[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2211":3,"related-2211":65},{"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,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":50,"slug":53,"view_count":39,"doi":8,"paper":54,"created_at":64},2211,"《视觉-语言模型对农业的了解比其表现出来的更多 基于评分标准的验证可弥补差距》","https:\u002F\u002Farcxiv.org\u002Fabs\u002F2609.09417","论文针对视觉-语言模型(VLM)在农作物病害、虫害、损伤、品质、物种识别等零样本任务中表现不佳的问题,构建包含116个数据集、834个类别、8324张图像的基准测试。线性探测显示VLM视觉编码器已能将与自监督DINOv3基线几乎同等可分离的农业特征编码,排除弱视觉表示为主要瓶颈。论文提出Probabilistic Pivot Tournament (PPT)验证器,通过基于固定诊断评分标准的K候选响应选择,使评判F1较未辅助基线近乎翻倍,在病害任务中将Gemma 4 E4B-it's病害F1提升至0.71,超过其自身上限0.60。",null,"arXiv 2609.09417 2026年9月","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,8,1,"构建116数据集、8324图像的农业VLM基准，提出PPT评分标准验证器使病害F1近乎翻倍，方法新颖、数据扎实，对农业AI落地有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","病害识别","多模态大模型","零样本学习",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"unclassified","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":48,"reason":49},[37,43],{"code":38,"score":39,"supported":13,"title":40,"abstract":41,"tags":42},"I2",3,[],[],[26,28,27],{"code":44,"score":20,"supported":13,"title":45,"abstract":46,"tags":47},"I1",[],[],[25],true,"缺少足够的主题证据",[51,52],"农业人工智能 多模态大模型 零样本学习 智慧农业","农业人工智能 多模态大模型","农业人工智能多模态大模型零样本学习智慧农业-2211",{"doi":8,"openalex_id":8,"authors":55,"venue":8,"cited_by_count":56,"oa_url":8,"card":57,"direction":61,"ingested_from":63},[],0,{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"论文构建农业VLM基准，发现视觉编码器不弱，提出PPT验证器按评分标准选答案，大幅提升识别F1。","116数据集8324图基准，线性探测对比DINOv3，提出PPT概率枢轴锦标赛验","VLM农业知识被输出瓶颈掩盖，PPT使病害F1近翻倍，Gemma提升至0.71超其上限0.60。","农业人工智能与决策模型","可探索将评分标准验证器嵌入农业VLM推理链，或迁移到遥感、品质分级等更多农业零样本任务。","agent","2026-09-12T00:06:40.201845Z",{"total":66,"page":20,"page_size":66,"items":67},6,[68,142,209,271,329,390],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":73,"content":8,"source_name":74,"source_url":71,"published_at":75,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":76,"score_detail":77,"sources":81,"tags":83,"primary_topic":86,"secondary_topics":91,"classification":92,"search_phrases":106,"slug":109,"view_count":56,"doi":110,"paper":111,"created_at":141},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。","Bioinformatics","2026-10-20T00:00:00Z",81,{"impact":16,"substance":17,"depth":16,"authority":78,"freshness":79,"relevant":20,"comment":80},14,9,"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。",[82],{"name":74,"url":71},[25,26,84,28,85],"植物表型","芸薹属",{"code":38,"slug":87,"name":88,"path":89,"parent_code":90},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":93,"locked":13,"version":33,"method":34,"evidence":94},"classified",{"candidates":95,"agricultural_evidence":48},[96,102],{"code":38,"score":19,"supported":48,"title":97,"abstract":98,"tags":101},[],[99,100],"深度学习","deep learning",[26,28],{"code":44,"score":20,"supported":13,"title":103,"abstract":104,"tags":105},[],[],[25],[107,108],"pxGPT 植物表型","Brassica 表型 多模态大模型","pxGPT植物表型-5562","10.1093\u002Fbioinformatics\u002Fbtag781",{"doi":110,"openalex_id":112,"authors":113,"venue":74,"cited_by_count":56,"oa_url":132,"card":133,"direction":139,"ingested_from":140},"W7223127960",[114,117,119,122,125,128,130],{"name":115,"orcid":116},"Po‐Xing Zheng","https:\u002F\u002Forcid.org\u002F0000-0001-5827-5865",{"name":118,"orcid":8},"Jheng-Yan Ou",{"name":120,"orcid":121},"Yu-Lin Wu","https:\u002F\u002Forcid.org\u002F0009-0003-8596-9047",{"name":123,"orcid":124},"Ruei-Shiuan Wang","https:\u002F\u002Forcid.org\u002F0009-0007-5159-1129",{"name":126,"orcid":127},"WU Ting-li","https:\u002F\u002Forcid.org\u002F0000-0001-7996-5515",{"name":129,"orcid":8},"Te-Chang Hsu",{"name":131,"orcid":8},"Yao-Cheng Lin","https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtag781\u002F72201337\u002Fbtag781.pdf",{"tldr":134,"method":135,"finding":136,"direction":137,"opportunity":138},"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",{"id":143,"title":144,"url":145,"summary":146,"summary_zh":147,"content":8,"source_name":148,"source_url":145,"published_at":149,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":150,"score_detail":151,"sources":154,"tags":156,"primary_topic":159,"secondary_topics":160,"classification":161,"search_phrases":195,"slug":198,"view_count":56,"doi":199,"paper":200,"created_at":208},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":19,"substance":78,"depth":152,"authority":66,"freshness":79,"relevant":20,"comment":153},12,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[155],{"name":148,"url":145},[25,26,157,27,158],"决策支持系统","灌溉管理",{"code":38,"slug":87,"name":88,"path":89,"parent_code":90},[],{"status":93,"locked":13,"version":33,"method":34,"evidence":162},{"candidates":163,"agricultural_evidence":48},[164,170,178,185,191],{"code":38,"score":165,"supported":48,"title":166,"abstract":168,"tags":169},11,[167],"decision support",[],[26,157,27],{"code":171,"score":172,"supported":48,"title":173,"abstract":174,"tags":177},"H3",7,[],[175,176],"灌溉","irrigation",[158],{"code":179,"score":66,"supported":48,"title":180,"abstract":181,"tags":184},"H2",[],[182,183],"土壤","soil",[],{"code":186,"score":39,"supported":48,"title":187,"abstract":188,"tags":190},"F3",[],[189],"肥料",[],{"code":44,"score":20,"supported":13,"title":192,"abstract":193,"tags":194},[],[],[25],[196,197],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":199,"openalex_id":201,"authors":202,"venue":148,"cited_by_count":56,"oa_url":145,"card":203,"direction":139,"ingested_from":140},"W7222785888",[],{"tldr":204,"method":205,"finding":206,"direction":61,"opportunity":207},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":8,"content":8,"source_name":214,"source_url":8,"published_at":215,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":216,"sources":218,"tags":220,"primary_topic":223,"secondary_topics":229,"classification":230,"search_phrases":258,"slug":261,"view_count":250,"doi":262,"paper":263,"created_at":270},5301,"MambaSeed:基于Mamba的轻量级混合视觉网络用于番茄叶病识别(PLOS ONE)","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0359350","PLOS ONE于2026-10-15发表论文MambaSeed,由Wang X, Zhang H, Chen Y, Li Y, Qin Y, Lv Z等团队提出基于MambaVisionMixer的轻量级混合视觉网络,配套局部连续分支注入(LCBI)机制;在番茄、玉米、马铃薯数据集上识别准确率分别达98.97%、98.06%和95.87%,模型仅1.13M参数、0.240G MACs,在RTX 4090上延迟3.39ms\u002F图像,为资源受限农田环境提供紧凑、高效、鲁棒的病害识别方案。","PLOS ONE(2026-10-15)","2026-10-15T00:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":217},"方法新颖、指标扎实的轻量级病害识别论文，对资源受限农田场景有实用价值，值得进入每日精选。",[219],{"name":214,"url":212},[25,26,221,27,222],"番茄","轻量级模型",{"code":224,"slug":225,"name":226,"path":227,"parent_code":228},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":93,"locked":13,"version":33,"method":34,"evidence":231},{"candidates":232,"agricultural_evidence":48},[233,237,243,249,254],{"code":224,"score":152,"supported":48,"title":234,"abstract":235,"tags":236},[221],[221],[221],{"code":238,"score":39,"supported":48,"title":239,"abstract":240,"tags":242},"A3",[],[241],"玉米",[],{"code":244,"score":39,"supported":48,"title":245,"abstract":246,"tags":248},"A5",[],[247],"马铃薯",[],{"code":38,"score":250,"supported":13,"title":251,"abstract":252,"tags":253},2,[],[],[26,27],{"code":44,"score":20,"supported":13,"title":255,"abstract":256,"tags":257},[],[],[25],[259,260],"MambaSeed 番茄叶病","MambaVisionMixer 病害识别","MambaSeed番茄叶病-5301","10.1371\u002Fjournal.pone.0359350",{"doi":262,"openalex_id":8,"authors":264,"venue":8,"cited_by_count":56,"oa_url":8,"card":265,"direction":61,"ingested_from":63},[],{"tldr":266,"method":267,"finding":268,"direction":61,"opportunity":269},"提出轻量级混合视觉网络MambaSeed，实现番茄等作物叶病高效识别。","基于MambaVisionMixer与局部连续分支注入机制，在三种作物病害数据集","番茄、玉米、马铃薯识别准确率达98.97%、98.06%、95.87%，仅1.13M参数、3.39m","可探索该轻量模型在移动端与边缘设备的田间部署，及多作物跨域泛化与病害早期识别。","2026-10-19T00:07:48.589614Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":8,"source_name":277,"source_url":274,"published_at":278,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":279,"score_detail":280,"sources":283,"tags":285,"primary_topic":288,"secondary_topics":289,"classification":290,"search_phrases":305,"slug":308,"view_count":20,"doi":309,"paper":310,"created_at":328},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-16T00:00:00Z",63,{"impact":152,"substance":16,"depth":281,"authority":19,"freshness":79,"relevant":20,"comment":282},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。",[284],{"name":277,"url":274},[25,26,286,28,287],"智能体","农业决策支持",{"code":38,"slug":87,"name":88,"path":89,"parent_code":90},[],{"status":93,"locked":13,"version":33,"method":34,"evidence":291},{"candidates":292,"agricultural_evidence":48},[293,297,301],{"code":38,"score":78,"supported":48,"title":294,"abstract":295,"tags":296},[167],[167],[26,28,286],{"code":179,"score":66,"supported":48,"title":298,"abstract":299,"tags":300},[],[182,183],[],{"code":44,"score":20,"supported":13,"title":302,"abstract":303,"tags":304},[],[],[25],[306,307],"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":309,"openalex_id":311,"authors":312,"venue":277,"cited_by_count":56,"oa_url":274,"card":323,"direction":61,"ingested_from":140},"W7222605322",[313,315,317,319,321],{"name":314,"orcid":8},"Tanmay Bhamre",{"name":316,"orcid":8},"Yash Barde",{"name":318,"orcid":8},"Devendra Yeole",{"name":320,"orcid":8},"Gajanan Chaudhary",{"name":322,"orcid":8},"Amarsinh Verpe",{"tldr":324,"method":325,"finding":326,"direction":61,"opportunity":327},"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。","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":330,"title":331,"url":332,"summary":333,"summary_zh":8,"content":8,"source_name":334,"source_url":8,"published_at":215,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":335,"score_detail":336,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":350,"classification":351,"search_phrases":378,"slug":381,"view_count":39,"doi":8,"paper":382,"created_at":389},5064,"Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20544","Haimanot Bitew Alemayehu、Simachew Melaku Ayalew、Chilotie Desalew Mengesha、Ayisheshim Almaw Bogale、Anteneh Birhan Yenew、Abrham Tegegne Bogale在arXiv 2026-10-15上传的预印本,面向埃塞俄比亚粮食安全作物马铃薯,提出可部署于边缘设备的轻量卷积神经网络,完成健康、早疫病、晚疫病三类分类。模型在PlantVillage(2,215张图像)训练,测试准确率98.66%;在埃塞俄比亚Banja镇田间独立采集的247张图像测试中准确率为90.28%,体现独立田间验证相较受控基准更接近真实部署条件。模型仅含446,787个可训练参数、约1.70MB,训练周期较VGG16和MobileNetV2更短。【预印本未审稿;仅在埃塞俄比亚一地进行田间验证,跨地区泛化能力未测】","arXiv preprint 2026-10-15",71,{"impact":337,"substance":338,"depth":339,"authority":79,"freshness":12,"relevant":20,"comment":340},15,20,17,"预印本提出仅1.7MB的轻量CNN实现马铃薯早疫病、晚疫病识别，含独立田间验证，方法扎实但尚未审稿、泛化性待验证，属细分领域技术进展。",[342],{"name":334,"url":332},[25,26,247,344,27],"边缘计算",{"code":244,"slug":346,"name":347,"path":348,"parent_code":349},"tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[],{"status":93,"locked":13,"version":33,"method":34,"evidence":352},{"candidates":353,"agricultural_evidence":48},[354,359,365,369,373],{"code":244,"score":152,"supported":48,"title":355,"abstract":357,"tags":358},[356],"potato",[247],[247],{"code":360,"score":39,"supported":48,"title":361,"abstract":362,"tags":364},"A8",[],[363],"粮食安全",[],{"code":38,"score":250,"supported":13,"title":366,"abstract":367,"tags":368},[],[],[26,27],{"code":44,"score":20,"supported":13,"title":370,"abstract":371,"tags":372},[],[],[25],{"code":374,"score":20,"supported":13,"title":375,"abstract":376,"tags":377},"I5",[],[],[344],[379,380],"埃塞俄比亚 马铃薯 晚疫病 检测","轻量CNN 马铃薯 叶片病害","埃塞俄比亚马铃薯晚疫病检测-5064",{"doi":8,"openalex_id":8,"authors":383,"venue":8,"cited_by_count":56,"oa_url":8,"card":384,"direction":61,"ingested_from":63},[],{"tldr":385,"method":386,"finding":387,"direction":61,"opportunity":388},"提出轻量CNN实现马铃薯早疫病与晚疫病三分类，并在埃塞俄比亚田间验证。","轻量卷积神经网络，PlantVillage 2215张训练，Banja镇247张","测试集准确率98.66%，独立田间测试降至90.28%，模型仅1.70MB。","可研究跨地区\u002F多作物泛化、田间复杂背景鲁棒性及边缘设备实时部署优化。","2026-10-16T00:14:01.369956Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":8,"content":8,"source_name":395,"source_url":8,"published_at":396,"category":397,"cover_url":8,"hotness":12,"is_selected":13,"score":398,"score_detail":399,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":422,"slug":425,"view_count":20,"doi":8,"paper":8,"created_at":426},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":16,"substance":400,"depth":339,"authority":78,"freshness":19,"relevant":20,"comment":401},21,"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。",[403],{"name":395,"url":393},[25,26,28,405,406],"害虫识别","枸杞",{"code":38,"slug":87,"name":88,"path":89,"parent_code":90},[],{"status":93,"locked":13,"version":33,"method":34,"evidence":410},{"candidates":411,"agricultural_evidence":48},[412,418],{"code":38,"score":413,"supported":48,"title":414,"abstract":415,"tags":417},5,[],[416],"artificial intelligence",[26,28],{"code":44,"score":20,"supported":13,"title":419,"abstract":420,"tags":421},[],[],[25],[423,424],"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"]