[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2280":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":57},2280,"Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112390","Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts。Computers and Electronics in Agriculture","利用二维光谱表示和多任务学习结合多门混合专家模型预测植物叶片功能性状。",null,"Computers and Electronics in Agriculture","2026-09-12T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,14,8,1,"核心期刊论文，提出二维光谱表征与多门专家混合多任务学习预测叶片功能性状，方法新颖、对作物表型与遥感监测有参考价值，但属细分方法进展，未达重大突破层级。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","遥感","作物表型","多任务学习",0,"10.1016\u002Fj.compag.2026.112390",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":9,"card":50,"direction":54,"ingested_from":56},"W7212395422",[37,39,41,43,45,47],{"name":38,"orcid":9},"Jianping Huang",{"name":40,"orcid":9},"Xin Zhang",{"name":42,"orcid":9},"Guanglai Wang",{"name":44,"orcid":9},"Chong Mo",{"name":46,"orcid":9},"Zhenghang Wang",{"name":48,"orcid":49},"Wenlong Song","https:\u002F\u002Forcid.org\u002F0000-0002-8810-532X",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"用二维光谱表示与多门混合专家多任务学习预测植物叶片功能性状。","二维光谱表示、多任务学习、多门混合专家模型。","该方法能同时准确预测多种叶片功能性状，优于单任务模型。","农业遥感与作物表型","可探索将该多任务框架迁移到多作物、多时相的高光谱表型监测中。","openalex","2026-09-13T23:30:01.702074Z"]