[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5081":3,"related-5081":146},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":114,"slug":117,"view_count":104,"doi":118,"paper":119,"created_at":145},5081,"Nanobubble-Driven Precision Aeration: Deciphering Abiotic Hypoxia Responses via Multi-Omics for Next-Generation Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fantiox15101332","Soil salinity and drought can restrict oxygen availability in the root zone, adding hypoxic stress to the ionic, osmotic, and hydraulic constraints experienced by crops. Nanobubble (NB) water offers a chemical-free approach for delivering highly dispersed oxygen and sustaining dissolved oxygen during irrigation. This review connects the physicochemical behavior of NBs with plant responses to salinity- and drought-associated hypoxia. It examines restoration of aerobic respiration and ATP homeostasis, modulation of ethylene response factor group VII and hypoxia-responsive gene signaling, recovery of ion transport and aquaporin function, redox regulation, hormonal cross-talk, and rhizosphere microbiome responses. Evidence from rice, maize, wheat, cotton, and controlled-environment systems is evaluated alongside opportunities for nanoparticle co-delivery, nanofertilizers, seed priming, and sensor-guided irrigation. The review also outlines how transcriptomics, proteomics, metabolomics, epigenomics, ionomics, and phenomics can resolve NB-mediated mechanisms across the plant–soil continuum. Current evidence supports NB irrigation as a promising precision-aeration platform, but field-scale validation, standardized NB characterization, dose–response studies, energy and cost assessments, and long-term environmental safety analyses remain necessary before broad agricultural deployment.","土壤盐渍化和干旱会限制根区氧气供应，在作物已承受的离子、渗透和水力胁迫之外，进一步叠加低氧胁迫。纳米气泡（NB）水提供了一种无需化学添加的方式，可输送高度分散的氧气并在灌溉过程中维持溶解氧水平。本文综述将纳米气泡的物理化学行为与植物对盐渍化和干旱相关低氧的响应联系起来，考察有氧呼吸和ATP稳态的恢复、乙烯响应因子第VII组及低氧响应基因信号的调控、离子转运和水通道蛋白功能的恢复、氧化还原调节、激素互作以及根际微生物组响应。文章评估了来自水稻、玉米、小麦、棉花及受控环境系统的证据，并探讨了纳米颗粒共递送、纳米肥料、种子引发和传感器引导灌溉的应用机会。综述还概述了转录组学、蛋白质组学、代谢组学、表观基因组学、离子组学和表型组学如何解析纳米气泡在植物–土壤连续体中的介导机制。当前证据支持纳米气泡灌溉作为一种有前景的精准增氧平台，但在广泛农业应用之前，仍需进行田间规模验证、标准化纳米气泡表征、剂量–响应研究、能耗与成本评估以及长期环境安全性分析。",null,"Antioxidants","2026-10-15T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,14,9,1,"综述系统梳理纳米气泡增氧缓解盐旱缺氧胁迫的机制与多组学解析路径，学术价值高但尚处田间验证前阶段，属细分领域前沿进展。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","精准灌溉","多组学","土壤盐渍化","纳米气泡",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,52,58,66,74,81,88,95,102,109],{"code":32,"score":20,"supported":45,"title":46,"abstract":47,"tags":51},true,[],[48,49,33,50],"土壤","根际","rhizosphere",[],{"code":53,"score":20,"supported":45,"title":54,"abstract":56,"tags":57},"I1",[55],"smart agriculture",[],[26],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":65},"H3",7,[],[63,64],"灌溉","irrigation",[27],{"code":67,"score":68,"supported":45,"title":69,"abstract":70,"tags":73},"A1",6,[],[71,72],"水稻","rice",[],{"code":75,"score":68,"supported":45,"title":76,"abstract":77,"tags":80},"A2",[],[78,79],"小麦","wheat",[],{"code":82,"score":68,"supported":45,"title":83,"abstract":84,"tags":87},"A3",[],[85,86],"玉米","maize",[],{"code":89,"score":68,"supported":45,"title":90,"abstract":91,"tags":94},"A6",[],[92,93],"棉花","cotton",[],{"code":96,"score":68,"supported":45,"title":97,"abstract":98,"tags":101},"H7",[],[99,100],"干旱","drought",[],{"code":103,"score":104,"supported":45,"title":105,"abstract":106,"tags":108},"F3",3,[],[107],"肥料",[],{"code":110,"score":21,"supported":14,"title":111,"abstract":112,"tags":113},"F2",[],[],[28],[115,116],"纳米气泡 灌溉 增氧","盐碱地 缺氧 多组学","纳米气泡灌溉增氧-5081","10.3390\u002Fantiox15101332",{"doi":118,"openalex_id":120,"authors":121,"venue":10,"cited_by_count":137,"oa_url":6,"card":138,"direction":142,"ingested_from":144},"W7222342675",[122,125,128,131,134],{"name":123,"orcid":124},"Hafiz Abdul Kareem","https:\u002F\u002Forcid.org\u002F0000-0002-8209-0446",{"name":126,"orcid":127},"Sana Saleem","https:\u002F\u002Forcid.org\u002F0000-0001-9836-2744",{"name":129,"orcid":130},"Muhammad Bilal Hafeez","https:\u002F\u002Forcid.org\u002F0000-0001-6719-6667",{"name":132,"orcid":133},"Naeem Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-5023-8362",{"name":135,"orcid":136},"Da‐Chuan Yin","https:\u002F\u002Forcid.org\u002F0000-0003-2161-5867",0,{"tldr":139,"method":140,"finding":141,"direction":142,"opportunity":143},"综述纳米气泡灌溉缓解盐旱根区缺氧的机制，并展望多组学与智能农业应用。","综述多组学（转录\u002F蛋白\u002F代谢\u002F表型等）与纳米气泡水灌溉证据。","纳米气泡灌溉可恢复呼吸与离子稳态，但田间验证与标准仍缺乏。","智慧农业 \u002F 农业物联网","可研究纳米气泡剂量-响应与传感器引导灌溉闭环，结合多组学解析根际机制。","openalex","2026-10-16T23:30:09.487745Z",{"total":68,"page":21,"page_size":68,"items":147},[148,234,309,430,497,578],{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":154,"source_url":151,"published_at":155,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":156,"score_detail":157,"sources":162,"tags":164,"primary_topic":168,"secondary_topics":174,"classification":181,"search_phrases":207,"slug":210,"view_count":137,"doi":211,"paper":212,"created_at":233},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":158,"substance":18,"depth":159,"authority":160,"freshness":20,"relevant":21,"comment":161},12,17,13,"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。",[163],{"name":154,"url":151},[26,165,166,29,167],"农业人工智能","遥感","新疆农业",{"code":169,"slug":170,"name":171,"path":172,"parent_code":173},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[175,176],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":177,"slug":178,"name":179,"path":180,"parent_code":173},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":182},{"candidates":183,"agricultural_evidence":45},[184,190,194,199,203],{"code":169,"score":185,"supported":45,"title":186,"abstract":188,"tags":189},15,[187],"remote sensing",[166,187],[166],{"code":32,"score":19,"supported":45,"title":191,"abstract":192,"tags":193},[33],[48,33],[],{"code":177,"score":20,"supported":45,"title":195,"abstract":197,"tags":198},[196],"deep learning",[],[165],{"code":96,"score":104,"supported":45,"title":200,"abstract":201,"tags":202},[],[99],[],{"code":53,"score":21,"supported":14,"title":204,"abstract":205,"tags":206},[],[],[26],[208,209],"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":211,"openalex_id":213,"authors":214,"venue":154,"cited_by_count":137,"oa_url":151,"card":227,"direction":231,"ingested_from":144},"W7221816183",[215,217,220,222,224],{"name":216,"orcid":9},"Mingjie Qian",{"name":218,"orcid":219},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":221,"orcid":9},"Peng Wang",{"name":223,"orcid":9},"Hangyuan Liu",{"name":225,"orcid":226},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":228,"method":229,"finding":230,"direction":231,"opportunity":232},"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":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":240,"source_url":237,"published_at":241,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":242,"score_detail":243,"sources":247,"tags":249,"primary_topic":253,"secondary_topics":254,"classification":257,"search_phrases":280,"slug":283,"view_count":137,"doi":284,"paper":285,"created_at":308},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":244,"substance":18,"depth":159,"authority":160,"freshness":245,"relevant":21,"comment":246},16,8,"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。",[248],{"name":240,"url":237},[26,250,251,252,29],"农业遥感","可解释AI","深度学习",{"code":177,"slug":178,"name":179,"path":180,"parent_code":173},[255,256],{"code":169,"slug":170,"name":171,"path":172,"parent_code":173},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":258},{"candidates":259,"agricultural_evidence":45},[260,264,268,272,276],{"code":177,"score":244,"supported":45,"title":261,"abstract":262,"tags":263},[196],[252,196],[251,252],{"code":169,"score":185,"supported":45,"title":265,"abstract":266,"tags":267},[187],[166,187],[250],{"code":32,"score":19,"supported":45,"title":269,"abstract":270,"tags":271},[33],[48,33],[],{"code":96,"score":104,"supported":45,"title":273,"abstract":274,"tags":275},[],[99],[],{"code":53,"score":21,"supported":14,"title":277,"abstract":278,"tags":279},[],[],[26],[281,282],"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":284,"openalex_id":286,"authors":287,"venue":240,"cited_by_count":137,"oa_url":237,"card":303,"direction":231,"ingested_from":144},"W7221715654",[288,291,294,296,299,301],{"name":289,"orcid":290},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":292,"orcid":293},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":295,"orcid":9},"Junchi Ma",{"name":297,"orcid":298},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":300,"orcid":9},"Jiahao Qi",{"name":302,"orcid":9},"Yuan Jing",{"tldr":304,"method":305,"finding":306,"direction":231,"opportunity":307},"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":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":317,"score_detail":318,"sources":321,"tags":323,"primary_topic":327,"secondary_topics":328,"classification":329,"search_phrases":394,"slug":397,"view_count":137,"doi":398,"paper":399,"created_at":429},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":319,"substance":18,"depth":17,"authority":19,"freshness":245,"relevant":21,"comment":320},22,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[322],{"name":315,"url":312},[26,165,324,325,326,28],"种业振兴","精准农业","土壤健康",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":330},{"candidates":331,"agricultural_evidence":45},[332,339,343,347,351,355,362,368,375,381,385,390],{"code":53,"score":333,"supported":45,"title":334,"abstract":336,"tags":338},19,[335],"precision agriculture",[325,337,335],"数字孪生",[26,325],{"code":32,"score":60,"supported":45,"title":340,"abstract":341,"tags":342},[],[48,33],[326],{"code":67,"score":68,"supported":45,"title":344,"abstract":345,"tags":346},[],[71,72],[],{"code":75,"score":68,"supported":45,"title":348,"abstract":349,"tags":350},[],[78,79],[],{"code":82,"score":68,"supported":45,"title":352,"abstract":353,"tags":354},[],[85,86],[],{"code":356,"score":68,"supported":45,"title":357,"abstract":358,"tags":361},"A8",[],[359,360],"粮食安全","food security",[],{"code":96,"score":68,"supported":45,"title":363,"abstract":364,"tags":367},[],[365,366],"气候变化","climate change",[],{"code":369,"score":68,"supported":45,"title":370,"abstract":371,"tags":374},"H8",[],[372,373],"可持续农业","sustainable agriculture",[],{"code":177,"score":376,"supported":45,"title":377,"abstract":378,"tags":380},4,[],[379],"artificial intelligence",[165],{"code":103,"score":104,"supported":45,"title":382,"abstract":383,"tags":384},[],[107],[],{"code":386,"score":21,"supported":14,"title":387,"abstract":388,"tags":389},"F1",[],[],[324],{"code":110,"score":21,"supported":14,"title":391,"abstract":392,"tags":393},[],[],[28],[395,396],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":398,"openalex_id":400,"authors":401,"venue":315,"cited_by_count":137,"oa_url":312,"card":422,"direction":428,"ingested_from":144},"W7221567543",[402,405,407,410,412,414,417,420],{"name":403,"orcid":404},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":406,"orcid":9},"Wang Qingxia",{"name":408,"orcid":409},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":411,"orcid":9},"Shuzhen Ye",{"name":413,"orcid":9},"Zhijuan Ji",{"name":415,"orcid":416},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":418,"orcid":419},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":421,"orcid":9},"Younan Ouyang",{"tldr":423,"method":424,"finding":425,"direction":426,"opportunity":427},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","农业人工智能与决策模型","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":446,"secondary_topics":449,"classification":451,"search_phrases":476,"slug":479,"view_count":480,"doi":481,"paper":482,"created_at":496},3184,"Soil Compaction and Irrigation Management: Implications of Soil Hydraulic Changes for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181853","Soil compaction is one of the main forms of physical soil degradation affecting agricultural productivity. Although its effects on soil structure and crop growth have been extensively investigated, their implications for irrigation management have received little attention. This review synthesises current knowledge on how soil compaction modifies the hydraulic functioning of agricultural soils and discusses the consequences for irrigation scheduling and precision irrigation. The available evidence shows that soil compaction reduces water infiltration and saturated hydraulic conductivity while altering soil water retention and plant-available water. Surface sealing and crust formation can further restrict infiltration, particularly when bare soil is exposed to high-energy rainfall or sprinkler irrigation. These changes modify water movement, redistribution and storage within the soil profile, affecting root development, plant physiological responses and crop productivity. The magnitude of these effects depends on soil texture, soil water status, crop species and environmental conditions. Conventional irrigation scheduling generally assumes stable soil hydraulic properties and may not adequately represent compacted soils. Recent advances in soil moisture sensing, crop modelling, remote sensing and decision support systems provide new opportunities to incorporate soil structural variability into irrigation management. Combining precision irrigation with preventive traffic management and practices that enhance soil structural and biological resilience may improve irrigation efficiency, optimise water use and enhance the long-term sustainability of irrigated agricultural systems.","土壤压实是影响农业生产力的土壤物理退化的主要形式之一。尽管其对土壤结构和作物生长的影响已得到广泛研究，但其对灌溉管理的影响却鲜受关注。本文综述了关于土壤压实如何改变农业土壤水力功能的现有知识，并探讨了其对灌溉制度和精准灌溉的影响。现有证据表明，土壤压实降低了水分入渗和饱和导水率，同时改变了土壤持水性和植物有效水量。地表封闭和结皮的形成会进一步限制入渗，尤其是当裸露土壤暴露于高能量降雨或喷灌时。这些变化改变了土壤剖面中水分的运动、再分布和储存，进而影响根系发育、植物生理响应和作物生产力。这些影响的程度取决于土壤质地、土壤水分状况、作物种类和环境条件。传统灌溉制度通常假设土壤水力性质稳定，可能无法充分反映压实土壤的情况。土壤水分传感、作物建模、遥感和决策支持系统方面的最新进展，为将土壤结构变异性纳入灌溉管理提供了新的机遇。将精准灌溉与预防性机械作业管理及增强土壤结构和生物韧性的措施相结合，有望提高灌溉效率、优化水资源利用，并增强灌溉农业系统的长期可持续性。","Agronomy","2026-09-20T00:00:00Z",78,{"impact":17,"substance":18,"depth":159,"authority":19,"freshness":245,"relevant":21,"comment":440},"系统综述土壤压实对土壤水力特性与灌溉管理的影响，提出将土壤结构变异纳入精准灌溉决策，对智慧农业与节水灌溉有参考价值。",[442],{"name":436,"url":433},[26,250,27,444,445],"土壤墒情监测","土壤压实",{"code":59,"slug":64,"name":447,"path":448,"parent_code":36},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[450],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":452},{"candidates":453,"agricultural_evidence":45},[454,459,463,467,471],{"code":59,"score":17,"supported":45,"title":455,"abstract":456,"tags":458},[64],[63,457,64],"水资源",[27],{"code":32,"score":19,"supported":45,"title":460,"abstract":461,"tags":462},[33],[48,33],[],{"code":53,"score":20,"supported":45,"title":464,"abstract":465,"tags":466},[335],[],[26],{"code":169,"score":60,"supported":45,"title":468,"abstract":469,"tags":470},[],[166,187],[250],{"code":177,"score":104,"supported":45,"title":472,"abstract":473,"tags":475},[],[474],"decision support",[],[477,478],"土壤压实 精准灌溉","土壤水力特性 灌溉管理","土壤压实精准灌溉-3184",2,"10.3390\u002Fagronomy16181853",{"doi":481,"openalex_id":483,"authors":484,"venue":436,"cited_by_count":137,"oa_url":433,"card":491,"direction":231,"ingested_from":144},"W7213866178",[485,488],{"name":486,"orcid":487},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":489,"orcid":490},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":492,"method":493,"finding":494,"direction":142,"opportunity":495},"综述土壤压实改变水力特性对灌溉管理的影响，提出将结构变异纳入精准灌溉。","文献综述，整合土壤水力、传感、作物模型与遥感技术。","压实降低入渗与导水率，传统灌溉调度假设不成立，需纳入结构变异。","可开发融合土壤压实空间变异与实时传感的精准灌溉决策模型，填补结构退化下的调度空白。","2026-09-22T23:30:25.358657Z",{"id":498,"title":499,"url":500,"summary":501,"summary_zh":502,"content":9,"source_name":436,"source_url":500,"published_at":503,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":504,"sources":506,"tags":508,"primary_topic":511,"secondary_topics":512,"classification":514,"search_phrases":542,"slug":545,"view_count":21,"doi":546,"paper":547,"created_at":577},2664,"Soil Salinity Mapping from UAV-Borne Hyperspectral Imagery with Soil Moisture Correction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181812","Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with Rp2 = 0.87, RMSEP = 0.67 mS·cm−1, and RPD = 2.93. Compared with the Ref–Stacking model, Rp2 increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm−1 (from 1.25 to 0.67 mS·cm−1). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture.","土壤盐渍化对全球农业可持续发展和生态安全构成重大威胁，每年造成大量作物损失。基于无人机的高光谱遥感凭借其高空间分辨率和灵活的数据获取能力，为土壤盐分监测提供了有前景的解决方案。然而，土壤水分会改变电磁辐射的散射和吸收特性，从而改变土壤光谱反射率并掩盖与盐分相关的诊断性光谱特征，这可能降低传统盐分估算模型的精度。本研究评估了六种光谱变换方法——原始反射率数据（Ref）、一阶导数（FDR）、分段直接标准化（PDS）、正交信号校正（OSC）、FDR + PDS和FDR + OSC——并结合三种机器学习算法：K近邻（KNN）、支持向量回归（SVR）和多层感知机（MLP）。进一步构建了集成这些基学习器的Stacking集成模型，以提高水分干扰下的土壤盐分反演精度。结果表明，Stacking模型在所评估的模型中达到了最高的精度和稳定性。与XGBoost和随机森林（RF）的额外比较进一步证实了所提出的Stacking框架的竞争性性能。FDR + OSC–Stacking组合取得了最佳的验证表现，Rp2 = 0.87，RMSEP = 0.67 mS·cm−1，RPD = 2.93。与Ref–Stacking模型相比，Rp2提高了0.32（从0.55增至0.87），而RMSEP降低了0.58 mS·cm−1（从1.25降至0.67 mS·cm−1）。结果表明，PDS在校正水分相关光谱变异方面效果有限，而OSC在保留与盐分估算相关的光谱信息的同时，更有效地减轻了水分干扰。在所评估的机器学习模型中，Stacking集成模型取得了优于MLP、SVR和KNN的预测性能。此外，FDR + OSC–Stacking组合在所评估的建模框架中表现最佳，并成功应用于无人机高光谱影像进行EC1:5的空间制图。这些发现证明了将适当的光谱校正与Stacking相结合用于基于无人机的土壤盐分评估的潜力，并为精准农业中的针对性盐分管理提供了有用的技术支持。","2026-09-15T00:00:00Z",{"impact":244,"substance":319,"depth":17,"authority":160,"freshness":20,"relevant":21,"comment":505},"该研究提出FDR+OSC光谱校正与Stacking集成模型相结合的无人机高光谱土壤盐分反演方法，验证精度显著提升（Rp²=0.87），为盐渍化农田精准管理提供了可落地的技术方案，专业深度与信息增量均较突出。",[507],{"name":436,"url":500},[26,509,325,510,29],"无人机","遥感监测",{"code":169,"slug":170,"name":171,"path":172,"parent_code":173},[513],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":515},{"candidates":516,"agricultural_evidence":45},[517,522,526,530,536],{"code":169,"score":17,"supported":45,"title":518,"abstract":520,"tags":521},[519],"hyperspectral",[166,187,519],[510],{"code":32,"score":19,"supported":45,"title":523,"abstract":524,"tags":525},[33],[48,33],[],{"code":53,"score":245,"supported":45,"title":527,"abstract":528,"tags":529},[],[325,335],[26,325],{"code":177,"score":68,"supported":45,"title":531,"abstract":532,"tags":535},[],[533,534],"机器学习","machine learning",[],{"code":537,"score":104,"supported":45,"title":538,"abstract":539,"tags":541},"J3",[],[540],"全球农业",[],[543,544],"土壤盐渍化 智慧农业 精准农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业精准农业遥感监测-2664","10.3390\u002Fagronomy16181812",{"doi":546,"openalex_id":548,"authors":549,"venue":436,"cited_by_count":137,"oa_url":500,"card":572,"direction":231,"ingested_from":144},"W7213244904",[550,552,554,556,558,560,562,564,567,569],{"name":551,"orcid":9},"Haiye Yu",{"name":553,"orcid":9},"Muyan Yu",{"name":555,"orcid":9},"Ranzhe Jiang",{"name":557,"orcid":9},"Xin Zhang",{"name":559,"orcid":9},"Zhu Guo",{"name":561,"orcid":9},"Yaohui Fu",{"name":563,"orcid":9},"Xingbang Liu",{"name":565,"orcid":566},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":568,"orcid":9},"Bingze Li",{"name":570,"orcid":571},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":573,"method":574,"finding":575,"direction":231,"opportunity":576},"用无人机高光谱结合水分校正与Stacking集成模型反演土壤盐分。","六种光谱变换与KNN\u002FSVR\u002FMLP及Stacking，对比XGBoost、RF","FDR+OSC-Stacking最优，Rp²=0.87，OSC比PDS更能消除水分干扰。","可探索水分校正与深度学习结合、多时相无人机盐分动态监测及跨区域迁移。","2026-09-16T23:30:29.016721Z",{"id":579,"title":580,"url":581,"summary":582,"summary_zh":583,"content":9,"source_name":584,"source_url":581,"published_at":585,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":156,"score_detail":586,"sources":588,"tags":590,"primary_topic":592,"secondary_topics":593,"classification":596,"search_phrases":617,"slug":620,"view_count":21,"doi":621,"paper":622,"created_at":640},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":160,"substance":18,"depth":159,"authority":160,"freshness":245,"relevant":21,"comment":587},"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。",[589],{"name":584,"url":581},[26,165,533,27,591],"土壤墒情",{"code":177,"slug":178,"name":179,"path":180,"parent_code":173},[594,595],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":59,"slug":64,"name":447,"path":448,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":597},{"candidates":598,"agricultural_evidence":45},[599,603,607,613],{"code":177,"score":244,"supported":45,"title":600,"abstract":601,"tags":602},[534],[533,534],[165,533],{"code":32,"score":185,"supported":45,"title":604,"abstract":605,"tags":606},[33],[48,33],[591],{"code":59,"score":13,"supported":45,"title":608,"abstract":609,"tags":612},[],[63,610,64,611],"蒸散发","evapotranspiration",[27],{"code":53,"score":20,"supported":45,"title":614,"abstract":615,"tags":616},[335],[],[26],[618,619],"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":621,"openalex_id":623,"authors":624,"venue":584,"cited_by_count":137,"oa_url":9,"card":635,"direction":426,"ingested_from":144},"W7213181547",[625,628,631,633],{"name":626,"orcid":627},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":629,"orcid":630},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":632,"orcid":9},"Kinnoree R. Pasha",{"name":634,"orcid":9},"Dilruba Yeasmin",{"tldr":636,"method":637,"finding":638,"direction":426,"opportunity":639},"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"]