[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-977":3,"related-977":126},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":33,"secondary_topics":39,"classification":45,"search_phrases":120,"slug":123,"view_count":124,"doi":8,"paper":8,"created_at":125},977,"“十里不同天”也能做出大模型 贵州以数据要素开辟山地智慧农业新空间","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260827\u002F20260827_1\u002Fnmrb_20260827_13389_1_2092723210403549199.html","清镇骆家桥蔬菜基地、安顺数智大米三万余亩分散稻田、湄潭茶园、黔东南蓝莓产业园，山地设施农业种植定额大模型、山地气象大模型、蛋链AI大模型、山地浆果专属大模型等一批本土行业大模型多点开花，覆盖蔬菜、茶叶、中药材、畜禽养殖、水稻种植、蓝莓等山地特色主导产业。麻江县搭建蓝莓产业互联网大数据服务平台，收录117类蓝莓种质资源数据；贵州两山天麻集团搭建区块链数字平台实现天麻全流程可追溯；山地气象数据集完成千万元级数据资产入表，贵州积极推进农业数据资产入表试点。贵州坚持普惠导向，常态化开展数字化种植技能培训，让山区小农户共享数字红利。",null,"农民日报 2026-08-27 第1版 · 2026-08-27","2026-08-27T00:00:00Z","报道",10,true,87,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},24,22,18,13,1,"央媒报道贵州山地智慧农业多行业大模型应用与数据资产入表，具省级产业级影响，信息详实，时效满分。",[23],{"name":9,"url":6},[25,26,27,28,29,30,31,32],"数据要素","蓝莓","贵州","农业","山地智慧农业","山地大模型","茶园","数据资产入表",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[40],{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",{"status":46,"locked":47,"version":48,"method":49,"evidence":50},"classified",false,"2026-10-22.2","rules",{"candidates":51,"agricultural_evidence":13},[52,58,63,70,78,85,91,97,103,109,115],{"code":34,"score":53,"supported":13,"title":54,"abstract":56,"tags":57},11,[55],"大模型",[55],[],{"code":41,"score":59,"supported":13,"title":60,"abstract":61,"tags":62},9,[25],[],[25],{"code":64,"score":65,"supported":13,"title":66,"abstract":68,"tags":69},"I1",8,[67],"智慧农业",[],[],{"code":71,"score":72,"supported":13,"title":73,"abstract":74,"tags":77},"A1",6,[],[75,76],"水稻","稻田",[],{"code":79,"score":80,"supported":13,"title":81,"abstract":82,"tags":84},"B1",3,[],[83],"蔬菜",[],{"code":86,"score":80,"supported":13,"title":87,"abstract":88,"tags":90},"B3",[],[89],"茶叶",[],{"code":92,"score":80,"supported":13,"title":93,"abstract":94,"tags":96},"B6",[],[95],"中药材",[],{"code":98,"score":80,"supported":13,"title":99,"abstract":100,"tags":102},"C",[],[101],"畜禽",[],{"code":104,"score":80,"supported":13,"title":105,"abstract":106,"tags":108},"F1",[],[107],"种质资源",[],{"code":110,"score":80,"supported":13,"title":111,"abstract":112,"tags":114},"G2",[],[113],"小农户",[],{"code":116,"score":20,"supported":47,"title":117,"abstract":118,"tags":119},"B2",[],[],[26],[121,122],"山地智慧农业 数据资产入表 山地大模型 数据要素","山地智慧农业 数据资产入表","山地智慧农业数据资产入表山地大模型数据要素-977",5,"2026-08-27T00:05:17.187627Z",{"total":72,"page":20,"page_size":72,"items":127},[128,241,282,345,384,433],{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":8,"source_name":134,"source_url":131,"published_at":135,"category":136,"cover_url":8,"hotness":12,"is_selected":47,"score":137,"score_detail":138,"sources":143,"tags":145,"primary_topic":149,"secondary_topics":150,"classification":157,"search_phrases":212,"slug":215,"view_count":216,"doi":217,"paper":218,"created_at":240},2160,"An Optimized Temporal Attention-based Deep Learning Framwork for Accurate Crop Yield Prediction from Multi-source Agricultural Data","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fag.d-6558","Background: In precision agriculture, crop yield prediction is crucial for supporting effective management of crops, timely settlement of farmers crop insurances, food security planning and serve as decision tool for sustainable agricultural achievements. However, prediction of crop yield is difficult due to the complex interaction behavior among diverse conditions in weather, soil properties, multispectral remote sensing observations and the dynamics in crop growth. Despite existing AI techniques like machine learning and deep learning models outperformed in predicting crop yield, they are often not able to capture the temporal dependencies. Furthermore, determining optimal hyperparameters for model training remains a challenging task, significantly influencing prediction accuracy, model convergence and generalization performance. Methods: To overcome this, the present study aimed to predict the crop yield using temporal attention (TA) based Artificial bee colony (ABC) optimized Bi - LSTM model using multi source agricultural data. The proposed model learns the sequential and temporal dependencies from both past and future time steps simultaneously, from the historical weather variables, soil properties, remote sensing derived normalized difference vegetation indices (NDVI) and historical yield data. The ABC algorithm was employed to automatically optimize the model hyperparameters with minimum run time. Result: The model is trained using district wise rice yield records of Tamil Nadu, historical weather variables, Sentinel-2 derived NDVI and soil properties. The dataset collected for major rice growing districts from 1995 to 2025 (30 years). The model shows the superior performance over conventional methods like random forest, support vector regression, deep neural network, convolutional neural network, long short-term memory models with lesser root mean square error (RMSE = 298.01 kg ha-1) and higher coefficient of determination, R2 (81.76%). Also, the training time of TA - ABC - Bi-LSTM model is comparatively less than the comparative models because of ABC optimization algorithm tuned the best hyperparameters compared to traditional trial and error manual method.","背景：在精准农业中，作物产量预测对于支持有效的作物管理、农民作物保险的及时理赔、粮食安全规划以及作为实现可持续农业的决策工具至关重要。然而，由于天气、土壤属性、多光谱遥感观测等多种条件之间复杂的交互作用以及作物生长的动态变化，作物产量预测仍然困难。尽管现有的机器学习、深度学习等人工智能技术在作物产量预测方面表现优异，但往往无法捕捉时间依赖性。此外，确定模型训练的最优超参数仍是一项具有挑战性的任务，显著影响预测精度、模型收敛性和泛化性能。方法：为解决上述问题，本研究旨在利用基于时间注意力（TA）的人工蜂群（ABC）算法优化的双向长短期记忆（Bi-LSTM）模型，结合多源农业数据预测作物产量。所提出的模型能够同时从历史和未来时间步中学习序列和时间依赖关系，数据来源包括历史气象变量、土壤属性、遥感衍生的归一化植被指数（NDVI）以及历史产量数据。采用ABC算法以最短运行时间自动优化模型超参数。结果：该模型使用泰米尔纳德邦各地区的稻米产量记录、历史气象变量、Sentinel-2衍生的NDVI和土壤属性进行训练。数据集收集了1995年至2025年（30年）主要水稻种植区的数据。该模型相比随机森林、支持向量回归、深度神经网络、卷积神经网络、长短期记忆模型等传统方法表现出更优的性能，均方根误差更低（RMSE = 298.01 kg ha-1），决定系数R²更高（81.76%）。此外，TA-ABC-Bi-LSTM模型的训练时间相比对比模型更短，因为ABC优化算法相比传统的试错手动方法能够调优出最佳超参数。","Agricultural Science Digest - A Research Journal","2026-09-10T00:00:00Z","论文",76,{"impact":139,"substance":140,"depth":18,"authority":141,"freshness":59,"relevant":20,"comment":142},16,21,12,"提出时序注意力结合人工蜂群优化的Bi-LSTM产量预测模型，基于30年多源数据取得较高精度，方法新颖且数据规模可观，对精准农业与农业保险决策有参考价值。",[144],{"name":134,"url":131},[67,146,75,147,148],"农业人工智能","遥感","作物产量预测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[151,156],{"code":152,"slug":153,"name":154,"path":155,"parent_code":38},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},{"status":46,"locked":47,"version":48,"method":49,"evidence":158},{"candidates":159,"agricultural_evidence":13},[160,168,174,179,185,192,199,206],{"code":34,"score":18,"supported":13,"title":161,"abstract":163,"tags":167},[162],"deep learning",[164,165,166,162],"深度学习","机器学习","machine learning",[146],{"code":152,"score":53,"supported":13,"title":169,"abstract":170,"tags":173},[],[147,171,172],"remote sensing","multispectral",[148,147],{"code":41,"score":53,"supported":13,"title":175,"abstract":177,"tags":178},[176],"agricultural data",[176],[],{"code":71,"score":12,"supported":13,"title":180,"abstract":181,"tags":184},[],[75,182,183],"稻米","rice",[75],{"code":64,"score":186,"supported":13,"title":187,"abstract":188,"tags":191},7,[],[189,190],"精准农业","precision agriculture",[67],{"code":193,"score":72,"supported":13,"title":194,"abstract":195,"tags":198},"A8",[],[196,197],"粮食安全","food security",[],{"code":200,"score":72,"supported":13,"title":201,"abstract":202,"tags":205},"H2",[],[203,204],"土壤","soil",[],{"code":207,"score":80,"supported":13,"title":208,"abstract":209,"tags":211},"H8",[],[210],"可持续农业",[],[213,214],"作物产量预测 农业人工智能 智慧农业 水稻","作物产量预测 农业人工智能","作物产量预测农业人工智能智慧农业水稻-2160",2,"10.18805\u002Fag.d-6558",{"doi":217,"openalex_id":219,"authors":220,"venue":134,"cited_by_count":231,"oa_url":131,"card":232,"direction":238,"ingested_from":239},"W7212119230",[221,223,225,227,229],{"name":222,"orcid":8},"M. 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Evidence from rural industrial integration, data factor allocation, and spatial effects in China","https:\u002F\u002Fwww.lnu.edu.cn\u002Finfo\u002F15114\u002F84907.htm","辽宁大学经济学院和军教授等在《Frontiers in Sustainable Food Systems》2026年第10卷发表的论文,基于中国省级面板数据,采用Super-SBM模型与GML指数,发现农业数字化通过农村产业融合中介与数据要素配置调节显著提升农业绿色全要素生产率,并对邻近地区产生正向溢出,为农业绿色转型提供理论依据。","Frontiers in Sustainable Food Systems 10(2026)",71,{"impact":18,"substance":140,"depth":290,"authority":19,"freshness":216,"relevant":20,"comment":291},17,"基于省级面板数据的实证研究，方法规范、结论有新意，但属学术论文且发布日期不明，时效性弱，适合作为研究参考而非每日精选头条。",[293],{"name":287,"url":285},[25,295,296,297,298],"空间溢出","农业数字化","绿色全要素生产率","农村产业融合",{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},[],{"status":46,"locked":47,"version":48,"method":49,"evidence":302},{"candidates":303,"agricultural_evidence":13},[304,308,313,318,322,326],{"code":41,"score":269,"supported":13,"title":305,"abstract":306,"tags":307},[],[25],[25],{"code":309,"score":20,"supported":47,"title":310,"abstract":311,"tags":312},"G4",[],[],[298],{"code":314,"score":20,"supported":47,"title":315,"abstract":316,"tags":317},"H4",[],[],[297],{"code":38,"score":20,"supported":47,"title":319,"abstract":320,"tags":321},[],[],[296],{"code":64,"score":20,"supported":47,"title":323,"abstract":324,"tags":325},[],[],[296],{"code":327,"score":20,"supported":47,"title":328,"abstract":329,"tags":330},"I4",[],[],[295],[332,333],"辽宁大学 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