[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1586":3,"related-1586":91},{"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":75,"slug":78,"view_count":79,"doi":8,"paper":80,"created_at":90},1586,"A physics-guided hybrid framework for peanut nitrogen monitoring (Computers and Electronics in Agriculture)","https:\u002F\u002Fnxy.scau.edu.cn\u002F2026\u002F0903\u002Fc14467a442979\u002Fpage.htm","华南农业大学农学院张雷课题组在国际学术期刊《Computers and Electronics in Agriculture》（中科院一区 Top，影响因子 10.3）发表研究论文。该研究聚焦花生冠层氮素含量监测过程中传统遥感模型对大量实测样本依赖强、跨环境适应性不足等问题，构建了融合辐射传输机理与机器学习的物理-数据融合监测框架 POSNH。该方法降低了模型对大量实测样本的依赖，并提高了其在不同品种、生育时期和环境条件下的预测精度与泛化能力，为花生氮素快速诊断、精准施肥研究和智慧农田管理提供了新的技术途径。",null,"华南农业大学农学院","2026-09-03T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,17,14,8,1,"研究提出物理-数据融合框架，提升花生氮素监测精度与泛化能力，对精准施肥和智慧农田管理有实质贡献。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","花生","遥感","氮素监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,63,70],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},12,true,[48],"peanut",[28],[28],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"I2",4,[],[56],"机器学习",[27],{"code":59,"score":53,"supported":46,"title":60,"abstract":61,"tags":62},"I3",[],[29],[29],{"code":64,"score":65,"supported":46,"title":66,"abstract":67,"tags":69},"F3",3,[],[68],"施肥",[],{"code":71,"score":21,"supported":13,"title":72,"abstract":73,"tags":74},"I1",[],[],[26],[76,77],"农业人工智能 智慧农业 氮素监测 花生","农业人工智能 智慧农业","农业人工智能智慧农业氮素监测花生-1586",6,{"doi":8,"openalex_id":8,"authors":81,"venue":8,"cited_by_count":82,"oa_url":8,"card":83,"direction":87,"ingested_from":89},[],0,{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"提出物理-数据融合框架POSNH，用于花生冠层氮素含量遥感监测，降低样本依赖并提高泛化能力。","融合辐射传输机理与机器学习，构建物理-数据融合监测框架POSNH。","POSNH降低了模型对大量实测样本的依赖，提高了跨品种、生育期和环境条件下的预测精度与泛化能力。","农业遥感与作物表型","可探索将物理-数据融合框架推广至其他作物或氮素以外的营养元素监测，并研究其在不同传感器和尺度下的适用性。","agent","2026-09-04T00:05:34.437930Z",{"total":79,"page":21,"page_size":79,"items":92},[93,155,250,313,380,446],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":8,"source_name":99,"source_url":96,"published_at":100,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":101,"score_detail":102,"sources":106,"tags":108,"primary_topic":111,"secondary_topics":112,"classification":113,"search_phrases":129,"slug":132,"view_count":53,"doi":133,"paper":134,"created_at":154},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","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",69,{"impact":45,"substance":16,"depth":103,"authority":19,"freshness":104,"relevant":21,"comment":105},16,9,"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。",[107],{"name":99,"url":96},[26,27,109,28,110],"病虫害识别","图像分割",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":114},{"candidates":115,"agricultural_evidence":46},[116,121,125],{"code":32,"score":117,"supported":46,"title":118,"abstract":119,"tags":120},15,[48],[28,48],[28],{"code":52,"score":65,"supported":13,"title":122,"abstract":123,"tags":124},[],[],[27,110,109],{"code":71,"score":21,"supported":13,"title":126,"abstract":127,"tags":128},[],[],[26],[130,131],"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":133,"openalex_id":135,"authors":136,"venue":99,"cited_by_count":82,"oa_url":8,"card":147,"direction":151,"ingested_from":153},"W7215079154",[137,140,143,145],{"name":138,"orcid":139},"Song Liu","https:\u002F\u002Forcid.org\u002F0009-0002-6649-4990",{"name":141,"orcid":142},"Chunjian Hua","https:\u002F\u002Forcid.org\u002F0000-0001-5397-2688",{"name":144,"orcid":8},"俞建峰 Yu Jianfeng",{"name":146,"orcid":8},"Ying Chen",{"tldr":148,"method":149,"finding":150,"direction":151,"opportunity":152},"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。","农业人工智能与决策模型","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。","openalex","2026-10-02T23:30:02.241779Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":8,"source_name":161,"source_url":158,"published_at":162,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":163,"score_detail":164,"sources":167,"tags":169,"primary_topic":172,"secondary_topics":173,"classification":183,"search_phrases":212,"slug":215,"view_count":53,"doi":216,"paper":217,"created_at":249},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","2026-09-11T00:00:00Z",80,{"impact":16,"substance":165,"depth":16,"authority":19,"freshness":20,"relevant":21,"comment":166},22,"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。",[168],{"name":161,"url":158},[26,170,27,29,171],"无人机","大豆育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[174,179],{"code":52,"slug":175,"name":176,"path":177,"parent_code":178},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":59,"slug":180,"name":181,"path":182,"parent_code":178},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":13,"version":40,"method":41,"evidence":184},{"candidates":185,"agricultural_evidence":46},[186,192,199,205],{"code":32,"score":117,"supported":46,"title":187,"abstract":189,"tags":191},[188],"soybean",[190,188],"大豆",[171],{"code":52,"score":12,"supported":46,"title":193,"abstract":194,"tags":198},[],[195,196,197],"深度学习","deep learning","artificial intelligence",[27],{"code":59,"score":12,"supported":46,"title":200,"abstract":201,"tags":204},[],[29,202,203],"remote sensing","multispectral",[29],{"code":71,"score":206,"supported":46,"title":207,"abstract":208,"tags":211},7,[],[209,210],"精准农业","precision agriculture",[26],[213,214],"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","10.3390\u002Frs18183121",{"doi":216,"openalex_id":218,"authors":219,"venue":161,"cited_by_count":82,"oa_url":158,"card":244,"direction":87,"ingested_from":153},"W7212223520",[220,223,226,229,232,234,236,239,242],{"name":221,"orcid":222},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":224,"orcid":225},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":227,"orcid":228},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":230,"orcid":231},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":233,"orcid":8},"Aviskar Giri",{"name":235,"orcid":8},"Ashutosh Pawar",{"name":237,"orcid":238},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":240,"orcid":241},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":243,"orcid":8},"Felix Fritschi",{"tldr":245,"method":246,"finding":247,"direction":87,"opportunity":248},"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。","2026-09-13T23:30:22.776177Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":8,"source_name":256,"source_url":253,"published_at":257,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":259,"sources":261,"tags":263,"primary_topic":264,"secondary_topics":265,"classification":267,"search_phrases":286,"slug":288,"view_count":65,"doi":289,"paper":290,"created_at":312},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":165,"depth":17,"authority":45,"freshness":206,"relevant":21,"comment":260},"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。",[262],{"name":256,"url":253},[26,27,190,29],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[266],{"code":59,"slug":180,"name":181,"path":182,"parent_code":178},{"status":39,"locked":13,"version":40,"method":41,"evidence":268},{"candidates":269,"agricultural_evidence":46},[270,274,278,282],{"code":32,"score":117,"supported":46,"title":271,"abstract":272,"tags":273},[188],[190,188],[190],{"code":59,"score":45,"supported":46,"title":275,"abstract":276,"tags":277},[202],[29],[29],{"code":71,"score":21,"supported":13,"title":279,"abstract":280,"tags":281},[],[],[26],{"code":52,"score":21,"supported":13,"title":283,"abstract":284,"tags":285},[],[],[27],[287,77],"农业人工智能 智慧农业 大豆 遥感","农业人工智能智慧农业大豆遥感-1444","10.3390\u002Fagriculture16171895",{"doi":289,"openalex_id":291,"authors":292,"venue":256,"cited_by_count":82,"oa_url":253,"card":307,"direction":87,"ingested_from":153},"W7204914742",[293,296,298,301,304],{"name":294,"orcid":295},"Chulin Pan","https:\u002F\u002Forcid.org\u002F0000-0003-3976-465X",{"name":297,"orcid":8},"Jiachen Ju",{"name":299,"orcid":300},"Hongpeng Guo","https:\u002F\u002Forcid.org\u002F0000-0003-0625-8929",{"name":302,"orcid":303},"Yufeng Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-8783-9786",{"name":305,"orcid":306},"Shuang Xu","https:\u002F\u002Forcid.org\u002F0000-0002-3470-0609",{"tldr":308,"method":309,"finding":310,"direction":87,"opportunity":311},"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","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":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":320,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":321,"sources":323,"tags":325,"primary_topic":328,"secondary_topics":329,"classification":330,"search_phrases":352,"slug":355,"view_count":82,"doi":356,"paper":357,"created_at":379},4325,"Precision agriculture reveals crop-yield footprints associated with distance to natural-seminatural habitats","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104997","Context Modern agricultural expansion has simplified landscapes and reduced natural and semi-natural habitats (NSNH), potentially disrupting ecological processes supporting crop production. Many of these processes are distance-dependent, potentially leaving spatial footprints in crop yield that vary with landscape context. Yet, their influence on crop yields at sub-field scales remains poorly understood. Precision agriculture offers opportunities to address this gap. Objective To evaluate how sunflower distance-to-NSNH - yield relationships vary across landscape contexts within contrasting landscape settings using precision agriculture data. Methods High-resolution yield-monitor data (∼700,000 observations) from 54 yield maps (Pampas, Argentina) were combined with landscape metrics derived from land-cover products, and distance-yield relationships were analysed using mixed-effects models. These relationships were evaluated across gradients of NSNH cover (landscape context) and between zones representing contrasting landscape settings. Results and conclusions Distance-yield relationships were non-linear, varied with landscape context, and differed between landscape settings. In simplified landscapes, yields declined beyond a few hundred meters from NSNH, with steeper declines under higher NSNH cover. In the more complex landscape setting, where NSNH were more abundant and distances to habitat were shorter, yield responses were weak across the observed NSNH gradient. These patterns may reflect distance-mediated ecological processes associated with NSNH, although their observational nature precludes attribution to specific mechanisms. Expanding the dataset across broader landscape gradients would strengthen the mechanistic interpretation of these patterns. Significance Precision agriculture can help identify crop-yield footprints consistent with distance-mediated ecological processes at sub-field scales, improving understanding of landscape-crop interactions and supporting ecologically informed landscape management.","背景 现代农业扩张使景观趋于简化，并减少了自然与半自然生境（NSNH），可能破坏了支撑作物生产的生态过程。其中许多过程具有距离依赖性，可能在作物产量中留下随景观背景而变化的时空印记。然而，这些过程在田块尺度以下对作物产量的影响仍知之甚少。精准农业为解决这一空白提供了机遇。目的 利用精准农业数据，评估在不同景观情境下，向日葵距自然与半自然生境的距离—产量关系如何随景观背景而变化。方法 将来自54幅产量图的高分辨率产量监测数据（约70万条观测值，阿根廷潘帕斯地区）与基于土地覆盖产品衍生的景观指标相结合，并采用混合效应模型分析距离—产量关系。这些关系在自然与半自然生境覆盖度梯度（景观背景）以及代表不同景观情境的区域之间进行了评估。结果与结论 距离—产量关系呈非线性，随景观背景而变化，并在不同景观情境之间存在差异。在简化景观中，产量在距自然与半自然生境数百米之外下降，且在自然与半自然生境覆盖度较高时下降更陡。在更为复杂的景观情境中，自然与半自然生境更为丰富且距生境距离更短，产量响应在所观测的自然与半自然生境梯度上较弱。这些格局可能反映了与自然与半自然生境相关的距离介导生态过程，尽管其观测性质使其无法归因于具体机制。将数据集扩展至更广泛的景观梯度将增强对这些格局的机制性解释。意义 精准农业有助于识别与田块尺度以下距离介导生态过程相一致的作物产量印记，增进对景观—作物相互作用的理解，并支持基于生态学信息的景观管理。","Agricultural Systems","2026-10-06T00:00:00Z",{"impact":103,"substance":165,"depth":16,"authority":19,"freshness":104,"relevant":21,"comment":322},"基于70万条产量监测数据揭示田块尺度产量与自然栖息地距离的非线性关系，方法新颖、数据规模大，对生态化农田管理有参考价值。",[324],{"name":319,"url":316},[26,209,29,326,327],"作物产量","农业生态",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":331},{"candidates":332,"agricultural_evidence":46},[333,337,342,347],{"code":71,"score":103,"supported":46,"title":334,"abstract":335,"tags":336},[210],[209,210],[26,209],{"code":32,"score":65,"supported":46,"title":338,"abstract":339,"tags":341},[],[340],"向日葵",[],{"code":59,"score":343,"supported":13,"title":344,"abstract":345,"tags":346},2,[],[],[326,29],{"code":348,"score":21,"supported":13,"title":349,"abstract":350,"tags":351},"H6",[],[],[327],[353,354],"潘帕斯 向日葵 产量 自然栖息地","精准农业 产量监测 景观","潘帕斯向日葵产量自然栖息地-4325","10.1016\u002Fj.agsy.2026.104997",{"doi":356,"openalex_id":358,"authors":359,"venue":319,"cited_by_count":82,"oa_url":316,"card":374,"direction":87,"ingested_from":153},"W7220681058",[360,363,366,369,371],{"name":361,"orcid":362},"Facundo José Oddi","https:\u002F\u002Forcid.org\u002F0000-0003-4478-978X",{"name":364,"orcid":365},"Matías G. Goldenberg","https:\u002F\u002Forcid.org\u002F0000-0002-9498-2951",{"name":367,"orcid":368},"Paula Zermoglio","https:\u002F\u002Forcid.org\u002F0000-0002-6056-5084",{"name":370,"orcid":8},"Tomás González",{"name":372,"orcid":373},"Lucas A. Garibaldi","https:\u002F\u002Forcid.org\u002F0000-0003-0725-4049",{"tldr":375,"method":376,"finding":377,"direction":87,"opportunity":378},"利用精准农业产量数据揭示向日葵产量随距自然半自然生境距离变化的非线性关系。","54幅产量图约70万条监测数据，结合土地覆盖景观指标与混合效应模型。","简化景观中产量随距生境距离增加而下降，复杂景观中响应微弱。","可扩展至多作物多区域，结合机制模型量化生境距离对产量的因果贡献。","2026-10-07T23:30:03.865642Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":8,"source_name":99,"source_url":383,"published_at":100,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":397,"search_phrases":412,"slug":415,"view_count":343,"doi":416,"paper":417,"created_at":445},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》",72,{"impact":45,"substance":17,"depth":18,"authority":19,"freshness":104,"relevant":21,"comment":388},"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。",[390],{"name":99,"url":383},[26,27,392,209,393],"目标检测","花生播种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[396],{"code":52,"slug":175,"name":176,"path":177,"parent_code":178},{"status":39,"locked":13,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":46},[400,404,408],{"code":52,"score":103,"supported":46,"title":401,"abstract":402,"tags":403},[196],[195,196],[27,392],{"code":32,"score":19,"supported":46,"title":405,"abstract":406,"tags":407},[48],[28,48],[],{"code":71,"score":343,"supported":13,"title":409,"abstract":410,"tags":411},[],[],[26,209],[413,414],"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":416,"openalex_id":418,"authors":419,"venue":99,"cited_by_count":82,"oa_url":8,"card":440,"direction":151,"ingested_from":153},"W7215133023",[420,423,425,427,429,431,433,435,438],{"name":421,"orcid":422},"Baogang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3430-7508",{"name":424,"orcid":8},"Enxi Liu",{"name":426,"orcid":8},"Yijia Chen",{"name":428,"orcid":8},"Zhipeng Feng",{"name":430,"orcid":8},"Yuhuan Li",{"name":432,"orcid":8},"Zunmin Liu",{"name":434,"orcid":8},"Jingchun Tang",{"name":436,"orcid":437},"Jiang Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6746-9987",{"name":439,"orcid":8},"Fazhan Yang",{"tldr":441,"method":442,"finding":443,"direction":151,"opportunity":444},"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":447,"title":448,"url":449,"summary":450,"summary_zh":8,"content":451,"source_name":452,"source_url":8,"published_at":453,"category":454,"cover_url":8,"hotness":12,"is_selected":46,"score":455,"score_detail":456,"sources":462,"tags":464,"primary_topic":469,"secondary_topics":470,"classification":471,"search_phrases":538,"slug":541,"view_count":79,"doi":8,"paper":8,"created_at":542},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":457,"substance":458,"depth":459,"authority":460,"freshness":20,"relevant":21,"comment":461},26,24,19,13,"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。",[463],{"name":452,"url":449},[26,27,465,466,467,468],"智能农机","齐鲁农云","农业数据要素","数字农业发展县",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":472},{"candidates":473,"agricultural_evidence":46},[474,479,483,489,495,501,507,513,519,524,529,533],{"code":71,"score":117,"supported":46,"title":475,"abstract":476,"tags":478},[26],[26,477],"数字农业",[26],{"code":32,"score":79,"supported":46,"title":480,"abstract":481,"tags":482},[],[190,28],[],{"code":484,"score":65,"supported":46,"title":485,"abstract":486,"tags":488},"A2",[],[487],"小麦",[],{"code":490,"score":65,"supported":46,"title":491,"abstract":492,"tags":494},"A3",[],[493],"玉米",[],{"code":496,"score":65,"supported":46,"title":497,"abstract":498,"tags":500},"A8",[],[499],"单产提升",[],{"code":502,"score":65,"supported":46,"title":503,"abstract":504,"tags":506},"C",[],[505],"畜牧",[],{"code":508,"score":65,"supported":46,"title":509,"abstract":510,"tags":512},"D",[],[511],"渔业",[],{"code":514,"score":65,"supported":46,"title":515,"abstract":516,"tags":518},"F1",[],[517],"种质资源",[],{"code":64,"score":65,"supported":46,"title":520,"abstract":521,"tags":523},[],[522],"水肥一体化",[],{"code":525,"score":21,"supported":13,"title":526,"abstract":527,"tags":528},"F5",[],[],[465],{"code":52,"score":21,"supported":13,"title":530,"abstract":531,"tags":532},[],[],[27],{"code":534,"score":21,"supported":13,"title":535,"abstract":536,"tags":537},"I6",[],[],[467],[539,540],"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"]