[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3515":3,"related-3515":57},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":56},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》",null,"International Journal of Systems Assurance Engineering and Management","2026-09-24T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,14,15,13,8,1,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","精准施肥","遥感","土壤制图",[33,34],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515",0,"10.1007\u002Fs13198-026-03435-1",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":9,"card":49,"direction":53,"ingested_from":55},"W7214144821",[41,44,46],{"name":42,"orcid":43},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":42,"orcid":45},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":47,"orcid":48},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","农业人工智能与决策模型","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","openalex","2026-09-25T23:30:49.869002Z",{"total":58,"page":22,"page_size":58,"items":59},6,[60,106,143,174,201,244],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":9,"source_name":66,"source_url":63,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":72,"tags":74,"search_phrases":77,"slug":80,"view_count":36,"doi":81,"paper":82,"created_at":105},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International",66,{"impact":17,"substance":69,"depth":70,"authority":17,"freshness":21,"relevant":22,"comment":71},18,16,"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[73],{"name":66,"url":63},[27,28,30,75,76],"GIS","干旱监测",[78,79],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":81,"openalex_id":83,"authors":84,"venue":66,"cited_by_count":36,"oa_url":63,"card":99,"direction":103,"ingested_from":55},"W7214156857",[85,87,89,91,93,95,97],{"name":86,"orcid":9},"V. Dhanalakshmi",{"name":88,"orcid":9},"N. Manikandan",{"name":90,"orcid":9},"V. S. Jinsy",{"name":92,"orcid":9},"K. V. Sumesh",{"name":94,"orcid":9},"P. Nideesh",{"name":96,"orcid":9},"P. S. Manju",{"name":98,"orcid":9},"N. Gopika",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","农业遥感与作物表型","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":114,"is_selected":14,"score":115,"score_detail":116,"sources":118,"tags":122,"search_phrases":125,"slug":128,"view_count":36,"doi":129,"paper":130,"created_at":142},3462,"AI For Sustainable Development Opportunities & Innovation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933761","Abstract Artificial Intelligence is increasingly being explored as a tool for accelerating progress toward sustainable development. AI can analyse large datasets, identify patterns, forecast events, optimise systems and support decision-making across agriculture, energy, water, healthcare, education, cities, industry and environmental management. Recent research shows that AI-for-SDG research is expanding rapidly, while also revealing gaps in social inclusion, governance and assessment of AI’s own environmental footprint. This project examines the major opportunities for AI-enabled sustainable development, including smart agriculture, renewable-energy optimisation, climate and disaster forecasting, intelligent waste management, sustainable cities, healthcare and education. It also discusses innovation pathways such as machine learning, computer vision, remote sensing, generative AI, digital twins and edge AI. The study emphasises that technological capability alone is insufficient: responsible AI requires reliable data, transparency, privacy, human oversight, equitable access, energy-efficient computing and lifecycle environmental assessment.","摘要 人工智能正日益被视为加速可持续发展进程的工具。人工智能可以分析大型数据集、识别模式、预测事件、优化系统，并在农业、能源、水资源、医疗、教育、城市、工业与环境管理等领域支持决策。近期研究表明，人工智能促进可持续发展目标（SDG）的研究正在迅速扩展，同时也揭示了在社会包容、治理以及人工智能自身环境足迹评估方面的不足。本项目考察了人工智能赋能可持续发展的主要机遇，包括智慧农业、可再生能源优化、气候与灾害预测、智能废物管理、可持续城市、医疗和教育。项目还讨论了机器学习、计算机视觉、遥感、生成式人工智能、数字孪生和边缘人工智能等创新路径。研究强调，仅靠技术能力是不够的：负责任的人工智能需要可靠的数据、透明度、隐私保护、人类监督、公平获取、节能计算以及生命周期环境评估。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,68,{"impact":69,"substance":70,"depth":19,"authority":20,"freshness":58,"relevant":22,"comment":117},"系统梳理AI赋能农业等可持续发展领域的机遇与治理挑战，属综合性研究综述，对智慧农业方向有参考价值但非突破性成果。",[119,120],{"name":112,"url":109},{"name":112,"url":121},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[123,27,28,124,30],"数字乡村","可持续发展",[126,127],"AI 可持续发展 智慧农业","农业人工智能 可持续发展 数字乡村 智慧农业","AI可持续发展智慧农业-3462","10.5281\u002Fzenodo.22933761",{"doi":129,"openalex_id":131,"authors":132,"venue":112,"cited_by_count":36,"oa_url":109,"card":135,"direction":141,"ingested_from":55},"W7214172367",[133],{"name":134,"orcid":9},"Saloni Ananda Patil",{"tldr":136,"method":137,"finding":138,"direction":139,"opportunity":140},"综述AI在可持续发展各领域的机会与创新路径，并强调负责任AI的治理要求。","文献综述，覆盖机器学习、计算机视觉、遥感、数字孪生与边缘AI等。","AI-for-SDG研究快速扩张，但社会包容、治理与AI自身环境足迹评估仍存缺口。","农业绿色发展与碳","可量化AI自身能耗与碳足迹，并评估其在农业减排中的净环境效益。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:08.894307Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":9,"content":9,"source_name":148,"source_url":9,"published_at":149,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":150,"score_detail":151,"sources":156,"tags":158,"search_phrases":161,"slug":164,"view_count":22,"doi":9,"paper":165,"created_at":173},3326,"Research on the Application of Agricultural Big Data in Plant Growth Prediction——基于多源数据同化与混合智能（MDA-HI）框架","https:\u002F\u002Fwww.icck.org\u002Ffilebob\u002Fuploads\u002Fstorage\u002FDIA_ANrbHOHS4uBBpnVQ6.pdf","《Digital Intelligence in Agriculture》2026年第2卷第2期。Wei Yongqiang等提出Multi-source Data Assimilation and Hybrid Intelligence（MDA-HI）框架，将基于过程的作物模型与集成机器学习算法（包括基于Transformer的架构和物理信息神经网络）相结合。在2023—2025年中国多生态区主要作物（水稻、小麦、玉米、番茄）的实证验证中：MDA-HI模型实现了产量预测RMSE平均减少42.7%、关键物候期预测减少38.1%。水稻-小麦轮作系统大规模案例研究显示数据驱动处方可将氮肥使用减少22.5%、灌溉水减少18.3%，同时产量增加5.1%。","《Digital Intelligence in Agriculture》2026; 2(2):54-67","2026-09-17T00:00:00Z",85,{"impact":152,"substance":153,"depth":154,"authority":18,"freshness":58,"relevant":22,"comment":155},22,24,19,"多源数据同化与混合智能框架在四大作物上验证，减肥节水增产数据扎实，方法新颖且具产业推广价值。",[157],{"name":148,"url":146},[27,28,29,159,160],"农业大数据","作物生长预测",[162,163],"MDA-HI 多源数据同化 作物模型","水稻小麦轮作 氮肥减量 产量预测","MDA-HI多源数据同化作物模型-3326",{"doi":9,"openalex_id":9,"authors":166,"venue":9,"cited_by_count":36,"oa_url":9,"card":167,"direction":53,"ingested_from":172},[],{"tldr":168,"method":169,"finding":170,"direction":53,"opportunity":171},"提出MDA-HI框架，融合过程模型与混合智能，用于作物生长与产量预测。","多源数据同化结合Transformer与物理信息神经网络，在中国多生态区验证。","产量预测RMSE降42.7%，氮肥减22.5%、灌溉水减18.3%，产量增5.1%。","可探索轻量化MDA-HI在边缘设备部署及跨区域迁移能力，降低小农户应用门槛。","agent","2026-09-24T00:04:02.947080Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":9,"content":9,"source_name":179,"source_url":9,"published_at":149,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":183,"tags":185,"search_phrases":189,"slug":192,"view_count":36,"doi":9,"paper":193,"created_at":200},3324,"Diag-STFN：全球收获前作物产量预测的诊断时空多模态融合网络——覆盖38国玉米29国小麦（Ecological Informatics 2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37354974?&&labelLang=hun","《Ecological Informatics》2026年第96期：Zhuang等提出Diag-STFN——一种诊断时空多模态融合网络，用于全球收获前作物产量预测。该网络基于数据集特征选择模型结构，以确定是否需要时间趋势耦合和空间模块激活。在三种前置期（早、中、晚季）下，基于覆盖38国玉米和29国小麦的CY-Bench基准数据集进行评估。结果表明，所提方法在所有前置期均实现了两种作物的最低汇总NRMSE，并在MAPE和KGE等补充指标上保持领先。消融研究表明诊断模块选择提供了主要的性能提升；方差分解显示性能差异在国家之间大于模型之间。","《Ecological Informatics》96 (2026) 103860",78,{"impact":69,"substance":152,"depth":69,"authority":18,"freshness":58,"relevant":22,"comment":182},"方法新颖、覆盖38国玉米与29国小麦的全球收获前产量预测研究，学术价值突出但产业落地尚早，适合作为前沿技术资讯收录。",[184],{"name":179,"url":177},[27,28,186,187,188,30],"产量预测","小麦","玉米",[190,191],"Diag-STFN 作物产量预测","CY-Bench 玉米 小麦","Diag-STFN作物产量预测-3324",{"doi":9,"openalex_id":9,"authors":194,"venue":9,"cited_by_count":36,"oa_url":9,"card":195,"direction":53,"ingested_from":172},[],{"tldr":196,"method":197,"finding":198,"direction":53,"opportunity":199},"提出诊断式时空多模态融合网络Diag-STFN，实现全球收获前玉米小麦产量预测。","基于CY-Bench基准，按数据特征诊断选择时间趋势与空间模块，覆盖38国玉米2","各前置期均取得最低NRMSE，诊断模块选择贡献最大，国家间差异大于模型间差异。","可探索自适应诊断机制迁移至其他作物，并针对国家间差异开展区域化建模与不确定性量化。","2026-09-24T00:04:02.684732Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":213,"tags":215,"search_phrases":218,"slug":221,"view_count":36,"doi":222,"paper":223,"created_at":243},3277,"Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193272","Soil moisture (SM) is critical for climate, water, and agriculture, but Soil Moisture Active Passive (SMAP) passive microwave products have coarse resolution, limiting regional applications. This study develops an SM downscaling framework based on Transformer and its variants (PatchTST and iTransformer), integrating multi-source satellite and groundwater data to generate 1 km daily SM products (2015–2022). Compared with Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network–LSTM (CNN-LSTM), Transformer and its variants achieve superior accuracy and generalization. Validated against in situ measurements and SMCI1.0, the Transformer-downscaled SM product achieved the best accuracy with ubRMSE = 0.0372 m3\u002Fm3 and RMSE = 0.0591 m3\u002Fm3. The downscaled SM dataset not only captured finer spatial details but also preserved the spatial patterns and seasonal dynamics of the original SMAP product and showed good responsiveness to precipitation events. Feature importance analysis revealed that, aside from precipitation, the diurnal land surface temperature difference had a greater impact on SM than individual daytime or nighttime land surface temperature, ranking just below vegetation indices and soil texture factors, while groundwater level showed higher importance than elevation and surface temperature. This study confirms the effectiveness of Transformer-based models for SM spatial downscaling, providing a novel framework integrating remote sensing and deep hydrological information to generate accurate 1 km SM products.","土壤水分（SM）对气候、水资源和农业至关重要，但土壤水分主动被动（SMAP）被动微波产品分辨率较粗，限制了区域应用。本研究构建了一个基于Transformer及其变体（PatchTST和iTransformer）的土壤水分降尺度框架，融合多源卫星和地下水数据，生成1 km日尺度土壤水分产品（2015—2022年）。与随机森林（RF）、长短期记忆网络（LSTM）和卷积神经网络—长短期记忆网络（CNN-LSTM）相比，Transformer及其变体取得了更高的精度和泛化能力。利用站点实测数据和SMCI1.0进行验证，Transformer降尺度土壤水分产品精度最优，ubRMSE = 0.0372 m³\u002Fm³，RMSE = 0.0591 m³\u002Fm³。降尺度土壤水分数据集不仅捕捉到了更精细的空间细节，还保留了原始SMAP产品的空间格局和季节动态，并对降水事件表现出良好的响应。特征重要性分析表明，除降水外，昼夜地表温差对土壤水分的影响大于单独的白天或夜间地表温度，其重要性仅次于植被指数和土壤质地因子，而地下水埋深的重要性高于高程和地表温度。本研究证实了基于Transformer的模型在土壤水分空间降尺度中的有效性，为融合遥感和深层水文信息生成准确的1 km土壤水分产品提供了一种新框架。","Remote Sensing","2026-09-22T00:00:00Z",81,{"impact":69,"substance":152,"depth":69,"authority":18,"freshness":211,"relevant":22,"comment":212},9,"基于Transformer的SMAP土壤水分1km降尺度研究，方法新颖、验证充分，对区域农业旱情监测有实用价值。",[214],{"name":207,"url":204},[27,28,216,30,217],"深度学习","土壤墒情",[219,220],"SMAP 土壤水分 降尺度","Transformer 土壤水分 安徽","SMAP土壤水分降尺度-3277","10.3390\u002Frs18193272",{"doi":222,"openalex_id":224,"authors":225,"venue":207,"cited_by_count":36,"oa_url":204,"card":238,"direction":103,"ingested_from":55},"W7208807695",[226,228,230,232,234,236],{"name":227,"orcid":9},"Yuyang Fan",{"name":229,"orcid":9},"Jianwei Ma",{"name":231,"orcid":9},"Mengmeng Li",{"name":233,"orcid":9},"Changqing Ke",{"name":235,"orcid":9},"Bin Cheng",{"name":237,"orcid":9},"Zheng Duan",{"tldr":239,"method":240,"finding":241,"direction":103,"opportunity":242},"基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。","Transformer、PatchTST、iTransformer，融合多源卫星","Transformer降尺度产品精度最优（ubRMSE=0.0372），保留原产品时空格局并响应降水","可探索Transformer降尺度产品在区域干旱监测、灌溉决策及作物估产中的耦合应用。","2026-09-23T23:30:19.132307Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":9,"content":9,"source_name":249,"source_url":9,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":250,"score_detail":251,"sources":253,"tags":255,"search_phrases":258,"slug":261,"view_count":36,"doi":9,"paper":262,"created_at":269},3250,"农机化研究2026(11)：基于多源感知与语义分割的果树行识别与路径跟踪控制——郑州工业应用技术学院许洋洋等 DeepLabv3+ F1达0.94","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F166268.html","农机化研究2026年第11期发表郑州工业应用技术学院许洋洋、柴亚珂、王莹、牛瑞利团队论文：针对果园中裸露土壤、遮蔽物干扰和冠层阴影等因素导致导航路径难以稳定提取的问题，提出融合多源感知与语义分割的自走式植保机果树识别与路径控制方法。首先利用无人机获取多光谱影像并生成正射影像（DOM）与数字表面模型（DSM），计算归一化差异绿度指数（NDGI），增强果树冠层与背景的可分性；然后采用U-Net\u002FDeepLabv3+语义分割网络在保留空间上下文信息的同时实现果树行\u002F背景端到端分割。田间对比试验结果表明，DeepLabv3+整体分割F1可达0.94，每样区生成导航线断裂1.2次，且转向工况下横向误差RMSE为0.11 m，显著优于传统阈值分割和SVM方法。","农机化研究",79,{"impact":70,"substance":152,"depth":69,"authority":20,"freshness":13,"relevant":22,"comment":252},"多源遥感与语义分割结合的果园导航研究，F1达0.94且横向误差0.11米，方法新颖、数据扎实，具备行业参考价值。",[254],{"name":249,"url":247},[27,28,256,30,257],"农机导航","果园植保",[259,260],"郑州工业应用技术学院 果树行识别","DeepLabv3 果园导航 路径跟踪","郑州工业应用技术学院果树行识别-3250",{"doi":9,"openalex_id":9,"authors":263,"venue":9,"cited_by_count":36,"oa_url":9,"card":264,"direction":103,"ingested_from":172},[],{"tldr":265,"method":266,"finding":267,"direction":103,"opportunity":268},"提出融合无人机多光谱与语义分割的果树行识别与路径跟踪方法，实现植保机自主导航。","无人机多光谱DOM\u002FDSM与NDGI，U-Net\u002FDeepLabv3+语义分割，","DeepLabv3+分割F1达0.94，导航线断裂1.2次，转向横向误差RMSE 0.11 m，优于","可探索多光谱与DSM特征融合的轻量化分割模型，并迁移至多树种、多季节果园导航。","2026-09-23T00:04:33.496233Z"]