[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2064":3,"related-2064":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":22,"tags":24,"primary_topic":9,"secondary_topics":30,"classification":31,"search_phrases":81,"slug":84,"view_count":20,"doi":85,"paper":86,"created_at":107},2064,"Spatial modeling of subsurface soil texture in semi-arid regions: Evaluating pure machine learning against hybrid regression kriging using Sentinel-1 and Sentinel-2 data","https:\u002F\u002Fdoi.org\u002F10.12912\u002F27197050\u002F235746","Accurate mapping of subsurface soil texture is important for sustainable land management and precision agriculture in semi-arid environments, where soil observations are often spatially sparse.This study evaluates a multitemporal Digital Soil Mapping framework combining wet-season Sentinel-1 synthetic aperture radar (SAR) and dry-season Sentinel-2 optical observations for mapping clay, silt, and sand fractions at 30-40 cm depth in the Tal Kaif district of northern Iraq.Four hyperparameter-optimized machine-learning algorithms -Random Forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and artificial neural network (ANN\u002F MLP) -were benchmarked using strict five-fold out-of-fold validation.Pure machine-learning models showed limited independent predictive performance for all three fractions, with negative R² values and RPD values below 1.0.The best pure-ML models achieved RMSE values of 9.726% for clay, 9.201% for silt, and 6.890% for sand.Residual variograms demonstrated moderate to strong spatial dependence, supporting the incorporation of spatially structured residuals through Regression Kriging (RK).Hybrid RK produced fraction-specific effects: RMSE decreased slightly for clay (9.726% to 9.689%) and more clearly for sand (6.890% to 6.702%), whereas silt performance deteriorated slightly (9.201% to 9.334%).The results therefore provide partial support for the hypothesis that geostatistical modelling can complement pure machine learning, but do not demonstrate a universal improvement from RK.The principal limitation is the indirect relationship between surface remote-sensing observations and subsurface texture at 30-40 cm depth, together with the sparse sampling network.Nevertheless, the resulting spatially continuous maps identified a predominance of fine-textured classes, particularly silty clay loam and silty clay, providing spatial information relevant to irrigation planning, precision agriculture, and soil-management decisions.The study contributes evidence that integrating environmental feature-space modelling with spatial residual structure can provide a more spatially informed framework for subsurface soil-texture mapping in data-sparse semi-arid environments, while highlighting the need for fraction-specific evaluation and independent validation in future studies.","在半干旱环境中，地下土壤质地的精确制图对可持续土地管理和精准农业具有重要意义，而这类地区的土壤观测数据通常在空间上较为稀疏。本研究评估了一种多时相数字土壤制图框架，该框架结合湿季Sentinel-1合成孔径雷达（SAR）和干季Sentinel-2光学观测数据，用于绘制伊拉克北部Tal Kaif地区30–40 cm深度黏粒、粉粒和砂粒含量。研究采用严格的五折交叉验证（out-of-fold）对四种经超参数优化的机器学习算法——随机森林（RF）、极端梯度提升（XGBoost）、支持向量回归（SVR）和人工神经网络（ANN\u002FMLP）——进行了基准测试。纯机器学习模型对三种粒级的独立预测性能均有限，R²为负值，RPD低于1.0。最佳纯机器学习模型的RMSE分别为：黏粒9.726%、粉粒9.201%、砂粒6.890%。残差变异函数表现出中等至强的空间依赖性，支持通过回归克里金（RK）纳入具有空间结构的残差。混合RK产生了粒级特异性效果：黏粒的RMSE略有下降（9.726%降至9.689%），砂粒下降更为明显（6.890%降至6.702%），而粉粒性能略有恶化（9.201%升至9.334%）。因此，研究结果部分支持了地统计建模可以补充纯机器学习的假设，但并未证明RK能带来普遍性改善。主要局限性在于地表遥感观测与30–40 cm深度地下质地之间的间接关系，以及采样网络稀疏。尽管如此，所生成的空间连续图件识别出细质地类别占主导，尤其是粉质黏壤土和粉质黏土，为灌溉规划、精准农业和土壤管理决策提供了相关空间信息。本研究提供了证据表明，将环境特征空间建模与空间残差结构相结合，可为数据稀疏的半干旱环境中地下土壤质地制图提供更具空间信息的框架，同时强调未来研究需要进行粒级特异性评估和独立验证。",null,"Ecological Engineering & Environmental Technology","2026-09-08T00:00:00Z","论文",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":17,"relevant":20,"comment":21},8,18,16,1,"基于Sentinel-1\u002F2多时相遥感的半干旱区地下土壤质地制图研究，方法对比严谨但结论为部分支持，属细分领域方法学进展，公共价值有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"机器学习","精准农业","遥感","数字土壤","土壤制图",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":80},[37,46,52,59,67,73],{"code":38,"score":39,"supported":40,"title":41,"abstract":43,"tags":45},"H2",15,true,[42],"soil",[44,42],"土壤",[29],{"code":47,"score":39,"supported":40,"title":48,"abstract":50,"tags":51},"I2",[49],"machine learning",[25,49],[25],{"code":53,"score":54,"supported":40,"title":55,"abstract":56,"tags":58},"I1",7,[],[26,57],"precision agriculture",[26],{"code":60,"score":61,"supported":40,"title":62,"abstract":63,"tags":66},"H3",6,[],[64,65],"灌溉","irrigation",[],{"code":68,"score":69,"supported":40,"title":70,"abstract":71,"tags":72},"I3",4,[],[27],[27],{"code":74,"score":75,"supported":40,"title":76,"abstract":77,"tags":79},"H7",3,[],[78],"干旱",[],"多个产业证据相当，需要确认主归属",[82,83],"土壤制图 数字土壤 机器学习 精准农业","土壤制图 数字土壤","土壤制图数字土壤机器学习精准农业-2064","10.12912\u002F27197050\u002F235746",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":98,"oa_url":99,"card":100,"direction":104,"ingested_from":106},"W7211972432",[89,92,95],{"name":90,"orcid":91},"Riyad Hazem Zubair","https:\u002F\u002Forcid.org\u002F0009-0000-0947-2184",{"name":93,"orcid":94},"Muntadher Aidi Shareef","https:\u002F\u002Forcid.org\u002F0000-0002-9089-652X",{"name":96,"orcid":97},"Abdelmalek Toumi","https:\u002F\u002Forcid.org\u002F0000-0001-8415-0871",0,"https:\u002F\u002Fwww.ecoeet.com\u002Fpdf-235746-144067?filename=Spatial-modeling-of-subsu.pdf",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"用Sentinel-1\u002F2多时相数据结合纯机器学习与回归克里金，制图半干旱区30-40cm土壤质地。","Sentinel-1 SAR与Sentinel-2光学数据，RF\u002FXGBoost","纯ML预测能力有限（R²为负），回归克里金仅对黏粒和砂粒略有改善，粉粒反而变差。","农业遥感与作物表型","表层遥感与深层土壤质地关系间接，可探索深度函数建模、多源数据融合与分粒级独立验证方法。","openalex","2026-09-10T23:30:30.335801Z",{"total":61,"page":20,"page_size":61,"items":109},[110,174,249,304,386,457],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":9,"content":9,"source_name":115,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":121,"tags":123,"primary_topic":127,"secondary_topics":132,"classification":133,"search_phrases":159,"slug":162,"view_count":163,"doi":9,"paper":164,"created_at":173},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":18,"substance":118,"depth":18,"authority":119,"freshness":75,"relevant":20,"comment":120},22,13,"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[122],{"name":115,"url":113},[124,125,25,126,26,27],"智慧农业","产量预测","玉米",{"code":128,"slug":129,"name":126,"path":130,"parent_code":131},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":134,"locked":14,"version":33,"method":34,"evidence":135},"classified",{"candidates":136,"agricultural_evidence":40},[137,141,146,151,155],{"code":53,"score":119,"supported":40,"title":138,"abstract":139,"tags":140},[26],[26],[124,26],{"code":128,"score":142,"supported":40,"title":143,"abstract":144,"tags":145},12,[126],[126],[126],{"code":68,"score":147,"supported":40,"title":148,"abstract":149,"tags":150},5,[],[27],[125,27],{"code":47,"score":69,"supported":40,"title":152,"abstract":153,"tags":154},[],[25],[25],{"code":38,"score":75,"supported":40,"title":156,"abstract":157,"tags":158},[],[44],[],[160,161],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",2,{"doi":9,"openalex_id":9,"authors":165,"venue":9,"cited_by_count":98,"oa_url":9,"card":166,"direction":170,"ingested_from":172},[],{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","智慧农业 \u002F 农业物联网","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","agent","2026-10-09T00:05:02.451957Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"primary_topic":9,"secondary_topics":191,"classification":192,"search_phrases":222,"slug":225,"view_count":98,"doi":226,"paper":227,"created_at":248},4311,"Cross-Regional Digital Soil Mapping of OpenLandMap-Derived USDA Soil Texture Classes Using Remote Sensing and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192147","Soil is a fundamental environmental resource that supports food production and sustainable agricultural systems. Soil’s texture is a key soil property that affects its capacity to hold water, infiltration rate, fertility, erosion risk, and crop productivity. Traditionally, it is determined using hydrometer or pipette analysis. These laboratory procedures are costly and time-consuming, typically requiring approximately two working days, which limits their practicality for large-scale assessments. This paper proposes a cross-regional digital soil texture classification system based on remote sensing data and machine learning in Malaysia and Pakistan. The two countries were chosen because they represent contrasting environmental conditions: Malaysia has humid tropical conditions, and Pakistan has arid and semi-arid conditions. The OpenLandMap United States Department of Agriculture (USDA) soil texture classes served as the reference labels, and the spectral bands from Landsat, together with spectral indices and Shuttle Radar Topography Mission (SRTM) elevation, were used as predictor variables. The three ensemble models, including Random Forest (RF), XGBoost, and CatBoost, were evaluated for multi-class classification of USDA soil texture. The main Landsat-based Malaysia–Pakistan dataset produced the best results, with RF achieving the highest accuracy of 71.45%, XGBoost 71.31%, and CatBoost 67.33%. Additional experiments using 2025 Landsat 8\u002F9 and Sentinel-2 imagery achieved lower accuracies of 63.00% and 56.10%, respectively, while five-fold spatial block cross-validation produced a mean accuracy of 59.27 ± 1.84%. To strengthen dataset reliability, representative OpenLandMap-derived soil texture classes were externally validated using PCRWR sand–silt–clay fractions plotted on the USDA soil texture triangle and independent Malaysian soil resource references. The results of the feature importance analysis showed that elevation, Bare Soil Index (BSI), Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) were important predictors for classifying OpenLandMap-derived USDA soil texture classes. These show that the Landsat-based spectral and terrain features, when used in conjunction with ensemble learning, could assist cross-regional soil texture classification for the USDA. However, class imbalance, spectral overlap between soil texture classes, temporal variability, and resolution mismatch between satellite predictors and soil labels remain important challenges. The findings of this study provide a useful foundation for large-scale soil texture mapping and future spatial validation across different environmental regions.","土壤是支撑粮食生产和可持续农业系统的基础环境资源。土壤质地是影响土壤持水能力、入渗速率、肥力、侵蚀风险和作物生产力的关键土壤属性。传统上，土壤质地采用比重计法或吸管法测定。这些实验室方法成本高、耗时长，通常需要约两个工作日，限制了其在大规模评估中的实用性。本文提出了一种基于遥感数据和机器学习的跨区域数字土壤质地分类系统，研究区域为马来西亚和巴基斯坦。选择这两个国家是因为它们代表了截然不同的环境条件：马来西亚属于湿润热带条件，巴基斯坦属于干旱和半干旱条件。以OpenLandMap提供的美国农业部（USDA）土壤质地类别作为参考标签，以Landsat光谱波段、光谱指数和航天飞机雷达地形测绘任务（SRTM）高程数据作为预测变量。评估了三种集成模型，包括随机森林（RF）、XGBoost和CatBoost，用于USDA土壤质地的多类分类。基于Landsat的马来西亚—巴基斯坦主数据集取得了最佳结果，RF达到最高精度71.45%，XGBoost为71.31%，CatBoost为67.33%。使用2025年Landsat 8\u002F9和Sentinel-2影像的额外实验分别取得了较低的精度，为63.00%和56.10%，而五折空间块交叉验证产生的平均精度为59.27 ± 1.84%。为增强数据集可靠性，使用PCRWR砂—粉—黏粒比例绘制在USDA土壤质地三角图上，并结合独立的马来西亚土壤资源参考资料，对具有代表性的OpenLandMap衍生土壤质地类别进行了外部验证。特征重要性分析结果表明，高程、裸土指数（BSI）、归一化差异水体指数（NDWI）和归一化差异植被指数（NDVI）是分类OpenLandMap衍生USDA土壤质地类别的重要预测因子。这些结果表明，基于Landsat的光谱和地形特征与集成学习结合使用时，可辅助USDA的跨区域土壤质地分类。然而，类别不平衡、土壤质地类别之间的光谱重叠、时间变异性以及卫星预测因子与s之间的分辨率不匹配","Agriculture","2026-10-05T00:00:00Z",77,{"impact":19,"substance":184,"depth":18,"authority":119,"freshness":185,"relevant":20,"comment":186},21,9,"跨区域遥感与集成学习土壤质地分类研究，方法扎实、结论有参考价值，但精度有限，属细分领域进展。",[188],{"name":180,"url":177},[190,124,25,27,29],"数字农业",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":193},{"candidates":194,"agricultural_evidence":40,"reason":80},[195,199,203,208,212,218],{"code":38,"score":39,"supported":40,"title":196,"abstract":197,"tags":198},[42],[44,42],[29],{"code":47,"score":39,"supported":40,"title":200,"abstract":201,"tags":202},[49],[25,49],[25],{"code":68,"score":39,"supported":40,"title":204,"abstract":206,"tags":207},[205],"remote sensing",[27,205],[27],{"code":74,"score":75,"supported":40,"title":209,"abstract":210,"tags":211},[],[78],[],{"code":213,"score":75,"supported":40,"title":214,"abstract":215,"tags":217},"H8",[],[216],"可持续农业",[],{"code":53,"score":163,"supported":14,"title":219,"abstract":220,"tags":221},[],[],[190,124],[223,224],"OpenLandMap USDA 土壤质地","Landsat 土壤质地 机器学习","OpenLandMapUSDA土壤质地-4311","10.3390\u002Fagriculture16192147",{"doi":226,"openalex_id":228,"authors":229,"venue":180,"cited_by_count":98,"oa_url":177,"card":243,"direction":104,"ingested_from":106},"W7220411242",[230,232,234,237,240],{"name":231,"orcid":9},"Umair Maqsood",{"name":233,"orcid":9},"Fatima Iftikhar",{"name":235,"orcid":236},"Kashif Sattar","https:\u002F\u002Forcid.org\u002F0000-0001-8620-9419",{"name":238,"orcid":239},"Iftikhar A. Ahmad","https:\u002F\u002Forcid.org\u002F0000-0003-3719-2387",{"name":241,"orcid":242},"Madini Obad Alassafi","https:\u002F\u002Forcid.org\u002F0000-0001-9919-8368",{"tldr":244,"method":245,"finding":246,"direction":104,"opportunity":247},"用Landsat光谱与地形特征结合集成学习，实现马来西亚与巴基斯坦跨区域USDA土壤质地分类。","Landsat光谱波段、光谱指数与SRTM高程，RF\u002FXGBoost\u002FCatBo","RF精度最高71.45%，高程、BSI、NDWI、NDVI为关键预测因子，跨区域可行但精度受限。","可探索多源遥感融合与类别不平衡校正，提升跨气候区土壤质地制图精度与迁移能力。","2026-10-06T23:30:20.282911Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":9,"content":9,"source_name":254,"source_url":9,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":256,"score_detail":257,"sources":261,"tags":263,"primary_topic":267,"secondary_topics":271,"classification":272,"search_phrases":291,"slug":294,"view_count":98,"doi":9,"paper":295,"created_at":303},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",70,{"impact":142,"substance":258,"depth":259,"authority":119,"freshness":17,"relevant":20,"comment":260},20,17,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[262],{"name":254,"url":252},[264,25,26,265,266],"农业人工智能","土壤数据","作物分类",{"code":38,"slug":42,"name":268,"path":269,"parent_code":270},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":134,"locked":14,"version":33,"method":34,"evidence":273},{"candidates":274,"agricultural_evidence":40},[275,279,283,287],{"code":38,"score":258,"supported":40,"title":276,"abstract":277,"tags":278},[44,42],[44],[265],{"code":47,"score":163,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[264,25],{"code":53,"score":20,"supported":14,"title":284,"abstract":285,"tags":286},[],[],[26],{"code":68,"score":20,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[266],[292,293],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":9,"openalex_id":9,"authors":296,"venue":9,"cited_by_count":98,"oa_url":9,"card":297,"direction":301,"ingested_from":172},[],{"tldr":298,"method":299,"finding":300,"direction":301,"opportunity":302},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","农业人工智能与决策模型","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","2026-10-21T00:10:08.428584Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":309,"content":9,"source_name":310,"source_url":307,"published_at":311,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":312,"score_detail":313,"sources":318,"tags":320,"primary_topic":322,"secondary_topics":327,"classification":328,"search_phrases":354,"slug":357,"view_count":69,"doi":358,"paper":359,"created_at":385},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":314,"substance":315,"depth":316,"authority":39,"freshness":185,"relevant":20,"comment":317},26,24,19,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[319],{"name":310,"url":307},[124,264,26,27,321],"全球评估",{"code":47,"slug":323,"name":324,"path":325,"parent_code":326},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":134,"locked":14,"version":33,"method":34,"evidence":329},{"candidates":330,"agricultural_evidence":40},[331,336,340,344,348],{"code":53,"score":332,"supported":40,"title":333,"abstract":334,"tags":335},11,[],[124,26,57],[124,26],{"code":47,"score":54,"supported":40,"title":337,"abstract":338,"tags":339},[],[25,49],[264],{"code":60,"score":61,"supported":40,"title":341,"abstract":342,"tags":343},[],[64,65],[],{"code":68,"score":69,"supported":40,"title":345,"abstract":346,"tags":347},[],[27],[27],{"code":349,"score":75,"supported":40,"title":350,"abstract":351,"tags":353},"F4",[],[352],"除草",[],[355,356],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":358,"openalex_id":360,"authors":361,"venue":310,"cited_by_count":98,"oa_url":307,"card":380,"direction":170,"ingested_from":106},"W7222974188",[362,365,368,371,374,377],{"name":363,"orcid":364},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":366,"orcid":367},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":369,"orcid":370},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":372,"orcid":373},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":375,"orcid":376},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":378,"orcid":379},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":381,"method":382,"finding":383,"direction":170,"opportunity":384},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":393,"published_at":394,"category":12,"cover_url":9,"hotness":395,"is_selected":14,"score":396,"score_detail":397,"sources":400,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":441,"slug":444,"view_count":98,"doi":445,"paper":446,"created_at":456},5322,"MACHINE LEARNING BASED PREDICTION OF HYDROGEOLOGICAL PARAMETERS AND GROUNDWATER POTENTIAL INDEX USING GEOSPATIAL AND BOREHOLE DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23434956","Abstract: Groundwater is a critical natural resource for domestic consumption, agricultural production, industrial development, and ecological sustainability. Accurate prediction of hydrogeological parameters and groundwater potential is essential for sustainable groundwater management, particularly in regions experiencing irregular rainfall, increasing water demand, and declining groundwater levels. This research paper presents a machine learning-based framework for predicting hydrogeological parameters and developing a Groundwater Potential Index (GWPI) by integrating geospatial datasets with borehole records. The proposed approach combines terrain characteristics, geological formations, soil properties, land use and land cover, drainage density, rainfall, slope, and remotely sensed indices with borehole-derived information, including water-table depth, aquifer thickness, lithology, and well yield. Machine learning algorithms such as Random Forest, Support Vector Regression, Gradient Boosting, and Artificial Neural Networks can be employed to estimate hydrogeological variables and identify areas with different groundwater potential levels. The predicted parameters are integrated within a geospatial framework to generate a spatially distributed groundwater potential index. Model performance can be assessed using the coefficient of determination (\\(R^2\\)), root mean square error (RMSE), and mean absolute error (MAE). The proposed framework offers a reproducible and scalable approach for groundwater exploration, borehole siting, and regional water-resource planning. Since no study-area dataset or model outputs have been provided, the paper presents a methodological framework rather than claiming experimentally validated results. Keywords: Machine Learning, Groundwater Potential Index, Hydrogeological Parameters, Geospatial Analysis, Borehole Data, Random Forest, Remote Sensing, Groundwater Management.","摘要：地下水是生活用水、农业生产、工业发展和生态可持续性的关键自然资源。准确预测水文地质参数和地下水潜力对于可持续地下水管理至关重要，尤其是在降雨不规律、用水需求不断增加和地下水位持续下降的地区。本文提出了一种基于机器学习的框架，通过将地理空间数据集与钻孔记录相结合，预测水文地质参数并构建地下水潜力指数（GWPI）。所提方法综合了地形特征、地质构造、土壤性质、土地利用与土地覆盖、河网密度、降雨、坡度以及遥感指数，并结合钻孔衍生信息，包括水位埋深、含水层厚度、岩性和井出水量。可采用随机森林、支持向量回归、梯度提升和人工神经网络等机器学习算法估算水文地质变量，并识别具有不同地下水潜力水平的区域。预测参数在地理空间框架内进行集成，以生成空间分布的地下水潜力指数。模型性能可通过决定系数（\\(R^2\\)）、均方根误差（RMSE）和平均绝对误差（MAE）进行评估。所提框架为地下水勘探、钻孔选址和区域水资源规划提供了一种可重复且可扩展的方法。由于未提供研究区数据集或模型输出，本文呈现的是方法学框架，而非声称经过实验验证的结果。关键词：机器学习，地下水潜力指数，水文地质参数，地理空间分析，钻孔数据，随机森林，遥感，地下水管理。","ACCENT JOURNAL OF  ECONOMICS ECOLOGY  & ENGINEERING","https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fview\u002F6754","2026-10-17T00:00:00Z",25,47,{"impact":17,"substance":142,"depth":398,"authority":69,"freshness":185,"relevant":20,"comment":399},14,"方法框架类论文，无实测数据与验证结果，主题与农业水资源相关但公共价值有限，不宜进入每日精选。",[401,402],{"name":392,"url":393},{"name":392,"url":403},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[25,27,405,406],"水资源管理","地下水监测",{"code":47,"slug":323,"name":324,"path":325,"parent_code":326},[],{"status":134,"locked":14,"version":33,"method":34,"evidence":410},{"candidates":411,"agricultural_evidence":40},[412,416,420,424,429,435],{"code":47,"score":39,"supported":40,"title":413,"abstract":414,"tags":415},[49],[25,49],[25],{"code":68,"score":54,"supported":40,"title":417,"abstract":418,"tags":419},[],[27,205],[27],{"code":38,"score":61,"supported":40,"title":421,"abstract":422,"tags":423},[],[44,42],[],{"code":60,"score":69,"supported":40,"title":425,"abstract":426,"tags":428},[],[427],"水资源",[405],{"code":430,"score":75,"supported":40,"title":431,"abstract":432,"tags":434},"I4",[],[433],"空间分析",[],{"code":436,"score":75,"supported":40,"title":437,"abstract":438,"tags":440},"I7",[],[439],"methodological framework",[],[442,443],"机器学习 地下水潜力指数","钻孔数据 水文地质参数","机器学习地下水潜力指数-5322","10.5281\u002Fzenodo.23434956",{"doi":445,"openalex_id":447,"authors":448,"venue":392,"cited_by_count":98,"oa_url":393,"card":451,"direction":104,"ingested_from":106},"W7222805722",[449],{"name":450,"orcid":9},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":452,"method":453,"finding":454,"direction":104,"opportunity":455},"提出融合地理空间与钻孔数据的机器学习框架，预测水文地质参数与地下水潜力指数。","随机森林、SVR、梯度提升、神经网络结合遥感与钻孔数据建模。","构建了可复现的地下水潜力评估方法框架，尚未经实测数据验证。","可将该框架落地到具体农业区，用实测钻孔数据验证并耦合灌溉需水预测。","2026-10-19T23:30:22.978385Z",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":464,"score_detail":465,"sources":467,"tags":469,"primary_topic":473,"secondary_topics":477,"classification":478,"search_phrases":500,"slug":503,"view_count":98,"doi":504,"paper":505,"created_at":526},5319,"Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5f22w","Accurate in-season crop mapping is vital for national food security, yet traditional ground-based estimation methods in Bangladesh often lack precision. This study develops a robust machine-learning framework to map Boro rice in the Manikganj district by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. To overcome the challenge of seasonal cloud cover, Sentinel-1 VH polarization was utilized to identify unique backscatter reductions during the transplantation phase (late February to early March) caused by specular reflection from flooded fields. By combining these SAR signatures with high-resolution spectral indices (NDVI, EVI, and BSI) from Sentinel-2 during peak growth stages, the research captures the distinct phenological signatures of paddy rice. A pixel-based Random Forest algorithm was applied within the Google Earth Engine (GEE) platform, utilizing about 200 field-verified reference points per sub-district (1,385 in total) for training and validation. The results demonstrated high classification performance, with overall accuracies ranging from 88.1% to 94.9% across the seven sub-districts. Furthermore, a comparison with official government statistics revealed a minimal total area difference of only 2.34%, underscoring the reliability of this integrated satellite approach for operational crop monitoring.","准确的当季作物制图对国家粮食安全至关重要，然而孟加拉国传统的基于地面估计的方法往往缺乏精度。本研究开发了一个稳健的机器学习框架，通过整合Sentinel-1合成孔径雷达（SAR）和Sentinel-2光学影像，对马尼格甘杰县的Boro水稻进行制图。为克服季节性云覆盖的挑战，研究利用Sentinel-1 VH极化识别插秧期（2月下旬至3月上旬）由淹水田块镜面反射所引起的独特后向散射降低特征。通过将这些SAR特征与Sentinel-2在生长旺盛期的高分辨率光谱指数（NDVI、EVI和BSI）相结合，研究捕捉到了水稻独特的物候特征。在Google Earth Engine（GEE）平台上应用了基于像元的随机森林算法，利用每个乡约200个实地验证参考点（共计1，385个）进行训练和验证。结果表明分类性能较高，七个乡的总体精度在88.1%至94.9%之间。此外，与政府官方统计数据相比，总面积差异仅为2.34%，凸显了这种综合卫星方法在业务化作物监测中的可靠性。","OpenAlex",79,{"impact":18,"substance":118,"depth":18,"authority":142,"freshness":185,"relevant":20,"comment":466},"方法扎实、精度可靠的水稻遥感制图研究，对农业遥感业务化监测有参考价值，但属区域性案例，影响层级有限。",[468],{"name":463,"url":460},[470,471,25,472,27],"水稻","粮食安全","作物制图",{"code":474,"slug":475,"name":470,"path":476,"parent_code":131},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[],{"status":134,"locked":14,"version":33,"method":34,"evidence":479},{"candidates":480,"agricultural_evidence":40},[481,486,492,496],{"code":474,"score":18,"supported":40,"title":482,"abstract":483,"tags":485},[475],[470,475,484],"paddy",[470],{"code":487,"score":54,"supported":40,"title":488,"abstract":489,"tags":491},"A8",[],[471,490],"food security",[471],{"code":47,"score":69,"supported":40,"title":493,"abstract":494,"tags":495},[],[25],[25],{"code":68,"score":20,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[27],[501,502],"Sentinel-1 Sentinel-2 水稻制图","孟加拉国 Boro 水稻 遥感","Sentinel-1Sentinel-2水稻制图-5319","10.31223\u002Fx5f22w",{"doi":504,"openalex_id":506,"authors":507,"venue":9,"cited_by_count":98,"oa_url":520,"card":521,"direction":104,"ingested_from":106},"W7222810209",[508,510,513,516,518],{"name":509,"orcid":9},"Mst. Farida Perveen",{"name":511,"orcid":512},"Sravan Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-6979-1726",{"name":514,"orcid":515},"Abdullah Al Mehadi","https:\u002F\u002Forcid.org\u002F0000-0002-9002-633X",{"name":517,"orcid":9},"Saima Kazmina",{"name":519,"orcid":9},"Amio Al Saem","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15552\u002Fdownload\u002F26951\u002F",{"tldr":522,"method":523,"finding":524,"direction":104,"opportunity":525},"融合Sentinel-1 SAR与Sentinel-2光学影像，用随机森林在GEE上绘制孟加拉国Bo","Sentinel-1 VH后向散射与Sentinel-2 NDVI\u002FEVI\u002FBS","七个分县总体精度88.1%–94.9%，与官方统计面积仅差2.34%。","可将该SAR-光学协同框架迁移至其他多云雨稻区，并探索多时相特征优选与跨季泛化。","2026-10-19T23:30:22.772950Z"]