[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1635":3,"related-1635":112},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":72,"slug":75,"view_count":67,"doi":76,"paper":77,"created_at":111},1635,"Invisible Threats, Relentless Hunters: Biosurveillance of Airborne Plant Pathogens","https:\u002F\u002Fdoi.org\u002F10.1146\u002Fannurev-phyto-011325-093123","Airborne dispersal enables plant pathogens to travel across fields, regions, and continents, fueling rapid epidemics and emerging disease threats. Biosurveillance, the systematic monitoring of airborne inoculum, offers the opportunity to detect pathogens before symptoms appear and informs timely, risk-based management. Recent advances in air sampling, molecular diagnostics, metagenomics, and imaging technologies have expanded the scale and resolution of pathogen monitoring, from single-species qPCR assays to community-level aerobiome surveys. Integration of biosurveillance data with decision-support systems, remote sensing, and artificial intelligence is transforming early-warning capabilities and providing novel insights into pathogen ecology, evolution, and fungicide resistance. Yet major challenges remain, including assay standardization, data interpretation, and translation into actionable tools for growers. This review synthesizes current approaches, highlights case studies in which biosurveillance has advanced disease management, and outlines future directions toward coordinated surveillance networks and precision agriculture applications.","空气传播使植物病原体能够跨越田间、区域乃至大陆进行扩散，从而引发快速蔓延的流行病和新兴病害威胁。生物监测（Biosurveillance），即对空气传播接种体的系统性监测，为在症状出现前检测病原体提供了可能，并支持基于风险的及时管理决策。近年来，空气采样、分子诊断、宏基因组学及成像技术的进步，已将病原体监测的规模与分辨率从单一物种的qPCR检测拓展至群落层面的空气微生物组调查。将生物监测数据与决策支持系统、遥感技术及人工智能相结合，正不断提升早期预警能力，并为病原体生态学、进化及杀菌剂抗性提供新的见解。然而，重大挑战依然存在，包括检测方法的标准化、数据解读以及将其转化为种植者可操作的工具。本综述综合了当前的研究方法，重点介绍了生物监测推动病害管理的典型案例，并展望了迈向协同监测网络与精准农业应用的未来方向。",null,"Annual Review of Phytopathology","2026-09-03T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,15,7,1,"系统综述气传病害生物监测技术进展，对智慧植保与精准农业有重要参考价值。",[25],{"name":10,"url":6},[27,28,29],"农业人工智能","植物病害","生物监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":46},[43,52,58,65],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},"I1",6,true,[],[49,50],"精准农业","precision agriculture",[],{"code":31,"score":45,"supported":46,"title":53,"abstract":54,"tags":57},[],[55,56],"遥感","remote sensing",[],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":64},"I2",4,[],[63],"artificial intelligence",[27],{"code":66,"score":67,"supported":46,"title":68,"abstract":69,"tags":71},"I7",3,[],[70],"研究方法",[],[73,74],"农业人工智能 植物病害 生物监测","农业人工智能 植物病害","农业人工智能植物病害生物监测-1635","10.1146\u002Fannurev-phyto-011325-093123",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":102,"oa_url":6,"card":103,"direction":109,"ingested_from":110},"W7207561022",[80,82,84,86,88,90,92,94,96,98,100],{"name":81,"orcid":9},"L. M. Quesada-Ocampo",{"name":83,"orcid":9},"T. Miles",{"name":85,"orcid":9},"M. Prieto-Torres",{"name":87,"orcid":9},"M. Chilvers",{"name":89,"orcid":9},"S. G. Crandall",{"name":91,"orcid":9},"D. Gent",{"name":93,"orcid":9},"K.M. Gold",{"name":95,"orcid":9},"L. Heger",{"name":97,"orcid":9},"M. Kudenov",{"name":99,"orcid":9},"R. P. Naegele",{"name":101,"orcid":9},"L. Xiang",0,{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"综述空气传播植物病原体的生物监测技术及其在病害预警中的应用。","综述空气采样、分子诊断、宏基因组学及成像技术，结合决策支持系统、遥感和AI。","生物监测可提前检测病原体，但标准化、数据解读和实用工具转化仍是挑战。","智慧农业 \u002F 农业物联网","可研究将生物监测数据与遥感、AI结合，开发标准化、可操作的病害预警系统，并探索病原体传播的生态学机制。","农业遥感与作物表型","openalex","2026-09-04T23:30:31.964985Z",{"total":45,"page":22,"page_size":45,"items":113},[114,225,317,368,444,514],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":128,"tags":130,"primary_topic":135,"secondary_topics":136,"classification":141,"search_phrases":197,"slug":200,"view_count":102,"doi":201,"paper":202,"created_at":224},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":124,"substance":19,"depth":125,"authority":126,"freshness":13,"relevant":22,"comment":127},12,16,9,"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[129],{"name":120,"url":117},[131,27,132,133,134],"智慧农业","无损检测","多光谱成像","食品质量检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[137],{"code":59,"slug":138,"name":139,"path":140,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":14,"version":39,"method":40,"evidence":142},{"candidates":143,"agricultural_evidence":46},[144,149,159,166,173,180,187,193],{"code":31,"score":124,"supported":46,"title":145,"abstract":147,"tags":148},[146],"multispectral",[146],[133],{"code":59,"score":13,"supported":46,"title":150,"abstract":151,"tags":158},[],[152,153,154,155,156,63,157],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[27],{"code":160,"score":21,"supported":46,"title":161,"abstract":162,"tags":165},"E4",[],[163,164],"食品安全","food safety",[132],{"code":167,"score":45,"supported":46,"title":168,"abstract":169,"tags":172},"A1",[],[170,171],"稻米","rice",[],{"code":174,"score":45,"supported":46,"title":175,"abstract":176,"tags":179},"B1",[],[177,178],"番茄","tomato",[],{"code":181,"score":45,"supported":46,"title":182,"abstract":183,"tags":186},"B2",[],[184,185],"苹果","apple",[],{"code":188,"score":67,"supported":46,"title":189,"abstract":190,"tags":192},"E1",[],[191],"农产品加工",[],{"code":44,"score":22,"supported":14,"title":194,"abstract":195,"tags":196},[],[],[131],[198,199],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":201,"openalex_id":203,"authors":204,"venue":120,"cited_by_count":102,"oa_url":117,"card":217,"direction":223,"ingested_from":110},"W7223139905",[205,207,209,211,213,215],{"name":206,"orcid":9},"Eshwari Take",{"name":208,"orcid":9},"Avanish Raut",{"name":210,"orcid":9},"Vedant Joshi",{"name":212,"orcid":9},"Shravani Akolkar",{"name":214,"orcid":9},"R. M. Gharat",{"name":216,"orcid":9},"Shruti Dive",{"tldr":218,"method":219,"finding":220,"direction":221,"opportunity":222},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":230,"content":9,"source_name":231,"source_url":228,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":233,"sources":236,"tags":238,"primary_topic":242,"secondary_topics":248,"classification":256,"search_phrases":285,"slug":288,"view_count":102,"doi":289,"paper":290,"created_at":316},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy",80,{"impact":125,"substance":18,"depth":19,"authority":234,"freshness":13,"relevant":22,"comment":235},14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[237],{"name":231,"url":228},[131,27,239,240,241],"农业遥感","氮素监测","紫花苜蓿",{"code":243,"slug":244,"name":245,"path":246,"parent_code":247},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[249,250],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":251,"slug":252,"name":253,"path":254,"parent_code":255},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":38,"locked":14,"version":39,"method":40,"evidence":257},{"candidates":258,"agricultural_evidence":46},[259,264,270,276,281],{"code":31,"score":19,"supported":46,"title":260,"abstract":262,"tags":263},[261],"hyperspectral",[55,56,261],[239],{"code":243,"score":20,"supported":46,"title":265,"abstract":267,"tags":269},[266],"alfalfa",[268,266],"苜蓿",[241],{"code":251,"score":13,"supported":46,"title":271,"abstract":272,"tags":275},[],[273,268,274],"饲草","forage",[241],{"code":59,"score":13,"supported":46,"title":277,"abstract":278,"tags":280},[],[152,153,155,156,279],"decision support",[27],{"code":44,"score":22,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[131],[286,287],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":289,"openalex_id":291,"authors":292,"venue":231,"cited_by_count":102,"oa_url":228,"card":311,"direction":109,"ingested_from":110},"W7223154576",[293,295,297,299,301,304,306,309],{"name":294,"orcid":9},"Shuai Fu",{"name":296,"orcid":9},"Yunhao Li",{"name":298,"orcid":9},"Chunli Miao",{"name":300,"orcid":9},"Jie Liu",{"name":302,"orcid":303},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":305,"orcid":9},"Yongjia Hu",{"name":307,"orcid":308},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":310,"orcid":9},"吴彩霞",{"tldr":312,"method":313,"finding":314,"direction":109,"opportunity":315},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":324,"score_detail":325,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":335,"classification":337,"search_phrases":352,"slug":355,"view_count":102,"doi":356,"paper":357,"created_at":367},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)",67,{"impact":326,"substance":17,"depth":327,"authority":124,"freshness":13,"relevant":22,"comment":328},8,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[330],{"name":323,"url":320},[131,27,332,55,333],"产量预测","可解释性",{"code":59,"slug":138,"name":139,"path":140,"parent_code":35},[336],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":338},{"candidates":339,"agricultural_evidence":46},[340,344,348],{"code":59,"score":124,"supported":46,"title":341,"abstract":342,"tags":343},[157],[154],[27],{"code":31,"score":326,"supported":46,"title":345,"abstract":346,"tags":347},[],[55,56],[332,55],{"code":44,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[131],[353,354],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":356,"openalex_id":358,"authors":359,"venue":323,"cited_by_count":102,"oa_url":320,"card":362,"direction":109,"ingested_from":110},"W7223114002",[360],{"name":361,"orcid":9},"Kadiatou Barry",{"tldr":363,"method":364,"finding":365,"direction":109,"opportunity":366},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":369,"title":370,"url":371,"summary":372,"summary_zh":373,"content":9,"source_name":323,"source_url":371,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":377,"tags":379,"primary_topic":383,"secondary_topics":389,"classification":396,"search_phrases":428,"slug":431,"view_count":102,"doi":432,"paper":433,"created_at":443},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":19,"substance":17,"depth":125,"authority":124,"freshness":126,"relevant":22,"comment":376},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[378],{"name":323,"url":371},[27,380,55,381,382],"农业物联网","智慧灌溉","节水农业",{"code":384,"slug":385,"name":386,"path":387,"parent_code":388},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[390,395],{"code":391,"slug":392,"name":393,"path":394,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":397},{"candidates":398,"agricultural_evidence":46},[399,406,413,417,421],{"code":384,"score":400,"supported":46,"title":401,"abstract":402,"tags":405},19,[385],[403,404,385],"灌溉","水资源",[381,382],{"code":391,"score":19,"supported":46,"title":407,"abstract":409,"tags":412},[408],"IoT",[410,411,408],"物联网","internet of things",[380],{"code":31,"score":20,"supported":46,"title":414,"abstract":415,"tags":416},[56],[55,56],[55],{"code":59,"score":124,"supported":46,"title":418,"abstract":419,"tags":420},[63],[63],[27],{"code":422,"score":45,"supported":46,"title":423,"abstract":424,"tags":427},"H8",[],[425,426],"可持续农业","sustainable agriculture",[],[429,430],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":432,"openalex_id":434,"authors":435,"venue":323,"cited_by_count":102,"oa_url":371,"card":438,"direction":107,"ingested_from":110},"W7223090389",[436],{"name":437,"orcid":9},"Patil",{"tldr":439,"method":440,"finding":441,"direction":107,"opportunity":442},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":9,"source_name":450,"source_url":447,"published_at":451,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":452,"score_detail":453,"sources":455,"tags":457,"primary_topic":459,"secondary_topics":461,"classification":463,"search_phrases":495,"slug":498,"view_count":102,"doi":499,"paper":500,"created_at":513},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":19,"substance":18,"depth":19,"authority":124,"freshness":326,"relevant":22,"comment":454},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[456],{"name":450,"url":447},[131,27,458,332,55],"水稻",{"code":167,"slug":171,"name":458,"path":460,"parent_code":247},"\u002Ftopic\u002Ffield-crops\u002Frice",[462],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":464},{"candidates":465,"agricultural_evidence":46},[466,470,474,481,487,491],{"code":167,"score":20,"supported":46,"title":467,"abstract":468,"tags":469},[171],[458,171],[458],{"code":31,"score":326,"supported":46,"title":471,"abstract":472,"tags":473},[],[55,56],[332,55],{"code":475,"score":45,"supported":46,"title":476,"abstract":477,"tags":480},"A8",[],[478,479],"粮食安全","food security",[],{"code":482,"score":67,"supported":46,"title":483,"abstract":484,"tags":486},"J1",[],[485],"农业规划",[],{"code":44,"score":22,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[131],{"code":59,"score":22,"supported":14,"title":492,"abstract":493,"tags":494},[],[],[27],[496,497],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":499,"openalex_id":501,"authors":502,"venue":450,"cited_by_count":102,"oa_url":447,"card":508,"direction":109,"ingested_from":110},"W7222942064",[503,505],{"name":504,"orcid":9},"Nayana Venkatesh",{"name":506,"orcid":507},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":509,"method":510,"finding":511,"direction":109,"opportunity":512},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":515,"title":516,"url":517,"summary":518,"summary_zh":519,"content":9,"source_name":520,"source_url":517,"published_at":521,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":522,"score_detail":523,"sources":527,"tags":529,"primary_topic":532,"secondary_topics":533,"classification":540,"search_phrases":566,"slug":569,"view_count":102,"doi":570,"paper":571,"created_at":591},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":124,"substance":524,"depth":327,"authority":525,"freshness":126,"relevant":22,"comment":526},21,13,"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。",[528],{"name":520,"url":517},[131,27,55,530,531],"土壤盐渍化","新疆农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[534,539],{"code":535,"slug":536,"name":537,"path":538,"parent_code":388},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":59,"slug":138,"name":139,"path":140,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":541},{"candidates":542,"agricultural_evidence":46},[543,547,552,556,562],{"code":31,"score":20,"supported":46,"title":544,"abstract":545,"tags":546},[56],[55,56],[55],{"code":535,"score":234,"supported":46,"title":548,"abstract":549,"tags":551},[536],[550,536],"土壤",[],{"code":59,"score":126,"supported":46,"title":553,"abstract":554,"tags":555},[156],[],[27],{"code":557,"score":67,"supported":46,"title":558,"abstract":559,"tags":561},"H7",[],[560],"干旱",[],{"code":44,"score":22,"supported":14,"title":563,"abstract":564,"tags":565},[],[],[131],[567,568],"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":570,"openalex_id":572,"authors":573,"venue":520,"cited_by_count":102,"oa_url":517,"card":586,"direction":109,"ingested_from":110},"W7221816183",[574,576,579,581,583],{"name":575,"orcid":9},"Mingjie Qian",{"name":577,"orcid":578},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":580,"orcid":9},"Peng Wang",{"name":582,"orcid":9},"Hangyuan Liu",{"name":584,"orcid":585},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":587,"method":588,"finding":589,"direction":109,"opportunity":590},"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"]