[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5166":3,"related-5166":112},{"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":37,"classification":38,"search_phrases":83,"slug":86,"view_count":87,"doi":88,"paper":89,"created_at":111},5166,"Application of the SCS–CN Method Integrated with GIS and Remote Sensing for Watershed Runoff Estimation: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i105735","Accurate estimation of runoff is crucial for watershed planning, soil and water conservation, flood mitigation, and the management of ungauged watersheds. The Soil Conservation Service Curve Number (SCS-CN) method has been extensively used because of its minimal input requirements and its ability to model the spatial influence of land use, soil properties, and hydrologic soil groups. This review compiles applications of the GIS-based SCS-CN method reported for watersheds in semi-arid and monsoon-affected countries such as India, Saudi Arabia, Afghanistan, China, Iraq, and Ethiopia. The reviewed literature employed rainfall data, land-use\u002Fland-cover data, soil maps, hydrologic soil groups, digital elevation models, and watershed morphometry to calculate curve numbers and direct runoff. The reported CN values varied substantially, from about 40 under dry conditions to more than 90 under urban, rocky, agricultural, or poorly infiltrating conditions. Runoff estimates ranged from relatively low values in arid catchments to more than 1,300 mm per year in high-rainfall watersheds. Several studies reported high R2 correlations between observed and estimated runoff, typically ranging from 0.73 to 0.98. Important limitations identified in the review include the sensitivity of results to the choice of curve number, antecedent moisture condition, initial abstraction assumption, land-use classification, soil-group assignment, rainfall representation, and the lack of observed discharge data. The overall design of the SCS-CN method and its integration with RS and GIS offer a practical approach to assessing spatial runoff and prioritising watersheds, but the method requires local calibration, uncertainty analysis, and validation against observed rainfall–runoff data.","径流的准确估算对于流域规划、水土保持、洪水缓解以及无资料流域的管理至关重要。土壤保持服务曲线数（SCS-CN）方法因其输入需求少、能够模拟土地利用、土壤性质和水文土壤组的空间影响而被广泛使用。本综述汇编了基于GIS的SCS-CN方法在半干旱和受季风影响国家（如印度、沙特阿拉伯、阿富汗、中国、伊拉克和埃塞俄比亚）流域的应用报道。所综述的文献利用降雨数据、土地利用\u002F土地覆盖数据、土壤图、水文土壤组、数字高程模型和流域形态计量来计算曲线数和直接径流。报道的CN值差异很大，从干旱条件下的约40到城市、岩石、农业或入渗不良条件下的90以上不等。径流估算值从干旱集水区的相对较低值到高降雨流域的年径流量超过1300 mm不等。多项研究报告了观测径流与估算径流之间的高R²相关性，通常在0.73至0.98之间。本综述识别出的重要局限性包括：结果对曲线数选择、前期土壤湿润条件、初始损失假设、土地利用分类、土壤组赋值、降雨代表性以及缺乏实测流量数据的敏感性。SCS-CN方法的总体设计及其与遥感和GIS的集成，为评估空间径流和流域优先排序提供了一种实用方法，但该方法需要本地校准、不确定性分析，并依据实测降雨—径流数据进行验证。",null,"International Journal of Environment and Climate Change","2026-10-16T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,12,9,1,"综述系统梳理了GIS与遥感集成的SCS-CN径流估算方法及其局限，对农业信息化中的流域水资源与水土保持管理有参考价值，但属方法综述、影响面有限，未达每日精选门槛。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","GIS","水土保持","流域管理","水文模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"partial","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46,"reason":82},[44,51,56,64,70,77],{"code":45,"score":20,"supported":46,"title":47,"abstract":49,"tags":50},"I3",true,[48],"remote sensing",[27],[27],{"code":52,"score":20,"supported":46,"title":53,"abstract":54,"tags":55},"I4",[28],[28],[28],{"code":57,"score":58,"supported":46,"title":59,"abstract":60,"tags":63},"H2",6,[],[61,62],"土壤","soil",[],{"code":65,"score":66,"supported":46,"title":67,"abstract":68,"tags":69},"H6",4,[],[29],[29],{"code":71,"score":72,"supported":46,"title":73,"abstract":74,"tags":76},"H7",3,[],[75],"干旱",[],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"H3",[],[],[31],"一级领域明确，二级方向需要细分",[84,85],"SCS-CN GIS 遥感 径流估算","水土保持 水文模型 流域管理 遥感","SCS-CNGIS遥感径流估算-5166",0,"10.9734\u002Fijecc\u002F2026\u002Fv16i105735",{"doi":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":87,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7222632567",[92,94,96,98,100,102],{"name":93,"orcid":9},"Sudhir Lad",{"name":95,"orcid":9},"R. D. Bansod",{"name":97,"orcid":9},"Bhaupatil Gavit",{"name":99,"orcid":9},"Sachin Nandgude",{"name":101,"orcid":9},"Shailendra Gadge",{"name":103,"orcid":9},"Mahesh Patil",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"综述GIS与遥感集成的SCS-CN法在半干旱与季风区流域径流估算的应用。","汇总印度、中国等国文献，用降雨、土地利用、土壤图、DEM与流域形态计算CN和径流","CN约40至90以上，年径流差异大，R²多在0.73至0.98，但结果对CN与前期湿度等敏感。","农业遥感与作物表型","可研究CN本地化校准与不确定性量化，并结合实测径流验证，提升无资料流域径流估算可靠性。","openalex","2026-10-17T23:30:28.373754Z",{"total":58,"page":22,"page_size":58,"items":113},[114,181,253,328,401,465],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":127,"tags":129,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":160,"slug":163,"view_count":87,"doi":164,"paper":165,"created_at":180},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z",70,{"impact":20,"substance":124,"depth":125,"authority":20,"freshness":21,"relevant":22,"comment":126},20,17,"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[128],{"name":120,"url":117},[130,27,28,131,132],"农业人工智能","野生动物保护","人兽冲突",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":136},{"candidates":137,"agricultural_evidence":46,"reason":82},[138,146,152,156],{"code":139,"score":13,"supported":46,"title":140,"abstract":141,"tags":145},"I2",[],[142,143,144],"机器学习","machine learning","decision support",[130],{"code":52,"score":13,"supported":46,"title":147,"abstract":148,"tags":151},[],[149,28,150],"地理信息","geographic information",[28],{"code":45,"score":66,"supported":46,"title":153,"abstract":154,"tags":155},[],[27],[27],{"code":65,"score":22,"supported":14,"title":157,"abstract":158,"tags":159},[],[],[131],[161,162],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":164,"openalex_id":166,"authors":167,"venue":120,"cited_by_count":87,"oa_url":117,"card":174,"direction":179,"ingested_from":110},"W7222999966",[168,170,172],{"name":169,"orcid":9},"Bhutia Dechen Doma",{"name":171,"orcid":9},"Swarup Das",{"name":173,"orcid":9},"Mandal Rakesh Kumar",{"tldr":175,"method":176,"finding":177,"direction":108,"opportunity":178},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","农业人工智能与决策模型","2026-10-21T23:30:43.936453Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":193,"tags":195,"primary_topic":199,"secondary_topics":200,"classification":201,"search_phrases":236,"slug":239,"view_count":87,"doi":240,"paper":241,"created_at":252},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":17,"substance":19,"depth":191,"authority":72,"freshness":17,"relevant":22,"comment":192},15,"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[194],{"name":187,"url":184},[196,27,28,197,198],"决策支持系统","水稻种植","土地评价",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":202},{"candidates":203,"agricultural_evidence":46,"reason":82},[204,208,212,221,228,232],{"code":139,"score":20,"supported":46,"title":205,"abstract":206,"tags":207},[144],[144],[196],{"code":52,"score":20,"supported":46,"title":209,"abstract":210,"tags":211},[28],[28],[28],{"code":213,"score":13,"supported":46,"title":214,"abstract":215,"tags":220},"A1",[],[216,217,218,219],"水稻","稻田","rice","paddy",[197],{"code":222,"score":58,"supported":46,"title":223,"abstract":224,"tags":227},"A8",[],[225,226],"粮食安全","food security",[],{"code":57,"score":58,"supported":46,"title":229,"abstract":230,"tags":231},[],[61,62],[],{"code":45,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[27],[237,238],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":240,"openalex_id":242,"authors":243,"venue":187,"cited_by_count":87,"oa_url":184,"card":247,"direction":179,"ingested_from":110},"W7222963628",[244],{"name":245,"orcid":246},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":248,"method":249,"finding":250,"direction":108,"opportunity":251},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":120,"source_url":256,"published_at":259,"category":12,"cover_url":9,"hotness":260,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":269,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":311,"slug":314,"view_count":66,"doi":315,"paper":316,"created_at":327},2317,"\"Bharat @2047: A Geographical Perspective on Sustainable and Inclusive Regional Development\"","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22706245","India’s vision of becoming a developed nation by 2047 depends on achieving balanced development across its diverse regions. Differences in landforms, climate, soils, water resources, population, agriculture, infrastructure and economic activities have created uneven development opportunities. While some regions have experienced rapid urbanization, industrialization and improved connectivity, rural, remote and environmentally fragile areas continue to face developmental challenges. This paper examines sustainable and inclusive regional development under Viksit Bharat @2047 from a geographical perspective. It focuses on regional disparities, rural transformation, urbanization, agriculture, infrastructure, natural-resource management, environmental sustainability, climate resilience, digital inclusion and tourism. Special attention is given to GIS and Remote Sensing for understanding spatial patterns and supporting development planning. The study is based on secondary information from government publications, institutional reports, academic literature and development documents, using a descriptive and analytical approach. The study argues that a uniform development model may not address India’s regional diversity effectively. Development strategies should consider local resources, environmental conditions, population characteristics, connectivity and economic potential. Strengthening rural–urban linkages, improving infrastructure, promoting sustainable resource use and expanding digital access can help reduce regional inequalities. A geographically sensitive approach can make Viksit Bharat @2047 more inclusive, sustainable and responsive to the varied needs of different regions.","印度到2047年成为发达国家的愿景，取决于能否在其多样化的各地区实现均衡发展。地貌、气候、土壤、水资源、人口、农业、基础设施和经济活动的差异，造成了发展机会的不均衡。一些地区经历了快速的城市化、工业化和连通性改善，而农村、偏远和环境脆弱地区仍面临发展挑战。本文从地理学视角考察“Viksit Bharat @2047”框架下的可持续和包容性区域发展。研究聚焦于区域差距、农村转型、城市化、农业、基础设施、自然资源管理、环境可持续性、气候韧性、数字包容和旅游业。特别关注地理信息系统（GIS）和遥感在理解空间格局及支持发展规划方面的作用。本研究基于政府出版物、机构报告、学术文献和发展文件的二手资料，采用描述性和分析性方法。研究认为，统一的发展模式可能无法有效应对印度的区域多样性。发展战略应考虑当地资源、环境条件、人口特征、连通性和经济潜力。加强城乡联系、改善基础设施、促进资源可持续利用和扩大数字接入，有助于缩小区域不平等。一种具有地理敏感性的方法可以使“Viksit Bharat @2047”更具包容性、可持续性，并更好地回应不同地区的多样化需求。","2026-09-30T00:00:00Z",25,57,{"impact":19,"substance":263,"depth":191,"authority":20,"freshness":87,"relevant":22,"comment":264},14,"从地理视角讨论印度2047区域均衡发展，涉及遥感与GIS应用及农村转型，对农业信息化有参考价值，但属宏观综述且时效性差。",[266,267],{"name":120,"url":256},{"name":120,"url":268},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22706244",[270,27,271,28,272],"数字乡村","农业可持续发展","区域协调发展",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":276},{"candidates":277,"agricultural_evidence":46,"reason":82},[278,283,287,291,296,301,306],{"code":45,"score":279,"supported":46,"title":280,"abstract":281,"tags":282},7,[],[27,48],[27],{"code":52,"score":279,"supported":46,"title":284,"abstract":285,"tags":286},[],[149,28],[28],{"code":57,"score":72,"supported":46,"title":288,"abstract":289,"tags":290},[],[61],[],{"code":78,"score":72,"supported":46,"title":292,"abstract":293,"tags":295},[],[294],"水资源",[],{"code":71,"score":72,"supported":46,"title":297,"abstract":298,"tags":300},[],[299],"climate resilience",[],{"code":302,"score":22,"supported":14,"title":303,"abstract":304,"tags":305},"H8",[],[],[271],{"code":307,"score":22,"supported":14,"title":308,"abstract":309,"tags":310},"I6",[],[],[270],[312,313],"农业可持续发展 区域协调发展 数字乡村 遥感","农业可持续发展 区域协调发展","农业可持续发展区域协调发展数字乡村遥感-2317","10.5281\u002Fzenodo.22706245",{"doi":315,"openalex_id":317,"authors":318,"venue":120,"cited_by_count":87,"oa_url":256,"card":321,"direction":108,"ingested_from":110},"W7212248989",[319],{"name":320,"orcid":9},"Jayram Sunil Pawar",{"tldr":322,"method":323,"finding":324,"direction":325,"opportunity":326},"从地理视角分析印度2047年可持续包容性区域发展，强调因地制宜与GIS遥感支持规划。","基于政府报告等二手资料，采用描述性与分析性方法，结合GIS和遥感。","统一发展模式难以应对区域多样性，需因地制宜并加强城乡联系与数字接入。","数字乡村与农业信息化","可探究GIS\u002F遥感如何量化区域差异并支撑差异化数字乡村发展策略。","2026-09-13T23:30:22.373477Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":382,"slug":385,"view_count":22,"doi":386,"paper":387,"created_at":400},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":17,"substance":263,"depth":338,"authority":58,"freshness":17,"relevant":22,"comment":339},13,"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[341],{"name":334,"url":331},[343,130,344,27,345],"智慧农业","物联网","农业教育",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":349},{"candidates":350,"agricultural_evidence":46,"reason":82},[351,357,361,367,373,377],{"code":352,"score":191,"supported":46,"title":353,"abstract":355,"tags":356},"I1",[354],"smart agriculture",[343,354],[343],{"code":45,"score":279,"supported":46,"title":358,"abstract":359,"tags":360},[],[27,48],[27],{"code":362,"score":279,"supported":46,"title":363,"abstract":364,"tags":366},"I5",[],[344,365],"internet of things",[344],{"code":78,"score":58,"supported":46,"title":368,"abstract":369,"tags":372},[],[370,371],"灌溉","irrigation",[],{"code":52,"score":58,"supported":46,"title":374,"abstract":375,"tags":376},[],[149,28],[],{"code":139,"score":66,"supported":46,"title":378,"abstract":379,"tags":381},[],[380],"artificial intelligence",[130],[383,384],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":386,"openalex_id":388,"authors":389,"venue":334,"cited_by_count":87,"oa_url":393,"card":394,"direction":399,"ingested_from":110},"W7221997102",[390],{"name":391,"orcid":392},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":395,"method":396,"finding":397,"direction":325,"opportunity":398},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:07.890026Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":443,"slug":446,"view_count":22,"doi":447,"paper":448,"created_at":464},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,{"impact":18,"substance":124,"depth":18,"authority":263,"freshness":17,"relevant":22,"comment":411},"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[413],{"name":407,"url":404},[343,130,415,27,416],"精准农业","多源数据融合",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":420},{"candidates":421,"agricultural_evidence":46,"reason":82},[422,427,433,437],{"code":352,"score":19,"supported":46,"title":423,"abstract":425,"tags":426},[424],"precision agriculture",[415,424],[343,415],{"code":139,"score":191,"supported":46,"title":428,"abstract":430,"tags":432},[429],"deep learning",[431,429],"深度学习",[130],{"code":45,"score":191,"supported":46,"title":434,"abstract":435,"tags":436},[48],[27,48],[27],{"code":362,"score":279,"supported":46,"title":438,"abstract":439,"tags":442},[],[440,441],"边缘计算","edge computing",[416],[444,445],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":447,"openalex_id":449,"authors":450,"venue":407,"cited_by_count":87,"oa_url":404,"card":459,"direction":108,"ingested_from":110},"W7221565940",[451,454,457],{"name":452,"orcid":453},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":455,"orcid":456},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":458,"orcid":9},"Cundeng Wang",{"tldr":460,"method":461,"finding":462,"direction":108,"opportunity":463},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":9,"source_name":120,"source_url":468,"published_at":471,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":472,"score_detail":473,"sources":476,"tags":478,"primary_topic":482,"secondary_topics":483,"classification":484,"search_phrases":504,"slug":507,"view_count":22,"doi":508,"paper":509,"created_at":534},4503,"FUSING DEEP LEARNING AND REMOTE SENSING DATA TO ESTIMATE NON-OPTICALLY ACTIVE WATER QUALITY VARIABLES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23243307","The efficient monitoring of inland water bodies such as rivers and lakes is crucial not only for ensuring human health and ecosystem balance, but also for the effective management of water resources. Two water quality variables of high importance and interest are Dissolved Oxygen (DO) and pH. The former variable is an ecosystem health index as it is connected to hypoxia events, while the latter one is linked to biochemical processes and the acidity of water. Currently, conventional water quality monitoring methods (e.g., via in-situ point sampling) are accurate for the particular point, but costly and time-consuming. On the other hand, satellite remote sensing provides a remedy, as it offers high spatial and temporal coverage. Most of the water quality applications of remote sensing data have focused on optically active parameters like Chlorophyll-a and Colored Dissolved Organic Matter (CDOM), but the estimation of non-optically active variables such as pH and DO poses a significant challenge. This study introduces a novel approach, that fuses Earth Observation (EO) and in-situ data, in a deep learning context, to provide estimations of non-optically active variables, characterized by high accuracy, high spatial coverage and low latency. The approach also considers the data imbalance challenge, which is common in environmental datasets. The approach is applied to Lake Yliki in central Greece. The Boeotikos Kifissos river basin, which lies upstream of Lake Yliki, is dominated by intense agricultural activity. The lake is also part of Athens’ water distribution system, making its efficient monitoring even more important.","对河流、湖泊等内陆水体的高效监测，不仅对保障人类健康和生态系统平衡至关重要，也对水资源的有效管理不可或缺。溶解氧（Dissolved Oxygen, DO）和pH值是两项备受关注且极为重要的水质变量。前者作为生态系统健康指标，与缺氧事件密切相关；后者则与生物化学过程及水体酸度相关。目前，传统的水质监测方法（如原位定点采样）对特定点位而言较为准确，但成本高昂且耗时费力。相比之下，卫星遥感提供了一种解决方案，因其具备较高的时空覆盖能力。遥感数据在水质方面的应用大多集中于光学活性参数，如叶绿素a和有色溶解有机物（Colored Dissolved Organic Matter, CDOM），而对pH值和溶解氧等非光学活性变量的估算则构成重大挑战。本研究提出了一种新方法，在深度学习框架下融合地球观测（Earth Observation, EO）数据与原位数据，以实现对非光学活性变量的高精度、高空间覆盖度和低延迟估算。该方法还考虑了环境数据集中常见的数据不平衡问题。该方法已应用于希腊中部的伊利基湖（Lake Yliki）。位于伊利基湖上游的维奥蒂亚基菲索斯河流域（Boeotikos Kifissos river basin）以密集的农业活动为主。该湖也是雅典供水系统的组成部分，因此对其开展高效监测尤为重要。","2026-10-08T00:00:00Z",81,{"impact":18,"substance":474,"depth":18,"authority":338,"freshness":13,"relevant":22,"comment":475},22,"将深度学习与遥感、地面实测数据融合估算内陆水体非光学活性水质参数，方法新颖且面向农业流域与饮用水源监测，具备较高专业参考价值。",[477],{"name":120,"url":468},[431,479,27,480,481],"农业面源污染","水资源管理","水质监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":9},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":485},{"candidates":486,"agricultural_evidence":46,"reason":82},[487,491,495,499],{"code":139,"score":191,"supported":46,"title":488,"abstract":489,"tags":490},[429],[431,429],[431],{"code":45,"score":191,"supported":46,"title":492,"abstract":493,"tags":494},[48],[27,48],[27],{"code":78,"score":66,"supported":46,"title":496,"abstract":497,"tags":498},[],[294],[480],{"code":500,"score":22,"supported":14,"title":501,"abstract":502,"tags":503},"H5",[],[],[479],[505,506],"Yliki湖 水质 遥感","溶解氧 pH 遥感 估算","Yliki湖水质遥感-4503","10.5281\u002Fzenodo.23243307",{"doi":508,"openalex_id":510,"authors":511,"venue":120,"cited_by_count":87,"oa_url":468,"card":529,"direction":108,"ingested_from":110},"W7220943541",[512,515,518,521,523,526],{"name":513,"orcid":514},"Vasiliki Thomopoulou","https:\u002F\u002Forcid.org\u002F0009-0000-8361-6879",{"name":516,"orcid":517},"Panagiotis Kossieris","https:\u002F\u002Forcid.org\u002F0000-0002-5201-3079",{"name":519,"orcid":520},"Georgios Bariamis","https:\u002F\u002Forcid.org\u002F0000-0002-9548-3054",{"name":522,"orcid":9},"Konstantinos Peroulis",{"name":524,"orcid":525},"G. Katsouras","https:\u002F\u002Forcid.org\u002F0000-0003-3249-3113",{"name":527,"orcid":528},"Christos Makropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-0308-4265",{"tldr":530,"method":531,"finding":532,"direction":108,"opportunity":533},"融合深度学习与遥感及实地数据，估算湖泊非光学活性水质变量（溶解氧和pH）。","深度学习融合地球观测与实地数据，处理数据不平衡，应用于希腊Yliki湖。","该方法能高精度、高覆盖、低延迟地估算非光学活性水质变量。","可探索将此类方法迁移至农业流域水质监测，结合农业活动数据提升预警能力。","2026-10-09T23:30:25.773729Z"]