[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2087":3,"related-2087":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":8,"secondary_topics":32,"classification":33,"search_phrases":70,"slug":73,"view_count":74,"doi":8,"paper":8,"created_at":75},2087,"江苏省农业科学院信息所举办\"智说三农·三农论智\"智慧农业学术论坛 浙江浙大与省农科院专家作报告","https:\u002F\u002Fiai.jaas.ac.cn\u002Fgzdt\u002Fart\u002F2026\u002Fart_bc316a767d624b76844e0d6eff013e1a.html","9月2日下午江苏农科院信息所举办第21期\"智说三农·三农论智\"智慧农业学术论坛,邀请浙江大学智能农业装备研究所副所长林涛研究员作《人工智能赋能设施农业生产管控》报告,浙江省农业科学院数字农业研究所副所长孔德栋研究员作《AI驱动型数智农业工厂关键技术与产业应用》报告,聚焦植物工厂与智慧渔业两大方向。",null,"信息所举办第二十一期“智说三农·三农论智”智慧农业学术论坛\n\n文章来源：信息所 发布时间：2026-09-07 09:35 阅读：次\n\n9月2日下午，信息所举办第二十一期“智说三农·三农论智”智慧农业学术论坛，特别邀请浙江大学智能农业装备研究所副所长林涛研究员和浙江省农业科学院数字农业研究所副所长孔德栋研究员作学术报告。信息所相关负责人及院内相关科技人员、研究生参加论坛。\n\n林涛以《人工智能赋能设施农业生产管控》为题，围绕农业AI系统观，从机器人自动化、高频数据采集、作物生长模型及环境调控等维度，阐述了从单点技术突破向垂直场景闭环解决方案转变的发展趋势；重点分享了团队参加第四届荷兰瓦赫宁根大学温室管理挑战赛并获得冠军的实战经验，验证了AI驱动温室生产管控可有效提升投入产出比。报告还介绍了植物工厂内部自主巡检无人机、草莓生长模型与能耗模型耦合、设施农业大模型应用探索等前沿研究成果。\n\n孔德栋以《AI驱动型数智农业工厂关键技术与产业应用》为题作学术报告。报告从数据、算力、算法切入，阐述了 “先工程落地、后科学研究”的产业化思路。内容聚焦植物工厂和智慧渔业两大方向。植物工厂方面，团队在智能光调控、农业专用空调系统、绳驱欠驱动采摘机械手等方面取得突破，通过光生物学研究与第一性原理重构硬件系统，大幅降低了成本，综合成本达到国际领先水平。智慧渔业方面，团队从零搭建标准化、模块化、去中心化的技术体系，构建多模态协同感知矩阵与跨区域云管控平台，实现了高密度养殖稳产降本，并推广至国内多省区及海外多国。\n\n与会青年科研人员围绕报告内容及科研实践中遇到的问题与困惑，与两位专家展开了热烈讨论和深入交流。\n\n“智说三农·三农论智”智慧农业学术论坛是信息所智慧农业创新团队打造的品牌学术平台，将持续邀请国内外相关领域有影响力的专家学者来院做专题报告，旨在促进合作交流、提高学术水平、提升协同创新能力、共促农业产业转型。\n\n![Image 1](https:\u002F\u002Fiai.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F938139336\u002Fpicture\u002F20268\u002FS970562c819074d469b06a90889d2e7fe-650.png)\n\n![Image 2](https:\u002F\u002Fiai.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F938139336\u002Fpicture\u002F20268\u002FSa6004fb881d74fe08594601d196be2cb-650.png)\n\n![Image 3](https:\u002F\u002Fiai.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F938139336\u002Fpicture\u002F20268\u002FSdc404c23ce084a419d20490108cef785-650.png)\n\n![Image 4](https:\u002F\u002Fiai.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F938139336\u002Fpicture\u002F20268\u002FS06d169c25c9f41b1a4a0bd7f2fd2c4c2-670.png)","江苏省农业科学院","2026-09-02T00:00:00Z","报道",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,18,14,13,6,1,"省级科研机构学术论坛，两位专家报告含AI温室管控、植物工厂降本与智慧渔业落地等实质技术进展，具备一定行业参考价值，但属会议报道、时效略滞后。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","设施农业","智慧渔业","植物工厂",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":69},[39,46,52,57,64],{"code":40,"score":17,"supported":41,"title":42,"abstract":43,"tags":45},"I1",true,[27],[27,44],"数字农业",[27],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":51},"B7",4,[],[31],[31],{"code":53,"score":48,"supported":41,"title":54,"abstract":55,"tags":56},"D5",[],[30],[30],{"code":58,"score":59,"supported":41,"title":60,"abstract":61,"tags":63},"D",3,[],[62],"渔业",[],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I2",[],[],[28],"多个产业证据相当，需要确认主归属",[71,72],"农业人工智能 智慧农业 智慧渔业 植物工厂","农业人工智能 智慧农业","农业人工智能智慧农业智慧渔业植物工厂-2087",2,"2026-09-11T00:04:20.152178Z",{"total":21,"page":22,"page_size":21,"items":77},[78,157,210,274,317,380],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":8,"source_name":84,"source_url":81,"published_at":85,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":87,"score_detail":88,"sources":92,"tags":94,"primary_topic":97,"secondary_topics":102,"classification":103,"search_phrases":128,"slug":131,"view_count":59,"doi":132,"paper":133,"created_at":156},4108,"Visión artificial para la detección de plagas en cultivos de tomate: revisión sistemática de la literatura","https:\u002F\u002Fdoi.org\u002F10.36825\u002Friti.14.34.010","Phytosanitary monitoring is an essential activity for reducing production losses caused by pests in tomato crops grown under protected agriculture conditions. In recent years, computer vision and deep learning methods have gained relevance as tools capable of automating agricultural image detection and analysis processes. Despite these advances, there is still a need to determine which techniques have been used, under what conditions they have been evaluated, and what performance they have reported, particularly in protected agriculture and small-scale production contexts. This study aims to examine research published on these technologies and assess their potential incorporation into participatory agricultural monitoring schemes. To this end, a systematic review of scientific literature published between 2020 and 2026 was conducted following the methodological guidelines proposed by Kitchenham. The review was developed through the stages of planning, study search and selection, and information synthesis, considering previously established inclusion and exclusion criteria. As a result, fifty studies related to image processing, artificial intelligence, and phytosanitary monitoring were selected and analyzed. Among the main findings of the review, several studies reported high-performance levels for deep learning models in tasks involving the automatic identification of symptoms associated with pests and other crop-related conditions. The synthesis identified the main techniques, data, application contexts, and reported performance levels, providing reference elements for assessing their potential incorporation into participatory agricultural monitoring schemes.","植物检疫监测是减少保护地番茄种植中由有害生物造成的产量损失的一项重要活动。近年来，计算机视觉和深度学习方法作为能够自动化农业图像检测与分析流程的工具而日益受到重视。尽管取得了这些进展，但仍需明确已采用了哪些技术、这些技术在何种条件下进行了评估，以及它们报告了怎样的性能，尤其是在保护地农业和小规模生产情境下。本研究旨在考察已发表的关于这些技术的研究，并评估其纳入参与式农业监测方案的潜力。为此，按照Kitchenham提出的方法学指南，对2020年至2026年间发表的科学文献进行了系统综述。该综述通过规划、研究检索与筛选以及信息综合等阶段展开，并依据事先确立的纳入与排除标准。结果共筛选并分析了50项与图像处理、人工智能和植物检疫监测相关的研究。在综述的主要发现中，多项研究报告了深度学习模型在自动识别与有害生物相关的症状及其他作物相关状况的任务中具有高性能水平。该综合识别了主要技术、数据、应用情境及所报告的性能水平，为评估其纳入参与式农业监测方案的潜力提供了参考要素。","Revista de Investigación en Tecnologías de la Información","2026-10-02T00:00:00Z","论文",63,{"impact":89,"substance":18,"depth":17,"authority":13,"freshness":90,"relevant":22,"comment":91},12,8,"系统综述梳理2020-2026年计算机视觉与深度学习在番茄病虫害检测中的应用，方法规范、结论有参考价值，但属细分领域综述，公共影响有限。",[93],{"name":84,"url":81},[27,28,29,95,96],"番茄种植","病虫害监测",{"code":65,"slug":98,"name":99,"path":100,"parent_code":101},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":104,"locked":14,"version":35,"method":36,"evidence":105},"classified",{"candidates":106,"agricultural_evidence":41},[107,116,124],{"code":65,"score":13,"supported":41,"title":108,"abstract":109,"tags":115},[],[110,111,112,113,114],"深度学习","计算机视觉","deep learning","artificial intelligence","computer vision",[28],{"code":117,"score":118,"supported":41,"title":119,"abstract":120,"tags":123},"B1",7,[],[121,122],"番茄","tomato",[95],{"code":40,"score":22,"supported":14,"title":125,"abstract":126,"tags":127},[],[],[27],[129,130],"计算机视觉 番茄 病虫害检测","深度学习 设施农业 植保监测","计算机视觉番茄病虫害检测-4108","10.36825\u002Friti.14.34.010",{"doi":132,"openalex_id":134,"authors":135,"venue":84,"cited_by_count":148,"oa_url":81,"card":149,"direction":153,"ingested_from":155},"W7216085028",[136,138,141,144,146],{"name":137,"orcid":8},"Jesús Alberto Alarcón Luna",{"name":139,"orcid":140},"María Salomé Alejandre Apolinar","https:\u002F\u002Forcid.org\u002F0009-0008-0232-8217",{"name":142,"orcid":143},"Hugo Amores Pérez","https:\u002F\u002Forcid.org\u002F0009-0009-0260-4872",{"name":145,"orcid":8},"Jacel Adame García",{"name":147,"orcid":8},"María Angélica Cerdán",0,{"tldr":150,"method":151,"finding":152,"direction":153,"opportunity":154},"系统综述2020-2026年计算机视觉与深度学习在番茄设施栽培虫害检测中的研究。","遵循Kitchenham指南系统检索筛选50篇文献，归纳技术、数据与性能。","深度学习模型在虫害症状自动识别任务中普遍报告高性能，但多限于特定条件。","农业人工智能与决策模型","面向设施农业与小规模生产的轻量化模型及参与式监测集成仍属研究空白。","openalex","2026-10-03T23:30:53.163959Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":8,"content":8,"source_name":162,"source_url":8,"published_at":163,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":164,"score_detail":165,"sources":169,"tags":171,"primary_topic":174,"secondary_topics":179,"classification":184,"search_phrases":205,"slug":208,"view_count":59,"doi":8,"paper":8,"created_at":209},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z",82,{"impact":18,"substance":166,"depth":18,"authority":17,"freshness":167,"relevant":22,"comment":168},22,9,"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[170],{"name":162,"url":160},[27,28,29,172,173],"温室番茄","生长预测",{"code":47,"slug":175,"name":176,"path":177,"parent_code":178},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[180],{"code":117,"slug":181,"name":182,"path":183,"parent_code":178},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":104,"locked":14,"version":35,"method":36,"evidence":185},{"candidates":186,"agricultural_evidence":41},[187,192,197,201],{"code":47,"score":89,"supported":41,"title":188,"abstract":190,"tags":191},[189],"温室",[189],[172],{"code":117,"score":193,"supported":41,"title":194,"abstract":195,"tags":196},11,[121],[121],[],{"code":40,"score":22,"supported":14,"title":198,"abstract":199,"tags":200},[],[],[27],{"code":65,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[28],[206,207],"中国农科院环发所 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模型","中国农科院环发所温室番茄生长预测-3935","2026-10-02T11:53:11.319506Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":8,"content":8,"source_name":215,"source_url":8,"published_at":216,"category":217,"cover_url":8,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":222,"tags":224,"primary_topic":227,"secondary_topics":233,"classification":234,"search_phrases":269,"slug":272,"view_count":59,"doi":8,"paper":8,"created_at":273},3925,"上海市崇明区印发《推进乡村全面振兴\"十五五\"规划》","https:\u002F\u002Fwww.shcm.gov.cn\u002Fgovxxgk\u002Fqzfbgs\u002F2026-09-29\u002F48dc8816-cdd5-4c14-b6ee-82c0750118bf.html","《规划》提出崇明要突破目标识别、智能控制、自动采摘等核心技术，鼓励开发农业智能体、柔性农业机器人；布局\"AI+\"未来农业场景，推动AI技术与农业生产深度融合，聚焦菌菇全自动生产、白山羊智慧养殖、工厂化水产养殖等领域，构建\"感知—认知—决策—执行\"的智能闭环，打造\"AI+现代农业智慧应用样板区\"；同时确保常年蔬菜种植面积稳定在7.5万亩，推进8000余亩蔬菜规模化基地建设，建成2000亩集中连片的高标准设施菜田。","上海市崇明区人民政府·2026年09月29日","2026-09-29T08:00:00Z","政策",84,{"impact":166,"substance":220,"depth":18,"authority":20,"freshness":90,"relevant":22,"comment":221},23,"省级区级“十五五”乡村振兴规划，明确农业智能体、柔性机器人、智能闭环等AI+农业落地路径与设施菜田数据，政策条款具体、增量信息足，值得进入每日精选。",[223],{"name":215,"url":213},[27,28,225,29,226],"农业机器人","十五五规划",{"code":228,"slug":229,"name":230,"path":231,"parent_code":232},"F6","agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",[],{"status":104,"locked":14,"version":35,"method":36,"evidence":235},{"candidates":236,"agricultural_evidence":41},[237,241,245,251,256,260,264],{"code":228,"score":48,"supported":41,"title":238,"abstract":239,"tags":240},[],[225],[225],{"code":117,"score":59,"supported":41,"title":242,"abstract":243,"tags":244},[],[182],[],{"code":246,"score":59,"supported":41,"title":247,"abstract":248,"tags":250},"C2",[],[249],"山羊",[],{"code":58,"score":59,"supported":41,"title":252,"abstract":253,"tags":255},[],[254],"水产",[],{"code":40,"score":22,"supported":14,"title":257,"abstract":258,"tags":259},[],[],[27],{"code":65,"score":22,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[28],{"code":265,"score":22,"supported":14,"title":266,"abstract":267,"tags":268},"J1",[],[],[226],[270,271],"崇明区 十五五 乡村振兴规划","崇明 AI 现代农业 智慧应用样板区","崇明区十五五乡村振兴规划-3925","2026-10-02T11:53:08.632722Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":8,"content":279,"source_name":280,"source_url":8,"published_at":281,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":282,"score_detail":283,"sources":286,"tags":288,"primary_topic":291,"secondary_topics":292,"classification":293,"search_phrases":312,"slug":315,"view_count":48,"doi":8,"paper":8,"created_at":316},3751,"设施技术国产化让每公斤番茄综合生产成本降至5元","https:\u002F\u002Fnync.yn.gov.cn\u002Fhtml\u002F2026\u002Fyunnongkuanxun-new_0923\u002F1428950.html","云南首个AI+农业全链路示范基地位于昆明市宜良县匡远街道温泉社区。嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造国产化AI种植模式，通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。","位于昆明市宜良县匡远街道温泉社区的番茄示范基地即将在国庆假期进入采收期。记者近日来到该基地，探访这座云南首个“AI+农业全链路”示范基地。\n\n清晨，基地里一串串番茄在简易薄膜温室内泛着诱人的青红光泽。没有泥泞的土地，没有汗流浃背的弯腰劳作，眼前这座“番茄工厂”安静得只听见蜜蜂授粉时的嗡鸣声和水肥滴灌的微响。\n\n上午8时，有工人陆续进场，偌大的基地只需三五位工人就能打理完毕。物联网感知终端静静矗立，实时采集光照强度、空气温湿度、水肥浓度和作物长势。种苗定植、枝蔓生长、开花坐果到成熟采收，每一个环节都有“数字管家”全程值守。嘉措（云南）农业科技有限公司农场场长李良坐在棚外，掏出手机轻点屏幕，棚内温湿度曲线、土壤含水量、水肥配比参数一目了然。指尖滑动间，灌溉阀自动开启，水肥顺着滴灌带精准送达番茄根部。\n\n“过去种番茄靠经验，现在靠算法。”云南指月科技有限公司项目经理尹文鑫指着棚内的国产化智慧灌溉设备说。这套系统由嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造，创新构建“全域监测+AI算法+智慧灌溉”三位一体的数字化种植模式。\n\n“基地里的全套设备都是国产的。”尹文鑫提高音量信心十足地介绍。长期以来，国内高端设施农业赛道被海外技术路线主导。参照国外植物工厂及高端玻璃温室模式，依托全套进口水肥设备与闭环控制系统虽能实现稳定高产，但每公斤番茄综合生产成本高达13元至15元，加之进口设备造价高、配件周期长、操作门槛高等痛点，技术落地难度极大。而该基地探索的国产化AI种植模式，正在彻底改写这一成本结构。\n\n国产化的意义，最终落在基地每一颗番茄的身价上。通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。同时，国产化硬件的使用，进一步压缩了采购、部署与运维成本，凸显整套方案规模化落地的性价比。\n\n“云”上管棚，“数”里种田。从传感器、灌溉阀到核心控制系统，基地核心硬件已实现全部国产配套；从数据采集、模型训练到智能决策输出，AI技术真正扎根田间。对种植户而言，AI不再是遥远的科技概念，而是手机里能调水肥、棚里能提产量、年底能多增收的实用“新农具”。（记者：王淑娟）","云南省农业农村厅","2026-09-23T12:00:00Z",78,{"impact":166,"substance":166,"depth":284,"authority":193,"freshness":21,"relevant":22,"comment":285},17,"云南首个AI+农业全链路示范基地，以国产化硬件与算法将番茄综合成本从13-15元\u002F公斤降至5元，数据具体、多方信源，具备行业示范价值。",[287],{"name":280,"url":277},[27,28,29,289,121,290],"云南","国产化替代",{"code":117,"slug":181,"name":182,"path":183,"parent_code":178},[],{"status":104,"locked":14,"version":35,"method":36,"evidence":294},{"candidates":295,"agricultural_evidence":41},[296,300,304,308],{"code":117,"score":89,"supported":41,"title":297,"abstract":298,"tags":299},[121],[121],[121],{"code":47,"score":59,"supported":41,"title":301,"abstract":302,"tags":303},[],[189],[],{"code":40,"score":22,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[27],{"code":65,"score":22,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[28],[313,314],"云南 宜良 AI番茄 示范基地","嘉措农业 指月科技 智慧灌溉","云南宜良AI番茄示范基地-3751","2026-09-29T00:08:25.735852Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":8,"source_name":323,"source_url":320,"published_at":324,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":333,"secondary_topics":334,"classification":335,"search_phrases":352,"slug":355,"view_count":21,"doi":356,"paper":357,"created_at":379},3606,"A decision-making method for light regulation of cucumber seedlings considering changes of temperature and CO2 in protected agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104601","Light serves as the primary energy source for photosynthesis and significantly influences plant morphology and biomass accumulation. In protected agricultural systems, light environment parameters (intensity and spectral quality) critically determine crop productivity. A data-driven framework for light optimisation in cucumber seedlings was developed. First, an artificial neural network was established to predict net photosynthetic rate using empirical data spanning diverse environmental regimes. Then, to jointly maximise photosynthetic efficiency and minimise energy consumption, two key innovations were implemented. The U-chord method was employed to derive target light intensities, and a hybrid cubic spline-global Newton algorithm was designed for spectral quality optimisation. These subsystems were fused through support vector regression to construct adaptive decision-making models for real-time light management. The results showed that the artificial neural network model achieved exceptional Pn prediction accuracy with coefficient of determination > 0.95, root mean square error \u003C 1.6 μmol m −2 s −1 , and mean absolute error ≤ 1.2 μmol m −2 s −1 . The target light intensity points effectively delineated the light-limited phase of the Pn–PPFD response from the light-saturated phase. Final decision-making models showed high generalisability (coefficient of determination > 0.97 for both spectral and intensity decision-making models). Simulations quantified the advantages of the proposed strategy. Compared to conventional maximum Pn-oriented strategies, the proposed method reduced lighting energy consumption by 40-46%, while dynamic spectral adjustments enhanced photosynthetic efficiency by 5% relative to static controls. This study effectively improved the efficiency of light energy utilisation, and provided a theoretical method for light regulation in protected agriculture.","光作为光合作用的主要能量来源，显著影响植物形态和生物量积累。在设施农业系统中，光环境参数（光强与光谱质量）对作物生产力具有决定性作用。本研究开发了一种数据驱动的黄瓜幼苗光优化框架。首先，基于涵盖多种环境条件的经验数据，建立了人工神经网络（artificial neural network）以预测净光合速率。随后，为实现光合效率最大化与能耗最小化的协同优化，实施了两项关键创新：采用U弦法（U-chord method）确定目标光强，并设计了三次样条-全局牛顿混合算法（hybrid cubic spline-global Newton algorithm）用于光谱质量优化。通过支持向量回归（support vector regression）融合各子系统，构建了用于实时光管理的自适应决策模型。结果表明，人工神经网络模型实现了优异的净光合速率预测精度，决定系数>0.95，均方根误差\u003C1.6 μmol m⁻² s⁻¹，平均绝对误差≤1.2 μmol m⁻² s⁻¹。目标光强点有效划分了净光合速率-光合光子通量密度响应曲线中的光限制阶段与光饱和阶段。最终决策模型表现出良好的泛化能力（光谱决策模型与光强决策模型的决定系数均>0.97）。仿真量化了所提策略的优势。与传统最大净光合速率导向策略相比，该方法将补光能耗降低了40%~46%，同时动态光谱调节使光合效率较静态对照提高了5%。本研究有效提升了光能利用效率，为设施农业光调控提供了理论方法。","Biosystems Engineering","2026-09-26T00:00:00Z",81,{"impact":18,"substance":166,"depth":18,"authority":19,"freshness":167,"relevant":22,"comment":327},"该研究提出数据驱动的设施黄瓜育苗光调控决策方法，节能40-46%且提升光合效率，方法新颖、数据可靠，对智慧农业光环境管理有较高参考价值。",[329],{"name":323,"url":320},[27,28,29,331,332],"黄瓜育苗","光环境调控",{"code":117,"slug":181,"name":182,"path":183,"parent_code":178},[],{"status":104,"locked":14,"version":35,"method":36,"evidence":336},{"candidates":337,"agricultural_evidence":41},[338,344,348],{"code":117,"score":19,"supported":41,"title":339,"abstract":341,"tags":343},[340],"cucumber",[342,340],"黄瓜",[],{"code":40,"score":22,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[27],{"code":65,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[28],[353,354],"设施农业 光环境 黄瓜育苗","光合速率 光强 光谱 优化","设施农业光环境黄瓜育苗-3606","10.1016\u002Fj.biosystemseng.2026.104601",{"doi":356,"openalex_id":358,"authors":359,"venue":323,"cited_by_count":148,"oa_url":320,"card":374,"direction":153,"ingested_from":155},"W7214421951",[360,363,366,368,371],{"name":361,"orcid":362},"Pan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-5184-5674",{"name":364,"orcid":365},"Huimin Li","https:\u002F\u002Forcid.org\u002F0009-0001-9124-7628",{"name":367,"orcid":8},"Jinghua Xu",{"name":369,"orcid":370},"Miao Lu","https:\u002F\u002Forcid.org\u002F0000-0001-6539-2170",{"name":372,"orcid":373},"Jin Ping Hu","https:\u002F\u002Forcid.org\u002F0000-0001-5532-6890",{"tldr":375,"method":376,"finding":377,"direction":153,"opportunity":378},"构建数据驱动光调控决策框架，优化黄瓜幼苗光强与光谱以提升光合效率并降低能耗。","人工神经网络预测净光合速率，U弦法确定光强，三次样条-全局牛顿法优化光谱，支持向","模型预测精度高，较传统策略降低能耗40-46%，动态光谱调整提升光合效率5%。","可探索多环境因子耦合的实时闭环光调控，并迁移至其他设施作物验证泛化性。","2026-09-27T23:30:09.843641Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":8,"source_name":386,"source_url":383,"published_at":387,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":282,"score_detail":388,"sources":390,"tags":392,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":414,"slug":417,"view_count":59,"doi":418,"paper":419,"created_at":443},3473,"TB-YOLOv8n: an improved YOLOv8n-based model for tip-burn detection in plant factory-grown pakchoi under LED lighting","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933084","Introduction Plant factory-grown pakchoi is prone to tip-burn under high-density cultivation, limited airflow, and rapid growth conditions, which reduces leaf integrity and commercial quality. Image-based detection of small or mild visible tip-burn symptoms remains challenging because the affected regions are usually small, irregularly shaped, low in color contrast, and easily disturbed by red-blue LED lighting, leaf overlap, curled leaf margins, and heart-leaf occlusion. Methods To address these challenges, this study constructed a pakchoi tip-burn image dataset under plant factory conditions and proposed TB-YOLOv8n, a lightweight detection model based on YOLOv8n. AFGCAttention was introduced at the end of the backbone to enhance informative channel responses related to subtle tip-burn regions and suppress redundant background information. GLSA was embedded between the backbone and neck to integrate local texture details with global spatial context under leaf overlap and low-contrast symptom conditions. In addition, a P2-enhanced BiFPN neck was constructed to strengthen cross-scale feature fusion and preserve fine spatial information for small visible tip-burn regions. Results Experimental results showed that TB-YOLOv8n achieved a precision of 89.4%, a recall of 87.6%, an mAP@0.5 of 93.2% ± 0.3%, and an mAP@0.5–0.95 of 59.0% ± 0.3%, which were 4.2, 3.9, 3.4, and 10.0 percentage points higher than those of the original YOLOv8n, respectively. The model contained only 2.2 M parameters, required 7.6 G FLOPs, had a model size of 4.8 MB, and achieved a model-forward inference speed of 138 FPS on the RTX 4090 and 33.0 FPS on the Jetson AGX Orin. Ablation experiments, heatmap visualization, and comparisons with mainstream detection models further confirmed the effectiveness of the proposed improvements. Discussion The results indicate that TB-YOLOv8n achieves a favorable balance among detection accuracy, lightweight structure, and real-time inference capability. The proposed model provides a basis for visible tip-burn monitoring and subsequent production decision support in plant factories.","引言 植物工厂栽培的小白菜在高密度种植、气流受限和快速生长条件下易发生焦边，导致叶片完整性和商品品质下降。基于图像的检测方法在识别微小或轻微可见的焦边症状时仍面临挑战，因为受害区域通常面积小、形状不规则、颜色对比度低，且易受红蓝LED光照、叶片重叠、叶缘卷曲和心叶遮挡的干扰。方法 为解决上述问题，本研究构建了植物工厂条件下的小白菜焦边图像数据集，并提出了TB-YOLOv8n，一种基于YOLOv8n的轻量化检测模型。在主干网络末端引入AFGCAttention，以增强与细微焦边区域相关的信息性通道响应并抑制冗余背景信息。在主干网络与颈部网络之间嵌入GLSA，以在叶片重叠和低对比度症状条件下整合局部纹理细节与全局空间上下文。此外，构建了P2增强的BiFPN颈部网络，以加强跨尺度特征融合并保留小面积可见焦边区域的精细空间信息。结果 实验结果表明，TB-YOLOv8n的精确率为89.4%，召回率为87.6%，mAP@0.5为93.2% ± 0.3%，mAP@0.5–0.95为59.0% ± 0.3%，分别比原始YOLOv8n提高了4.2、3.9、3.4和10.0个百分点。该模型仅含2.2 M参数，需要7.6 G FLOPs，模型大小为4.8 MB，在RTX 4090上的模型前向推理速度为138 FPS，在Jetson AGX Orin上为33.0 FPS。消融实验、热力图可视化以及与主流检测模型的比较进一步证实了所提改进的有效性。讨论 结果表明，TB-YOLOv8n在检测精度、轻量化结构和实时推理能力之间取得了良好平衡。所提模型为植物工厂中可见焦边监测及后续生产决策支持提供了依据。","Frontiers in Plant Science","2026-09-23T00:00:00Z",{"impact":17,"substance":166,"depth":18,"authority":19,"freshness":167,"relevant":22,"comment":389},"面向植物工厂小白菜干烧心的轻量化检测模型，方法新颖、指标扎实且可实时部署，对设施蔬菜智能监测有实用参考价值。",[391],{"name":386,"url":383},[27,28,31,393,394],"病害检测","小白菜",{"code":47,"slug":175,"name":176,"path":177,"parent_code":178},[],{"status":104,"locked":14,"version":35,"method":36,"evidence":398},{"candidates":399,"agricultural_evidence":41},[400,405,410],{"code":47,"score":17,"supported":41,"title":401,"abstract":403,"tags":404},[402],"plant factory",[31,402],[31],{"code":65,"score":48,"supported":41,"title":406,"abstract":407,"tags":409},[],[408],"decision support",[28],{"code":40,"score":22,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[27],[415,416],"植物工厂 小白菜 干烧心 检测","TB-YOLOv8n 干烧心 识别","植物工厂小白菜干烧心检测-3473","10.3389\u002Ffpls.2026.1933084",{"doi":418,"openalex_id":420,"authors":421,"venue":386,"cited_by_count":148,"oa_url":436,"card":437,"direction":442,"ingested_from":155},"W7214074852",[422,425,427,429,431,433],{"name":423,"orcid":424},"Peng Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4589-0137",{"name":426,"orcid":8},"Wenwen Hu",{"name":428,"orcid":8},"Xiang Ma",{"name":430,"orcid":8},"Shipu Xu",{"name":432,"orcid":8},"Zhiwen Zhou",{"name":434,"orcid":435},"Yunsheng Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0348-4608","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1933084\u002Fpdf",{"tldr":438,"method":439,"finding":440,"direction":153,"opportunity":441},"提出轻量模型TB-YOLOv8n，在植物工厂LED光下检测小白菜干烧心。","构建植物工厂小白菜干烧心图像数据集，改进YOLOv8n加入AFGCAttenti","mAP@0.5达93.2%，较YOLOv8n提升3.4个百分点，仅2.2M参数、138FPS。","可延伸至多作物苗期胁迫早期检测，结合时序图像与决策模型实现预警。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:11.141664Z"]