[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3821":3,"related-3821":96},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":77,"slug":80,"view_count":21,"doi":81,"paper":82,"created_at":95},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,false,65,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},18,14,12,9,1,"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。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23036818",[28,29,30,31,32],"智慧农业","农业机器人","生物育种","精准农业","垂直农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":49},[46,54,60,67,72],{"code":47,"score":48,"supported":49,"title":50,"abstract":51,"tags":53},"I1",11,true,[],[28,31,52],"precision agriculture",[28,31],{"code":34,"score":55,"supported":49,"title":56,"abstract":57,"tags":59},7,[],[32,58],"hydroponic",[32],{"code":61,"score":62,"supported":49,"title":63,"abstract":64,"tags":66},"F5",3,[],[65],"收割机",[],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"F2",[],[],[30],{"code":73,"score":21,"supported":14,"title":74,"abstract":75,"tags":76},"F6",[],[],[29],[78,79],"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":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":87,"oa_url":6,"card":88,"direction":92,"ingested_from":94},"W7214716174",[85],{"name":86,"orcid":9},"R.L. Poonguzhali",0,{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-30T23:30:09.413038Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,183,261,330,423,489],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":107,"is_selected":14,"score":108,"score_detail":109,"sources":115,"tags":117,"primary_topic":120,"secondary_topics":121,"classification":122,"search_phrases":153,"slug":156,"view_count":21,"doi":157,"paper":158,"created_at":182},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",10,76,{"impact":17,"substance":110,"depth":111,"authority":112,"freshness":113,"relevant":21,"comment":114},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。",[116],{"name":105,"url":102},[28,31,32,118,119],"水培","无土栽培",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":123},{"candidates":124,"agricultural_evidence":49},[125,130,134,141,147],{"code":47,"score":126,"supported":49,"title":127,"abstract":128,"tags":129},19,[52],[28,31,52],[28,31],{"code":34,"score":97,"supported":49,"title":131,"abstract":132,"tags":133},[],[119],[32,119,118],{"code":135,"score":97,"supported":49,"title":136,"abstract":137,"tags":140},"I7",[],[138,139],"文献计量","bibliometric",[],{"code":142,"score":62,"supported":49,"title":143,"abstract":144,"tags":146},"H3",[],[145],"水资源",[],{"code":148,"score":62,"supported":49,"title":149,"abstract":150,"tags":152},"H8",[],[151],"可持续农业",[],[154,155],"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":157,"openalex_id":159,"authors":160,"venue":105,"cited_by_count":87,"oa_url":102,"card":177,"direction":92,"ingested_from":94},"W7221762528",[161,164,167,169,172,175],{"name":162,"orcid":163},"Monica Dutta","https:\u002F\u002Forcid.org\u002F0000-0002-6235-9794",{"name":165,"orcid":166},"Deepali Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-6771-2534",{"name":168,"orcid":9},"Walaa Jamal Alharthi",{"name":170,"orcid":171},"Mohammad Zubair Khan","https:\u002F\u002Forcid.org\u002F0000-0002-2409-7172",{"name":173,"orcid":174},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":176,"orcid":9},"Sapna Juneja",{"tldr":178,"method":179,"finding":180,"direction":92,"opportunity":181},"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":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":10,"source_url":186,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":198,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":243,"slug":246,"view_count":87,"doi":247,"paper":248,"created_at":260},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。","2026-10-21T00:00:00Z",71,{"impact":19,"substance":192,"depth":17,"authority":107,"freshness":20,"relevant":21,"comment":193},22,"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。",[195,196],{"name":10,"url":186},{"name":10,"url":197},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527545",[28,32,199,200,201],"设施园艺","咖啡种植","等离子种子处理",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":205},{"candidates":206,"agricultural_evidence":49},[207,212,219,226,233,239],{"code":34,"score":107,"supported":49,"title":208,"abstract":210,"tags":211},[209],"greenhouse",[],[32,199],{"code":213,"score":55,"supported":49,"title":214,"abstract":215,"tags":218},"B3",[],[216,217],"咖啡","coffee",[200],{"code":220,"score":97,"supported":49,"title":221,"abstract":222,"tags":225},"F3",[],[223,224],"施肥","fertigation",[],{"code":227,"score":97,"supported":49,"title":228,"abstract":229,"tags":232},"I2",[],[230,231],"计算机视觉","computer vision",[],{"code":234,"score":62,"supported":49,"title":235,"abstract":236,"tags":238},"I3",[],[237],"multispectral",[],{"code":47,"score":21,"supported":14,"title":240,"abstract":241,"tags":242},[],[],[28],[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":10,"cited_by_count":87,"oa_url":186,"card":254,"direction":259,"ingested_from":94},"W7223104009",[251],{"name":252,"orcid":253},"Ahmed Hala","https:\u002F\u002Forcid.org\u002F0000-0002-7749-2299",{"tldr":255,"method":256,"finding":257,"direction":92,"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","2026-10-22T11:20:31.314461Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":9,"source_name":267,"source_url":264,"published_at":268,"category":12,"cover_url":9,"hotness":107,"is_selected":14,"score":269,"score_detail":270,"sources":273,"tags":275,"primary_topic":279,"secondary_topics":280,"classification":287,"search_phrases":305,"slug":308,"view_count":87,"doi":309,"paper":310,"created_at":329},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,{"impact":113,"substance":17,"depth":271,"authority":97,"freshness":113,"relevant":21,"comment":272},16,"论文将Laravel与Android集成用于水培温室多参数监测，方法具体、数据连续24小时完整，但属单点应用研究，公共影响有限，适合作为技术案例而非每日精选头条。",[274],{"name":267,"url":264},[28,276,31,277,278],"物联网","温室大棚","水培蔬菜",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[281],{"code":282,"slug":283,"name":284,"path":285,"parent_code":286},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":41,"locked":14,"version":42,"method":43,"evidence":288},{"candidates":289,"agricultural_evidence":49},[290,296,301],{"code":282,"score":17,"supported":49,"title":291,"abstract":293,"tags":295},[292],"IoT",[276,294,292],"internet of things",[276],{"code":34,"score":18,"supported":49,"title":297,"abstract":298,"tags":300},[209],[299,209],"温室",[],{"code":47,"score":113,"supported":49,"title":302,"abstract":303,"tags":304},[],[31,52],[28,31],[306,307],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":309,"openalex_id":311,"authors":312,"venue":267,"cited_by_count":87,"oa_url":323,"card":324,"direction":92,"ingested_from":94},"W7222850582",[313,315,318,321],{"name":314,"orcid":9},"Ramadanzah Ramadanzah",{"name":316,"orcid":317},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":319,"orcid":320},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":322,"orcid":9},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":325,"method":326,"finding":327,"direction":92,"opportunity":328},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":336,"source_url":333,"published_at":337,"category":12,"cover_url":9,"hotness":107,"is_selected":49,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":347,"secondary_topics":348,"classification":354,"search_phrases":394,"slug":397,"view_count":21,"doi":398,"paper":399,"created_at":422},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":17,"substance":192,"depth":17,"authority":18,"freshness":20,"relevant":21,"comment":340},"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[342],{"name":336,"url":333},[28,344,345,31,346],"农业人工智能","智能灌溉","温室种植",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[349],{"code":142,"slug":350,"name":351,"path":352,"parent_code":353},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":41,"locked":14,"version":42,"method":43,"evidence":355},{"candidates":356,"agricultural_evidence":49},[357,362,367,371,378,384,390],{"code":142,"score":17,"supported":49,"title":358,"abstract":359,"tags":361},[350],[360,145,350],"灌溉",[345],{"code":34,"score":363,"supported":49,"title":364,"abstract":365,"tags":366},15,[209],[299,209],[346],{"code":47,"score":113,"supported":49,"title":368,"abstract":369,"tags":370},[],[31,52],[28,31],{"code":372,"score":97,"supported":49,"title":373,"abstract":374,"tags":377},"H2",[],[375,376],"土壤","soil",[],{"code":379,"score":62,"supported":49,"title":380,"abstract":381,"tags":383},"B1",[],[382],"番茄",[],{"code":385,"score":62,"supported":49,"title":386,"abstract":387,"tags":389},"H7",[],[388],"气候变化",[],{"code":227,"score":21,"supported":14,"title":391,"abstract":392,"tags":393},[],[],[344],[395,396],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":398,"openalex_id":400,"authors":401,"venue":336,"cited_by_count":87,"oa_url":333,"card":416,"direction":421,"ingested_from":94},"W7220746994",[402,405,408,410,413],{"name":403,"orcid":404},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":406,"orcid":407},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":409,"orcid":9},"Abdelkader Mami",{"name":411,"orcid":412},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":414,"orcid":415},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":417,"method":418,"finding":419,"direction":92,"opportunity":420},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","农业人工智能与决策模型","2026-10-07T23:30:43.848308Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":107,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":440,"secondary_topics":441,"classification":442,"search_phrases":459,"slug":462,"view_count":446,"doi":463,"paper":464,"created_at":488},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":271,"substance":433,"depth":17,"authority":18,"freshness":20,"relevant":21,"comment":434},21,"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。",[436],{"name":429,"url":426},[28,344,32,438,439],"作物模型","光环境调控",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":443},{"candidates":444,"agricultural_evidence":49},[445,450,455],{"code":34,"score":446,"supported":49,"title":447,"abstract":448,"tags":449},4,[],[32],[32],{"code":227,"score":451,"supported":14,"title":452,"abstract":453,"tags":454},2,[],[],[438,344],{"code":47,"score":21,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[28],[460,461],"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":463,"openalex_id":465,"authors":466,"venue":429,"cited_by_count":87,"oa_url":9,"card":483,"direction":421,"ingested_from":94},"W7215104938",[467,470,473,475,478,480],{"name":468,"orcid":469},"Eunjeong Lim","https:\u002F\u002Forcid.org\u002F0009-0008-2621-8602",{"name":471,"orcid":472},"Da-Seul Choi","https:\u002F\u002Forcid.org\u002F0009-0008-3239-9817",{"name":474,"orcid":9},"Hyeonjee Kim",{"name":476,"orcid":477},"Taewon Moon","https:\u002F\u002Forcid.org\u002F0000-0002-1593-7870",{"name":479,"orcid":9},"Myung-min Oh",{"name":481,"orcid":482},"Tae In Ahn","https:\u002F\u002Forcid.org\u002F0000-0001-5939-7450",{"tldr":484,"method":485,"finding":486,"direction":421,"opportunity":487},"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。","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":490,"title":491,"url":492,"summary":493,"summary_zh":9,"content":9,"source_name":494,"source_url":9,"published_at":495,"category":12,"cover_url":9,"hotness":107,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":504,"secondary_topics":505,"classification":511,"search_phrases":527,"slug":530,"view_count":531,"doi":9,"paper":532,"created_at":540},2397,"System-Level Smart Robotic Harvesting for High-Value Greenhouse Crops: A Review","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1795","Agronomy 2026, 16(18):1795（2026-09-13）。江苏大学Junyi Wang等综述188项主要研究，以'具身智能'为分析视角连接感知、可采性评估、决策、操控、反馈、故障传播与恢复。研究表明系统集成是主要瓶颈：可观测性不完整、场景状态短、接触不确定性、结果验证弱导致完整任务成功率、有效下降效。提出六维生产相关评价体系。","MDPI Agronomy | 2026-09-13","2026-09-13T00:00:00Z",82,{"impact":17,"substance":192,"depth":17,"authority":18,"freshness":107,"relevant":21,"comment":498},"江苏大学团队基于188项研究的系统级综述，指出系统集成是可观测性、接触不确定性与故障恢复等瓶颈，并提出六维生产评价体系，对温室智能采摘研发具有较高参考价值。",[500],{"name":494,"url":492},[28,29,502,503,346],"具身智能","采摘机器人",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[506],{"code":73,"slug":507,"name":508,"path":509,"parent_code":510},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":41,"locked":14,"version":42,"method":43,"evidence":512},{"candidates":513,"agricultural_evidence":49},[514,519,523],{"code":73,"score":107,"supported":49,"title":515,"abstract":517,"tags":518},[516],"robotic harvesting",[],[29,503],{"code":34,"score":20,"supported":49,"title":520,"abstract":521,"tags":522},[209],[],[346],{"code":47,"score":21,"supported":14,"title":524,"abstract":525,"tags":526},[],[],[28],[528,529],"农业机器人 采摘机器人 具身智能 智慧农业","农业机器人 采摘机器人","农业机器人采摘机器人具身智能智慧农业-2397",5,{"doi":9,"openalex_id":9,"authors":533,"venue":9,"cited_by_count":87,"oa_url":9,"card":534,"direction":421,"ingested_from":539},[],{"tldr":535,"method":536,"finding":537,"direction":421,"opportunity":538},"综述188项研究，从具身智能视角分析温室高价值作物智能采收的系统集成瓶颈。","综述188项研究，以具身智能视角串联感知、可采性评估、决策、操控与反馈。","系统集成是主要瓶颈，可观测性不完整与接触不确定性导致完整任务成功率下降。","可探索面向温室采收的具身智能系统集成与六维生产评价体系，弥补任务级成功率验证空白。","agent","2026-09-14T00:06:33.089876Z"]