[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3136":3,"related-3136":70},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"search_phrases":29,"slug":32,"view_count":33,"doi":34,"paper":35,"created_at":69},3136,"Integrated water–carbon management reshapes trade-offs among greenhouse gas emissions, productivity, and economic returns in rice systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104991","Integrated water–carbon management reshapes trade-offs among greenhouse gas emissions, productivity, and economic returns in rice systems。Agricultural Systems",null,"Agricultural Systems","2026-09-22T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,14,1,"核心期刊新研究，提出水碳协同管理以平衡稻田减排、产量与收益，方法新颖、结论有实质增量，对农业绿色低碳与智慧灌溉有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"水稻","农业温室气体","水碳协同","稻田减排","农业经济评估",[30,31],"水稻 水碳管理 温室气体","稻田 减排 经济收益","水稻水碳管理温室气体-3136",0,"10.1016\u002Fj.agsy.2026.104991",{"doi":34,"openalex_id":36,"authors":37,"venue":9,"cited_by_count":33,"oa_url":8,"card":8,"direction":8,"ingested_from":68},"W7213974400",[38,40,42,44,46,48,51,54,56,58,60,62,65,66],{"name":39,"orcid":8},"Zewei Jiang",{"name":41,"orcid":8},"Shihong Yang",{"name":43,"orcid":8},"Qingqing Pang",{"name":45,"orcid":8},"Pete Smith",{"name":47,"orcid":8},"Mohamed Abdalla",{"name":49,"orcid":50},"Matthias Kuhnert","https:\u002F\u002Forcid.org\u002F0000-0003-3284-2133",{"name":52,"orcid":53},"Saeed Karbin","https:\u002F\u002Forcid.org\u002F0000-0002-8327-8037",{"name":55,"orcid":8},"Yi Xu",{"name":57,"orcid":8},"Jie Zhang",{"name":59,"orcid":8},"Haonan Qiu",{"name":61,"orcid":8},"Xishan Song",{"name":63,"orcid":64},"Qinbo Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5327-8837",{"name":55,"orcid":8},{"name":67,"orcid":8},"Yakov Kuzyakov","openalex","2026-09-22T23:30:05.426576Z",{"total":71,"page":19,"page_size":71,"items":72},6,[73,119,159,185,206,230],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":8,"source_name":79,"source_url":76,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":80,"score_detail":81,"sources":87,"tags":89,"search_phrases":94,"slug":97,"view_count":33,"doi":98,"paper":99,"created_at":118},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。","Journal of Experimental Agriculture International",72,{"impact":82,"substance":83,"depth":84,"authority":85,"freshness":12,"relevant":19,"comment":86},12,20,17,13,"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[88],{"name":79,"url":76},[24,90,91,92,93],"产量预测","遥感","作物模型","灌溉管理",[95,96],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168","10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":98,"openalex_id":100,"authors":101,"venue":79,"cited_by_count":33,"oa_url":76,"card":112,"direction":116,"ingested_from":68},"W7213966772",[102,104,106,108,110],{"name":103,"orcid":8},"Rana Prathap",{"name":105,"orcid":8},"G. Ravi Babu",{"name":107,"orcid":8},"V. Muthayya Chowdary",{"name":109,"orcid":8},"K.Krupavathi",{"name":111,"orcid":8},"K. Chandrasekhar",{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","农业遥感与作物表型","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","2026-09-22T23:30:22.711640Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":8,"source_name":125,"source_url":122,"published_at":126,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":127,"score_detail":128,"sources":131,"tags":133,"search_phrases":137,"slug":140,"view_count":33,"doi":141,"paper":142,"created_at":158},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture","2026-09-21T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":85,"freshness":129,"relevant":19,"comment":130},9,"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[132],{"name":125,"url":122},[134,135,24,91,136],"智慧农业","农业人工智能","作物表型",[138,139],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":141,"openalex_id":143,"authors":144,"venue":125,"cited_by_count":33,"oa_url":122,"card":153,"direction":116,"ingested_from":68},"W7213887432",[145,148,150],{"name":146,"orcid":147},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":149,"orcid":8},"Noboru Noguchi",{"name":151,"orcid":152},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":154,"method":155,"finding":156,"direction":116,"opportunity":157},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":8,"content":164,"source_name":165,"source_url":8,"published_at":166,"category":167,"cover_url":8,"hotness":12,"is_selected":13,"score":168,"score_detail":169,"sources":173,"tags":175,"search_phrases":180,"slug":183,"view_count":33,"doi":8,"paper":8,"created_at":184},3118,"广州市农业农村科学院赴花都区开展晚造粮食生产技术指导——专家团队深入水稻\u002F鲜食玉米连片种植区","https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fgzdt\u002Fcontent\u002Fpost_11014185.html","广州市农业农村科学院联合花都区农业技术管理中心组建农技专家服务团队，深入花都区粮食生产核心基地开展常态化、精准化田间技术帮扶和实地指导服务。在花东镇水稻种植片区晚造水稻已进入穗分化关键阶段，专家现场指导农户科学追施促穗肥；针对采用自留种的田块开展手把手实操教学、现场示范田间除杂技术；在花山镇鲜食玉米生产基地引导种植户实行错期分批播种；叮嘱种植主体密切监测草地贪夜蛾、茎腐病、南方锈病等重大病虫害发生动态。广州市农业农村科学院长期扎根花都农业生产一线、多方联动共建多处百亩连片水稻示范田。","[](javascript:void(0))\n\n[![Image 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全力打造全过程人民民主省...](https:\u002F\u002Fwww.gd.gov.cn\u002Fxxts\u002Fcontent\u002Fpost_4956000.html \"省委人大工作会议召开坚持好完善好运行好人民代表大会制度 全力打造全过程人民民主省域样板为广东增创新优势实现新突破提供有力制度保障黄坤明讲话 孟凡利主持 黄楚平林克庆出席\")2026-09-15\n\n*   [番禺区：“百千万工程”总师聘任暨艺术乡建“共创活...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fqzdt\u002Fcontent\u002Fpost_11014163.html \" 番禺区：“百千万工程”总师聘任暨艺术乡建“共创活...\") 2026-09-21\n*   [番禺区：入选广东省美丽县城典型案例](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fqzdt\u002Fcontent\u002Fpost_11002088.html \" 番禺区：入选广东省美丽县城典型案例\") 2026-09-14\n*   [南沙区：开展违规网具集中销毁专项行动](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fqzdt\u002Fcontent\u002Fpost_10995760.html \" 南沙区：开展违规网具集中销毁专项行动\") 2026-09-08\n\n[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Ftzgg\u002Findex.html)[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fgkmlpt\u002Findex#15306)[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fgkmlpt\u002Fpolicy#411)\n*   通知公告\n*   规划统计\n*   政策解读\n\n*   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\"种植管理\")[农环植保](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Fsn\u002Fnhzb\u002Findex.html \"农环植保\")[农机管理](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Fsn\u002Fnjgl\u002Findex.html \"农机管理\")[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Findex.html)\n\n#### [热点专题](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002Findex.html)\n\n*   [《广州市加快建设都市现代农业强市规划（2024—...](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002Fjsdsxdnyqsgh\u002F \"《广州市加快建设都市现代农业强市规划（2024—2035年）》内容解读（视频）\")\n*   [广州市畜牧兽医屠管行业普法直通车](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002F2025xmsytghypfztc\u002F \"广州市畜牧兽医屠管行业普法直通车\")\n*   [广州市打造美丽中国城市样板美丽乡村优秀案例](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002F2025mlxcyxal\u002F \"广州市打造美丽中国城市样板美丽乡村优秀案例\")\n[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002Findex.html)\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fc_cat?name=ftyg) 接访预告\n\n*   [信访工作条例](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_8373419.html \"信访工作条例\") 2022-06-27 \n*   [广东省信访条例](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_8090013.html \"广东省信访条例\") 2022-02-22 \n*   [广州市农业农村局来信、来访指南](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10033656.html \"广州市农业农村局来信、来访指南\") 2026-06-05 \n*   [广州市农业农村局领导9月接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_11012694.html \"广州市农业农村局领导9月接访预告\") 2026-09-20 \n*   [广州市农业农村局领导8月接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10967166.html \"广州市农业农村局领导8月接访预告\") 2026-08-17 \n*   [广州市农业农村局领导7月接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10905195.html \"广州市农业农村局领导7月接访预告\") 2026-07-16 \n*   [广州市农业农村局6月份局领导接访安排](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10862146.html \"广州市农业农村局6月份局领导接访安排\") 2026-06-18 \n*   [广州市农业农村局5月份局领导接访安排](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10827005.html \"广州市农业农村局5月份局领导接访安排\") 2026-05-25 \n*   [4月23日广州市农业农村局领导接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10773365.html \"4月23日广州市农业农村局领导接访预告\") 2026-04-16 \n*   [3月23日广州市农业农村局领导接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10728537.html \"3月23日广州市农业农村局领导接访预告\") 2026-03-16 \n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fc_cat?name=wsdc) 网上调查\n\n![Image 7](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_01.jpg)\n\n*   [广州市农业农村局关于2026年中央一号文件的调查...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F50869 \"广州市农业农村局关于2026年中央一号文件的调查问卷\")\n*   [广州市农业农村局关于2025年中央一号文件的调查...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F44211 \"广州市农业农村局关于2025年中央一号文件的调查问卷\")\n*   [广州市农业农村局关于2024年中央一号文件的调查...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F36886 \"广州市农业农村局关于2024年中央一号文件的调查问卷\")\n*   [广州市农业农村局关于广州市落实中央工作会议精神的...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F28959 \"广州市农业农村局关于广州市落实中央工作会议精神的调查问卷\")\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fc_cat?name=yjzj)民意征集\n\n*   [广州市农业农村局关于公开征求《广州市加快农业农村...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F52347 \"广州市农业农村局关于公开征求《广州市加快农业农村现代化“十五五”规划（征求意见稿）》意见的通知\")2026-08-21\n*   [广州市农业农村局关于公开征求《广州市本地农产品稳...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F51945 \"广州市农业农村局关于公开征求《广州市本地农产品稳产保供重点生产主体培育与管理办法》意见的公告\")2026-07-31\n*   [广州市农业农村局关于公开征求《广州市畜禽养殖管理...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F51346 \"广州市农业农村局关于公开征求《广州市畜禽养殖管理办法（征求意见稿）》 及公平竞争意见和建议的公告\")2026-07-03\n*   [广州市农业农村局关于公开征求《广州市本地农产品稳...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F50320 \"广州市农业农村局关于公开征求《广州市本地农产品稳产保供重点生产主体培育与管理办法》意见的公告\")2026-05-14\n*   [广州市农业农村局关于公开征求《广州市农业农村专家...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F49164 \"广州市农业农村局关于公开征求《广州市农业农村专家库管理办法（修订稿·征求意见稿）》意见的通知\")2026-03-05\n*   [广州市农业农村局关于公开征求《广州市养殖水域滩涂...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F48522 \"广州市农业农村局关于公开征求《广州市养殖水域滩涂规划（2019～2030年）》部分内容修改意见的公告\")2026-01-15\n\n[![Image 8: 局长信箱](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_btn1.jpg)](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fldxx)[![Image 9: 政府12345](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_btn2.jpg)](https:\u002F\u002Fwww.gz.gov.cn\u002Fgz12345\u002F)[![Image 10](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_btn04.jpg)](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fdwzsk?via=pc)\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Ffwyypt\u002Findex.html) 服务应用平台\n\n*   [市农业农村专家库](https:\u002F\u002Fsnyj.nyncj.gz.gov.cn\u002Fportal\u002F \"市农业农村专家库\")\n*   [财政专项资金申请](https:\u002F\u002F112.94.68.237\u002Fportal\u002F \"财政专项资金申请\")\n*   [农产品价格采集系统](https:\u002F\u002Fwww.abuya.com.cn:6888\u002Fjsp\u002Flogin\u002Flogin.jsp \"农产品价格采集系统\")\n*   [农机购置补贴平台](http:\u002F\u002F210.76.75.39:2018\u002FGouZBT2021To23_Fromal\u002F \"农机购置补贴平台\")\n*   [集体产权流转平台](https:\u002F\u002F112.94.70.20:8088\u002F \"集体产权流转平台\")\n*   [智慧畜牧兽医平台](http:\u002F\u002Fwww.gzxm.org.cn\u002Famaq-sso-server\u002Flogin;jsessionid=2D3E225ED88BC1346655DD3033C2FD61 \"智慧畜牧兽医平台\")\n*   [农博士综合服务平台](https:\u002F\u002Fwww.gznbs.com:8081\u002Fnyjnewnbs\u002Fjsp\u002Flogin\u002Flogin.jsp \"农博士综合服务平台\")\n\n##### [更多>>](https:\u002F\u002Fwww.abuya.com.cn:6888\u002Fweb\u002Findex.html) 数据发布\n\n*   [黄沙水产交易市场行情分析（9月14日—9月18日)](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fsjfb\u002Fgzscxq\u002Fcontent\u002Fpost_11010229.html \"黄沙水产交易市场行情分析（9月14日—9月18日)\")\n*   [广州花卉研究中心有限公司市场行情分析（9月14日—9月18日)](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fsjfb\u002Fgzscxq\u002Fcontent\u002Fpost_11009991.html \"广州花卉研究中心有限公司市场行情分析（9月14日—9月18日)\")\n\n*   [三农微博](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fwfw\u002Fsnwb\u002Findex.html)\n*   [三农微信](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fwfw\u002Fsnwx\u002Findex.html)\n*   [农药使用名录](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fnysyml\u002Findex.html)\n*   [主推技术](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fztjs\u002Findex.html)\n*   [主导品种](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fzdpz\u002Findex.html)\n*   [农事管理](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fnsgl\u002Findex.html)\n*   [下载区](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fxzq\u002Findex.html)\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Findex.html) 常用数据\n\n*   [广州农产品价格数据发布](https:\u002F\u002Fwww.abuya.com.cn:6888\u002Fweb\u002Findex.html \"广州农产品价格数据发布\")\n*   [广州市畜禽屠宰企业名单（2026年9月）](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_10708260.html \"广州市畜禽屠宰企业名单（2026年9月）\")\n*   [广州市2025年12月水产养殖病害预测预报](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_10602819.html \"广州市2025年12月水产养殖病害预测预报\")\n*   [广州市2024年10月水产养殖病害预测预报](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_9924348.html \"广州市2024年10月水产养殖病害预测预报\")\n*   [广州市2023年12月水产养殖病害预测预报](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_9376420.html \"广州市2023年12月水产养殖病害预测预报\")\n*   [【一图读懂】《2023年广州市农业主导品种和主推...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_9131131.html \"【一图读懂】《2023年广州市农业主导品种和主推技术》\")\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Fsn\u002Findex.html)三农\n\n*   [广州农情](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fgznq\u002Findex.html \"广州农情\")\n*   [三品一标](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fspyb\u002Findex.html \"三品一标\")\n*   [省名特优新](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fsmtyx\u002Findex.html \"省名特优新\")\n*   [“粤字号”农业品牌](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fsmpcp\u002Findex.html \"“粤字号”农业品牌\")\n*   [畜禽屠宰企业](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fxqtzqy\u002Findex.html \"畜禽屠宰企业\")\n\n 您现在的位置： [首页](http:\u002F\u002Fnyncj.gz.gov.cn\u002F)>[政务](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Ftzgg\u002F)>[政务要闻](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002F)>[工作动态](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fgzdt\u002F)\n\n**政务要闻**[工作动态](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fgzdt)[区镇连线](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fqzdt)[政声传递](http:\u002F\u002Fwww.gd.gov.cn\u002Fxxts\u002F)[图片新闻](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Ftpxw)[三农要闻](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fsnyw)[他山之石](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Ftszs)[媒体报道](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fmtbd)**政务要闻**\n\n## 农技赋能助丰收 精准服务保粮安 —— 广州市农业农村科学院赴花都区开展晚造粮食生产技术指导\n\n###### 来源： **广州市农业农村科学院** 时间： 2026-09-21 14:56:53  浏览次数：_35_\n\n为深入贯彻落实国家粮食安全战略，进一步压紧压实粮食生产责任，持续巩固晚造粮食生产良好态势，近日，广州市农业农村科学院联合花都区农业技术管理中心，组建农技专家服务团队，深入花都区粮食生产核心基地，开展常态化、精准化田间技术帮扶和实地指导服务，全力护航秋粮丰产丰收。\n\n当前正值晚造粮食作物生长发育的关键时期，田间管理的质量直接关系秋粮收成。专家团队先后深入水稻、鲜食玉米连片种植区，实地察看作物长势，系统掌握田间管护情况，围绕水肥精准调控、绿色病虫害防控、自留种提纯复壮、粮食产销对接、惠农补贴政策申报等现实问题，现场为种植户答疑解惑，切实提升农户科学种粮技术水平，充分激发各类种粮主体的生产积极性。\n\n在花东镇水稻种植片区，晚造水稻已进入穗分化关键阶段。农技专家现场指导农户科学追施促穗肥，为实现穗大粒多、稳产丰产筑牢生长根基。针对采用自留种的田块，专家开展手把手实操教学，现场示范田间除杂技术，严守种子纯度关口，从源头上保障稻谷品质。在花山镇鲜食玉米生产基地，专家引导种植户实行错期分批播种，有效规避大批量集中上市带来的收购价格下行风险。同时，叮嘱种植主体密切监测草地贪夜蛾、茎腐病、南方锈病等重大病虫害发生动态，秉持“预防为主、综合防治”理念，扎实做好病虫害监测与防控工作。\n\n广州市农业农村科学院长期扎根花都农业生产一线，持续开展粮食作物新优品种选育与示范推广，多方联动共建多处百亩连片水稻示范田，推进规模化示范种植。针对华南地区台风多发、降雨集中，水稻成熟收割期易发生倒伏、穗上发芽等突出生产难题，下一步，将以品种改良创新、机插秧技术集成应用为双抓手，协同攻关破解生产痛点，充分挖掘粮食单产提升潜力，全力保障广州市粮食生产安全。\n\n![Image 11: 图片1.png](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fimg\u002F1\u002F1692\u002F1692565\u002F11014185.png)![Image 12: 图片2.png](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fimg\u002F1\u002F1692\u002F1692566\u002F11014185.png)\n\n[![Image 13: 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\"隐私安全\")|[使用帮助](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fwzxg\u002Fsybz\u002Findex.html \"使用帮助\")\n\n广州市农业农村科学院版权所有，未经授权禁止复制或建立镜像\n\n主办单位：广州市农业农村局  运营维护及技术支持：广州市农业农村科学院  访问人数统计：_9684890_\n\n[![Image 24](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002Fghs.png)公安机关备案号：44011102001333](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=44011102001333)[粤ICP备20004752号-1](http:\u002F\u002Fbeian.miit.gov.cn\u002F)网站标识码：4401000027\n\n[![Image 25](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002Fnred.png)](http:\u002F\u002Fbszs.conac.cn\u002Fsitename?method=show&id=080C6254AE2B3C44E053022819AC84D6)\n\n![Image 26](https:\u002F\u002Fzfwzgl.www.gov.cn\u002Fexposure\u002Fimages\u002Fjiucuo.png?v=4401000027)\n\n![Image 27](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002Fwzafw.png)","广州市农业农村科学院","2026-09-18T00:00:00Z","报道",39,{"impact":170,"substance":170,"depth":71,"authority":12,"freshness":171,"relevant":19,"comment":172},8,7,"地市级农科院常规技术指导通稿，正文缺失、信息增量有限，仅具地方服务动态价值，不建议进入每日精选。",[174],{"name":165,"url":162},[24,176,177,178,179],"鲜食玉米","晚造粮食生产","农技指导","广州花都",[181,182],"广州农科院 花都 晚造","水稻 鲜食玉米 技术指导","广州农科院花都晚造-3118","2026-09-22T00:05:36.766978Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":8,"content":190,"source_name":191,"source_url":8,"published_at":192,"category":167,"cover_url":8,"hotness":12,"is_selected":13,"score":193,"score_detail":194,"sources":196,"tags":198,"search_phrases":201,"slug":204,"view_count":33,"doi":8,"paper":8,"created_at":205},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。","## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z",76,{"impact":17,"substance":17,"depth":84,"authority":129,"freshness":71,"relevant":19,"comment":195},"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[197],{"name":191,"url":188},[134,135,24,199,200],"精准农业","无人機巡田",[202,203],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101","2026-09-22T00:05:33.996455Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":8,"content":8,"source_name":211,"source_url":8,"published_at":212,"category":167,"cover_url":8,"hotness":12,"is_selected":213,"score":214,"score_detail":215,"sources":218,"tags":220,"search_phrases":225,"slug":228,"view_count":33,"doi":8,"paper":8,"created_at":229},3032,"中国农科院基因组所超级稻种质创新团队揭示水稻器官边界建成的分子机制登The Plant Cell","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F94a1ad7de45342d0b77e6c154d55e0ab.htm","中国农业科学院农业基因组研究所超级稻种质创新团队近日揭示水稻器官边界建成的分子机制，相关研究成果发表在《植物细胞》（The Plant Cell）上。研究发现水稻中存在一类转录因子，在器官边界特异表达，像\"分子开关\"调控下游功能基因，可同时调控分蘖起始、叶枕发育等多类边界发育过程，最终决定水稻分蘖数、叶片夹角等多项核心产量性状。研究完善了单子叶植物器官边界发育的基础理论，为禾本科作物的株型改良提供了基因靶标，得到国家自然科学基金、中国农业科学院科技创新工程、广东省重点领域研发计划等项目资助。","中国农业科学院","2026-09-20T00:00:00Z",true,87,{"impact":17,"substance":17,"depth":16,"authority":216,"freshness":12,"relevant":19,"comment":217},15,"国家级科研机构在核心期刊发表的水稻器官边界分子机制突破，为禾本科作物株型改良提供基因靶标，专业增量与权威性俱佳，值得进入每日精选。",[219],{"name":211,"url":209},[24,221,222,223,224],"种业振兴","分子育种","农业科技","株型改良",[226,227],"中国农科院基因组所 水稻 器官边界","The Plant Cell 水稻 分蘖 分子机制","中国农科院基因组所水稻器官边界-3032","2026-09-21T00:04:33.543357Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":8,"source_name":236,"source_url":233,"published_at":237,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":80,"score_detail":238,"sources":241,"tags":243,"search_phrases":245,"slug":248,"view_count":33,"doi":249,"paper":250,"created_at":265},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",{"impact":82,"substance":239,"depth":84,"authority":85,"freshness":129,"relevant":19,"comment":240},21,"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[242],{"name":236,"url":233},[134,135,24,244,91],"多模态融合",[246,247],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":249,"openalex_id":251,"authors":252,"venue":236,"cited_by_count":33,"oa_url":233,"card":259,"direction":263,"ingested_from":68},"W7213619014",[253,255,257],{"name":254,"orcid":8},"Nurfadhilah Mardianti Andini",{"name":256,"orcid":8},"Mike Yuliana",{"name":258,"orcid":8},"Moch. Zen Samsono Hadi",{"tldr":260,"method":261,"finding":262,"direction":263,"opportunity":264},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","智慧农业 \u002F 农业物联网","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","2026-09-20T23:30:08.419613Z"]