{"name":374,"orcid":9},"Bruno Betoni Parodi",{"name":376,"orcid":377},"Cornelia Weltzien","https:\u002F\u002Forcid.org\u002F0000-0002-2951-3614",{"name":379,"orcid":9},"Marina M. -C. Höhne",{"tldr":381,"method":382,"finding":383,"direction":89,"opportunity":384},"提出LeafTrackNet深度学习框架，实现俯视植物表型中稳健的叶片追踪。","基于深度学习的叶片追踪框架，用于俯视植物表型图像序列。","该框架能稳健追踪叶片，提升植物表型分析的自动化与准确性。","可探索多物种、遮挡与生长形变下的长期叶片追踪及三维表型融合。","2026-09-17T23:30:01.562015Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":9,"content":391,"source_name":392,"source_url":9,"published_at":393,"category":223,"cover_url":9,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":403,"classification":404,"search_phrases":428,"slug":431,"view_count":358,"doi":9,"paper":9,"created_at":432},2106,"中国农业大学马韫韬教授到上海农科院作\"AI与植物表型\"专题学术报告 推动智慧农业多学科交叉","https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742","9月2日下午上海农科院信息所举办\"AI时代的智慧农业与植物表型\"专题学术报告会,特邀中国农业大学教授、博士生导师、作物表型三维仿真首席科学家马韫韬作专题学术报告。马韫韬重点介绍了棉花育种表型研究进展,基于DINO视觉大模型实现棉铃精准分割与最佳集中收获期量化,首创无人机交叉环绕飞行三维重建技术,为棉花机采化育种、产量预测与基因挖掘提供高效技术手段。","尊敬的用户，您好！\n\n网站不支持您所使用的浏览器版本（可能会出现网页变形等问题）。为了更好地展示页面效果，请您使用以下浏览器（点击图标会跳转到相关浏览器的官方网站下载页面）。\n\n*   [![Image 3: Chrome](https:\u002F\u002Fwww.saas.sh.cn\u002Fcontent\u002F_common\u002Fbase\u002Fimg\u002Ficon-browser\u002Fbr-chrome.png)Chrome](https:\u002F\u002Fwww.google.cn\u002Fchrome\u002F)\n*   [![Image 4: Firefox](https:\u002F\u002Fwww.saas.sh.cn\u002Fcontent\u002F_common\u002Fbase\u002Fimg\u002Ficon-browser\u002Fbr-firefox.png)Firefox](https:\u002F\u002Fwww.firefox.com\u002F)\n*   [![Image 5: edge](https:\u002F\u002Fwww.saas.sh.cn\u002Fcontent\u002F_common\u002Fbase\u002Fimg\u002Ficon-browser\u002Fbr-edge.png)新Edge](https:\u002F\u002Fwww.microsoft.com\u002Fzh-cn\u002Fedge)\n*   [![Image 6: 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次【字体：[小](javascript:;)[大](javascript:;)】\n\n9月2日下午，信息所举办“AI时代的智慧农业与植物表型”专题学术报告会，特邀中国农业大学教授、博士生导师、作物表型三维仿真首席科学家马韫韬作专题学术报告。所长施建春主持会议，信息所、园艺所、生态所、质标所科技人员和学生共同参加交流学习。\n\n报告会上，马韫韬结合团队近几年的研究积累，围绕人工智能时代智慧农业与植物表型技术的前沿趋势和最新研究进展作了系统分享。在设施温室方面，团队利用三维仿真模型、微气候仿真方法开展了基于流体力学的温室骨架设计和安全性分析，优化后的温室结构进一步提升了环境调控能力；针对设施内遥感手段缺乏的问题，自主研制RGB、多光谱、热红外多元传感器装备，结合人工智能与视觉大模型实现作物株高、叶面积等表型指标的自动提取与动态监测。\n\n在大田育种表型方面，马韫韬重点介绍了棉花育种表型研究进展。团队开展了基于视觉大模型的棉花单株多性状表型提取与稳定性分析方法；建立基于DINO视觉大模型的棉花集中吐絮特征筛选与采收决策技术体系，实现棉铃精准分割与最佳集中收获期量化；首创无人机交叉环绕飞行三维重建技术，为棉花机采化育种、产量预测与基因挖掘提供了高效技术手段。\n\n交流环节，与会人员围绕温室环境仿真、多模态数据融合、单株表型提取、无人机低空航线设计、三维点云与AI训练等话题与马韫韬展开深入探讨。马韫韬结合计算机与农学交叉背景，就学科交叉融合和研究生培养分享了宝贵经验。\n\n本次报告会是信息所“智慧农业与高通量表型技术前沿”系列学术报告活动之一。报告内容前沿务实，进一步开阔了科研人员的学术视野，促进了人工智能、作物表型、农业信息技术等多学科交叉融合，为推进我院智慧农业科技创新和成果转化注入了新动能。\n\n![Image 9: 5f39e0ad07e8461bb2d0dac0a5ddad26.jpg](https:\u002F\u002Fwww.saas.sh.cn\u002Fupload\u002Fmain\u002Fcontentmanage\u002Farticle\u002Fimage\u002F2026\u002F09\u002F07\u002F5f39e0ad07e8461bb2d0dac0a5ddad26.jpg)\n\n![Image 10: 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18](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 23](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 19](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 24](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 20](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 25](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 21](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 26](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 22](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 27](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 23](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 28](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 24](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 29](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 25](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 30](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 26](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 31](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 27](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证\n\n![Image 32](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)\n\n加载中...\n\n刷新\n\n切换至语音验证码\n\n[Audio 28](https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fjcdt\u002Fcontent_56742)播放语音验证码\n\n刷新\n\n返回文字点选验证","上海市农业科学院","2026-09-02T00:00:00Z",43,{"impact":45,"substance":13,"depth":20,"authority":13,"freshness":95,"relevant":21,"comment":396},"省级农科院举办的AI与植物表型学术报告，属细分领域学术交流动态，有交叉学科价值但公共影响有限，时效性一般。",[398],{"name":392,"url":389},[26,27,400,28,401],"学术交流","棉花育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":405},{"candidates":406,"agricultural_evidence":46},[407,411,416,423],{"code":53,"score":162,"supported":46,"title":408,"abstract":409,"tags":410},[26],[26],[26],{"code":32,"score":287,"supported":46,"title":412,"abstract":413,"tags":415},[],[414],"大模型",[27],{"code":417,"score":418,"supported":46,"title":419,"abstract":420,"tags":422},"A6",3,[],[421],"棉花",[],{"code":350,"score":418,"supported":46,"title":424,"abstract":425,"tags":427},[],[426],"作物表型",[],[429,430],"农业人工智能 学术交流 智慧农业 棉花育种","农业人工智能 学术交流","农业人工智能学术交流智慧农业棉花育种-2106","2026-09-11T00:04:21.627848Z"]