[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-273":3,"related-273":64},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":59,"slug":62,"view_count":13,"doi":8,"paper":8,"created_at":63},273,"ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.01530","2026年8月2日arXiv上线cs.CV预印本ST-LoRA,提出面向农业不确定性感知语义分割的LoRA高效集成框架。研究针对数字农业决策支持对预测精度与不确定性校准的双重需求,以及集成方法计算与内存开销大的瓶颈,提出在单训练轨迹下构建多样化集成成员:每个成员共享冻结预训练骨干网络,仅通过轻量级低秩适配器(LoRA)差异,训练参数控制在全模型的10%以内,保持集成多样性。实验采用两套农业数据集:GrowliFlower-L(花椰菜,露天)和BUP20(甜椒,温室),覆盖分布内性能、分布偏移下的校准以及分布外检测。研究发现,前馈层而非注意力层是密集预测场景下LoRA的关键靶点(颠覆语言模型仅关注注意力的惯例),ST-LoRA在分割精度与校准上匹配或超越全秩集成,且显著降低训练时间、推理延迟、内存占用与存储需求;优于快照集成、MC Dropout与深度确定性不确定性等高效基线。",null,"[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.01530)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2608.01530v1)\n\n> Abstract:Reliable decision-support in digital agriculture requires accurate predictions and well-calibrated uncertainty estimates, particularly for dense prediction tasks such as semantic segmentation. Ensemble methods provide strong uncertainty quantification, but their computational and memory demands limit practical use, while single-model approximations often trade off uncertainty quality for efficiency. We propose ST-LoRA, a parameter-efficient ensemble framework that builds diverse ensemble members from a single training trajectory by combining Low-Rank Adaptation (LoRA) with snapshot ensembling. Each member shares a frozen pretrained backbone and differs only in lightweight low-rank adapters, reducing trainable parameters to under 10% of the full model while preserving ensemble diversity. We evaluate across two agricultural datasets - GrowliFlower-L (cauliflower, open field) and BUP20 (sweet pepper, glasshouse) - using SegFormer and Mask2Former, covering in-distribution performance, calibration under distribution shift, and out-of-distribution detection. Ablations show feed-forward layers, not attention layers, are the critical LoRA target for dense prediction, contrary to the attention-only convention from language models. ST-LoRA matches or exceeds full-rank ensembles in segmentation accuracy and calibration across both datasets and architectures, while substantially reducing training time, inference latency, memory footprint, and storage requirements. Against efficient baselines - Snapshot Ensemble, MC Dropout, and Deep Deterministic Uncertainty - ST-LoRA consistently matches or outperforms them in image\u002Fpixel-level OoD detection, calibration stability under shift, and cross-seed variance, with far fewer parameters and lower compute. These results show LoRA-efficient ensemble adaptation is a highly effective, practical approach for uncertainty-aware agricultural vision systems.\n\nComments:Submitted to Computers and Electronics in Agriculture (Elsevier). Currently under review (first revision round)\nSubjects:Computer Vision and Pattern Recognition (cs.CV)\nCite as:[arXiv:2608.01530](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.01530) [cs.CV]\n(or [arXiv:2608.01530v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.01530v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.01530](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.01530)\n\narXiv-issued DOI via DataCite\n\n## Submission history\n\nFrom: Mohamed Farag [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F7540b145\u002F2608.01530)] \n\n**[v1]** Sun, 2 Aug 2026 22:49:28 UTC (22,114 KB)","arXiv 2608.01530 (cs.CV) 2026-08-02","2026-08-02T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,20,12,5,1,"提出ST-LoRA方法，在农业语义分割中实现高效不确定性估计，方法新颖且实验扎实，对农业AI落地有实际价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"预印本","arXiv","ST-LoRA","农业语义分割","不确定性校准","LoRA集成",{"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":48},[46,53],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},3,true,[],[51],"温室",[],{"code":54,"score":47,"supported":48,"title":55,"abstract":56,"tags":58},"I1",[],[57],"数字农业",[],[60,61],"不确定性校准 农业语义分割 预印本 LoRA集成","不确定性校准 农业语义分割","不确定性校准农业语义分割预印本LoRA集成-273","2026-08-07T00:18:31.362185Z",{"total":65,"page":22,"page_size":65,"items":66},6,[67,153,208,263,311,352],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":72,"content":8,"source_name":73,"source_url":70,"published_at":74,"category":12,"cover_url":8,"hotness":75,"is_selected":14,"score":76,"score_detail":77,"sources":80,"tags":84,"primary_topic":90,"secondary_topics":91,"classification":92,"search_phrases":132,"slug":135,"view_count":136,"doi":137,"paper":138,"created_at":152},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":20,"substance":18,"depth":17,"authority":13,"freshness":78,"relevant":22,"comment":79},9,"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。",[81,82],{"name":73,"url":70},{"name":73,"url":83},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527545",[85,86,87,88,89],"智慧农业","垂直农业","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":93},{"candidates":94,"agricultural_evidence":48},[95,100,108,115,122,128],{"code":34,"score":13,"supported":48,"title":96,"abstract":98,"tags":99},[97],"greenhouse",[],[86,87],{"code":101,"score":102,"supported":48,"title":103,"abstract":104,"tags":107},"B3",7,[],[105,106],"咖啡","coffee",[88],{"code":109,"score":65,"supported":48,"title":110,"abstract":111,"tags":114},"F3",[],[112,113],"施肥","fertigation",[],{"code":116,"score":65,"supported":48,"title":117,"abstract":118,"tags":121},"I2",[],[119,120],"计算机视觉","computer vision",[],{"code":123,"score":47,"supported":48,"title":124,"abstract":125,"tags":127},"I3",[],[126],"multispectral",[],{"code":54,"score":22,"supported":14,"title":129,"abstract":130,"tags":131},[],[],[85],[133,134],"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":137,"openalex_id":139,"authors":140,"venue":73,"cited_by_count":136,"oa_url":70,"card":144,"direction":150,"ingested_from":151},"W7223104009",[141],{"name":142,"orcid":143},"Ahmed Hala","https:\u002F\u002Forcid.org\u002F0000-0002-7749-2299",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"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",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":8,"content":8,"source_name":158,"source_url":156,"published_at":159,"category":12,"cover_url":8,"hotness":13,"is_selected":48,"score":160,"score_detail":161,"sources":164,"tags":166,"primary_topic":172,"secondary_topics":173,"classification":174,"search_phrases":187,"slug":190,"view_count":136,"doi":191,"paper":192,"created_at":207},5540,"A national study of the effectiveness and co-benefits from actions to mitigate water quality impairment and greenhouse gas emissions from dairy farms in New Zealand","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105026","A national study of the effectiveness and co-benefits from actions to mitigate water quality impairment and greenhouse gas emissions from dairy farms in New Zealand。Agricultural Systems","Agricultural Systems","2026-10-22T00:00:00Z",86,{"impact":18,"substance":18,"depth":17,"authority":162,"freshness":13,"relevant":22,"comment":163},14,"新西兰全国性奶牛场水环境与温室气体协同减排研究，数据规模大、结论有政策参考价值，但属境外研究，对国内读者直接相关性有限。",[165],{"name":158,"url":156},[167,168,169,170,171],"奶业","新西兰","温室气体减排","水质保护","农业环境政策",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":175},{"candidates":176,"agricultural_evidence":48},[177,182],{"code":34,"score":178,"supported":48,"title":179,"abstract":180,"tags":181},11,[97],[97],[],{"code":183,"score":22,"supported":14,"title":184,"abstract":185,"tags":186},"C2",[],[],[167],[188,189],"新西兰 奶牛场 温室气体 减排","农业环境政策 温室气体减排 水质保护 新西兰","新西兰奶牛场温室气体减排-5540","10.1016\u002Fj.agsy.2026.105026",{"doi":191,"openalex_id":193,"authors":194,"venue":158,"cited_by_count":136,"oa_url":156,"card":8,"direction":8,"ingested_from":151},"W7223139855",[195,198,201,203,205],{"name":196,"orcid":197},"Richard William McDowell","https:\u002F\u002Forcid.org\u002F0000-0003-3911-4825",{"name":199,"orcid":200},"Katrina Ann Macintosh","https:\u002F\u002Forcid.org\u002F0000-0002-7232-400X",{"name":202,"orcid":8},"A. Wright-Stow",{"name":204,"orcid":8},"P. Astruc",{"name":206,"orcid":8},"C.X.O. Thiange","2026-10-22T11:20:11.497856Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":8,"content":8,"source_name":213,"source_url":8,"published_at":74,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":214,"sources":220,"tags":222,"primary_topic":227,"secondary_topics":228,"classification":234,"search_phrases":250,"slug":253,"view_count":136,"doi":8,"paper":254,"created_at":262},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",{"impact":215,"substance":216,"depth":217,"authority":218,"freshness":13,"relevant":22,"comment":219},16,21,17,13,"论文解析日光温室番茄全生育期环境特征并提出温光湿耦合智能调控方案，结论有新意，对设施农业信息化有参考价值。",[221],{"name":213,"url":211},[85,223,224,225,226],"设施农业","环境调控","温室番茄","智能决策",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[229],{"code":230,"slug":231,"name":232,"path":233,"parent_code":38},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":41,"locked":14,"version":42,"method":43,"evidence":235},{"candidates":236,"agricultural_evidence":48},[237,241,246],{"code":34,"score":218,"supported":48,"title":238,"abstract":239,"tags":240},[51],[51],[225,224],{"code":230,"score":178,"supported":48,"title":242,"abstract":244,"tags":245},[243],"番茄",[243],[],{"code":54,"score":22,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[85],[251,252],"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":255,"venue":8,"cited_by_count":136,"oa_url":8,"card":256,"direction":148,"ingested_from":261},[],{"tldr":257,"method":258,"finding":259,"direction":148,"opportunity":260},"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":264,"title":265,"url":266,"summary":267,"summary_zh":8,"content":268,"source_name":269,"source_url":8,"published_at":8,"category":270,"cover_url":8,"hotness":13,"is_selected":14,"score":271,"score_detail":272,"sources":274,"tags":276,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":306,"slug":309,"view_count":136,"doi":8,"paper":8,"created_at":310},5356,"泰国榴莲专家一行到云南省农科院热经所考察 双方达成合作意愿","https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html","10 月 8—10 日,泰国榴莲专家组一行 5 人到访云南省农业科学院热带亚热带经济作物研究所(简称热经所)考察。热经所尼章光研究员、王美存副研究员陪同调研了热经所榴莲温室种植和大田种植的情况,双方围绕制约潞江坝种植榴莲发展的寒、旱、风核心问题相互交流,共同培育出适合云南、泰国的榴莲优良品种和提升云南、泰国榴莲的种植技术水平,达成合作意愿。","[![Image 2](https:\u002F\u002Fwww.31food.com\u002Fimages\u002FVersion\u002F2025\u002Flogo.png)](https:\u002F\u002Fwww.31food.com\u002F)\n\n资讯中心\n\n资讯\n\n__\n\n*   资讯\n*   技术\n*   展会\n\n__ 搜索\n\n[![Image 3: 广告招租](https:\u002F\u002Fimg48.31food.com\u002F6\u002F20251022\u002F638967411604383587436.jpg)](https:\u002F\u002Fwww.31food.com\u002FAdvertisInfo_PageView.aspx?ID=1706 \"广告招租\")\n\n  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关键词：[榴莲产业](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)[热带作物](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)[国际合作](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)阅读量：39\n\n**导读：**10 月 8-10 日，泰国榴莲专家组到访云南农科院热经所考察，实地调研温室与大田榴莲种植，交流防寒旱风及病害技术难题，座谈介绍云南榴莲产业现状，双方就品种选育与种植技术达成合作意向。 \n\n 2026年10月8日-10月10日，泰国榴莲专家一行5人至云南省农业科学院热带亚热带经济作物研究所(简称：热经所)考察。  \n\n 热经所尼章光研究员、王美存副研究员陪同调研了热经所榴莲温室种植和大田种植的情况，期间共同交流了制约潞江坝种植榴莲发展的寒、旱、风的核心问题，相互对如何突破防寒、防旱、防风、叶枯病等技术进行了深入讨论。对此，泰国榴莲专家给出了一些宝贵的经验和一些建设性建议。  \n\n 在座谈交流会上，热经所副所长张翠仙副研究员首先介绍了热经所的基本情况；接着介绍了孟定、金平、勐腊、德宏等云南榴莲种植区域的气候特色；然后对热经所在各榴莲种植区域的基地建设情况、引进品种、各区域的榴莲种植规模、结果情况等作了详细阐述。泰国榴莲专家对如何提升榴莲的果实品质和提高果农效益，从种植区域多样性、土壤、气候、生产季节、种植区域特色品牌创制等5个方面展开了细致讲解。最后双方针对共同培育出适合云南、泰国的榴莲优良品种和提升云南、泰国榴莲的种植技术水平展开了讨论并且双方达成了合作意愿。  \n\n![Image 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- 食品行业“互联网+”服务平台\n\n服务咨询：0571-87209775 采购热线：0571-87759925  媒体合作：0571-87759680\n\n在线客服：[QQ联系](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)采购咨询：[QQ联系](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)媒体合作：[QQ联系](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)会展合作：[QQ联系](https:\u002F\u002Fwww.31food.com\u002Fnews\u002F43969.html)\n\nCopyright 2026 31food.com All Rights Reserved 法律顾问：浙江天册律师事务所 贾熙明律师[ICP备案号：浙B2-20100369-21](https:\u002F\u002Fbeian.miit.gov.cn\u002F#\u002FIntegrated\u002Findex)\n\n![Image 46](https:\u002F\u002Fwww.hbzhan.com\u002Fimages\u002Fnews\u002FweixinCode.jpg)公众号\n\n![Image 47](https:\u002F\u002Fwww.hbzhan.com\u002Fimages\u002Fnews\u002FmobileCode.jpg)APP\n\n[](javascript:)\n\n![Image 48](https:\u002F\u002Fpublic.mtnets.com\u002FImages\u002Fpublic\u002Fling.png)\n\n温馨提示\n\n您的咨询有回复啦，请查看！\n\n[查看新消息](javascript:void(0);)","食品生意网 2026-10","报道",52,{"impact":20,"substance":162,"depth":178,"authority":78,"freshness":65,"relevant":22,"comment":273},"省级科研机构与泰国专家的技术交流，有实质内容但属常规外事考察，影响限于榴莲细分产业，可作行业动态而非每日精选头条。",[275],{"name":269,"url":266},[277,278,279,280,281],"国际合作","品种选育","热带作物","榴莲产业","种植技术",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":285},{"candidates":286,"agricultural_evidence":48},[287,291,296,301],{"code":34,"score":47,"supported":48,"title":288,"abstract":289,"tags":290},[],[51],[],{"code":292,"score":22,"supported":14,"title":293,"abstract":294,"tags":295},"B2",[],[],[280],{"code":297,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},"F1",[],[],[278],{"code":302,"score":22,"supported":14,"title":303,"abstract":304,"tags":305},"J3",[],[],[277],[307,308],"云南省农科院热经所 泰国榴莲 合作","云南 榴莲 品种选育","云南省农科院热经所泰国榴莲合作-5356","2026-10-20T02:47:44.829356Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":8,"content":8,"source_name":316,"source_url":8,"published_at":317,"category":270,"cover_url":8,"hotness":13,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":328,"secondary_topics":329,"classification":330,"search_phrases":347,"slug":350,"view_count":136,"doi":8,"paper":8,"created_at":351},5345,"上海农科院历时 10 年选育沪翠甜 6 号哈密瓜在甘肃张掖发布","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-14\u002Fdoc-inivctrv8581700.shtml","上海市农业科学院垂直农业研究中心西甜瓜创新团队首席科学家张永平研究员带领团队历时 10 年选育的哈密瓜新品种沪翠甜 6 号于 10 月 13 日在甘肃张掖正式发布。该品种果肉呈翠绿色,口感酥脆、汁水充足,带淡淡奶香,单瓜重量约 2 公斤,果腔小、糖度梯度小,中心糖度最高可达 18 度以上、边糖 10 度以上。最大特点是采摘即食,秋冬季采收后存放 30 天仍可保持脆甜口感。已在甘肃、内蒙古、宁夏、山东、河南、上海、海南等全国甜瓜主要产区布局种植,基地全部采用现代化温室大棚模式。","新浪财经(上观新闻转载) 2026-10-14","2026-10-14T00:00:00Z",67,{"impact":17,"substance":19,"depth":162,"authority":78,"freshness":65,"relevant":22,"comment":320},"省级科研机构历时十年育成耐储运、采摘即食的甜瓜新品种，并已在全国主要产区布局，具备产业推广价值，但属单一品种发布，影响范围有限。",[322],{"name":316,"url":314},[223,324,325,326,327],"种业振兴","品种推广","西甜瓜育种","采摘即食",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":331},{"candidates":332,"agricultural_evidence":48},[333,337,342],{"code":34,"score":65,"supported":48,"title":334,"abstract":335,"tags":336},[],[51,86],[],{"code":230,"score":47,"supported":48,"title":338,"abstract":339,"tags":341},[],[340],"甜瓜",[],{"code":297,"score":343,"supported":14,"title":344,"abstract":345,"tags":346},2,[],[],[325,324],[348,349],"上海农科院 沪翠甜6号","沪翠甜6号 哈密瓜 张掖","上海农科院沪翠甜6号-5345","2026-10-20T02:47:44.034119Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":8,"source_name":358,"source_url":355,"published_at":359,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":318,"score_detail":360,"sources":363,"tags":365,"primary_topic":370,"secondary_topics":371,"classification":379,"search_phrases":427,"slug":430,"view_count":136,"doi":8,"paper":431,"created_at":449},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",{"impact":20,"substance":216,"depth":217,"authority":78,"freshness":361,"relevant":22,"comment":362},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。",[364],{"name":358,"url":355},[85,366,367,368,369],"农业人工智能","农业物联网","温室环境","小气候预测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[372,378],{"code":373,"slug":374,"name":375,"path":376,"parent_code":377},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":230,"slug":231,"name":232,"path":233,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":380},{"candidates":381,"agricultural_evidence":48},[382,389,393,401,407,413,420],{"code":373,"score":383,"supported":48,"title":384,"abstract":386,"tags":388},15,[385],"IoT",[387,385],"物联网",[367],{"code":34,"score":162,"supported":48,"title":390,"abstract":391,"tags":392},[97],[51,97],[],{"code":230,"score":78,"supported":48,"title":394,"abstract":395,"tags":400},[],[243,396,397,398,399],"辣椒","黄瓜","tomato","cucumber",[],{"code":54,"score":102,"supported":48,"title":402,"abstract":403,"tags":406},[],[404,405],"精准农业","precision agriculture",[85],{"code":116,"score":102,"supported":48,"title":408,"abstract":409,"tags":412},[],[410,411],"深度学习","deep learning",[366],{"code":414,"score":65,"supported":48,"title":415,"abstract":416,"tags":419},"H2",[],[417,418],"土壤","soil",[],{"code":421,"score":65,"supported":48,"title":422,"abstract":423,"tags":426},"H3",[],[424,425],"灌溉","irrigation",[],[428,429],"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":432,"authors":433,"venue":358,"cited_by_count":136,"oa_url":8,"card":444,"direction":148,"ingested_from":151},"W7222909837",[434,436,438,440,442],{"name":435,"orcid":8},"Siman Emmanuel*",{"name":437,"orcid":8},"Edward Onyebueke Agu",{"name":439,"orcid":8},"Musa Gani",{"name":441,"orcid":8},"Oladunjoye John Abiodun",{"name":443,"orcid":8},"Anagu Emmanuel John",{"tldr":445,"method":446,"finding":447,"direction":148,"opportunity":448},"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"]