[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2218":3,"related-2218":80},{"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":21,"tags":23,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":69,"created_at":79},2218,"《基于现役作物多模态无人机图像的大豆种子成分基因型感知预测》","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3121","Vasit Sagan等(圣路易斯大学、密苏里大学)开发端到端卷积神经网络框架,从基于无人机的多传感器图像及相关基因型和物候元数据估算大豆八种种子性状(蛋白质、油、蔗糖、纤维、淀粉、灰分、复合和简单碳水化合物)。2020-2021年密苏里州两个农场共采集372份大豆样本,在四个时间点捕获多光谱、热成像和LiDAR数据。蔗糖预测精度最高(R²=0.80),其次为简单碳水化合物(R²=0.70)和淀粉(R²=0.55)。多光谱图像在所有模态中提供最稳健的估计。",null,"Remote Sensing 2026年9月11日","2026-09-11T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,14,1,"多模态无人机遥感结合深度学习预测大豆种子成分，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","无人机","农业人工智能","农业遥感","大豆育种",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,54,59],{"code":30,"score":43,"supported":44,"title":45,"abstract":47,"tags":48},12,true,[46],"大豆",[46],[28],{"code":50,"score":19,"supported":13,"title":51,"abstract":52,"tags":53},"I1",[],[],[24],{"code":55,"score":19,"supported":13,"title":56,"abstract":57,"tags":58},"I2",[],[],[26],{"code":60,"score":19,"supported":13,"title":61,"abstract":62,"tags":63},"I3",[],[],[27],[65,66],"农业人工智能 农业遥感 大豆育种 智慧农业","农业人工智能 农业遥感","农业人工智能农业遥感大豆育种智慧农业-2218",3,{"doi":8,"openalex_id":8,"authors":70,"venue":8,"cited_by_count":71,"oa_url":8,"card":72,"direction":76,"ingested_from":78},[],0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"用无人机多模态图像和CNN预测大豆八种种子成分，蔗糖精度最高。","端到端CNN，融合多光谱、热成像、LiDAR及基因型物候元数据，372份样本。","多光谱最稳健，蔗糖R²=0.80，简单碳水化合物0.70，淀粉0.55。","农业遥感与作物表型","可探索多模态融合与基因型-表型关联，提升低精度性状预测及跨年份泛化。","agent","2026-09-12T00:06:40.739496Z",{"total":81,"page":19,"page_size":81,"items":82},6,[83,178,244,317,372,468],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":90,"score_detail":91,"sources":94,"tags":96,"primary_topic":98,"secondary_topics":99,"classification":109,"search_phrases":138,"slug":141,"view_count":142,"doi":143,"paper":144,"created_at":177},2320,"Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183121","Geospatial artificial intelligence (GeoAI) integrates multimodal remote sensing with deep learning to model complex agricultural systems at scale. Within this framework, accurate and non-destructive prediction of seed composition from in-season standing crops is essential for breeding and precision agriculture. This study developed an end-to-end convolutional neural network (CNN) framework to estimate eight seed traits (protein, oil, sucrose, fiber, starch, ash, complex and simple carbohydrates) from UAV-based multisensor imagery and associated genotype and phenological metadata. A total of 372 soybean samples were collected over two growing seasons (2020–2021) from two fields in Missouri, with UAV flights capturing multispectral (MSI), thermal (THR), and LiDAR (LDR) data at four time points spanning vegetative to reproductive growth stages. CNN models were trained in single- and multi-date configurations, incorporating genotype (GEN) and days after sowing (DAS) as additional features. The highest accuracy was achieved for sucrose (R2 = 0.80), followed by simple carbohydrate (R2 = 0.70) and starch (R2 = 0.55), with notable gains from GEN and DAS. Multi-date models incorporating earlier acquisitions often matched or outperformed later or all-date combinations. Among modalities, MSI provided the most robust estimates, with limited added value from LDR or THR. Unlike feature-based pipelines prone to multicollinearity, this image-to-trait approach enables automated, scalable prediction of soybean seed composition for in-season, field-level assessment.","地理空间人工智能（GeoAI）将多模态遥感与深度学习相结合，以在大尺度上对复杂农业系统进行建模。在该框架下，对当季未收获作物进行准确且无损的种子成分预测，对于育种和精准农业至关重要。本研究开发了一种端到端的卷积神经网络（CNN）框架，利用基于无人机（UAV）的多传感器影像以及相关的基因型和物候元数据，估算八种种子性状（蛋白质、油分、蔗糖、纤维、淀粉、灰分、复杂碳水化合物和简单碳水化合物）。研究在2020—2021两个生长季内，从密苏里州两块田地共采集372份大豆样本，无人机飞行在从营养生长期到生殖生长期的四个时间点获取了多光谱（MSI）、热红外（THR）和激光雷达（LDR）数据。CNN模型以单日期和多日期配置进行训练，并将基因型（GEN）和播种后天数（DAS）作为附加特征纳入。蔗糖的估算精度最高（R² = 0.80），其次为简单碳水化合物（R² = 0.70）和淀粉（R² = 0.55），且GEN和DAS的加入带来了显著提升。纳入早期采集数据的多日期模型往往与后期或全日期组合的表现相当，甚至更优。在各模态中，MSI提供了最稳健的估算结果，而LDR或THR的附加价值有限。与易受多重共线性影响的特征工程流程不同，这种从图像到性状的方法能够实现大豆种子成分的自动化、可扩展预测，用于当季田块尺度的评估。","Remote Sensing",80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":92,"relevant":19,"comment":93},8,"基于多模态无人机影像与基因型信息的大豆籽粒成分无损预测研究，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[95],{"name":89,"url":86},[24,25,26,97,28],"遥感",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[100,105],{"code":55,"slug":101,"name":102,"path":103,"parent_code":104},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":60,"slug":106,"name":107,"path":108,"parent_code":104},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":37,"locked":13,"version":38,"method":39,"evidence":110},{"candidates":111,"agricultural_evidence":44},[112,118,125,131],{"code":30,"score":113,"supported":44,"title":114,"abstract":116,"tags":117},15,[115],"soybean",[46,115],[28],{"code":55,"score":12,"supported":44,"title":119,"abstract":120,"tags":124},[],[121,122,123],"深度学习","deep learning","artificial intelligence",[26],{"code":60,"score":12,"supported":44,"title":126,"abstract":127,"tags":130},[],[97,128,129],"remote sensing","multispectral",[97],{"code":50,"score":132,"supported":44,"title":133,"abstract":134,"tags":137},7,[],[135,136],"精准农业","precision agriculture",[24],[139,140],"农业人工智能 大豆育种 智慧农业 无人机","农业人工智能 大豆育种","农业人工智能大豆育种智慧农业无人机-2320",4,"10.3390\u002Frs18183121",{"doi":143,"openalex_id":145,"authors":146,"venue":89,"cited_by_count":71,"oa_url":86,"card":171,"direction":76,"ingested_from":176},"W7212223520",[147,150,153,156,159,161,163,166,169],{"name":148,"orcid":149},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":151,"orcid":152},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":154,"orcid":155},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":157,"orcid":158},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":160,"orcid":8},"Aviskar Giri",{"name":162,"orcid":8},"Ashutosh Pawar",{"name":164,"orcid":165},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":167,"orcid":168},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":170,"orcid":8},"Felix Fritschi",{"tldr":172,"method":173,"finding":174,"direction":76,"opportunity":175},"用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。","372份大豆样本，多光谱\u002F热红外\u002FLiDAR影像加基因型与播后天数，端到端CNN","蔗糖预测最优(R²=0.80)，基因型与播期提升精度，多光谱最稳健。","可探索基因型-表型-环境交互建模，并迁移至其他作物与多时相早期预测。","openalex","2026-09-13T23:30:22.776177Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":8,"content":8,"source_name":183,"source_url":8,"published_at":184,"category":11,"cover_url":8,"hotness":12,"is_selected":44,"score":185,"score_detail":186,"sources":192,"tags":194,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":231,"slug":234,"view_count":142,"doi":8,"paper":235,"created_at":243},3125,"Full-Season Agentic Farm System FAIRY: Event-Driven Multi-Agent Orchestration for Soybean Production（FAIRY 全季节智能体农场系统：大豆生产的事件驱动多智能体编排）","https:\u002F\u002Faiagentstore.ai\u002Fai-agent-news\u002Ftopic\u002Fagriculture-food\u002F2026-08-11","哈尔滨工业大学研究人员发布并部署全栈、事件驱动的智能体引擎 FAIRY 于中国运行中的大豆研究农场。FAIRY 集成传感器、无人机、卫星植被产品、机械 API、作物过程模型与多智能体编排层，执行起垄→播种→灌溉→病虫害防治→收获→干燥→存储工作流，并在 64 垄研究场上跨 100 个全季节场景评估 9 个智能体控制器。这是智能体系统能够在大农业时间尺度和延迟结果下进行推理的最清晰演示之一，将农业中的智能体工作从实验室演示推进到全过程评估。同期 arXiv 推出 HarvestBench 基准将 LLM 驱动智能体置于农场网格世界（拖拉机面临动物选择绕行或碾压），结果显示模型差异巨大、对道德简报高度敏感、避免意愿具有可衡量的价格弹性。","Harbin Institute of Technology \u002F arXiv","2026-09-18T00:00:00Z",89,{"impact":187,"substance":188,"depth":189,"authority":18,"freshness":190,"relevant":19,"comment":191},24,23,19,9,"哈工大在真实大豆农场部署全季节事件驱动多智能体系统并配套 HarvestBench 基准，是农业智能体从演示走向全过程评估的标志性进展，专业深度与信息增量俱佳。",[193],{"name":183,"url":181},[24,195,26,27,196,197],"无人农场","多智能体","大豆生产",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":201},{"candidates":202,"agricultural_evidence":44},[203,207,213,218,223,227],{"code":30,"score":189,"supported":44,"title":204,"abstract":205,"tags":206},[46,115],[46],[],{"code":208,"score":68,"supported":44,"title":209,"abstract":210,"tags":212},"H3",[],[211],"灌溉",[],{"code":55,"score":214,"supported":13,"title":215,"abstract":216,"tags":217},2,[],[],[26,196],{"code":219,"score":19,"supported":13,"title":220,"abstract":221,"tags":222},"F6",[],[],[195],{"code":50,"score":19,"supported":13,"title":224,"abstract":225,"tags":226},[],[],[24],{"code":60,"score":19,"supported":13,"title":228,"abstract":229,"tags":230},[],[],[27],[232,233],"哈工大 FAIRY 大豆","HarvestBench 智能体 农场","哈工大FAIRY大豆-3125",{"doi":8,"openalex_id":8,"authors":236,"venue":8,"cited_by_count":71,"oa_url":8,"card":237,"direction":241,"ingested_from":78},[],{"tldr":238,"method":239,"finding":240,"direction":241,"opportunity":242},"部署全季节事件驱动多智能体系统FAIRY，在大豆农场完成从起垄到存储的全流程评估。","集成传感器、无人机、卫星、作物模型与机械API，用9个智能体控制器在64垄100","智能体系统能在大农业时间尺度下推理，模型差异大且对道德简报敏感。","农业人工智能与决策模型","可研究多智能体在长周期、延迟反馈农业任务中的鲁棒性与伦理约束机制。","2026-09-22T00:05:38.611717Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":8,"source_name":250,"source_url":247,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":252,"score_detail":253,"sources":257,"tags":259,"primary_topic":262,"secondary_topics":263,"classification":265,"search_phrases":283,"slug":286,"view_count":214,"doi":287,"paper":288,"created_at":316},3986,"YOLOv8-MST: a lightweight and efficient real-time detection and counting deep learning algorithm for double-grain peanut seeding","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112481","YOLOv8-MST: a lightweight and efficient real-time detection and counting deep learning algorithm for double-grain peanut seeding。Computers and Electronics in Agriculture","YOLOv8-MST：一种用于双粒花生播种的轻量高效实时检测与计数深度学习算法。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",72,{"impact":43,"substance":254,"depth":255,"authority":18,"freshness":190,"relevant":19,"comment":256},20,17,"核心期刊发表的轻量化实时检测计数算法，对花生精量播种智能化有实用价值，但属细分技术进展，影响面有限。",[258],{"name":250,"url":247},[24,26,260,135,261],"目标检测","花生播种",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[264],{"code":55,"slug":101,"name":102,"path":103,"parent_code":104},{"status":37,"locked":13,"version":38,"method":39,"evidence":266},{"candidates":267,"agricultural_evidence":44},[268,273,279],{"code":55,"score":269,"supported":44,"title":270,"abstract":271,"tags":272},16,[122],[121,122],[26,260],{"code":30,"score":18,"supported":44,"title":274,"abstract":276,"tags":278},[275],"peanut",[277,275],"花生",[],{"code":50,"score":214,"supported":13,"title":280,"abstract":281,"tags":282},[],[],[24,135],[284,285],"YOLOv8-MST 花生播种","双粒花生 实时检测计数","YOLOv8-MST花生播种-3986","10.1016\u002Fj.compag.2026.112481",{"doi":287,"openalex_id":289,"authors":290,"venue":250,"cited_by_count":71,"oa_url":8,"card":311,"direction":241,"ingested_from":176},"W7215133023",[291,294,296,298,300,302,304,306,309],{"name":292,"orcid":293},"Baogang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3430-7508",{"name":295,"orcid":8},"Enxi Liu",{"name":297,"orcid":8},"Yijia Chen",{"name":299,"orcid":8},"Zhipeng Feng",{"name":301,"orcid":8},"Yuhuan Li",{"name":303,"orcid":8},"Zunmin Liu",{"name":305,"orcid":8},"Jingchun Tang",{"name":307,"orcid":308},"Jiang Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6746-9987",{"name":310,"orcid":8},"Fazhan Yang",{"tldr":312,"method":313,"finding":314,"direction":241,"opportunity":315},"提出YOLOv8-MST轻量算法，实现双粒花生播种实时检测与计数。","基于YOLOv8改进的轻量深度学习模型，用于花生播种图像检测计数。","YOLOv8-MST在保持高精度的同时实现轻量化实时检测与计数。","可探索轻量模型在多种作物播种质量实时监测中的泛化与边缘部署。","2026-10-02T23:30:03.835851Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":8,"source_name":250,"source_url":320,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":333,"search_phrases":348,"slug":351,"view_count":142,"doi":352,"paper":353,"created_at":371},3971,"A gradient-enhanced lightweight network for peanut leaf disease segmentation in complex field scenarios","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112482","A gradient-enhanced lightweight network for peanut leaf disease segmentation in complex field scenarios。Computers and Electronics in Agriculture","一种用于复杂田间场景下花生叶部病害分割的梯度增强轻量级网络",69,{"impact":43,"substance":16,"depth":269,"authority":18,"freshness":190,"relevant":19,"comment":325},"发表于农业信息领域核心期刊的轻量级分割网络研究，方法有创新但属细分技术进展，公共影响有限。",[327],{"name":250,"url":320},[24,26,329,277,330],"病虫害识别","图像分割",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":334},{"candidates":335,"agricultural_evidence":44},[336,340,344],{"code":30,"score":113,"supported":44,"title":337,"abstract":338,"tags":339},[275],[277,275],[277],{"code":55,"score":68,"supported":13,"title":341,"abstract":342,"tags":343},[],[],[26,330,329],{"code":50,"score":19,"supported":13,"title":345,"abstract":346,"tags":347},[],[],[24],[349,350],"花生叶片病害 图像分割","花生 病害识别 轻量网络","花生叶片病害图像分割-3971","10.1016\u002Fj.compag.2026.112482",{"doi":352,"openalex_id":354,"authors":355,"venue":250,"cited_by_count":71,"oa_url":8,"card":366,"direction":241,"ingested_from":176},"W7215079154",[356,359,362,364],{"name":357,"orcid":358},"Song Liu","https:\u002F\u002Forcid.org\u002F0009-0002-6649-4990",{"name":360,"orcid":361},"Chunjian Hua","https:\u002F\u002Forcid.org\u002F0000-0001-5397-2688",{"name":363,"orcid":8},"俞建峰 Yu Jianfeng",{"name":365,"orcid":8},"Ying Chen",{"tldr":367,"method":368,"finding":369,"direction":241,"opportunity":370},"提出梯度增强轻量网络，用于复杂田间场景下花生叶部病害分割。","梯度增强模块与轻量网络结合，在复杂田间图像上分割花生叶部病害。","该方法在复杂田间场景下实现花生叶部病害的轻量高效分割。","可探索轻量分割模型在移动端部署及多作物病害泛化能力。","2026-10-02T23:30:02.241779Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":8,"content":377,"source_name":378,"source_url":8,"published_at":379,"category":380,"cover_url":8,"hotness":12,"is_selected":44,"score":381,"score_detail":382,"sources":386,"tags":388,"primary_topic":393,"secondary_topics":394,"classification":395,"search_phrases":463,"slug":466,"view_count":81,"doi":8,"paper":8,"created_at":467},3924,"山东省人民政府办公厅印发《关于大力发展智慧农业的实施意见》（鲁政办字〔2026〕85号）","https:\u002F\u002Fny.zibo.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_5879_3024122.html","《意见》提出到2030年山东将建设80个数字农业发展县，重点打造300个高水平智慧农业应用场景，全省农业生产信息化率达到60%以上，粮油作物水肥一体化应用面积达到2000万亩。围绕\"加力推进优势产业智慧转型\"，《意见》部署智慧赋能主要作物单产提升、设施农业改造升级、畜牧业节本增效、渔业产业提档升级四大方向，加快\"北斗+智慧农业\"应用推广，构建小麦、玉米、大豆、花生等主要作物数字化种植技术体系；建设省级种质资源数据库平台，推动经验育种向智能设计育种转变。","*   [](https:\u002F\u002Fny.zibo.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_5879_3024122.html)\n\n*   [](https:\u002F\u002Fny.zibo.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_5879_3024122.html)\n\n[首页](https:\u002F\u002Fny.zibo.gov.cn\u002Findex.html)>[三农业务](https:\u002F\u002Fny.zibo.gov.cn\u002Fcol\u002Fcol980\u002Findex.html)>[政策法规](https:\u002F\u002Fny.zibo.gov.cn\u002Fcol\u002Fcol5879\u002Findex.html)\n\n山东省人民政府办公厅关于大力发展智慧农业的实施意见\n* * *\n发布日期： 2026-09-30 13:58:48 浏览次数:字体：[ [大](javascript:doZoom(17))[中](javascript:doZoom(15))[小](javascript:doZoom(13)) ]\n鲁政办字〔2026〕85号\n\n各市人民政府，各县（市、区）人民政府，省政府有关部门、有关直属机构，有关高等院校：\n\n为贯彻落实党中央、国务院关于智慧农业的决策部署，深入实施“人工智能＋农业”行动，加快推进物联网、大数据、人工智能、机器人等现代信息技术在农业农村领域全方位、全链条推广应用，打造全国智慧农业发展高地，助力农业农村现代化，现提出如下实施意见。\n\n**一、加力推进优势产业智慧转型**\n\n（一）智慧赋能主要作物单产提升。利用卫星遥感、无人机、地面传感等技术，统筹布局“四情”监测网络，深化农业气象灾害风险减量工作，提升实时感知和预警能力。推进农业生产数字化改造，大力推广应用智能农业机械化、水肥一体化智慧管控等技术，加强农田新型基础设施建设和改造升级，探索粮食减损智能系统推广，到2030年粮油作物水肥一体化应用面积达到2000万亩。促进良田、良种、良机、良法与数字化技术有机融合，开展“北斗＋智慧农业”支撑粮油作物大面积单产提升应用推广，构建小麦、玉米、大豆、花生等主要作物数字化种植技术体系，提升大面积单产水平。（牵头单位：省农业农村厅，责任单位：省大数据局、省气象局）\n\n（二）智慧赋能设施农业改造升级。有序推进集中连片老旧低效设施蔬菜基地、果园、茶园等更新改造，建设标准化、智慧化现代设施。加快精准感知、智能调控等数字化技术装备集成应用，推广应用设施农业智能体，实现全生长周期智能化决策。引导规模化设施农业经营主体，部署应用生产经营全过程数字化管理平台，推进设施农业智慧化管理。（牵头单位：省农业农村厅，责任单位：省发展改革委、省自然资源厅、省大数据局）\n\n（三）智慧赋能畜牧业节本增效。引导规模养殖场（户）进行数字化改造，建立电子养殖档案，推进养殖、饲料兽药、屠宰等环节数据互联互通，实现直联直报。鼓励规模养殖场（户）、畜牧屠宰企业加强重点环节监测预警，完善从生产到屠宰的全链条数字化监管。大力推广精准饲喂、智能环控、自动巡检、个体识别等智慧化养殖装备与技术，全面提高综合养殖效益。（牵头单位：省畜牧局，责任单位：省农业农村厅、省大数据局）\n\n（四）智慧赋能渔业产业提档升级。分类有序推进池塘、工厂化等养殖模式智能化改造，科学配备饲料精准投喂、智能增氧、循环水处理、水质在线监测等智能化设施。深化海洋牧场现代化建设，因地制宜推广应用水域环境监测、水下巡检、智能采捕等先进技术装备。推进苗种繁育、健康养殖、产品加工、冷链物流、综合服务等全链条数字化融合发展。强化渔政执法数字化建设，加快海洋渔船设施装备更新改造，利用渔船监控平台，推广AIS防避碰和北斗船位终端的应用，保障渔业生产安全。（牵头单位：省农业农村厅，责任单位：省工业和信息化厅）\n\n**二、加力推进关键领域智慧化建设**\n\n（五）大力发展智能农机装备。加快发展适用于精准作业、果蔬采摘、病虫害巡检、畜禽健康监测、深海养殖监控等复杂场景的智能农机、农业机器人等智能装备。扩大无人机在植保、施肥、农情监测等方面普及应用。鼓励农机制造企业在农机装备出厂前加装北斗导航、传感器等智能化设备，推动农业基础设施与智能农机协同联动。支持“无人农场”建设。（牵头单位：省工业和信息化厅，责任单位：省科技厅、省农业农村厅、省畜牧局）\n\n（六）加快智慧育种技术应用。加大种质资源数据收集与利用力度，建设省级种质资源数据库平台。推动人工智能、大数据等新一代信息技术与传统育种深度融合。支持科研机构和种业企业联合打造智能育种平台，开发智能设计育种工具，推动经验育种向智能设计育种转变，加速育种创新。以寿光、兰陵、莘县、济阳等蔬菜生产大县为重点，推动集约化数字种苗工厂建设。（省发展改革委、省科技厅、省农业农村厅按职责分工负责）\n\n（七）推进农业农村管理服务数字化。建好用好“齐鲁农云”山东省数字农业农村综合管理服务平台，支持开展省市共建试点工作，打造覆盖全省、上下贯通、业务协同的农业农村“数字大脑”。拓展“鲁农码”应用领域，实现涉农业务“一码通行”、服务事项“一码通办”，推进全省农业农村用地“一张图”管理。推动现代农事综合服务中心数字化建设，促进农业社会化服务资源高效整合与供需智能匹配，推动涉农数据价值转化。（省自然资源厅、省农业农村厅、省大数据局按职责分工负责）\n\n（八）加快全产业链数字化改造。充分发挥农业“链主”企业、龙头企业等示范作用，带动产业链上下游各类经营主体进行数字化改造。支持农产品精深加工数字化技术及智能装备研发与应用，推动人工智能技术在农产品分选、加工过程质量控制、供应链协同优化等关键环节的深度融合，建设特色农业“大脑”。积极推动农产品质量安全承诺达标合格证信息化，推进“区块链＋”在农产品质量安全管理中的应用，探索建立食用农产品全链条信息化追溯体系。强化农产品市场监测预警，引导产业有序发展。建好用好“齐鲁农超”平台，加快推动农村电商平台经济高质量发展，不断提升“好品山东 优特农品”品牌影响力。（省发展改革委、省工业和信息化厅、省农业农村厅、省商务厅、省市场监管局、省畜牧局按职责分工负责）\n\n**三、加力推进数字生态效能释放**\n\n（九）强化数据治理。健全农业农村数据管理制度，完善数据资源目录，加强涉农数据治理，建设农业农村高质量数据集。探索构建涉农数据资产价值转化路径，推动数据在农业生产、经营、管理、服务等各环节的应用，充分发挥数据要素价值。搭建安全可信的数据共享与交易环境，构建农业行业可信数据空间，推进网络安全与数据安全防护体系建设。（省委网信办、省农业农村厅、省大数据局按职责分工负责）\n\n（十）强化技术创新。实施“人工智能＋农业”行动，鼓励企业与科研院所、高校联合开展农业传感器与专用芯片、模型算法等研发攻关与成果转化，深入推进人工智能大模型、大数据分析等技术在农业农村领域融合应用，加快构建“空天地一体化”数字农业技术体系。鼓励各地根据发展需求，有针对性地组织开展智慧农业技术研发。强化企业创新主体地位，积极培育智慧农业领军企业，整合科研院所、高校、龙头企业和新型生产经营组织等资源，形成“产学研用”一体化创新模式。（省教育厅、省科技厅、省农业农村厅、省畜牧局按职责分工负责）\n\n（十一）强化技术推广。鼓励涉农高校、科研机构、推广单位与经营主体开展智慧农业技术集成应用示范，实施省农业重大技术协同推广计划，遴选发布一批智慧农业主推技术。加快推进物联网、大数据、人工智能、北斗导航、高分遥感等现代信息技术推广应用。加快制（修）订智慧农业标准规范，建立健全智慧农业技术推广服务体系，加强智慧农业技术指导和推广。鼓励有条件的农业社会化服务主体加快应用智慧农业技术，提供农情遥感监测、农事作业、经营管理、防灾减灾等技术服务。（省教育厅、省科技厅、省农业农村厅、省畜牧局按职责分工负责）\n\n（十二）强化场景应用。开展实数融合促进行动，依托全省农业数字赋能平台等，推进数字农业发展县建设，探索形成区域性的智慧农业解决方案。加快粮食作物、设施园艺、畜牧水产、特色产业及智慧气象、农机服务等重点领域的场景创新与推广，指导各地根据产业发展实际，因地制宜打造一批技术融合度高、应用实效性强、复制推广性好的智慧农业应用场景。到2030年，建设80个数字农业发展县，重点打造300个高水平的智慧农业应用场景，全省农业生产信息化率达到60％以上。（省农业农村厅、省大数据局、省畜牧局、省气象局按职责分工负责）\n\n**四、切实做好组织保障工作**\n\n由农业农村部门牵头，加强与发展改革、财政、科技、工业和信息化、自然资源、畜牧兽医、大数据、气象等部门沟通配合，强化省、市、县三级协同联动，确保任务落到实处。统筹乡村振兴重大专项资金等各级相关领域财政资金推进智慧农业建设，重点支持关键技术攻关、数字农业发展县和应用场景建设。省级每年支持建设30个智慧农业应用场景。优化金融服务落实税收优惠政策，支持智慧农业科技企业和创新平台发展。推动高校、科研院所与智慧农业企业深化合作，培养精通信息技术与农业产业的复合型人才。持续推动高素质农民培育、基层农技推广队伍素质提升等项目，提升生产经营主体数字素养与技能。推进智慧农业领域从业人员职业技能培训与评价，吸引各类人才投身智慧农业发展。（省委金融办、省发展改革委、省教育厅、省科技厅、省工业和信息化厅、省财政厅、省人力资源社会保障厅、省自然资源厅、省农业农村厅、省大数据局、省畜牧局、山东金融监管局、省税务局、省气象局按职责分工负责）\n\n山东省人民政府办公厅\n\n2026年9月18日\n[【打印本页】](javascript:window.print())[【关闭窗口】](javascript:window.opener=null;window.open('','_self');window.close();)","淄博市农业农村局·2026年09月30日","2026-09-30T05:58:48Z","政策",90,{"impact":383,"substance":187,"depth":189,"authority":384,"freshness":92,"relevant":19,"comment":385},26,13,"省级智慧农业顶层政策文件，条款与量化目标明确，对全国农业信息化建设具有示范参考价值，值得进入每日精选。",[387],{"name":378,"url":375},[24,26,389,390,391,392],"智能农机","齐鲁农云","农业数据要素","数字农业发展县",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":396},{"candidates":397,"agricultural_evidence":44},[398,403,407,413,419,425,431,437,443,449,454,458],{"code":50,"score":113,"supported":44,"title":399,"abstract":400,"tags":402},[24],[24,401],"数字农业",[24],{"code":30,"score":81,"supported":44,"title":404,"abstract":405,"tags":406},[],[46,277],[],{"code":408,"score":68,"supported":44,"title":409,"abstract":410,"tags":412},"A2",[],[411],"小麦",[],{"code":414,"score":68,"supported":44,"title":415,"abstract":416,"tags":418},"A3",[],[417],"玉米",[],{"code":420,"score":68,"supported":44,"title":421,"abstract":422,"tags":424},"A8",[],[423],"单产提升",[],{"code":426,"score":68,"supported":44,"title":427,"abstract":428,"tags":430},"C",[],[429],"畜牧",[],{"code":432,"score":68,"supported":44,"title":433,"abstract":434,"tags":436},"D",[],[435],"渔业",[],{"code":438,"score":68,"supported":44,"title":439,"abstract":440,"tags":442},"F1",[],[441],"种质资源",[],{"code":444,"score":68,"supported":44,"title":445,"abstract":446,"tags":448},"F3",[],[447],"水肥一体化",[],{"code":450,"score":19,"supported":13,"title":451,"abstract":452,"tags":453},"F5",[],[],[389],{"code":55,"score":19,"supported":13,"title":455,"abstract":456,"tags":457},[],[],[26],{"code":459,"score":19,"supported":13,"title":460,"abstract":461,"tags":462},"I6",[],[],[391],[464,465],"山东 智慧农业 实施意见","鲁政办字 85号 智慧农业","山东智慧农业实施意见-3924","2026-10-02T11:53:08.547636Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":8,"content":473,"source_name":474,"source_url":8,"published_at":184,"category":475,"cover_url":8,"hotness":12,"is_selected":13,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":486,"secondary_topics":487,"classification":488,"search_phrases":515,"slug":518,"view_count":68,"doi":8,"paper":8,"created_at":519},3297,"山区油菜智能化监测成果在十堰国家农业科技园区落地推广——天空地一体化监测技术与山区油菜大数据平台","https:\u002F\u002Fwww.iarrp.cn\u002Fysdt\u002Fkydt\u002Fd6e5d598bf9f4f998f7f08d7c811699c.htm","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题山区油菜产业智能化关键技术和机械装备研究示范成果应用推广会在十堰国家农业科技园区召开。项目首席科学家梅德圣研究员及行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山区县基层农技推广部门、区域内油菜种植大户代表参会。课题组精准锚定秦巴山区油菜生产实际需求，构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判；研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备。","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题“山区油菜产业智能化关键技术和机械装备研究示范”成果应用推广会在十堰国家农业科技园区顺利召开。本次会议聚焦山区油菜产业智能化转型痛点难点，集中交付项目核心科技成果，开展大数据平台实操培训，搭建成果落地、技术普及、产业赋能的产学研交流平台，助力秦巴山区油菜产业摆脱传统生产模式，全面迈向数字化、智能化、高效化高质量发展新阶段。项目首席科学家梅德圣研究员等行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山市区县基层农技推广部门，区域内油菜种植大户用户代表参会参训。\n\n我国秦巴等山区油菜种植体量较大，是保障区域油料供给、带动农民增收、助推乡村产业振兴的特色优势产业。但受山地、坡地为主的特殊地形制约，区域内耕地零散、地块不规则，长期存在机械化作业难、生产管理粗放、长势监测滞后、病虫害防控被动、生产数据缺失等突出问题，生产成本偏高、产量稳定性不足，严重制约山区油菜产业提质增效与绿色可持续发展。课题组精准锚定秦巴山区油菜生产实际需求，依托高水平产学研协同攻关体系，针对性开展智能技术改良、专用装备适配、数字体系构建与本地化示范推广，形成一套适配山地生产场景的成熟技术成果与应用模式。\n\n会上，课题组详细介绍了课题研发成果、技术创新亮点与本地化示范成效，顺利完成系列核心成果集中交付。中国农业科学院农业资源与农业区划研究所宋茜研究员表示，课题创新构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判，可为田间科学施肥、绿色防控、适时收获、精准植保提供精准数据支撑，改变山区油菜传统凭经验、粗放式的生产管理模式。同时，课题研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备，有效破解山地农机作业难度大、精度低、损耗高的产业难题，全面提升山区油菜机械化、标准化作业水平。\n\n为切实打通科技成果落地“最后一公里”，会议同步开展山区油菜大数据平台专项实操培训。项目技术专家围绕平台功能架构、操作流程、数据填报、智能分析、后台运维等核心内容开展系统讲解，结合秦巴山区油菜生产真实场景进行实操演示，现场解答基层农技人员、种植大户在平台使用、技术落地、田间实操中的各类问题。培训内容贴合一线生产需求、实操性极强，有效提升了基层技术人员数字化应用能力，为成果规模化落地推广夯实人才基础。\n\n据了解，课题以十堰柳陂镇示范基地为核心辐射支点，持续拓展示范应用版图，辐射带动五峰、安阳、大柳等多个乡镇开展规模化示范种植，常态化开展技术培训、智能设备实操演示、田间观摩交流、入户指导等配套服务，不断优化完善技术应用模式，成功探索形成一套适配秦巴山区、可复制、可推广的智慧油菜绿色高效生产模式。通过智能化技术与装备的落地应用，有效实现油菜稳产提质、生产降本增效，显著提升山区油菜产业标准化、智能化、现代化发展水平。\n\n与会代表一致认为，该课题成果针对性解决了山区油菜生产的系列瓶颈问题，构建了适配山地特征的智慧油菜生产体系，为山区油料产业高质量发展提供了优秀示范样板。下一步，课题各方将持续深化产学研协同合作，依托十堰国家农业科技园区平台优势，持续迭代优化大数据平台功能与智能装备性能，持续扩大示范推广覆盖面，常态化开展基层技术赋能服务，全力推动智慧油菜生产技术在秦巴武陵山区落地普及、提质增效，持续助力区域油料稳产保供、农业绿色发展、农民增收致富与乡村全面振兴。","中国农业科学院农业资源与农业区划研究所 2026年09月18日","报道",70,{"impact":16,"substance":16,"depth":18,"authority":18,"freshness":81,"relevant":19,"comment":478},"国家重点研发计划课题成果在十堰国家农业科技园区落地推广，天空地一体化监测与山区油菜大数据平台具备实质技术内容与示范价值，值得进入每日精选。",[480],{"name":474,"url":471},[24,27,482,483,484,485],"成果转化","油菜","天空地一体化","大数据平台",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":489},{"candidates":490,"agricultural_evidence":44},[491,495,501,507,511],{"code":30,"score":43,"supported":44,"title":492,"abstract":493,"tags":494},[483],[483],[483],{"code":496,"score":142,"supported":44,"title":497,"abstract":498,"tags":500},"G1",[],[499],"农技推广",[482],{"code":502,"score":68,"supported":44,"title":503,"abstract":504,"tags":506},"F4",[],[505],"植保",[],{"code":50,"score":19,"supported":13,"title":508,"abstract":509,"tags":510},[],[],[24],{"code":60,"score":19,"supported":13,"title":512,"abstract":513,"tags":514},[],[],[27],[516,517],"十堰 山区油菜 智能化监测","天空地一体化 油菜大数据平台","十堰山区油菜智能化监测-3297","2026-09-24T00:03:59.027363Z"]