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数智京园 建设指南","北京 智慧设施 种植 分级建设","北京数智京园建设指南-5257","2026-10-19T00:07:31.752116Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":8,"content":8,"source_name":223,"source_url":8,"published_at":224,"category":11,"cover_url":8,"hotness":12,"is_selected":41,"score":225,"score_detail":226,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":236,"classification":238,"search_phrases":259,"slug":262,"view_count":54,"doi":8,"paper":8,"created_at":263},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",82,{"impact":18,"substance":227,"depth":18,"authority":228,"freshness":20,"relevant":21,"comment":229},22,15,"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。",[231],{"name":223,"url":221},[91,233,26,93,234],"农业人工智能","生长预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[237],{"code":98,"slug":99,"name":100,"path":101,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":239},{"candidates":240,"agricultural_evidence":13},[241,246,250,254],{"code":33,"score":242,"supported":13,"title":243,"abstract":244,"tags":245},12,[49],[49],[93],{"code":98,"score":47,"supported":13,"title":247,"abstract":248,"tags":249},[111],[111],[],{"code":53,"score":21,"supported":41,"title":251,"abstract":252,"tags":253},[],[],[91],{"code":255,"score":21,"supported":41,"title":256,"abstract":257,"tags":258},"I2",[],[],[233],[260,261],"中国农科院环发所 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Along with ensuring the continued improvement of the technology itself, educating the public about aquaponics using a systems-thinking approach is equally important. Agent-based modeling was used to develop an aquaponics model that will allow any student to have an active learning experience with a generalized operation of the system while also exploring systems-thinking principles. By developing a model, individuals interested in learning are not limited by what their educational institutions can provide, which is often restricted by a lack of resources. The aquaponics agent-based model presented in this study allows the user to change the fish and plant types, climate, and season and display the corresponding greenhouse gas emissions when the theoretical system is operating. The number of plants harvested and the total greenhouse gas emissions are the two stochastic outputs of the model, given potential variations in timelines, system efficiencies, and general human error. Greenhouse gas emissions were compared with those reported in the relevant literature to inform parameter selection. The overall goal of this model is to provide an interactive and broadly accessible educational opportunity.","鱼菜共生（Aquaponics）是水产养殖（即水生物种的繁殖）与水培（即无土栽培植物）的结合，目前正被研究作为一种更可持续的粮食生产方式。在确保该技术本身持续改进的同时，利用系统思维方法向公众普及鱼菜共生知识同样重要。本研究采用基于主体的建模（Agent-based modeling）方法开发了一个鱼菜共生模型，使任何学生都能通过该系统的广义操作获得主动学习体验，同时探索系统思维原则。通过开发这一模型，有兴趣的学习者不再受限于其教育机构所能提供的条件——这些条件往往因资源匮乏而受到制约。本研究提出的鱼菜共生基于主体的模型允许用户更改鱼类和植物类型、气候和季节，并展示理论系统运行时的相应温室气体排放量。考虑到时间线、系统效率和一般人为误差的潜在变化，收获植物数量和温室气体排放总量是该模型的两个随机输出。温室气体排放量已与相关文献中报告的数据进行比较，以指导参数选择。该模型的总体目标是提供一种具有交互性且可广泛获取的教育机会。","Frontiers in Sustainable Food Systems","2026-09-30T00:00:00Z",61,{"impact":274,"substance":18,"depth":83,"authority":86,"freshness":71,"relevant":21,"comment":275},8,"该论文提出基于智能体的鱼菜共生教学模型，可模拟鱼类、植物、气候与季节变化并输出温室气体排放，方法新颖且具教育推广价值，但属细分领域学术成果，公共影响有限。",[277],{"name":270,"url":267},[91,26,279,280,281],"温室气体","水产养殖","系统思维",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":285},{"candidates":286,"agricultural_evidence":13},[287,293,301,307,312],{"code":33,"score":85,"supported":13,"title":288,"abstract":290,"tags":292},[289],"greenhouse",[49,291,289],"无土栽培",[],{"code":294,"score":295,"supported":13,"title":296,"abstract":297,"tags":300},"D",7,[],[298,299],"水产","aquaculture",[280],{"code":302,"score":54,"supported":13,"title":303,"abstract":304,"tags":306},"H2",[],[305],"soil",[],{"code":308,"score":21,"supported":41,"title":309,"abstract":310,"tags":311},"H4",[],[],[279],{"code":53,"score":21,"supported":41,"title":313,"abstract":314,"tags":315},[],[],[91],[317,318],"鱼菜共生 温室气体 模型","智慧农业 水产养殖 温室气体 系统思维","鱼菜共生温室气体模型-3874","10.3389\u002Ffsufs.2026.1862992",{"doi":320,"openalex_id":322,"authors":323,"venue":270,"cited_by_count":122,"oa_url":267,"card":333,"direction":337,"ingested_from":339},"W7214975686",[324,327,329,331],{"name":325,"orcid":326},"Marissa Breitenstein","https:\u002F\u002Forcid.org\u002F0009-0005-2148-8812",{"name":328,"orcid":8},"Elisabeth Bautista",{"name":330,"orcid":8},"Alexis Daniels",{"name":332,"orcid":8},"Andrea Hicks",{"tldr":334,"method":335,"finding":336,"direction":337,"opportunity":338},"开发鱼菜共生系统智能体模型，模拟不同鱼、植物、气候和季节下的温室气体排放，用于教学。","基于智能体的建模（ABM），参数参考文献，输出随机性结果。","模型可交互展示鱼菜共生系统的温室气体排放，促进系统思维学习。","农业绿色发展与碳","可扩展模型纳入更多环境与经济指标，或开发面向公众的在线交互平台。","openalex","2026-10-01T23:30:07.298849Z",{"id":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":348,"category":80,"cover_url":8,"hotness":12,"is_selected":41,"score":349,"score_detail":350,"sources":352,"tags":354,"primary_topic":357,"secondary_topics":358,"classification":364,"search_phrases":390,"slug":393,"view_count":54,"doi":394,"paper":395,"created_at":406},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。","Journal of Environmental and Sustainability Science","2026-09-16T00:00:00Z",74,{"impact":228,"substance":142,"depth":85,"authority":242,"freshness":12,"relevant":21,"comment":351},"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[353],{"name":347,"url":344},[91,233,26,355,356],"决策支持系统","病虫害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[359],{"code":255,"slug":360,"name":361,"path":362,"parent_code":363},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":41,"version":42,"method":43,"evidence":365},{"candidates":366,"agricultural_evidence":13},[367,375,379,386],{"code":255,"score":368,"supported":13,"title":369,"abstract":372,"tags":374},27,[370,371],"machine learning","decision support",[373,370,371],"机器学习",[233,355],{"code":33,"score":19,"supported":13,"title":376,"abstract":377,"tags":378},[289],[49,289],[],{"code":380,"score":71,"supported":13,"title":381,"abstract":382,"tags":385},"F4",[],[383,384],"农药","pesticide",[],{"code":53,"score":21,"supported":41,"title":387,"abstract":388,"tags":389},[],[],[91],[391,392],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统病虫害预警智慧农业-2682","10.36099\u002Fjess.v1i3.001",{"doi":394,"openalex_id":396,"authors":397,"venue":347,"cited_by_count":122,"oa_url":344,"card":400,"direction":404,"ingested_from":339},"W7213235680",[398],{"name":399,"orcid":8},"S. Nasiketha",{"tldr":401,"method":402,"finding":403,"direction":404,"opportunity":405},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","农业人工智能与决策模型","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","2026-09-16T23:30:51.573509Z"]