[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1643":3,"related-1643":117},{"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":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":42,"search_phrases":96,"slug":99,"view_count":21,"doi":100,"paper":101,"created_at":116},1643,"Random Forest-Based Groundwater Potential Zone Mapping in Latur District, Maharashtra, India","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84777","Groundwater is a major source of water for agriculture, domestic use, and rural development in semi-arid regions of India. In Latur District, Maharashtra, groundwater occurrence is spatially variable because the Deccan basalt terrain has low primary porosity and depends mainly on weathered material, fractures, joints, vesicular zones, lineaments, and favourable surface conditions. This research applies Remote Sensing, Geographic Information System (GIS), and Random Forest (RF) machine learning to delineate groundwater potential zones (GWPZs) in Latur District. Seven spatial conditioning parameters— lithology, geomorphology, soil, slope, drainage density, lineament density, and land use\u002Fland cover (LULC)—were integrated with groundwater-well observations. A total of 109 wells were classified using a 12.8 m mean-depth threshold. Seventy percent of the observations were used for RF training and 30% were retained for independent validation. All input maps were standardized to WGS 84\u002FUTM Zone 43N and a 30 m raster grid. The RF probability output was classified into very-low, low, moderate, high, and very-high groundwater-potential zones. The model produced an ROC-AUC value of 0.8192, showing good discriminative capability between groundwater occurrence and non-occurrence conditions. The final GWPZ map provides a district-scale planning tool for groundwater exploration, recharge planning, conservation measures, and future field investigation.","地下水是印度半干旱地区农业、生活用水及农村发展的主要水源。在马哈拉施特拉邦拉图尔县，由于德干玄武岩地形原生孔隙度低，地下水赋存主要依赖于风化层、裂隙、节理、气孔带、线性构造及有利的地表条件，因此其空间分布具有显著差异性。本研究运用遥感、地理信息系统（GIS）及随机森林（RF）机器学习方法，对拉图尔县地下水潜力区（GWPZs）进行圈定。研究整合了七个空间条件参数——岩性、地貌、土壤、坡度、排水密度、线性构造密度及土地利用\u002F土地覆盖（LULC），并与地下水井观测数据相结合。以12.8米平均水深为阈值，对总计109口井进行了分类。其中70%的观测数据用于随机森林模型训练，其余30%保留用于独立验证。所有输入图件均统一至WGS 84\u002FUTM 43N带坐标系，并采用30米栅格分辨率。随机森林模型输出的概率结果被划分为极低、低、中、高和极高五个地下水潜力等级。模型ROC-AUC值为0.8192，显示出对地下水赋存与非赋存条件的良好判别能力。最终生成的地下水潜力区图件为区域尺度的地下水勘探、补给规划、保护措施及未来野外调查提供了规划工具。",null,"International Journal for Research in Applied Science and Engineering Technology","2026-09-03T00:00:00Z","论文",10,false,49,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},8,15,16,2,1,"利用随机森林与遥感GIS绘制地下水潜力图，方法可靠，但区域局限，时效性低。",[24],{"name":10,"url":6},[26,27,28,29],"农业人工智能","机器学习","遥感","地下水",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,57,62,69,77,84,91],{"code":31,"score":49,"supported":50,"title":51,"abstract":52,"tags":56},9,true,[],[53,54,55],"地理信息","GIS","geographic information",[],{"code":38,"score":17,"supported":50,"title":58,"abstract":59,"tags":61},[],[27,60],"machine learning",[26,27],{"code":63,"score":64,"supported":50,"title":65,"abstract":66,"tags":68},"I3",7,[],[28,67],"remote sensing",[28],{"code":70,"score":71,"supported":50,"title":72,"abstract":73,"tags":76},"G",6,[],[74,75],"农村发展","rural development",[],{"code":78,"score":71,"supported":50,"title":79,"abstract":80,"tags":83},"H2",[],[81,82],"土壤","soil",[],{"code":85,"score":86,"supported":50,"title":87,"abstract":88,"tags":90},"H7",3,[],[89],"干旱",[],{"code":92,"score":21,"supported":14,"title":93,"abstract":94,"tags":95},"H3",[],[],[29],[97,98],"农业人工智能 机器学习 地下水 遥感","农业人工智能 机器学习","农业人工智能机器学习地下水遥感-1643","10.22214\u002Fijraset.2026.84777",{"doi":100,"openalex_id":102,"authors":103,"venue":10,"cited_by_count":108,"oa_url":6,"card":109,"direction":113,"ingested_from":115},"W7207838377",[104,106],{"name":105,"orcid":9},"Suchivrutta R. Kamble",{"name":107,"orcid":9},"Ganesh K. Patil",0,{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"利用随机森林结合遥感与GIS绘制印度Latur地区地下水潜力图。","随机森林模型，集成7个空间参数与109口井数据。","模型ROC-AUC为0.8192，能有效区分地下水潜力区。","农业遥感与作物表型","可扩展至其他干旱区，结合实时水文数据与深度学习提升精度。","openalex","2026-09-04T23:30:32.476791Z",{"total":71,"page":21,"page_size":71,"items":118},[119,193,256,318,426,517],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":127,"is_selected":14,"score":128,"score_detail":129,"sources":134,"tags":138,"primary_topic":141,"secondary_topics":142,"classification":144,"search_phrases":175,"slug":178,"view_count":179,"doi":180,"paper":181,"created_at":192},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,72,{"impact":130,"substance":131,"depth":130,"authority":132,"freshness":86,"relevant":21,"comment":133},18,20,13,"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。",[135,136],{"name":125,"url":122},{"name":125,"url":137},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705829",[139,140,26,28,53],"数字乡村","智慧农业",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[143],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":14,"version":44,"method":45,"evidence":145},{"candidates":146,"agricultural_evidence":50},[147,153,159,166,170],{"code":38,"score":148,"supported":50,"title":149,"abstract":151,"tags":152},12,[150],"artificial intelligence",[150],[26],{"code":31,"score":49,"supported":50,"title":154,"abstract":155,"tags":158},[],[53,156,54,157,55],"空间分析","spatial analysis",[],{"code":160,"score":64,"supported":50,"title":161,"abstract":162,"tags":165},"I1",[],[163,164],"精准农业","precision agriculture",[140],{"code":63,"score":21,"supported":14,"title":167,"abstract":168,"tags":169},[],[],[28],{"code":171,"score":21,"supported":14,"title":172,"abstract":173,"tags":174},"I6",[],[],[139],[176,177],"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",4,"10.5281\u002Fzenodo.22705830",{"doi":180,"openalex_id":182,"authors":183,"venue":125,"cited_by_count":108,"oa_url":122,"card":186,"direction":191,"ingested_from":115},"W7212270621",[184],{"name":185,"orcid":9},"Lalitkumar G. Thakur",{"tldr":187,"method":188,"finding":189,"direction":113,"opportunity":190},"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。","数字乡村与农业信息化","2026-09-13T23:30:29.245252Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":125,"source_url":196,"published_at":126,"category":12,"cover_url":9,"hotness":127,"is_selected":14,"score":199,"score_detail":200,"sources":202,"tags":206,"primary_topic":208,"secondary_topics":209,"classification":211,"search_phrases":240,"slug":242,"view_count":86,"doi":243,"paper":244,"created_at":255},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":148,"substance":13,"depth":148,"authority":148,"freshness":108,"relevant":21,"comment":201},"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。",[203,204],{"name":125,"url":196},{"name":125,"url":205},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22720771",[140,26,28,53,207],"灾害预警",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[210],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":14,"version":44,"method":45,"evidence":212},{"candidates":213,"agricultural_evidence":50},[214,220,224,228,232,236],{"code":38,"score":130,"supported":50,"title":215,"abstract":216,"tags":219},[150],[217,27,60,218,150],"深度学习","deep learning",[26],{"code":31,"score":49,"supported":50,"title":221,"abstract":222,"tags":223},[],[53,156,54,157,55],[],{"code":63,"score":64,"supported":50,"title":225,"abstract":226,"tags":227},[],[28,67],[28],{"code":78,"score":71,"supported":50,"title":229,"abstract":230,"tags":231},[],[81,82],[],{"code":85,"score":179,"supported":50,"title":233,"abstract":234,"tags":235},[],[89],[207],{"code":160,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[140],[241,177],"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":243,"openalex_id":245,"authors":246,"venue":125,"cited_by_count":108,"oa_url":196,"card":249,"direction":254,"ingested_from":115},"W7212350488",[247],{"name":248,"orcid":9},"Omprakash Wamanrao Jadhav",{"tldr":250,"method":251,"finding":252,"direction":113,"opportunity":253},"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。","智慧农业 \u002F 农业物联网","2026-09-13T23:30:09.354631Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":9,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":264,"sources":266,"tags":268,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":295,"slug":298,"view_count":108,"doi":299,"paper":300,"created_at":317},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":108,"substance":108,"depth":108,"authority":108,"freshness":108,"relevant":108,"comment":265},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[267],{"name":262,"url":259},[27,269,270,271],"遥感监测","土地利用","城市扩张",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[274],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":14,"version":44,"method":45,"evidence":276},{"candidates":277,"agricultural_evidence":50},[278,282,286,290],{"code":38,"score":18,"supported":50,"title":279,"abstract":280,"tags":281},[60],[27,60],[27],{"code":31,"score":49,"supported":50,"title":283,"abstract":284,"tags":285},[],[156,54,157],[],{"code":63,"score":64,"supported":50,"title":287,"abstract":288,"tags":289},[],[28,67],[269],{"code":291,"score":21,"supported":14,"title":292,"abstract":293,"tags":294},"H6",[],[],[270],[296,297],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":299,"openalex_id":301,"authors":302,"venue":262,"cited_by_count":108,"oa_url":259,"card":312,"direction":113,"ingested_from":115},"W7223022155",[303,306,308,310],{"name":304,"orcid":305},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":307,"orcid":9},"Goanar Jal Dup",{"name":309,"orcid":9},"Weyesa Merga Chemeda",{"name":311,"orcid":9},"Shankar Karuppannan",{"tldr":313,"method":314,"finding":315,"direction":113,"opportunity":316},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":9,"source_name":324,"source_url":321,"published_at":325,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":330,"tags":332,"primary_topic":334,"secondary_topics":335,"classification":342,"search_phrases":401,"slug":404,"view_count":108,"doi":405,"paper":406,"created_at":425},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":130,"substance":131,"depth":328,"authority":132,"freshness":17,"relevant":21,"comment":329},17,"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。",[331],{"name":324,"url":321},[140,26,333,163,269],"农业大数据",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[336,337],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":338,"slug":339,"name":340,"path":341,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":43,"locked":14,"version":44,"method":45,"evidence":343},{"candidates":344,"agricultural_evidence":50},[345,349,353,360,364,368,375,379,385,389,395],{"code":38,"score":13,"supported":50,"title":346,"abstract":347,"tags":348},[],[27,60,150],[26],{"code":31,"score":49,"supported":50,"title":350,"abstract":351,"tags":352},[],[53,54,55],[],{"code":338,"score":49,"supported":50,"title":354,"abstract":355,"tags":359},[],[356,357,358],"物联网","internet of things","IoT",[],{"code":160,"score":17,"supported":50,"title":361,"abstract":362,"tags":363},[],[163,164],[140,163],{"code":63,"score":64,"supported":50,"title":365,"abstract":366,"tags":367},[],[28,67],[269],{"code":369,"score":71,"supported":50,"title":370,"abstract":371,"tags":374},"A8",[],[372,373],"粮食安全","food security",[],{"code":78,"score":71,"supported":50,"title":376,"abstract":377,"tags":378},[],[81,82],[],{"code":92,"score":71,"supported":50,"title":380,"abstract":381,"tags":384},[],[382,383],"灌溉","irrigation",[],{"code":171,"score":179,"supported":50,"title":386,"abstract":387,"tags":388},[],[333],[333],{"code":390,"score":86,"supported":50,"title":391,"abstract":392,"tags":394},"F3",[],[393],"养分管理",[],{"code":396,"score":86,"supported":50,"title":397,"abstract":398,"tags":400},"F5",[],[399],"农业机械",[],[402,403],"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":405,"openalex_id":407,"authors":408,"venue":324,"cited_by_count":108,"oa_url":321,"card":419,"direction":254,"ingested_from":115},"W7220374836",[409,411,413,415,417],{"name":410,"orcid":9},"Kodavali Aswini",{"name":412,"orcid":9},"Raju Marimuthu",{"name":414,"orcid":9},"Guru Gurusamy",{"name":416,"orcid":9},"Kannan Pandian",{"name":418,"orcid":9},"Anandham Rangasamy",{"tldr":420,"method":421,"finding":422,"direction":423,"opportunity":424},"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":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":9,"source_name":432,"source_url":429,"published_at":433,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":434,"score_detail":435,"sources":437,"tags":439,"primary_topic":442,"secondary_topics":448,"classification":451,"search_phrases":495,"slug":498,"view_count":21,"doi":499,"paper":500,"created_at":516},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":130,"substance":131,"depth":19,"authority":148,"freshness":49,"relevant":21,"comment":436},"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。",[438],{"name":432,"url":429},[140,26,440,163,441],"病虫害识别","蔬菜种植",{"code":443,"slug":444,"name":445,"path":446,"parent_code":447},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[449,450],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":14,"version":44,"method":45,"evidence":452},{"candidates":453,"agricultural_evidence":50},[454,459,467,471,475,482,486,490],{"code":38,"score":455,"supported":50,"title":456,"abstract":457,"tags":458},19,[150],[27,60,150],[26,440],{"code":443,"score":130,"supported":50,"title":460,"abstract":462,"tags":466},[461],"vegetable",[445,463,464,461,465],"番茄","辣椒","tomato",[441],{"code":31,"score":49,"supported":50,"title":468,"abstract":469,"tags":470},[],[53,54,55],[],{"code":160,"score":17,"supported":50,"title":472,"abstract":473,"tags":474},[],[163,164],[140,163],{"code":476,"score":71,"supported":50,"title":477,"abstract":478,"tags":481},"F4",[],[479,480],"农药","pesticide",[],{"code":78,"score":71,"supported":50,"title":483,"abstract":484,"tags":485},[],[81,82],[],{"code":92,"score":71,"supported":50,"title":487,"abstract":488,"tags":489},[],[382,383],[],{"code":63,"score":86,"supported":50,"title":491,"abstract":492,"tags":494},[],[493],"multispectral",[],[496,497],"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":499,"openalex_id":501,"authors":502,"venue":432,"cited_by_count":108,"oa_url":9,"card":511,"direction":423,"ingested_from":115},"W7215128283",[503,505,507,509],{"name":504,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":506,"orcid":9},"Dr. SK Kshirsagar",{"name":508,"orcid":9},"MG Agale",{"name":510,"orcid":9},"GS Shinde",{"tldr":512,"method":513,"finding":514,"direction":254,"opportunity":515},"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":518,"title":519,"url":520,"summary":521,"summary_zh":522,"content":9,"source_name":523,"source_url":520,"published_at":524,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":525,"score_detail":526,"sources":529,"tags":531,"primary_topic":533,"secondary_topics":534,"classification":535,"search_phrases":558,"slug":560,"view_count":21,"doi":561,"paper":562,"created_at":587},1981,"Hydrochemistry and modeling nitrate concentration in farmland groundwater under different hydrological seasons by integrating hybrid quantum-classical ML, virtual sample generation and AlphaEarth Foundation","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-30-5647-2026","Abstract. Precise seasonal prediction of groundwater nitrate concentrations in intensive agricultural areas faces challenges such as data sparsity, strong spatiotemporal heterogeneity, and complex hydro-biogeochemical processes. To address these issues, this study proposes an integrated prediction framework combining hybrid quantum-classical machine learning, advanced virtual sample generation (t-SNE-GMM-KNN), and remote sensing foundation model semantic embedding (AEF). Modeling was conducted across the 2022–2023 normal, dry, and wet seasons in Xiong'an New Area. Hydrochemical types were dominated by Ca-Mg-HCO3−, controlled by mineral dissolution and evaporation. Nitrate concentrations were highest in the dry season (mean 42.93 mg L−1), driven by evaporative concentration. Spatially, high-value zones shifted: southeast (normal), central (dry), and northwest (wet). MixSIAR modeling based on isotopes indicated domestic sewage and livestock manure (74.1 %) as dominant sources. The t-SNE-GMM-KNN strategy mitigated small-sample bias while preserving nonlinear structure. When virtual samples were augmented to 10-fold, the Random Forest R2 in the dry season increased from 0.284 to > 0.85. Furthermore, a hybrid quantum-classical Random Forest exhibited superior robustness for data sparsity, achieving peak performance in the normal season (R2 = 0.962, RMSE = 5.73 mg L−1). Additionally, using only AEF embeddings achieved screening-level accuracy (R2 up to 0.860), providing a feasible rapid survey scheme for extensive unmonitored regions. Correlation analysis identified TDS and EC as persistent top predictors (r > 0.8). This comprehensive framework offers a robust solution for seasonal nitrate prediction and sustainable water management.","摘要：集约化农业区地下水硝酸盐浓度的精准季节预测面临数据稀疏、时空异质性强及水文生物地球化学过程复杂等挑战。针对上述问题，本研究提出了一种集成预测框架，融合了混合量子-经典机器学习、先进虚拟样本生成技术（t-SNE-GMM-KNN）及遥感基础模型语义嵌入（AEF）。研究在雄安新区2022–2023年平水期、枯水期和丰水期开展了建模分析。水化学类型以Ca-Mg-HCO₃⁻为主，受矿物溶解和蒸发作用控制。硝酸盐浓度在枯水期最高（均值42.93 mg L⁻¹），主要受蒸发浓缩作用驱动。空间上，高值区呈迁移特征：平水期位于东南部，枯水期移至中部，丰水期则分布于西北部。基于同位素的MixSIAR模型表明，生活污水和畜禽粪便（74.1%）为主要污染源。t-SNE-GMM-KNN策略在保持非线性结构的同时缓解了小样本偏差；当虚拟样本扩充至10倍时，枯水期随机森林模型的R²从0.284提升至0.85以上。此外，混合量子-经典随机森林模型在数据稀疏条件下表现出优异的鲁棒性，在平水期达到最佳性能（R² = 0.962，RMSE = 5.73 mg L⁻¹）。仅使用AEF嵌入特征即可达到筛选级精度（R²最高达0.860），为广域无监测区域提供了可行的快速调查方案。相关性分析识别出TDS和EC为持续主导预测因子（r > 0.8）。该综合框架为季节性硝酸盐预测和可持续水资源管理提供了稳健的解决方案。","Hydrology and earth system sciences","2026-09-08T00:00:00Z",73,{"impact":130,"substance":527,"depth":130,"authority":132,"freshness":20,"relevant":21,"comment":528},22,"研究提出结合量子机器学习与遥感基础模型的农田地下水硝酸盐预测框架，方法新颖，数据详实，对农业面源污染治理有参考价值。",[530],{"name":523,"url":520},[26,27,28,29,532],"硝酸盐",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":536},{"candidates":537,"agricultural_evidence":50},[538,542,546,553],{"code":38,"score":17,"supported":50,"title":539,"abstract":540,"tags":541},[],[27,60],[26,27],{"code":63,"score":64,"supported":50,"title":543,"abstract":544,"tags":545},[],[28,67],[28],{"code":547,"score":71,"supported":50,"title":548,"abstract":549,"tags":552},"C",[],[550,551],"畜禽","livestock",[],{"code":92,"score":179,"supported":50,"title":554,"abstract":555,"tags":557},[],[556],"水资源",[29],[559,98],"农业人工智能 机器学习 地下水 硝酸盐","农业人工智能机器学习地下水硝酸盐-1981","10.5194\u002Fhess-30-5647-2026",{"doi":561,"openalex_id":563,"authors":564,"venue":523,"cited_by_count":108,"oa_url":520,"card":582,"direction":254,"ingested_from":115},"W7126035353",[565,567,570,572,575,577,579],{"name":566,"orcid":9},"Junjie Xu",{"name":568,"orcid":569},"Xin Wei","https:\u002F\u002Forcid.org\u002F0000-0002-5207-6141",{"name":571,"orcid":9},"Yilei Yu",{"name":573,"orcid":574},"Lihu Yang","https:\u002F\u002Forcid.org\u002F0000-0002-4580-4972",{"name":576,"orcid":9},"Yuanzheng Zhai",{"name":578,"orcid":9},"Cuicui Lv",{"name":580,"orcid":581},"Xinzhe Song","https:\u002F\u002Forcid.org\u002F0000-0002-6638-7805",{"tldr":583,"method":584,"finding":585,"direction":423,"opportunity":586},"提出集成混合量子-经典机器学习、虚拟样本生成和遥感基础模型的框架，预测雄安农田地下水硝酸盐浓度。","混合量子-经典随机森林、t-SNE-GMM-KNN虚拟样本、AlphaEarth","干季硝酸盐最高（42.93 mg\u002FL），虚拟样本扩增10倍使R2从0.284升至>0.85，混合模型","可探索将虚拟样本生成与基础模型嵌入结合，应用于其他数据稀疏的农业环境监测，如土壤养分或农药残留预测。","2026-09-09T23:30:08.416152Z"]