[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4208":3,"related-4208":101},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":100},4208,"High-Efficiency Solar-Piezo Hybrid Energy Harvester for Long-Term Autonomous Operation of Smart Agriculture Sensor Nodes","https:\u002F\u002Fdoi.org\u002F10.31838\u002Fece\u002F03.02.06","The paper presents the design and consideration of a highly efficient hybrid energy harvesting system, combined solar and piezo energy harvesting components to power autonomous wireless sensor nodes (WSNs) used in smart agriculture. The most important goal is to provide a maintenance free, long term operation of the sensor nodes in the envisioned variable conditions of environment. The suggested system consists of a monocrystalline solar panel and piezoelectric transducers that allow to focus both ambient light and mechanics vibrations. In-order to get maximum energy extraction, intelligent maximum power point tracking (MPPT) based power conditioning circuit is implemented, and the energy generated is stored using hybrid supercapacitor-Li-ion battery system. Up to the area of evaluation of the performance of the designed system in the real conditions, a particular WSN platform was created and deployed both in a greenhouse and an open field setting. The experimental output shows that the hybrid energy harvester records a 30-45 percent rise in energy capture than the solar-only systems. Moreover, efficiency of conversion of power is increased by about 22 % by using the MPPT implementation. The sensor nodes exhibited an outstanding reliability, surpassing 98 per cent of uptime in a 30-day non-stop deployment, and over 12 hours working autonomously without any illumination. The innovation in this study is the strength of the energy harvesting system, where supplementary sources are also utilized to increase the resilience and extend the operation weeks of smart agriculture WSNs, considering the shortcomings of using single sources in the uncontrollable outdoor situations.","本文介绍了一种高效混合能量收集系统的设计与思考，该系统结合了太阳能和压电能量收集组件，为智慧农业中使用的自主无线传感器节点（WSN）供电。最重要的目标是在预期的多变环境条件下，实现传感器节点的免维护、长期运行。所提出的系统由单晶硅太阳能板和压电换能器组成，能够同时利用环境光和机械振动。为了实现最大能量提取，采用了基于智能最大功率点跟踪（MPPT）的功率调节电路，并将产生的能量通过混合超级电容器-锂离子电池系统进行存储。为了在实际条件下评估所设计系统的性能，专门搭建了一个WSN平台，并在温室和露天田地环境中进行了部署。实验结果表明，与仅使用太阳能的系统相比，混合能量收集器的能量采集量提高了30%至45%。此外，通过采用MPPT实现，功率转换效率提高了约22%。传感器节点表现出卓越的可靠性，在30天不间断部署中正常运行时间超过98%，并在无任何光照条件下自主运行超过12小时。本研究的创新之处在于该能量收集系统的稳健性，其中还利用了辅助能源来增强智慧农业WSN的韧性并延长其运行周数，考虑到在不可控的户外环境中使用单一能源的局限性。",null,"Progress in Electronics and Communication Engineering","https:\u002F\u002Fecejournals.in\u002Findex.php\u002FPECE\u002Farticle\u002Fview\u002F429","2026-10-22T00:00:00Z","论文",10,false,70,{"impact":18,"substance":19,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,12,0,1,"论文提出太阳能-压电混合能量采集方案，实测能量捕获提升30-45%、节点在线率超98%，对智慧农业长期免维护传感部署有实质参考价值，但发表于半月后、时效性不足，暂不宜进入每日精选。",[25],{"name":10,"url":11},[27,28,29,30,31],"智慧农业","农业物联网","农业传感器","能量采集","无线传感器",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":46,"locked":15,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":54},[51,59,65,72],{"code":52,"score":53,"supported":54,"title":55,"abstract":57,"tags":58},"I1",15,true,[56],"smart agriculture",[27,56],[27],{"code":33,"score":60,"supported":54,"title":61,"abstract":63,"tags":64},11,[62],"harvester",[62],[],{"code":40,"score":66,"supported":54,"title":67,"abstract":68,"tags":71},8,[],[69,70],"无线传感","wireless sensor",[29,28],{"code":73,"score":74,"supported":54,"title":75,"abstract":76,"tags":79},"B7",6,[],[77,78],"温室","greenhouse",[],[81,82],"太阳能 压电 混合能量采集","农业无线传感器 自供电","太阳能压电混合能量采集-4208",2,"10.31838\u002Fece\u002F03.02.06",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":21,"oa_url":11,"card":93,"direction":97,"ingested_from":99},"W7219845622",[89,91],{"name":90,"orcid":9},"N. K. Havalam",{"name":92,"orcid":9},"Ragaba Mean Bosco",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"设计太阳能-压电混合能量收集系统，为智慧农业无线传感器节点提供长期免维护供电。","单晶硅太阳能板与压电换能器混合，MPPT电路及超级电容-锂离子电池储能，温室和露","混合系统能量捕获比纯太阳能高30-45%，MPPT提升转换效率约22%，30天运行时间超98%，无光","智慧农业 \u002F 农业物联网","可探索混合能量收集与低功耗传感器调度协同优化，以及基于环境预测的动态能量管理策略。","openalex","2026-10-05T23:30:08.764944Z",{"total":74,"page":22,"page_size":74,"items":102},[103,202,302,372,427,503],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":13,"cover_url":9,"hotness":111,"is_selected":15,"score":112,"score_detail":113,"sources":117,"tags":122,"primary_topic":127,"secondary_topics":132,"classification":135,"search_phrases":186,"slug":189,"view_count":22,"doi":190,"paper":191,"created_at":201},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",25,81,{"impact":19,"substance":114,"depth":18,"authority":115,"freshness":66,"relevant":22,"comment":116},20,13,"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[118,119],{"name":109,"url":106},{"name":120,"url":121},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,123,124,28,125,126],"农业人工智能","农业机器人","精准农业","自动驾驶农机",{"code":128,"slug":129,"name":130,"path":131,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[133,134],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":136},{"candidates":137,"agricultural_evidence":54},[138,147,155,162,169,174,181],{"code":128,"score":18,"supported":54,"title":139,"abstract":141,"tags":146},[140],"artificial intelligence",[142,143,144,140,145],"机器学习","计算机视觉","machine learning","computer vision",[123],{"code":33,"score":148,"supported":54,"title":149,"abstract":151,"tags":154},17,[150],"agricultural machinery",[152,153,150],"农业机械","收割机",[],{"code":40,"score":14,"supported":54,"title":156,"abstract":157,"tags":161},[],[158,159,160],"物联网","internet of things","IoT",[28],{"code":163,"score":74,"supported":54,"title":164,"abstract":165,"tags":168},"I3",[],[166,167],"遥感","remote sensing",[],{"code":52,"score":170,"supported":54,"title":171,"abstract":172,"tags":173},5,[],[125],[27,125],{"code":175,"score":176,"supported":54,"title":177,"abstract":178,"tags":180},"F4",3,[],[179],"除草",[],{"code":182,"score":22,"supported":15,"title":183,"abstract":184,"tags":185},"F6",[],[],[124],[187,188],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":190,"openalex_id":192,"authors":193,"venue":109,"cited_by_count":21,"oa_url":106,"card":196,"direction":97,"ingested_from":99},"W7221138004",[194],{"name":195,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":197,"method":198,"finding":199,"direction":97,"opportunity":200},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":9,"source_name":208,"source_url":205,"published_at":209,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":210,"score_detail":211,"sources":213,"tags":215,"primary_topic":217,"secondary_topics":218,"classification":220,"search_phrases":283,"slug":286,"view_count":66,"doi":287,"paper":288,"created_at":301},3536,"Functional photonic and optoelectronic materials and devices for climate-resilient smart agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmats.2026.1943334","The variation in the temperature, precipitation, and extreme events has rendered climate change very threatening to crop production and the overall world food security. The prospects of sustainable and climate resilient agricultural production is brought up with the use of photonics and optoelectronics coupled with smart farming technology. The paper offers an overall foundation of smart agricultural systems that are assisted by photonics and optoelectronics that combine the state-of-the-art optical sensors, Internet of Things (IoT) communications, and machine learning algorithms in the crop and soil monitoring. Due to optical sensing technologies including fiber optic sensors, LiDAR, fluorescence spectroscopy, and hyperspectral imaging, optical sensors can also be used in high resolution and non-destructive measurement of physiological properties of plants, nutrient status, water stress and disease incidence. With the help of optoelectronic devices, it is possible to carry out the accurate signal processing, data collection, automated control of irrigation, fertigation and microclimate regulating systems. When sensor-based information is incorporated with any decision support systems the quality of early stress detection is improved, wastage of resources is minimized and the effect on the environment is minimized. Multispectral and temporal data based on machine learning models enhance forecasting of climate risk, diseases, and pest outbreaks forecasting, and crop yield forecasting. The system will aid in making the contemporary crop production systems more sustainable and resilient in the long-term due to the fact that it will provide scalable and flexible solutions across the agroecological regions.","温度、降水及极端事件的变化使气候变化对作物生产和全球粮食安全构成严重威胁。光子学与光电子学结合智慧农业技术，为实现可持续且气候韧性的农业生产带来了前景。本文提供了由光子学和光电子学辅助的智慧农业系统的总体基础，该系统将先进的光学传感器、物联网（IoT）通信和机器学习算法结合应用于作物与土壤监测。借助光纤传感器、激光雷达（LiDAR）、荧光光谱和高光谱成像等光学传感技术，光学传感器还可用于高分辨率、非破坏性地测量植物生理特性、养分状况、水分胁迫和病害发生情况。借助光电子器件，可以实现精确的信号处理、数据采集以及灌溉、施肥和微气候调节系统的自动控制。当基于传感器的信息与决策支持系统相结合时，早期胁迫检测的质量得以提高，资源浪费降至最低，对环境的影响也降至最小。基于机器学习模型的多光谱和时间序列数据增强了气候风险、病虫害暴发预测以及作物产量预测的能力。该系统将有助于使当代作物生产系统在长期内更具可持续性和韧性，因为它将为各农业生态区域提供可扩展且灵活的解决方案。","Frontiers in Materials","2026-09-25T00:00:00Z",76,{"impact":18,"substance":114,"depth":148,"authority":115,"freshness":66,"relevant":22,"comment":212},"系统综述光子与光电子技术在气候韧性智慧农业中的应用，方法覆盖全面、结论可靠，对农业信息化领域有较高参考价值。",[214],{"name":208,"url":205},[27,28,142,29,216],"光谱遥感",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[219],{"code":128,"slug":129,"name":130,"path":131,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":221},{"candidates":222,"agricultural_evidence":54},[223,227,231,236,243,250,257,264,271,277],{"code":52,"score":20,"supported":54,"title":224,"abstract":225,"tags":226},[56],[27],[27],{"code":40,"score":60,"supported":54,"title":228,"abstract":229,"tags":230},[],[158,159,160],[29,28],{"code":128,"score":14,"supported":54,"title":232,"abstract":233,"tags":235},[],[142,144,234],"decision support",[142],{"code":237,"score":74,"supported":54,"title":238,"abstract":239,"tags":242},"A8",[],[240,241],"粮食安全","food security",[],{"code":244,"score":74,"supported":54,"title":245,"abstract":246,"tags":249},"F3",[],[247,248],"施肥","fertigation",[],{"code":251,"score":74,"supported":54,"title":252,"abstract":253,"tags":256},"H2",[],[254,255],"土壤","soil",[],{"code":258,"score":74,"supported":54,"title":259,"abstract":260,"tags":263},"H3",[],[261,262],"灌溉","irrigation",[],{"code":265,"score":74,"supported":54,"title":266,"abstract":267,"tags":270},"H7",[],[268,269],"气候变化","climate change",[],{"code":163,"score":74,"supported":54,"title":272,"abstract":273,"tags":276},[],[274,275],"multispectral","hyperspectral",[],{"code":278,"score":176,"supported":54,"title":279,"abstract":280,"tags":282},"H6",[],[281],"农业生态",[],[284,285],"光子学 光电材料 智慧农业","光纤传感 高光谱成像 作物监测","光子学光电材料智慧农业-3536","10.3389\u002Ffmats.2026.1943334",{"doi":287,"openalex_id":289,"authors":290,"venue":208,"cited_by_count":21,"oa_url":205,"card":296,"direction":97,"ingested_from":99},"W7214293156",[291,293],{"name":292,"orcid":9},"Karthika Vishnu Priya Kathula",{"name":294,"orcid":295},"Murugesan Mohana Keerthi","https:\u002F\u002Forcid.org\u002F0000-0002-2018-0200",{"tldr":297,"method":298,"finding":299,"direction":97,"opportunity":300},"综述光子与光电子材料器件结合物联网和机器学习，支撑气候韧性智慧农业的作物与土壤监测。","综述光纤传感、LiDAR、荧光光谱、高光谱成像与IoT、机器学习融合方案。","光学传感与光电器件可实现无损高分辨监测，提升早期胁迫检测并减少资源浪费。","可探索低成本光子传感器与轻量ML模型在田间边缘端的集成及跨生态区泛化验证。","2026-09-26T23:30:09.644423Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":9,"source_name":308,"source_url":305,"published_at":309,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":310,"score_detail":311,"sources":314,"tags":316,"primary_topic":318,"secondary_topics":319,"classification":321,"search_phrases":339,"slug":342,"view_count":84,"doi":343,"paper":344,"created_at":371},2154,"Ambient RF energy harvesting using voltage doubler rectifier for battery-free agricultural sensor networks: Design, statistical analysis, and machine learning validation","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0350236","The growing adoption of precision agriculture requires sustainable power sources for wireless sensor networks (WSNs) deployed in remote and resource-constrained environments. Conventional battery-powered systems are constrained by limited-service life, maintenance requirements, and environmental concerns. This study presents the design, experimental validation, statistical evaluation, and machine learning (ML)-based modeling of an ambient radio-frequency (RF) energy harvesting system for agricultural monitoring applications. The proposed system harvests RF energy from AM\u002FFM broadcasting (558 kHz–108 MHz) and cellular communication bands (800–2100 MHz) using a frequency-selective broadband antenna, an integrated diplexer, switchable L-section impedance-matching networks, and a Villard voltage-doubler rectifier based on low forward-voltage OA79 germanium diodes. Experimental measurements from 30 independent repeated trials yielded a mean open-circuit output voltage of 4.07 ± 0.08 V (95% CI: 4.04–4.10 V) and a loaded output voltage of 2.11 ± 0.11 V, corresponding to a harvested power of approximately 44.5 μW across a 100 kΩ load. One-way analysis of variance (ANOVA) demonstrated significant differences among diode types (F (2,87) = 687.4, p \u003C 0.001), while Tukey’s post hoc test confirmed the superior performance of OA79 diodes compared with 1N4148 and 1N5819 alternatives. Among the evaluated ML models, Gradient Boosting regression achieved the highest predictive accuracy, with R² = 0.963 and RMSE = 0.071 V. SHapley Additive exPlanations (SHAP) analysis identified diode forward voltage and ambient RF power density as the most influential factors affecting output voltage prediction. The results indicate the potential applicability of ambient multi-band RF energy harvesting as a supplementary energy source for low-power agricultural sensing applications operating under duty-cycled conditions. By combining broadband energy harvesting, statistical validation, and predictive modeling, the proposed framework establishes a systematic methodology for evaluating self-powered agricultural IoT systems operating under variable RF environments.","精准农业的日益普及，对部署在偏远且资源受限环境中的无线传感器网络（WSN）提出了可持续供电的需求。传统电池供电系统受限于使用寿命有限、维护需求以及环境问题。本研究介绍了一种面向农业监测应用的环境射频（RF）能量收集系统的设计、实验验证、统计评估以及基于机器学习（ML）的建模。所提出的系统利用频率选择性宽带天线、集成双工器、可切换L型阻抗匹配网络以及基于低正向电压OA79锗二极管的Villard倍压整流器，从AM\u002FFM广播频段（558 kHz–108 MHz）和蜂窝通信频段（800–2100 MHz）收集射频能量。来自30次独立重复试验的实验测量结果得出，平均开路输出电压为4.07 ± 0.08 V（95% CI：4.04–4.10 V），负载输出电压为2.11 ± 0.11 V，对应在100 kΩ负载上收集的功率约为44.5 μW。单因素方差分析（ANOVA）表明，不同二极管类型之间存在显著差异（F (2,87) = 687.4，p \u003C 0.001），而Tukey事后检验证实，与1N4148和1N5819替代方案相比，OA79二极管具有更优越的性能。在评估的机器学习模型中，梯度提升回归取得了最高的预测精度，R² = 0.963，RMSE = 0.071 V。SHapley加性解释（SHAP）分析确定，二极管正向电压和环境射频功率密度是影响输出电压预测的最重要因素。结果表明，环境多频段射频能量收集作为在占空比条件下运行的低功耗农业传感应用的补充能源，具有潜在适用性。通过将宽带能量收集、统计验证和预测建模相结合，所提出的框架建立了一种系统化方法，用于评估在多变射频环境下运行的自供电农业物联网系统。","PLoS ONE","2026-09-10T00:00:00Z",71,{"impact":20,"substance":312,"depth":148,"authority":115,"freshness":66,"relevant":22,"comment":313},21,"面向无电池农业传感网络的射频能量采集研究，方法完整、统计与机器学习验证扎实，但属实验室原型阶段，产业影响有限。",[315],{"name":308,"url":305},[27,123,28,317,30],"无线传感器网络",{"code":128,"slug":129,"name":130,"path":131,"parent_code":44},[320],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":322},{"candidates":323,"agricultural_evidence":54},[324,328,333],{"code":128,"score":53,"supported":54,"title":325,"abstract":326,"tags":327},[144],[142,144],[123],{"code":40,"score":60,"supported":54,"title":329,"abstract":330,"tags":332},[],[158,331,69,160,70],"农业传感",[28,317],{"code":52,"score":334,"supported":54,"title":335,"abstract":336,"tags":338},7,[],[125,337],"precision agriculture",[27],[340,341],"无线传感器网络 农业人工智能 农业物联网 智慧农业","无线传感器网络 农业人工智能","无线传感器网络农业人工智能农业物联网智慧农业-2154","10.1371\u002Fjournal.pone.0350236",{"doi":343,"openalex_id":345,"authors":346,"venue":308,"cited_by_count":21,"oa_url":305,"card":366,"direction":97,"ingested_from":99},"W7212168596",[347,349,351,353,355,357,359,361,364],{"name":348,"orcid":9},"Md. Atik Hasan Nishat",{"name":350,"orcid":9},"Prithwiraj Biswas Pallab",{"name":352,"orcid":9},"Nowrin Jannat",{"name":354,"orcid":9},"Saleha Nasrin Mishu",{"name":356,"orcid":9},"Md Fahad Ullah Utsho",{"name":358,"orcid":9},"Md. Bipul Islam",{"name":360,"orcid":9},"Riaz Uddin Mondal",{"name":362,"orcid":363},"Md. Firoz Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2721-0596",{"name":365,"orcid":9},"M. Hasnat Kabir",{"tldr":367,"method":368,"finding":369,"direction":97,"opportunity":370},"设计多频段环境RF能量收集系统，为无电池农业传感器网络供电并验证。","Villard倍压整流器、宽带天线、ANOVA统计与梯度提升\u002FSHAP机器学习建","OA79锗二极管性能最优，输出约44.5μW，梯度提升预测R²达0.963。","可探索多源环境能量混合收集与自适应功率管理，提升农业物联网节点长期自持能力。","2026-09-11T23:30:16.081985Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":377,"content":9,"source_name":109,"source_url":375,"published_at":378,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":379,"score_detail":380,"sources":383,"tags":385,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":411,"slug":414,"view_count":21,"doi":415,"paper":416,"created_at":426},5554,"Smart Agriculture using IoT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531185","The Smart Agriculture Using IoT system uses different sensors installed in the agricultural field to collect data about soil and environmental conditions. The collected data is sent to a microcontroller, such as an ESP32 or Arduino with a suitable communication module. When the soil moisture level becomes lower than the required level, the system can automatically switch on the water pump. When sufficient moisture is detected, the pump switches off. Farmers can also monitor field conditions through a mobile application or web dashboard. This system reduces manual work and helps farmers use water and other resources efficiently.","基于物联网的智慧农业系统利用安装在农田中的各类传感器采集土壤与环境状况数据。采集到的数据被传送至微控制器，如ESP32或配备合适通信模块的Arduino。当土壤湿度低于所需水平时，系统可自动开启水泵；当检测到湿度充足时，水泵则关闭。农民还可通过移动应用程序或网页仪表盘监测田间状况。该系统减少了人工劳作，帮助农民高效利用水资源及其他资源。","2026-10-21T00:00:00Z",49,{"impact":66,"substance":20,"depth":14,"authority":381,"freshness":14,"relevant":22,"comment":382},9,"论文介绍基于ESP32\u002FArduino的土壤湿度自动灌溉与远程监测方案，属常规技术实现，方法新颖性有限，但主题贴合智慧农业且时效性强，可作为一般资讯收录。",[384],{"name":109,"url":375},[27,28,386,387],"智能灌溉","土壤墒情",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":15,"version":47,"method":48,"evidence":391},{"candidates":392,"agricultural_evidence":54},[393,397,401,405],{"code":52,"score":53,"supported":54,"title":394,"abstract":395,"tags":396},[56],[27,56],[27],{"code":40,"score":53,"supported":54,"title":398,"abstract":399,"tags":400},[160],[158,160],[28],{"code":251,"score":334,"supported":54,"title":402,"abstract":403,"tags":404},[],[254,255],[387],{"code":258,"score":406,"supported":54,"title":407,"abstract":408,"tags":410},4,[],[409],"水资源",[386],[412,413],"ESP32 土壤湿度 自动灌溉","农业物联网 移动端监测","ESP32土壤湿度自动灌溉-5554","10.5281\u002Fzenodo.23531185",{"doi":415,"openalex_id":417,"authors":418,"venue":109,"cited_by_count":21,"oa_url":375,"card":421,"direction":97,"ingested_from":99},"W7223137846",[419],{"name":420,"orcid":9},"Prachi Khairnar",{"tldr":422,"method":423,"finding":424,"direction":97,"opportunity":425},"基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。","部署土壤与环境传感器，经ESP32\u002FArduino采集数据并联动水泵。","系统可自动启停水泵并通过手机或网页端远程监控，节水省工。","可引入多源传感融合与预测性灌溉决策，弥补仅靠阈值控制的局限。","2026-10-22T11:20:18.299106Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":109,"source_url":430,"published_at":433,"category":13,"cover_url":9,"hotness":111,"is_selected":15,"score":434,"score_detail":435,"sources":437,"tags":441,"primary_topic":444,"secondary_topics":448,"classification":450,"search_phrases":480,"slug":483,"view_count":21,"doi":484,"paper":485,"created_at":502},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",73,{"impact":19,"substance":312,"depth":148,"authority":115,"freshness":21,"relevant":22,"comment":436},"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[438,439],{"name":109,"url":430},{"name":109,"url":440},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,123,28,442,443],"区块链溯源","精准灌溉",{"code":258,"slug":262,"name":445,"path":446,"parent_code":447},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[449],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":451},{"candidates":452,"agricultural_evidence":54},[453,457,461,465,471,476],{"code":258,"score":53,"supported":54,"title":454,"abstract":455,"tags":456},[262],[261,262],[443],{"code":40,"score":14,"supported":54,"title":458,"abstract":459,"tags":460},[],[158,159,160],[28],{"code":128,"score":406,"supported":54,"title":462,"abstract":463,"tags":464},[],[140],[123],{"code":466,"score":176,"supported":54,"title":467,"abstract":468,"tags":470},"G2",[],[469],"小农户",[],{"code":472,"score":22,"supported":15,"title":473,"abstract":474,"tags":475},"E4",[],[],[442],{"code":52,"score":22,"supported":15,"title":477,"abstract":478,"tags":479},[],[],[27],[481,482],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":484,"openalex_id":486,"authors":487,"venue":109,"cited_by_count":21,"oa_url":430,"card":496,"direction":97,"ingested_from":99},"W7223034914",[488,491,493],{"name":489,"orcid":490},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":492,"orcid":9},"Dr.T.Baskar",{"name":494,"orcid":495},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":497,"method":498,"finding":499,"direction":500,"opportunity":501},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":504,"title":505,"url":506,"summary":507,"summary_zh":508,"content":9,"source_name":509,"source_url":506,"published_at":510,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":210,"score_detail":511,"sources":514,"tags":516,"primary_topic":519,"secondary_topics":520,"classification":522,"search_phrases":538,"slug":541,"view_count":21,"doi":542,"paper":543,"created_at":563},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",{"impact":512,"substance":312,"depth":18,"authority":20,"freshness":381,"relevant":22,"comment":513},16,"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[515],{"name":509,"url":506},[27,123,28,517,518],"大语言模型","传感器标准化",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[521],{"code":128,"slug":129,"name":130,"path":131,"parent_code":44},{"status":46,"locked":15,"version":47,"method":48,"evidence":523},{"candidates":524,"agricultural_evidence":54},[525,529,534],{"code":40,"score":18,"supported":54,"title":526,"abstract":527,"tags":528},[160],[158,159,160],[28],{"code":128,"score":512,"supported":54,"title":530,"abstract":531,"tags":533},[234],[532,234],"large language model",[123,517],{"code":52,"score":334,"supported":54,"title":535,"abstract":536,"tags":537},[],[27,56],[27],[539,540],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":542,"openalex_id":544,"authors":545,"venue":509,"cited_by_count":21,"oa_url":506,"card":558,"direction":97,"ingested_from":99},"W7222920424",[546,548,550,552,554,556],{"name":547,"orcid":9},"Leqi Lai",{"name":549,"orcid":9},"Langtao Duan",{"name":551,"orcid":9},"Yuhao Chen",{"name":553,"orcid":9},"Yuhang Zheng",{"name":555,"orcid":9},"Jinyu Yang",{"name":557,"orcid":9},"Mingyao Wen",{"tldr":559,"method":560,"finding":561,"direction":97,"opportunity":562},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z"]