[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3814":3,"related-3814":130},{"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":50,"search_phrases":98,"slug":101,"view_count":22,"doi":102,"paper":103,"created_at":129},3814,"Optimizing biogas slurry and biochar co-application to enhance soil microenvironment, mitigate greenhouse gas emissions, and increase tomato yield in greenhouse systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1866425","To support agricultural carbon peaking and carbon neutrality goals, this study investigated the uncertainties surrounding greenhouse gas emissions and nutrient leaching associated with biogas slurry utilization. It systematically explored the mechanisms and mitigation pathways of co-applying biogas slurry, biochar, and chemical fertilizers for reducing greenhouse gas emissions and nutrient losses under intensive agricultural management characterized by excessive irrigation and fertilizer application. Under equivalent N, P, and K inputs and consistent irrigation levels, different substitution ratios of biogas slurry for chemical fertilizers were evaluated in a point-source underground irrigation system. Treatments included CF, FR, BS25, BS50, BS75, and BS100, with corresponding biochar treatments CF + C, FR + C, BS25 + C, BS50 + C, BS75 + C, and BS100 + C. Tomato yield, soil physical properties, microbial biomass, microbial gene copy numbers, and soil greenhouse gas emissions were measured. Key influencing factors and optimal management strategies were identified using multivariate statistical analysis and a multi-criteria decision approach. The results indicated that biogas slurry combined with biochar significantly improved the soil microenvironment in greenhouse tomato systems by enhancing soil temperature (ST), soil moisture content (SMC), and water-filled pore space (WFPS), especially during the flowering stage in both spring-summer and autumn-winter periods. Biochar also significantly increased microbial biomass carbon (MBC), while microbial gene copy numbers, including AOA, AOB, and mcrA, showed clear seasonal variation. Regarding greenhouse gas emissions, co-application of biogas slurry and biochar significantly affected CO 2 fluxes, particularly from days 8–119, promoting CO 2 emissions. Biochar effectively suppressed N 2 O emissions, promoted CH 4 emissions during days 1–7, and reduced CH 4 fluxes from days 8–119. The BS75 + C treatment significantly increased tomato yield to 151.34 t ha −1 , reduced global warming potential to 9491.43 kg ha −1 , and lowered greenhouse gas emission intensity. Compared with other treatments, BS75 + C consistently showed superior agronomic and environmental performance. Comprehensive analysis showed that ST, SMC, and MBC mainly influenced CO 2 emissions; ST, AOA, and AOB influenced N 2 O emissions; and mcrA, WFPS, and ST influenced CH 4 emissions. Multi-criteria decision analysis identified BS75 + C as the optimal strategy for maximizing yield while minimizing greenhouse gas emissions and improving biogas slurry utilization efficiency in sustainable greenhouse agriculture.","为支撑农业碳达峰与碳中和目标，本研究探讨了沼液利用过程中温室气体排放与养分淋失的不确定性，系统揭示了在过量灌溉与施肥的集约化农业管理条件下，沼液、生物炭与化肥配施减少温室气体排放和养分损失的机理与调控路径。在等氮、磷、钾投入及一致灌溉水平下，采用点源地下灌溉系统，评估了不同沼液替代化肥比例的效果。处理包括CF、FR、BS25、BS50、BS75和BS100，以及相应的生物炭处理CF + C、FR + C、BS25 + C、BS50 + C、BS75 + C和BS100 + C。测定了番茄产量、土壤物理性质、微生物生物量、微生物基因拷贝数及土壤温室气体排放。通过多元统计分析和多准则决策方法，确定了关键影响因素和最优管理策略。结果表明，沼液配施生物炭通过提高土壤温度(ST)、土壤含水量(SMC)和充水孔隙度(WFPS)，显著改善了温室番茄系统的土壤微环境，尤其在春夏季和秋冬季的开花期。生物炭还显著提高了微生物生物量碳(MBC)，而微生物基因拷贝数(包括AOA、AOB和mcrA)则表现出明显的季节性变化。在温室气体排放方面，沼液与生物炭配施显著影响CO₂通量，尤其在8—119天期间促进CO₂排放。生物炭有效抑制了N₂O排放，在1—7天促进CH₄排放，并在8—119天降低CH₄通量。BS75 + C处理显著提高了番茄产量至151.34 t ha⁻¹，将全球增温潜势降至9491.43 kg ha⁻¹，并降低了温室气体排放强度。与其他处理相比，BS75 + C在农艺和环境性能方面始终表现优越。综合分析表明，ST、SMC和MBC主要影响CO₂排放；ST、AOA和AOB影响N₂O排放；mcrA、WFPS和ST影响CH₄排放。多准则决策分析确定BS75 + C为最优策",null,"Frontiers in Sustainable Food Systems","2026-09-29T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"该研究通过沼液与生物炭配施实现温室番茄增产与温室气体减排，方法系统、数据扎实，对农业绿色低碳发展具有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"生物炭","温室番茄","农业碳中和","沼液利用","减排增效",{"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",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,69,76,84,91],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":63},15,true,[60],"greenhouse",[62,60],"温室",[28],{"code":45,"score":57,"supported":58,"title":65,"abstract":66,"tags":68},[46],[67,46],"土壤",[27],{"code":40,"score":70,"supported":58,"title":71,"abstract":73,"tags":75},14,[72],"tomato",[74,72],"番茄",[],{"code":77,"score":78,"supported":58,"title":79,"abstract":80,"tags":83},"F3",6,[],[81,82],"施肥","fertilizer",[],{"code":85,"score":78,"supported":58,"title":86,"abstract":87,"tags":90},"H3",[],[88,89],"灌溉","irrigation",[],{"code":92,"score":93,"supported":58,"title":94,"abstract":95,"tags":97},"H4",3,[],[96],"农业碳",[],[99,100],"沼液 生物炭 温室番茄","BS75 C 温室气体减排","沼液生物炭温室番茄-3814","10.3389\u002Ffsufs.2026.1866425",{"doi":102,"openalex_id":104,"authors":105,"venue":10,"cited_by_count":121,"oa_url":6,"card":122,"direction":126,"ingested_from":128},"W7214739741",[106,109,112,114,117,119],{"name":107,"orcid":108},"Qinglin Sa","https:\u002F\u002Forcid.org\u002F0009-0007-5248-566X",{"name":110,"orcid":111},"Jian Zheng","https:\u002F\u002Forcid.org\u002F0009-0003-5723-1950",{"name":113,"orcid":9},"Ke Zhang",{"name":115,"orcid":116},"Yan Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5950-8904",{"name":118,"orcid":9},"Xuqin FU",{"name":120,"orcid":9},"Shikun Sun",0,{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"研究沼液与生物炭配施对温室番茄土壤微环境、温室气体排放及产量的影响。","等氮磷钾滴灌试验，设沼液替代化肥比例与生物炭处理，测产量、土壤及气体。","BS75+C处理番茄产量最高达151.34 t\u002Fha，全球增温潜势最低，减排增产效果最优。","农业绿色发展与碳","可探究不同气候区或土壤类型下沼液生物炭配施的长期固碳减排效应与微生物机制。","openalex","2026-09-30T23:30:07.179554Z",{"total":78,"page":22,"page_size":78,"items":131},[132,185,232,272,343,387],{"id":133,"title":134,"url":135,"summary":136,"summary_zh":9,"content":9,"source_name":137,"source_url":9,"published_at":138,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":139,"score_detail":140,"sources":144,"tags":146,"primary_topic":151,"secondary_topics":152,"classification":154,"search_phrases":171,"slug":174,"view_count":121,"doi":9,"paper":175,"created_at":184},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","2026-10-21T00:00:00Z",77,{"impact":17,"substance":141,"depth":142,"authority":20,"freshness":13,"relevant":22,"comment":143},21,17,"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。",[145],{"name":137,"url":135},[147,148,149,28,150],"智慧农业","设施农业","环境调控","智能决策",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[153],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":155},{"candidates":156,"agricultural_evidence":58},[157,161,166],{"code":33,"score":20,"supported":58,"title":158,"abstract":159,"tags":160},[62],[62],[28,149],{"code":40,"score":162,"supported":58,"title":163,"abstract":164,"tags":165},11,[74],[74],[],{"code":167,"score":22,"supported":14,"title":168,"abstract":169,"tags":170},"I1",[],[],[147],[172,173],"日光温室 番茄 环境调控","温室番茄 智能闭环控制","日光温室番茄环境调控-5536",{"doi":9,"openalex_id":9,"authors":176,"venue":9,"cited_by_count":121,"oa_url":9,"card":177,"direction":181,"ingested_from":183},[],{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"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。","智慧农业 \u002F 农业物联网","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":186,"title":187,"url":188,"summary":189,"summary_zh":9,"content":190,"source_name":191,"source_url":9,"published_at":192,"category":193,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":198,"tags":200,"primary_topic":204,"secondary_topics":205,"classification":207,"search_phrases":227,"slug":230,"view_count":22,"doi":9,"paper":9,"created_at":231},4259,"中国农科院构建知识与数据双驱动模型精准预测温室番茄生长","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572492&mobile=1&type=detail","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提出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。成果发表在《Computers and Electronics in Agriculture》。","գйũҵѧԺũҵɳչоʩֲﻷ̴Ŷӹ֪ʶ˫ģͣʵҷĸ߾Ԥ⣬оɹڡũҵѧComputers and Electronics in Agricultureϡ\n\n׼ܿƵĺǰᡣȻͳģͲȡѡޣģͳȱԼԤʧ档ˣںϻʱɣʵָ߾Ԥǹؼս","科学网 2026-10-04","2026-10-04T00:00:00Z","报道",82,{"impact":18,"substance":141,"depth":142,"authority":70,"freshness":196,"relevant":22,"comment":197},8,"国家级科研机构在智慧种植领域提出知识数据双驱动模型，方法新颖、结论可靠，对温室作物精准管理有参考价值。",[199],{"name":191,"url":188},[147,201,28,202,203],"农业人工智能","知识数据双驱动","生长预测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[206],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":208},{"candidates":209,"agricultural_evidence":58},[210,214,218,222],{"code":40,"score":162,"supported":58,"title":211,"abstract":212,"tags":213},[74],[74],[],{"code":33,"score":21,"supported":58,"title":215,"abstract":216,"tags":217},[62],[],[28],{"code":167,"score":22,"supported":14,"title":219,"abstract":220,"tags":221},[],[],[147],{"code":223,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},"I2",[],[],[201],[228,229],"中国农科院 温室番茄 生长预测","知识数据双驱动 模型 番茄","中国农科院温室番茄生长预测-4259","2026-10-06T00:05:01.795653Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":9,"content":9,"source_name":237,"source_url":9,"published_at":238,"category":193,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":239,"sources":241,"tags":243,"primary_topic":244,"secondary_topics":245,"classification":247,"search_phrases":267,"slug":270,"view_count":93,"doi":9,"paper":9,"created_at":271},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","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，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种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。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z",{"impact":19,"substance":18,"depth":19,"authority":57,"freshness":21,"relevant":22,"comment":240},"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[242],{"name":237,"url":235},[147,201,148,28,203],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[246],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":248},{"candidates":249,"agricultural_evidence":58},[250,255,259,263],{"code":33,"score":251,"supported":58,"title":252,"abstract":253,"tags":254},12,[62],[62],[28],{"code":40,"score":162,"supported":58,"title":256,"abstract":257,"tags":258},[74],[74],[],{"code":167,"score":22,"supported":14,"title":260,"abstract":261,"tags":262},[],[],[147],{"code":223,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[201],[268,269],"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 模型","中国农科院环发所温室番茄生长预测-3935","2026-10-02T11:53:11.319506Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":292,"secondary_topics":293,"classification":295,"search_phrases":320,"slug":323,"view_count":22,"doi":324,"paper":325,"created_at":342},2928,"Biochar 2.0 in Climate-Smart Agriculture: A Critical Review of Designer Biochars, Soil Microbiome Responses, Nutrient-Use Efficiency and Greenhouse-Gas Mitigation","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajsspn\u002F2026\u002Fv12i4764","Biochar has moved from a broadly promoted soil amendment to a material that is deliberately engineered for defined agronomic and climatic functions. This transition, described here as a shift towards designer biochars, raises questions that the earlier literature was not designed to answer: whether tailoring feedstock, pyrolysis conditions and post-synthesis modification produces reproducible gains in nutrient-use efficiency, whether the accompanying soil microbiome changes are functionally meaningful or largely descriptive, and whether greenhouse-gas benefits observed in short experiments persist under field management and withstand carbon-accounting scrutiny. This critical narrative review examines the evidence linking biochar properties to soil microbial responses, nutrient retention and transformation, and the mitigation of nitrous oxide, methane and carbon dioxide in agricultural soils. Literature was identified through structured searching of open scholarly indexes, agricultural and intergovernmental repositories, and citation tracking, with every retained source verified against an authoritative bibliographic record. The synthesis indicates that the strongest and most reproducible effects are pedoclimatic rather than material-specific: responses concentrate in acidic, coarse-textured, nutrient-depleted and tropical soils, whereas temperate fertile systems frequently show negligible agronomic gain. Microbiome studies consistently report compositional turnover with little change in alpha diversity, and the functional interpretation of these shifts rests heavily on marker-gene abundance rather than demonstrated process rates. Nitrous oxide suppression is well supported in aggregate but varies by a factor of five across syntheses, and recent field work shows that freshly applied high-rate biochar can increase emissions. Methane outcomes in flooded systems depend on water management and application history. Carbon permanence remains contested because the principal proxies used for crediting are informative about carbonisation rather than about in-soil residence under field weathering. Confidence is highest for liming, cation retention and short-term nitrogen conservation, and lowest for long-term net climate benefit at landscape scale. Priorities include multi-year factorial field trials that pair engineered materials with process-level measurement, standardised reporting of biochar properties, and accounting frameworks calibrated on weathered rather than freshly produced material.","生物炭已从一种被广泛推广的土壤改良剂，转变为一种为特定农艺和气候功能而刻意设计的材料。这一转变——本文将其描述为向“设计型生物炭”的转向——提出了早期文献未曾设计回答的问题：定制原料、热解条件和合成后修饰是否能在养分利用效率上产生可重复的增益；伴随而来的土壤微生物组变化在功能上是否有意义，还是主要停留在描述层面；以及在短期实验中观察到的温室气体效益能否在田间管理下持续存在，并经受住碳核算的审视。这篇批判性叙述综述考察了将生物炭性质与土壤微生物响应、养分保持与转化，以及农业土壤中氧化亚氮、甲烷和二氧化碳减排联系起来的证据。文献通过结构化检索开放学术索引、农业和政府间知识库以及引文追踪来识别，所有保留的来源均对照权威书目记录进行了核实。综合结果表明，最强且最可重复的效应是土壤气候性的，而非材料特异性的：响应集中于酸性、粗质地、养分贫瘠和热带土壤，而温带肥沃系统往往显示可忽略的农艺增益。微生物组研究一致报告组成上的更替而α多样性变化甚微，且对这些变化的功能解读在很大程度上依赖于标记基因丰度，而非已证实的过程速率。氧化亚氮抑制在总体上得到较好支持，但不同综合研究之间相差可达五倍，且近期田间工作表明，新施用的高用量生物炭可能增加排放。淹水系统中的甲烷结果取决于水分管理和施用历史。碳永久性仍存争议，因为用于信用核算的主要替代指标所反映的是碳化程度，而非田间风化条件下的土壤内驻留时间。置信度最高的是石灰效应、阳离子保持和短期氮素保全，最低的是景观尺度上的长期净气候效益。优先事项包括：将工程化材料与过程水平测量相结合的多年度析因田间试验、生物炭性质的标准化报告，以及基于风化而非","Asian Journal of Soil Science and Plant Nutrition","2026-09-18T00:00:00Z",80,{"impact":19,"substance":18,"depth":282,"authority":20,"freshness":196,"relevant":22,"comment":283},19,"系统评述设计型生物炭在土壤微生物、养分效率与温室气体减排上的证据强度与不确定性，学术增量扎实，对农业绿色低碳与土壤健康方向有参考价值。",[285],{"name":278,"url":275},[27,287,288,289,290,291],"土壤健康","气候智慧农业","土壤微生物","温室气体减排","养分利用效率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[294],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":296},{"candidates":297,"agricultural_evidence":58},[298,302,306,311,315],{"code":45,"score":142,"supported":58,"title":299,"abstract":300,"tags":301},[46],[67,46],[287,289,27],{"code":33,"score":70,"supported":58,"title":303,"abstract":304,"tags":305},[60],[62,60],[],{"code":167,"score":196,"supported":58,"title":307,"abstract":309,"tags":310},[308],"smart agriculture",[],[],{"code":77,"score":22,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[291],{"code":316,"score":22,"supported":14,"title":317,"abstract":318,"tags":319},"H7",[],[],[288],[321,322],"生物炭 温室气体 减排","养分利用效率 气候智慧农业 温室气体减排 土壤微生物","生物炭温室气体减排-2928","10.9734\u002Fajsspn\u002F2026\u002Fv12i4764",{"doi":324,"openalex_id":326,"authors":327,"venue":278,"cited_by_count":121,"oa_url":275,"card":337,"direction":181,"ingested_from":128},"W7213554252",[328,330,333,335],{"name":329,"orcid":9},"K. G. Rosin",{"name":331,"orcid":332},"Aditya V Machnoor","https:\u002F\u002Forcid.org\u002F0009-0001-5777-9188",{"name":334,"orcid":9},"Rakesh Kokatnoor",{"name":336,"orcid":9},"Arun S. Kalasad",{"tldr":338,"method":339,"finding":340,"direction":126,"opportunity":341},"综述设计型生物炭在气候智慧农业中的效果，指出其农艺与减排效益高度依赖土壤气候条件。","结构化文献检索与引文追踪，整合生物炭性质、土壤微生物、养分与温室气体证据。","最强效应集中于酸性贫瘠热带土壤，温带肥沃土壤增益有限；氧化亚氮抑制变异大，碳持久性存疑。","需多年多因子田间试验，结合过程速率测量与风化态碳核算，验证设计型生物炭的长期净气候效益。","2026-09-19T23:30:11.160909Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":9,"content":348,"source_name":349,"source_url":9,"published_at":350,"category":193,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":354,"tags":356,"primary_topic":358,"secondary_topics":359,"classification":361,"search_phrases":381,"slug":384,"view_count":385,"doi":9,"paper":9,"created_at":386},2082,"中国农科院农业环境与可持续发展研究所构建知识与数据双驱动模型赋能温室番茄生长精准预测","https:\u002F\u002Fieda.caas.cn\u002Fxwzx\u002Fkyjz\u002Faa737d68a96d4bbbb9e1dc8b8076592a.htm","中国农科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联合荷兰瓦赫宁根大学,提出知识与数据双驱动的尺度自适应时序卷积混合模型(SATCN-Informer),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(TOMSIM)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精准预测。研究成果发表于《农业计算机与电子学》(Computers and Electronics in Agriculture)。","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联合荷兰瓦赫宁根大学在温室番茄生长建模方面取得进展。相关研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。\n\n![Image 1: image.png](https:\u002F\u002Fieda.caas.cn\u002Fimages\u002F2026-09\u002Feacc678d60c145b18d62aa0f87ba78b8.png)\n\n_知识与数据双驱动的SATCN-Informer模型框架_\n\n作物生长精准估算是温室智控的核心前提。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。\n\n研究团队以番茄为对象，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（SATCN-Informer）。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（TOMSIM）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。\n\n该研究得到中央级公益性科研院所基本科研业务费资助。\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281","中国农业科学院","2026-09-09T00:00:00Z",84,{"impact":18,"substance":141,"depth":19,"authority":57,"freshness":196,"relevant":22,"comment":353},"国家级科研机构联合瓦赫宁根大学提出知识与数据双驱动的温室番茄生长预测模型，方法新颖、应用价值明确，值得进入每日精选。",[355],{"name":349,"url":346},[147,201,148,28,357],"作物生长模型",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[360],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":362},{"candidates":363,"agricultural_evidence":58},[364,368,372,377],{"code":40,"score":162,"supported":58,"title":365,"abstract":366,"tags":367},[74],[74],[],{"code":33,"score":21,"supported":58,"title":369,"abstract":370,"tags":371},[62],[],[28],{"code":223,"score":373,"supported":14,"title":374,"abstract":375,"tags":376},2,[],[],[357,201],{"code":167,"score":22,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[147],[382,383],"作物生长模型 农业人工智能 智慧农业 温室番茄","作物生长模型 农业人工智能","作物生长模型农业人工智能智慧农业温室番茄-2082",4,"2026-09-11T00:04:19.821301Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":404,"secondary_topics":405,"classification":413,"search_phrases":454,"slug":457,"view_count":121,"doi":9,"paper":458,"created_at":476},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":251,"substance":141,"depth":142,"authority":21,"freshness":196,"relevant":22,"comment":397},"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。",[399],{"name":393,"url":390},[147,201,401,402,403],"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},[406,412],{"code":407,"slug":408,"name":409,"path":410,"parent_code":411},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":414},{"candidates":415,"agricultural_evidence":58},[416,422,426,433,440,446,450],{"code":407,"score":57,"supported":58,"title":417,"abstract":419,"tags":421},[418],"IoT",[420,418],"物联网",[401],{"code":33,"score":70,"supported":58,"title":423,"abstract":424,"tags":425},[60],[62,60],[],{"code":40,"score":21,"supported":58,"title":427,"abstract":428,"tags":432},[],[74,429,430,72,431],"辣椒","黄瓜","cucumber",[],{"code":167,"score":434,"supported":58,"title":435,"abstract":436,"tags":439},7,[],[437,438],"精准农业","precision agriculture",[147],{"code":223,"score":434,"supported":58,"title":441,"abstract":442,"tags":445},[],[443,444],"深度学习","deep learning",[201],{"code":45,"score":78,"supported":58,"title":447,"abstract":448,"tags":449},[],[67,46],[],{"code":85,"score":78,"supported":58,"title":451,"abstract":452,"tags":453},[],[88,89],[],[455,456],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":459,"authors":460,"venue":393,"cited_by_count":121,"oa_url":9,"card":471,"direction":181,"ingested_from":128},"W7222909837",[461,463,465,467,469],{"name":462,"orcid":9},"Siman Emmanuel*",{"name":464,"orcid":9},"Edward Onyebueke Agu",{"name":466,"orcid":9},"Musa Gani",{"name":468,"orcid":9},"Oladunjoye John Abiodun",{"name":470,"orcid":9},"Anagu Emmanuel John",{"tldr":472,"method":473,"finding":474,"direction":181,"opportunity":475},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z"]