[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-135":3,"related-135":51},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":45,"slug":48,"view_count":49,"doi":8,"paper":8,"created_at":50},135,"作物制图模型可跨区域\"迁移应用\"——中国农科院资划所构建自适应作物制图模型,无需目标区域地面样本","https:\u002F\u002Fnew.qq.com\u002Frain\u002Fa\u002F20260727A09G1900","中国农业科学院农业资源与农业区划研究所农业绿色发展科学与工程团队构建了一种面向跨区域应用的作物制图模型,可在目标区域缺少地面样本标签的情况下开展作物分类。研究成果发表在《农业中的计算机与电子学(Computers and Electronics in Agriculture)》上。传统作物制图依赖大量地面样本,采集成本高、周期长;不同地区气候条件、种植方式和作物生长特征差异明显,模型跨区域应用难度较大。该模型通过多个分类算法的协同约束,学习不同区域间相对稳定的作物识别特征,减少区域差异对模型迁移应用的影响,研究团队在多个国家开展实验验证其稳定性。",null,"window.DATA = {\"url\":\"https:\u002F\u002Fview.inews.qq.com\u002Fa\u002F20260727A09G1900\",\"article_id\":\"20260727A09G1900\",\"atype\":\"0\",\"article_type\":\"0\",\"title\":\"作物制图模型可跨区域“迁移应用”\",\"desc\":\"近日，中国农业科学院农业资源与农业区划研究所农业绿色发展科学与工程团队构建了一种面向跨区域应用的作物制图模型，可在目标区域缺少地面样本标签的情况下开展作物分类。相关研究成果发表在《农业中的计算机与电子学（Computers and Electronics in Agriculture）》上。传统作物制图依赖大量地面样本，采集成本高、周期长...\",\"iNewsRecommendLevel\":1,\"abstract\":\"近日，中国农业科学院农业资源与农业区划研究所农业绿色发展科学与工程团队构建了一种面向跨区域应用的作物制图模型，可在目标区域缺少地面样本标签的情况下开展作物分类。相关研究成果发表在《农业中的计算机与电子学（Computers and Electronics in Agriculture）》上。传统作物制图依赖大量地面样本，采集成本高、周期长...\",\"catalog1\":\"agriculture\",\"ad_channel_sign\":\"news\",\"introduction\":\"\",\"media\":\"中国农业科学院\",\"media_id\":\"20124819\",\"pubtime\":\"2026-07-27 16:59:15\",\"comment_id\":\"1260727605102445\",\"political\":0,\"cmsId\":\"20260727A09G1900\",\"cms_id\":\"20260727A09G1900\",\"closeAllAd\":0,\"closeAllFavorite\":false,\"originContent\":{\"directory\":{\"enable\":2},\"text\":\"\\u003cdiv class=\\\"rich_media_content\\\"\\u003e\\u003csection data-nest-level=\\\"2\\\" data-pm-slice=\\\"0 0 []\\\" style=\\\"box-sizing: border-box; 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font-size: 17px; font-style: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6\\\"\\u003e近日，中国农业科学院\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6\\\"\\u003e农业资源与农业区划研究所\\u003c\u002Fspan\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6\\\"\\u003e农业绿色发展科学与工程团队构建了一种面向跨区域应用的作物制图模型，可在目标区域缺少地面样本标签的情况下开展作物分类。相关研究成果发表在\\u003cspan style=\\\"color: rgb(4, 102, 141); font-weight: bold\\\" textstyle=\\\"\\\"\\u003e《农业中的计算机与电子学（Computers and Electronics in Agriculture）》\\u003c\u002Fspan\\u003e上。\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fp\\u003e\\u003c\u002Fsection\\u003e\\u003csection data-nest-level=\\\"5\\\" style=\\\"box-sizing: border-box; display: flex; flex-flow: row; justify-content: flex-start; margin: 0px\\\"\\u003e\\u003csection data-nest-level=\\\"6\\\" style=\\\"align-self: flex-end; box-sizing: border-box; display: inline-block; flex: 0 0 0%; height: auto; margin: 0px 4px 0px 0px; vertical-align: bottom; width: auto\\\"\\u003e\\u003csection data-nest-level=\\\"7\\\" style=\\\"-moz-transform: perspective(0px); -o-transform: perspective(0px); -webkit-transform: perspective(0px); box-sizing: border-box; transform: perspective(0px); transform-style: flat\\\"\\u003e\\u003csection data-nest-level=\\\"8\\\" style=\\\"-moz-transform: rotateX(180deg); -o-transform: rotateX(180deg); -webkit-transform: rotateX(180deg); box-sizing: border-box; text-align: center; transform: rotateX(180deg)\\\"\\u003e\\u003csection data-nest-level=\\\"9\\\" style=\\\"background-color: rgb(28, 136, 136); box-sizing: border-box; display: inline-block; height: 10px; overflow: hidden; vertical-align: top; width: 3px\\\"\\u003e\\u003csvg data-nest-level=\\\"10\\\" style=\\\"float: left; line-height: 0; vertical-align: top; width: 0\\\" viewBox=\\\"0 0 1 1\\\"\\u003e\\u003c\u002Fsvg\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003csection data-nest-level=\\\"6\\\" style=\\\"align-self: flex-end; box-sizing: border-box; display: inline-block; flex: 0 0 0%; height: auto; vertical-align: bottom; width: auto\\\"\\u003e\\u003csection data-nest-level=\\\"7\\\" style=\\\"-moz-transform: perspective(0px); -o-transform: perspective(0px); -webkit-transform: perspective(0px); box-sizing: border-box; transform: perspective(0px); transform-style: flat\\\"\\u003e\\u003csection data-nest-level=\\\"8\\\" style=\\\"-moz-transform: rotateX(180deg); -o-transform: rotateX(180deg); -webkit-transform: rotateX(180deg); box-sizing: border-box; margin: 0px; text-align: center; transform: rotateX(180deg)\\\"\\u003e\\u003csection data-nest-level=\\\"9\\\" style=\\\"background-color: rgb(28, 136, 136); box-sizing: border-box; display: inline-block; height: 16px; overflow: hidden; vertical-align: top; width: 3px\\\"\\u003e\\u003csvg data-nest-level=\\\"10\\\" style=\\\"float: left; line-height: 0; vertical-align: top; width: 0\\\" viewBox=\\\"0 0 1 1\\\"\\u003e\\u003c\u002Fsvg\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003csection data-nest-level=\\\"6\\\" style=\\\"align-self: flex-end; border-bottom: 1px solid rgb(28, 136, 136); border-bottom-right-radius: 0px; box-sizing: border-box; display: inline-block; flex: 0 0 auto; height: auto; margin: 0px; padding: 4px 4px 4px 11px; vertical-align: bottom; width: 100px\\\"\\u003e\\u003csection data-nest-level=\\\"7\\\" style=\\\"box-sizing: border-box; text-align: justify\\\"\\u003e\\u003cp data-nest-level=\\\"8\\\" style=\\\"box-sizing: border-box; margin: 0px; padding: 0px; white-space: normal\\\"\\u003e\\u003cspan data-nest-level=\\\"9\\\" leaf=\\\"\\\"\\u003e\\u003cbr\u002F\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fp\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003c\u002Fsection\\u003e\\u003csection nodeleaf=\\\"\\\" style=\\\"margin-bottom: 8px; margin-top: 8px; text-align: center\\\"\\u003e\\u003c!--IMG_0--\\u003e\\u003c\u002Fsection\\u003e\\u003cp data-pm-slice=\\\"0 0 []\\\" style=\\\"margin-top: 8px\\\"\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6\\\"\\u003e传统作物制图依赖大量地面样本，采集成本高、周期长；同时，不同地区气候条件、种植方式和作物生长特征差异明显，导致模型跨区域应用难度较大。\\u003c\u002Fspan\\u003e\\u003c!--EOP_0--\\u003e\\u003c\u002Fp\\u003e\\u003c!--PARAGRAPH_0--\\u003e\\u003cp style=\\\"margin-top: 8px\\\"\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6\\\"\\u003e该研究构建自适应作物制图模型。该模型通过多个分类算法的协同约束，学习不同区域间相对稳定的作物识别特征，减少区域差异对模型迁移应用的影响，并可在缺少目标区域标签的情况下完成作物制图。研究团队在多个国家开展实验，该模型在多数\\u003c!--VERTICAL_CARD_BEGIN_0--\\u003e跨区域迁移\\u003c!--VERTICAL_CARD_END_0--\\u003e场景中表现稳定，可为地面样本不足地区的作物制图提供技术支撑。\\u003c\u002Fspan\\u003e\\u003c!--EOP_1--\\u003e\\u003c\u002Fp\\u003e\\u003c!--PARAGRAPH_1--\\u003e\\u003cp style=\\\"margin-top: 8px\\\"\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"color: rgba(0, 0, 0, 0.9); 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font-size: 15px\\\" textstyle=\\\"\\\"\\u003e来源\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan class=\\\"js_darkmode__13\\\" data-nest-level=\\\"4\\\" leaf=\\\"\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; visibility: visible; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan style=\\\"color: rgb(136, 136, 136); font-size: 15px\\\" textstyle=\\\"\\\"\\u003e丨资划所\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan data-nest-level=\\\"4\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cspan data-nest-level=\\\"5\\\" leaf=\\\"\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; visibility: visible; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cbr\u002F\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fsection\\u003e\\u003csection class=\\\"js_darkmode__11\\\" data-nest-level=\\\"3\\\" data-pm-slice=\\\"0 0 []\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: 0.544px; margin: 0px; max-width: 100%; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; visibility: visible; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan data-nest-level=\\\"4\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cspan data-nest-level=\\\"5\\\" leaf=\\\"\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; visibility: visible; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan style=\\\"color: rgb(136, 136, 136); font-size: 15px\\\" textstyle=\\\"\\\"\\u003e作者丨\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan data-pm-slice=\\\"0 0 []\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; visibility: visible; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; visibility: visible; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan style=\\\"color: rgb(136, 136, 136); font-size: 15px\\\" textstyle=\\\"\\\"\\u003e高懋芳\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cspan leaf=\\\"\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initi\n[…全文已截断…]","中国农业科学院资划所 2026-07-27","2026-07-27T00:00:00Z","报道",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,20,16,14,4,1,"国家级科研机构在作物制图模型跨区域应用上的重要突破，方法新颖，但发布已超一周，时效性稍弱。",[25],{"name":10,"url":6},[27,28,29,30],"中国农科院","机器学习","作物制图","跨区域迁移",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":43,"reason":44},[38],{"code":39,"score":22,"supported":14,"title":40,"abstract":41,"tags":42},"I2",[],[],[28],true,"缺少足够的主题证据",[46,47],"中国农科院 跨区域迁移 作物制图 机器学习","中国农科院 跨区域迁移","中国农科院跨区域迁移作物制图机器学习-135",9,"2026-08-01T06:01:54.174615Z",{"total":52,"page":22,"page_size":52,"items":53},6,[54,135,201,293,361,414],{"id":55,"title":56,"url":57,"summary":58,"summary_zh":59,"content":8,"source_name":60,"source_url":57,"published_at":61,"category":62,"cover_url":8,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":68,"tags":70,"primary_topic":74,"secondary_topics":79,"classification":80,"search_phrases":105,"slug":108,"view_count":109,"doi":110,"paper":111,"created_at":134},5319,"Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5f22w","Accurate in-season crop mapping is vital for national food security, yet traditional ground-based estimation methods in Bangladesh often lack precision. This study develops a robust machine-learning framework to map Boro rice in the Manikganj district by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. To overcome the challenge of seasonal cloud cover, Sentinel-1 VH polarization was utilized to identify unique backscatter reductions during the transplantation phase (late February to early March) caused by specular reflection from flooded fields. By combining these SAR signatures with high-resolution spectral indices (NDVI, EVI, and BSI) from Sentinel-2 during peak growth stages, the research captures the distinct phenological signatures of paddy rice. A pixel-based Random Forest algorithm was applied within the Google Earth Engine (GEE) platform, utilizing about 200 field-verified reference points per sub-district (1,385 in total) for training and validation. The results demonstrated high classification performance, with overall accuracies ranging from 88.1% to 94.9% across the seven sub-districts. Furthermore, a comparison with official government statistics revealed a minimal total area difference of only 2.34%, underscoring the reliability of this integrated satellite approach for operational crop monitoring.","准确的当季作物制图对国家粮食安全至关重要，然而孟加拉国传统的基于地面估计的方法往往缺乏精度。本研究开发了一个稳健的机器学习框架，通过整合Sentinel-1合成孔径雷达（SAR）和Sentinel-2光学影像，对马尼格甘杰县的Boro水稻进行制图。为克服季节性云覆盖的挑战，研究利用Sentinel-1 VH极化识别插秧期（2月下旬至3月上旬）由淹水田块镜面反射所引起的独特后向散射降低特征。通过将这些SAR特征与Sentinel-2在生长旺盛期的高分辨率光谱指数（NDVI、EVI和BSI）相结合，研究捕捉到了水稻独特的物候特征。在Google Earth Engine（GEE）平台上应用了基于像元的随机森林算法，利用每个乡约200个实地验证参考点（共计1，385个）进行训练和验证。结果表明分类性能较高，七个乡的总体精度在88.1%至94.9%之间。此外，与政府官方统计数据相比，总面积差异仅为2.34%，凸显了这种综合卫星方法在业务化作物监测中的可靠性。","OpenAlex","2026-10-18T00:00:00Z","论文",79,{"impact":65,"substance":17,"depth":65,"authority":66,"freshness":49,"relevant":22,"comment":67},18,12,"方法扎实、精度可靠的水稻遥感制图研究，对农业遥感业务化监测有参考价值，但属区域性案例，影响层级有限。",[69],{"name":60,"url":57},[71,72,28,29,73],"水稻","粮食安全","遥感",{"code":75,"slug":76,"name":71,"path":77,"parent_code":78},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":81,"locked":14,"version":34,"method":35,"evidence":82},"classified",{"candidates":83,"agricultural_evidence":43},[84,89,96,100],{"code":75,"score":65,"supported":43,"title":85,"abstract":86,"tags":88},[76],[71,76,87],"paddy",[71],{"code":90,"score":91,"supported":43,"title":92,"abstract":93,"tags":95},"A8",7,[],[72,94],"food security",[72],{"code":39,"score":21,"supported":43,"title":97,"abstract":98,"tags":99},[],[28],[28],{"code":101,"score":22,"supported":14,"title":102,"abstract":103,"tags":104},"I3",[],[],[73],[106,107],"Sentinel-1 Sentinel-2 水稻制图","孟加拉国 Boro 水稻 遥感","Sentinel-1Sentinel-2水稻制图-5319",0,"10.31223\u002Fx5f22w",{"doi":110,"openalex_id":112,"authors":113,"venue":8,"cited_by_count":109,"oa_url":126,"card":127,"direction":131,"ingested_from":133},"W7222810209",[114,116,119,122,124],{"name":115,"orcid":8},"Mst. Farida Perveen",{"name":117,"orcid":118},"Sravan Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-6979-1726",{"name":120,"orcid":121},"Abdullah Al Mehadi","https:\u002F\u002Forcid.org\u002F0000-0002-9002-633X",{"name":123,"orcid":8},"Saima Kazmina",{"name":125,"orcid":8},"Amio Al Saem","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15552\u002Fdownload\u002F26951\u002F",{"tldr":128,"method":129,"finding":130,"direction":131,"opportunity":132},"融合Sentinel-1 SAR与Sentinel-2光学影像，用随机森林在GEE上绘制孟加拉国Bo","Sentinel-1 VH后向散射与Sentinel-2 NDVI\u002FEVI\u002FBS","七个分县总体精度88.1%–94.9%，与官方统计面积仅差2.34%。","农业遥感与作物表型","可将该SAR-光学协同框架迁移至其他多云雨稻区，并探索多时相特征优选与跨季泛化。","openalex","2026-10-19T23:30:22.772950Z",{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":8,"source_name":141,"source_url":138,"published_at":142,"category":62,"cover_url":8,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":147,"tags":149,"primary_topic":152,"secondary_topics":157,"classification":158,"search_phrases":176,"slug":179,"view_count":109,"doi":180,"paper":181,"created_at":200},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy","2026-10-21T00:00:00Z",73,{"impact":66,"substance":18,"depth":145,"authority":20,"freshness":13,"relevant":22,"comment":146},17,"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[148],{"name":141,"url":138},[150,29,73,151],"智慧农业","SAR",{"code":101,"slug":153,"name":154,"path":155,"parent_code":156},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":81,"locked":14,"version":34,"method":35,"evidence":159},{"candidates":160,"agricultural_evidence":43},[161,167,171],{"code":101,"score":13,"supported":43,"title":162,"abstract":163,"tags":166},[],[73,164,165],"remote sensing","multispectral",[73],{"code":90,"score":52,"supported":43,"title":168,"abstract":169,"tags":170},[],[72,94],[],{"code":172,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},"I1",[],[],[150],[177,178],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":180,"openalex_id":182,"authors":183,"venue":141,"cited_by_count":109,"oa_url":138,"card":195,"direction":131,"ingested_from":133},"W7223117203",[184,187,189,192],{"name":185,"orcid":186},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":188,"orcid":8},"Wentao Lv",{"name":190,"orcid":191},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":193,"orcid":194},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":196,"method":197,"finding":198,"direction":131,"opportunity":199},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":8,"source_name":141,"source_url":204,"published_at":142,"category":62,"cover_url":8,"hotness":13,"is_selected":14,"score":207,"score_detail":208,"sources":212,"tags":214,"primary_topic":218,"secondary_topics":223,"classification":234,"search_phrases":269,"slug":272,"view_count":109,"doi":273,"paper":274,"created_at":292},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。",77,{"impact":19,"substance":209,"depth":145,"authority":210,"freshness":13,"relevant":22,"comment":211},21,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[213],{"name":141,"url":204},[150,28,215,216,217],"遥感监测","土壤墒情","冬小麦",{"code":219,"slug":220,"name":221,"path":222,"parent_code":78},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[224,230],{"code":225,"slug":226,"name":227,"path":228,"parent_code":229},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":231,"name":232,"path":233,"parent_code":156},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":81,"locked":14,"version":34,"method":35,"evidence":235},{"candidates":236,"agricultural_evidence":43},[237,243,249,254,258,265],{"code":219,"score":238,"supported":43,"title":239,"abstract":241,"tags":242},26,[240,220],"winter wheat",[221,240,220],[217],{"code":225,"score":244,"supported":43,"title":245,"abstract":246,"tags":248},15,[226],[247,226],"土壤",[216],{"code":39,"score":244,"supported":43,"title":250,"abstract":252,"tags":253},[251],"machine learning",[28,251],[28],{"code":101,"score":13,"supported":43,"title":255,"abstract":256,"tags":257},[],[73,164,165],[215],{"code":259,"score":52,"supported":43,"title":260,"abstract":261,"tags":264},"H3",[],[262,263],"灌溉","irrigation",[],{"code":172,"score":21,"supported":43,"title":266,"abstract":267,"tags":268},[],[150],[150],[270,271],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":273,"openalex_id":275,"authors":276,"venue":141,"cited_by_count":109,"oa_url":204,"card":286,"direction":291,"ingested_from":133},"W7223156204",[277,280,282,284],{"name":278,"orcid":279},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":281,"orcid":8},"Yi Zhang",{"name":283,"orcid":8},"WEI Lei",{"name":285,"orcid":8},"Chengcai Zhang",{"tldr":287,"method":288,"finding":289,"direction":131,"opportunity":290},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":8,"source_name":299,"source_url":296,"published_at":300,"category":62,"cover_url":8,"hotness":13,"is_selected":14,"score":109,"score_detail":301,"sources":303,"tags":305,"primary_topic":308,"secondary_topics":309,"classification":315,"search_phrases":338,"slug":341,"view_count":109,"doi":342,"paper":343,"created_at":360},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":109,"substance":109,"depth":109,"authority":109,"freshness":109,"relevant":109,"comment":302},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[304],{"name":299,"url":296},[28,215,306,307],"土地利用","城市扩张",{"code":39,"slug":231,"name":232,"path":233,"parent_code":156},[310],{"code":311,"slug":312,"name":313,"path":314,"parent_code":156},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":81,"locked":14,"version":34,"method":35,"evidence":316},{"candidates":317,"agricultural_evidence":43},[318,322,329,333],{"code":39,"score":244,"supported":43,"title":319,"abstract":320,"tags":321},[251],[28,251],[28],{"code":311,"score":49,"supported":43,"title":323,"abstract":324,"tags":328},[],[325,326,327],"空间分析","GIS","spatial analysis",[],{"code":101,"score":91,"supported":43,"title":330,"abstract":331,"tags":332},[],[73,164],[215],{"code":334,"score":22,"supported":14,"title":335,"abstract":336,"tags":337},"H6",[],[],[306],[339,340],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":342,"openalex_id":344,"authors":345,"venue":299,"cited_by_count":109,"oa_url":296,"card":355,"direction":131,"ingested_from":133},"W7223022155",[346,349,351,353],{"name":347,"orcid":348},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":350,"orcid":8},"Goanar Jal Dup",{"name":352,"orcid":8},"Weyesa Merga Chemeda",{"name":354,"orcid":8},"Shankar Karuppannan",{"tldr":356,"method":357,"finding":358,"direction":131,"opportunity":359},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":8,"content":8,"source_name":366,"source_url":8,"published_at":61,"category":62,"cover_url":8,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":371,"tags":373,"primary_topic":378,"secondary_topics":379,"classification":380,"search_phrases":400,"slug":403,"view_count":109,"doi":8,"paper":404,"created_at":413},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18",70,{"impact":66,"substance":18,"depth":145,"authority":210,"freshness":369,"relevant":22,"comment":370},8,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[372],{"name":366,"url":364},[374,28,375,376,377],"农业人工智能","精准农业","土壤数据","作物分类",{"code":225,"slug":226,"name":227,"path":228,"parent_code":229},[],{"status":81,"locked":14,"version":34,"method":35,"evidence":381},{"candidates":382,"agricultural_evidence":43},[383,387,392,396],{"code":225,"score":18,"supported":43,"title":384,"abstract":385,"tags":386},[247,226],[247],[376],{"code":39,"score":388,"supported":14,"title":389,"abstract":390,"tags":391},2,[],[],[374,28],{"code":172,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[375],{"code":101,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[377],[401,402],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":8,"openalex_id":8,"authors":405,"venue":8,"cited_by_count":109,"oa_url":8,"card":406,"direction":410,"ingested_from":412},[],{"tldr":407,"method":408,"finding":409,"direction":410,"opportunity":411},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","农业人工智能与决策模型","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","agent","2026-10-21T00:10:08.428584Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":8,"source_name":420,"source_url":417,"published_at":421,"category":62,"cover_url":8,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":425,"tags":427,"primary_topic":430,"secondary_topics":431,"classification":438,"search_phrases":478,"slug":481,"view_count":109,"doi":482,"paper":483,"created_at":504},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":65,"substance":17,"depth":65,"authority":20,"freshness":369,"relevant":22,"comment":424},"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[426],{"name":420,"url":417},[150,374,28,428,429],"食品安全","文献计量",{"code":39,"slug":231,"name":232,"path":233,"parent_code":156},[432],{"code":433,"slug":434,"name":435,"path":436,"parent_code":437},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":81,"locked":14,"version":34,"method":35,"evidence":439},{"candidates":440,"agricultural_evidence":43},[441,451,456,462,469,474],{"code":39,"score":442,"supported":43,"title":443,"abstract":445,"tags":450},19,[444],"artificial intelligence",[446,28,447,251,448,444,449],"深度学习","计算机视觉","deep learning","computer vision",[374,28],{"code":433,"score":244,"supported":43,"title":452,"abstract":454,"tags":455},[453],"food safety",[428,453],[428],{"code":457,"score":244,"supported":43,"title":458,"abstract":460,"tags":461},"I7",[459],"bibliometric",[429,459],[429],{"code":463,"score":52,"supported":43,"title":464,"abstract":465,"tags":468},"H7",[],[466,467],"气候变化","climate change",[],{"code":90,"score":470,"supported":43,"title":471,"abstract":472,"tags":473},3,[],[94],[],{"code":172,"score":22,"supported":14,"title":475,"abstract":476,"tags":477},[],[],[150],[479,480],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":482,"openalex_id":484,"authors":485,"venue":420,"cited_by_count":109,"oa_url":8,"card":499,"direction":410,"ingested_from":133},"W7222920742",[486,488,490,493,496],{"name":487,"orcid":8},"Harang Lee",{"name":489,"orcid":8},"지은서",{"name":491,"orcid":492},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":494,"orcid":495},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":497,"orcid":498},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":500,"method":501,"finding":502,"direction":410,"opportunity":503},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z"]