[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-410":3,"related-410":68},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":62,"slug":65,"view_count":66,"doi":8,"paper":8,"created_at":67},410,"'空地协同'助再生稻增产增效——安徽农业大学吴文革团队头季稻桩碾压率降低75%","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260807\u002F20260807_3\u002Fnmrb_20260807_13371_3_2085482741848182821.html","农民日报8月7日报道:安徽怀宁县平山镇万亩再生稻示范基地,早熟区头季稻平均亩产干谷746.6公斤,无人机精准作业,宽窄行稻株排列整齐、长势喜人。安徽农业大学吴文革教授团队历经十余年攻关,创新形成'空地协同'一体化技术方案:在育秧环节,自主研制整盘振动式水稻精准育秧播种装备,实现每穴2-4粒精量播种;在机插环节,通过宽窄行种植预先为收割机履带留出行走通道;收获环节,头季稻收割前,无人机对田块进行航测,精准识别预留宽行位置,提取北斗坐标后智能规划收获路径。应用该方案后,头季稻桩机收碾压率较传统模式降低75%,再生季亩产提高约50%,两季亩产稳定在950公斤左右。",null,"农民日报 2026-08-07","2026-08-07T01:00:00Z","报道",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,20,16,12,5,1,"报道了安徽农大团队在再生稻领域的创新技术，具有省级农业科技进展价值，数据详实，来源权威，但时效性稍差。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","再生稻","安徽省农科院","空地协同","吴文革",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},15,true,[27],[34,27],[27],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"F5",3,[],[55],"收割机",[],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],[63,64],"安徽省农科院 智慧农业 空地协同 再生稻","安徽省农科院 智慧农业","安徽省农科院智慧农业空地协同再生稻-410",4,"2026-08-11T23:57:01.875298Z",{"total":69,"page":21,"page_size":69,"items":70},6,[71,182,224,278,353,429],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":92,"secondary_topics":93,"classification":100,"search_phrases":147,"slug":150,"view_count":151,"doi":152,"paper":153,"created_at":181},3477,"Ratoon rice under a changing climate: Physiological responses and management strategies","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16473","Ratoon rice has gained attention as a sustainable strategy for maintaining rice productivity under changing climatic conditions, where rising temperatures, irregular rainfall, droughts, floods and other extreme weather events threaten crop productivity and global food security. Ratoon rice has emerged as a resource efficient and climate smart production method as it makes use of the regenerative ability of plants to generate a second harvest from stubble of the main crop. Compared to conventional rice cultivation, ratoon rice requires less labor, reduced inputs and a shorter growing period while improving land, water and nutrient use efficiency. However, the physiological responses of rice plants to changing environmental conditions are important for the success of ratoon crops. Bud regeneration, carbohydrate reserve mobilisation, hormonal regulation, photosynthetic efficiency and source sink relationship play a critical role in determining ratoon establishment and yield under climatic stress. Ratoon rice is more resilient to heat, drought, flooding and other environmental challenges when the appropriate management techniques such as optimal stubble height, balanced nutrition, effective irrigation and plant growth regulators are used. Furthermore, improving soil health through organic amendments and beneficial microbial interactions contributes to greater stress tolerance and sustainable productivity. Ratoon rice production is still limited despite its potential due to automation issues, yield instability and climate related hazards. To enhance the adaptation under shifting climatic conditions, future research should concentrate on creating climate-resilient ratoon cultivars, precision nutrient and water management technologies and decision support systems. This review summarises the physiological response of ratoon rice to climate change and highlights effective management strategies that can enhance productivity, resource use efficiency and sustainability, thereby supporting resilient rice production and long term food security.","再生稻作为一种在气候变化条件下维持水稻生产力的可持续策略而受到关注，气温升高、降雨不规律、干旱、洪涝及其他极端天气事件正威胁作物生产力和全球粮食安全。再生稻已成为一种资源高效且气候智慧型的生产方式，因为它利用植物的再生能力，从主作物的稻桩上获得第二次收获。与传统水稻栽培相比，再生稻所需劳动力更少、投入更低、生育期更短，同时提高了土地、水分和养分利用效率。然而，水稻植株对环境条件变化的生理响应对于再生稻作的成功至关重要。在气候胁迫下，芽再生、碳水化合物储备动员、激素调控、光合效率及源库关系在决定再生稻建成和产量方面发挥着关键作用。当采用适宜的管理技术，如最佳留桩高度、平衡营养、有效灌溉和植物生长调节剂时，再生稻对高温、干旱、洪涝及其他环境挑战具有更强的适应力。此外，通过有机改良和有益微生物互作改善土壤健康，有助于提高抗逆性和可持续生产力。尽管再生稻生产潜力巨大，但由于自动化问题、产量不稳定及气候相关灾害，其发展仍然受限。为增强在不断变化的气候条件下的适应性，未来研究应集中于培育气候韧性再生稻品种、精准养分和水分管理技术及决策支持系统。本综述总结了再生稻对气候变化的生理响应，并重点介绍了能够提高生产力、资源利用效率和可持续性的有效管理策略，从而支持具有韧性的水稻生产和长期粮食安全。","Plant Science Today","2026-09-23T00:00:00Z","论文",73,{"impact":45,"substance":17,"depth":82,"authority":83,"freshness":84,"relevant":21,"comment":85},17,13,8,"综述系统梳理再生稻应对气候变化的生理机制与管理策略，专业性强但属学术综述，产业即时影响有限。",[87],{"name":77,"url":74},[26,89,27,90,91],"粮食安全","气候变化","水稻栽培",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[94],{"code":95,"slug":96,"name":97,"path":98,"parent_code":99},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":13,"version":40,"method":41,"evidence":101},{"candidates":102,"agricultural_evidence":46},[103,108,116,123,130,137,143],{"code":32,"score":104,"supported":46,"title":105,"abstract":106,"tags":107},18,[33],[34,27,33],[27],{"code":95,"score":12,"supported":46,"title":109,"abstract":110,"tags":115},[],[90,111,112,113,114],"干旱","洪涝","climate change","drought",[90],{"code":117,"score":118,"supported":46,"title":119,"abstract":120,"tags":122},"A8",7,[],[89,121],"food security",[89],{"code":124,"score":69,"supported":46,"title":125,"abstract":126,"tags":129},"H2",[],[127,128],"土壤","soil",[],{"code":131,"score":69,"supported":46,"title":132,"abstract":133,"tags":136},"H3",[],[134,135],"灌溉","irrigation",[],{"code":138,"score":52,"supported":46,"title":139,"abstract":140,"tags":142},"I2",[],[141],"decision support",[],{"code":58,"score":21,"supported":13,"title":144,"abstract":145,"tags":146},[],[],[26],[148,149],"再生稻 气候变化 栽培管理","Plant Science Today 再生稻","再生稻气候变化栽培管理-3477",2,"10.14719\u002Fpst.16473",{"doi":152,"openalex_id":154,"authors":155,"venue":77,"cited_by_count":171,"oa_url":172,"card":173,"direction":179,"ingested_from":180},"W7214098380",[156,159,162,165,168],{"name":157,"orcid":158},"R Abirami","https:\u002F\u002Forcid.org\u002F0009-0002-8075-9908",{"name":160,"orcid":161},"R Vigneshwari","https:\u002F\u002Forcid.org\u002F0000-0002-4417-1034",{"name":163,"orcid":164},"K. Malarkodi","https:\u002F\u002Forcid.org\u002F0000-0002-8974-9389",{"name":166,"orcid":167},"N Sakthivel","https:\u002F\u002Forcid.org\u002F0009-0005-8248-3812",{"name":169,"orcid":170},"Koothan Vanitha","https:\u002F\u002Forcid.org\u002F0000-0001-8516-9275",0,"https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16473\u002F16260",{"tldr":174,"method":175,"finding":176,"direction":177,"opportunity":178},"综述再生稻在气候变化下的生理响应与栽培管理策略，以提升产量与可持续性。","文献综述，聚焦再生芽、碳储备、激素、光合及源库关系等生理机制。","优化留桩高度、水肥与生长调节剂可增强再生稻抗逆性，但自动化与产量稳定性仍受限。","农业绿色发展与碳","可研发气候韧性再生稻品种及精准水肥决策支持系统，并探索微生物互作提升抗逆性。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:11.458343Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":8,"content":187,"source_name":188,"source_url":8,"published_at":189,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":190,"score_detail":191,"sources":194,"tags":196,"primary_topic":200,"secondary_topics":201,"classification":202,"search_phrases":219,"slug":222,"view_count":171,"doi":8,"paper":8,"created_at":223},5450,"福建农科院水稻所「禾两优631」再生稻示范 两季亩产 1364.52 公斤","https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fhtml\u002Ffjsnykxy\u002F2026-10-19\u002F849994658.html","10 月 15 日，福建省农业科学院水稻所在尤溪县西城镇麻洋村举行「禾两优631」作再生稻高产栽培示范验收会，中国科学院韩斌、钱前院士等到场测产。经测产该品种再生季亩产 484.72 公斤，加上 8 月头季亩产 879.8 公斤，两季合计亩产 1364.52 公斤，是麻洋村基地连续第 26 年开展再生稻高产示范的新进展。「禾两优631」为两系杂交中稻，2025 年通过福建省中稻审定（闽审稻 20251009），抗白叶枯病、中抗稻瘟病、再生力强，2026 年福建省农业主导品种。","10月15日，我院水稻所在尤溪县西城镇麻洋村举行新品种“禾两优631”作再生稻高产栽培示范验收会。中国科学院韩斌院士、钱前院士以及南京农业大学、中国农业科学院作物科学研究所、上海市农业科学院、福建省农业农村厅、福建省农学会和三明市农业农村局的专家莅临现场测产。谢华安院士，我院党委委员、副院长魏辉研究员到会指导，院科研处处长赵健研究员主持验收会。\n\n经测产，“禾两优631”再生季亩产达484.72公斤，加上今年8月头季亩产879.8公斤，两季合计亩产1364.52公斤。水稻所又一新品种实现再生稻栽培高产，也是该基地连续第26年开展再生稻高产栽培示范取得的新进展。验收专家们表示，“禾两优631”头季与再生季表现均衡，再生季484.72公斤的产量表明该品种具有较强再生力和推广潜力，也验证了配套栽培技术在大面积示范中的稳定性。两季合计1364.52公斤，对提升稻田综合效益、稳定粮食生产、促进农民增收具有重要意义。\n\n尤溪县是全国高产再生稻发祥地。自2000年以来，我院水稻所的科研人员风雨无阻、年复一年，在同一片土地上持续开展再生稻品种试验、高产栽培和示范推广。截至目前，麻洋村百亩示范基地已示范种植15个强再生力杂交稻品种。麻洋村百亩示范基地已成为福建再生稻品种与技术的重要展示窗口。谢华安院士团队经数十年持续攻关，不仅选育出一批高产、抗逆、优质、广适的强再生力水稻新品种；还系统构建了水稻再生力评价体系，创建了以“双调双控”为核心的再生稻高效生产关键技术体系和省工节本的机械化生产技术体系，相关科研成果获得福建省科技进步一等奖2项。这既是26年厚积薄发的集中体现，也是对持之以恒开展基础研究和应用推广的最好肯定。\n\n“禾两优631”是我院水稻所主持选育的两系杂交中稻品种，2025年通过福建省中稻审定（闽审稻20251009），进入国家长江中下游中籼迟熟组区域试验（第2年）。 “禾两优631”田间表现产量高（LAX1\u002FPTB1\u002FNOG1）、抗白叶枯病（Xa23）、中抗稻瘟病（Pi1\u002FPi2\u002FPikh）、再生力强（psr1）、适应性强（OsLG3）、抗倒伏，是一个综合性状优良的杂交稻品种。“禾两优631”被评为2024年全国种子双交会水稻地展评价优良品种、2025年福建省优质稻田间展示综合表现优异十大品种、2026年福建省农业主导品种。“禾两优631”的育成和应用，将为福建省乃至长江中下游地区的粮食生产作出更大贡献。\n\n站在新的历史起点上，水稻所将继续保持“咬定青山不放松”的战略定力，以更高标准、更实举措推进再生稻科研与产业高质量发展，在融合分子育种等现代技术手段强化育种攻关、推进农机农艺融合、切实提升生产效率和深化示范推广与产业带动等方面，加快技术成果在全省乃至南方稻区的推广应用，为南方稻区再生稻产业发展提供可借鉴的样板，为保障国家粮食安全、促进农业增效和农民增收作出新的更大贡献。（文\u002F水稻所 朱永生，图\u002F水稻所 蔡秋华）","福建省农业科学院 2026-10-19","2026-10-19T00:00:00Z",82,{"impact":16,"substance":16,"depth":18,"authority":192,"freshness":84,"relevant":21,"comment":193},14,"省级科研机构发布的新品种实收测产数据具体，两季亩产1364.52公斤且连续26年示范，具行业参考价值，值得进入每日精选。",[195],{"name":188,"url":185},[197,89,27,198,199],"种业振兴","水稻育种","高产示范",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":203},{"candidates":204,"agricultural_evidence":46},[205,210,214],{"code":32,"score":206,"supported":46,"title":207,"abstract":208,"tags":209},24,[34,27],[34,27],[27,198],{"code":117,"score":151,"supported":13,"title":211,"abstract":212,"tags":213},[],[],[89,199],{"code":215,"score":21,"supported":13,"title":216,"abstract":217,"tags":218},"F1",[],[],[197],[220,221],"禾两优631 再生稻","福建农科院 水稻所 尤溪","禾两优631再生稻-5450","2026-10-21T00:10:02.519850Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":8,"content":229,"source_name":230,"source_url":8,"published_at":231,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":232,"score_detail":233,"sources":236,"tags":238,"primary_topic":242,"secondary_topics":243,"classification":250,"search_phrases":273,"slug":276,"view_count":171,"doi":8,"paper":8,"created_at":277},5432,"广东农科院水稻所主办 2026 年再生稻技术培训和现场观摩会在始兴举行","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F573077.shtm","广东省农业科学院水稻研究所主办的 2026 年再生稻技术培训和现场观摩会在韶关市始兴县举行，面向粤北「一季有余、两季不足」光温受限区域挖掘周年粮食产能。始兴县澄江镇善亨村 750 亩再生稻示范基地在省农科院水稻所、省农技推广中心和省水稻产业技术体系技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种并开展品种对比试验。近年来始兴再生稻已连续 3 年实现头季、再生季两季亩产突破「吨粮」。","广东省农业技术推广中心种植业技术与种业推广部部长罗学梅在总结中充分肯定再生稻项目取得的技术突破和示范成效，并就下一阶段强化技术指导、完善示范推广体系、推动再生稻扩大应用等工作作出部署。他指出，要进一步加强科研、推广、种业和种植主体协同联动，加快适宜品种、先进技术和标准模式落地，为稳定粮食生产提供有力支撑。\n\n随后，与会人员前往始兴县澄江镇善亨村750亩再生稻示范基地开展现场观摩。该基地在广东省农业科学院水稻研究所、广东省农业技术推广中心以及广东省水稻产业技术体系的技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种，并开展品种对比试验。技术人员走进田间，结合不同品种长势，围绕品种筛选、栽培管理、促芽肥施用、病虫害防控和机械化收割等关键环节进行讲解，并面对面解答参会人员提出的生产问题。\n\n参训人员沿着田埂仔细察看苗情和穗型，认真听取田间讲解，踊跃交流技术要点，现场学习氛围浓厚。此次活动将室内授课与田间教学有机结合，进一步提升了基层农技人员和种植主体对再生稻技术的认识，增强了推广应用信心。与会人员表示，将结合本地生产实际，把学习成果转化为田间管理措施，积极提升再生稻种植水平。\n\n版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。","科学网 2026-10-17","2026-10-17T00:00:00Z",58,{"impact":18,"substance":83,"depth":234,"authority":19,"freshness":69,"relevant":21,"comment":235},11,"省级科研机构主办的再生稻技术培训与田间观摩，有示范基地和品种对比等实质内容，但属常规推广活动，影响力限于区域。",[237],{"name":230,"url":227},[239,34,27,240,241],"广东","农技推广","粮食生产",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[244],{"code":245,"slug":246,"name":247,"path":248,"parent_code":249},"G1","extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"status":39,"locked":13,"version":40,"method":41,"evidence":251},{"candidates":252,"agricultural_evidence":46},[253,259,264,269],{"code":32,"score":254,"supported":46,"title":255,"abstract":256,"tags":258},27,[34,27],[34,257,27],"杂交稻",[27,34],{"code":245,"score":45,"supported":46,"title":260,"abstract":262,"tags":263},[261],"技术培训",[240,261],[240],{"code":117,"score":66,"supported":46,"title":265,"abstract":266,"tags":268},[],[267],"粮食产能",[241],{"code":36,"score":21,"supported":13,"title":270,"abstract":271,"tags":272},[],[],[241],[274,275],"广东农科院 再生稻 始兴","再生稻 技术培训 观摩会","广东农科院再生稻始兴-5432","2026-10-21T00:10:01.007510Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":8,"source_name":284,"source_url":281,"published_at":285,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":286,"score_detail":287,"sources":289,"tags":291,"primary_topic":295,"secondary_topics":296,"classification":303,"search_phrases":333,"slug":336,"view_count":171,"doi":337,"paper":338,"created_at":352},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":104,"substance":16,"depth":104,"authority":19,"freshness":84,"relevant":21,"comment":288},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[290],{"name":284,"url":281},[26,292,34,293,294],"农业人工智能","产量预测","遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[297],{"code":298,"slug":299,"name":300,"path":301,"parent_code":302},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":304},{"candidates":305,"agricultural_evidence":46},[306,310,315,319,325,329],{"code":32,"score":45,"supported":46,"title":307,"abstract":308,"tags":309},[33],[34,33],[34],{"code":298,"score":84,"supported":46,"title":311,"abstract":312,"tags":314},[],[294,313],"remote sensing",[293,294],{"code":117,"score":69,"supported":46,"title":316,"abstract":317,"tags":318},[],[89,121],[],{"code":320,"score":52,"supported":46,"title":321,"abstract":322,"tags":324},"J1",[],[323],"农业规划",[],{"code":58,"score":21,"supported":13,"title":326,"abstract":327,"tags":328},[],[],[26],{"code":138,"score":21,"supported":13,"title":330,"abstract":331,"tags":332},[],[],[292],[334,335],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":337,"openalex_id":339,"authors":340,"venue":284,"cited_by_count":171,"oa_url":281,"card":346,"direction":350,"ingested_from":180},"W7222942064",[341,343],{"name":342,"orcid":8},"Nayana Venkatesh",{"name":344,"orcid":345},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":347,"method":348,"finding":349,"direction":350,"opportunity":351},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","农业遥感与作物表型","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":8,"source_name":359,"source_url":356,"published_at":189,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":360,"score_detail":361,"sources":364,"tags":366,"primary_topic":369,"secondary_topics":370,"classification":371,"search_phrases":410,"slug":413,"view_count":171,"doi":414,"paper":415,"created_at":428},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development",80,{"impact":104,"substance":16,"depth":104,"authority":83,"freshness":362,"relevant":21,"comment":363},9,"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[365],{"name":359,"url":356},[26,292,34,367,368],"数字孪生","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":372},{"candidates":373,"agricultural_evidence":46},[374,382,388,395,399,406],{"code":58,"score":375,"supported":46,"title":376,"abstract":378,"tags":381},19,[377],"digital twin",[379,367,380,377],"精准农业","precision agriculture",[367,26],{"code":32,"score":104,"supported":46,"title":383,"abstract":385,"tags":387},[384],"paddy",[34,386,33,384],"稻田",[34],{"code":389,"score":69,"supported":46,"title":390,"abstract":391,"tags":394},"F4",[],[392,393],"农药","pesticide",[],{"code":131,"score":69,"supported":46,"title":396,"abstract":397,"tags":398},[],[134,135],[],{"code":400,"score":69,"supported":46,"title":401,"abstract":402,"tags":405},"I5",[],[403,404],"物联网","IoT",[],{"code":138,"score":66,"supported":46,"title":407,"abstract":408,"tags":409},[],[141],[292],[411,412],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":414,"openalex_id":416,"authors":417,"venue":359,"cited_by_count":171,"oa_url":8,"card":422,"direction":179,"ingested_from":180},"W7222969403",[418,420],{"name":419,"orcid":8},"Jyoti Rani",{"name":421,"orcid":8},"Dr.  Narendra S.  Jadhav",{"tldr":423,"method":424,"finding":425,"direction":426,"opportunity":427},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","农业人工智能与决策模型","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","2026-10-20T23:30:12.636299Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":8,"content":187,"source_name":434,"source_url":8,"published_at":435,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":436,"score_detail":437,"sources":439,"tags":441,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":461,"slug":462,"view_count":171,"doi":8,"paper":8,"created_at":463},5291,"福建农科院水稻所\"双抗\"杂交稻新品种\"禾两优631\"两季亩产达1364.52公斤","https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fhtml\u002Fsdyjs\u002F2026-10-16\u002F1322929416.html","10月15日福建省农科院水稻所在尤溪县西城镇麻洋村举行\"禾两优631\"作再生稻高产栽培示范验收会,中国科学院韩斌院士、钱前院士等专家现场测产;经测产禾两优631再生季亩产达484.72公斤,加上8月头季亩产879.8公斤,两季合计亩产1364.52公斤;尤溪麻洋村百亩示范基地已示范种植15个强再生力杂交稻品种,谢华安院士团队长期攻关。","福建省农业科学院(2026-10-16)","2026-10-16T00:00:00Z",81,{"impact":16,"substance":16,"depth":18,"authority":83,"freshness":84,"relevant":21,"comment":438},"省级科研机构发布的新品种测产数据具体，两季亩产1364.52公斤且连续26年示范，具备行业参考价值，值得进入每日精选。",[440],{"name":434,"url":432},[197,89,27,442,443],"杂交水稻","高产栽培",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":447},{"candidates":448,"agricultural_evidence":46},[449,453,457],{"code":32,"score":254,"supported":46,"title":450,"abstract":451,"tags":452},[34,257],[34,257,27],[27,442],{"code":117,"score":151,"supported":13,"title":454,"abstract":455,"tags":456},[],[],[89,443],{"code":215,"score":21,"supported":13,"title":458,"abstract":459,"tags":460},[],[],[197],[220,221],"禾两优631再生稻-5291","2026-10-19T00:07:42.101315Z"]