[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3893":3,"related-3893":108},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":79,"slug":82,"view_count":22,"doi":83,"paper":84,"created_at":107},3893,"Modeling the productive potential of coffee trees in the Cerrado of Minas Gerais through hierarchical landscape analysis and remote sensing data","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2516","Brazil stands out as the world’s largest producer and exporter of Arabica coffee, one of the world’s most important commodities, which plays a crucial role in the country’s economy. In this context, this study develops a model to classify the productive potential of coffee trees based on hierarchical landscape analysis and coffee plant metrics, applied to different farms in the Córrego Feio watershed, Patrocínio, Minas Gerais. We employed a model based on landscape metrics (slope, water flow, vegetation, and slope shape), inspired by Ross’s (1994) proposal for Environmental Fragility. Each variable was reclassified according to hierarchical levels from the literature and empirical observations of the area. The data were integrated to generate two products: Factor T, which represents the potential of the area based on static soil and relief variables, and Calculated Potential, which combines Factor T with the NDVI vegetation index for a dynamic assessment. The application of the model demonstrated a strong capacity to interpret the qualitative hierarchy of productive potential, supporting the delineation of differentiated management zones. The results indicated that productive potential is more strongly associated with soil and topographic factors than with the vegetation index. It is concluded that the proposed method is a valuable tool for precision agriculture, allowing the delineation of management zones for the application of inputs in a more efficient and sustainable manner, contributing to resource conservation and environmental protection. Key words: Arabica coffee; environmental fragility; hierarchical analysis; vegetation index; watershed.","巴西是全球最大的阿拉比卡咖啡（Arabica coffee）生产国和出口国，阿拉比卡咖啡作为全球最重要的商品之一，在该国经济中发挥着至关重要的作用。在此背景下，本研究开发了一个基于层次景观分析和咖啡植株指标的咖啡树生产潜力分类模型，并将其应用于米纳斯吉拉斯州帕特罗西尼奥市Córrego Feio流域的不同农场。我们采用了一个基于景观指标（坡度、水流、植被和坡形）的模型，其灵感来源于Ross（1994）提出的环境脆弱性方案。每个变量根据文献和该区域经验观察所得的层次等级进行重新分类。数据经过整合生成了两个产品：因子T，代表基于静态土壤和地形变量的区域潜力；以及计算潜力，将因子T与NDVI植被指数相结合以进行动态评估。模型的应用展现出强大的能力，能够解释生产潜力的定性层次，支持差异化经营区的划分。结果表明，生产潜力与土壤和地形因子的关联性强于与植被指数的关联性。研究认为，所提出的方法是一种有价值的精准农业工具，能够以更高效和可持续的方式划定管理区以施用投入品，有助于资源保护和环境保护。关键词：阿拉比卡咖啡；环境脆弱性；层次分析；植被指数；流域。",null,"Coffee Science","2026-09-30T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"基于景观分析与NDVI的咖啡生产潜力分级模型，方法新颖、结论可靠，对精准农业管理分区有实用价值，但属区域性研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","遥感","管理分区","咖啡种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,65,71],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},15,true,[55],"coffee",[57,55],"咖啡",[31],{"code":40,"score":60,"supported":53,"title":61,"abstract":63,"tags":64},9,[62],"remote sensing",[],[29],{"code":66,"score":21,"supported":53,"title":67,"abstract":68,"tags":70},"I1",[],[28,69],"precision agriculture",[27,28],{"code":72,"score":73,"supported":53,"title":74,"abstract":75,"tags":78},"H2",6,[],[76,77],"土壤","soil",[],[80,81],"Minas Gerais 咖啡 遥感","Córrego Feio 流域 咖啡 生产潜力","MinasGerais咖啡遥感-3893","10.25186\u002F.v21i.2516",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":99,"oa_url":6,"card":100,"direction":104,"ingested_from":106},"W7214888407",[87,90,93,96],{"name":88,"orcid":89},"Jorge Eduardo Ferreira Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6376",{"name":91,"orcid":92},"Mateus Duarte Segismundo","https:\u002F\u002Forcid.org\u002F0009-0001-1289-406X",{"name":94,"orcid":95},"George Deroco Martins","https:\u002F\u002Forcid.org\u002F0000-0001-9738-7325",{"name":97,"orcid":98},"João Vítor Meza Bravo","https:\u002F\u002Forcid.org\u002F0000-0002-5457-3192",0,{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"基于景观指标与NDVI构建模型，对巴西塞拉多咖啡树生产潜力进行分级分类。","层次化景观分析（坡度、水流、植被、坡形）结合NDVI，生成因子T与计算潜力。","生产潜力与土壤和地形因素关联强于植被指数，可划分差异化精准管理区。","农业遥感与作物表型","可引入多时相遥感与机器学习，动态预测咖啡生产潜力并优化管理区划分。","openalex","2026-10-01T23:30:28.485534Z",{"total":73,"page":22,"page_size":73,"items":109},[110,179,255,313,405,480],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":9,"content":9,"source_name":115,"source_url":113,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":121,"tags":123,"primary_topic":126,"secondary_topics":127,"classification":129,"search_phrases":145,"slug":148,"view_count":149,"doi":150,"paper":151,"created_at":178},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","Research Square","2026-09-24T00:00:00Z",51,{"impact":21,"substance":119,"depth":52,"authority":73,"freshness":21,"relevant":22,"comment":120},14,"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[122],{"name":115,"url":113},[27,124,29,125,31],"无人机","NDVI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[128],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":130},{"candidates":131,"agricultural_evidence":53},[132,137,141],{"code":40,"score":20,"supported":53,"title":133,"abstract":135,"tags":136},[134],"multispectral",[134],[125,29],{"code":33,"score":17,"supported":53,"title":138,"abstract":139,"tags":140},[55],[55],[31],{"code":66,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[27],[146,147],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619",5,"10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":150,"openalex_id":152,"authors":153,"venue":115,"cited_by_count":99,"oa_url":177,"card":9,"direction":104,"ingested_from":106},"W7214220732",[154,156,158,160,162,165,167,169,172,175],{"name":155,"orcid":9},"Gabriel de Morais Campos",{"name":157,"orcid":9},"Aline Bhering Silva",{"name":159,"orcid":9},"Cileimar Aparecida da Silva",{"name":161,"orcid":9},"Vanda Maria Salles Andrade",{"name":163,"orcid":164},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":166,"orcid":9},"Andressa Barcellos Silva",{"name":168,"orcid":9},"Marco Thúlio Gonçalves Vieira",{"name":170,"orcid":171},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":173,"orcid":174},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":176,"orcid":9},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":193,"primary_topic":197,"secondary_topics":198,"classification":200,"search_phrases":225,"slug":228,"view_count":22,"doi":229,"paper":230,"created_at":254},4598,"Seasonal compatibility of RGB and multispectral vegetation indices in Amazon Cacao","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00815-x","Abstract Precision agriculture in cacao cultivation is constrained by multispectral sensor costs, limiting accessibility for smallholder farmers. This study evaluated the seasonal compatibility between RGB-derived visible reflectance and multispectral vegetation indices (NDVI, GNDVI, NDRE) to establish a low-cost monitoring approach for Eastern Amazon cacao plantations. We collected spectral data from 1,431 random sampling points across a 0.9-ha commercial cacao plantation during four seasonal periods (February, May, August, and November 2025) using UAV-mounted RGB and multispectral sensors. Pearson correlations and linear regression models assessed relationships between mean visible reflectance [(R + G+B)\u002F3] and multispectral indices. Geostatistical analysis characterized spatial dependence structure across seasons. GNDVI demonstrated superior compatibility across all seasons (mean R² = 59.3%; range: 52.5–62.9%), outperforming NDRE (44.2%) and NDVI (40.8%) by 15–19% points. Temporal stability was 2.9× higher for GNDVI (SD = 4.7 pp) than NDRE (14.1 pp) and NDVI (12.7 pp). Peak compatibility occurred in May (R² = 62.9%) during wet-to-dry transition. The conversion equation (GNDVI = 0.509–0.000012 × Visible Reflectance; RMSE = 0.077) enables reliable estimation, retaining 59% of multispectral information. Strong spatial dependence (Co\u002F(Co + C) \u003C 10%) was confirmed across all periods, with August providing optimal structure (range = 11.4 m) for management zone delineation. GNDVI is the most reliable RGB proxy for cacao monitoring, democratizing precision agriculture access through affordable technology while maintaining robust spatial and temporal performance year-round.","摘要 可可种植中的精准农业受到多光谱传感器成本的制约，限制了小农户的可及性。本研究评估了RGB衍生的可见光反射率与多光谱植被指数（NDVI、GNDVI、NDRE）之间的季节性兼容性，以建立一种面向东亚马逊可可种植园的低成本监测方法。我们使用无人机搭载的RGB和多光谱传感器，在四个季节时段（2025年2月、5月、8月和11月）从一片0.9公顷商业可可种植园的1，431个随机采样点采集了光谱数据。采用Pearson相关分析和线性回归模型评估平均可见光反射率[（R + G + B）\u002F3]与多光谱指数之间的关系。地统计学分析刻画了各季节的空间依赖结构。GNDVI在所有季节均表现出最优兼容性（平均R² = 59.3%；范围：52.5–62.9%），较NDRE（44.2%）和NDVI（40.8%）高出15–19个百分点。GNDVI的时间稳定性（SD = 4.7个百分点）是NDRE（14.1个百分点）和NDVI（12.7个百分点）的2.9倍。最高兼容性出现在5月（R² = 62.9%），即湿季向干季过渡期。转换方程（GNDVI = 0.509–0.000012 × 可见光反射率；RMSE = 0.077）可实现可靠估算，保留了59%的多光谱信息。所有时期均确认存在强空间依赖性（Co\u002F(Co + C) \u003C 10%），其中8月提供了最优结构（变程 = 11.4 m），适合用于管理分区划分。GNDVI是可可监测中最可靠的RGB替代指标，通过可负担的技术使精准农业更广泛可及，同时全年保持稳健的空间和时间性能。","Applied Geomatics","2026-10-08T00:00:00Z",68,{"impact":21,"substance":189,"depth":19,"authority":20,"freshness":60,"relevant":22,"comment":190},21,"以低成本RGB替代多光谱监测亚马逊可可，数据扎实、结论明确，对精准农业降本有参考价值，但属细分作物研究，公共影响有限。",[192],{"name":185,"url":182},[27,29,194,195,196],"植被指数","可可种植","无人机监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[199],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":201},{"candidates":202,"agricultural_evidence":53},[203,209,213,218],{"code":33,"score":119,"supported":53,"title":204,"abstract":206,"tags":208},[205],"cacao",[207,205],"可可",[],{"code":40,"score":20,"supported":53,"title":210,"abstract":211,"tags":212},[134],[134],[194,29],{"code":66,"score":214,"supported":53,"title":215,"abstract":216,"tags":217},7,[],[28,69],[27],{"code":219,"score":73,"supported":53,"title":220,"abstract":221,"tags":224},"G2",[],[222,223],"小农户","smallholder",[],[226,227],"亚马逊 可可 无人机 遥感","GNDVI RGB 植被指数 可可","亚马逊可可无人机遥感-4598","10.1007\u002Fs12518-026-00815-x",{"doi":229,"openalex_id":231,"authors":232,"venue":185,"cited_by_count":99,"oa_url":248,"card":249,"direction":104,"ingested_from":106},"W7221039911",[233,236,239,242,245],{"name":234,"orcid":235},"Charlyngton da Silva e Silva","https:\u002F\u002Forcid.org\u002F0009-0003-9332-4302",{"name":237,"orcid":238},"Luiz Antônio Soares Cardoso","https:\u002F\u002Forcid.org\u002F0000-0001-6868-7847",{"name":240,"orcid":241},"Ronilson de Souza Santos","https:\u002F\u002Forcid.org\u002F0000-0002-2683-456X",{"name":243,"orcid":244},"Carlos Rodrigo Tanajura Caldeira","https:\u002F\u002Forcid.org\u002F0000-0001-7102-7735",{"name":246,"orcid":247},"Norberto Cornejo Noronha","https:\u002F\u002Forcid.org\u002F0000-0001-7216-8687","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00815-x.pdf",{"tldr":250,"method":251,"finding":252,"direction":104,"opportunity":253},"评估RGB与多光谱植被指数在亚马逊可可园的季节性兼容性，以低成本监测替代多光谱传感器。","无人机RGB与多光谱传感器采集1431个样点，用相关、回归和地统计分析四季数据。","GNDVI兼容性最佳（R²=59.3%），时空稳定性最高，5月表现最优，可保留59%多光谱信息。","可探索RGB代理指数在其他热带作物和不同冠层结构下的普适性，并优化低成本管理分区方法。","2026-10-10T23:30:31.142887Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":9,"content":9,"source_name":260,"source_url":9,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":266,"tags":268,"primary_topic":271,"secondary_topics":272,"classification":279,"search_phrases":300,"slug":303,"view_count":22,"doi":9,"paper":304,"created_at":312},1367,"基于无人机多源遥感特征的茶树生长与氮素状况深度学习监测","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F17\u002F2923","南京农业大学园艺学院团队提出融合光谱、纹理与谐波特征的多域融合指标，结合无人机数据与深度学习模型，用于茶树生物量与氮素积累量估算。实验表明多域融合指数显著优于传统光谱指标，可实现对茶园生物量与氮素状况的高精度反演，为近实时茶园监测提供工具。","Remote Sensing · MDPI · 2026-09-01","2026-08-31T16:00:00Z",72,{"impact":52,"substance":18,"depth":264,"authority":17,"freshness":214,"relevant":22,"comment":265},18,"研究提出多域融合指标，提升茶树氮素监测精度，对智慧茶园有实用价值。",[267],{"name":260,"url":258},[27,124,29,269,270],"氮素监测","茶树",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[273,278],{"code":274,"slug":275,"name":276,"path":277,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":280},{"candidates":281,"agricultural_evidence":53},[282,287,292,296],{"code":33,"score":283,"supported":53,"title":284,"abstract":285,"tags":286},11,[270],[270],[],{"code":274,"score":283,"supported":53,"title":288,"abstract":290,"tags":291},[289],"深度学习",[289],[],{"code":40,"score":60,"supported":53,"title":293,"abstract":294,"tags":295},[29],[],[29],{"code":66,"score":22,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[27],[301,302],"智慧农业 氮素监测 无人机 茶树","智慧农业 氮素监测","智慧农业氮素监测无人机茶树-1367",{"doi":9,"openalex_id":9,"authors":305,"venue":9,"cited_by_count":99,"oa_url":9,"card":306,"direction":104,"ingested_from":311},[],{"tldr":307,"method":308,"finding":309,"direction":104,"opportunity":310},"融合无人机多源遥感特征与深度学习，高精度估算茶树生物量与氮素积累量。","融合光谱、纹理、谐波特征的多域融合指标，结合无人机数据与深度学习模型。","多域融合指数显著优于传统光谱指标，实现茶园生物量与氮素高精度反演。","可探索多域融合特征在其他作物或不同生长阶段的适用性，或结合时序数据预测氮素动态。","agent","2026-09-02T00:05:07.021728Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":9,"source_name":319,"source_url":316,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":325,"tags":327,"primary_topic":330,"secondary_topics":331,"classification":341,"search_phrases":389,"slug":392,"view_count":393,"doi":9,"paper":394,"created_at":404},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":323,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":324},16,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[326],{"name":319,"url":316},[27,28,29,328,329],"盐碱地治理","土壤监测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[332,337],{"code":333,"slug":334,"name":335,"path":336,"parent_code":44},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":72,"slug":77,"name":338,"path":339,"parent_code":340},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":342},{"candidates":343,"agricultural_evidence":53},[344,348,356,360,364,370,377,383],{"code":40,"score":264,"supported":53,"title":345,"abstract":346,"tags":347},[62],[29,62,134],[29],{"code":333,"score":19,"supported":53,"title":349,"abstract":351,"tags":355},[350],"GIS",[352,353,350,354],"地理信息","空间分析","geographic information",[],{"code":72,"score":52,"supported":53,"title":357,"abstract":358,"tags":359},[77],[76,77],[328],{"code":66,"score":13,"supported":53,"title":361,"abstract":362,"tags":363},[69],[],[27,28],{"code":274,"score":73,"supported":53,"title":365,"abstract":366,"tags":369},[],[367,368],"机器学习","machine learning",[],{"code":371,"score":372,"supported":53,"title":373,"abstract":374,"tags":376},"H3",3,[],[375],"灌溉",[],{"code":378,"score":372,"supported":53,"title":379,"abstract":380,"tags":382},"H7",[],[381],"干旱",[],{"code":384,"score":372,"supported":53,"title":385,"abstract":386,"tags":388},"I7",[],[387],"methodological framework",[],[390,391],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":395,"authors":396,"venue":319,"cited_by_count":99,"oa_url":316,"card":399,"direction":104,"ingested_from":106},"W7221843179",[397],{"name":398,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":400,"method":401,"finding":402,"direction":104,"opportunity":403},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":9,"source_name":411,"source_url":408,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":412,"sources":414,"tags":416,"primary_topic":419,"secondary_topics":420,"classification":422,"search_phrases":462,"slug":465,"view_count":22,"doi":466,"paper":467,"created_at":479},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",{"impact":17,"substance":189,"depth":19,"authority":20,"freshness":60,"relevant":22,"comment":413},"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[415],{"name":411,"url":408},[27,28,29,417,418],"土壤健康","覆盖作物",{"code":72,"slug":77,"name":338,"path":339,"parent_code":340},[421],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":423},{"candidates":424,"agricultural_evidence":53},[425,429,433,437,444,451,457],{"code":72,"score":52,"supported":53,"title":426,"abstract":427,"tags":428},[77],[76,77],[417],{"code":40,"score":17,"supported":53,"title":430,"abstract":431,"tags":432},[62],[29],[29],{"code":66,"score":21,"supported":53,"title":434,"abstract":435,"tags":436},[],[28,69],[27,28],{"code":438,"score":73,"supported":53,"title":439,"abstract":440,"tags":443},"A4",[],[441,442],"油菜","rapeseed",[],{"code":445,"score":73,"supported":53,"title":446,"abstract":447,"tags":450},"H8",[],[448,449],"可持续农业","sustainable agriculture",[],{"code":452,"score":372,"supported":53,"title":453,"abstract":454,"tags":456},"B1",[],[455],"萝卜",[],{"code":458,"score":22,"supported":14,"title":459,"abstract":460,"tags":461},"A7",[],[],[418],[463,464],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":466,"openalex_id":468,"authors":469,"venue":411,"cited_by_count":99,"oa_url":9,"card":474,"direction":104,"ingested_from":106},"W7221669942",[470,472],{"name":471,"orcid":9},"Ali Mirzakhani Nafchi",{"name":473,"orcid":9},"Salman Mirzaee",{"tldr":475,"method":476,"finding":477,"direction":104,"opportunity":478},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":486,"source_url":483,"published_at":487,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":488,"score_detail":489,"sources":492,"tags":494,"primary_topic":497,"secondary_topics":498,"classification":500,"search_phrases":525,"slug":528,"view_count":22,"doi":529,"paper":530,"created_at":560},4081,"Aerial Phenotyping of Coffee Flowering Using Deep Learning for Early Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15193019","Early yield estimation is important for improving decision-making and management practices in coffee production systems. However, approaches that use aerial imagery to characterize flowering and relate it to subsequent yield remain limited. This study evaluated the potential of RGB imagery acquired by remotely piloted aircraft (RPA) combined with Deep Learning techniques to classify coffee plants according to three levels of a flowering proxy and to investigate its relationship with yield. A dataset comprising 1593 segmented RGB images was classified into Low, Medium, and High flowering levels using a custom convolutional neural network and three transfer learning architectures: VGG16, ResNet50V2, and MobileNetV2. ResNet50V2 was selected for final evaluation based on its validation performance and achieved 92.12% accuracy, 92.40% macro-precision, 91.81% macro-recall, and 92.06% macro-F1 on the test dataset. The flowering proxy showed a positive association with field-observed yield, with Spearman’s ρ = 0.701 (p \u003C 0.001) and R2 = 0.528 for the combined dataset of Arara and Mundo Novo. In consolidated out-of-sample predictions, the model achieved R2 = 0.489, MAE = 1.03 kg plant−1, and RMSE = 1.18 kg plant−1. Performance differed between cultivars, with consolidated R2 values of 0.567 for Arara and 0.329 for Mundo Novo. Spatial analysis also showed correspondence between the distribution of flowering levels derived from RGB imagery and the subsequent spatial distribution of yield. These findings indicate that RPA-based RGB imagery and Deep Learning can provide spatialized information related to coffee flowering and its productive performance, with potential applications in precision agriculture. Further evaluation across production cycles, cultivars, and environmental conditions is needed to assess the robustness and generalization of the approach.","早期产量估测对于改善咖啡生产系统中的决策与管理实践具有重要意义。然而，利用航空影像表征开花并将其与后续产量相关联的方法仍然有限。本研究评估了由遥控驾驶航空器（RPA）获取的RGB影像结合深度学习技术，根据开花代理指标的三个水平对咖啡植株进行分类，并探讨其与产量之间关系的潜力。利用自定义卷积神经网络和三种迁移学习架构——VGG16、ResNet50V2和MobileNetV2，将包含1593张分割RGB图像的数据集分类为低、中、高开花水平。基于验证性能，选择ResNet50V2进行最终评估，其在测试数据集上达到了92.12%的准确率、92.40%的宏精确率、91.81%的宏召回率和92.06%的宏F1值。开花代理指标与田间观测产量呈正相关，Arara和Mundo Novo合并数据集的Spearman's ρ = 0.701（p \u003C 0.001），R² = 0.528。在合并的样本外预测中，模型达到了R² = 0.489、MAE = 1.03 kg plant⁻¹和RMSE = 1.18 kg plant⁻¹。不同品种间的表现存在差异，Arara和Mundo Novo的合并R²值分别为0.567和0.329。空间分析还表明，由RGB影像得出的开花水平分布与后续产量的空间分布之间存在对应关系。这些发现表明，基于RPA的RGB影像和深度学习能够提供与咖啡开花及其生产性能相关的空间化信息，在精准农业中具有应用潜力。需要在不同生产周期、品种和环境条件下进行进一步评估，以检验该方法的稳健性和泛化能力。","Plants","2026-10-02T00:00:00Z",78,{"impact":323,"substance":490,"depth":264,"authority":20,"freshness":60,"relevant":22,"comment":491},22,"该研究将无人机RGB影像与深度学习结合用于咖啡花期分类和早期产量预测，方法新颖、数据扎实、结论可靠，对智慧农业和精准农业有参考价值，但属于细分作物研究，影响范围有限。",[493],{"name":486,"url":483},[27,495,496,29,31],"农业人工智能","产量预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[499],{"code":274,"slug":275,"name":276,"path":277,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":501},{"candidates":502,"agricultural_evidence":53},[503,507,512,516,521],{"code":33,"score":52,"supported":53,"title":504,"abstract":505,"tags":506},[55],[57,55],[31],{"code":274,"score":52,"supported":53,"title":508,"abstract":510,"tags":511},[509],"deep learning",[289,509],[495],{"code":66,"score":214,"supported":53,"title":513,"abstract":514,"tags":515},[],[28,69],[27],{"code":333,"score":73,"supported":53,"title":517,"abstract":518,"tags":520},[],[353,519],"spatial analysis",[],{"code":40,"score":393,"supported":14,"title":522,"abstract":523,"tags":524},[],[],[496,29],[526,527],"无人机 RGB 咖啡 花期 产量预测","ResNet50V2 咖啡 深度学习","无人机RGB咖啡花期产量预测-4081","10.3390\u002Fplants15193019",{"doi":529,"openalex_id":531,"authors":532,"venue":486,"cited_by_count":99,"oa_url":483,"card":555,"direction":104,"ingested_from":106},"W7216244707",[533,535,538,541,543,546,549,552],{"name":534,"orcid":9},"Elias Horácio Zavala",{"name":536,"orcid":537},"Gabriel Araújo e Silva Ferraz","https:\u002F\u002Forcid.org\u002F0000-0001-6403-2210",{"name":539,"orcid":540},"Felipe Andrés Obando Vega","https:\u002F\u002Forcid.org\u002F0000-0001-7241-7065",{"name":542,"orcid":9},"Mirian de Lourdes Oliveira",{"name":544,"orcid":545},"Milene Alves de Figueiredo Carvalho","https:\u002F\u002Forcid.org\u002F0000-0001-6138-8182",{"name":547,"orcid":548},"Samuel Henrique Braga da Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-8783-0378",{"name":550,"orcid":551},"Andrea Pagliai","https:\u002F\u002Forcid.org\u002F0000-0002-0455-7426",{"name":553,"orcid":554},"Daniele Sarri","https:\u002F\u002Forcid.org\u002F0000-0001-6990-7573",{"tldr":556,"method":557,"finding":558,"direction":104,"opportunity":559},"用无人机RGB影像和深度学习对咖啡开花分级，实现早期产量预测。","1593张分割RGB图，自定义CNN与VGG16\u002FResNet50V2\u002FMobi","ResNet50V2开花分级准确率92.12%，开花等级与产量正相关（ρ=0.701）。","可跨生产周期、品种和环境验证模型泛化性，并融合多源遥感提升早期产量预测稳健性。","2026-10-03T23:30:27.930711Z"]