[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2859":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"view_count":30,"doi":31,"paper":32,"created_at":41},2859,"县域大豆产量预测的深度学习方法:利用大规模环境数据——Frontiers in Artificial Intelligence","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1914697\u002Ffull","论文提出了基于深度学习的大规模环境数据县域大豆产量预测方法。利用县级大豆产量数据和环境变量构建预测模型,评估多种深度学习架构(卷积神经网络、循环神经网络、Transformer等)在大豆产量预测中的表现,并与传统统计回归模型进行比较。结果显示,深度学习方法在预测精度和稳定性方面优于传统模型,能够更好地捕捉环境因素与产量之间的非线性关系,为农业政策制定和粮食安全评估提供数据支持。",null,"Frontiers in Artificial Intelligence","2026-09-17T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},16,21,17,13,1,"论文以县级大规模环境数据对比多种深度学习架构与传统回归模型，方法新颖、结论可靠，对农业政策与粮食安全评估有参考价值，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","粮食安全","大豆产量预测","环境数据",0,"10.3389\u002Ffrai.2026.1914697\u002Ffull",{"doi":31,"openalex_id":8,"authors":33,"venue":8,"cited_by_count":30,"oa_url":8,"card":34,"direction":38,"ingested_from":40},[],{"tldr":35,"method":36,"finding":37,"direction":38,"opportunity":39},"用深度学习结合大规模环境数据预测县域大豆产量，并对比多种网络架构与传统模型。","县级大豆产量与环境变量数据，采用CNN、RNN、Transformer等深度学习","深度学习在预测精度和稳定性上优于传统统计回归，能更好捕捉非线性关系。","农业人工智能与决策模型","可探索多模态环境数据融合与可解释性，提升跨区域迁移和极端气候下的预测鲁棒性。","agent","2026-09-18T00:03:31.241582Z"]