[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3523":3,"related-3523":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},3523,"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10458-y","Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system。Precision Agriculture",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,17,14,8,1,"精准农业核心期刊论文，探讨土壤空间变异对变量播种与施肥效果的影响，方法新颖、结论可靠，对智慧农业田间管理有参考价值，但属细分领域研究，影响范围有限。",[24],{"name":9,"url":6},[26,27,28,29,30,31],"智慧农业","变量施肥","玉米","精准农业","向日葵","变量播种",[33,34],"Precision Agriculture 变量播种 变量施肥","灌溉玉米 向日葵 土壤变异","PrecisionAgriculture变量播种变量施肥-3523",0,"10.1007\u002Fs11119-026-10458-y",{"doi":37,"openalex_id":39,"authors":40,"venue":9,"cited_by_count":36,"oa_url":8,"card":8,"direction":8,"ingested_from":54},"W7214357812",[41,44,46,48,50,52],{"name":42,"orcid":43},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":45,"orcid":8},"J. A. Martínez-Casasnovas",{"name":47,"orcid":8},"S. Artero",{"name":49,"orcid":8},"A. Vigo",{"name":51,"orcid":8},"M. Vidal",{"name":53,"orcid":8},"F. J. García-Ramos","openalex","2026-09-26T23:30:03.127069Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,107,149,187,242,270],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":8,"source_name":65,"source_url":62,"published_at":66,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":67,"score_detail":68,"sources":72,"tags":74,"search_phrases":77,"slug":80,"view_count":36,"doi":81,"paper":82,"created_at":106},3516,"Approaches to forecast soil nutrient dynamics for precision agriculture and sustainable fertiliser management: A review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16160","Predictive modelling of soil nutrient dynamics is an essential tool for promoting sustainable agricultural practices and environmentally responsible farming methods. The statistical and machine learning techniques used to forecast the availability and dynamics of soil nutrients are summarised in this review. The core frameworks for measuring spatio-temporal nutritional variability are established by traditional statistical approaches such as time-series models autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), multivariate techniques (Principal component analysis (PCA) and factor analysis) and geostatistical tools (kriging). By capturing intricate nonlinear interactions within heterogeneous agroecosystems, machine learning techniques like random forest, support vector machines and ensemble approaches (XGBoost, LightGBM and AdaBoost) provide higher prediction accuracy. Forecasting capabilities are further enhanced by hybrid frameworks [Autoregressive integrated moving average with exogenous variables–artificial neural network. (ARIMAX-ANN)] and deep learning architectures (Convolutional neural network (CNN), long short-term memory (LSTM), ANN). With R2 values above 0.93 and notable decreases in prediction errors, ensemble approaches routinely perform better than traditional linear models. Nevertheless, persistent challenges include data quality limitations, spatial sampling constraints, insufficient environmental covariates and reduced model transferability across diverse pedoclimatic regions. Integrating high-resolution soil properties, climatic variables, terrain attributes and spectral information with advanced modelling architectures remains crucial for enhancing predictive reliability, ultimately supporting precision nutrient management, improved fertiliser efficiency and environmentally responsible agricultural systems.","土壤养分动态的预测建模是推动可持续农业实践和环境友好型耕作方法的重要工具。本综述总结了用于预测土壤养分有效性及其动态变化的统计与机器学习技术。传统统计方法，如时间序列模型自回归积分滑动平均模型（ARIMA）、季节性自回归积分滑动平均模型（SARIMA）、多变量技术（主成分分析（PCA）和因子分析）以及地统计工具（克里金法），为量化养分的时空变异性奠定了核心框架。通过捕捉异质性农业生态系统中复杂的非线性相互作用，随机森林、支持向量机和集成方法（XGBoost、LightGBM和AdaBoost）等机器学习技术可实现更高的预测精度。混合框架[含外生变量的自回归积分滑动平均模型–人工神经网络（ARIMAX-ANN）]和深度学习架构（卷积神经网络（CNN）、长短期记忆网络（LSTM）、人工神经网络（ANN））进一步增强了预测能力。集成方法的R²值超过0.93，且预测误差显著降低，其表现通常优于传统线性模型。然而，持续存在的挑战包括数据质量限制、空间采样约束、环境协变量不足以及模型在不同土壤气候区域间可迁移性降低等问题。将高分辨率土壤属性、气候变量、地形属性和光谱信息与先进建模架构相结合，对于提高预测可靠性仍然至关重要，最终可为精准养分管理、提高肥料利用效率以及环境友好型农业系统提供支撑。","Plant Science Today","2026-09-24T00:00:00Z",79,{"impact":17,"substance":69,"depth":17,"authority":70,"freshness":20,"relevant":21,"comment":71},22,13,"系统综述土壤养分动态预测的统计与机器学习方法，方法体系完整、结论有量化支撑，对精准施肥与农业信息化有参考价值，但属综述类论文，产业级影响有限。",[73],{"name":65,"url":62},[26,27,75,29,76],"机器学习","土壤养分",[78,79],"土壤养分 预测模型 精准农业","机器学习 施肥管理 可持续农业","土壤养分预测模型精准农业-3516","10.14719\u002Fpst.16160",{"doi":81,"openalex_id":83,"authors":84,"venue":65,"cited_by_count":36,"oa_url":62,"card":100,"direction":104,"ingested_from":54},"W7214167059",[85,88,91,94,97],{"name":86,"orcid":87},"R Rathna","https:\u002F\u002Forcid.org\u002F0009-0004-7797-2673",{"name":89,"orcid":90},"B Sivasankari","https:\u002F\u002Forcid.org\u002F0000-0001-9921-8170",{"name":92,"orcid":93},"R. Gangai Selvi","https:\u002F\u002Forcid.org\u002F0000-0002-4475-2293",{"name":95,"orcid":96},"J Prabhakaran","https:\u002F\u002Forcid.org\u002F0000-0001-7339-175X",{"name":98,"orcid":99},"K. G. Sabarinathan","https:\u002F\u002Forcid.org\u002F0000-0002-8659-6479",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"综述土壤养分动态预测的统计与机器学习方法，比较精度与局限。","综述ARIMA、地统计、随机森林、XGBoost、CNN\u002FLSTM及混合模型。","集成与深度学习模型精度更高（R²>0.93），但数据质量与跨区迁移性仍是瓶颈。","农业人工智能与决策模型","可研究多源遥感与气候数据融合的迁移学习模型，提升跨区域养分预测泛化能力。","2026-09-25T23:30:54.950445Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":8,"source_name":113,"source_url":110,"published_at":114,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":115,"score_detail":116,"sources":120,"tags":122,"search_phrases":125,"slug":128,"view_count":36,"doi":129,"paper":130,"created_at":148},2113,"Orthogonal delineation of site-specific management zones using soil data: A multi-property optimization approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112399","Orthogonal delineation of site-specific management zones using soil data: A multi-property optimization approach。Computers and Electronics in Agriculture","基于土壤数据的特定管理区正交划分：一种多属性优化方法。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-11T00:00:00Z",72,{"impact":16,"substance":117,"depth":18,"authority":19,"freshness":118,"relevant":21,"comment":119},20,9,"核心期刊论文，提出基于多属性优化的管理分区正交划分方法，方法新颖且对精准农业变量管理有直接参考价值，但属细分领域学术进展，公共影响有限。",[121],{"name":113,"url":110},[26,27,29,123,124],"土壤数据","管理分区",[126,127],"变量施肥 土壤数据 智慧农业 管理分区","变量施肥 土壤数据","变量施肥土壤数据智慧农业管理分区-2113","10.1016\u002Fj.compag.2026.112399",{"doi":129,"openalex_id":131,"authors":132,"venue":113,"cited_by_count":36,"oa_url":141,"card":142,"direction":146,"ingested_from":54},"W7157647968",[133,136,138],{"name":134,"orcid":135},"Salvador J. Vicencio-Medina","https:\u002F\u002Forcid.org\u002F0000-0002-3285-097X",{"name":137,"orcid":8},"Armin Lüer‐Villagra",{"name":139,"orcid":140},"Gonzalo Méndez-Vogel","https:\u002F\u002Forcid.org\u002F0000-0002-6455-7305","https:\u002F\u002Ffigshare.com\u002Farticles\u002Fjournal_contribution\u002FOrthogonal_delineation_of_site-specific_management_zones_using_soil_data_a_multi-property_optimization_approach\u002F32095483",{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"提出多属性优化方法，利用土壤数据正交划分特定地点管理分区。","多属性优化与正交划分，基于土壤属性数据。","多属性优化可有效生成管理分区，提升分区独立性。","农业遥感与作物表型","可结合遥感与实时传感器数据，发展动态管理分区与变量施肥决策。","2026-09-11T23:30:01.392377Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":8,"source_name":113,"source_url":152,"published_at":155,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":156,"score_detail":157,"sources":161,"tags":163,"search_phrases":165,"slug":168,"view_count":21,"doi":169,"paper":170,"created_at":186},1754,"Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112349","Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops。Computers and Electronics in Agriculture","低成本自主无人地面车辆用于玉米作物原位磷筛查以支持分区施肥。农业计算机与电子","2026-09-05T00:00:00Z",62,{"impact":16,"substance":17,"depth":158,"authority":19,"freshness":159,"relevant":21,"comment":160},16,2,"低成本无人车用于磷素原位检测，支持玉米分区施肥，技术可行但时效性低。",[162],{"name":113,"url":152},[26,164,27,28],"农业机器人",[166,167],"农业机器人 变量施肥 智慧农业 玉米","农业机器人 变量施肥","农业机器人变量施肥智慧农业玉米-1754","10.1016\u002Fj.compag.2026.112349",{"doi":169,"openalex_id":171,"authors":172,"venue":113,"cited_by_count":36,"oa_url":8,"card":180,"direction":184,"ingested_from":54},"W7208817219",[173,175,178],{"name":174,"orcid":8},"Lalo Villacorta",{"name":176,"orcid":177},"Adrian Medrano","https:\u002F\u002Forcid.org\u002F0009-0009-2010-3828",{"name":179,"orcid":8},"Miguel Lara",{"tldr":181,"method":182,"finding":183,"direction":184,"opportunity":185},"研发低成本自主地面机器人，原位筛查玉米磷含量，支持分区施肥。","低成本UGV搭载原位传感器，结合自主导航与磷筛查技术。","实现低成本、自主的田间磷检测，为分区施肥提供依据。","智慧农业 \u002F 农业物联网","可延伸研究多养分原位传感与自主决策，或结合无人机与地面机器人协同，提升精准施肥效率。","2026-09-06T23:30:01.426286Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":8,"content":8,"source_name":192,"source_url":190,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":194,"score_detail":195,"sources":198,"tags":200,"search_phrases":203,"slug":206,"view_count":36,"doi":207,"paper":208,"created_at":241},1600,"Integrating proximal soil sensing and Sentinel-1\u002F2 remote sensing for management zone delineation and monitoring of variable-rate nitrogen fertilization in winter barley","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102542","Integrating proximal soil sensing and Sentinel-1\u002F2 remote sensing for management zone delineation and monitoring of variable-rate nitrogen fertilization in winter barley。Smart Agricultural Technology","Smart Agricultural Technology","2026-09-01T00:00:00Z",66,{"impact":16,"substance":117,"depth":17,"authority":16,"freshness":196,"relevant":21,"comment":197},4,"研究结合近地传感与卫星遥感，为冬大麦管理分区和变量施氮提供新方法，具实践价值。",[199],{"name":192,"url":190},[26,27,29,201,202],"遥感","冬大麦",[204,205],"变量施肥 智慧农业 精准农业 冬大麦","变量施肥 智慧农业","变量施肥智慧农业精准农业冬大麦-1600","10.1016\u002Fj.atech.2026.102542",{"doi":207,"openalex_id":209,"authors":210,"venue":192,"cited_by_count":36,"oa_url":190,"card":8,"direction":8,"ingested_from":54},"W7207736997",[211,213,215,218,221,223,225,227,229,231,233,235,238],{"name":212,"orcid":8},"Nazerke Amangeldy",{"name":214,"orcid":8},"Eduardo Garcia-Braga",{"name":216,"orcid":217},"Yasmina Chourak","https:\u002F\u002Forcid.org\u002F0000-0001-7634-3497",{"name":219,"orcid":220},"Gerard Portal","https:\u002F\u002Forcid.org\u002F0000-0003-0797-6711",{"name":222,"orcid":8},"Hongzhen Luo",{"name":224,"orcid":8},"Isi Bardají",{"name":226,"orcid":8},"Mercè Vall-llossera",{"name":228,"orcid":8},"Rosa Vilaplana Ventura",{"name":230,"orcid":8},"Antonios Morellos",{"name":232,"orcid":8},"Manuel Vázquez-Arellano",{"name":234,"orcid":8},"Erik Meers",{"name":236,"orcid":237},"Estefanía Blanch","https:\u002F\u002Forcid.org\u002F0000-0002-8438-2776",{"name":239,"orcid":240},"Abdul Mounem Mouazen","https:\u002F\u002Forcid.org\u002F0000-0002-0354-0067","2026-09-04T23:30:04.649483Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":8,"content":8,"source_name":9,"source_url":245,"published_at":247,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":248,"score_detail":249,"sources":252,"tags":254,"search_phrases":256,"slug":259,"view_count":36,"doi":260,"paper":261,"created_at":269},1211,"A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10437-3","A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data。Precision Agriculture","2026-08-25T00:00:00Z",70,{"impact":250,"substance":117,"depth":17,"authority":250,"freshness":159,"relevant":21,"comment":251},15,"论文提出结合土壤传感与卫星NDVI的变量播种决策框架，方法新颖，数据可靠，但时效性较低。",[253],{"name":9,"url":245},[26,28,29,201,255],"土壤传感",[257,258],"土壤传感 智慧农业 精准农业 玉米","土壤传感 智慧农业","土壤传感智慧农业精准农业玉米-1211","10.1007\u002Fs11119-026-10437-3",{"doi":260,"openalex_id":262,"authors":263,"venue":9,"cited_by_count":36,"oa_url":8,"card":8,"direction":8,"ingested_from":54},"W7204189193",[264,266,268],{"name":265,"orcid":8},"Jialu Sun",{"name":232,"orcid":267},"https:\u002F\u002Forcid.org\u002F0000-0001-7820-5143",{"name":239,"orcid":240},"2026-09-01T04:03:03.736293Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":8,"content":8,"source_name":275,"source_url":8,"published_at":66,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":67,"score_detail":276,"sources":278,"tags":280,"search_phrases":284,"slug":287,"view_count":36,"doi":8,"paper":288,"created_at":296},3600,"Development of an Adaptive Sprayer Control System for UAV-Based Spot Spraying in Soybean（基于植被覆盖率的自适应无人机大豆点喷控制系统研发）","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260924002257098.htm","本研究开发了一套集成无人机的自适应喷雾控制系统，将冠层感知与基于阈值的点喷技术相连接。研究人员利用大豆单独覆盖度作为局部杂草斑块检测的基准基线，室内与田间估算结果的R²值分别为0.91和0.69。在播种后24天，采用25%的阈值可将潜在误喷激活率限制在4.8%。室内测试中，自动喷雾执行准确率达到97.3%。研究指出杂草可导致大豆超过30%的产量损失，全球年经济损失达330亿美元。无人机喷洒避免了与土壤的直接接触，减少了土壤压实和作物损伤的风险，提高了关键除草窗口期的操作灵活性。","Smart Agricultural Technology 7.1 2026-09-24",{"impact":17,"substance":69,"depth":17,"authority":70,"freshness":20,"relevant":21,"comment":277},"该研究提出基于冠层覆盖度的无人机自适应点喷控制方法，数据详实、结论可靠，对精准植保技术研发具有参考价值，值得进入每日精选。",[279],{"name":275,"url":273},[26,29,281,282,283],"大豆","无人机植保","杂草管理",[285,286],"无人机 大豆 点喷","自适应喷雾 大豆 杂草","无人机大豆点喷-3600",{"doi":8,"openalex_id":8,"authors":289,"venue":8,"cited_by_count":36,"oa_url":8,"card":290,"direction":184,"ingested_from":295},[],{"tldr":291,"method":292,"finding":293,"direction":184,"opportunity":294},"研发基于冠层覆盖率的无人机自适应点喷系统，实现大豆田间精准除草。","用无人机冠层感知与阈值点喷控制，室内外估算覆盖度R²为0.91和0.69。","25%阈值下误喷激活率仅4.8%，室内自动喷雾执行准确率达97.3%。","可探索多作物、多生育期自适应阈值与杂草识别模型融合，提升田间鲁棒性。","agent","2026-09-27T00:05:18.158668Z"]