[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3976":3,"related-3976":95},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":62,"slug":65,"view_count":46,"doi":66,"paper":67,"created_at":94},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。",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,14,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","垂直农业","作物模型","光环境调控",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,51,57],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},4,true,[],[29],[29],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"I2",2,[],[],[30,28],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[27],[63,64],"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":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":86,"oa_url":9,"card":87,"direction":91,"ingested_from":93},"W7215104938",[70,73,76,78,81,83],{"name":71,"orcid":72},"Eunjeong Lim","https:\u002F\u002Forcid.org\u002F0009-0008-2621-8602",{"name":74,"orcid":75},"Da-Seul Choi","https:\u002F\u002Forcid.org\u002F0009-0008-3239-9817",{"name":77,"orcid":9},"Hyeonjee Kim",{"name":79,"orcid":80},"Taewon Moon","https:\u002F\u002Forcid.org\u002F0000-0002-1593-7870",{"name":82,"orcid":9},"Myung-min Oh",{"name":84,"orcid":85},"Tae In Ahn","https:\u002F\u002Forcid.org\u002F0000-0001-5939-7450",0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"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。","openalex","2026-10-02T23:30:02.603545Z",{"total":96,"page":22,"page_size":96,"items":97},6,[98,181,286,366,441,487],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":106,"is_selected":14,"score":107,"score_detail":108,"sources":112,"tags":116,"primary_topic":120,"secondary_topics":121,"classification":122,"search_phrases":162,"slug":165,"view_count":86,"doi":166,"paper":167,"created_at":180},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":109,"substance":110,"depth":19,"authority":13,"freshness":21,"relevant":22,"comment":111},12,22,"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。",[113,114],{"name":104,"url":101},{"name":104,"url":115},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527545",[27,29,117,118,119],"设施园艺","咖啡种植","等离子种子处理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":123},{"candidates":124,"agricultural_evidence":47},[125,130,138,145,151,158],{"code":33,"score":13,"supported":47,"title":126,"abstract":128,"tags":129},[127],"greenhouse",[],[29,117],{"code":131,"score":132,"supported":47,"title":133,"abstract":134,"tags":137},"B3",7,[],[135,136],"咖啡","coffee",[118],{"code":139,"score":96,"supported":47,"title":140,"abstract":141,"tags":144},"F3",[],[142,143],"施肥","fertigation",[],{"code":52,"score":96,"supported":47,"title":146,"abstract":147,"tags":150},[],[148,149],"计算机视觉","computer vision",[],{"code":152,"score":153,"supported":47,"title":154,"abstract":155,"tags":157},"I3",3,[],[156],"multispectral",[],{"code":58,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[27],[163,164],"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":166,"openalex_id":168,"authors":169,"venue":104,"cited_by_count":86,"oa_url":101,"card":173,"direction":179,"ingested_from":93},"W7223104009",[170],{"name":171,"orcid":172},"Ahmed Hala","https:\u002F\u002Forcid.org\u002F0000-0002-7749-2299",{"tldr":174,"method":175,"finding":176,"direction":177,"opportunity":178},"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","2026-10-22T11:20:31.314461Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":194,"tags":196,"primary_topic":200,"secondary_topics":201,"classification":213,"search_phrases":263,"slug":266,"view_count":86,"doi":9,"paper":267,"created_at":285},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":109,"substance":18,"depth":191,"authority":21,"freshness":192,"relevant":22,"comment":193},17,8,"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。",[195],{"name":187,"url":184},[27,28,197,198,199],"农业物联网","温室环境","小气候预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[202,208],{"code":203,"slug":204,"name":205,"path":206,"parent_code":207},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":209,"slug":210,"name":211,"path":212,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":40,"locked":14,"version":41,"method":42,"evidence":214},{"candidates":215,"agricultural_evidence":47},[216,223,228,237,243,249,256],{"code":203,"score":217,"supported":47,"title":218,"abstract":220,"tags":222},15,[219],"IoT",[221,219],"物联网",[197],{"code":33,"score":20,"supported":47,"title":224,"abstract":225,"tags":227},[127],[226,127],"温室",[],{"code":209,"score":21,"supported":47,"title":229,"abstract":230,"tags":236},[],[231,232,233,234,235],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":58,"score":132,"supported":47,"title":238,"abstract":239,"tags":242},[],[240,241],"精准农业","precision agriculture",[27],{"code":52,"score":132,"supported":47,"title":244,"abstract":245,"tags":248},[],[246,247],"深度学习","deep learning",[28],{"code":250,"score":96,"supported":47,"title":251,"abstract":252,"tags":255},"H2",[],[253,254],"土壤","soil",[],{"code":257,"score":96,"supported":47,"title":258,"abstract":259,"tags":262},"H3",[],[260,261],"灌溉","irrigation",[],[264,265],"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":268,"authors":269,"venue":187,"cited_by_count":86,"oa_url":9,"card":280,"direction":177,"ingested_from":93},"W7222909837",[270,272,274,276,278],{"name":271,"orcid":9},"Siman Emmanuel*",{"name":273,"orcid":9},"Edward Onyebueke Agu",{"name":275,"orcid":9},"Musa Gani",{"name":277,"orcid":9},"Oladunjoye John Abiodun",{"name":279,"orcid":9},"Anagu Emmanuel John",{"tldr":281,"method":282,"finding":283,"direction":177,"opportunity":284},"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":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":306,"search_phrases":336,"slug":339,"view_count":22,"doi":340,"paper":341,"created_at":365},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":296,"depth":191,"authority":297,"freshness":192,"relevant":22,"comment":298},20,13,"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。",[300],{"name":292,"url":289},[27,240,29,302,303],"水培","无土栽培",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":307},{"candidates":308,"agricultural_evidence":47},[309,314,318,325,330],{"code":58,"score":310,"supported":47,"title":311,"abstract":312,"tags":313},19,[241],[27,240,241],[27,240],{"code":33,"score":96,"supported":47,"title":315,"abstract":316,"tags":317},[],[303],[29,303,302],{"code":319,"score":96,"supported":47,"title":320,"abstract":321,"tags":324},"I7",[],[322,323],"文献计量","bibliometric",[],{"code":257,"score":153,"supported":47,"title":326,"abstract":327,"tags":329},[],[328],"水资源",[],{"code":331,"score":153,"supported":47,"title":332,"abstract":333,"tags":335},"H8",[],[334],"可持续农业",[],[337,338],"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":340,"openalex_id":342,"authors":343,"venue":292,"cited_by_count":86,"oa_url":289,"card":360,"direction":177,"ingested_from":93},"W7221762528",[344,347,350,352,355,358],{"name":345,"orcid":346},"Monica Dutta","https:\u002F\u002Forcid.org\u002F0000-0002-6235-9794",{"name":348,"orcid":349},"Deepali Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-6771-2534",{"name":351,"orcid":9},"Walaa Jamal Alharthi",{"name":353,"orcid":354},"Mohammad Zubair Khan","https:\u002F\u002Forcid.org\u002F0000-0002-2409-7172",{"name":356,"orcid":357},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":359,"orcid":9},"Sapna Juneja",{"tldr":361,"method":362,"finding":363,"direction":177,"opportunity":364},"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":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":9,"source_name":372,"source_url":369,"published_at":373,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":377,"tags":379,"primary_topic":381,"secondary_topics":382,"classification":388,"search_phrases":415,"slug":418,"view_count":46,"doi":419,"paper":420,"created_at":440},4569,"SMART GREENHOUSE MEASUREMENT AND MONITORING SYSTEM BASED ON IOT AND COMPUTER VISION","https:\u002F\u002Fdoi.org\u002F10.46242\u002Fjstiuh.v83i05.6189","This paper presents a smart greenhouse measurement and monitoring system integrating IoT and computer vision for non-invasive plant growth observation and remote supervision. The proposed architecture combines environmental sensors, STM32-based data acquisition and control modules, a Raspberry Pi 4 with a USB camera, a lightweight deep learning-based stem segmentation module, Firebase Realtime Database, and a web application for monitoring and control. In the computer vision module, tomato plant images are processed directly on the Raspberry Pi to segment stem regions and estimate plant height from calibrated image measurements, enabling edge-based phenotypic monitoring without destructive sampling or manual measurement. Experimental results on a tomato cultivation prototype show that the system can collect temperature, air humidity, soil moisture, and light intensity data in real time while providing automated plant height estimation from RGB images. During the reported period, temperature ranged from 27.8°C to 38.8°C, air humidity from 40.2% to 77.0%, soil moisture from 52.0% to 82.4%, and plant height increased from 65.8 cm to 77.0 cm. The segmentation results demonstrate the feasibility of extracting stem-level structural information under practical greenhouse conditions, including complex background, leaf occlusion, and non-uniform illumination. The prototype confirms the potential of a low-cost smart greenhouse monitoring system and provides a practical basis for future expansion toward richer agronomic indicators, such as stem width, leaf area, canopy coverage, growth rate estimation, and automated environmental control.","本文提出了一种集成物联网与计算机视觉的智能温室测量与监测系统，用于非侵入式植物生长观测与远程监管。所提出的架构结合了环境传感器、基于STM32的数据采集与控制模块、配备USB摄像头的Raspberry Pi 4、基于轻量级深度学习的茎秆分割模块、Firebase实时数据库以及用于监测与控制的Web应用程序。在计算机视觉模块中，番茄植株图像直接在Raspberry Pi上处理，以分割茎秆区域并根据标定后的图像测量值估算株高，从而实现基于边缘的表型监测，无需破坏性采样或人工测量。在番茄栽培原型上的实验结果表明，该系统能够实时采集温度、空气湿度、土壤湿度和光照强度数据，同时基于RGB图像提供自动株高估算。在报告期内，温度范围为27.8°C至38.8°C，空气湿度为40.2%至77.0%，土壤湿度为52.0%至82.4%，株高从65.8 cm增长至77.0 cm。分割结果证明了在实际温室条件下（包括复杂背景、叶片遮挡和非均匀光照）提取茎秆级结构信息的可行性。该原型验证了低成本智能温室监测系统的潜力，并为未来扩展至更丰富的农艺指标（如茎宽、叶面积、冠层覆盖度、生长速率估算和自动化环境控制）提供了实践基础。","Journal of Science and Technology - IUH","2026-10-08T00:00:00Z",66,{"impact":109,"substance":296,"depth":17,"authority":13,"freshness":192,"relevant":22,"comment":376},"低成本IoT与边缘视觉融合的温室监测原型，方法可行、数据翔实，但属细分技术验证，产业影响有限。",[378],{"name":372,"url":369},[27,28,148,197,380],"温室种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[383,387],{"code":52,"slug":384,"name":385,"path":386,"parent_code":207},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":203,"slug":204,"name":205,"path":206,"parent_code":207},{"status":40,"locked":14,"version":41,"method":42,"evidence":389},{"candidates":390,"agricultural_evidence":47},[391,395,399,403,407,411],{"code":52,"score":310,"supported":47,"title":392,"abstract":393,"tags":394},[149],[246,148,247,149],[28,148],{"code":33,"score":217,"supported":47,"title":396,"abstract":397,"tags":398},[127],[226,127],[380],{"code":203,"score":217,"supported":47,"title":400,"abstract":401,"tags":402},[219],[221,219],[197],{"code":209,"score":96,"supported":47,"title":404,"abstract":405,"tags":406},[],[231,234],[],{"code":250,"score":96,"supported":47,"title":408,"abstract":409,"tags":410},[],[253,254],[],{"code":58,"score":22,"supported":14,"title":412,"abstract":413,"tags":414},[],[],[27],[416,417],"Raspberry Pi 温室监测","番茄 株高 图像识别","RaspberryPi温室监测-4569","10.46242\u002Fjstiuh.v83i05.6189",{"doi":419,"openalex_id":421,"authors":422,"venue":372,"cited_by_count":86,"oa_url":434,"card":435,"direction":177,"ingested_from":93},"W7221046739",[423,425,427,429,431],{"name":424,"orcid":9},"HOANG THI TU UYEN",{"name":426,"orcid":9},"NGUYEN HOANH",{"name":428,"orcid":9},"NGO THANH QUYEN",{"name":430,"orcid":9},"Lam Binh Minh",{"name":432,"orcid":433},"Nguyễn Trung Nhân","https:\u002F\u002Forcid.org\u002F0000-0001-9328-7693","https:\u002F\u002Fjst.iuh.edu.vn\u002Findex.php\u002Fjst-iuh\u002Farticle\u002Fdownload\u002F6189\u002F1131",{"tldr":436,"method":437,"finding":438,"direction":177,"opportunity":439},"集成物联网与计算机视觉的智能温室系统，实现非破坏性植物生长监测与远程管控。","环境传感器、STM32、树莓派4、USB摄像头、轻量茎分割模型、Firebase","原型可实时采集温湿度、土壤水分和光照，并从RGB图像估计株高，验证了边缘表型监测可行性。","可扩展至茎宽、叶面积、冠层覆盖度等更丰富农艺指标，并实现自动化环境控制闭环。","2026-10-10T23:30:11.420750Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":9,"content":446,"source_name":447,"source_url":9,"published_at":448,"category":449,"cover_url":9,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":459,"secondary_topics":460,"classification":462,"search_phrases":482,"slug":485,"view_count":53,"doi":9,"paper":9,"created_at":486},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) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,王佳 来源：中国科学报 发布时间：2026\u002F10\u002F4 22:09:23 选择字号：小 中 大 双驱动模型可精准预测温室番茄生长态势 近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，相关研究成果发表在《农业计算机与电子学》（_**Computers and Electronics in Agriculture**_）上。 作物生长精准估算是温室智能控制的核心前提。然而，传统机理模型参数获取难、泛化有限，而纯数据驱动模型常因缺乏生理约束致预测失真。因此，如何融合环境时序数据与作物生长规律，实现高精度且生理合理的生长预测仍是关键挑战。 科研团队以番茄为对象，提出一种知识与数据双驱动的尺度自适应时序卷积混合模型。该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。该模型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、产量预测具有重要应用价值。 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*   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[更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n\n[关于我们](http:\u002F\u002Fwww.sciencenet.cn\u002Faboutus.aspx) | [网站声明](http:\u002F\u002Fwww.sciencenet.cn\u002Fshengming.aspx) | [服务条款](http:\u002F\u002Fwww.sciencenet.cn\u002Ftiaokuang.aspx) | [联系方式](http:\u002F\u002Fwww.sciencenet.cn\u002Fcontact.aspx) | [举报](https:\u002F\u002Fwww.sciencenet.cn\u002Fjubao.aspx) | [中国科学报社](https:\u002F\u002Fstimes.sciencenet.cn\u002F)  \n[京ICP备07017567号-12](http:\u002F\u002Fwww.beian.miit.gov.cn\u002F)互联网新闻信息服务许可证10120230008 京公网安备 11010802032783  \nCopyright @ 2007-2026 中国科学报社 All Rights Reserved  \n地址：北京市海淀区中关村南一条乙三号 电话：010-62580783","科学网 中国农业科学院 2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":110,"substance":18,"depth":191,"authority":20,"freshness":96,"relevant":22,"comment":452},"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。",[454],{"name":447,"url":444},[27,28,456,457,458],"作物生长模型","设施番茄","温室环控",{"code":209,"slug":210,"name":211,"path":212,"parent_code":37},[461],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":463},{"candidates":464,"agricultural_evidence":47},[465,469,474,478],{"code":209,"score":109,"supported":47,"title":466,"abstract":467,"tags":468},[231],[231],[457],{"code":33,"score":470,"supported":47,"title":471,"abstract":472,"tags":473},11,[226],[226],[],{"code":52,"score":53,"supported":14,"title":475,"abstract":476,"tags":477},[],[],[456,28],{"code":58,"score":22,"supported":14,"title":479,"abstract":480,"tags":481},[],[],[27],[483,484],"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":488,"title":489,"url":490,"summary":491,"summary_zh":492,"content":9,"source_name":493,"source_url":490,"published_at":494,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":495,"score_detail":496,"sources":498,"tags":500,"primary_topic":502,"secondary_topics":503,"classification":508,"search_phrases":541,"slug":544,"view_count":22,"doi":545,"paper":546,"created_at":568},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":19,"substance":110,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":497},"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。",[499],{"name":493,"url":490},[27,28,501,240,380],"智能灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[504],{"code":257,"slug":261,"name":505,"path":506,"parent_code":507},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":509},{"candidates":510,"agricultural_evidence":47},[511,515,519,523,527,531,537],{"code":257,"score":19,"supported":47,"title":512,"abstract":513,"tags":514},[261],[260,328,261],[501],{"code":33,"score":217,"supported":47,"title":516,"abstract":517,"tags":518},[127],[226,127],[380],{"code":58,"score":192,"supported":47,"title":520,"abstract":521,"tags":522},[],[240,241],[27,240],{"code":250,"score":96,"supported":47,"title":524,"abstract":525,"tags":526},[],[253,254],[],{"code":209,"score":153,"supported":47,"title":528,"abstract":529,"tags":530},[],[231],[],{"code":532,"score":153,"supported":47,"title":533,"abstract":534,"tags":536},"H7",[],[535],"气候变化",[],{"code":52,"score":22,"supported":14,"title":538,"abstract":539,"tags":540},[],[],[28],[542,543],"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":545,"openalex_id":547,"authors":548,"venue":493,"cited_by_count":86,"oa_url":490,"card":563,"direction":91,"ingested_from":93},"W7220746994",[549,552,555,557,560],{"name":550,"orcid":551},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":553,"orcid":554},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":556,"orcid":9},"Abdelkader Mami",{"name":558,"orcid":559},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":561,"orcid":562},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":564,"method":565,"finding":566,"direction":177,"opportunity":567},"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。","2026-10-07T23:30:43.848308Z"]