[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5655":3,"related-5655":77},{"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,"embedding_ready":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":62,"slug":65,"view_count":66,"doi":8,"paper":67,"created_at":76},5655,"面向节能温室运行的LED照明控制策略比较研究","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.28570","预印本，未同行评审。在同一温室并行对比常开、启发式与基于马尔可夫链太阳预报的预测补光控制，2026年2—4月番茄两重复试验显示预测策略达到目标日光积分且成本较常开降低65%，单位电费干物质18.976克、约为常开两倍多；仅单温室验证。",null,"arXiv 2026-10-20","2026-10-19T16:00:00Z","论文",10,false,66,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":19,"relevant":20,"comment":21},12,21,17,8,1,"预印本单温室验证，节能数据有参考价值但可靠性有限，属细分技术进展，可作候选而非重点精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","LED补光","温室环控","节能控制","番茄栽培",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,57],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},11,true,[47],"温室",[47],[],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"B1",3,[],[55],"番茄",[],{"code":58,"score":20,"supported":13,"title":59,"abstract":60,"tags":61},"I1",[],[],[25],[63,64],"温室 LED补光 节能控制","马尔可夫链 太阳预报 补光","温室LED补光节能控制-5655",0,{"doi":8,"openalex_id":8,"authors":68,"venue":8,"cited_by_count":66,"oa_url":8,"card":69,"direction":73,"ingested_from":75},[],{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"在同一温室对比三种LED补光控制策略，预测策略节能且增产。","并行对比常开、启发式与马尔可夫链太阳预报预测控制，番茄两重复试验。","预测策略达标日光积分且成本降65%，单位电费干物质约为常开两倍多。","智慧农业 \u002F 农业物联网","仅单温室验证，可扩展多温室多作物验证，并融合实时传感与电价优化控制。","agent","2026-10-23T00:32:42.146061Z",{"total":78,"page":20,"page_size":78,"items":79},6,[80,132,181,262,310,407],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":8,"content":85,"source_name":86,"source_url":8,"published_at":87,"category":88,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":13,"score":89,"score_detail":90,"sources":94,"tags":96,"primary_topic":100,"secondary_topics":104,"classification":106,"search_phrases":127,"slug":130,"view_count":119,"doi":8,"paper":8,"created_at":131},4459,"中国农科院环发所构建知识与数据双驱动模型精准预测温室番茄生长态势","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572492.shtm","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以番茄为对象，构建知识与数据双驱动的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；研究成果发表在Computers and Electronics in Agriculture；仅依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具重要应用价值。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | 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相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n 无标题页 \n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572492)\n\n相关新闻 相关论文\n*   1\n*   [《科学》（20261001出版）一周论文导读](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572487.shtm)\n\n*   2\n*   [2026年度湖南省科学技术奖提名工作启动](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572486.shtm)\n\n*   3\n*   [河南中央引导地方科技发展资金项目结项验收结果公示](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572485.shtm)\n\n*   4\n*   [江西组织开展2027年度自然科学基金项目申报工作](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572484.shtm)\n\n*   5\n*   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2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":91,"substance":17,"depth":18,"authority":92,"freshness":78,"relevant":20,"comment":93},22,14,"国家级科研机构在智慧农业领域的模型方法突破，方法新颖、结论可靠，对温室智能环控与产量预测有应用价值，值得进入每日精选。",[95],{"name":86,"url":83},[25,97,98,99,27],"农业人工智能","作物生长模型","设施番茄",{"code":51,"slug":101,"name":102,"path":103,"parent_code":35},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[105],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":107},{"candidates":108,"agricultural_evidence":45},[109,113,117,123],{"code":51,"score":16,"supported":45,"title":110,"abstract":111,"tags":112},[55],[55],[99],{"code":31,"score":44,"supported":45,"title":114,"abstract":115,"tags":116},[47],[47],[],{"code":118,"score":119,"supported":13,"title":120,"abstract":121,"tags":122},"I2",2,[],[],[98,97],{"code":58,"score":20,"supported":13,"title":124,"abstract":125,"tags":126},[],[],[25],[128,129],"中国农科院环发所 温室番茄 生长预测","知识与数据双驱动 番茄 干物质","中国农科院环发所温室番茄生长预测-4459","2026-10-09T00:04:56.060677Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":8,"content":8,"source_name":137,"source_url":8,"published_at":138,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":13,"score":139,"score_detail":140,"sources":145,"tags":147,"primary_topic":151,"secondary_topics":152,"classification":153,"search_phrases":168,"slug":171,"view_count":66,"doi":8,"paper":172,"created_at":180},5652,"Bombadil：面向植物电生理信号的基础模型","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.26597","预印本，未同行评审。以100个植物物种、十类环境（实验室、温室、商业果园、天然林）超400万小时植物电信号无标签预训练基础模型，可从同一信号追踪七项胁迫生理指标变化，并覆盖训练中无标签的物种与胁迫类型；跨场景田间验证仍有限。","arXiv 2026-10-19","2026-10-18T16:00:00Z",74,{"impact":141,"substance":91,"depth":142,"authority":143,"freshness":78,"relevant":20,"comment":144},18,19,9,"预印本尚未经同行评审，但以400万小时、100个物种的植物电信号预训练基础模型，在方法新颖性与数据规模上具有实质增量，值得关注但田间验证有限。",[146],{"name":137,"url":135},[25,97,148,149,150],"基础模型","植物电生理","胁迫监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":154},{"candidates":155,"agricultural_evidence":45},[156,160,164],{"code":31,"score":52,"supported":45,"title":157,"abstract":158,"tags":159},[],[47],[],{"code":58,"score":20,"supported":13,"title":161,"abstract":162,"tags":163},[],[],[25],{"code":118,"score":20,"supported":13,"title":165,"abstract":166,"tags":167},[],[],[97],[169,170],"Bombadil 植物电信号 基础模型","植物电生理 胁迫监测","Bombadil植物电信号基础模型-5652",{"doi":8,"openalex_id":8,"authors":173,"venue":8,"cited_by_count":66,"oa_url":8,"card":174,"direction":178,"ingested_from":75},[],{"tldr":175,"method":176,"finding":177,"direction":178,"opportunity":179},"构建植物电生理信号基础模型，用超400万小时无标签数据预训练。","100个物种、十类环境超400万小时植物电信号无标签预训练。","同一信号可追踪七项胁迫生理指标，并泛化到未见物种与胁迫类型。","农业人工智能与决策模型","跨场景田间验证不足，可研究多环境迁移与胁迫早期预警的落地评估。","2026-10-23T00:32:41.455307Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":8,"source_name":187,"source_url":184,"published_at":188,"category":11,"cover_url":8,"hotness":189,"is_selected":13,"embedding_ready":13,"score":190,"score_detail":191,"sources":193,"tags":197,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":243,"slug":246,"view_count":66,"doi":247,"paper":248,"created_at":261},5566,"Accelerated Precision Cultivation of Coffea arabica in a Vertical Hybrid Greenhouse Unit Integrated with Non-Thermal Dielectric Barrier Discharge Plasma Seed Priming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23509132","Indoor controlled environment agriculture (CEA) offers substantial opportunities to insulate perennial crops from climate volatility and optimize resource efficiency, yet high-density cultivation of coffee (Coffea arabica) remains constrained by long ontogenetic establishment periods, slow seed germination, and physical root-zone limitations. In this work, we propose, model, and optimize a four-stage Vertical Hybrid Coffee Cultivation Unit (VH-CCU™) coupled with an upstream non-thermal dielectric barrier discharge (DBD) plasma seed conditioning skid. Pre-sowing treatment of Coffea arabica var. Kholani seeds using a 50 W, 10 kHz atmospheric helium DBD reactor induced sub-micron coat etching and accelerated imbibition by 10% to 12%, advancing emergence by approximately two weeks and boosting the Seedling Vigor Index from 475.9 to 766.6 (+61.1%) at 120 s exposure without generating toxic chemical effluents. Downstream cultivation integrates rooftop agrivoltaics (Stage 1), tiered pyramid micro-droplet fertigation (Stage 2), an engineered shallow volcanic-coir substrate bed (Stage 3), and an enclosed aeroponic sub-chamber (“basement”) for automated, non-destructive root phenotyping via multispectral computer vision (Stage 4). A 46-run Box–Behnken response surface Design of Experiments (DoE) evaluated five design factors across biological, hydraulic, and energetic responses. Multi-objective numerical optimization identified an optimal operating regime (X∗ 1 = 214 s plasma exposure, X∗ 2 = 1.21 L\u002Fplant\u002Fday drip volume, X∗ 3 = 25.5 cm bed depth, X∗ 4 = 16.7 mol\u002Fm2\u002Fday DLI, and X∗ 5 = adaptive edge-vision mist control), which achieved an experimental water-use efficiency of 4.72 g dry biomass\u002FL, compressed time to first bearing from 36–48 months down to 17–18 months, and maintained active root health above 96%. Techno economic modeling reveals that while high specific energy requirements (∼241 kWh\u002Fkg of green coffee) preclude commodity grade scaling, targeted micro-lot specialty production (SCA score 88+) paired with rooftop solar offsets yields a simple payback of 5.1 years and a 10-year Net Present Value of +$169,400. This hybrid framework establishes an empirical basis for the rapid, resource-efficient indoor production of high-grade coffee.","室内受控环境农业（CEA）为多年生作物规避气候波动和优化资源效率提供了重要机遇，但咖啡（Coffea arabica）的高密度栽培仍受限于漫长的个体发育建成期、缓慢的种子萌发以及根区物理空间限制。本研究提出、建模并优化了一种四阶段垂直混合咖啡栽培单元（VH-CCU™），并耦合上游非热介质阻挡放电（DBD）等离子体种子调理装置。采用50 W、10 kHz大气氦气DBD反应器对Kholani品种咖啡种子进行播前处理，诱导了亚微米级种皮刻蚀，使吸胀速率提高10%至12%，出苗提前约两周，在120 s暴露条件下将幼苗活力指数从475.9提升至766.6（+61.1%），且不产生有毒化学废液。下游栽培集成了屋顶农业光伏（第1阶段）、分层金字塔微滴灌施肥（第2阶段）、工程化浅层火山椰糠基质床（第3阶段）以及封闭式气雾栽培地下子室（第4阶段），后者通过多光谱计算机视觉实现自动化非破坏性根系表型分析。通过46组Box–Behnken响应面试验设计（DoE），评估了五个设计因子在生物学、水力学和能量学响应方面的表现。多目标数值优化确定了最优运行方案（X∗1 = 214 s等离子体暴露时间，X∗2 = 1.21 L\u002F株\u002F天滴灌量，X∗3 = 25.5 cm基质床深度，X∗4 = 16.7 mol\u002Fm²\u002F天日光积分，X∗5 = 自适应边缘视觉喷雾控制），实现了4.72 g干生物量\u002FL的实验水分利用效率，将首次结果期从36–48个月缩短至17–18个月，并维持根系健康率在96%以上。技术经济建模表明，尽管高比能耗（约241 kWh\u002Fkg生咖啡）限制了商品级规模化生产，但面向微批次精品生产（SCA评分88+）并配合屋顶太阳能抵消，可实现5.1年简单投资回收期和+$169,400的10年净现值。该混合框架为高等级咖啡的快速、资源高效室内生产奠定了实证基础。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",25,71,{"impact":16,"substance":91,"depth":141,"authority":12,"freshness":143,"relevant":20,"comment":192},"方法新颖、数据扎实的室内咖啡栽培论文，但属细分作物与预印本平台，产业影响有限，可作主题聚合而非每日精选头条。",[194,195],{"name":187,"url":184},{"name":187,"url":196},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527545",[25,198,199,200,201],"垂直农业","设施园艺","咖啡种植","等离子种子处理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":205},{"candidates":206,"agricultural_evidence":45},[207,212,220,227,233,239],{"code":31,"score":12,"supported":45,"title":208,"abstract":210,"tags":211},[209],"greenhouse",[],[198,199],{"code":213,"score":214,"supported":45,"title":215,"abstract":216,"tags":219},"B3",7,[],[217,218],"咖啡","coffee",[200],{"code":221,"score":78,"supported":45,"title":222,"abstract":223,"tags":226},"F3",[],[224,225],"施肥","fertigation",[],{"code":118,"score":78,"supported":45,"title":228,"abstract":229,"tags":232},[],[230,231],"计算机视觉","computer vision",[],{"code":234,"score":52,"supported":45,"title":235,"abstract":236,"tags":238},"I3",[],[237],"multispectral",[],{"code":58,"score":20,"supported":13,"title":240,"abstract":241,"tags":242},[],[],[25],[244,245],"Coffea arabica 垂直温室 等离子种子处理","VH-CCU 咖啡 室内栽培","Coffeaarabica垂直温室等离子种子处理-5566","10.5281\u002Fzenodo.23509132",{"doi":247,"openalex_id":249,"authors":250,"venue":187,"cited_by_count":66,"oa_url":184,"card":254,"direction":259,"ingested_from":260},"W7223104009",[251],{"name":252,"orcid":253},"Ahmed Hala","https:\u002F\u002Forcid.org\u002F0000-0002-7749-2299",{"tldr":255,"method":256,"finding":257,"direction":73,"opportunity":258},"提出并优化等离子体种子预处理+四阶段垂直混合温室单元，加速咖啡室内栽培。","50W氦DBD等离子体处理种子，Box-Behnken响应面试验与多目标优化建模","出苗提前约两周，活力指数增61.1%，首果期缩至17-18个月，水分利用效率4.72g\u002FL。","等离子体种子预处理与室内垂直栽培耦合可拓展至其他多年生高值作物，并需验证规模化能耗经济性。","农业遥感与作物表型","openalex","2026-10-22T11:20:31.314461Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":8,"content":8,"source_name":267,"source_url":8,"published_at":188,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":13,"score":268,"score_detail":269,"sources":273,"tags":275,"primary_topic":280,"secondary_topics":281,"classification":283,"search_phrases":298,"slug":301,"view_count":66,"doi":8,"paper":302,"created_at":309},5536,"日光温室番茄全生育期环境特征解析与智能调控决策支持方法","https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F10\u002F20261021145441802156759.shtm","研究解析日光温室番茄全生育期环境特征，发现该类温室气候缓冲能力较强、有效积温与番茄生理需求呈双峰匹配，但相对增益指标揭示单位时间热量供给失衡、深冬热量不足以及光能结构性不足、湿度调控昼夜失衡等问题。作者建议构建温度—光照—湿度耦合的智能闭环控制系统。","Agriculture 2026-10-21",77,{"impact":270,"substance":17,"depth":18,"authority":271,"freshness":12,"relevant":20,"comment":272},16,13,"论文解析日光温室番茄全生育期环境特征并提出温光湿耦合智能调控方案，结论有新意，对设施农业信息化有参考价值。",[274],{"name":267,"url":265},[25,276,277,278,279],"设施农业","环境调控","温室番茄","智能决策",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[282],{"code":51,"slug":101,"name":102,"path":103,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":284},{"candidates":285,"agricultural_evidence":45},[286,290,294],{"code":31,"score":271,"supported":45,"title":287,"abstract":288,"tags":289},[47],[47],[278,277],{"code":51,"score":44,"supported":45,"title":291,"abstract":292,"tags":293},[55],[55],[],{"code":58,"score":20,"supported":13,"title":295,"abstract":296,"tags":297},[],[],[25],[299,300],"日光温室 番茄 环境调控","温室番茄 智能闭环控制","日光温室番茄环境调控-5536",{"doi":8,"openalex_id":8,"authors":303,"venue":8,"cited_by_count":66,"oa_url":8,"card":304,"direction":73,"ingested_from":75},[],{"tldr":305,"method":306,"finding":307,"direction":73,"opportunity":308},"解析日光温室番茄全生育期环境特征并提出智能调控决策支持方法。","基于全生育期环境监测数据，分析积温匹配与相对增益指标。","温室缓冲力强但深冬热量不足、光能结构性不足、湿度昼夜失衡。","可研发温度—光照—湿度耦合的智能闭环控制算法，并验证其对番茄产量与能耗的协同优化效果。","2026-10-22T00:07:10.129353Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":8,"source_name":316,"source_url":313,"published_at":317,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":13,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":327,"secondary_topics":328,"classification":336,"search_phrases":384,"slug":387,"view_count":66,"doi":8,"paper":388,"created_at":406},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":16,"substance":17,"depth":18,"authority":143,"freshness":19,"relevant":20,"comment":320},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[322],{"name":316,"url":313},[25,97,324,325,326],"农业物联网","温室环境","小气候预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[329,335],{"code":330,"slug":331,"name":332,"path":333,"parent_code":334},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":51,"slug":101,"name":102,"path":103,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":337},{"candidates":338,"agricultural_evidence":45},[339,346,350,358,364,370,377],{"code":330,"score":340,"supported":45,"title":341,"abstract":343,"tags":345},15,[342],"IoT",[344,342],"物联网",[324],{"code":31,"score":92,"supported":45,"title":347,"abstract":348,"tags":349},[209],[47,209],[],{"code":51,"score":143,"supported":45,"title":351,"abstract":352,"tags":357},[],[55,353,354,355,356],"辣椒","黄瓜","tomato","cucumber",[],{"code":58,"score":214,"supported":45,"title":359,"abstract":360,"tags":363},[],[361,362],"精准农业","precision 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John",{"tldr":402,"method":403,"finding":404,"direction":73,"opportunity":405},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":8,"content":412,"source_name":413,"source_url":8,"published_at":414,"category":415,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":13,"score":416,"score_detail":417,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":444,"slug":447,"view_count":20,"doi":8,"paper":8,"created_at":448},5257,"北京发布《北京市2026年智慧设施(种植)建设指南》分级建设\"数智京园\"","https:\u002F\u002Fk.sina.com.cn\u002Farticle_7517400647_1c0126e4705909clmq.html","北京市农业农村局印发《2026年智慧设施(种植)建设指南》,按基础版、升级版分级建设,适配不同主体不同设施不同生产需求,坚持\"优先选用适用技术,避免过度智能化浪费\";依托\"数智京园\"开展国产化智能控制技术\"赛马制\"验证,目前连栋温室国产系统产量与对照系统逐步齐平、商品果率提升。","设备好不好用，模型能不能适应自家大棚，经营主体更愿意到现场看一看。市场上智慧农业产品种类繁多，质量与售后不一，导致“不会选”。“数智京园”把验证、示范、选型推荐连起来，让农户先看实效再决定。\n\n在示范园区，合作社理事长、家庭农场主走进温室，查看智能调控效果，体验系统操作流程。不少园区在观摩后主动联系，希望接入“数智京园”。和合园作为集成展示点，集中呈现环境感知、卷膜卷被、水肥一体化、高压微雾，以及多光谱表型监测、智能植保、采摘机器人等装备，面向“头雁”、设施产业集群和产业园主体开展观摩，避免盲目跟风买高配、不适配设备。\n\n让农户放心选择，先要把技术的实际能力测清楚。首农翠湖智慧农业创新工场（以下简称“翠湖工场”）使用“数智京园”开展数字化管理，并另行开展连栋温室国产化智能控制技术“赛马制”验证。“验证不仅要看设备能否执行指令，还要看执行得是否及时、调控是否高效。”翠湖工场科技总监王利利介绍，国产系统产量与对照系统逐步齐平、商品果率提升后，园区选用国产系统也更有底气。\n\n让技术在真实场景中经受检验，也是“数智京园”推进应用的重要做法。围绕番茄、草莓、黄瓜、叶菜等作物，以及土壤、基质等栽培方式，在不同区域、不同茬口开展对照验证，检验环境控制、水肥调控的效果，再把成熟技术集成到“数智京园”中，形成可供不同园区选择的方案。2026年，北京市农业农村局印发了《北京市2026年智慧设施（种植）建设指南》，按照基础版、升级版分级建设，适配不同主体、不同设施和不同生产需求，坚持“优先选用适用技术，避免过度智能化浪费”，解决“不会选”。\n\n验证之后，还要让农户看得见、学得会。京郊的示范园区成为现场课堂，经营主体进棚看设备运行、体验农事排产，了解改造投入和生产收益。技术服务人员开展手把手培训，配合图文操作手册，帮助农户掌握使用方法。设备出现异常，通过远程诊断和现场维修处理，减少“坏了没人管”的顾虑。京品码追溯、智能采收秤等把生产、采收、包装数据打通，既方便劳动力考核，也方便消费者扫码看全过程。\n\n从验证到示范，再到推广，“数智京园”正把技术选择、生产应用和后续服务连成一条线。随着这套模式在更多设施大棚落地生根，智能管控将在京郊的一茬茬农事里持续发挥作用，让省下来的人工、节约的水肥和增加的收益，结出更多硕果，为首都乡村全面振兴注入强劲的数字动能。\n\n特别声明：以上文章内容仅代表作者本人观点，不代表新浪网观点或立场。如有关于作品内容、版权或其它问题请于作品发表后的30日内与新浪网联系。","新浪网转载(2026-10-19)","2026-10-19T00:00:00Z","政策",70,{"impact":141,"substance":418,"depth":270,"authority":78,"freshness":12,"relevant":20,"comment":419},20,"省级智慧设施建设指南，含分级建设、验证示范与推广服务链条等实质内容，对设施农业数字化有参考价值，但来源为商业门户转载，权威性一般。",[421],{"name":413,"url":410},[423,25,276,424,425],"数字乡村","北京","建设指南",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":429},{"candidates":430,"agricultural_evidence":45},[431,435,439],{"code":31,"score":52,"supported":45,"title":432,"abstract":433,"tags":434},[],[47],[],{"code":58,"score":20,"supported":13,"title":436,"abstract":437,"tags":438},[],[],[25],{"code":440,"score":20,"supported":13,"title":441,"abstract":442,"tags":443},"I6",[],[],[423],[445,446],"北京 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