[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3367":3,"related-3367":101},{"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,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":100},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。",null,"Preprints.org","2026-09-22T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,6,8,1,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","农业人工智能","巴西农业","农村数字化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,65,71],{"code":46,"score":21,"supported":47,"title":48,"abstract":49,"tags":51},"I1",true,[],[27,50],"digital agriculture",[27,28],{"code":33,"score":20,"supported":47,"title":53,"abstract":54,"tags":57},[],[55,56],"空间分析","spatial analysis",[],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":64},"G1",3,[],[63],"技术培训",[],{"code":66,"score":60,"supported":47,"title":67,"abstract":68,"tags":70},"I6",[],[69],"agricultural data",[],{"code":72,"score":22,"supported":14,"title":73,"abstract":74,"tags":75},"I2",[],[],[29],[77,78],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367",4,"10.20944\u002Fpreprints202609.1880.v1",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":92,"oa_url":6,"card":93,"direction":97,"ingested_from":99},"W7214109425",[85,88,90],{"name":86,"orcid":87},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":89,"orcid":9},"Eduardo Dias",{"name":91,"orcid":9},"Lidia Scoton",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","数字乡村与农业信息化","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","openalex","2026-09-24T23:30:27.046035Z",{"total":20,"page":22,"page_size":20,"items":102},[103,180,294,385,479,538],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":128,"classification":130,"search_phrases":163,"slug":166,"view_count":22,"doi":167,"paper":168,"created_at":179},4491,"INTEGRATING \"SMART AGRICULTURE\" TECHNOLOGIES INTO THE EDUCATIONAL PROCESS IN AGRICULTURAL EDUCATION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23242369","This thesis discusses the pedagogical potential of integrating “Smart Agriculture” technologies into the teaching of specialized subjects at higher agricultural education institutions. Internet of Things (IoT), sensors, digital monitoring, Geographic Information Systems (GIS), remote sensing, and artificial intelligence tools enable students to connect theoretical knowledge with real-world agrotechnological tasks. It is proposed that these technologies should be used in lessons not merely as demonstration tools, but as means of identifying problems, collecting and analyzing data, and making practical decisions.","本论文探讨了将“智慧农业”技术融入高等农业院校专业课程教学的教学潜力。物联网（IoT）、传感器、数字监测、地理信息系统（GIS）、遥感以及人工智能工具能够帮助学生将理论知识与现实农业技术任务相联系。本文提出，这些技术不应仅在课堂上作为演示工具使用，而应作为发现问题、收集与分析数据以及做出实际决策的手段。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",64,{"impact":113,"substance":17,"depth":114,"authority":113,"freshness":115,"relevant":22,"comment":116},12,15,9,"探讨将智慧农业技术融入高等农业教学的论文，属细分领域教育研究，有方法性内容但影响面有限。",[118],{"name":109,"url":106},[28,29,120,121,122],"物联网","遥感","农业教育",{"code":124,"slug":125,"name":126,"path":127,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[129],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":131},{"candidates":132,"agricultural_evidence":47},[133,138,144,151,158],{"code":46,"score":114,"supported":47,"title":134,"abstract":136,"tags":137},[135],"smart agriculture",[28,135],[28],{"code":124,"score":13,"supported":47,"title":139,"abstract":140,"tags":143},[],[120,141,142],"internet of things","IoT",[120],{"code":33,"score":115,"supported":47,"title":145,"abstract":146,"tags":150},[],[147,148,149],"地理信息","GIS","geographic information",[],{"code":152,"score":153,"supported":47,"title":154,"abstract":155,"tags":157},"I3",7,[],[121,156],"remote sensing",[121],{"code":72,"score":80,"supported":47,"title":159,"abstract":160,"tags":162},[],[161],"artificial intelligence",[29],[164,165],"智慧农业 农业教育 课程","农业人工智能 农业教育 智慧农业 物联网","智慧农业农业教育课程-4491","10.5281\u002Fzenodo.23242369",{"doi":167,"openalex_id":169,"authors":170,"venue":109,"cited_by_count":92,"oa_url":106,"card":173,"direction":178,"ingested_from":99},"W7221022747",[171],{"name":172,"orcid":9},"Shamsiyeva Ogiloy",{"tldr":174,"method":175,"finding":176,"direction":97,"opportunity":177},"探讨将智慧农业技术融入高等农业教育，作为学生解决实际农艺问题的教学工具。","整合物联网、传感器、GIS、遥感与AI工具于专业课程教学。","技术不应仅作演示，而应成为发现问题、采集分析数据并决策的手段。","可研究智慧农业教学对学生数据决策能力的量化影响及课程设计模式。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:08.054996Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":187,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":193,"tags":195,"primary_topic":199,"secondary_topics":203,"classification":206,"search_phrases":269,"slug":272,"view_count":92,"doi":273,"paper":274,"created_at":293},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":190,"substance":18,"depth":19,"authority":191,"freshness":21,"relevant":22,"comment":192},18,13,"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[194],{"name":186,"url":183},[28,29,196,197,198],"农业大数据","精准农业","遥感监测",{"code":72,"slug":200,"name":201,"path":202,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[204,205],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":124,"slug":125,"name":126,"path":127,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":207},{"candidates":208,"agricultural_evidence":47},[209,215,219,223,228,232,239,246,253,257,263],{"code":72,"score":13,"supported":47,"title":210,"abstract":211,"tags":214},[],[212,213,161],"机器学习","machine learning",[29],{"code":33,"score":115,"supported":47,"title":216,"abstract":217,"tags":218},[],[147,148,149],[],{"code":124,"score":115,"supported":47,"title":220,"abstract":221,"tags":222},[],[120,141,142],[],{"code":46,"score":21,"supported":47,"title":224,"abstract":225,"tags":227},[],[197,226],"precision agriculture",[28,197],{"code":152,"score":153,"supported":47,"title":229,"abstract":230,"tags":231},[],[121,156],[198],{"code":233,"score":20,"supported":47,"title":234,"abstract":235,"tags":238},"A8",[],[236,237],"粮食安全","food security",[],{"code":240,"score":20,"supported":47,"title":241,"abstract":242,"tags":245},"H2",[],[243,244],"土壤","soil",[],{"code":247,"score":20,"supported":47,"title":248,"abstract":249,"tags":252},"H3",[],[250,251],"灌溉","irrigation",[],{"code":66,"score":80,"supported":47,"title":254,"abstract":255,"tags":256},[],[196],[196],{"code":258,"score":60,"supported":47,"title":259,"abstract":260,"tags":262},"F3",[],[261],"养分管理",[],{"code":264,"score":60,"supported":47,"title":265,"abstract":266,"tags":268},"F5",[],[267],"农业机械",[],[270,271],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":273,"openalex_id":275,"authors":276,"venue":186,"cited_by_count":92,"oa_url":183,"card":287,"direction":178,"ingested_from":99},"W7220374836",[277,279,281,283,285],{"name":278,"orcid":9},"Kodavali Aswini",{"name":280,"orcid":9},"Raju Marimuthu",{"name":282,"orcid":9},"Guru Gurusamy",{"name":284,"orcid":9},"Kannan Pandian",{"name":286,"orcid":9},"Anandham Rangasamy",{"tldr":288,"method":289,"finding":290,"direction":291,"opportunity":292},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","农业人工智能与决策模型","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","2026-10-06T23:30:11.880721Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":305,"tags":307,"primary_topic":310,"secondary_topics":316,"classification":319,"search_phrases":363,"slug":366,"view_count":22,"doi":367,"paper":368,"created_at":384},4038,"Use of artificial intelligence (AI) in vegetable crops: A review","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2664844x.2026.v8.i10a.1995","Artificial Intelligence (AI) is emerging technology in vegetable production, enabling data-driven decision making and precision agriculture practices. AI refers to the intelligence exhibited by machines that can perceive their environment, process large datasets, and make decisions to optimize agricultural productivity. With the integration of technologies such as machine learning, sensors, drones, satellites, Geographic Information Systems (GIS), and Global Positioning Systems (GPS), AI enables efficient monitoring and management of crop production systems In vegetable cultivation, AI technologies assist farmers in several critical operations including soil monitoring, climate assessment, pest and disease diagnosis, irrigation scheduling, and yield prediction. Sensors such as wind vane, cup count anemometer, rain gauge, lux meter, soil moisture sensor, and crop canopy sensors collect real-time environmental and crop data. These data was processed through data loggers and machine learning algorithms to provide accurate recommendations for crop management. Drone-based imaging systems and multispectral cameras further support precision farming by identifying weeds, nutrient deficiencies and disease symptoms at early stages. The use of AI in vegetables also highlights image-based predictive agriculture systems where captured images are processed through stages such as. image cleaning, feature extraction, and model training to detect pests and diseases. Several case studies demonstrate the use of AI in vegetable crops such as tomato, capsicum, and chilli for monitoring canopy growth, irrigation management. Additionally, robotic technologies in harvesting address labor shortages in agriculture. Crop yield prediction of AI in vegetables increases by 40% sensors and algorithms reduces labor cost by 30% moisture sensor increases water use efficiency by 40% spraying done reduces use of pesticide by 60% robotic arms increases harvesting accuracy by 40% artificial neural network data integration saves 25% cost in disease monitoring.","人工智能（AI）正在成为蔬菜生产领域的新兴技术，推动数据驱动决策与精准农业实践。AI是指机器所表现出的智能，能够感知环境、处理大规模数据集并做出决策以优化农业生产力。通过整合机器学习、传感器、无人机、卫星、地理信息系统（GIS）和全球定位系统（GPS）等技术，AI实现了对作物生产系统的高效监测与管理。在蔬菜种植中，AI技术可协助农民完成多项关键作业，包括土壤监测、气候评估、病虫害诊断、灌溉调度和产量预测。风向标、杯式风速计、雨量计、照度计、土壤水分传感器和作物冠层传感器等设备可采集实时的环境与作物数据。这些数据通过数据记录器和机器学习算法进行处理，为作物管理提供精准建议。基于无人机的成像系统与多光谱相机通过早期识别杂草、养分缺乏和病害症状，进一步支持精准农业。AI在蔬菜中的应用还突出了基于图像的预测性农业系统，该系统将采集的图像经过图像清洗、特征提取和模型训练等阶段处理，以检测病虫害。多个案例研究展示了AI在番茄、辣椒和尖椒等蔬菜作物中用于监测冠层生长和灌溉管理的应用。此外，收获机器人技术可缓解农业劳动力短缺问题。AI在蔬菜中的作物产量预测通过传感器和算法提高了40%，劳动力成本降低了30%，土壤水分传感器使水分利用效率提高了40%，喷雾作业使农药使用量减少了60%，机械臂使收获准确率提高了40%，人工神经网络数据集成使病害监测成本节省了25%。","International Journal of Agriculture and Food Science","2026-10-01T00:00:00Z",75,{"impact":190,"substance":18,"depth":17,"authority":113,"freshness":115,"relevant":22,"comment":304},"系统综述AI在蔬菜生产中的应用，含产量、成本、水肥药效等量化数据，对智慧农业主题有聚合价值。",[306],{"name":300,"url":297},[28,29,308,197,309],"病虫害识别","蔬菜种植",{"code":311,"slug":312,"name":313,"path":314,"parent_code":315},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[317,318],{"code":72,"slug":200,"name":201,"path":202,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":320},{"candidates":321,"agricultural_evidence":47},[322,327,335,339,343,350,354,358],{"code":72,"score":323,"supported":47,"title":324,"abstract":325,"tags":326},19,[161],[212,213,161],[29,308],{"code":311,"score":190,"supported":47,"title":328,"abstract":330,"tags":334},[329],"vegetable",[313,331,332,329,333],"番茄","辣椒","tomato",[309],{"code":33,"score":115,"supported":47,"title":336,"abstract":337,"tags":338},[],[147,148,149],[],{"code":46,"score":21,"supported":47,"title":340,"abstract":341,"tags":342},[],[197,226],[28,197],{"code":344,"score":20,"supported":47,"title":345,"abstract":346,"tags":349},"F4",[],[347,348],"农药","pesticide",[],{"code":240,"score":20,"supported":47,"title":351,"abstract":352,"tags":353},[],[243,244],[],{"code":247,"score":20,"supported":47,"title":355,"abstract":356,"tags":357},[],[250,251],[],{"code":152,"score":60,"supported":47,"title":359,"abstract":360,"tags":362},[],[361],"multispectral",[],[364,365],"AI 蔬菜 精准农业","蔬菜 病虫害 图像识别","AI蔬菜精准农业-4038","10.33545\u002F2664844x.2026.v8.i10a.1995",{"doi":367,"openalex_id":369,"authors":370,"venue":300,"cited_by_count":92,"oa_url":9,"card":379,"direction":291,"ingested_from":99},"W7215128283",[371,373,375,377],{"name":372,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":374,"orcid":9},"Dr. SK Kshirsagar",{"name":376,"orcid":9},"MG Agale",{"name":378,"orcid":9},"GS Shinde",{"tldr":380,"method":381,"finding":382,"direction":178,"opportunity":383},"综述人工智能在蔬菜作物生产中的应用，涵盖监测、诊断、灌溉、产量预测与采收机器人。","综述机器学习、传感器、无人机、卫星、GIS\u002FGPS及图像处理在蔬菜生产中的应用案","AI使蔬菜产量预测提高40%、节水40%、减农药60%、采收准确率提高40%。","可针对小农户蔬菜场景开发低成本多源数据融合模型，并验证AI长期经济与生态效益。","2026-10-02T23:30:40.356457Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":390,"content":9,"source_name":391,"source_url":388,"published_at":392,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":393,"score_detail":394,"sources":396,"tags":398,"primary_topic":400,"secondary_topics":401,"classification":412,"search_phrases":449,"slug":452,"view_count":453,"doi":454,"paper":455,"created_at":478},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","2026-09-24T00:00:00Z",66,{"impact":113,"substance":190,"depth":17,"authority":113,"freshness":21,"relevant":22,"comment":395},"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。",[397],{"name":391,"url":388},[28,29,121,148,399],"干旱监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[402,408],{"code":403,"slug":404,"name":405,"path":406,"parent_code":407},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":152,"slug":409,"name":410,"path":411,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":413},{"candidates":414,"agricultural_evidence":47},[415,419,425,429,435,439,445],{"code":33,"score":190,"supported":47,"title":416,"abstract":417,"tags":418},[148],[147,55,148,56,149],[148],{"code":403,"score":114,"supported":47,"title":420,"abstract":422,"tags":424},[421],"drought",[423,421],"干旱",[399],{"code":152,"score":114,"supported":47,"title":426,"abstract":427,"tags":428},[156],[121,156],[121],{"code":72,"score":13,"supported":47,"title":430,"abstract":431,"tags":434},[],[432,212,213,433,161],"深度学习","deep learning",[29],{"code":240,"score":20,"supported":47,"title":436,"abstract":437,"tags":438},[],[243,244],[],{"code":247,"score":20,"supported":47,"title":440,"abstract":441,"tags":444},[],[442,443],"水资源","evapotranspiration",[],{"code":46,"score":22,"supported":14,"title":446,"abstract":447,"tags":448},[],[],[28],[450,451],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":454,"openalex_id":456,"authors":457,"venue":391,"cited_by_count":92,"oa_url":388,"card":472,"direction":476,"ingested_from":99},"W7214156857",[458,460,462,464,466,468,470],{"name":459,"orcid":9},"V. Dhanalakshmi",{"name":461,"orcid":9},"N. Manikandan",{"name":463,"orcid":9},"V. S. Jinsy",{"name":465,"orcid":9},"K. V. Sumesh",{"name":467,"orcid":9},"P. Nideesh",{"name":469,"orcid":9},"P. S. Manju",{"name":471,"orcid":9},"N. Gopika",{"tldr":473,"method":474,"finding":475,"direction":476,"opportunity":477},"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":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":109,"source_url":482,"published_at":485,"category":12,"cover_url":9,"hotness":486,"is_selected":14,"score":487,"score_detail":488,"sources":490,"tags":494,"primary_topic":496,"secondary_topics":497,"classification":499,"search_phrases":522,"slug":525,"view_count":80,"doi":526,"paper":527,"created_at":537},2333,"Empowering India's Digital Future through Artificial Intelligence in Geography","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705830","The convergence of Geographic Information Systems (GIS), high-resolution Earth observation (EO) systems, and Artificial Intelligence (AI) has established a new paradigm: Geospatial Artificial Intelligence (GeoAI). In the context of India's rapid digital transformation, GeoAI serves as a core technology driving sustainable urbanization, precision agriculture, disaster risk reduction, and multi-modal logistics planning. Anchored by strategic national frameworks such as the National Geospatial Policy (NGP), PM Gati Shakti National Master Plan, and the SVAMITVA scheme, this paper examines how AI-integrated spatial analysis accelerates India's digital governance. Through technical methodologies, statistical hypothesis testing, applied case studies, and systemic evaluation, this study demonstrates the pivotal role of GeoAI in building resilient spatial data infrastructure and offers strategic policy recommendations for scalable national adoption.","地理信息系统（GIS）、高分辨率对地观测（EO）系统与人工智能（AI）的融合，已确立了一种新范式：地理空间人工智能（GeoAI）。在印度快速数字化转型的背景下，GeoAI成为推动可持续城市化、精准农业、灾害风险降低和多式联运物流规划的核心技术。本文以国家地理空间政策（NGP）、PM Gati Shakti国家总体规划以及SVAMITVA计划等战略性国家框架为依托，考察了AI集成的空间分析如何加速印度的数字治理。通过技术方法、统计假设检验、应用案例研究和系统性评估，本研究表明GeoAI在构建韧性空间数据基础设施中发挥关键作用，并为全国范围的可扩展应用提供了战略性政策建议。","2026-09-30T00:00:00Z",25,72,{"impact":190,"substance":18,"depth":190,"authority":191,"freshness":60,"relevant":22,"comment":489},"论文系统阐述GeoAI在印度精准农业、数字治理中的应用与政策建议，方法扎实但发布日期在未来，时效性存疑。",[491,492],{"name":109,"url":482},{"name":109,"url":493},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705829",[495,28,29,121,147],"数字乡村",{"code":72,"slug":200,"name":201,"path":202,"parent_code":37},[498],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":500},{"candidates":501,"agricultural_evidence":47},[502,506,510,514,518],{"code":72,"score":113,"supported":47,"title":503,"abstract":504,"tags":505},[161],[161],[29],{"code":33,"score":115,"supported":47,"title":507,"abstract":508,"tags":509},[],[147,55,148,56,149],[],{"code":46,"score":153,"supported":47,"title":511,"abstract":512,"tags":513},[],[197,226],[28],{"code":152,"score":22,"supported":14,"title":515,"abstract":516,"tags":517},[],[],[121],{"code":66,"score":22,"supported":14,"title":519,"abstract":520,"tags":521},[],[],[495],[523,524],"农业人工智能 地理信息 数字乡村 智慧农业","农业人工智能 地理信息","农业人工智能地理信息数字乡村智慧农业-2333","10.5281\u002Fzenodo.22705830",{"doi":526,"openalex_id":528,"authors":529,"venue":109,"cited_by_count":92,"oa_url":482,"card":532,"direction":97,"ingested_from":99},"W7212270621",[530],{"name":531,"orcid":9},"Lalitkumar G. Thakur",{"tldr":533,"method":534,"finding":535,"direction":476,"opportunity":536},"本文探讨地理空间人工智能（GeoAI）如何推动印度数字治理与可持续发展。","结合GIS、高分辨率对地观测与AI，采用案例研究、统计检验和政策评估。","GeoAI在精准农业、灾害减灾等领域关键，需国家政策框架支持规模化应用。","可研究GeoAI在印度小农精准农业中的适配性、数据鸿沟与政策落地机制。","2026-09-13T23:30:29.245252Z",{"id":539,"title":540,"url":541,"summary":542,"summary_zh":543,"content":9,"source_name":109,"source_url":541,"published_at":485,"category":12,"cover_url":9,"hotness":486,"is_selected":14,"score":544,"score_detail":545,"sources":547,"tags":551,"primary_topic":553,"secondary_topics":554,"classification":556,"search_phrases":583,"slug":585,"view_count":60,"doi":586,"paper":587,"created_at":597},2297,"Uses of Artificial Intelligence In Geography","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22720772","Abstract Artificial Intelligence (AI) is becoming an important technology in the field of Geography. It helps geographers collect, process, analyze, and interpret large amounts of geographical data quickly and accurately. AI is widely used with Geographic Information Systems (GIS), Remote Sensing, satellite images, GPS, and spatial databases. It can help in land-use and land-cover mapping, urban planning, population analysis, disaster management, climate studies, agriculture, and environmental monitoring. AI-based techniques such as machine learning and deep learning can identify patterns and changes in geographical areas that may be difficult to detect through traditional methods. AI also helps in predicting natural hazards such as floods, droughts, landslides, and forest fires. In urban areas, it supports transportation planning, infrastructure development, and smart-city management. In agriculture, AI can assist in crop monitoring, soil analysis, and yield prediction. However, the effective use of AI requires reliable data, skilled users, proper technology, and attention to data privacy and accuracy. Thus, Artificial Intelligence has great potential to improve geographical research, spatial analysis, planning, and sustainable development.","摘要 人工智能（AI）正成为地理学领域的一项重要技术。它帮助地理学者快速、准确地采集、处理、分析和解释大量地理数据。人工智能与地理信息系统（GIS）、遥感、卫星影像、全球定位系统（GPS）及空间数据库广泛结合使用。它可辅助土地利用与土地覆盖制图、城市规划、人口分析、灾害管理、气候研究、农业及环境监测。基于人工智能的技术，如机器学习和深度学习，能够识别地理区域中传统方法难以探测的模式与变化。人工智能还有助于预测洪水、干旱、滑坡和森林火灾等自然灾害。在城市地区，它支持交通规划、基础设施建设及智慧城市管理。在农业领域，人工智能可协助作物监测、土壤分析及产量预测。然而，人工智能的有效应用需要可靠的数据、熟练的使用者、适当的技术，并需关注数据隐私与准确性。因此，人工智能在改进地理研究、空间分析、规划及可持续发展方面具有巨大潜力。",46,{"impact":113,"substance":13,"depth":113,"authority":113,"freshness":92,"relevant":22,"comment":546},"综述性论文，泛谈AI在地理与农业中的应用，缺乏新数据与新方法，且发布日期在未来，时效性不足，不宜进入每日精选。",[548,549],{"name":109,"url":541},{"name":109,"url":550},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22720771",[28,29,121,147,552],"灾害预警",{"code":72,"slug":200,"name":201,"path":202,"parent_code":37},[555],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":557},{"candidates":558,"agricultural_evidence":47},[559,563,567,571,575,579],{"code":72,"score":190,"supported":47,"title":560,"abstract":561,"tags":562},[161],[432,212,213,433,161],[29],{"code":33,"score":115,"supported":47,"title":564,"abstract":565,"tags":566},[],[147,55,148,56,149],[],{"code":152,"score":153,"supported":47,"title":568,"abstract":569,"tags":570},[],[121,156],[121],{"code":240,"score":20,"supported":47,"title":572,"abstract":573,"tags":574},[],[243,244],[],{"code":403,"score":80,"supported":47,"title":576,"abstract":577,"tags":578},[],[423],[552],{"code":46,"score":22,"supported":14,"title":580,"abstract":581,"tags":582},[],[],[28],[584,524],"农业人工智能 地理信息 智慧农业 灾害预警","农业人工智能地理信息智慧农业灾害预警-2297","10.5281\u002Fzenodo.22720772",{"doi":586,"openalex_id":588,"authors":589,"venue":109,"cited_by_count":92,"oa_url":541,"card":592,"direction":178,"ingested_from":99},"W7212350488",[590],{"name":591,"orcid":9},"Omprakash Wamanrao Jadhav",{"tldr":593,"method":594,"finding":595,"direction":476,"opportunity":596},"综述人工智能在地理学中的应用，涵盖GIS、遥感、城市规划、农业与环境监测等。","综述性分析，涉及机器学习、深度学习与GIS、遥感、GPS等地理数据技术。","AI能高效识别地理模式与变化，预测自然灾害，并助力农业监测与智慧城市管理。","可聚焦AI在农业遥感中的可解释性与小样本迁移学习，提升作物监测精度与泛化能力。","2026-09-13T23:30:09.354631Z"]