[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2272":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":8,"paper":31,"created_at":40},2272,"MDPI Remote Sensing 18(18):3107 MODIS NDVI与机器学习作物产量预测比较研究","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3107","Arai与Sanwal在MDPI Remote Sensing发表研究,基于印度2000-2026年州级数据,随机森林在保留时间顺序的走前验证中MAPE=11.4%、R²=0.982,优于梯度提升(MAPE=13.0%)。MODIS NDVI年最大值与粮食产量相关性r≈0.84,提供了可扩展的食品安全评估与农业决策支持实用路线。",null,"MDPI Remote Sensing 18(18):3107","2026-09-09T16:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,6,1,"基于印度2000-2026年州级数据的MODIS NDVI与机器学习产量预测对比研究，方法严谨、结论可靠，对农业遥感估产具有参考价值，但属境外案例、非国内政策或产业突破，适合作为专业精选而非头条。",[23],{"name":9,"url":6},[25,26,27,28,29],"农业人工智能","粮食安全","机器学习","遥感","作物产量预测",0,{"doi":8,"openalex_id":8,"authors":32,"venue":8,"cited_by_count":30,"oa_url":8,"card":33,"direction":37,"ingested_from":39},[],{"tldr":34,"method":35,"finding":36,"direction":37,"opportunity":38},"基于印度州级MODIS NDVI与机器学习比较作物产量预测，随机森林优于梯度提升。","用2000-2026年印度州级MODIS NDVI年最大值与随机森林、梯度提升走","随机森林MAPE=11.4%、R²=0.982，NDVI与产量相关性r≈0.84。","农业遥感与作物表型","可在中国等区域验证NDVI-产量模型迁移性，并融合多源遥感与气象提升预测鲁棒性。","agent","2026-09-13T00:04:05.087805Z"]