[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1505":3,"related-1505":95},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":80,"slug":83,"view_count":51,"doi":8,"paper":84,"created_at":94},1505,"Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operations","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.00106","[预印本]本文介绍 FAIRY 智慧农业智能体系统，已部署在哈尔滨工业大学智慧农业站点的运营大豆研究农场。FAIRY 能执行和评估贯穿整季的时空农艺操作，覆盖起垄、播种、灌溉、施肥、病虫害处理、收获、谷物处理、干燥与储存等全流程，融合生产级机械 API、固定土壤与冠层传感器、多光谱与热成像无人机、卫星植被产品、气象站、标定作物过程模型、农艺记录以及多年产量历史。该智能体系统以\"万物皆事件\"为执行范式，在此基础上叠加多智能体控制器与编排后端、知识库原子技能层、前沿与边缘模型执行层等。",null,"arXiv 2609.00106","2026-08-31T09:53:17Z","论文",10,false,75,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,12,7,1,"FAIRY系统实现大豆全流程智能管理，技术集成度高，具示范意义。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","智能体","大豆","大田种植",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,49,55,62,68,74],{"code":31,"score":18,"supported":44,"title":45,"abstract":47,"tags":48},true,[46],"soybean",[28],[28],{"code":50,"score":51,"supported":44,"title":52,"abstract":53,"tags":54},"I1",4,[],[25],[25],{"code":56,"score":57,"supported":44,"title":58,"abstract":59,"tags":61},"F3",3,[],[60],"施肥",[],{"code":63,"score":57,"supported":44,"title":64,"abstract":65,"tags":67},"H2",[],[66],"土壤",[],{"code":69,"score":57,"supported":44,"title":70,"abstract":71,"tags":73},"H3",[],[72],"灌溉",[],{"code":75,"score":76,"supported":13,"title":77,"abstract":78,"tags":79},"I2",2,[],[],[26,27],[81,82],"农业人工智能 大田种植 智慧农业 智能体","农业人工智能 大田种植","农业人工智能大田种植智慧农业智能体-1505",{"doi":8,"openalex_id":8,"authors":85,"venue":8,"cited_by_count":86,"oa_url":8,"card":87,"direction":91,"ingested_from":93},[],0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"部署并评估了全季大豆农场的智慧农业智能体系统FAIRY，实现全流程农艺操作。","融合机械API、多源传感器、无人机、卫星、气象站及作物模型，采用多智能体控制器与","FAIRY能执行并评估整季时空农艺操作，覆盖从起垄到储存的全流程。","智慧农业 \u002F 农业物联网","可研究智能体系统在不同作物、地域的泛化性，以及人机协同决策与异常处理机制。","agent","2026-09-03T00:06:44.823744Z",{"total":96,"page":20,"page_size":96,"items":97},6,[98,178,251,327,405,460],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":104,"source_url":101,"published_at":105,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":106,"score_detail":107,"sources":111,"tags":113,"primary_topic":116,"secondary_topics":117,"classification":118,"search_phrases":139,"slug":142,"view_count":76,"doi":143,"paper":144,"created_at":177},1512,"Research on interaction mechanisms and operating parameter optimization of soybean threshing based on the discrete element method","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112354","Research on interaction mechanisms and operating parameter optimization of soybean threshing based on the discrete element method。Computers and Electronics in Agriculture","基于离散元法的大豆脱粒交互机理与作业参数优化研究。《计算机与电子在农业中的应用》","Computers and Electronics in Agriculture","2026-09-02T00:00:00Z",67,{"impact":18,"substance":16,"depth":108,"authority":109,"freshness":19,"relevant":20,"comment":110},16,14,"论文聚焦大豆脱粒过程仿真与参数优化，方法新颖，对农机智能化有参考价值，但影响范围限于细分领域。",[112],{"name":104,"url":101},[25,26,28,114,115],"农机装备","离散元法",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":119},{"candidates":120,"agricultural_evidence":44},[121,126,131,135],{"code":31,"score":122,"supported":44,"title":123,"abstract":124,"tags":125},15,[46],[28,46],[28],{"code":127,"score":20,"supported":13,"title":128,"abstract":129,"tags":130},"F5",[],[],[114],{"code":50,"score":20,"supported":13,"title":132,"abstract":133,"tags":134},[],[],[25],{"code":75,"score":20,"supported":13,"title":136,"abstract":137,"tags":138},[],[],[26],[140,141],"农业人工智能 农机装备 智慧农业 离散元法","农业人工智能 农机装备","农业人工智能农机装备智慧农业离散元法-1512","10.1016\u002Fj.compag.2026.112354",{"doi":143,"openalex_id":145,"authors":146,"venue":104,"cited_by_count":86,"oa_url":8,"card":171,"direction":91,"ingested_from":176},"W7206167949",[147,149,152,155,157,160,163,165,168],{"name":148,"orcid":8},"Fuqiang Gou",{"name":150,"orcid":151},"Zhenjie Qian","https:\u002F\u002Forcid.org\u002F0000-0003-1897-2396",{"name":153,"orcid":154},"Youliang Ni","https:\u002F\u002Forcid.org\u002F0000-0001-5621-8752",{"name":156,"orcid":8},"Chengqian Jin",{"name":158,"orcid":159},"Tengxiang Yang","https:\u002F\u002Forcid.org\u002F0000-0003-2768-7546",{"name":161,"orcid":162},"Guangyue Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-4051-8616",{"name":164,"orcid":8},"Zheng Liu",{"name":166,"orcid":167},"Man Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6246-1801",{"name":169,"orcid":170},"Zeyu Cai","https:\u002F\u002Forcid.org\u002F0000-0003-3443-2277",{"tldr":172,"method":173,"finding":174,"direction":91,"opportunity":175},"用离散元法研究大豆脱粒的相互作用机制并优化操作参数。","离散元法模拟与试验验证，优化操作参数。","揭示了脱粒过程机理，确定了最优参数组合。","可结合传感器实时监测脱粒过程，开发自适应控制策略，实现智能脱粒。","openalex","2026-09-03T23:30:02.277672Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":8,"source_name":184,"source_url":181,"published_at":185,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":186,"score_detail":187,"sources":190,"tags":192,"primary_topic":194,"secondary_topics":195,"classification":202,"search_phrases":222,"slug":225,"view_count":57,"doi":226,"paper":227,"created_at":250},1444,"An Improved Transformer-KAN Model for Soybean Mapping Based on Multi-Temporal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16171895","Accurate and transferable soybean mapping is essential for agricultural monitoring and area verification, yet conventional Transformer models can be limited in modeling complex nonlinear phenological relationships and maintaining training stability. This study proposes an improved Transformer-KAN model for multi-temporal Sentinel-2 data. Temporal positional encoding, multi-head self-attention with relative positional bias, and a Pre-LayerNorm residual structure are introduced to strengthen phenological sequence modeling, while FastKAN replaces the conventional MLP-based feed-forward network to enhance nonlinear feature representation. The model was trained using 2023 samples from Hailun City and directly evaluated in Bozhou, McLean, and Cass without retraining or fine-tuning. Cross-year transferability was further evaluated by applying the Hailun-trained model to data from Bozhou and McLean from 2021 to 2025. The proposed model achieved overall accuracies of 0.975, 0.983, 0.958, and 0.966 in Hailun, Bozhou, McLean, and Cass, respectively, with corresponding Kappa coefficients of 0.895, 0.887, 0.883, and 0.916. Complexity analysis showed that Transformer-KAN required 0.8142 M parameters and 1.1967 M FLOPs, with an average inference time of 3.0191 ms per sample, compared with 0.6130 M parameters, 0.7971 M FLOPs, and 1.4009 ms per sample for the conventional Transformer, indicating that the improved feature representation was accompanied by increased computational complexity. Cross-year classification performance remained generally high from 2021 to 2025, although interannual variations were observed due to differences in crop growth conditions, phenological timing, and image acquisition quality. Discrepancies between remote-sensing-derived and officially reported soybean areas were mainly related to differences in statistical definitions and residual classification uncertainties, rather than model transferability. Overall, Transformer-KAN provides accurate and transferable soybean mapping and can serve as a spatially explicit complement to official agricultural statistics.","准确且可迁移的大豆制图对于农业监测和面积核查至关重要，然而传统Transformer模型在建模复杂非线性物候关系及维持训练稳定性方面可能存在局限。本研究提出一种改进的Transformer-KAN模型，用于多时相Sentinel-2数据。引入时间位置编码、带相对位置偏置的多头自注意力机制以及Pre-LayerNorm残差结构，以增强物候序列建模能力，同时采用FastKAN替代传统基于MLP的前馈网络，以提升非线性特征表达能力。该模型利用2023年海伦市样本进行训练，并在北州、麦克莱恩和卡斯直接评估，无需重新训练或微调。跨年度迁移能力进一步通过将海伦训练模型应用于2021至2025年北州和麦克莱恩的数据进行评估。所提模型在海伦、北州、麦克莱恩和卡斯的总体精度分别为0.975、0.983、0.958和0.966，对应的Kappa系数分别为0.895、0.887、0.883和0.916。复杂度分析表明，Transformer-KAN需要0.8142 M参数和1.1967 M FLOPs，平均每样本推理时间为3.0191 ms，而传统Transformer为0.6130 M参数、0.7971 M FLOPs和每样本1.4009 ms，说明改进的特征表示伴随着计算复杂度的增加。2021至2025年跨年度分类性能总体保持较高水平，但由于作物生长条件、物候时序和影像获取质量的差异，存在年际波动。遥感反演大豆面积与官方统计面积之间的差异主要与统计口径差异和残余分类不确定性有关，而非模型迁移能力所致。总体而言，Transformer-KAN能够提供准确且可迁移的大豆制图结果，可作为官方农业统计的空间显式补充。","Agriculture","2026-09-01T00:00:00Z",79,{"impact":16,"substance":188,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":189},22,"提出改进的Transformer-KAN模型用于大豆遥感制图，跨区域跨年份验证精度高，对农业监测有实质贡献。",[191],{"name":184,"url":181},[25,26,28,193],"遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[196],{"code":197,"slug":198,"name":199,"path":200,"parent_code":201},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":203},{"candidates":204,"agricultural_evidence":44},[205,209,214,218],{"code":31,"score":122,"supported":44,"title":206,"abstract":207,"tags":208},[46],[28,46],[28],{"code":197,"score":18,"supported":44,"title":210,"abstract":212,"tags":213},[211],"remote sensing",[193],[193],{"code":50,"score":20,"supported":13,"title":215,"abstract":216,"tags":217},[],[],[25],{"code":75,"score":20,"supported":13,"title":219,"abstract":220,"tags":221},[],[],[26],[223,224],"农业人工智能 智慧农业 大豆 遥感","农业人工智能 智慧农业","农业人工智能智慧农业大豆遥感-1444","10.3390\u002Fagriculture16171895",{"doi":226,"openalex_id":228,"authors":229,"venue":184,"cited_by_count":86,"oa_url":181,"card":244,"direction":248,"ingested_from":176},"W7204914742",[230,233,235,238,241],{"name":231,"orcid":232},"Chulin Pan","https:\u002F\u002Forcid.org\u002F0000-0003-3976-465X",{"name":234,"orcid":8},"Jiachen Ju",{"name":236,"orcid":237},"Hongpeng Guo","https:\u002F\u002Forcid.org\u002F0000-0003-0625-8929",{"name":239,"orcid":240},"Yufeng Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-8783-9786",{"name":242,"orcid":243},"Shuang Xu","https:\u002F\u002Forcid.org\u002F0000-0002-3470-0609",{"tldr":245,"method":246,"finding":247,"direction":248,"opportunity":249},"提出改进的Transformer-KAN模型，基于多时相遥感数据实现高精度、可迁移的大豆种植区制图。","结合时间位置编码、相对位置偏置、Pre-LayerNorm和FastKAN替换M","模型在四个区域精度达0.958-0.983，跨年迁移稳定，但计算复杂度高于传统Transformer","农业遥感与作物表型","可探索降低模型复杂度或轻量化设计，以平衡精度与效率，并研究跨区域物候差异对迁移的影响。","2026-09-02T23:30:53.720188Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":8,"source_name":257,"source_url":254,"published_at":258,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":259,"score_detail":260,"sources":264,"tags":266,"primary_topic":270,"secondary_topics":271,"classification":273,"search_phrases":298,"slug":301,"view_count":51,"doi":302,"paper":303,"created_at":326},4998,"Unmanned aerial vehicle time‐series phenotyping illuminates genotype‐specific responses to off‐target dicamba exposure in soybean","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70118","Abstract Dicamba, a synthetic auxin herbicide, is widely used for postemergence weed control. However, the high potential for volatility and spray drift raises concerns about damage to nontarget dicot crops lacking the dicamba monooxygenase transgene, which provides bioengineered resistance. Our objective was to evaluate the practical application of unmanned aerial vehicle (UAV)‐based multispectral imaging for quantifying off‐target dicamba injury, using non‐genetically modified (GM) soybean ( Glycine max (L.) Merr.) genotypes with known tolerant (T) and sensitive (S) responses to provide a reference framework. We conducted field experiments in 2021 and 2022 using varying dicamba rates. Temporal visual injury estimates (0–5 scale) were recorded approximately every 5 days for 6–8 weeks. Significant differences were detected between T and S lines, especially with 1\u002F500 single and weekly repeated treatments, which provided the largest separation. To augment the precision of visual injury assessment, UAV‐based multispectral imagery from each of multiple flights was used to calculate four vegetation indices (VIs): green leaf index (GLI), normalized excess green index (ExG2), normalized green red difference index (NGRDI), and red edge normalized difference vegetation index (RENDVI). These VIs captured differences between T and S lines, indicating dicamba injury can be quantified through remote sensing across contrasting soybean genotypes. Strong negative correlations were observed between visual injury scores and VI scores across treatments at each flight date, with overall correlations for GLI, ExG2, NGRDI, and RENDVI being −0.43, −0.43, −0.44, and −0.39 in 2021 and −0.54, −0.54, −0.64, and −0.57 in 2022. These results support the utility of UAV‐derived VIs for detecting and quantifying off‐target herbicide injury in soybean, facilitating scientific research efforts to develop non‐GM‐tolerant genotypes.","摘要 麦草畏（dicamba）是一种合成生长素类除草剂，广泛用于苗后杂草防除。然而，其高挥发性和喷雾飘移潜力引发了人们对非靶标双子叶作物的担忧，这些作物缺乏提供生物工程抗性的麦草畏单加氧酶（dicamba monooxygenase）转基因。本研究旨在评估基于无人机（UAV）的多光谱成像在量化非靶标麦草畏药害中的实际应用，利用对麦草畏具有已知耐受（T）和敏感（S）响应的非转基因（GM）大豆（Glycine max (L.) Merr.）基因型作为参考框架。我们于2021年和2022年开展了田间试验，使用不同的麦草畏剂量。在约6–8周内，每约5天记录一次时序视觉药害评估（0–5级）。在T和S品系之间检测到显著差异，尤其是在1\u002F500单次和每周重复处理下，差异最为明显。为提高视觉药害评估的精度，利用多次飞行获取的无人机多光谱影像计算了4种植被指数（VIs）：绿叶指数（GLI）、归一化超绿指数（ExG2）、归一化绿红差异指数（NGRDI）和红边归一化差异植被指数（RENDVI）。这些植被指数捕捉到了T和S品系之间的差异，表明麦草畏药害可通过遥感在不同大豆基因型间进行量化。在各飞行日期的所有处理中，视觉药害评分与植被指数评分之间均观察到强负相关，2021年GLI、ExG2、NGRDI和RENDVI的总体相关系数分别为−0.43、−0.43、−0.44和−0.39，2022年分别为−0.54、−0.54、−0.64和−0.57。这些结果支持无人机衍生植被指数在大豆非靶标除草剂药害检测和量化中的实用性，有助于推动开发非转基因耐受基因型的科学研究工作。","The Plant Phenome Journal","2026-10-14T00:00:00Z",77,{"impact":108,"substance":188,"depth":16,"authority":261,"freshness":262,"relevant":20,"comment":263},13,8,"该研究利用无人机多光谱时序表型定量评估大豆非靶标除草剂药害，方法新颖、数据扎实，对智慧农业遥感监测具有参考价值。",[265],{"name":257,"url":254},[25,28,267,268,269],"无人机遥感","植被指数","除草剂药害",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[272],{"code":197,"slug":198,"name":199,"path":200,"parent_code":201},{"status":38,"locked":13,"version":39,"method":40,"evidence":274},{"candidates":275,"agricultural_evidence":44},[276,280,287,294],{"code":31,"score":122,"supported":44,"title":277,"abstract":278,"tags":279},[46],[28,46],[28],{"code":197,"score":281,"supported":44,"title":282,"abstract":283,"tags":286},11,[],[193,284,211,285],"无人机多光谱","multispectral",[267,268],{"code":288,"score":96,"supported":44,"title":289,"abstract":290,"tags":293},"F4",[],[291,292],"除草","weed control",[],{"code":50,"score":20,"supported":13,"title":295,"abstract":296,"tags":297},[],[],[25],[299,300],"无人机 多光谱 大豆 药害","麦草畏 飘移 大豆 遥感","无人机多光谱大豆药害-4998","10.1002\u002Fppj2.70118",{"doi":302,"openalex_id":304,"authors":305,"venue":257,"cited_by_count":86,"oa_url":254,"card":321,"direction":248,"ingested_from":176},"W7221759072",[306,309,312,315,318],{"name":307,"orcid":308},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":310,"orcid":311},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":313,"orcid":314},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":316,"orcid":317},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":319,"orcid":320},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":322,"method":323,"finding":324,"direction":248,"opportunity":325},"用无人机多光谱时序表型量化非转基因大豆受除草剂飘移药害的基因型差异。","2021-2022田间试验，无人机多光谱影像计算GLI、ExG2、NGRDI、R","植被指数与目测药害评分显著负相关，能区分耐性与敏感基因型，2022年相关性更强。","可拓展到多作物、多除草剂飘移的无人机早期诊断，并融合机器学习构建药害分级预警模型。","2026-10-15T23:30:20.537887Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":8,"source_name":104,"source_url":330,"published_at":333,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":334,"score_detail":335,"sources":339,"tags":341,"primary_topic":345,"secondary_topics":346,"classification":351,"search_phrases":370,"slug":373,"view_count":76,"doi":374,"paper":375,"created_at":404},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》","2026-10-01T00:00:00Z",72,{"impact":18,"substance":17,"depth":336,"authority":109,"freshness":337,"relevant":20,"comment":338},17,9,"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。",[340],{"name":104,"url":330},[25,26,342,343,344],"目标检测","精准农业","花生播种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[347],{"code":75,"slug":348,"name":349,"path":350,"parent_code":201},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":13,"version":39,"method":40,"evidence":352},{"candidates":353,"agricultural_evidence":44},[354,360,366],{"code":75,"score":108,"supported":44,"title":355,"abstract":357,"tags":359},[356],"deep 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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":18,"substance":16,"depth":108,"authority":109,"freshness":337,"relevant":20,"comment":413},"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。",[415],{"name":104,"url":408},[25,26,417,364,418],"病虫害识别","图像分割",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":422},{"candidates":423,"agricultural_evidence":44},[424,428,432],{"code":31,"score":122,"supported":44,"title":425,"abstract":426,"tags":427},[362],[364,362],[364],{"code":75,"score":57,"supported":13,"title":429,"abstract":430,"tags":431},[],[],[26,418,417],{"code":50,"score":20,"supported":13,"title":433,"abstract":434,"tags":435},[],[],[25],[437,438],"花生叶片病害 图像分割","花生 病害识别 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Chen",{"tldr":455,"method":456,"finding":457,"direction":402,"opportunity":458},"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":461,"title":462,"url":463,"summary":464,"summary_zh":8,"content":465,"source_name":466,"source_url":8,"published_at":467,"category":468,"cover_url":8,"hotness":12,"is_selected":44,"score":469,"score_detail":470,"sources":475,"tags":477,"primary_topic":482,"secondary_topics":483,"classification":484,"search_phrases":550,"slug":553,"view_count":96,"doi":8,"paper":8,"created_at":554},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":471,"substance":472,"depth":473,"authority":261,"freshness":262,"relevant":20,"comment":474},26,24,19,"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。",[476],{"name":466,"url":463},[25,26,478,479,480,481],"智能农机","齐鲁农云","农业数据要素","数字农业发展县",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":485},{"candidates":486,"agricultural_evidence":44},[487,492,496,502,508,514,520,526,532,537,541,545],{"code":50,"score":122,"supported":44,"title":488,"abstract":489,"tags":491},[25],[25,490],"数字农业",[25],{"code":31,"score":96,"supported":44,"title":493,"abstract":494,"tags":495},[],[28,364],[],{"code":497,"score":57,"supported":44,"title":498,"abstract":499,"tags":501},"A2",[],[500],"小麦",[],{"code":503,"score":57,"supported":44,"title":504,"abstract":505,"tags":507},"A3",[],[506],"玉米",[],{"code":509,"score":57,"supported":44,"title":510,"abstract":511,"tags":513},"A8",[],[512],"单产提升",[],{"code":515,"score":57,"supported":44,"title":516,"abstract":517,"tags":519},"C",[],[518],"畜牧",[],{"code":521,"score":57,"supported":44,"title":522,"abstract":523,"tags":525},"D",[],[524],"渔业",[],{"code":527,"score":57,"supported":44,"title":528,"abstract":529,"tags":531},"F1",[],[530],"种质资源",[],{"code":56,"score":57,"supported":44,"title":533,"abstract":534,"tags":536},[],[535],"水肥一体化",[],{"code":127,"score":20,"supported":13,"title":538,"abstract":539,"tags":540},[],[],[478],{"code":75,"score":20,"supported":13,"title":542,"abstract":543,"tags":544},[],[],[26],{"code":546,"score":20,"supported":13,"title":547,"abstract":548,"tags":549},"I6",[],[],[480],[551,552],"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"]