[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5566":3,"related-5566":109},{"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":24,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":88,"slug":91,"view_count":92,"doi":93,"paper":94,"created_at":108},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",25,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,10,9,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527545",[29,30,31,32,33],"智慧农业","垂直农业","设施园艺","咖啡种植","等离子种子处理",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":48},[47,53,61,69,76,83],{"code":35,"score":20,"supported":48,"title":49,"abstract":51,"tags":52},true,[50],"greenhouse",[],[30,31],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":60},"B3",7,[],[58,59],"咖啡","coffee",[32],{"code":62,"score":63,"supported":48,"title":64,"abstract":65,"tags":68},"F3",6,[],[66,67],"施肥","fertigation",[],{"code":70,"score":63,"supported":48,"title":71,"abstract":72,"tags":75},"I2",[],[73,74],"计算机视觉","computer vision",[],{"code":77,"score":78,"supported":48,"title":79,"abstract":80,"tags":82},"I3",3,[],[81],"multispectral",[],{"code":84,"score":22,"supported":14,"title":85,"abstract":86,"tags":87},"I1",[],[],[29],[89,90],"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",0,"10.5281\u002Fzenodo.23509132",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":92,"oa_url":6,"card":100,"direction":106,"ingested_from":107},"W7223104009",[97],{"name":98,"orcid":99},"Ahmed Hala","https:\u002F\u002Forcid.org\u002F0000-0002-7749-2299",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"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。","智慧农业 \u002F 农业物联网","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",{"total":63,"page":22,"page_size":63,"items":110},[111,196,266,330,383,477],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":119,"score_detail":120,"sources":126,"tags":128,"primary_topic":132,"secondary_topics":133,"classification":134,"search_phrases":166,"slug":169,"view_count":22,"doi":170,"paper":171,"created_at":195},4984,"A Comparative Review of Soilless Precision Agriculture Techniques and Emerging Smart Farming Technologies","https:\u002F\u002Fdoi.org\u002F10.38094\u002Fjastt721251","The rapidly diminishing arable land, uncertain climatic conditions, and lack of ample natural resources are worsening the urgency for adopting sustainable agricultural systems. Soilless cultivation methods promote enhanced resource utilization and better productivity. These methods, like aeroponics, hydroponics, and aquaponics, are evolving as smart farming techniques, providing notable advantages over traditional farming methods, enhancing water conservation, resource utilization, and enabling cultivation in places with reduced area and less water. The concept of soilless cultivation provides multifold advantages over traditional cultivation methods by further making provision for cultivating in the closest proximity to urban farmers, like residential rooftops. A bibliometric analysis is also conducted to highlight the recent trends in smart precision farming technologies and the choice of substrate for each technique. In this article, various methods of soilless smart precision agriculture are identified and compared with traditional cultivation methods. Moreover, aspects like economic sustainability and recent research trends are analyzed to obtain insight into the three basic classes of soilless cultivation, namely aeroponics, aquaponics, and hydroponics. Furthermore, the study also focuses on briefly analyzing the economics of soilless techniques. The study concludes by outlining the major challenges and possible future enhancements in this domain that can lead to the evolution of smart, efficient, and resilient agricultural systems.","可耕地迅速减少、气候条件不稳定以及充足自然资源的匮乏，正日益加剧采用可持续农业体系的紧迫性。无土栽培方法促进了资源利用率的提高和生产力的提升。气雾栽培、水培和鱼菜共生等方法正演变为智慧农业技术，相较传统耕作方式具有显著优势，能够增强节水能力、提高资源利用率，并使得在面积有限、水资源匮乏的地区进行种植成为可能。无土栽培的概念相较传统栽培方法具有多重优势，还能进一步满足在城市农户附近（如住宅屋顶）进行种植的需求。本文还进行了文献计量分析，以揭示智慧精准农业技术的最新趋势以及每种技术对基质的选择。本文识别了多种无土智慧精准农业方法，并将其与传统栽培方法进行比较。此外，还分析了经济可持续性和近期研究趋势等方面，以深入了解无土栽培的三大基本类别，即气雾栽培、鱼菜共生和水培。进一步地，本研究还着重简要分析了无土栽培技术的经济性。研究最后概述了该领域的主要挑战及可能的未来改进方向，这些改进有望推动智慧、高效且具有韧性的农业体系的发展。","Journal of Applied Science and Technology Trends","2026-10-14T00:00:00Z",76,{"impact":19,"substance":121,"depth":122,"authority":123,"freshness":124,"relevant":22,"comment":125},20,17,13,8,"系统比较无土栽培三类技术与智慧农业趋势，含文献计量分析，对设施农业与都市农业有参考价值。",[127],{"name":117,"url":114},[29,129,30,130,131],"精准农业","水培","无土栽培",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":135},{"candidates":136,"agricultural_evidence":48},[137,143,147,154,160],{"code":84,"score":138,"supported":48,"title":139,"abstract":141,"tags":142},19,[140],"precision agriculture",[29,129,140],[29,129],{"code":35,"score":63,"supported":48,"title":144,"abstract":145,"tags":146},[],[131],[30,131,130],{"code":148,"score":63,"supported":48,"title":149,"abstract":150,"tags":153},"I7",[],[151,152],"文献计量","bibliometric",[],{"code":155,"score":78,"supported":48,"title":156,"abstract":157,"tags":159},"H3",[],[158],"水资源",[],{"code":161,"score":78,"supported":48,"title":162,"abstract":163,"tags":165},"H8",[],[164],"可持续农业",[],[167,168],"无土栽培 智慧农业 技术对比","水培 气雾培 鱼菜共生 精准农业","无土栽培智慧农业技术对比-4984","10.38094\u002Fjastt721251",{"doi":170,"openalex_id":172,"authors":173,"venue":117,"cited_by_count":92,"oa_url":114,"card":190,"direction":104,"ingested_from":107},"W7221762528",[174,177,180,182,185,188],{"name":175,"orcid":176},"Monica Dutta","https:\u002F\u002Forcid.org\u002F0000-0002-6235-9794",{"name":178,"orcid":179},"Deepali Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-6771-2534",{"name":181,"orcid":9},"Walaa Jamal Alharthi",{"name":183,"orcid":184},"Mohammad Zubair Khan","https:\u002F\u002Forcid.org\u002F0000-0002-2409-7172",{"name":186,"orcid":187},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":189,"orcid":9},"Sapna Juneja",{"tldr":191,"method":192,"finding":193,"direction":104,"opportunity":194},"综述无土精准农业技术，比较气雾培、水培和鱼菜共生，并分析智能农业趋势。","文献综述与文献计量分析，比较无土栽培技术、基质选择及经济可持续性。","无土栽培在节水、资源利用和城市近郊种植上优于传统农业，但经济性和技术挑战仍存。","可研究无土栽培与物联网、AI决策模型的深度融合，优化基质选择和能耗经济性。","2026-10-15T23:30:08.336176Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":204,"score_detail":205,"sources":210,"tags":212,"primary_topic":216,"secondary_topics":217,"classification":218,"search_phrases":235,"slug":238,"view_count":222,"doi":239,"paper":240,"created_at":265},3976,"Model-based optimization of dynamic photon allocation for maximizing biomass production under a fixed total light integral in vertical farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112489","Model-based optimization of dynamic photon allocation for maximizing biomass production under a fixed total light integral in vertical farming。Computers and Electronics in Agriculture","基于模型的动态光子分配优化，以在垂直农业固定总光积分下最大化生物量生产。","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",78,{"impact":206,"substance":207,"depth":19,"authority":208,"freshness":21,"relevant":22,"comment":209},16,21,14,"核心期刊论文，提出固定光积分下动态光子分配模型优化方法，对垂直农业光环境精准调控有参考价值，但属细分领域方法研究，产业影响有限。",[211],{"name":202,"url":199},[29,213,30,214,215],"农业人工智能","作物模型","光环境调控",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":219},{"candidates":220,"agricultural_evidence":48},[221,226,231],{"code":35,"score":222,"supported":48,"title":223,"abstract":224,"tags":225},4,[],[30],[30],{"code":70,"score":227,"supported":14,"title":228,"abstract":229,"tags":230},2,[],[],[214,213],{"code":84,"score":22,"supported":14,"title":232,"abstract":233,"tags":234},[],[],[29],[236,237],"垂直农业 光积分 生物量","动态光子分配 模型优化","垂直农业光积分生物量-3976","10.1016\u002Fj.compag.2026.112489",{"doi":239,"openalex_id":241,"authors":242,"venue":202,"cited_by_count":92,"oa_url":9,"card":259,"direction":263,"ingested_from":107},"W7215104938",[243,246,249,251,254,256],{"name":244,"orcid":245},"Eunjeong Lim","https:\u002F\u002Forcid.org\u002F0009-0008-2621-8602",{"name":247,"orcid":248},"Da-Seul Choi","https:\u002F\u002Forcid.org\u002F0009-0008-3239-9817",{"name":250,"orcid":9},"Hyeonjee Kim",{"name":252,"orcid":253},"Taewon Moon","https:\u002F\u002Forcid.org\u002F0000-0002-1593-7870",{"name":255,"orcid":9},"Myung-min Oh",{"name":257,"orcid":258},"Tae In Ahn","https:\u002F\u002Forcid.org\u002F0000-0001-5939-7450",{"tldr":260,"method":261,"finding":262,"direction":263,"opportunity":264},"在固定总光照积分下，用模型优化垂直农业中动态光子分配以最大化生物量。","基于作物生长模型的优化方法，模拟不同光子分配策略。","动态光子分配策略可提升固定光积分下的生物量产量。","农业人工智能与决策模型","可结合实时传感与作物状态反馈，发展自适应光配方优化，并验证多作物多场景泛化性。","2026-10-02T23:30:02.603545Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":10,"source_url":269,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":276,"tags":280,"primary_topic":283,"secondary_topics":284,"classification":285,"search_phrases":314,"slug":317,"view_count":22,"doi":318,"paper":319,"created_at":329},3821,"Transforming Agriculture Through Technology And Smart Farming Innovations","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23036819","Abstract In the contemporary agricultural landscape, a significant paradigm shift is taking place as farmers across the globe increasingly adopt advanced technologies and innovative farming practices. This transformation highlights the profound impact of modern agricultural technologies and the key trends shaping the future of food production. A central component of this revolution is the integration of precision agriculture, wherein farmers utilize cutting-edge tools such as GPS systems, sensors, and drones to optimize resource allocation. This approach enhances crop management efficiency, reduces resource wastage, and improves overall productivity. The rise of smart farming has introduced data-driven decision-making processes, enabling farmers to monitor, analyze, and manage crops with remarkable accuracy. In addition, advancements in biotechnology and genetic engineering have facilitated the development of genetically modified crops with improved resilience, enhanced nutritional value, and greater resistance to pests and diseases. These innovations not only increase agricultural output but also promote sustainability and environmentally responsible farming practices. Moreover, the emergence of vertical farming and controlled-environment agriculture marks a significant departure from conventional farming methods. By employing hydroponic and aeroponic systems in vertically structured environments, crops can be cultivated efficiently with minimal land use and optimized resource consumption. This method is particularly advantageous in urban areas where arable land is limited, offering a sustainable solution to growing food demands. Furthermore, robotics and automation are reshaping agricultural operations. Technologies such as autonomous tractors, robotic harvesters, and drone-assisted planting systems are reducing reliance on manual labor while enhancing operational efficiency. These advancements help address labor shortages and strengthen the overall resilience and sustainability of the agricultural sector.","在当代农业格局中，随着全球农民日益采用先进技术和创新耕作方式，正在发生重大的范式转变。这一变革凸显了现代农业技术的深远影响以及塑造未来粮食生产的关键趋势。这场革命的核心组成部分是精准农业的整合，农民利用GPS系统、传感器和无人机等前沿工具来优化资源配置。这种方法提高了作物管理效率，减少了资源浪费，并提升了整体生产力。智慧农业的兴起引入了数据驱动的决策过程，使农民能够以极高的精度监测、分析和管理作物。此外，生物技术和基因工程的进步促进了转基因作物的开发，这些作物具有更强的适应力、更高的营养价值和更强的抗病虫害能力。这些创新不仅提高了农业产量，还促进了可持续性和环境友好的耕作方式。此外，垂直农业和受控环境农业的出现标志着对传统耕作方式的重大突破。通过在垂直结构中采用水培和气培系统，作物可以在最少的土地利用和优化的资源消耗下高效种植。这种方法在城市地区尤为有利，因为那里可耕地有限，为日益增长的粮食需求提供了可持续的解决方案。此外，机器人技术和自动化正在重塑农业作业。自主拖拉机、机器人收割机和无人机辅助种植系统等技术正在减少对人工劳动的依赖，同时提高作业效率。这些进步有助于应对劳动力短缺问题，并增强农业部门的整体韧性和可持续性。","2026-09-30T00:00:00Z",65,{"impact":19,"substance":208,"depth":17,"authority":17,"freshness":21,"relevant":22,"comment":275},"Zenodo 平台上的智慧农业综述性论文，覆盖面广但缺乏新数据与具体案例，可作为主题聚合素材而非每日精选。",[277,278],{"name":10,"url":269},{"name":10,"url":279},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23036818",[29,281,282,129,30],"农业机器人","生物育种",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":286},{"candidates":287,"agricultural_evidence":48},[288,293,298,304,309],{"code":84,"score":289,"supported":48,"title":290,"abstract":291,"tags":292},11,[],[29,129,140],[29,129],{"code":35,"score":55,"supported":48,"title":294,"abstract":295,"tags":297},[],[30,296],"hydroponic",[30],{"code":299,"score":78,"supported":48,"title":300,"abstract":301,"tags":303},"F5",[],[302],"收割机",[],{"code":305,"score":22,"supported":14,"title":306,"abstract":307,"tags":308},"F2",[],[],[282],{"code":310,"score":22,"supported":14,"title":311,"abstract":312,"tags":313},"F6",[],[],[281],[315,316],"农业机器人 垂直农业 智慧农业 生物育种","农业机器人 垂直农业","农业机器人垂直农业智慧农业生物育种-3821","10.5281\u002Fzenodo.23036819",{"doi":318,"openalex_id":320,"authors":321,"venue":10,"cited_by_count":92,"oa_url":269,"card":324,"direction":104,"ingested_from":107},"W7214716174",[322],{"name":323,"orcid":9},"R.L. Poonguzhali",{"tldr":325,"method":326,"finding":327,"direction":104,"opportunity":328},"综述精准农业、智慧农业、生物技术、垂直农业与机器人自动化如何重塑未来粮食生产。","文献综述，整合GPS、传感器、无人机、基因工程、水培气培及自动化技术案例。","技术融合可提升效率、减少浪费、促进可持续，并应对劳动力短缺与耕地限制。","可量化评估垂直农业与机器人系统在中小农场的经济可行性及碳足迹，填补实证空白。","2026-09-30T23:30:09.413038Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":9,"content":9,"source_name":335,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":336,"score_detail":337,"sources":339,"tags":341,"primary_topic":346,"secondary_topics":347,"classification":353,"search_phrases":370,"slug":373,"view_count":92,"doi":9,"paper":374,"created_at":382},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":206,"substance":207,"depth":122,"authority":123,"freshness":20,"relevant":22,"comment":338},"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。",[340],{"name":335,"url":333},[29,342,343,344,345],"设施农业","环境调控","温室番茄","智能决策",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[348],{"code":349,"slug":350,"name":351,"path":352,"parent_code":39},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":42,"locked":14,"version":43,"method":44,"evidence":354},{"candidates":355,"agricultural_evidence":48},[356,361,366],{"code":35,"score":123,"supported":48,"title":357,"abstract":359,"tags":360},[358],"温室",[358],[344,343],{"code":349,"score":289,"supported":48,"title":362,"abstract":364,"tags":365},[363],"番茄",[363],[],{"code":84,"score":22,"supported":14,"title":367,"abstract":368,"tags":369},[],[],[29],[371,372],"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":9,"openalex_id":9,"authors":375,"venue":9,"cited_by_count":92,"oa_url":9,"card":376,"direction":104,"ingested_from":381},[],{"tldr":377,"method":378,"finding":379,"direction":104,"opportunity":380},"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。","agent","2026-10-22T00:07:10.129353Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":400,"secondary_topics":401,"classification":409,"search_phrases":454,"slug":457,"view_count":92,"doi":9,"paper":458,"created_at":476},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":17,"substance":207,"depth":122,"authority":21,"freshness":124,"relevant":22,"comment":393},"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。",[395],{"name":389,"url":386},[29,213,397,398,399],"农业物联网","温室环境","小气候预测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[402,408],{"code":403,"slug":404,"name":405,"path":406,"parent_code":407},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":349,"slug":350,"name":351,"path":352,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":410},{"candidates":411,"agricultural_evidence":48},[412,419,423,431,435,441,448],{"code":403,"score":413,"supported":48,"title":414,"abstract":416,"tags":418},15,[415],"IoT",[417,415],"物联网",[397],{"code":35,"score":208,"supported":48,"title":420,"abstract":421,"tags":422},[50],[358,50],[],{"code":349,"score":21,"supported":48,"title":424,"abstract":425,"tags":430},[],[363,426,427,428,429],"辣椒","黄瓜","tomato","cucumber",[],{"code":84,"score":55,"supported":48,"title":432,"abstract":433,"tags":434},[],[129,140],[29],{"code":70,"score":55,"supported":48,"title":436,"abstract":437,"tags":440},[],[438,439],"深度学习","deep learning",[213],{"code":442,"score":63,"supported":48,"title":443,"abstract":444,"tags":447},"H2",[],[445,446],"土壤","soil",[],{"code":155,"score":63,"supported":48,"title":449,"abstract":450,"tags":453},[],[451,452],"灌溉","irrigation",[],[455,456],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":459,"authors":460,"venue":389,"cited_by_count":92,"oa_url":9,"card":471,"direction":104,"ingested_from":107},"W7222909837",[461,463,465,467,469],{"name":462,"orcid":9},"Siman Emmanuel*",{"name":464,"orcid":9},"Edward Onyebueke Agu",{"name":466,"orcid":9},"Musa Gani",{"name":468,"orcid":9},"Oladunjoye John Abiodun",{"name":470,"orcid":9},"Anagu Emmanuel John",{"tldr":472,"method":473,"finding":474,"direction":104,"opportunity":475},"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":478,"title":479,"url":480,"summary":481,"summary_zh":9,"content":482,"source_name":483,"source_url":9,"published_at":484,"category":485,"cover_url":9,"hotness":20,"is_selected":14,"score":486,"score_detail":487,"sources":489,"tags":491,"primary_topic":495,"secondary_topics":496,"classification":497,"search_phrases":513,"slug":516,"view_count":22,"doi":9,"paper":9,"created_at":517},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":19,"substance":121,"depth":206,"authority":63,"freshness":20,"relevant":22,"comment":488},"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。",[490],{"name":483,"url":480},[492,29,342,493,494],"数字乡村","北京","建设指南",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":498},{"candidates":499,"agricultural_evidence":48},[500,504,508],{"code":35,"score":78,"supported":48,"title":501,"abstract":502,"tags":503},[],[358],[],{"code":84,"score":22,"supported":14,"title":505,"abstract":506,"tags":507},[],[],[29],{"code":509,"score":22,"supported":14,"title":510,"abstract":511,"tags":512},"I6",[],[],[492],[514,515],"北京 数智京园 建设指南","北京 智慧设施 种植 分级建设","北京数智京园建设指南-5257","2026-10-19T00:07:31.752116Z"]