[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2212":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},2212,"《基于遥感的农作物产量估算综述:机器学习技术及环境、算法和硬件限制》","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffpls.2026.1742689\u002Ffull","Muhammad等系统综述遥感技术在农作物和植物产量估算中的应用进展,提出将遥感方法学系统分类为:传感器方法、平台方法、分析与建模方法、机器学习方法。基于多项研究结果发现,基于深度学习的架构在精度、查准率、查全率和F1分数等关键评估指标上一致实现优越性能,这种性能优势源于其学习分层表示、捕捉复杂非线性关系、高效扩展大规模数据集的能力。论文还将局限性系统归纳为环境、算法、硬件操作和无线传感器网络四大类别。",null,"Frontiers in Plant Science 2026年9月","2026-09-07T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"系统综述遥感产量估算方法学并归纳环境、算法与硬件限制，方法分类清晰、结论可靠，对农业遥感与AI应用有较高参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","机器学习","遥感","产量估算",0,"10.3389\u002Ffpls.2026.1742689\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},"系统综述遥感估产方法，分类传感器、平台、建模与机器学习，指出深度学习性能最优。","文献综述，按传感器、平台、分析建模、机器学习四类归纳遥感估产方法。","深度学习在精度、查准率、召回率和F1上一致优于其他方法，局限归为环境、算法、硬件与无线传感器网络四类","农业遥感与作物表型","可针对综述指出的环境与硬件限制，研究轻量化深度学习模型在边缘设备上的实时估产。","agent","2026-09-12T00:06:40.290409Z"]