[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-43":3,"related-43":119},{"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":114,"slug":117,"view_count":19,"doi":8,"paper":8,"created_at":118},43,"潮涌黄河口 碱滩换新颜——黄三角农高区十载耕耘护粮安","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260729\u002F20260729_1\u002Fnmrb_20260729_13363_1_2082216350156165131.html","农民日报2026-07-29头版头条。黄河三角洲农业高新技术产业示范区运行10年，依托国家盐碱地综合利用技术创新中心，构建\"1+3+16+N\"协同创新体系。中国农科院李晓彬团队的垄作覆膜精准滴灌水肥剂一体化生境调控技术，使盐分7‰至14‰重度滩涂玉米出苗率突破90%、首年亩产可达460至550公斤。配套玉米伴生种植模式可使玉米单产提升7%至15%。园区首创盐碱地专用农机装备，节水25%-30%、肥料利用率提高20%以上、灌溉施肥用工节省85%以上。十年来已培育小麦、玉米、大豆、花生、藜麦、油菜等耐盐碱作物新品种（系），种质资源库存纳3.5万份耐盐材料，盐碱地特色产业链形成\"碱康农产品\"区域公用品牌。",null,"农民日报 · 2026-07-29","2026-07-28T23:00:00Z","报道",10,false,92,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},28,23,17,14,1,"全国性盐碱地治理重大突破，数据详实，央媒头版，时效性强。",[23],{"name":9,"url":6},[11,25,26,27,28,29],"智慧农业","盐碱地","黄三角农高区","耐盐碱种业","节水",{"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":45},[43,52,60,66,73,79,85,91,97,103,109],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":51},9,true,[],[48,49,50],"大豆","油菜","花生",[],{"code":53,"score":54,"supported":45,"title":55,"abstract":56,"tags":59},"F3",6,[],[57,58],"施肥","肥料",[],{"code":61,"score":62,"supported":45,"title":63,"abstract":64,"tags":65},"H2",4,[],[26],[26],{"code":67,"score":68,"supported":45,"title":69,"abstract":70,"tags":72},"A2",3,[],[71],"小麦",[],{"code":74,"score":68,"supported":45,"title":75,"abstract":76,"tags":78},"A3",[],[77],"玉米",[],{"code":80,"score":68,"supported":45,"title":81,"abstract":82,"tags":84},"A5",[],[83],"藜麦",[],{"code":86,"score":68,"supported":45,"title":87,"abstract":88,"tags":90},"A8",[],[89],"单产提升",[],{"code":92,"score":68,"supported":45,"title":93,"abstract":94,"tags":96},"F1",[],[95],"种质资源",[],{"code":98,"score":68,"supported":45,"title":99,"abstract":100,"tags":102},"F5",[],[101],"农机",[],{"code":104,"score":68,"supported":45,"title":105,"abstract":106,"tags":108},"H3",[],[107],"灌溉",[],{"code":110,"score":20,"supported":13,"title":111,"abstract":112,"tags":113},"I1",[],[],[25],[115,116],"黄三角农高区 耐盐碱种业 智慧农业 盐碱地","黄三角农高区 耐盐碱种业","黄三角农高区耐盐碱种业智慧农业盐碱地-43","2026-07-28T23:53:29.143281Z",{"total":54,"page":20,"page_size":54,"items":120},[121,213,306,373,452,510],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":8,"source_name":127,"source_url":124,"published_at":128,"category":129,"cover_url":8,"hotness":12,"is_selected":13,"score":130,"score_detail":131,"sources":138,"tags":140,"primary_topic":144,"secondary_topics":145,"classification":152,"search_phrases":181,"slug":184,"view_count":62,"doi":185,"paper":186,"created_at":212},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","论文",77,{"impact":132,"substance":133,"depth":134,"authority":135,"freshness":136,"relevant":20,"comment":137},16,22,18,13,8,"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。",[139],{"name":127,"url":124},[25,48,141,142,143],"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},[146],{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"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":153},{"candidates":154,"agricultural_evidence":45},[155,161,170,177],{"code":31,"score":156,"supported":45,"title":157,"abstract":159,"tags":160},15,[158],"soybean",[48,158],[48],{"code":147,"score":162,"supported":45,"title":163,"abstract":164,"tags":169},11,[],[165,166,167,168],"遥感","无人机多光谱","remote sensing","multispectral",[141,142],{"code":171,"score":54,"supported":45,"title":172,"abstract":173,"tags":176},"F4",[],[174,175],"除草","weed control",[],{"code":110,"score":20,"supported":13,"title":178,"abstract":179,"tags":180},[],[],[25],[182,183],"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":185,"openalex_id":187,"authors":188,"venue":127,"cited_by_count":204,"oa_url":124,"card":205,"direction":209,"ingested_from":211},"W7221759072",[189,192,195,198,201],{"name":190,"orcid":191},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":193,"orcid":194},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":196,"orcid":197},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":199,"orcid":200},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":202,"orcid":203},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",0,{"tldr":206,"method":207,"finding":208,"direction":209,"opportunity":210},"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。","农业遥感与作物表型","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。","openalex","2026-10-15T23:30:20.537887Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":8,"source_name":219,"source_url":216,"published_at":220,"category":129,"cover_url":8,"hotness":12,"is_selected":13,"score":221,"score_detail":222,"sources":224,"tags":226,"primary_topic":231,"secondary_topics":232,"classification":238,"search_phrases":261,"slug":264,"view_count":20,"doi":265,"paper":266,"created_at":305},4445,"Soil intrinsic penetration (SIP) index: decoupling environmental noise from mechanical resistance to improve soybean yield prediction","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75118-7","Soil compaction is a critical global agricultural concern, acting as a severe physical barrier that restricts root system expansion and reduces crop productivity. However, conventional Soil Penetration Resistance (PR) is a transient metric dictated by instantaneous hydraulic states rather than a stable structural property, leading to misleading interpretations in precision agriculture. This study aimed to develop and validate the Moisture-Normalized Soil Intrinsic Penetration (SIP) Index to isolate persistent anthropogenic structural degradation from environmental noise. Data were compiled from five field campaigns (2020–2025) conducted on a Typic Hapludox in Chapadão do Sul, Brazil. Three distinct sensing technologies: electronic (reference); hydraulic; and impact penetrometers, were evaluated across four depth intervals (0.0–0.4 m). The SIP Index was derived using a power-law normalization approach (SIP = PR x SW α x Clay β ), with empirical coefficients optimized via a Differential Evolution algorithm to maximize Spearman correlations with soybean yield. Results consistently identified the 0.10–0.20 m layer as the critical functional compaction barrier (traffic pan) across all sensing systems. While raw PR from continuous-insertion sensors (electronic and hydraulic) showed negligible or counter-intuitive yield correlations (ρ ≈ -0.01 to 0.08), the SIP Index drastically enhanced predictive capacity, reaching ρ = -0.39 for the electronic sensor, representing a substantial shift from the negligible associations observed with raw data, and ρ = -0.41 for the hydraulic system. Impact penetrometers exhibited higher inherent stability against moisture variations, rendering the SIP correction less significant for that device. Ultimately, the SIP framework effectively filters out pedogenic and hydraulic noise, providing a robust anthropogenic signature for delineating management zones and identifying true mechanical constraints to crop performance.","土壤压实是全球农业面临的严峻问题，作为一种严重的物理障碍，限制根系扩展并降低作物生产力。然而，传统的土壤穿透阻力（PR）是一种瞬时指标，受即时水分状态支配，而非稳定的结构属性，这导致在精准农业中产生误导性解读。本研究旨在开发并验证水分归一化土壤固有穿透（SIP）指数，以将持久性人为结构退化与环境噪声分离。数据来源于2020至2025年间在巴西沙帕当杜苏尔一处典型强淋溶老成土上开展的五次田间试验。评估了三种不同的传感技术：电子（参考）、液压和冲击式穿透仪，涵盖四个深度区间（0.0–0.4 m）。SIP指数采用幂律归一化方法（SIP = PR × SW^α × Clay^β）推导，经验系数通过差分进化算法优化，以最大化与大豆产量的Spearman相关性。结果一致表明，在所有传感系统中，0.10–0.20 m土层均为关键功能性压实障碍（机械压实层）。连续插入式传感器（电子和液压）的原始PR与产量相关性可忽略或呈反直觉关系（ρ ≈ -0.01至0.08），而SIP指数大幅提升了预测能力，电子传感器达到ρ = -0.39，相较于原始数据的可忽略关联实现了显著转变，液压系统达到ρ = -0.41。冲击式穿透仪对水分变化表现出更高的固有稳定性，因此SIP校正对该设备意义较小。最终，SIP框架有效滤除了成土和水分噪声，为划定管理分区和识别作物生长的真实机械限制提供了稳健的人为信号。","Scientific Reports","2026-10-06T00:00:00Z",76,{"impact":156,"substance":133,"depth":134,"authority":135,"freshness":136,"relevant":20,"comment":223},"提出水分归一化土壤内在穿透指数，用三种传感技术跨五年田间数据验证，显著提升大豆产量预测能力，方法新颖且对精准农业管理分区有实用价值。",[225],{"name":219,"url":216},[25,227,228,229,230],"精准农业","土壤传感","土壤压实","大豆产量",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[233],{"code":61,"slug":234,"name":235,"path":236,"parent_code":237},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":38,"locked":13,"version":39,"method":40,"evidence":239},{"candidates":240,"agricultural_evidence":45},[241,245,250,255],{"code":31,"score":19,"supported":45,"title":242,"abstract":243,"tags":244},[158],[48,158],[],{"code":61,"score":19,"supported":45,"title":246,"abstract":247,"tags":249},[234],[248,234],"土壤",[],{"code":110,"score":136,"supported":45,"title":251,"abstract":252,"tags":254},[],[227,253],"precision agriculture",[25,227],{"code":256,"score":68,"supported":45,"title":257,"abstract":258,"tags":260},"J3",[],[259],"全球农业",[],[262,263],"土壤穿透阻力 大豆产量","SIP 指数 土壤压实","土壤穿透阻力大豆产量-4445","10.1038\u002Fs41598-026-75118-7",{"doi":265,"openalex_id":267,"authors":268,"venue":219,"cited_by_count":204,"oa_url":297,"card":298,"direction":304,"ingested_from":211},"W7220671604",[269,272,274,277,280,283,286,289,291,294],{"name":270,"orcid":271},"Fábio Henrique Rojo Baio","https:\u002F\u002Forcid.org\u002F0000-0002-9522-0342",{"name":273,"orcid":8},"Job Teixeira de Oliveira",{"name":275,"orcid":276},"Cassiano Garcia Roque","https:\u002F\u002Forcid.org\u002F0000-0001-6872-0424",{"name":278,"orcid":279},"Ricardo Gava","https:\u002F\u002Forcid.org\u002F0000-0001-6268-5728",{"name":281,"orcid":282},"Larissa Pereira Ribeiro Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8121-0119",{"name":284,"orcid":285},"Cid Naudi Silva Campos","https:\u002F\u002Forcid.org\u002F0000-0001-6810-885X",{"name":287,"orcid":288},"Rafael Felippe Ratke","https:\u002F\u002Forcid.org\u002F0000-0001-6930-3913",{"name":290,"orcid":8},"Octávio Babosa Plaster",{"name":292,"orcid":293},"Dthenifer Cordeiro Santana","https:\u002F\u002Forcid.org\u002F0000-0001-7611-6040",{"name":295,"orcid":296},"Paulo Eduardo Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8236-542X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75118-7_reference.pdf",{"tldr":299,"method":300,"finding":301,"direction":302,"opportunity":303},"提出土壤固有穿透指数SIP，剔除水分等环境噪声，提升大豆产量预测精度。","基于2020-2025年巴西五季田间数据，用幂律归一化与差分进化算法优化系数。","0.10-0.20米为关键压实层，SIP使电子与液压传感器产量相关性提升至-0.39和-0.41。","智慧农业 \u002F 农业物联网","可探索SIP指数在不同土壤类型与作物上的普适性，并集成到实时传感与变量作业决策中。","数字乡村与农业信息化","2026-10-08T23:30:38.497161Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":8,"source_name":312,"source_url":309,"published_at":220,"category":129,"cover_url":8,"hotness":12,"is_selected":13,"score":313,"score_detail":314,"sources":316,"tags":318,"primary_topic":321,"secondary_topics":322,"classification":323,"search_phrases":345,"slug":348,"view_count":204,"doi":349,"paper":350,"created_at":372},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",79,{"impact":132,"substance":133,"depth":134,"authority":19,"freshness":44,"relevant":20,"comment":315},"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。",[317],{"name":312,"url":309},[25,227,165,319,320],"作物产量","农业生态",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":324},{"candidates":325,"agricultural_evidence":45},[326,330,335,340],{"code":110,"score":132,"supported":45,"title":327,"abstract":328,"tags":329},[253],[227,253],[25,227],{"code":31,"score":68,"supported":45,"title":331,"abstract":332,"tags":334},[],[333],"向日葵",[],{"code":147,"score":336,"supported":13,"title":337,"abstract":338,"tags":339},2,[],[],[319,165],{"code":341,"score":20,"supported":13,"title":342,"abstract":343,"tags":344},"H6",[],[],[320],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":312,"cited_by_count":204,"oa_url":309,"card":367,"direction":209,"ingested_from":211},"W7220681058",[353,356,359,362,364],{"name":354,"orcid":355},"Facundo José Oddi","https:\u002F\u002Forcid.org\u002F0000-0003-4478-978X",{"name":357,"orcid":358},"Matías G. Goldenberg","https:\u002F\u002Forcid.org\u002F0000-0002-9498-2951",{"name":360,"orcid":361},"Paula Zermoglio","https:\u002F\u002Forcid.org\u002F0000-0002-6056-5084",{"name":363,"orcid":8},"Tomás González",{"name":365,"orcid":366},"Lucas A. Garibaldi","https:\u002F\u002Forcid.org\u002F0000-0003-0725-4049",{"tldr":368,"method":369,"finding":370,"direction":209,"opportunity":371},"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":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":8,"source_name":379,"source_url":376,"published_at":380,"category":129,"cover_url":8,"hotness":12,"is_selected":13,"score":381,"score_detail":382,"sources":386,"tags":388,"primary_topic":392,"secondary_topics":393,"classification":399,"search_phrases":417,"slug":420,"view_count":336,"doi":421,"paper":422,"created_at":451},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":383,"substance":384,"depth":18,"authority":19,"freshness":44,"relevant":20,"comment":385},12,20,"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。",[387],{"name":379,"url":376},[25,389,390,227,391],"农业人工智能","目标检测","花生播种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[394],{"code":395,"slug":396,"name":397,"path":398,"parent_code":151},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":13,"version":39,"method":40,"evidence":400},{"candidates":401,"agricultural_evidence":45},[402,408,413],{"code":395,"score":132,"supported":45,"title":403,"abstract":405,"tags":407},[404],"deep learning",[406,404],"深度学习",[389,390],{"code":31,"score":19,"supported":45,"title":409,"abstract":411,"tags":412},[410],"peanut",[50,410],[],{"code":110,"score":336,"supported":13,"title":414,"abstract":415,"tags":416},[],[],[25,227],[418,419],"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":421,"openalex_id":423,"authors":424,"venue":379,"cited_by_count":204,"oa_url":8,"card":445,"direction":449,"ingested_from":211},"W7215133023",[425,428,430,432,434,436,438,440,443],{"name":426,"orcid":427},"Baogang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3430-7508",{"name":429,"orcid":8},"Enxi Liu",{"name":431,"orcid":8},"Yijia Chen",{"name":433,"orcid":8},"Zhipeng Feng",{"name":435,"orcid":8},"Yuhuan Li",{"name":437,"orcid":8},"Zunmin Liu",{"name":439,"orcid":8},"Jingchun Tang",{"name":441,"orcid":442},"Jiang Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6746-9987",{"name":444,"orcid":8},"Fazhan Yang",{"tldr":446,"method":447,"finding":448,"direction":449,"opportunity":450},"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。","农业人工智能与决策模型","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":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":8,"source_name":379,"source_url":455,"published_at":380,"category":129,"cover_url":8,"hotness":12,"is_selected":13,"score":381,"score_detail":458,"sources":460,"tags":462,"primary_topic":466,"secondary_topics":467,"classification":468,"search_phrases":484,"slug":487,"view_count":204,"doi":488,"paper":489,"created_at":509},3983,"Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112469","Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling。Computers and Electronics in Agriculture","基于SPH-FEM耦合的磨料气体射流油菜茎秆切割机理数值与实验研究。《计算机与电子农业》",{"impact":383,"substance":384,"depth":18,"authority":19,"freshness":44,"relevant":20,"comment":459},"核心期刊论文，SPH-FEM耦合方法研究油菜秸秆磨料气体射流切割机理，方法有新意但属细分领域基础研究，产业影响有限。",[461],{"name":379,"url":455},[25,49,463,464,465],"数值模拟","农业机械","秸秆处理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":469},{"candidates":470,"agricultural_evidence":45},[471,476,480],{"code":31,"score":156,"supported":45,"title":472,"abstract":474,"tags":475},[473],"rapeseed",[49,473],[49],{"code":98,"score":20,"supported":13,"title":477,"abstract":478,"tags":479},[],[],[464],{"code":110,"score":20,"supported":13,"title":481,"abstract":482,"tags":483},[],[],[25],[485,486],"油菜秸秆 磨料气体射流 切割","SPH-FEM 耦合 秸秆切割","油菜秸秆磨料气体射流切割-3983","10.1016\u002Fj.compag.2026.112469",{"doi":488,"openalex_id":490,"authors":491,"venue":379,"cited_by_count":204,"oa_url":8,"card":504,"direction":449,"ingested_from":211},"W7215128671",[492,495,497,499,501],{"name":493,"orcid":494},"Mun-Sung Han","https:\u002F\u002Forcid.org\u002F0009-0000-4043-6982",{"name":496,"orcid":8},"Xiaokang Qin",{"name":498,"orcid":8},"Kai Yu",{"name":500,"orcid":8},"Yun Chen",{"name":502,"orcid":503},"Kaixiong Hu","https:\u002F\u002Forcid.org\u002F0000-0001-9904-6137",{"tldr":505,"method":506,"finding":507,"direction":449,"opportunity":508},"采用SPH-FEM耦合方法数值与实验研究磨料气体射流切割油菜茎秆的机理。","SPH-FEM耦合数值模拟结合切割实验，分析磨料气体射流切割过程。","揭示了磨料气体射流切割油菜茎秆的力学机制与切割参数影响规律。","可将SPH-FEM耦合仿真拓展至其他作物茎秆切割，并融合机器视觉实现切割参数智能优化。","2026-10-02T23:30:03.593073Z",{"id":511,"title":512,"url":513,"summary":514,"summary_zh":515,"content":8,"source_name":379,"source_url":513,"published_at":380,"category":129,"cover_url":8,"hotness":12,"is_selected":13,"score":381,"score_detail":516,"sources":518,"tags":520,"primary_topic":523,"secondary_topics":524,"classification":525,"search_phrases":541,"slug":544,"view_count":336,"doi":545,"paper":546,"created_at":571},3977,"Development and evaluation of a chain-groove lateral seedling feeding device based on modular stepping misalignment for multi-row transplanting of rapeseed bare-root seedlings","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112468","Development and evaluation of a chain-groove lateral seedling feeding device based on modular stepping misalignment for multi-row transplanting of rapeseed bare-root seedlings。Computers and Electronics in Agriculture","基于模块化步进错位的链槽式侧向投苗装置开发与评估：面向油菜裸根苗多行移栽。Computers and Electronics in Agriculture",{"impact":383,"substance":384,"depth":18,"authority":19,"freshness":44,"relevant":20,"comment":517},"核心期刊论文，提出链槽式模块化错位送苗装置，对多行油菜裸苗移栽机械化有实质方法创新，但属细分领域技术进展，公共影响有限。",[519],{"name":379,"url":513},[25,521,227,49,522],"农业机器人","移栽机械",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":526},{"candidates":527,"agricultural_evidence":45},[528,532,536],{"code":31,"score":156,"supported":45,"title":529,"abstract":530,"tags":531},[473],[49,473],[49],{"code":110,"score":336,"supported":13,"title":533,"abstract":534,"tags":535},[],[],[25,227],{"code":537,"score":20,"supported":13,"title":538,"abstract":539,"tags":540},"F6",[],[],[521],[542,543],"油菜裸苗移栽 链槽式送苗装置","Computers and Electronics in Agriculture 移栽","油菜裸苗移栽链槽式送苗装置-3977","10.1016\u002Fj.compag.2026.112468",{"doi":545,"openalex_id":547,"authors":548,"venue":379,"cited_by_count":204,"oa_url":8,"card":566,"direction":302,"ingested_from":211},"W7215105622",[549,552,554,556,559,561,563],{"name":550,"orcid":551},"Hua Yuan","https:\u002F\u002Forcid.org\u002F0000-0001-7594-8718",{"name":553,"orcid":8},"Qingxi Liao",{"name":555,"orcid":8},"Zhengyu Liu",{"name":557,"orcid":558},"Zhe Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2653-0408",{"name":560,"orcid":8},"Lin Li",{"name":562,"orcid":8},"Yitao Liao",{"name":564,"orcid":565},"Wenli Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-8443-8987",{"tldr":567,"method":568,"finding":569,"direction":302,"opportunity":570},"研发并评估链槽式侧向供苗装置，实现油菜裸根苗多行移栽的模块化步进错位供苗。","设计链槽侧向供苗机构，采用模块化步进错位方案，并进行样机试验评估。","该装置能实现多行裸根苗有序错位供苗，提升移栽效率与供苗质量。","可结合视觉检测与伺服控制，研究供苗状态实时监测与自适应错位调节，提升多行移栽智能化水平。","2026-10-02T23:30:02.682513Z"]