[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3254":3,"related-3254":101},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":20,"doi":67,"paper":68,"created_at":100},3254,"End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112412","End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation。Computers and Electronics in Agriculture","端边云协同的水稻秧苗群体表型识别与诊断及其在立体水稻育秧中的应用。",null,"Computers and Electronics in Agriculture","2026-09-23T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,20,14,1,"核心期刊论文，提出端边云协同的水稻育苗群体表型识别诊断方法，方法新颖、时效性强，对智慧育苗有参考价值。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","边缘计算","水稻育苗","表型识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,48,53,58],{"code":31,"score":19,"supported":44,"title":45,"abstract":46,"tags":47},true,[32],[33,32],[],{"code":49,"score":20,"supported":14,"title":50,"abstract":51,"tags":52},"I1",[],[],[25],{"code":54,"score":20,"supported":14,"title":55,"abstract":56,"tags":57},"I2",[],[],[26],{"code":59,"score":20,"supported":14,"title":60,"abstract":61,"tags":62},"I5",[],[],[27],[64,65],"水稻育苗 群体表型 识别","垂直水稻育苗 端边云协同","水稻育苗群体表型识别-3254","10.1016\u002Fj.compag.2026.112412",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":92,"oa_url":9,"card":93,"direction":97,"ingested_from":99},"W7214024203",[71,73,76,78,80,82,84,86,89],{"name":72,"orcid":9},"Zheng Ma",{"name":74,"orcid":75},"Ruohan Wang","https:\u002F\u002Forcid.org\u002F0009-0009-7656-5132",{"name":77,"orcid":9},"Yuxuan Cai",{"name":79,"orcid":9},"Donghong Wen",{"name":81,"orcid":9},"Long Zhou",{"name":83,"orcid":9},"Yunzhe Huang",{"name":85,"orcid":9},"Yihua Liu",{"name":87,"orcid":88},"Zhongbin Su","https:\u002F\u002Forcid.org\u002F0000-0002-8966-8933",{"name":90,"orcid":91},"Hongbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7711-5744",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"提出端边云协同的水稻秧苗群体表型识别与诊断方法，用于垂直育秧场景。","端边云协同架构结合深度学习，对垂直育秧水稻秧苗群体图像进行表型识别与诊断。","端边云协同可实现水稻秧苗群体表型高效识别与诊断，支撑垂直育秧精准管理。","农业遥感与作物表型","可探索端边云协同下多尺度群体表型实时诊断与育秧环境调控闭环，弥补边缘算力与模型轻量化研究空白。","openalex","2026-09-23T23:30:01.424596Z",{"total":102,"page":20,"page_size":102,"items":103},6,[104,185,267,343,394,448],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":118,"tags":120,"primary_topic":123,"secondary_topics":124,"classification":131,"search_phrases":166,"slug":169,"view_count":92,"doi":170,"paper":171,"created_at":184},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":17,"substance":114,"depth":17,"authority":115,"freshness":116,"relevant":20,"comment":117},22,12,8,"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。",[119],{"name":110,"url":107},[25,26,33,121,122],"产量预测","遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[125],{"code":126,"slug":127,"name":128,"path":129,"parent_code":130},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":132},{"candidates":133,"agricultural_evidence":44},[134,139,144,151,158,162],{"code":31,"score":135,"supported":44,"title":136,"abstract":137,"tags":138},15,[32],[33,32],[33],{"code":126,"score":116,"supported":44,"title":140,"abstract":141,"tags":143},[],[122,142],"remote sensing",[121,122],{"code":145,"score":102,"supported":44,"title":146,"abstract":147,"tags":150},"A8",[],[148,149],"粮食安全","food security",[],{"code":152,"score":153,"supported":44,"title":154,"abstract":155,"tags":157},"J1",3,[],[156],"农业规划",[],{"code":49,"score":20,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[25],{"code":54,"score":20,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[26],[167,168],"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":170,"openalex_id":172,"authors":173,"venue":110,"cited_by_count":92,"oa_url":107,"card":179,"direction":97,"ingested_from":99},"W7222942064",[174,176],{"name":175,"orcid":9},"Nayana Venkatesh",{"name":177,"orcid":178},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":180,"method":181,"finding":182,"direction":97,"opportunity":183},"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。","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":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":193,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":247,"slug":250,"view_count":92,"doi":251,"paper":252,"created_at":266},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","2026-10-19T00:00:00Z",{"impact":17,"substance":114,"depth":17,"authority":194,"freshness":195,"relevant":20,"comment":196},13,9,"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。",[198],{"name":191,"url":188},[25,26,33,200,201],"数字孪生","病虫害预警",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":205},{"candidates":206,"agricultural_evidence":44},[207,215,221,228,235,241],{"code":49,"score":208,"supported":44,"title":209,"abstract":211,"tags":214},19,[210],"digital twin",[212,200,213,210],"精准农业","precision agriculture",[200,25],{"code":31,"score":17,"supported":44,"title":216,"abstract":218,"tags":220},[217],"paddy",[33,219,32,217],"稻田",[33],{"code":222,"score":102,"supported":44,"title":223,"abstract":224,"tags":227},"F4",[],[225,226],"农药","pesticide",[],{"code":229,"score":102,"supported":44,"title":230,"abstract":231,"tags":234},"H3",[],[232,233],"灌溉","irrigation",[],{"code":59,"score":102,"supported":44,"title":236,"abstract":237,"tags":240},[],[238,239],"物联网","IoT",[],{"code":54,"score":242,"supported":44,"title":243,"abstract":244,"tags":246},4,[],[245],"decision support",[26],[248,249],"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":251,"openalex_id":253,"authors":254,"venue":191,"cited_by_count":92,"oa_url":9,"card":259,"direction":265,"ingested_from":99},"W7222969403",[255,257],{"name":256,"orcid":9},"Jyoti Rani",{"name":258,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":260,"method":261,"finding":262,"direction":263,"opportunity":264},"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。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:12.636299Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":284,"secondary_topics":285,"classification":290,"search_phrases":312,"slug":315,"view_count":20,"doi":316,"paper":317,"created_at":342},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":115,"substance":18,"depth":17,"authority":19,"freshness":116,"relevant":20,"comment":277},"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[279],{"name":273,"url":270},[25,26,281,282,283],"可解释AI","农业气象","水稻产量预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[286],{"code":54,"slug":287,"name":288,"path":289,"parent_code":130},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":14,"version":39,"method":40,"evidence":291},{"candidates":292,"agricultural_evidence":44},[293,299,303,308],{"code":54,"score":208,"supported":44,"title":294,"abstract":296,"tags":298},[295],"machine learning",[297,295,245],"机器学习",[26,281],{"code":31,"score":19,"supported":44,"title":300,"abstract":301,"tags":302},[32],[33,32],[],{"code":304,"score":20,"supported":14,"title":305,"abstract":306,"tags":307},"H7",[],[],[282],{"code":49,"score":20,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[25],[313,314],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":316,"openalex_id":318,"authors":319,"venue":273,"cited_by_count":92,"oa_url":336,"card":337,"direction":265,"ingested_from":99},"W7221657053",[320,322,324,326,328,330,333],{"name":321,"orcid":9},"Noh-Hyun Lee",{"name":323,"orcid":9},"Wonjae Jeong",{"name":325,"orcid":9},"Hyeon-Ji Yang",{"name":327,"orcid":9},"Hana Cho",{"name":329,"orcid":9},"Hyeon-Yeong Ju",{"name":331,"orcid":332},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":334,"orcid":335},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":338,"method":339,"finding":340,"direction":263,"opportunity":341},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":9,"content":348,"source_name":349,"source_url":9,"published_at":274,"category":350,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":356,"tags":358,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":389,"slug":392,"view_count":92,"doi":9,"paper":9,"created_at":393},4950,"'袁稻思源'杂交水稻智能育种平台发布 业界首个智能育种平台落户湖南","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696096616441774642\u002F","10月11日,'袁稻思源'杂交水稻智能育种平台发布会在岳麓山实验室举行,由中国科学院自动化研究所联合湖南省农业科学院、北京中科闻歌科技股份有限公司共同研发。平台集成'问答助手''育种资源库''知识图谱''文献库'四大功能模块,收纳35570个水稻品种、689项性状、28648条基因数据和624757篇中英文文献。中国科学院院士李家洋、钱前、谢道昕,中国工程院院士柏连阳等共同见证。","来源：环球网\n\n水稻育种专家过去最头疼的是什么？翻资料。查一个品种亲本、找一个性状基因，动辄耗费大量时间。近期，中科闻歌带着决策智能入场了。10月11日，由湖南省农科院、中科院自动化所与中科闻歌联合打造的“袁稻思源”杂交水稻智能育种平台正式发布，把海量育种数据和AI决策能力送进育种一线。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F2397644cc26144379c7e432950a2deec~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792627757&x-signature=o8pNXOWnEqlIl4mnK12LZ8rHkeA%3D)\n**水稻育种：一个“数据富矿”**\n\n杂交水稻育种是一个典型的“数据富矿、决策洼地”领域。全球积累了海量品种、性状、基因和文献数据，但长期分散、难以检索，育种更多依赖科研人员的经验与记忆。为破解这一难题，今年7月2日，中国科学院自动化研究所、北京中科闻歌科技股份有限公司与湖南省农业科学院正式签约，围绕“人工智能+农业科研”开展联合攻关。“袁稻思源”正是这一合作的阶段性成果，平台名称取自袁隆平杂交水稻创新团队，寓意“饮水思源”，感怀袁隆平院士对中国水稻杂交育种作出的突出贡献。\n\n湖南省农科院院长余应弘介绍，“袁稻思源”是湖南落实国家种业振兴行动、推进育种数字化智能化转型的关键举措，旨在传承袁隆平院士科学精神，把数据资源和智能技术转化为育种创新的现实生产力。在中科闻歌的战略版图里，这次入局农业科研，是其“决策大模型”“AI国家队”能力向产业纵深的又一次延伸。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F328723c57ddce965a8efda71ad02f21a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792627757&x-signature=0csP6GpZya22ien7EebcU64%2FxRw%3D)\n**“袁稻思源”四大核心功能**\n\n“袁稻思源”平台构建四大核心功能模块，形成完整的智能育种知识和能力。\n\n数据资源库，将散落的育种数据统一“上牌”。目前平台收纳35570个水稻品种、689项性状、28648条基因数据和624757篇中英文文献。不育系按光敏、温敏、光温敏等维度精细分类，支持育种专种家像检索商品一样，快速定位目标种质。该数据资源库是“袁稻思源”平台的核心底座。\n\n基于资源库建立图谱，让品种、基因、文献等彼此“建联”。品种图谱勾画出每个品种的选育谱系和材料关系，基因图谱把基因、性状、品种与文献证据编织成一张可推理的网络。例如输入Y58S，系统可直接呈现该品种的千粒重、抗病性及相关基因。\n\n智能问答“小稻”，作为育种专家的对话式检索入口。专业科研人员只需自然语言，也可向平台提出专业问题，系统从文献和真实数据中生成答案，每条结论都标明来源，不凭空编造。在发布会现场，专家提问“第三代杂交水稻产业化有哪些障碍”，“小稻”当即从监管、生物安全、成本、知识产权等维度给出专业分析，并提示品种审定前需补充哪些数据。\n\n最后一个能力模块是育种智能体。品种审定智能体可根据品种性状，帮助科研人员预判审定通过率，杂交配组智能体可基于基因型数据预测候选组合的表现。育种家从“翻资料猜结果”变成“让AI算概率”，大幅提升工作效率。\n\n湖南省农科院副院长李莉现场演示了平台在育种资源查询、品种系谱呈现、基因关联展示等方面的能力，一套组合下来，育种专家从“盲试”变成“有据可依”。目前，平台已在第三代杂交水稻育种中开展应用示范，取得良好成效。\n\n**专家视角：这场变革会改变什么**\n\n平台发布当天，中国工程院院士、湖南省农科院党委书记柏连阳直言，“拥抱人工智能，是今天科研人员的必答题”。平台目前虽为初级版本，但已迈出AI赋能杂交水稻育种关键而可喜的一步。\n\n中国科学院院士、崖州湾国家实验室主任李家洋的判断更为前瞻：当前全球种业竞争加剧，育种正从“经验驱动”转向“数据驱动、设计育种”，“谁率先突破智能育种，谁就赢得下一轮农业产业革命的主动”。李家洋建议，平台可强化智能决策、深化协同创新、坚持服务产业，最终建设成为面向全国乃至全球的水稻智能育种公共产品。\n\n据中科闻歌方面表示，“袁稻思源”是决策智能第一次系统嵌入国家粮食安全的核心环节。从金融到政务，再到农业科研，AI行业正在把“数据决策”能力，嵌入更多关乎国计民生的领域。对育种行业而言，当AI助力科研人员更快、更准地“排雷”和“选路”，中国水稻育种的效率天花板或许才刚刚被打开。（心月）","环球网 今日头条 2026 年 10 月 13 日","报道",88,{"impact":353,"substance":354,"depth":17,"authority":194,"freshness":116,"relevant":20,"comment":355},26,23,"业界首个杂交水稻智能育种平台落地湖南，数据规模与AI决策功能具体，属种业数字化标志性进展，值得入选每日精选。",[357],{"name":349,"url":346},[25,26,359,360,361],"种业振兴","智能育种","杂交水稻",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":365},{"candidates":366,"agricultural_evidence":44},[367,371,376,381,385],{"code":31,"score":115,"supported":44,"title":368,"abstract":369,"tags":370},[33],[33],[361],{"code":372,"score":20,"supported":14,"title":373,"abstract":374,"tags":375},"F1",[],[],[359],{"code":377,"score":20,"supported":14,"title":378,"abstract":379,"tags":380},"F2",[],[],[360],{"code":49,"score":20,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[25],{"code":54,"score":20,"supported":14,"title":386,"abstract":387,"tags":388},[],[],[26],[390,391],"袁稻思源 杂交水稻 智能育种平台","湖南省农科院 中科闻歌 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每亩增收超百元","http:\u002F\u002Fwww.came.net.cn\u002FsecondLevelPage\u002Finfo\u002F218\u002F227189","10月8日,江苏盐城亭湖区智慧农场,'亭农云'平台整合土壤墒情传感器、田间气象站、虫情测报设备,24小时值守。AI模型对土壤养分、湿度、光照、虫情综合研判,自动生成'田间处方',对比传统种植,水肥资源浪费明显减少,粮食年均产量较普通地块提升8%以上,每亩增收超百元。一体化智能灌溉泵站配合电磁阀,一人10分钟能管好600亩田的水,新增有效耕种面积28亩,较传统渠系节地4.6%。","10月8日，在亭湖区智慧农场，亭湖区农业技术综合服务中心副主任陈乃祥蹲在东南角的田埂上，拨开一丛水稻，几株稻穗的颖壳上裹着黑色粉状物，捻一下，粉末沾在指尖，是黑穗病。\n\n“搁以前，四五个人跑两三天，才能巡完600亩稻田。”陈乃祥拍掉指尖的粉末站起身，“如今智能设备全天值守，系统自动识别，AI研判、精准施药。”\n\n话音刚落，一架植保无人机腾空而起，沿着预设航线飞向农田。\n\n这片稻田，是盐城以人工智能赋能传统农业的一个缩影。\n\n走进综合服务中心，屏幕上，整片稻田的状态尽收眼底。土壤墒情传感器、田间微型气象站、虫情测报设备24小时值守田间，农情数据实时回传“亭农云”平台，形成一个动态更新的农田数据库。\n\n![Image 1](http:\u002F\u002F202.127.42.160:3009\u002Fupload_file\u002F2026\u002F10\u002F14\u002F20261014144727766.png)\n\n“以前种地靠经验，施肥灌溉全凭眼力，多浇一寸水、多施一把肥是常事。”农场负责人王其玉回忆，每逢高温干旱或病虫害高发时节，管护人员要顶着烈日跑遍整片田，劳动强度大，还容易错过最佳管护窗口。\n\n如今，算法接过了老农的“经验账本”。AI模型对土壤养分、湿度、光照、虫情等数据综合研判，自动生成“田间处方”，哪一块田缺水、哪一片需要补肥、哪里存在虫害风险，一目了然。\n\n“传感器埋进土里，相当于给农田配备全天候‘体检医生’。”陈乃祥介绍，系统会根据水稻不同生长周期动态调整水肥供给，对比传统种植，水肥资源浪费明显减少，粮食年均产量较普通地块提升8%以上，每亩增收超百元。\n\n水的问题，同样交给算法。农场将原先“北大南小”的两块田整合为大田，创新采用“管渠结合、灌排一体”的田间渠系模式，斗渠改用大口径管道，农渠灌排两用。一体化智能灌溉泵站配合农渠首末端电磁阀，一人10分钟能管好600亩田的水。仅此一项，就新增有效耕种面积28亩，较传统灌排渠系模式节地4.6%。\n\n水稻植保，是水稻生产中的关键环节。传统模式靠人工巡查，覆盖面有限，发现虫害时往往已经扩散，用药量大、防治时机滞后。\n\n在这片智慧农场，一套“虫情监测—AI研判—无人机精准施药”的闭环管护体系，守护着稻田。AI图像识别系统自动统计虫口数量、识别害虫种类，田间摄像头实时捕捉稻株长势，结合气象站的温度、湿度、降雨数据，让预警跑在病害发生前。\n\n“系统通过对虫情数据的分析研判，确定防治对象、防治适期和频次，形成差异化植保作业方案，病虫害防治更精准。”市植物保护站副站长曹丽算了一笔账，农资成本降了，农药残留少了，稻米品质稳了，高温天气下人工植保的安全风险也避开了。\n\n管护体系，铺到了更多稻田里。全市依托“盐农云”平台搭建的智慧大田驾驶舱，将卫星遥感、无人机巡田、地面物联网设备拧成一股绳，1200余万亩稻麦实现全域动态监测。上线“盐小农”AI智能体，支持地块手绘评估、作物识别、长势监测、病害防治、产量预估等功能，通过遥感、传感、气象三重数据交叉校验，破解通用农业AI预测失真难题。\n\n视线转向广袤的大田，智能化装备成为田间主力军，弯腰劳作的农户越来越少。\n\n北斗导航赋能机器协同作业，覆盖耕地整地、播种插秧、植保防护、成熟收割全流程，自主规划路径、自动避障、自主作业。\n\n“规模化种植最头疼的是农时窗口短。”王其玉说，无人农机群组可多台设备协同开工，以往数十人才能干完的大田管护工作，如今少数技术人员在管控室就能统筹千亩农田。\n\n机器把重活接了过去，人也没闲着。“设备运维、模型调优、异常情况处置，离不开人。”陈乃祥现在去田里，多半是核实系统标出的异常点位，不用再一垄一垄地巡。\n\n机器承担起重复繁重的农事劳动，人转向技术管理与数据分析，“体力型”劳动力正转变成“技术型”。\n\n这背后，是全市层面的系统布局。今年3月，全省“人工智能+农业”推进会暨农业农村局长会议正式部署实施“人工智能+农业141”行动。盐城紧随其后，出台《盐城市科技赋能农业强市建设行动计划》，系统布局“人工智能+农业”。同时，依托“盐农云”农业农村大数据平台，探索“大屏管治理、手机惠农户、AI解难题”的智慧农业新路径。\n\n布局的成效，写在田野上。全市建成智慧农业园区18个、智慧农场63个，农业生产信息化率达60%，处于全省第一方阵；智能农机总量超2.3万台（套），农作物耕种收综合机械化率达90.5%。\n\n曾经“看天吃饭”的农田，正在AI与数字技术的加持下，开启“知天而作、数据种地”的全新农耕时代。\n\n亭湖区农业技术综合服务中心副主任陈乃祥说：“人工智能赋能农业，实验室算法再好，也要落到田地里接受检验。智慧农场的核心，不是简单把设备铺到田里，而是沉淀本土农田的真实数据，训练适配本地土壤、气候、作物的AI模型。\n\n传统农民靠代代积累的种地经验，智慧农业把千亩农田的经验沉淀为数据，转化成可复制的生产方案。我们当前遇到的最大课题，一方面是硬件设备要适应田间复杂多变的环境，另一方面，要让普通农户看得懂、用得上数字化工具，不能让智慧农业停留在示范地块。\n\n下一步，将持续推进农业数据融合，总结可复制的经验模式，将智慧农场的成功经验分享给周边种植大户、合作社，让更多农户享受到数字农业带来的红利，以数智力量夯实粮食安全根基。”","中国农业机械化信息网 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日",{"impact":455,"substance":114,"depth":402,"authority":195,"freshness":116,"relevant":20,"comment":456},24,"省级科研机构联合发布的杂交水稻智能育种平台，数据规模与功能模块具体，属种业数字化实质进展，值得进入每日精选。",[458],{"name":453,"url":451},[25,26,359,360,361],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":463},{"candidates":464,"agricultural_evidence":44},[465,469,474,478,482],{"code":31,"score":18,"supported":44,"title":466,"abstract":467,"tags":468},[33,219],[33],[361],{"code":372,"score":242,"supported":44,"title":470,"abstract":471,"tags":473},[],[472],"品种审定",[359],{"code":377,"score":20,"supported":14,"title":475,"abstract":476,"tags":477},[],[],[360],{"code":49,"score":20,"supported":14,"title":479,"abstract":480,"tags":481},[],[],[25],{"code":54,"score":20,"supported":14,"title":483,"abstract":484,"tags":485},[],[],[26],[487,488],"袁稻思源 智能育种平台","湖南省农科院 杂交水稻 智能育种","袁稻思源智能育种平台-4852",2,"2026-10-14T00:04:07.585104Z"]