[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2069":3,"related-2069":95},{"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":94},2069,"Nanomaterial-enabled Sensors for Soil and Crop Analysis in Agriculture: A Focused Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Facri\u002F2026\u002Fv26i92158","Rapid, sensitive and portable measurements are important for precision agriculture, particularly when soil and crop conditions vary spatially and temporally. This focused narrative review evaluates nanomaterial-enabled sensors for soil nutrient assessment, crop physiological monitoring and plant-disease detection. Four representative studies were examined across contrasting sensing architectures involving graphene derivatives, conductive nanofibres, gold nanoparticles and carbon nanotubes. The selected platforms targeted soil nitrate, indole-3-acetic acid, pathogen-associated DNA and a citrus Huanglongbing protein biomarker. Reported studies demonstrated low analytical detection limits and successful measurements in soil extracts, plant tissues or other agriculturally relevant samples. However, their evidence primarily supports analytical feasibility rather than routine field deployment or direct agronomic benefit. Important constraints include matrix interference, calibration stability, durability of biological recognition elements, electrode reproducibility, sample-preparation requirements, environmental safety and fabrication cost. Recent developments in wearable, wireless and portable sensing further indicate a shift towards integrated monitoring systems, but connectivity alone does not establish field readiness. Future work should emphasise multilocation validation, comparison with accepted reference methods, standardised calibration, long-term stability, batch reproducibility, safe device management and integration of sensor outputs with agricultural decision-support systems. Nanomaterial-enabled sensors therefore show promise as complementary analytical tools, while routine adoption requires reliable performance under real production conditions and evidence that measurements support better management decisions.","快速、灵敏且便携的测量对精准农业十分重要，尤其是当土壤和作物状况在空间和时间上存在变化时。本聚焦式叙述综述评估了用于土壤养分评估、作物生理监测和植物病害检测的纳米材料传感器。本文考察了四项代表性研究，涉及石墨烯衍生物、导电纳米纤维、金纳米颗粒和碳纳米管等不同传感架构。所选平台分别针对土壤硝酸盐、吲哚-3-乙酸、病原体相关DNA和柑橘黄龙病蛋白生物标志物。所报道的研究展示了较低的分析检测限，并成功实现了在土壤提取物、植物组织或其他农业相关样品中的测量。然而，其证据主要支持分析可行性，而非常规田间部署或直接农艺效益。重要限制包括基质干扰、校准稳定性、生物识别元件的耐久性、电极重现性、样品制备要求、环境安全性和制造成本。可穿戴、无线和便携式传感的最新进展进一步表明，传感正转向集成监测系统，但仅凭连接性并不能确立田间就绪性。未来工作应强调多地点验证、与公认参考方法的比较、标准化校准、长期稳定性、批次重现性、安全器件管理，以及将传感器输出与农业决策支持系统相整合。因此，纳米材料传感器作为补充性分析工具显示出前景，而常规采用则需要在真实生产条件下具备可靠性能，并提供测量能够支持更好管理决策的证据。",null,"Archives of Current Research International","2026-09-09T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,17,8,1,"综述系统梳理纳米材料传感器在土壤养分、作物生理与病害检测中的分析可行性，但作者明确指出其证据尚不足以支撑田间常规应用，属细分领域学术进展，适合主题聚合而非每日精选头条。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","纳米材料","农业传感器","土壤养分检测","作物病害监测",{"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":46},[44,51,59,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},14,true,[33],[49,33],"土壤",[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"I1",7,[],[56,57],"精准农业","precision agriculture",[26],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"B2",6,[],[64,65],"柑橘","citrus",[],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"I5",[],[],[28],[73,74],"作物病害监测 土壤养分检测 农业传感器 智慧农业","作物病害监测 土壤养分检测","作物病害监测土壤养分检测农业传感器智慧农业-2069",2,"10.9734\u002Facri\u002F2026\u002Fv26i92158",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":92,"ingested_from":93},"W7212060484",[81,83],{"name":82,"orcid":9},"N. Pruthviraj",{"name":84,"orcid":9},"V. Lakshmi",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"综述纳米材料传感器在土壤养分、作物生理和病害检测中的分析可行性及田间应用瓶颈。","聚焦综述4项研究，涵盖石墨烯、导电纳米纤维、金纳米颗粒和碳纳米管传感平台。","传感器检测限低但仅证明分析可行性，田间部署受基质干扰、校准稳定性等限制。","智慧农业 \u002F 农业物联网","需开展多地点验证、标准化校准及与决策支持系统集成，以证明田间实用性和农艺效益。","农业人工智能与决策模型","openalex","2026-09-10T23:30:53.443004Z",{"total":61,"page":21,"page_size":61,"items":96},[97,159,209,272,362,430],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":109,"tags":111,"primary_topic":115,"secondary_topics":116,"classification":122,"search_phrases":142,"slug":145,"view_count":85,"doi":146,"paper":147,"created_at":158},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-05T00:00:00Z",52,{"impact":20,"substance":107,"depth":45,"authority":61,"freshness":20,"relevant":21,"comment":108},16,"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[110],{"name":103,"url":100},[112,26,113,114,28],"数字农业","物联网","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[117],{"code":68,"slug":118,"name":119,"path":120,"parent_code":121},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":123},{"candidates":124,"agricultural_evidence":46},[125,129,138],{"code":32,"score":45,"supported":46,"title":126,"abstract":127,"tags":128},[33],[49,33],[],{"code":68,"score":130,"supported":46,"title":131,"abstract":132,"tags":137},11,[],[113,133,134,135,136],"无线传感","internet of things","IoT","wireless sensor",[28,113],{"code":52,"score":20,"supported":46,"title":139,"abstract":140,"tags":141},[],[56,57],[112,26],[143,144],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":146,"openalex_id":148,"authors":149,"venue":103,"cited_by_count":85,"oa_url":152,"card":153,"direction":90,"ingested_from":93},"W7220426775",[150],{"name":151,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":154,"method":155,"finding":156,"direction":90,"opportunity":157},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":103,"source_url":162,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":192,"slug":195,"view_count":21,"doi":196,"paper":197,"created_at":208},4069,"Adoption of Smart Soil Monitoring Systems among Biological Science Staff in Federal Polytechnic Bali: Trends, Benefits, and Challenges","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no10.2025.pg255.268","Smart Soil Monitoring Systems (SSMS) have emerged as innovative digital solutions for improving soil data collection, analyzing environmental variables, and enhancing precision in agricultural and scientific research. Despite the global shift toward smart agriculture and environmental sustainability, the level of awareness and adoption of SSMS in many Nigerian institutions remains significantly low. This study investigated the trends, benefits, and challenges in the adoption of Smart Soil Monitoring Systems among Biological Science staff of Federal Polytechnic Bali, Taraba State. A descriptive survey design was adopted, and a census sampling technique was used to involve all 20 Biological Science staff as respondents. A validated structured questionnaire titled “Adoption of Smart Soil Monitoring Systems Questionnaire” was used for data collection, while frequency, percentage, mean, and standard deviation served as analytical tools. Findings revealed a moderate level of awareness of SSMS but very low practical adoption due to limited access to modern monitoring technologies, lack of training, insufficient funding, and inadequate institutional policy support. However, respondents acknowledged clear benefits such as improved soil analysis accuracy, enhanced environmental sustainability, and better support for agricultural research and teaching. It was recommended that the institution should invest in digital soil monitoring tools, organize continuous capacitybuilding workshops, and encourage integration of SSMS into academic laboratory activities. This research contributes to the understanding of digital innovation adoption in higher education, particularly in the context of agricultural and environmental studies.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）已成为创新性数字解决方案，用于改善土壤数据采集、分析环境变量，并提升农业与科学研究中的精准度。尽管全球正朝着智慧农业和环境可持续方向转型，但尼日利亚许多机构对SSMS的认知和采用水平仍然显著偏低。本研究调查了塔拉巴州巴利联邦理工学院生物科学系教职工在采用智能土壤监测系统方面的趋势、益处与挑战。研究采用描述性调查设计，并运用普查抽样技术，将全部20名生物科学系教职工纳入为调查对象。数据收集使用了一份经过验证的结构化问卷，题为“智能土壤监测系统采用情况问卷”，分析工具包括频数、百分比、均值和标准差。研究发现，受访者对SSMS的认知处于中等水平，但实际采用率极低，原因包括获取现代监测技术的机会有限、缺乏培训、资金不足以及机构政策支持不到位。然而，受访者认可其明显益处，如提高土壤分析准确性、增强环境可持续性，以及更好地支持农业研究与教学。研究建议该机构应投资数字土壤监测工具，组织持续的能力建设研讨会，并鼓励将SSMS纳入学术实验室活动。本研究有助于理解高等教育中数字创新的采用情况，尤其是在农业与环境研究背景下。","2026-10-01T00:00:00Z",42,{"impact":168,"substance":45,"depth":17,"authority":168,"freshness":20,"relevant":21,"comment":169},4,"尼日利亚地方院校小样本调查，主题相关但影响范围与信源权威度有限，不建议进入每日精选。",[171],{"name":103,"url":162},[112,26,114,28,173],"尼日利亚",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":177},{"candidates":178,"agricultural_evidence":46},[179,183,188],{"code":32,"score":45,"supported":46,"title":180,"abstract":181,"tags":182},[33],[49,33],[],{"code":52,"score":20,"supported":46,"title":184,"abstract":185,"tags":187},[],[26,186],"smart agriculture",[112,26],{"code":68,"score":21,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[28],[193,194],"Federal Polytechnic Bali 土壤监测","尼日利亚 智慧土壤监测系统","FederalPolytechnicBali土壤监测-4069","10.56201\u002Fwjimt.v9.no10.2025.pg255.268",{"doi":196,"openalex_id":198,"authors":199,"venue":103,"cited_by_count":85,"oa_url":9,"card":202,"direction":90,"ingested_from":93},"W7216559522",[200],{"name":201,"orcid":9},"Usman Mohammed",{"tldr":203,"method":204,"finding":205,"direction":206,"opportunity":207},"调查尼日利亚一所理工学院生物科学教职工对智能土壤监测系统的认知、采用趋势、益处与挑战。","描述性问卷普查，20名教职工，频数、百分比、均值与标准差分析。","认知中等但实际采用率极低，受技术获取、培训、资金与政策支持不足制约，但益处获认可。","数字乡村与农业信息化","可研究发展中国家高校农业数字化采用障碍机制，并设计低成本培训与政策干预方案。","2026-10-03T23:30:14.576760Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":222,"tags":224,"primary_topic":229,"secondary_topics":230,"classification":236,"search_phrases":252,"slug":255,"view_count":85,"doi":256,"paper":257,"created_at":271},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":20,"substance":219,"depth":19,"authority":220,"freshness":13,"relevant":21,"comment":221},20,9,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[223],{"name":215,"url":212},[26,225,226,227,228],"农业人工智能","主动学习","线虫检测","土壤采样",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[231],{"code":232,"slug":233,"name":234,"path":235,"parent_code":121},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":237},{"candidates":238,"agricultural_evidence":46},[239,243,248],{"code":32,"score":45,"supported":46,"title":240,"abstract":241,"tags":242},[33],[49,33],[],{"code":232,"score":220,"supported":46,"title":244,"abstract":246,"tags":247},[245],"artificial intelligence",[],[225],{"code":52,"score":53,"supported":46,"title":249,"abstract":250,"tags":251},[],[56,57],[26],[253,254],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":256,"openalex_id":258,"authors":259,"venue":215,"cited_by_count":85,"oa_url":212,"card":266,"direction":92,"ingested_from":93},"W7223082195",[260,262,264],{"name":261,"orcid":9},"Amine Ben Salah",{"name":263,"orcid":9},"Ines Trabelsi",{"name":265,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":267,"method":268,"finding":269,"direction":92,"opportunity":270},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":284,"tags":286,"primary_topic":291,"secondary_topics":297,"classification":300,"search_phrases":338,"slug":341,"view_count":85,"doi":342,"paper":343,"created_at":361},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":107,"substance":281,"depth":19,"authority":282,"freshness":13,"relevant":21,"comment":283},21,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[285],{"name":278,"url":275},[26,287,288,289,290],"机器学习","遥感监测","土壤墒情","冬小麦",{"code":292,"slug":293,"name":294,"path":295,"parent_code":296},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[298,299],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":232,"slug":233,"name":234,"path":235,"parent_code":121},{"status":39,"locked":14,"version":40,"method":41,"evidence":301},{"candidates":302,"agricultural_evidence":46},[303,309,314,319,327,334],{"code":292,"score":304,"supported":46,"title":305,"abstract":307,"tags":308},26,[306,293],"winter wheat",[294,306,293],[290],{"code":32,"score":310,"supported":46,"title":311,"abstract":312,"tags":313},15,[33],[49,33],[289],{"code":232,"score":310,"supported":46,"title":315,"abstract":317,"tags":318},[316],"machine learning",[287,316],[287],{"code":320,"score":13,"supported":46,"title":321,"abstract":322,"tags":326},"I3",[],[323,324,325],"遥感","remote sensing","multispectral",[288],{"code":328,"score":61,"supported":46,"title":329,"abstract":330,"tags":333},"H3",[],[331,332],"灌溉","irrigation",[],{"code":52,"score":168,"supported":46,"title":335,"abstract":336,"tags":337},[],[26],[26],[339,340],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":342,"openalex_id":344,"authors":345,"venue":278,"cited_by_count":85,"oa_url":275,"card":355,"direction":90,"ingested_from":93},"W7223156204",[346,349,351,353],{"name":347,"orcid":348},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":350,"orcid":9},"Yi Zhang",{"name":352,"orcid":9},"WEI Lei",{"name":354,"orcid":9},"Chengcai Zhang",{"tldr":356,"method":357,"finding":358,"direction":359,"opportunity":360},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":367,"content":9,"source_name":368,"source_url":365,"published_at":369,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":370,"score_detail":371,"sources":373,"tags":375,"primary_topic":379,"secondary_topics":380,"classification":381,"search_phrases":407,"slug":410,"view_count":85,"doi":411,"paper":412,"created_at":429},5389,"A Wideband Flexible Waterproof Antenna for Internet of Underground Things: Design, Characterization, and Field Validation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204815","The Internet of Underground Things (IoUT) is seen as a new technology that could revolutionize soil management, improving irrigation efficiency, soil and plant health, and food safety using reliable and resilient wireless connectivity. Rapid deployment of IoUT in precision agriculture requires flexible, waterproof antennas specifically designed for operation in soil. This study introduces a methodology for the design and characterization of waterproof, flexible antennas that maintain robust performance in soil across varying gravimetric water contents (GWC). Applying this methodology, a wideband, waterproof, and flexible antenna was designed and fabricated. Its parameters and characteristics were evaluated numerically and experimentally in soils with different GWCs to determine its suitability for IoUT applications. Within the GWC range of 10% to 20%, which is optimal for supporting plant health and water use, the antenna demonstrates a wide bandwidth of 0.6 to 1.8 GHz (|S11| \u003C −10 dB) and radiation efficiency ranging from 3.56% to 0.15% at 868 MHz. Experimental results obtained in free space, dry sand, and garden soil agree reasonably well with simulation predictions, showing that the proposed antenna is suitable for IoUT applications. Field validation demonstrated that the antenna prototype maintained its impedance response after two months of in-soil operation, indicating stable performance over the evaluated period.","地下物联网（Internet of Underground Things, IoUT）被视为一项可能彻底改变土壤管理的新技术，通过可靠且具有韧性的无线连接提升灌溉效率、土壤与植物健康以及食品安全。在精准农业中快速部署IoUT需要专为土壤环境运行而设计的柔性防水天线。本研究提出了一种防水柔性天线的设计与表征方法，使天线在不同重量含水率（gravimetric water content, GWC）的土壤中保持稳健性能。应用该方法，设计并制作了一款宽带防水柔性天线。在不同GWC的土壤中对其参数与特性进行了数值仿真与实验评估，以确定其用于IoUT应用的适用性。在10%至20%的GWC范围内（该范围最有利于维持植物健康与水分利用），天线在868 MHz处展现出0.6至1.8 GHz的宽带宽（|S11| \u003C −10 dB）以及3.56%至0.15%的辐射效率。在自由空间、干沙和花园土壤中获得的实验结果与仿真预测吻合良好，表明所提出的天线适用于IoUT应用。现场验证表明，天线原型在土壤中运行两个月后仍保持其阻抗响应，表明在所评估时段内性能稳定。","Electronics","2026-10-19T00:00:00Z",70,{"impact":17,"substance":219,"depth":19,"authority":282,"freshness":20,"relevant":21,"comment":372},"面向地下物联网的柔性防水天线设计与田间验证，方法新颖、数据扎实，对精准农业无线感知有参考价值，但属细分技术进展，公共影响有限。",[374],{"name":368,"url":365},[26,113,376,377,378],"土壤水分","地下物联网","柔性天线",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":382},{"candidates":383,"agricultural_evidence":46},[384,388,392,399,403],{"code":32,"score":53,"supported":46,"title":385,"abstract":386,"tags":387},[],[49,33],[376],{"code":52,"score":53,"supported":46,"title":389,"abstract":390,"tags":391},[],[56,57],[26],{"code":393,"score":61,"supported":46,"title":394,"abstract":395,"tags":398},"E4",[],[396,397],"食品安全","food safety",[],{"code":328,"score":61,"supported":46,"title":400,"abstract":401,"tags":402},[],[331,332],[],{"code":68,"score":168,"supported":46,"title":404,"abstract":405,"tags":406},[],[113],[113],[408,409],"IoUT 柔性防水天线","地下物联网 土壤水分","IoUT柔性防水天线-5389","10.3390\u002Felectronics15204815",{"doi":411,"openalex_id":413,"authors":414,"venue":368,"cited_by_count":85,"oa_url":365,"card":424,"direction":90,"ingested_from":93},"W7222978032",[415,418,421],{"name":416,"orcid":417},"Nikolay Todorov Atanasov","https:\u002F\u002Forcid.org\u002F0000-0002-7707-0133",{"name":419,"orcid":420},"Blagovest Nikolaev Atanasov","https:\u002F\u002Forcid.org\u002F0009-0001-0145-5619",{"name":422,"orcid":423},"Gabriela Lachezarova Atanasova","https:\u002F\u002Forcid.org\u002F0000-0002-7177-8317",{"tldr":425,"method":426,"finding":427,"direction":90,"opportunity":428},"设计并验证了一款用于地下物联网的宽带柔性防水天线。","采用数值仿真与实验测试，在不同含水率土壤中评估天线性能。","在10%-20%含水率下带宽0.6-1.8GHz，埋土两个月阻抗响应稳定。","可探索天线辐射效率随土壤含水率下降的补偿机制，或结合土壤传感节点做长期田间组网验证。","2026-10-20T23:30:13.823825Z",{"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":448,"secondary_topics":449,"classification":450,"search_phrases":511,"slug":514,"view_count":501,"doi":515,"paper":516,"created_at":539},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。","Antioxidants","2026-10-15T00:00:00Z",80,{"impact":18,"substance":281,"depth":18,"authority":45,"freshness":220,"relevant":21,"comment":440},"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。",[442],{"name":436,"url":433},[26,444,445,446,447],"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},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":451},{"candidates":452,"agricultural_evidence":46},[453,459,463,467,474,478,485,492,499,506],{"code":32,"score":220,"supported":46,"title":454,"abstract":455,"tags":458},[],[49,456,33,457],"根际","rhizosphere",[],{"code":52,"score":220,"supported":46,"title":460,"abstract":461,"tags":462},[186],[],[26],{"code":328,"score":53,"supported":46,"title":464,"abstract":465,"tags":466},[],[331,332],[444],{"code":468,"score":61,"supported":46,"title":469,"abstract":470,"tags":473},"A1",[],[471,472],"水稻","rice",[],{"code":292,"score":61,"supported":46,"title":475,"abstract":476,"tags":477},[],[294,293],[],{"code":479,"score":61,"supported":46,"title":480,"abstract":481,"tags":484},"A3",[],[482,483],"玉米","maize",[],{"code":486,"score":61,"supported":46,"title":487,"abstract":488,"tags":491},"A6",[],[489,490],"棉花","cotton",[],{"code":493,"score":61,"supported":46,"title":494,"abstract":495,"tags":498},"H7",[],[496,497],"干旱","drought",[],{"code":500,"score":501,"supported":46,"title":502,"abstract":503,"tags":505},"F3",3,[],[504],"肥料",[],{"code":507,"score":21,"supported":14,"title":508,"abstract":509,"tags":510},"F2",[],[],[445],[512,513],"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":515,"openalex_id":517,"authors":518,"venue":436,"cited_by_count":85,"oa_url":433,"card":534,"direction":90,"ingested_from":93},"W7222342675",[519,522,525,528,531],{"name":520,"orcid":521},"Hafiz Abdul Kareem","https:\u002F\u002Forcid.org\u002F0000-0002-8209-0446",{"name":523,"orcid":524},"Sana Saleem","https:\u002F\u002Forcid.org\u002F0000-0001-9836-2744",{"name":526,"orcid":527},"Muhammad Bilal Hafeez","https:\u002F\u002Forcid.org\u002F0000-0001-6719-6667",{"name":529,"orcid":530},"Naeem Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-5023-8362",{"name":532,"orcid":533},"Da‐Chuan Yin","https:\u002F\u002Forcid.org\u002F0000-0003-2161-5867",{"tldr":535,"method":536,"finding":537,"direction":90,"opportunity":538},"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。","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。","2026-10-16T23:30:09.487745Z"]