[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1373":3,"related-1373":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":69,"created_at":79},1373,"农业信息物理系统：感知驱动的多机器人协同与非结构化环境物流研究进展","https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F17\u002F5514","江苏大学农业工程学院综述：分析从传统 2D 几何映射到融合 LiDAR、UAV、空间数据的多模态语义 3D 重建的飞跃；强调从集中式运筹到 MARL 多智能体强化学习去中心化数据驱动方法的算法演进；探讨微动预测控制与生态可持续指标在路径模型中的整合；总结开放大田高通量接力、密植果园 GNSS 拒止离散路由、温室人机协作三类典型场景；指出边缘算力、Sim-to-Real、经济闭环与人机协作伦理是规模化商业化的剩余障碍。",null,"Sensors · MDPI · 2026-08-31","2026-08-30T16:00:00Z","论文",10,false,71,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,12,3,1,"综述涵盖感知、算法与场景，对农业机器人领域有参考价值，但时效性稍弱。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","农业机器人","多智能体","数字孪生",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":43},[42,48,54,59],{"code":30,"score":19,"supported":43,"title":44,"abstract":45,"tags":47},true,[],[46],"温室",[],{"code":49,"score":50,"supported":13,"title":51,"abstract":52,"tags":53},"I1",2,[],[],[28,25],{"code":55,"score":20,"supported":13,"title":56,"abstract":57,"tags":58},"F6",[],[],[26],{"code":60,"score":20,"supported":13,"title":61,"abstract":62,"tags":63},"I2",[],[],[27],[65,66],"农业机器人 多智能体 数字孪生 智慧农业","农业机器人 多智能体","农业机器人多智能体数字孪生智慧农业-1373",5,{"doi":8,"openalex_id":8,"authors":70,"venue":8,"cited_by_count":71,"oa_url":8,"card":72,"direction":76,"ingested_from":78},[],0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"综述农业信息物理系统中感知驱动多机器人协同与非结构化环境物流的研究进展。","综述多模态语义3D重建、MARL多智能体强化学习、微动预测控制等方法。","识别出三类典型场景，指出边缘算力、Sim-to-Real等是商业化障碍。","农业人工智能与决策模型","可研究边缘算力与Sim-to-Real结合，实现农业机器人去中心化协同的规模化落地。","agent","2026-09-02T00:05:07.338781Z",{"total":81,"page":20,"page_size":81,"items":82},6,[83,155,231,283,357,409],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":11,"cover_url":8,"hotness":91,"is_selected":13,"score":92,"score_detail":93,"sources":97,"tags":101,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":137,"slug":140,"view_count":20,"doi":141,"paper":142,"created_at":154},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,65,{"impact":16,"substance":94,"depth":18,"authority":18,"freshness":95,"relevant":20,"comment":96},14,9,"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。",[98,99],{"name":89,"url":86},{"name":89,"url":100},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23036818",[25,26,102,103,104],"生物育种","精准农业","垂直农业",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":108},{"candidates":109,"agricultural_evidence":43},[110,116,122,128,133],{"code":49,"score":111,"supported":43,"title":112,"abstract":113,"tags":115},11,[],[25,103,114],"precision agriculture",[25,103],{"code":30,"score":117,"supported":43,"title":118,"abstract":119,"tags":121},7,[],[104,120],"hydroponic",[104],{"code":123,"score":19,"supported":43,"title":124,"abstract":125,"tags":127},"F5",[],[126],"收割机",[],{"code":129,"score":20,"supported":13,"title":130,"abstract":131,"tags":132},"F2",[],[],[102],{"code":55,"score":20,"supported":13,"title":134,"abstract":135,"tags":136},[],[],[26],[138,139],"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":141,"openalex_id":143,"authors":144,"venue":89,"cited_by_count":71,"oa_url":86,"card":147,"direction":151,"ingested_from":153},"W7214716174",[145],{"name":146,"orcid":8},"R.L. Poonguzhali",{"tldr":148,"method":149,"finding":150,"direction":151,"opportunity":152},"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",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":8,"source_name":161,"source_url":158,"published_at":162,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":163,"score_detail":164,"sources":170,"tags":172,"primary_topic":176,"secondary_topics":177,"classification":184,"search_phrases":207,"slug":210,"view_count":50,"doi":211,"paper":212,"created_at":230},3614,"Integration of digital twin and IoT for virtual greenhouse management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sciaf.2026.e03665","The convergence of Digital Twin (DT) and Internet of Things (IoT) technologies represents a key enabler of Agriculture 5.0, providing real-time synchronization between physical and virtual environments. When coupled with Virtual Reality (VR), these technologies enable immersive and interactive systems that enhance intelligent agriculture monitoring, control, and decision-making. This study proposes an advanced and fully integrated VR–DT–IoT framework for virtual greenhouse management, designed not only to simulate and visualize but also to synchronize and control the physical system in real time. The proposed framework achieves seamless cyber-physical fusion by establishing bidirectional communication among IoT sensors, DT models, and VR interfaces. This enables users to trigger actuator commands and observe instant physical feedback directly within the immersive environment. A real-world experimental greenhouse was developed as a testbed to validate the system’s architecture and functionality. IoT sensors continuously monitored environmental parameters with data streams dynamically synchronized to the DT model reproducing greenhouse dynamics. The VR interface provided context-aware interaction and spatial visualization, allowing users to explore live sensor data, analyze trends, and simulate adaptive control strategies in a realistic virtual environment. The experimental validation, conducted with 23 participants across seven training sessions, revealed a marked reduction in task completion time and error rates, along with high usability scores (SUS = 3.84\u002F5), confirming the system’s effectiveness in improving operational performance. The findings demonstrate that the proposed system significantly advances digital agriculture by merging modeling, real-time feedback, and immersive control into a unified human-in-the-loop platform. This research highlights the technological novelty and practical value of integrating DT, IoT, and VR into a single coherent framework, bridging physical and virtual domains.","数字孪生（Digital Twin, DT）与物联网（Internet of Things, IoT）技术的融合是农业5.0的关键使能技术，可实现物理环境与虚拟环境之间的实时同步。当与虚拟现实（Virtual Reality, VR）相结合时，这些技术能够构建沉浸式交互系统，从而增强智能农业的监测、控制与决策能力。本研究提出了一种先进且完全集成的VR–DT–IoT框架，用于虚拟温室管理，该框架不仅能够仿真和可视化，还能够实时同步和控制物理系统。所提出的框架通过建立IoT传感器、DT模型与VR界面之间的双向通信，实现了无缝的信息物理融合。用户可在沉浸式环境中直接触发执行器命令并观察即时物理反馈。本研究搭建了一个真实实验温室作为测试平台，以验证系统架构与功能。IoT传感器持续监测环境参数，数据流动态同步至DT模型，以复现温室动态变化。VR界面提供了情境感知交互与空间可视化功能，使用户能够在逼真的虚拟环境中探索实时传感器数据、分析趋势并仿真自适应控制策略。实验验证共有23名参与者参与，跨越七次培训课程，结果显示任务完成时间和错误率显著降低，同时可用性评分较高（SUS = 3.84\u002F5），证实了该系统在提升操作性能方面的有效性。研究结果表明，所提出的系统通过将建模、实时反馈与沉浸式控制融合为统一的人在回路平台，显著推进了数字农业发展。本研究凸显了将DT、IoT与VR集成到单一连贯框架中的技术创新性与实用价值，弥合了物理域与虚拟域之间的鸿沟。","Scientific African","2026-09-25T00:00:00Z",76,{"impact":165,"substance":166,"depth":16,"authority":167,"freshness":168,"relevant":20,"comment":169},16,21,13,8,"提出VR-DT-IoT全集成虚拟温室框架并经真实温室与23人实验验证，方法新颖、数据可靠，对智慧农业人机交互控制有参考价值。",[171],{"name":161,"url":158},[25,173,28,174,175],"农业物联网","虚拟现实","温室管理",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[178],{"code":179,"slug":180,"name":181,"path":182,"parent_code":183},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":37,"locked":13,"version":38,"method":39,"evidence":185},{"candidates":186,"agricultural_evidence":43},[187,195,202],{"code":49,"score":188,"supported":43,"title":189,"abstract":191,"tags":194},19,[190],"digital twin",[192,28,193,190],"数字农业","digital agriculture",[28,25],{"code":179,"score":16,"supported":43,"title":196,"abstract":198,"tags":201},[197],"IoT",[199,200,197],"物联网","internet of things",[173],{"code":30,"score":94,"supported":43,"title":203,"abstract":205,"tags":206},[204],"greenhouse",[46,204],[],[208,209],"数字孪生 物联网 虚拟温室","VR DT IoT 智慧农业","数字孪生物联网虚拟温室-3614","10.1016\u002Fj.sciaf.2026.e03665",{"doi":211,"openalex_id":213,"authors":214,"venue":161,"cited_by_count":71,"oa_url":224,"card":225,"direction":151,"ingested_from":153},"W7214397681",[215,218,221],{"name":216,"orcid":217},"Hicham Slimani","https:\u002F\u002Forcid.org\u002F0000-0002-7946-4064",{"name":219,"orcid":220},"Abdelilah Jilbab","https:\u002F\u002Forcid.org\u002F0000-0002-1577-9040",{"name":222,"orcid":223},"Jamal El Mhamdi","https:\u002F\u002Forcid.org\u002F0000-0001-8219-3560","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2468227626004904\u002Fpdf",{"tldr":226,"method":227,"finding":228,"direction":151,"opportunity":229},"提出VR-DT-IoT框架，实现虚拟温室实时同步与沉浸式控制。","构建真实温室试验台，集成IoT传感器、数字孪生模型与VR界面，23人测试。","任务完成时间和错误率显著降低，可用性高（SUS=3.84\u002F5）。","可探索多用户协同VR控制、AI预测性维护及跨温室数字孪生标准化。","2026-09-27T23:30:19.611108Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":8,"content":8,"source_name":236,"source_url":8,"published_at":237,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":248,"secondary_topics":249,"classification":255,"search_phrases":271,"slug":274,"view_count":68,"doi":8,"paper":275,"created_at":282},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":16,"substance":240,"depth":16,"authority":94,"freshness":12,"relevant":20,"comment":241},22,"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。",[243],{"name":236,"url":234},[25,26,245,246,247],"具身智能","采摘机器人","温室种植",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[250],{"code":55,"slug":251,"name":252,"path":253,"parent_code":254},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":37,"locked":13,"version":38,"method":39,"evidence":256},{"candidates":257,"agricultural_evidence":43},[258,263,267],{"code":55,"score":12,"supported":43,"title":259,"abstract":261,"tags":262},[260],"robotic harvesting",[],[26,246],{"code":30,"score":95,"supported":43,"title":264,"abstract":265,"tags":266},[204],[],[247],{"code":49,"score":20,"supported":13,"title":268,"abstract":269,"tags":270},[],[],[25],[272,273],"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",{"doi":8,"openalex_id":8,"authors":276,"venue":8,"cited_by_count":71,"oa_url":8,"card":277,"direction":76,"ingested_from":78},[],{"tldr":278,"method":279,"finding":280,"direction":76,"opportunity":281},"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。","2026-09-14T00:06:33.089876Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":8,"source_name":89,"source_url":286,"published_at":289,"category":11,"cover_url":8,"hotness":91,"is_selected":13,"score":14,"score_detail":290,"sources":292,"tags":296,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":339,"slug":342,"view_count":71,"doi":343,"paper":344,"created_at":356},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",{"impact":18,"substance":240,"depth":16,"authority":12,"freshness":95,"relevant":20,"comment":291},"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。",[293,294],{"name":89,"url":286},{"name":89,"url":295},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527545",[25,104,297,298,299],"设施园艺","咖啡种植","等离子种子处理",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":303},{"candidates":304,"agricultural_evidence":43},[305,309,316,323,329,335],{"code":30,"score":12,"supported":43,"title":306,"abstract":307,"tags":308},[204],[],[104,297],{"code":310,"score":117,"supported":43,"title":311,"abstract":312,"tags":315},"B3",[],[313,314],"咖啡","coffee",[298],{"code":317,"score":81,"supported":43,"title":318,"abstract":319,"tags":322},"F3",[],[320,321],"施肥","fertigation",[],{"code":60,"score":81,"supported":43,"title":324,"abstract":325,"tags":328},[],[326,327],"计算机视觉","computer vision",[],{"code":330,"score":19,"supported":43,"title":331,"abstract":332,"tags":334},"I3",[],[333],"multispectral",[],{"code":49,"score":20,"supported":13,"title":336,"abstract":337,"tags":338},[],[],[25],[340,341],"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":343,"openalex_id":345,"authors":346,"venue":89,"cited_by_count":71,"oa_url":286,"card":350,"direction":355,"ingested_from":153},"W7223104009",[347],{"name":348,"orcid":349},"Ahmed Hala","https:\u002F\u002Forcid.org\u002F0000-0002-7749-2299",{"tldr":351,"method":352,"finding":353,"direction":151,"opportunity":354},"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":358,"title":359,"url":360,"summary":361,"summary_zh":8,"content":8,"source_name":362,"source_url":8,"published_at":289,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":363,"score_detail":364,"sources":367,"tags":369,"primary_topic":374,"secondary_topics":375,"classification":381,"search_phrases":397,"slug":400,"view_count":71,"doi":8,"paper":401,"created_at":408},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":165,"substance":166,"depth":365,"authority":167,"freshness":12,"relevant":20,"comment":366},17,"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。",[368],{"name":362,"url":360},[25,370,371,372,373],"设施农业","环境调控","温室番茄","智能决策",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[376],{"code":377,"slug":378,"name":379,"path":380,"parent_code":34},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":37,"locked":13,"version":38,"method":39,"evidence":382},{"candidates":383,"agricultural_evidence":43},[384,388,393],{"code":30,"score":167,"supported":43,"title":385,"abstract":386,"tags":387},[46],[46],[372,371],{"code":377,"score":111,"supported":43,"title":389,"abstract":391,"tags":392},[390],"番茄",[390],[],{"code":49,"score":20,"supported":13,"title":394,"abstract":395,"tags":396},[],[],[25],[398,399],"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":402,"venue":8,"cited_by_count":71,"oa_url":8,"card":403,"direction":151,"ingested_from":78},[],{"tldr":404,"method":405,"finding":406,"direction":151,"opportunity":407},"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":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":8,"source_name":415,"source_url":412,"published_at":416,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":417,"score_detail":418,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":427,"classification":430,"search_phrases":474,"slug":477,"view_count":71,"doi":8,"paper":478,"created_at":496},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":18,"substance":166,"depth":365,"authority":95,"freshness":168,"relevant":20,"comment":419},"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。",[421],{"name":415,"url":412},[25,423,173,424,425],"农业人工智能","温室环境","小气候预测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[428,429],{"code":179,"slug":180,"name":181,"path":182,"parent_code":183},{"code":377,"slug":378,"name":379,"path":380,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":431},{"candidates":432,"agricultural_evidence":43},[433,438,442,450,454,460,467],{"code":179,"score":434,"supported":43,"title":435,"abstract":436,"tags":437},15,[197],[199,197],[173],{"code":30,"score":94,"supported":43,"title":439,"abstract":440,"tags":441},[204],[46,204],[],{"code":377,"score":95,"supported":43,"title":443,"abstract":444,"tags":449},[],[390,445,446,447,448],"辣椒","黄瓜","tomato","cucumber",[],{"code":49,"score":117,"supported":43,"title":451,"abstract":452,"tags":453},[],[103,114],[25],{"code":60,"score":117,"supported":43,"title":455,"abstract":456,"tags":459},[],[457,458],"深度学习","deep learning",[423],{"code":461,"score":81,"supported":43,"title":462,"abstract":463,"tags":466},"H2",[],[464,465],"土壤","soil",[],{"code":468,"score":81,"supported":43,"title":469,"abstract":470,"tags":473},"H3",[],[471,472],"灌溉","irrigation",[],[475,476],"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":8,"openalex_id":479,"authors":480,"venue":415,"cited_by_count":71,"oa_url":8,"card":491,"direction":151,"ingested_from":153},"W7222909837",[481,483,485,487,489],{"name":482,"orcid":8},"Siman Emmanuel*",{"name":484,"orcid":8},"Edward Onyebueke Agu",{"name":486,"orcid":8},"Musa Gani",{"name":488,"orcid":8},"Oladunjoye John Abiodun",{"name":490,"orcid":8},"Anagu Emmanuel John",{"tldr":492,"method":493,"finding":494,"direction":151,"opportunity":495},"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"]