[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3916":3,"related-3916":119},{"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":50,"search_phrases":90,"slug":93,"view_count":94,"doi":95,"paper":96,"created_at":118},3916,"Hyperspectral inversion of soil total nitrogen content using spectral and machine learning","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjb.v55i3.93850","The Agro-pastoral Transitional Ecotone of Northern Shaanxi is an ecologically fragile region. Soil nitrogen is a critical factor influencing crop productivity and regional ecological balance. Rapid and accurate monitoring of farmland soil total nitrogen (TN) content is therefore essential for implementing precision agriculture and ensuring sustainable land use in this area. This study focused on the cultivated soils of Dingbian County, a representative area within this ecotone. Field-collected soil samples underwent TN content determination and laboratory-based hyper spectral reflectance measurement. The original spectral reflectance (R) was first transformed using mathematical methods, including first-derivative (FDR), second-derivative (SDR), and logarithm of the reciprocal's first-derivative (LRFD) analysis, to enhance spectral information and suppress background noise. Subsequently, the successive projections algorithm (SPA) and correlation analysis (CA) were integrated to identify feature wavelengths highly correlated with soil TN from the transformed spectral data. Partial Least Squares Regression (PLSR), Decision Tree (DT), and Random Forest (RF) were developed to estimate soil TN content. Model performance was evaluated and compared using the coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: (1) Spectral derivative transformations effectively highlighted subtle information, with LRFD showing the greatest sensitivity to TN. Models built using LRFD-derived features consistently outperformed those based on original reflectance. (2) Among the machine learning algorithms, the Random Forest (RF) model demonstrated superior predictive performance on the validation set (R² = 0.85, RMSE = 0.11 g\u002Fkg, RPD = 2.15), indicating the highest stability and generalization capability. The PLSR model performed moderately well, while the DT model was prone to over fitting and yielded the lowest accuracy. (3) This study confirms that the integration of hyper spectral technology and machine learning algorithms enables efficient and precise estimation of soil TN content in farmland within the Northern Shaanxi agro-pastoral ecotone, providing a reliable technical framework for soil fertility assessment and precision nutrient management in the region. Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","陕北农牧交错带是生态脆弱区，土壤氮素是影响作物生产力和区域生态平衡的关键因子。快速、准确地监测农田土壤全氮（TN）含量，对于该区实施精准农业和保障土地可持续利用至关重要。本研究以该交错带典型区域定边县的耕作土壤为研究对象，对野外采集的土壤样品进行TN含量测定和实验室高光谱反射率测量。首先采用数学方法对原始光谱反射率（R）进行变换，包括一阶微分（FDR）、二阶微分（SDR）和倒数对数一阶微分（LRFD）分析，以增强光谱信息并抑制背景噪声。随后，将连续投影算法（SPA）与相关分析（CA）相结合，从变换后的光谱数据中筛选出与土壤TN高度相关的特征波长。构建偏最小二乘回归（PLSR）、决策树（DT）和随机森林（RF）模型估算土壤TN含量。采用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对模型性能进行评价与比较。结果表明：（1）光谱微分变换有效凸显了细微信息，其中LRFD对TN的敏感性最强，基于LRFD衍生特征构建的模型始终优于基于原始反射率的模型。（2）在机器学习算法中，随机森林（RF）模型在验证集上表现出优越的预测性能（R² = 0.85，RMSE = 0.11 g\u002Fkg，RPD = 2.15），具有最高的稳定性和泛化能力。PLSR模型表现中等，而DT模型易于过拟合，精度最低。（3）本研究证实，高光谱技术与机器学习算法相结合能够高效、精确地估算陕北农牧交错带农田土壤TN含量，为该区域土壤肥力评估和精准养分管理提供了可靠的技术框架。Bangladesh J. Bot. 55(3): 605-614, 2026 (September)",null,"Bangladesh Journal of Botany","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,"高光谱结合机器学习反演土壤全氮，方法对比扎实、结论可靠，对精准农业养分管理有参考价值，但属区域性田间研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准施肥","机器学习","遥感","土壤氮素",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,63,69,75,83],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":62},15,true,[60],"machine learning",[29,60],[29],{"code":40,"score":64,"supported":58,"title":65,"abstract":66,"tags":68},14,[41],[67,41],"土壤",[],{"code":46,"score":70,"supported":58,"title":71,"abstract":73,"tags":74},9,[72],"hyperspectral",[],[30],{"code":76,"score":77,"supported":58,"title":78,"abstract":79,"tags":82},"I1",7,[],[80,81],"精准农业","precision agriculture",[27],{"code":84,"score":85,"supported":58,"title":86,"abstract":87,"tags":89},"F3",4,[],[88],"养分管理",[28],[91,92],"定边县 土壤全氮 高光谱","陕北农牧交错带 土壤氮素 反演","定边县土壤全氮高光谱-3916",2,"10.3329\u002Fbjb.v55i3.93850",{"doi":95,"openalex_id":97,"authors":98,"venue":10,"cited_by_count":109,"oa_url":6,"card":110,"direction":116,"ingested_from":117},"W7214885868",[99,102,104,106],{"name":100,"orcid":101},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":103,"orcid":9},"Ni Wang",{"name":105,"orcid":9},"Jiancang Xie",{"name":107,"orcid":108},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",0,{"tldr":111,"method":112,"finding":113,"direction":114,"opportunity":115},"用高光谱与机器学习反演陕北农牧交错带农田土壤全氮含量。","野外采样测光谱，经FDR\u002FSDR\u002FLRFD变换，SPA+CA选特征波长，建PLS","LRFD变换最敏感，RF模型验证集R²=0.85、RPD=2.15，精度与泛化最优。","农业遥感与作物表型","可探索多源遥感与深度学习融合，提升生态脆弱区土壤氮素时空反演与制图能力。","农业人工智能与决策模型","openalex","2026-10-01T23:30:53.960449Z",{"total":120,"page":22,"page_size":120,"items":121},6,[122,206,281,368,441,492],{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":134,"tags":136,"primary_topic":140,"secondary_topics":141,"classification":144,"search_phrases":160,"slug":163,"view_count":109,"doi":164,"paper":165,"created_at":205},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":17,"substance":18,"depth":132,"authority":20,"freshness":21,"relevant":22,"comment":133},16,"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[135],{"name":128,"url":125},[29,30,137,138,139],"土壤碳","Landsat","数字土壤制图",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[142,143],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":145},{"candidates":146,"agricultural_evidence":58},[147,151,155],{"code":40,"score":64,"supported":58,"title":148,"abstract":149,"tags":150},[41],[67,41],[],{"code":33,"score":70,"supported":58,"title":152,"abstract":153,"tags":154},[60],[],[29],{"code":46,"score":21,"supported":58,"title":156,"abstract":157,"tags":159},[],[30,158],"remote sensing",[138,30],[161,162],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":164,"openalex_id":166,"authors":167,"venue":128,"cited_by_count":109,"oa_url":9,"card":200,"direction":114,"ingested_from":117},"W7222107492",[168,171,174,177,180,183,186,189,191,194,197],{"name":169,"orcid":170},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":172,"orcid":173},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":175,"orcid":176},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":178,"orcid":179},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":181,"orcid":182},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":184,"orcid":185},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":187,"orcid":188},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":190,"orcid":9},"A. E. Shatunov",{"name":192,"orcid":193},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":195,"orcid":196},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":198,"orcid":199},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":201,"method":202,"finding":203,"direction":114,"opportunity":204},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":9,"source_name":212,"source_url":209,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":218,"tags":220,"primary_topic":224,"secondary_topics":225,"classification":228,"search_phrases":255,"slug":258,"view_count":109,"doi":259,"paper":260,"created_at":280},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":17,"substance":216,"depth":19,"authority":20,"freshness":70,"relevant":22,"comment":217},21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[219],{"name":212,"url":209},[27,221,30,222,223],"农业人工智能","土壤盐渍化","新疆农业",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[226,227],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":229},{"candidates":230,"agricultural_evidence":58},[231,235,239,244,251],{"code":46,"score":57,"supported":58,"title":232,"abstract":233,"tags":234},[158],[30,158],[30],{"code":40,"score":64,"supported":58,"title":236,"abstract":237,"tags":238},[41],[67,41],[],{"code":33,"score":70,"supported":58,"title":240,"abstract":242,"tags":243},[241],"deep learning",[],[221],{"code":245,"score":246,"supported":58,"title":247,"abstract":248,"tags":250},"H7",3,[],[249],"干旱",[],{"code":76,"score":22,"supported":14,"title":252,"abstract":253,"tags":254},[],[],[27],[256,257],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":259,"openalex_id":261,"authors":262,"venue":212,"cited_by_count":109,"oa_url":209,"card":275,"direction":114,"ingested_from":117},"W7221816183",[263,265,268,270,272],{"name":264,"orcid":9},"Mingjie Qian",{"name":266,"orcid":267},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":269,"orcid":9},"Peng Wang",{"name":271,"orcid":9},"Hangyuan Liu",{"name":273,"orcid":274},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":276,"method":277,"finding":278,"direction":114,"opportunity":279},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":293,"tags":295,"primary_topic":297,"secondary_topics":298,"classification":301,"search_phrases":331,"slug":334,"view_count":120,"doi":335,"paper":336,"created_at":367},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":17,"substance":291,"depth":132,"authority":20,"freshness":21,"relevant":22,"comment":292},18,"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[294],{"name":287,"url":284},[27,221,80,30,296],"土壤碳汇",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[299,300],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":302},{"candidates":303,"agricultural_evidence":58},[304,308,313,317,321,327],{"code":40,"score":64,"supported":58,"title":305,"abstract":306,"tags":307},[41],[67,41],[],{"code":33,"score":13,"supported":58,"title":309,"abstract":310,"tags":312},[],[29,60,311],"artificial intelligence",[221],{"code":46,"score":13,"supported":58,"title":314,"abstract":315,"tags":316},[],[30,158,72],[30],{"code":76,"score":21,"supported":58,"title":318,"abstract":319,"tags":320},[],[80,81],[27,80],{"code":322,"score":85,"supported":58,"title":323,"abstract":324,"tags":326},"H4",[],[325],"carbon sequestration",[296],{"code":245,"score":246,"supported":58,"title":328,"abstract":329,"tags":330},[],[249],[],[332,333],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":335,"openalex_id":337,"authors":338,"venue":287,"cited_by_count":109,"oa_url":284,"card":362,"direction":114,"ingested_from":117},"W7213561504",[339,342,345,348,351,354,356,358,360],{"name":340,"orcid":341},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":343,"orcid":344},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":346,"orcid":347},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":349,"orcid":350},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":352,"orcid":353},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":355,"orcid":9},"Almaha Jamal",{"name":357,"orcid":9},"Afra Rashed",{"name":359,"orcid":9},"Hamda Yousif",{"name":361,"orcid":9},"Sarah Sadeq",{"tldr":363,"method":364,"finding":365,"direction":114,"opportunity":366},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":369,"title":370,"url":371,"summary":372,"summary_zh":373,"content":9,"source_name":374,"source_url":371,"published_at":375,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":377,"sources":379,"tags":381,"primary_topic":385,"secondary_topics":386,"classification":389,"search_phrases":412,"slug":415,"view_count":109,"doi":416,"paper":417,"created_at":440},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":132,"substance":216,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":378},"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[380],{"name":374,"url":371},[27,382,383,384,222],"农业遥感","可解释AI","深度学习",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[387,388],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":390},{"candidates":391,"agricultural_evidence":58},[392,396,400,404,408],{"code":33,"score":132,"supported":58,"title":393,"abstract":394,"tags":395},[241],[384,241],[383,384],{"code":46,"score":57,"supported":58,"title":397,"abstract":398,"tags":399},[158],[30,158],[382],{"code":40,"score":64,"supported":58,"title":401,"abstract":402,"tags":403},[41],[67,41],[],{"code":245,"score":246,"supported":58,"title":405,"abstract":406,"tags":407},[],[249],[],{"code":76,"score":22,"supported":14,"title":409,"abstract":410,"tags":411},[],[],[27],[413,414],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":416,"openalex_id":418,"authors":419,"venue":374,"cited_by_count":109,"oa_url":371,"card":435,"direction":114,"ingested_from":117},"W7221715654",[420,423,426,428,431,433],{"name":421,"orcid":422},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":424,"orcid":425},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":427,"orcid":9},"Junchi Ma",{"name":429,"orcid":430},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":432,"orcid":9},"Jiahao Qi",{"name":434,"orcid":9},"Yuan Jing",{"tldr":436,"method":437,"finding":438,"direction":114,"opportunity":439},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":9,"content":9,"source_name":446,"source_url":9,"published_at":447,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":448,"score_detail":449,"sources":452,"tags":454,"primary_topic":456,"secondary_topics":457,"classification":459,"search_phrases":479,"slug":482,"view_count":85,"doi":9,"paper":483,"created_at":491},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":291,"substance":450,"depth":291,"authority":64,"freshness":120,"relevant":22,"comment":451},22,"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[453],{"name":446,"url":444},[27,221,29,30,455],"土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[458],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":460},{"candidates":461,"agricultural_evidence":58},[462,466,471,475],{"code":33,"score":132,"supported":58,"title":463,"abstract":464,"tags":465},[311],[384,29],[221,29],{"code":40,"score":467,"supported":58,"title":468,"abstract":469,"tags":470},11,[41],[67],[],{"code":46,"score":85,"supported":58,"title":472,"abstract":473,"tags":474},[],[30],[30],{"code":76,"score":22,"supported":14,"title":476,"abstract":477,"tags":478},[],[],[27],[480,481],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":484,"venue":9,"cited_by_count":109,"oa_url":9,"card":485,"direction":116,"ingested_from":490},[],{"tldr":486,"method":487,"finding":488,"direction":116,"opportunity":489},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":9,"source_name":498,"source_url":495,"published_at":499,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":500,"sources":502,"tags":504,"primary_topic":507,"secondary_topics":512,"classification":515,"search_phrases":545,"slug":548,"view_count":22,"doi":549,"paper":550,"created_at":576},3863,"Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms","https:\u002F\u002Fdoi.org\u002F10.31489\u002F2026feb3\u002F137-151","Based on establishing the relationship between the morphological and anatomical properties of plant tissues and the recognition efficiency of the machine learning algorithm, a comprehensive assessment of the limitations of ML algorithms (using Random Forest as an example) in the multiclass identification of crops and weeds in the agroecosystems of northeastern Kazakhstan was carried out for the first time. The study substantiates the principal biological causes manifested in the optical overlap of classes in the hyperspectral feature space. Data collection was performed with a hyperspectral sensor in the VIS-NIR range. The analysis of spectral profiles was carried out based on variance distribution assessment using principal component analysis and the construction of reflectance curves. Classification of the extracted data was performed using the Random Forest algorithm. Spring wheat (Triticum aestivum) crops at different phenological phases and five dominant weed species (Euphorbia virgata, Descurainia sophia, Vicia hirsuta, Fallopia convolvulus, Echinochloa crus-galli) were selected as research objects. The algorithm demonstrated \"crop-weed\" segregation efficiency with an overall classification accuracy on test data of 72.94%. High identification accuracy was revealed for wheat (MCC=0.994) due to its spectral stability and pronounced red edge features (680-730 nm). Reduced accuracy in intra-class weed separation is driven by species-specific anatomy: thick cuticles in Euphorbia virgata increase reflectance, complex structure in Vicia hirsuta causes spectral dispersion, and mesophyll similarity between Echinochloa crus-galli and grasses leads to optical overlap with crop features. Thus, the model enables reliable crop-weed differentiation, a key requirement for precision agriculture and targeted agrochemical application.","在建立植物组织形态解剖特性与机器学习算法识别效率之间关系的基础上，首次对机器学习算法（以随机森林为例）在哈萨克斯坦东北部农业生态系统中作物与杂草多类识别方面的局限性进行了综合评估。研究证实了在高光谱特征空间中表现为类别光谱重叠的主要生物学成因。数据采集使用VIS-NIR波段的高光谱传感器完成。光谱剖面分析基于主成分分析的方差分布评估及反射率曲线构建进行。提取数据的分类采用随机森林算法完成。选取不同物候期的春小麦（Triticum aestivum）作物及五种优势杂草物种（Euphorbia virgata、Descurainia sophia、Vicia hirsuta、Fallopia convolvulus、Echinochloa crus-galli）作为研究对象。该算法在测试数据上实现了“作物-杂草”分离，总体分类精度为72.94%。小麦因其光谱稳定性及显著的红边特征（680-730 nm）而表现出较高的识别精度（MCC=0.994）。杂草类内分离精度降低由物种特异性解剖结构所致：Euphorbia virgata的厚角质层增加了反射率，Vicia hirsuta的复杂结构导致光谱离散，而Echinochloa crus-galli与禾本科植物之间的叶肉相似性导致其与作物特征发生光谱重叠。因此，该模型能够实现可靠的作物-杂草区分，这是精准农业和靶向农化施用的关键要求。","Fundamental and Experimental Biology","2026-09-29T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":70,"relevant":22,"comment":501},"高光谱结合机器学习实现麦田作物与杂草识别，方法新颖、结论具体，对精准农业有参考价值，但属细分领域研究，影响范围有限。",[503],{"name":498,"url":495},[27,29,505,506,30],"小麦","杂草识别",{"code":508,"slug":509,"name":505,"path":510,"parent_code":511},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[513,514],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":516},{"candidates":517,"agricultural_evidence":58},[518,522,526,530,534,540],{"code":508,"score":57,"supported":58,"title":519,"abstract":520,"tags":521},[509],[505,509],[505],{"code":33,"score":57,"supported":58,"title":523,"abstract":524,"tags":525},[60],[29,60],[29],{"code":46,"score":17,"supported":58,"title":527,"abstract":528,"tags":529},[72],[72],[30],{"code":76,"score":77,"supported":58,"title":531,"abstract":532,"tags":533},[],[80,81],[27],{"code":535,"score":246,"supported":58,"title":536,"abstract":537,"tags":539},"H6",[],[538],"农业生态",[],{"code":541,"score":22,"supported":14,"title":542,"abstract":543,"tags":544},"F4",[],[],[506],[546,547],"高光谱 小麦 杂草 识别","随机森林 作物 杂草 分类","高光谱小麦杂草识别-3863","10.31489\u002F2026feb3\u002F137-151",{"doi":549,"openalex_id":551,"authors":552,"venue":498,"cited_by_count":109,"oa_url":9,"card":571,"direction":116,"ingested_from":117},"W7214771122",[553,556,559,562,565,568],{"name":554,"orcid":555},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":557,"orcid":558},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":560,"orcid":561},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":563,"orcid":564},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":566,"orcid":567},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":569,"orcid":570},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":572,"method":573,"finding":574,"direction":114,"opportunity":575},"利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。","VIS-NIR高光谱传感器采集数据，PCA分析光谱，随机森林分类春小麦与五种杂草","作物-杂草总体分类精度72.94%，小麦识别精度高（MCC=0.994），杂草种间分离受解剖结构影响","可探索融合形态解剖特征与深度学习，提升杂草种间分类精度，服务精准施药。","2026-09-30T23:30:54.952093Z"]