[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3010":3,"related-3010":54},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":53},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。",null,"International Journal of Computer Applications","2026-09-18T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"面向小农户的精准水肥一体化多任务集成模型与物联网决策支持系统，数据规模与方法验证扎实，对智慧农业落地有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业物联网","小农户","精准灌溉",[32,33],"尼日利亚 精准灌溉 物联网","堆叠集成 施肥决策 小农户","尼日利亚精准灌溉物联网-3010",0,"10.5120\u002Fijca9a6e56b81235",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":46,"direction":50,"ingested_from":52},"W7213663876",[40,42,44],{"name":41,"orcid":9},"Awojide S.",{"name":43,"orcid":9},"Ikpotokin F.O.",{"name":45,"orcid":9},"Sadiq F.I.",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"构建多任务堆叠集成模型与物联网决策支持系统，实现小农户精准水肥一体化。","LSTM、XGBoost、随机森林堆叠集成，融合尼日利亚土壤气象、IoT与NDV","集成模型将施肥量MAE降低10.9%，需求与时机分类F1提升，系统部署零错误。","智慧农业 \u002F 农业物联网","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","openalex","2026-09-20T23:30:09.003454Z",{"total":55,"page":21,"page_size":55,"items":56},6,[57,93,119,151,185,222],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":64,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":66,"score_detail":67,"sources":71,"tags":75,"search_phrases":77,"slug":80,"view_count":35,"doi":81,"paper":82,"created_at":92},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-12T00:00:00Z",25,85,{"impact":18,"substance":68,"depth":17,"authority":19,"freshness":69,"relevant":21,"comment":70},23,9,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[72,73],{"name":63,"url":60},{"name":63,"url":74},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[76,26,27,28,29,30],"数字乡村",[78,79],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":81,"openalex_id":83,"authors":84,"venue":63,"cited_by_count":35,"oa_url":60,"card":87,"direction":50,"ingested_from":52},"W7212357737",[85],{"name":86,"orcid":9},"Pranit Kamble",{"tldr":88,"method":89,"finding":90,"direction":50,"opportunity":91},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":94,"title":95,"url":96,"summary":97,"summary_zh":9,"content":98,"source_name":99,"source_url":9,"published_at":100,"category":101,"cover_url":9,"hotness":13,"is_selected":102,"score":103,"score_detail":104,"sources":109,"tags":111,"search_phrases":114,"slug":117,"view_count":35,"doi":9,"paper":9,"created_at":118},3022,"农业农村部党组召开会议强调：大力推进\"人工智能+\"农业 拓展无人机、物联网等应用场景","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202609\u002Ft20260920_8872540.htm","农业农村部党组9月20日召开会议，传达学习习近平总书记关于山东青岛市北海造船厂一货轮火灾事故的重要指示精神，部署农业安全生产、农机装备产业发展等工作。会议强调，要大力推进\"人工智能+\"农业，拓展无人机、物联网等应用场景，让新质生产力更好赋能现代农业发展；要全力抓好\"三秋\"生产，分区域分作物指导抓细秋粮田管，强化农业防灾减灾救灾，精心组织开展秋收，压茬推进秋冬种，确保秋粮丰收到手、冬小麦冬油菜种足种好。","[![Image 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9月20日，农业农村部党组召开会议。部党组书记、部长张柱主持会议。\n\n会议传达学习习近平总书记关于山东青岛市北海造船厂一货轮火灾事故的重要指示精神，强调要深入学习领会，抓好贯彻落实，按照李强总理批示要求和国务院常务会议部署，时刻绷紧安全生产这根弦，树牢极限思维、底线思维，严格按照“三管三必须”要求，进一步压紧压实责任，全面加强农业安全生产监管，紧盯海洋渔船、农机、有限空间等重点领域，深入排查整治风险隐患，逐项整改落实到位，细化防范救援措施，坚决遏制重特大事故发生，以高水平安全保障农业农村高质量发展。\n\n会议学习贯彻习近平总书记关于发展先进制造业的重要指示精神，强调要把培育壮大农机装备产业作为农业现代化的关键支撑，坚持智能化、绿色化、融合化发展方向，聚焦高端智能、丘陵山区适用农机等突出短板，集中优势资源力量，攻关突破一批标志性整机和关键共性技术，加快中试验证 和 熟化 应用，让农业生产更多领域“有机可用、有好机用”。要大力推进“人工智能+”农业，拓展无人机、物联网等应用场景，让新质生产力更好赋能现代农业发展。\n\n会议强调，要全力抓好“三秋”生产，分区域分作物指导抓细秋粮田管，强化农业防灾减灾救灾，精心组织开展秋收，压茬推进秋冬种，确保秋粮丰收到手，冬小麦冬油菜种足种好。要着力强化农业科技装备支撑，系统谋划推进高标准农田建设，加快农业科技成果集成推广，加强优良品种选育，大力推广水肥一体化技术模式，加力推进粮油作物大面积单产提升。要积极稳妥深化农村改革，有序推进第二轮土地承包到期后再延长30年试点，推动农业社会化服务扩面提质，促进小农户和现代农业发展有机衔接。\n\n会议还研究了其他事项。\n\n![Image 3](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_back.png)\n返回顶部\n\n*   [外交部](https:\u002F\u002Fwww.fmprc.gov.cn\u002Fweb\u002F)\n*   [国防部](http:\u002F\u002Fwww.mod.gov.cn\u002F)\n*   [国家发展和改革委员会](https:\u002F\u002Fwww.ndrc.gov.cn\u002F)\n*   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人工智能","农业无人机 物联网 应用场景","农业农村部人工智能-3022","2026-09-21T00:04:31.528736Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":126,"sources":129,"tags":131,"search_phrases":134,"slug":137,"view_count":35,"doi":138,"paper":139,"created_at":150},2948,"Machine learning and remote sensing for smallholder precision agriculture in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04538-2","While machine learning (ML) and remote sensing (RS) are frequently heralded as the definitive solutions for agricultural resilience in Sub-Saharan Africa, a profound ‘implementation gap’ persists between laboratory-validated computational maturity and field-level utility for smallholder farmers. This systematic review, conducted under preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, critically analyzes why sophisticated models optimized for large-scale monocultures fail within the fragmented, intercropped landscapes of Ethiopia. By synthesizing empirical evidence across four domains—in-season crop yield forecasting, digital soil mapping, real-time biotic stress detection, and agro-meteorological modeling—the review uncover a fundamental scale mismatch between coarse-resolution satellite observations and sub-hectare micro-plots. The critique identifies localized data scarcity, hardware constraints, and the ‘last-mile’ connectivity divide as the primary friction points obstructing the transition from macro-level pixels to actionable, site-specific agricultural intelligence. Moving beyond simple summary, the study propose a strategic roadmap centered on decentralized edge computing, tinyML optimizations, and a restructuring of extension services to integrate digital intelligence into daily smallholder decision-making. These structural shifts are essential to bridge the digital divide and secure Ethiopia’s national food security against escalating climate variability. This review foregrounds the significance of digital agriculture within the context of the sustainable development goals (SDGs), specifically addressing SDG 2 (zero hunger) and SDG 13 (climate action) by enhancing crop productivity and building resilience in smallholder systems.","尽管机器学习（ML）与遥感（RS）常被标榜为撒哈拉以南非洲农业韧性的终极解决方案，但实验室验证的计算成熟度与小农户田间实用性之间仍存在深刻的“实施鸿沟”。本系统综述依据系统综述和荟萃分析首选报告条目（PRISMA）2020指南开展，批判性地分析了为何针对大规模单一种植优化的复杂模型在埃塞俄比亚碎片化、间作化的景观中失效。通过综合四个领域的实证证据——季内作物产量预测、数字土壤制图、实时生物胁迫检测和农业气象建模——本综述揭示了粗分辨率卫星观测与亚公顷微地块之间的根本性尺度错配。该批判性分析将局部数据稀缺、硬件约束和“最后一公里”连接鸿沟确定为阻碍从宏观像元向可操作、因地制宜的农业智能转化的主要摩擦点。本研究超越简单的总结，提出了一条以去中心化边缘计算、tinyML优化和推广服务体系重构为核心的战略路线图，旨在将数字智能融入小农户的日常决策。这些结构性转变对于弥合数字鸿沟、保障埃塞俄比亚在日益加剧的气候变率下的国家粮食安全至关重要。本综述凸显了数字农业在可持续发展目标（SDGs）背景下的重要意义，特别是通过提升作物生产力和增强小农系统韧性来回应SDG 2（零饥饿）和SDG 13（气候行动）。","Discover Sustainability",{"impact":17,"substance":127,"depth":17,"authority":19,"freshness":69,"relevant":21,"comment":128},21,"系统综述揭示机器学习与遥感在小农场景的落地鸿沟，并提出边缘计算与tinyML路线图，对数字农业与SDG研究有参考价值。",[130],{"name":125,"url":122},[26,27,29,132,133],"数字鸿沟","遥感",[135,136],"埃塞俄比亚 小农户 精准农业","机器学习 遥感 小农","埃塞俄比亚小农户精准农业-2948","10.1007\u002Fs43621-026-04538-2",{"doi":138,"openalex_id":140,"authors":141,"venue":125,"cited_by_count":35,"oa_url":122,"card":144,"direction":148,"ingested_from":52},"W7213562005",[142],{"name":143,"orcid":9},"Abrha Asefa",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"系统综述埃塞俄比亚小农精准农业中机器学习和遥感的应用鸿沟与出路。","PRISMA 2020 系统综述，综合四领域实证证据。","粗分辨率卫星与亚公顷微地块尺度不匹配，数据稀缺和连接鸿沟阻碍落地。","农业遥感与作物表型","面向碎片化间作小农的 tinyML 边缘计算与本地化数据采集，是填补落地鸿沟的关键方向。","2026-09-19T23:30:33.334404Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":63,"source_url":154,"published_at":157,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":158,"score_detail":159,"sources":162,"tags":166,"search_phrases":169,"slug":172,"view_count":35,"doi":173,"paper":174,"created_at":184},2866,"Upcoming Technologies for Agriculture: Innovations Shaping the Future of Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816586","Agriculture has always been a cornerstone of human civilization, providing food, raw materials, and employment. However, with the global population projected to reach nearly 10 billion by 2050, the demand for food production is expected to increase substantially (Godfray et al., 2010). Traditional farming methods are increasingly challenged by climate change, resource limitations, and environmental concerns. To address these challenges, upcoming technologies in agriculture promise to revolutionize farming practices by enhancing productivity, sustainability, and resilience. This article reviews key emerging technologies, including precision agriculture, artificial intelligence (AI), gene editing, drone and robotic systems, Internet of Things (IoT) sensors, and sustainable farming innovations. The integration of these technologies is expected to transform agriculture into a more efficient, data-driven, and environmentally friendly sector.","农业一直是人类文明的基石，为人类提供食物、原材料和就业机会。然而，随着全球人口预计到2050年将接近100亿，粮食生产需求预计将大幅增加（Godfray等，2010）。传统耕作方式日益受到气候变化、资源限制和环境问题的挑战。为应对这些挑战，农业领域的新兴技术有望通过提高生产力、可持续性和韧性来彻底变革耕作方式。本文综述了关键新兴技术，包括精准农业、人工智能（AI）、基因编辑、无人机与机器人系统、物联网（IoT）传感器以及可持续农业创新。这些技术的融合有望将农业转变为一个更高效、数据驱动且环境友好的产业。","2026-09-17T00:00:00Z",68,{"impact":17,"substance":160,"depth":107,"authority":19,"freshness":20,"relevant":21,"comment":161},14,"综述性论文系统梳理精准农业、AI、基因编辑等前沿技术，时效性尚可，但缺乏新数据与独家结论，适合作为主题聚合素材而非每日精选头条。",[163,164],{"name":63,"url":154},{"name":63,"url":165},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[26,27,28,167,168],"精准农业","基因编辑",[170,171],"精准农业 人工智能 无人机","农业物联网 传感器 机器人","精准农业人工智能无人机-2866","10.5281\u002Fzenodo.22816586",{"doi":173,"openalex_id":175,"authors":176,"venue":63,"cited_by_count":35,"oa_url":154,"card":179,"direction":50,"ingested_from":52},"W7213515489",[177],{"name":178,"orcid":9},"Zorawar Singh",{"tldr":180,"method":181,"finding":182,"direction":50,"opportunity":183},"综述精准农业、AI、基因编辑、无人机、物联网等新兴技术如何重塑未来农业。","文献综述，整合精准农业、AI、基因编辑、无人机、IoT等关键技术。","技术融合将推动农业向高效、数据驱动和环境友好方向转型。","可聚焦多技术集成落地中的成本、数据标准与农户采纳障碍等实证研究空白。","2026-09-18T23:30:14.975692Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":63,"source_url":188,"published_at":191,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":192,"score_detail":193,"sources":196,"tags":200,"search_phrases":203,"slug":205,"view_count":35,"doi":206,"paper":207,"created_at":221},2773,"IoT-Enabled Sensor Applications in Smart Healthcare, Smart Agriculture, Environmental Monitoring and Smart Energy & Safety Monitoring","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795428","The Internet of Things (IoT) is becoming a fundamental technology for the development of sensorized intelligent systems in several domains of society. This paper presents a structured literature review on architectures for sensing and intelligence in the scope of four research areas: Smart Healthcare, Smart Agriculture, Environmental Monitoring, and Smart Energy and Safety Monitoring. Recent journal articles, systematic reviews, and conference papers were analysed to report sensor modalities, communication architectures, and learning algorithms, namely machine learning (ML) and deep learning (DL), that have been proposed in literature for each of the above research areas. A consolidated inventory of algorithms that have been reported in the above surveyed works, as well as a comparison of sensing technologies, learning techniques, and their corresponding applications, are also presented. Finally, open challenges that have been found to be recurring, i.e., sensor calibration and drift, energy efficiency, security and privacy, heterogeneous data, and the gap between proof-of-concept prototypes and large-scale scalable and practical implementations, as well as future research directions, are outlined. The purpose of this work is to provide a useful, structured literature review that can serve as a background and reference for researchers interested in sensorized IoT applications using algorithmic approaches.","物联网（IoT）正在成为社会发展多个领域中传感智能系统开发的基础性技术。本文针对四个研究领域——智能医疗、智能农业、环境监测以及智能能源与安全监测——中的传感与智能架构进行了结构化文献综述。通过分析近期期刊论文、系统性综述和会议论文，报告了上述各研究领域中文献所提出的传感器模态、通信架构以及学习算法，即机器学习（ML）和深度学习（DL）。本文还提供了在上述综述工作中所报道算法的综合清单，以及传感技术、学习技术及其相应应用的对比。最后，概述了反复出现的开放挑战，即传感器校准与漂移、能效、安全与隐私、异构数据，以及概念验证原型与大规模可扩展实际实现之间的差距，并指出了未来研究方向。本工作的目的是提供一份有用的、结构化的文献综述，为对使用算法方法的传感物联网应用感兴趣的研究人员提供背景和参考。","2026-09-16T00:00:00Z",77,{"impact":17,"substance":106,"depth":194,"authority":19,"freshness":69,"relevant":21,"comment":195},17,"系统性文献综述，覆盖智慧农业等四大领域的传感与智能架构，方法梳理与开放挑战总结扎实，对农业物联网研究有参考价值，但非农业专属突破性成果。",[197,198],{"name":63,"url":188},{"name":63,"url":199},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795429",[26,27,28,201,202],"传感器","环境监测",[204,79],"农业人工智能 农业物联网 智慧农业 环境监测","农业人工智能农业物联网智慧农业环境监测-2773","10.5281\u002Fzenodo.22795428",{"doi":206,"openalex_id":208,"authors":209,"venue":63,"cited_by_count":35,"oa_url":188,"card":216,"direction":50,"ingested_from":52},"W7213433550",[210,212,214],{"name":211,"orcid":9},"Mrs.C.Nithya",{"name":213,"orcid":9},"Mr.M.K.Sampath",{"name":215,"orcid":9},"Mrs. P.Raga Keerthana",{"tldr":217,"method":218,"finding":219,"direction":50,"opportunity":220},"综述物联网传感与智能在医疗、农业、环境、能源安全四大领域的架构、算法与应用。","结构化文献综述，分析传感器模态、通信架构及机器学习\u002F深度学习算法。","梳理了各领域算法清单，指出传感器校准漂移、能效、安全隐私等共性挑战。","农业物联网中传感器校准漂移与跨域异构数据融合的轻量化算法研究尚存空白。","2026-09-17T23:30:13.984507Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":9,"source_name":228,"source_url":225,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":233,"tags":235,"search_phrases":237,"slug":240,"view_count":35,"doi":241,"paper":242,"created_at":266},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing","2026-09-15T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":160,"freshness":20,"relevant":21,"comment":232},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[234],{"name":228,"url":225},[26,27,236,133,30],"小麦",[238,239],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":241,"openalex_id":243,"authors":244,"venue":228,"cited_by_count":35,"oa_url":225,"card":261,"direction":148,"ingested_from":52},"W7213246708",[245,248,250,252,255,258],{"name":246,"orcid":247},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":249,"orcid":9},"Qian Cheng",{"name":251,"orcid":9},"Fuyi Duan",{"name":253,"orcid":254},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":256,"orcid":257},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":259,"orcid":260},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":262,"method":263,"finding":264,"direction":148,"opportunity":265},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z"]