[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5485":3,"related-5485":97},{"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":18,"tags":20,"primary_topic":25,"secondary_topics":31,"classification":37,"search_phrases":72,"slug":75,"view_count":15,"doi":76,"paper":77,"created_at":96},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。",null,"PLoS ONE","2026-10-20T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"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。",[19],{"name":10,"url":6},[21,22,23,24],"机器学习","遥感监测","土地利用","城市扩张",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[32],{"code":33,"slug":34,"name":35,"path":36,"parent_code":30},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,58,66],{"code":26,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},15,true,[47],"machine learning",[21,47],[21],{"code":33,"score":51,"supported":45,"title":52,"abstract":53,"tags":57},9,[],[54,55,56],"空间分析","GIS","spatial analysis",[],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":65},"I3",7,[],[63,64],"遥感","remote sensing",[22],{"code":67,"score":68,"supported":14,"title":69,"abstract":70,"tags":71},"H6",1,[],[],[23],[73,74],"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":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":15,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7223022155",[80,83,85,87],{"name":81,"orcid":82},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":84,"orcid":9},"Goanar Jal Dup",{"name":86,"orcid":9},"Weyesa Merga Chemeda",{"name":88,"orcid":9},"Shankar Karuppannan",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","农业遥感与作物表型","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。","openalex","2026-10-21T23:30:23.271293Z",{"total":98,"page":68,"page_size":98,"items":99},6,[100,228,312,385,474,543],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":116,"tags":118,"primary_topic":123,"secondary_topics":124,"classification":131,"search_phrases":202,"slug":205,"view_count":15,"doi":206,"paper":207,"created_at":227},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":110,"substance":111,"depth":112,"authority":113,"freshness":114,"relevant":68,"comment":115},18,20,17,13,8,"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。",[117],{"name":106,"url":103},[119,120,121,122,22],"智慧农业","农业人工智能","农业大数据","精准农业",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[125,126],{"code":33,"slug":34,"name":35,"path":36,"parent_code":30},{"code":127,"slug":128,"name":129,"path":130,"parent_code":30},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":38,"locked":14,"version":39,"method":40,"evidence":132},{"candidates":133,"agricultural_evidence":45},[134,139,145,152,158,162,169,176,183,189,196],{"code":26,"score":13,"supported":45,"title":135,"abstract":136,"tags":138},[],[21,47,137],"artificial intelligence",[120],{"code":33,"score":51,"supported":45,"title":140,"abstract":141,"tags":144},[],[142,55,143],"地理信息","geographic information",[],{"code":127,"score":51,"supported":45,"title":146,"abstract":147,"tags":151},[],[148,149,150],"物联网","internet of things","IoT",[],{"code":153,"score":114,"supported":45,"title":154,"abstract":155,"tags":157},"I1",[],[122,156],"precision agriculture",[119,122],{"code":59,"score":60,"supported":45,"title":159,"abstract":160,"tags":161},[],[63,64],[22],{"code":163,"score":98,"supported":45,"title":164,"abstract":165,"tags":168},"A8",[],[166,167],"粮食安全","food security",[],{"code":170,"score":98,"supported":45,"title":171,"abstract":172,"tags":175},"H2",[],[173,174],"土壤","soil",[],{"code":177,"score":98,"supported":45,"title":178,"abstract":179,"tags":182},"H3",[],[180,181],"灌溉","irrigation",[],{"code":184,"score":185,"supported":45,"title":186,"abstract":187,"tags":188},"I6",4,[],[121],[121],{"code":190,"score":191,"supported":45,"title":192,"abstract":193,"tags":195},"F3",3,[],[194],"养分管理",[],{"code":197,"score":191,"supported":45,"title":198,"abstract":199,"tags":201},"F5",[],[200],"农业机械",[],[203,204],"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":206,"openalex_id":208,"authors":209,"venue":106,"cited_by_count":15,"oa_url":103,"card":220,"direction":226,"ingested_from":95},"W7220374836",[210,212,214,216,218],{"name":211,"orcid":9},"Kodavali Aswini",{"name":213,"orcid":9},"Raju Marimuthu",{"name":215,"orcid":9},"Guru Gurusamy",{"name":217,"orcid":9},"Kannan Pandian",{"name":219,"orcid":9},"Anandham Rangasamy",{"tldr":221,"method":222,"finding":223,"direction":224,"opportunity":225},"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。","智慧农业 \u002F 农业物联网","2026-10-06T23:30:11.880721Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":234,"source_url":231,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":240,"tags":242,"primary_topic":247,"secondary_topics":253,"classification":256,"search_phrases":291,"slug":294,"view_count":295,"doi":296,"paper":297,"created_at":311},3617,"Applications and challenges of geospatial technologies in veterinary medicine","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44338-026-00259-y","Geospatial technologies are increasingly recognized as critical assets in veterinary medicine, providing robust frameworks for disease surveillance, risk mapping, and informed decision‑making. The objective of this narrative review is to summarize the role of Geographic Information Systems, Global Positioning System, and Remote Sensing in advancing veterinary medicine by synthesizing current applications, identifying prevailing challenges, and exploring future prospects. Collectively, these tools enable monitoring of disease dynamics, identification of epidemiological hotspots, outbreak prediction, and integration of spatial and epidemiological datasets to guide targeted interventions. For instance, GIS‑based mapping of Rift Valley fever outbreaks in East Africa has enabled rapid identification of epidemiological hotspots and guided vaccination campaigns, while RS‑derived vegetation indices have successfully predicted tick distribution patterns in southern Africa, improving targeted control strategies. Their utility extends beyond livestock health, contributing significantly to wildlife disease monitoring, zoonotic risk assessment, food safety assurance, and broader veterinary decision processes. Despite such successes, adoption remains limited, hindered by technical\u002Finfrastructure barriers, poor data quality, and ethical and policy challenges. In Ethiopia, recent surveys indicate that fewer than 20% of veterinary institutions actively use GIS platforms for routine surveillance, and integration of RS into national veterinary services is still limited to pilot projects. This gap underscores the urgent need for national geospatial data repositories, standardized protocols, and capacity‑building initiatives to ensure equitable access and sustainable utilization. Future opportunities lie in harnessing advances in Artificial Intelligence, Machine Learning, and cloud‑based GIS platforms, alongside global collaboration and training programs, to strengthen veterinary epidemiology and surveillance systems. Addressing these challenges will be essential for the sustainable integration of geospatial technologies into veterinary practice, thereby enhancing disease control, safeguarding public health, and supporting livestock productivity.","地理空间技术在兽医学中日益被视为关键资产，为疾病监测、风险制图和知情决策提供了稳健框架。本叙述性综述旨在总结地理信息系统（Geographic Information Systems, GIS）、全球定位系统（Global Positioning System, GPS）和遥感（Remote Sensing, RS）在推动兽医学发展中的作用，通过综合当前应用、识别主要挑战并探讨未来前景。总体而言，这些工具有助于监测疾病动态、识别流行病学热点、预测疫情，并整合空间与流行病学数据集以指导针对性干预。例如，基于GIS的东非裂谷热疫情制图能够快速识别流行病学热点并指导疫苗接种活动，而遥感衍生的植被指数已成功预测南部非洲蜱虫分布模式，改善了针对性控制策略。其效用不仅限于家畜健康，还对野生动物疾病监测、人兽共患病风险评估、食品安全保障及更广泛的兽医决策过程作出重要贡献。尽管取得了这些成功，其应用仍然有限，受到技术\u002F基础设施障碍、数据质量不佳以及伦理和政策挑战的制约。在埃塞俄比亚，近期调查表明，不到20%的兽医机构在常规监测中积极使用GIS平台，而遥感纳入国家兽医服务仍局限于试点项目。这一差距凸显了建立国家地理空间数据存储库、标准化方案和能力建设举措的迫切需求，以确保公平获取和可持续利用。未来的机遇在于利用人工智能、机器学习和基于云的GIS平台的进步，以及全球合作和培训项目，来加强兽医流行病学和监测系统。应对这些挑战对于将地理空间技术可持续地整合到兽医实践中至关重要，从而增强疾病控制、保障公共卫生并支持家畜生产力。","Discover Animals","2026-09-24T00:00:00Z",75,{"impact":110,"substance":111,"depth":112,"authority":238,"freshness":114,"relevant":68,"comment":239},12,"综述系统梳理GIS、GPS与遥感在兽医领域的应用与瓶颈，含东非裂谷热、南部非洲蜱虫分布及埃塞俄比亚使用率不足20%等实证数据，对农业信息化与动物疫病防控有参考价值。",[241],{"name":234,"url":231},[243,22,244,245,246],"动物疫病防控","人兽共患病","兽医信息化","地理信息系统",{"code":248,"slug":249,"name":250,"path":251,"parent_code":252},"C5","animal-health","疫病防控与兽药","\u002Ftopic\u002Flivestock\u002Fanimal-health","C",[254,255],{"code":33,"slug":34,"name":35,"path":36,"parent_code":30},{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":38,"locked":14,"version":39,"method":40,"evidence":257},{"candidates":258,"agricultural_evidence":45},[259,267,271,275,279,286],{"code":248,"score":260,"supported":45,"title":261,"abstract":263,"tags":266},19,[262],"veterinary",[264,262,265],"人兽共患","zoonotic",[244,243],{"code":33,"score":13,"supported":45,"title":268,"abstract":269,"tags":270},[],[142,55,143],[246],{"code":26,"score":51,"supported":45,"title":272,"abstract":273,"tags":274},[],[21,47,137],[],{"code":59,"score":60,"supported":45,"title":276,"abstract":277,"tags":278},[],[63,64],[22],{"code":280,"score":98,"supported":45,"title":281,"abstract":282,"tags":285},"E4",[],[283,284],"食品安全","food safety",[],{"code":252,"score":191,"supported":45,"title":287,"abstract":288,"tags":290},[],[289],"livestock",[],[292,293],"GIS 兽医 疾病监测","遥感 蜱虫 分布 预测","GIS兽医疾病监测-3617",2,"10.1007\u002Fs44338-026-00259-y",{"doi":296,"openalex_id":298,"authors":299,"venue":234,"cited_by_count":15,"oa_url":305,"card":306,"direction":226,"ingested_from":95},"W7214225608",[300,303],{"name":301,"orcid":302},"Assaye Wollelie Fentie","https:\u002F\u002Forcid.org\u002F0009-0005-9099-9819",{"name":304,"orcid":9},"Daniel Haile Asefa","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44338-026-00259-y.pdf",{"tldr":307,"method":308,"finding":309,"direction":93,"opportunity":310},"综述地理空间技术在兽医领域的应用、挑战与前景，聚焦疾病监测与风险制图。","叙述性综述，整合GIS、GPS、遥感在兽医流行病学中的应用案例与调查数据。","GIS\u002FRS能有效预测疫情和病媒分布，但应用受限于基础设施、数据质量及政策，如埃塞俄比亚使用率不足2","可探索AI\u002F机器学习与云GIS结合，构建兽医领域标准化地理空间数据平台与预警系统。","2026-09-27T23:30:26.015992Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":9,"source_name":318,"source_url":315,"published_at":319,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":324,"tags":326,"primary_topic":328,"secondary_topics":329,"classification":331,"search_phrases":367,"slug":370,"view_count":68,"doi":371,"paper":372,"created_at":384},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。","International Journal for Research in Applied Science and Engineering Technology","2026-09-03T00:00:00Z",49,{"impact":114,"substance":44,"depth":322,"authority":114,"freshness":295,"relevant":68,"comment":323},16,"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。",[325],{"name":318,"url":315},[120,21,63,327],"地下水",{"code":33,"slug":34,"name":35,"path":36,"parent_code":30},[330],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":38,"locked":14,"version":39,"method":40,"evidence":332},{"candidates":333,"agricultural_evidence":45},[334,338,342,346,353,357,363],{"code":33,"score":51,"supported":45,"title":335,"abstract":336,"tags":337},[],[142,55,143],[],{"code":26,"score":114,"supported":45,"title":339,"abstract":340,"tags":341},[],[21,47],[120,21],{"code":59,"score":60,"supported":45,"title":343,"abstract":344,"tags":345},[],[63,64],[63],{"code":347,"score":98,"supported":45,"title":348,"abstract":349,"tags":352},"G",[],[350,351],"农村发展","rural development",[],{"code":170,"score":98,"supported":45,"title":354,"abstract":355,"tags":356},[],[173,174],[],{"code":358,"score":191,"supported":45,"title":359,"abstract":360,"tags":362},"H7",[],[361],"干旱",[],{"code":177,"score":68,"supported":14,"title":364,"abstract":365,"tags":366},[],[],[327],[368,369],"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":371,"openalex_id":373,"authors":374,"venue":318,"cited_by_count":15,"oa_url":315,"card":379,"direction":93,"ingested_from":95},"W7207838377",[375,377],{"name":376,"orcid":9},"Suchivrutta R. Kamble",{"name":378,"orcid":9},"Ganesh K. Patil",{"tldr":380,"method":381,"finding":382,"direction":93,"opportunity":383},"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。","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。","2026-09-04T23:30:32.476791Z",{"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":236,"score_detail":393,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":406,"classification":409,"search_phrases":452,"slug":455,"view_count":68,"doi":456,"paper":457,"created_at":473},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",{"impact":110,"substance":111,"depth":322,"authority":238,"freshness":51,"relevant":68,"comment":394},"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。",[396],{"name":391,"url":388},[119,120,398,122,399],"病虫害识别","蔬菜种植",{"code":401,"slug":402,"name":403,"path":404,"parent_code":405},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[407,408],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"code":33,"slug":34,"name":35,"path":36,"parent_code":30},{"status":38,"locked":14,"version":39,"method":40,"evidence":410},{"candidates":411,"agricultural_evidence":45},[412,416,424,428,432,439,443,447],{"code":26,"score":260,"supported":45,"title":413,"abstract":414,"tags":415},[137],[21,47,137],[120,398],{"code":401,"score":110,"supported":45,"title":417,"abstract":419,"tags":423},[418],"vegetable",[403,420,421,418,422],"番茄","辣椒","tomato",[399],{"code":33,"score":51,"supported":45,"title":425,"abstract":426,"tags":427},[],[142,55,143],[],{"code":153,"score":114,"supported":45,"title":429,"abstract":430,"tags":431},[],[122,156],[119,122],{"code":433,"score":98,"supported":45,"title":434,"abstract":435,"tags":438},"F4",[],[436,437],"农药","pesticide",[],{"code":170,"score":98,"supported":45,"title":440,"abstract":441,"tags":442},[],[173,174],[],{"code":177,"score":98,"supported":45,"title":444,"abstract":445,"tags":446},[],[180,181],[],{"code":59,"score":191,"supported":45,"title":448,"abstract":449,"tags":451},[],[450],"multispectral",[],[453,454],"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":456,"openalex_id":458,"authors":459,"venue":391,"cited_by_count":15,"oa_url":9,"card":468,"direction":224,"ingested_from":95},"W7215128283",[460,462,464,466],{"name":461,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":463,"orcid":9},"Dr. SK Kshirsagar",{"name":465,"orcid":9},"MG Agale",{"name":467,"orcid":9},"GS Shinde",{"tldr":469,"method":470,"finding":471,"direction":226,"opportunity":472},"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":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":9,"source_name":480,"source_url":477,"published_at":481,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":482,"sources":484,"tags":486,"primary_topic":488,"secondary_topics":492,"classification":494,"search_phrases":521,"slug":524,"view_count":51,"doi":525,"paper":526,"created_at":542},3071,"Urban Influence on Landscape Transformation and Land Cover Interactions During Urban Expansion (1972–2022): A Remote Sensing and GIS-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0038","Abstract This study investigates how urban expansion influences land cover dynamics. An interactive analytical approach was adopted to examine the transitions among built-up areas, cropland, and bare land throughout the urbanization process. Remote Sensing and GIS techniques, in conjunction with the Land Transfer Matrix (LTM), Landscape Metrics (LM), Multi-buffer Ring (MBR) and complementary spatial tools, were employed to analyze the city of Tiaret in Algeria over the past half century (1972-2022). The results reveal a marked intensification of urbanization, with an Overall Average Annual Urban Expansion Rate (OA-AUER) of 28.3 ha and a concurrent rise in bare land driven by cropland conversion under urban pressure. Notably, 89.4 % of cropland loss initially transitions into bare land, while subsequent urban development originates from cropland (52 %) and bare land (47 %). The observed density patterns in peripheral areas confirm the significant influence of the urban core on surrounding land-cover transformations. These findings elucidate the urbanization process while considering the transitional role of bare land and further explain diffusion coalescence process. Further empirical research is recommended to refine theoretical frameworks on landscape transformation dynamics and to inform policies aimed at mitigating bare-land expansion in peri-urban areas, controlling cropland urbanization, and planning sustainable urban growth.","摘要 本研究探讨城市扩张如何影响土地覆盖动态。采用交互式分析方法，考察城市化进程中建成区、耕地和裸地之间的转换。运用遥感与地理信息系统技术，结合土地转移矩阵（LTM）、景观格局指数（LM）、多缓冲环（MBR）及辅助空间工具，对阿尔及利亚提亚雷特市过去半个世纪（1972—2022年）的变化进行了分析。结果表明，城市化显著加剧，年均城市扩张总面积率（OA-AUER）为28.3公顷，同时在城市压力下耕地转换导致裸地同步增加。值得注意的是，89.4%的耕地损失最初转变为裸地，而随后的城市发展来源于耕地（52%）和裸地（47%）。外围区域观测到的密度格局证实了城市核心对周边土地覆盖变化的显著影响。这些发现阐明了城市化过程，同时考虑了裸地的过渡性作用，并进一步解释了扩散聚合过程。建议开展进一步实证研究，以完善景观转型动态的理论框架，并为旨在减缓城郊地区裸地扩张、控制耕地城市化以及规划可持续城市增长的政策提供依据。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":483},"该论文聚焦阿尔及利亚Tiaret市城市扩张与土地覆盖变化，属城市地理与遥感应用研究，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[485],{"name":480,"url":477},[487,22,23,24],"耕地保护",{"code":59,"slug":489,"name":490,"path":491,"parent_code":30},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[493],{"code":33,"slug":34,"name":35,"path":36,"parent_code":30},{"status":38,"locked":14,"version":39,"method":40,"evidence":495},{"candidates":496,"agricultural_evidence":45},[497,501,506,512,517],{"code":59,"score":44,"supported":45,"title":498,"abstract":499,"tags":500},[64],[63,64],[22],{"code":33,"score":502,"supported":45,"title":503,"abstract":504,"tags":505},14,[55],[142,55],[],{"code":507,"score":191,"supported":45,"title":508,"abstract":509,"tags":511},"G2",[],[510],"land transfer",[],{"code":513,"score":68,"supported":14,"title":514,"abstract":515,"tags":516},"H1",[],[],[487],{"code":67,"score":68,"supported":14,"title":518,"abstract":519,"tags":520},[],[],[23],[522,523],"Tiaret 城市扩张 遥感","耕地 建设用地 转移矩阵","Tiaret城市扩张遥感-3071","10.2478\u002Fjlecol-2026-0038",{"doi":525,"openalex_id":527,"authors":528,"venue":480,"cited_by_count":15,"oa_url":536,"card":537,"direction":93,"ingested_from":95},"W7213652939",[529,531,533],{"name":530,"orcid":9},"Amina Kalbaza",{"name":532,"orcid":9},"Belkacem Marir",{"name":534,"orcid":535},"Farida Naceur","https:\u002F\u002Forcid.org\u002F0009-0004-1962-4937","https:\u002F\u002Freference-global.com\u002Fdownload\u002Farticle\u002F10.2478\u002Fjlecol-2026-0038.pdf",{"tldr":538,"method":539,"finding":540,"direction":93,"opportunity":541},"基于遥感与GIS分析阿尔及利亚提亚雷特1972—2022年城市扩张对景观与土地覆盖的影响。","遥感、GIS、土地转移矩阵、景观格局指数与多缓冲环分析。","耕地先转为裸地再被城市占用，城市核心显著驱动外围土地覆盖转变。","可延伸研究裸地作为耕地—城市转换中间态的机制，并构建城郊耕地流失预警模型。","2026-09-21T23:30:24.332177Z",{"id":544,"title":545,"url":546,"summary":547,"summary_zh":548,"content":9,"source_name":480,"source_url":546,"published_at":481,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":549,"sources":551,"tags":553,"primary_topic":555,"secondary_topics":556,"classification":557,"search_phrases":573,"slug":576,"view_count":185,"doi":577,"paper":578,"created_at":589},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":550},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[552],{"name":480,"url":546},[554,22,23,24],"深度学习",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":558},{"candidates":559,"agricultural_evidence":45},[560,565,569],{"code":26,"score":44,"supported":45,"title":561,"abstract":563,"tags":564},[562],"deep learning",[554,562],[554],{"code":59,"score":60,"supported":45,"title":566,"abstract":567,"tags":568},[],[63,64],[22],{"code":67,"score":68,"supported":14,"title":570,"abstract":571,"tags":572},[],[],[23],[574,575],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015","10.2478\u002Fjlecol-2026-0037",{"doi":577,"openalex_id":579,"authors":580,"venue":480,"cited_by_count":15,"oa_url":546,"card":584,"direction":93,"ingested_from":95},"W7213670913",[581],{"name":582,"orcid":583},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":585,"method":586,"finding":587,"direction":93,"opportunity":588},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z"]