[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4286":3,"related-4286":89},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":74,"slug":77,"view_count":65,"doi":8,"paper":78,"created_at":88},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。",null,"Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","机器学习","遥感","土壤监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,63,69],{"code":31,"score":50,"supported":51,"title":52,"abstract":54,"tags":56},16,true,[53],"artificial intelligence",[55,27],"深度学习",[26,27],{"code":38,"score":58,"supported":51,"title":59,"abstract":60,"tags":62},11,[39],[61],"土壤",[],{"code":64,"score":65,"supported":51,"title":66,"abstract":67,"tags":68},"I3",4,[],[28],[28],{"code":70,"score":20,"supported":13,"title":71,"abstract":72,"tags":73},"I1",[],[],[25],[75,76],"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":8,"openalex_id":8,"authors":79,"venue":8,"cited_by_count":80,"oa_url":8,"card":81,"direction":85,"ingested_from":87},[],0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"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。","农业人工智能与决策模型","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",{"total":19,"page":20,"page_size":19,"items":90},[91,164,245,317,402,468],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":8,"source_name":97,"source_url":94,"published_at":98,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":99,"score_detail":100,"sources":104,"tags":106,"primary_topic":107,"secondary_topics":108,"classification":110,"search_phrases":143,"slug":146,"view_count":147,"doi":148,"paper":149,"created_at":163},1328,"Explainable Machine Learning for Simultaneous Prediction of Soil Texture and Gravimetric Water Content: A SHAP-Driven Multi-Model Framework","https:\u002F\u002Fdoi.org\u002F10.70917\u002Fijcisim-2026-5335","Soil texture and gravimetric water content (GWC) are important properties which need to be determined accurately for proper irrigation and sustainable land management. Although machine learning (ML) has enhanced the ability to predict pedometrics, many of the high performing algorithms have been described as a \"black-box\" and practitioners have been reluctant to trust the model and connect the model output with soil physics. Furthermore, the existing approaches mostly assume independence between the attributes to be predicted and do not take into account their physical relationships. The present study aims to fill these voids by proposing a robust multi-model approach for simultaneous prediction of soil texture and GWC. A variety of data was used to assess state-of-the-art algorithms, such as XGBoost, CatBoost, and Random Forest. However, to achieve local and global interpretability, we integrated Shapley Additive Explanations (SHAP). This XAI-based approach filled the gap between computational intelligence and soil physics, allowing a fine-grained diagnosis of the contribution of the different features. The results show that the Random Forest framework outperformed in terms of concurrent performance with R2 value of 0.908, accuracy of 90.8% and Cohen's Kappa of 0.82 for soil texture and R2 value of 0.798 for GWC. Two features, Cation Exchange Capacity (CEC) and Organic Matter Percentage (OMP), were identified as the most influential using SHAP analysis, and they were found to exert the greatest influence on the moisture retention thresholds, with CEC being the most discriminative feature between the Clayey and Sandy texture classes, which aligns with the model's validity in light of the known pedology. This transparency and simultaneous monitoring of key soil properties makes it an appealing solution for precision agriculture and for resource management in the context of climate change.","土壤质地与重量含水量（GWC）是精准灌溉和可持续土地管理中需精确测定的重要属性。尽管机器学习（ML）增强了预测土壤计量学（pedometrics）的能力，但许多高性能算法被描述为“黑箱”，实践者往往不愿信任模型，也难以将模型输出与土壤物理学联系起来。此外，现有方法大多假设待预测属性之间相互独立，未考虑其物理关系。本研究旨在通过提出一种稳健的多模型方法，同时预测土壤质地和GWC，以填补这些空白。研究使用了多种数据评估先进算法，如XGBoost、CatBoost和随机森林（Random Forest）。然而，为实现局部和全局可解释性，我们整合了Shapley加法解释（SHAP）。这种基于可解释人工智能（XAI）的方法弥合了计算智能与土壤物理学之间的鸿沟，能够对不同特征的贡献进行细粒度诊断。结果表明，随机森林框架在并发性能上表现优异，土壤质地的R²值为0.908，准确率为90.8%，Cohen's Kappa系数为0.82，GWC的R²值为0.798。通过SHAP分析，阳离子交换量（CEC）和有机质百分比（OMP）被识别为最具影响力的两个特征，且它们对水分保持阈值的影响最大，其中CEC是区分黏土和砂土质地类别的最具判别力的特征，这与已知土壤学知识下模型的效度相吻合。这种对关键土壤属性的透明性和同步监测，使其成为精准农业及气候变化背景下资源管理的理想解决方案。","International Journal of Computer Information Systems and Industrial Management Applications","2026-08-30T00:00:00Z",73,{"impact":16,"substance":17,"depth":16,"authority":101,"freshness":102,"relevant":20,"comment":103},12,3,"研究提出可解释的机器学习框架，同时预测土壤质地与含水量，提升模型透明度，对精准农业有实质贡献。",[105],{"name":97,"url":94},[25,26,27,29],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[109],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":111},{"candidates":112,"agricultural_evidence":51},[113,118,122,129,136],{"code":31,"score":50,"supported":51,"title":114,"abstract":116,"tags":117},[115],"machine learning",[27,115],[26,27],{"code":38,"score":18,"supported":51,"title":119,"abstract":120,"tags":121},[39],[61,39],[],{"code":70,"score":123,"supported":51,"title":124,"abstract":125,"tags":128},7,[],[126,127],"精准农业","precision agriculture",[25],{"code":130,"score":19,"supported":51,"title":131,"abstract":132,"tags":135},"H3",[],[133,134],"灌溉","irrigation",[],{"code":137,"score":19,"supported":51,"title":138,"abstract":139,"tags":142},"H7",[],[140,141],"气候变化","climate change",[],[144,145],"农业人工智能 土壤监测 智慧农业 机器学习","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业机器学习-1328",2,"10.70917\u002Fijcisim-2026-5335",{"doi":148,"openalex_id":150,"authors":151,"venue":97,"cited_by_count":80,"oa_url":94,"card":156,"direction":85,"ingested_from":162},"W7204858035",[152,154],{"name":153,"orcid":8},"Chetana Shivanagi",{"name":155,"orcid":8},"Shivayogi  Ullagaddi",{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"提出可解释机器学习框架，同时预测土壤质地和含水量，提升模型透明度和可信度。","使用XGBoost、CatBoost、随机森林，结合SHAP进行局部和全局解释。","随机森林表现最佳，CEC和有机质是关键特征，与土壤物理规律一致。","智慧农业 \u002F 农业物联网","可探索将SHAP解释与因果推断结合，或扩展到其他土壤属性及动态预测，增强模型泛化性。","openalex","2026-09-01T23:30:40.247344Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":179,"tags":181,"primary_topic":184,"secondary_topics":188,"classification":191,"search_phrases":218,"slug":221,"view_count":80,"doi":222,"paper":223,"created_at":244},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":101,"substance":174,"depth":175,"authority":176,"freshness":177,"relevant":20,"comment":178},21,17,13,9,"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。",[180],{"name":170,"url":167},[25,26,28,182,183],"土壤盐渍化","新疆农业",{"code":64,"slug":185,"name":186,"path":187,"parent_code":35},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[189,190],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":192},{"candidates":193,"agricultural_evidence":51},[194,200,204,209,214],{"code":64,"score":195,"supported":51,"title":196,"abstract":198,"tags":199},15,[197],"remote sensing",[28,197],[28],{"code":38,"score":18,"supported":51,"title":201,"abstract":202,"tags":203},[39],[61,39],[],{"code":31,"score":177,"supported":51,"title":205,"abstract":207,"tags":208},[206],"deep learning",[],[26],{"code":137,"score":102,"supported":51,"title":210,"abstract":211,"tags":213},[],[212],"干旱",[],{"code":70,"score":20,"supported":13,"title":215,"abstract":216,"tags":217},[],[],[25],[219,220],"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":222,"openalex_id":224,"authors":225,"venue":170,"cited_by_count":80,"oa_url":167,"card":238,"direction":242,"ingested_from":162},"W7221816183",[226,228,231,233,235],{"name":227,"orcid":8},"Mingjie Qian",{"name":229,"orcid":230},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":232,"orcid":8},"Peng Wang",{"name":234,"orcid":8},"Hangyuan Liu",{"name":236,"orcid":237},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":239,"method":240,"finding":241,"direction":242,"opportunity":243},"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。","农业遥感与作物表型","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":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":8,"source_name":251,"source_url":248,"published_at":252,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":253,"score_detail":254,"sources":258,"tags":260,"primary_topic":263,"secondary_topics":264,"classification":267,"search_phrases":293,"slug":296,"view_count":147,"doi":297,"paper":298,"created_at":316},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)","Bangladesh Journal of Botany","2026-09-30T00:00:00Z",70,{"impact":101,"substance":255,"depth":175,"authority":176,"freshness":256,"relevant":20,"comment":257},20,8,"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。",[259],{"name":251,"url":248},[25,261,27,28,262],"精准施肥","土壤氮素",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[265,266],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":64,"slug":185,"name":186,"path":187,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":268},{"candidates":269,"agricultural_evidence":51},[270,274,278,283,287],{"code":31,"score":195,"supported":51,"title":271,"abstract":272,"tags":273},[115],[27,115],[27],{"code":38,"score":18,"supported":51,"title":275,"abstract":276,"tags":277},[39],[61,39],[],{"code":64,"score":177,"supported":51,"title":279,"abstract":281,"tags":282},[280],"hyperspectral",[],[28],{"code":70,"score":123,"supported":51,"title":284,"abstract":285,"tags":286},[],[126,127],[25],{"code":288,"score":65,"supported":51,"title":289,"abstract":290,"tags":292},"F3",[],[291],"养分管理",[261],[294,295],"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","10.3329\u002Fbjb.v55i3.93850",{"doi":297,"openalex_id":299,"authors":300,"venue":251,"cited_by_count":80,"oa_url":248,"card":311,"direction":85,"ingested_from":162},"W7214885868",[301,304,306,308],{"name":302,"orcid":303},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":305,"orcid":8},"Ni Wang",{"name":307,"orcid":8},"Jiancang Xie",{"name":309,"orcid":310},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":312,"method":313,"finding":314,"direction":242,"opportunity":315},"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。","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。","2026-10-01T23:30:53.960449Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":8,"source_name":323,"source_url":320,"published_at":324,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":332,"secondary_topics":333,"classification":336,"search_phrases":365,"slug":368,"view_count":19,"doi":369,"paper":370,"created_at":401},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":101,"substance":16,"depth":50,"authority":176,"freshness":256,"relevant":20,"comment":327},"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。",[329],{"name":323,"url":320},[25,26,126,28,331],"土壤碳汇",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[334,335],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":64,"slug":185,"name":186,"path":187,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":337},{"candidates":338,"agricultural_evidence":51},[339,343,347,351,355,361],{"code":38,"score":18,"supported":51,"title":340,"abstract":341,"tags":342},[39],[61,39],[],{"code":31,"score":12,"supported":51,"title":344,"abstract":345,"tags":346},[],[27,115,53],[26],{"code":64,"score":12,"supported":51,"title":348,"abstract":349,"tags":350},[],[28,197,280],[28],{"code":70,"score":256,"supported":51,"title":352,"abstract":353,"tags":354},[],[126,127],[25,126],{"code":356,"score":65,"supported":51,"title":357,"abstract":358,"tags":360},"H4",[],[359],"carbon sequestration",[331],{"code":137,"score":102,"supported":51,"title":362,"abstract":363,"tags":364},[],[212],[],[366,367],"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":369,"openalex_id":371,"authors":372,"venue":323,"cited_by_count":80,"oa_url":320,"card":396,"direction":242,"ingested_from":162},"W7213561504",[373,376,379,382,385,388,390,392,394],{"name":374,"orcid":375},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":377,"orcid":378},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":380,"orcid":381},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":383,"orcid":384},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":386,"orcid":387},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":389,"orcid":8},"Almaha Jamal",{"name":391,"orcid":8},"Afra Rashed",{"name":393,"orcid":8},"Hamda Yousif",{"name":395,"orcid":8},"Sarah Sadeq",{"tldr":397,"method":398,"finding":399,"direction":242,"opportunity":400},"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":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":8,"source_name":408,"source_url":405,"published_at":409,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":410,"sources":412,"tags":414,"primary_topic":417,"secondary_topics":418,"classification":423,"search_phrases":444,"slug":447,"view_count":20,"doi":448,"paper":449,"created_at":467},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",{"impact":176,"substance":174,"depth":175,"authority":176,"freshness":256,"relevant":20,"comment":411},"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[413],{"name":408,"url":405},[25,26,27,415,416],"精准灌溉","土壤墒情",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[419,420],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":130,"slug":134,"name":421,"path":422,"parent_code":42},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":44,"locked":13,"version":45,"method":46,"evidence":424},{"candidates":425,"agricultural_evidence":51},[426,430,434,440],{"code":31,"score":50,"supported":51,"title":427,"abstract":428,"tags":429},[115],[27,115],[26,27],{"code":38,"score":195,"supported":51,"title":431,"abstract":432,"tags":433},[39],[61,39],[416],{"code":130,"score":12,"supported":51,"title":435,"abstract":436,"tags":439},[],[133,437,134,438],"蒸散发","evapotranspiration",[415],{"code":70,"score":177,"supported":51,"title":441,"abstract":442,"tags":443},[127],[],[25],[445,446],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":448,"openalex_id":450,"authors":451,"venue":408,"cited_by_count":80,"oa_url":8,"card":462,"direction":85,"ingested_from":162},"W7213181547",[452,455,458,460],{"name":453,"orcid":454},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":456,"orcid":457},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":459,"orcid":8},"Kinnoree R. Pasha",{"name":461,"orcid":8},"Dilruba Yeasmin",{"tldr":463,"method":464,"finding":465,"direction":85,"opportunity":466},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":8,"source_name":474,"source_url":471,"published_at":475,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":484,"secondary_topics":485,"classification":487,"search_phrases":520,"slug":523,"view_count":102,"doi":524,"paper":525,"created_at":545},1910,"APPLICATION OF ARTIFICIAL INTELLIGENCE IN SOIL FERTILITY ASSESSMENT AND CROP YIELD PREDICTION IN SOUTHWEST NIGERIA: A STATISTICAL APPROACH","https:\u002F\u002Fdoi.org\u002F10.70382\u002Ftijsrat.v13i9.090","This study investigates the application of Artificial Intelligence (AI) techniques in soil fertility assessment and crop yield prediction in Southwest Nigeria. Traditional soil analysis methods are often time-consuming, expensive, and limited in predictive capability. This research integrates machine learning models to analyse soil parameters such as pH, nitrogen, phosphorus, potassium, and organic matter content to predict crop yield outcomes. Data were obtained from selected agricultural zones in Osun and Oyo States. Supervised learning algorithms including Linear Regression, Random Forest, and Support Vector Machines were employed. The results indicate that AI-based models significantly improve prediction accuracy compared to conventional statistical approaches, with Random Forest outperforming other models. The study demonstrates that AI can enhance decision-making in precision agriculture, optimize fertilizer application, and improve food security. It is recommended that stakeholders adopt AI-driven soil analysis systems to boost agricultural productivity in Nigeria.","本研究探讨了人工智能（AI）技术在尼日利亚西南部土壤肥力评估与作物产量预测中的应用。传统土壤分析方法往往耗时、成本高昂，且预测能力有限。本研究整合机器学习模型，分析土壤参数如pH值、氮、磷、钾及有机质含量，以预测作物产量结果。数据来源于奥孙州和奥约州选定的农业区域。采用了包括线性回归、随机森林和支持向量机在内的监督学习算法。结果表明，与传统统计方法相比，基于AI的模型显著提高了预测准确性，其中随机森林模型表现最优。研究证明，AI能够增强精准农业中的决策能力，优化肥料施用，并提升粮食安全水平。建议利益相关方采用AI驱动的土壤分析系统，以提高尼日利亚的农业生产率。","International Journal of Science Research and Technology","2026-09-07T00:00:00Z",56,{"impact":101,"substance":16,"depth":195,"authority":256,"freshness":102,"relevant":20,"comment":478},"研究AI在尼日利亚土壤评估与产量预测中的应用，方法常规但数据具体，对非洲农业智能化有参考价值。",[480],{"name":474,"url":471},[25,26,27,482,483],"作物产量预测","土壤肥力",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[486],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":488},{"candidates":489,"agricultural_evidence":51},[490,495,499,503,510,516],{"code":31,"score":491,"supported":51,"title":492,"abstract":493,"tags":494},19,[53],[27,115,53],[26,27],{"code":38,"score":195,"supported":51,"title":496,"abstract":497,"tags":498},[39],[61,39],[483],{"code":70,"score":123,"supported":51,"title":500,"abstract":501,"tags":502},[],[126,127],[25],{"code":504,"score":19,"supported":51,"title":505,"abstract":506,"tags":509},"A8",[],[507,508],"粮食安全","food security",[],{"code":288,"score":19,"supported":51,"title":511,"abstract":512,"tags":515},[],[513,514],"肥料","fertilizer",[],{"code":64,"score":20,"supported":13,"title":517,"abstract":518,"tags":519},[],[],[482],[521,522],"作物产量预测 农业人工智能 土壤肥力 智慧农业","作物产量预测 农业人工智能","作物产量预测农业人工智能土壤肥力智慧农业-1910","10.70382\u002Ftijsrat.v13i9.090",{"doi":524,"openalex_id":526,"authors":527,"venue":474,"cited_by_count":80,"oa_url":471,"card":540,"direction":85,"ingested_from":162},"W7211915991",[528,530,532,534,536,538],{"name":529,"orcid":8},"AJALA S. A.",{"name":531,"orcid":8},"OYEDEPO A. O.",{"name":533,"orcid":8},"OLOWOLAGBA M. A.",{"name":535,"orcid":8},"OGUNOLA Z. A.",{"name":537,"orcid":8},"BAKARE S. A.",{"name":539,"orcid":8},"SULU A. B.",{"tldr":541,"method":542,"finding":543,"direction":85,"opportunity":544},"研究AI在尼日利亚西南部土壤肥力评估和作物产量预测中的应用，发现随机森林模型表现最佳。","使用线性回归、随机森林和支持向量机等监督学习算法分析土壤参数预测作物产量。","AI模型显著提高预测精度，随机森林优于其他模型，可优化施肥和提升农业生产力。","可探索将AI土壤分析系统推广至其他地区，或结合遥感数据与实时传感器提升预测精度和实用性。","2026-09-08T23:30:32.423857Z"]