[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4062":3,"related-4062":127},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":88,"slug":91,"view_count":22,"doi":92,"paper":93,"created_at":126},4062,"Development and Field Validation of a Solar-Powered Smart Buoy for Saltwater Intrusion Monitoring","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsolar6050067","Rising sea levels and seasonal decreases in freshwater discharge are accelerating the penetration of saline water into freshwater agricultural canals, aquaculture, and irrigation systems in low-lying coastal areas, making saltwater intrusion a growing threat. This paper reports on the design, development and field validation of a real-time, solar-powered smart buoy for continuous saltwater intrusion monitoring in agricultural canals. The proposed system comprises a multi-parameter electrical conductivity sensor, a water-level sensor based on a pressure transducer, an ESP32 microcontroller, an energy subsystem powered by photovoltaics, and a cloud-based Internet of Things (IoT) platform capable of automatically sending notifications to a smartphone when predefined salinity thresholds are exceeded. Field experiments were carried out at seven monitoring stations along the Khlong Bang Mot canal, Bangkok, Thailand to assess (1) sensor accuracy, (2) power autonomy, (3) flotation stability, (4) communication reliability and (5) spatial salinity distribution under representative operating conditions. The monitoring platform showed measurement accuracy within the specified ±2% full-scale range for the salinity levels studied, and temperature compensation was effective in minimizing conductivity drift across different environmental conditions. The photovoltaic power subsystem provided ~60 h of continuous autonomous operation per battery charge cycle, and the reinforced buoy structure allowed stable operation at canal wave heights of 5–20 cm with a maximum tilt angle of 20.7° and rapid equilibrium recovery. The field deployment successfully detected localized saltwater intrusion and provided smartphone alerts in real time within 10 s of threshold exceedance. The proposed smart buoy provides a reliable, low-cost, and field-deployable solution for ongoing surveillance of saltwater intrusion in freshwater agricultural canals. The integrated platform combines environmental sensing, autonomous energy management, wireless communication and real-time decision support to provide a practical approach to improve irrigation management, reduce salinity-related agricultural losses and enhance climate resilience in vulnerable farming communities.","海平面上升和淡水排放量的季节性减少正在加速盐水侵入低洼沿海地区的淡水农业运河、水产养殖和灌溉系统，使盐水入侵成为日益严重的威胁。本文报道了一种用于农业运河盐水入侵连续监测的实时太阳能智能浮标的设计、开发和现场验证。所提出的系统包括多参数电导率传感器、基于压力传感器的水位传感器、ESP32微控制器、由光伏供电的能量子系统，以及一个基于云的物联网（IoT）平台，该平台能够在超过预设盐度阈值时自动向智能手机发送通知。在泰国曼谷Khlong Bang Mot运河沿线的七个监测站进行了现场实验，以评估（1）传感器精度，（2）电力自主性，（3）漂浮稳定性，（4）通信可靠性，以及（5）代表性运行条件下的空间盐度分布。该监测平台在所研究的盐度水平下表现出在规定的±2%满量程范围内的测量精度，并且温度补偿有效减少了不同环境条件下的电导率漂移。光伏发电子系统在每个电池充电循环中提供约60小时的连续自主运行，加固的浮标结构使其能够在5–20 cm的运河波高下稳定运行，最大倾斜角为20.7°，并能快速恢复平衡。现场部署成功检测到局部盐水入侵，并在超过阈值后10秒内通过智能手机实时发出警报。所提出的智能浮标为淡水农业运河中盐水入侵的持续监测提供了一种可靠、低成本且可现场部署的解决方案。该集成平台结合了环境传感、自主能量管理、无线通信和实时决策支持，为提高灌溉管理、减少与盐度相关的农业损失以及增强脆弱农业社区的气候韧性提供了一种实用方法。",null,"Solar","2026-10-02T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,21,17,13,8,1,"低成本太阳能IoT浮标实现农业渠道咸潮入侵实时监测与手机预警，方法扎实、现场验证充分，对沿海灌区管理有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","气候韧性","灌溉管理","盐渍化监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,53,61,69,76,83],{"code":33,"score":13,"supported":46,"title":47,"abstract":48,"tags":52},true,[],[49,50,51],"物联网","internet of things","IoT",[28],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":60},"H3",7,[],[58,59],"灌溉","irrigation",[30],{"code":62,"score":63,"supported":46,"title":64,"abstract":65,"tags":68},"D",6,[],[66,67],"水产","aquaculture",[],{"code":70,"score":71,"supported":46,"title":72,"abstract":73,"tags":75},"H7",4,[],[74],"climate resilience",[29],{"code":77,"score":78,"supported":46,"title":79,"abstract":80,"tags":82},"I2",3,[],[81],"decision support",[],{"code":84,"score":22,"supported":14,"title":85,"abstract":86,"tags":87},"I1",[],[],[27],[89,90],"太阳能智能浮标 咸潮入侵 监测","Khlong Bang Mot 运河 盐度监测","太阳能智能浮标咸潮入侵监测-4062","10.3390\u002Fsolar6050067",{"doi":92,"openalex_id":94,"authors":95,"venue":10,"cited_by_count":118,"oa_url":6,"card":119,"direction":123,"ingested_from":125},"W7216324832",[96,99,101,104,107,110,112,115],{"name":97,"orcid":98},"Wirote Ritthong","https:\u002F\u002Forcid.org\u002F0009-0007-0005-3750",{"name":100,"orcid":9},"Kittigorn Chalernphon",{"name":102,"orcid":103},"Nutthapong Kunla","https:\u002F\u002Forcid.org\u002F0000-0003-1177-6886",{"name":105,"orcid":106},"Pongthep Poungthong","https:\u002F\u002Forcid.org\u002F0000-0002-5278-5984",{"name":108,"orcid":109},"Naphat Albutt","https:\u002F\u002Forcid.org\u002F0009-0004-5806-7571",{"name":111,"orcid":9},"Akom Boonpunya",{"name":113,"orcid":114},"Mohammad Shabaz","https:\u002F\u002Forcid.org\u002F0000-0001-5106-7609",{"name":116,"orcid":117},"Khongdet Phasinam","https:\u002F\u002Forcid.org\u002F0000-0002-5795-9779",0,{"tldr":120,"method":121,"finding":122,"direction":123,"opportunity":124},"开发并田间验证太阳能智能浮标，实时监测农业运河盐水入侵并手机报警。","ESP32、电导率与压力水位传感器、光伏供电、IoT云平台，泰国7站测试。","盐度精度±2%，光伏续航约60小时，超阈值10秒内手机报警，可检测局部入侵。","智慧农业 \u002F 农业物联网","可扩展多参数融合与盐度预测模型，结合闸门调度实现盐水入侵主动防控。","openalex","2026-10-03T23:30:10.242752Z",{"total":63,"page":22,"page_size":63,"items":128},[129,190,293,367,428,519],{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":135,"source_url":132,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":142,"tags":144,"primary_topic":147,"secondary_topics":148,"classification":149,"search_phrases":174,"slug":177,"view_count":118,"doi":178,"paper":179,"created_at":189},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",49,{"impact":21,"substance":139,"depth":13,"authority":140,"freshness":13,"relevant":22,"comment":141},12,9,"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。",[143],{"name":135,"url":132},[27,28,145,146],"智能灌溉","土壤墒情",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":150},{"candidates":151,"agricultural_evidence":46},[152,158,162,169],{"code":84,"score":153,"supported":46,"title":154,"abstract":156,"tags":157},15,[155],"smart agriculture",[27,155],[27],{"code":33,"score":153,"supported":46,"title":159,"abstract":160,"tags":161},[51],[49,51],[28],{"code":163,"score":55,"supported":46,"title":164,"abstract":165,"tags":168},"H2",[],[166,167],"土壤","soil",[146],{"code":54,"score":71,"supported":46,"title":170,"abstract":171,"tags":173},[],[172],"水资源",[145],[175,176],"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":178,"openalex_id":180,"authors":181,"venue":135,"cited_by_count":118,"oa_url":132,"card":184,"direction":123,"ingested_from":125},"W7223137846",[182],{"name":183,"orcid":9},"Prachi Khairnar",{"tldr":185,"method":186,"finding":187,"direction":123,"opportunity":188},"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":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":210,"classification":216,"search_phrases":268,"slug":271,"view_count":118,"doi":272,"paper":273,"created_at":292},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":17,"substance":199,"depth":17,"authority":20,"freshness":140,"relevant":22,"comment":200},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[202],{"name":196,"url":193},[27,204,49,205,29],"农业人工智能","精准农业",{"code":77,"slug":207,"name":208,"path":209,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[211,212],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":54,"slug":59,"name":213,"path":214,"parent_code":215},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":217},{"candidates":218,"agricultural_evidence":46},[219,230,236,242,246,252,256,262],{"code":77,"score":17,"supported":46,"title":220,"abstract":222,"tags":229},[221],"artificial intelligence",[223,224,225,226,227,228],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[204],{"code":84,"score":231,"supported":46,"title":232,"abstract":234,"tags":235},16,[233],"precision agriculture",[205,233],[27,205],{"code":33,"score":13,"supported":46,"title":237,"abstract":238,"tags":241},[],[49,239,51,240],"边缘计算","edge computing",[49],{"code":54,"score":140,"supported":46,"title":243,"abstract":244,"tags":245},[],[58,172,59],[],{"code":70,"score":55,"supported":46,"title":247,"abstract":248,"tags":251},[],[249,250],"气候变化","climate change",[29],{"code":163,"score":63,"supported":46,"title":253,"abstract":254,"tags":255},[],[166,167],[],{"code":257,"score":78,"supported":46,"title":258,"abstract":259,"tags":261},"A7",[],[260],"间作",[],{"code":263,"score":78,"supported":46,"title":264,"abstract":265,"tags":267},"F3",[],[266],"养分管理",[],[269,270],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":272,"openalex_id":274,"authors":275,"venue":196,"cited_by_count":118,"oa_url":193,"card":286,"direction":123,"ingested_from":125},"W7223121456",[276,279,281,283],{"name":277,"orcid":278},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":280,"orcid":9},"Oluwadare OYEBODE",{"name":282,"orcid":9},"Iretiayo Adelaiye",{"name":284,"orcid":285},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":287,"method":288,"finding":289,"direction":290,"opportunity":291},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":135,"source_url":296,"published_at":299,"category":12,"cover_url":9,"hotness":300,"is_selected":14,"score":301,"score_detail":302,"sources":304,"tags":308,"primary_topic":311,"secondary_topics":312,"classification":314,"search_phrases":344,"slug":347,"view_count":118,"doi":348,"paper":349,"created_at":366},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",25,73,{"impact":199,"substance":18,"depth":19,"authority":20,"freshness":118,"relevant":22,"comment":303},"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。",[305,306],{"name":135,"url":296},{"name":135,"url":307},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,204,28,309,310],"区块链溯源","精准灌溉",{"code":54,"slug":59,"name":213,"path":214,"parent_code":215},[313],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":315},{"candidates":316,"agricultural_evidence":46},[317,321,325,329,335,340],{"code":54,"score":153,"supported":46,"title":318,"abstract":319,"tags":320},[59],[58,59],[310],{"code":33,"score":13,"supported":46,"title":322,"abstract":323,"tags":324},[],[49,50,51],[28],{"code":77,"score":71,"supported":46,"title":326,"abstract":327,"tags":328},[],[221],[204],{"code":330,"score":78,"supported":46,"title":331,"abstract":332,"tags":334},"G2",[],[333],"小农户",[],{"code":336,"score":22,"supported":14,"title":337,"abstract":338,"tags":339},"E4",[],[],[309],{"code":84,"score":22,"supported":14,"title":341,"abstract":342,"tags":343},[],[],[27],[345,346],"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":348,"openalex_id":350,"authors":351,"venue":135,"cited_by_count":118,"oa_url":296,"card":360,"direction":123,"ingested_from":125},"W7223034914",[352,355,357],{"name":353,"orcid":354},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":356,"orcid":9},"Dr.T.Baskar",{"name":358,"orcid":359},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":361,"method":362,"finding":363,"direction":364,"opportunity":365},"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":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":9,"source_name":373,"source_url":370,"published_at":374,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":378,"tags":380,"primary_topic":383,"secondary_topics":384,"classification":386,"search_phrases":402,"slug":405,"view_count":118,"doi":406,"paper":407,"created_at":427},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",76,{"impact":231,"substance":18,"depth":17,"authority":139,"freshness":140,"relevant":22,"comment":377},"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。",[379],{"name":373,"url":370},[27,204,28,381,382],"大语言模型","传感器标准化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[385],{"code":77,"slug":207,"name":208,"path":209,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":387},{"candidates":388,"agricultural_evidence":46},[389,393,398],{"code":33,"score":17,"supported":46,"title":390,"abstract":391,"tags":392},[51],[49,50,51],[28],{"code":77,"score":231,"supported":46,"title":394,"abstract":395,"tags":397},[81],[396,81],"large language model",[204,381],{"code":84,"score":55,"supported":46,"title":399,"abstract":400,"tags":401},[],[27,155],[27],[403,404],"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":406,"openalex_id":408,"authors":409,"venue":373,"cited_by_count":118,"oa_url":370,"card":422,"direction":123,"ingested_from":125},"W7222920424",[410,412,414,416,418,420],{"name":411,"orcid":9},"Leqi Lai",{"name":413,"orcid":9},"Langtao Duan",{"name":415,"orcid":9},"Yuhao Chen",{"name":417,"orcid":9},"Yuhang Zheng",{"name":419,"orcid":9},"Jinyu Yang",{"name":421,"orcid":9},"Mingyao Wen",{"tldr":423,"method":424,"finding":425,"direction":123,"opportunity":426},"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",{"id":429,"title":430,"url":431,"summary":432,"summary_zh":433,"content":9,"source_name":434,"source_url":431,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":439,"tags":441,"primary_topic":444,"secondary_topics":450,"classification":457,"search_phrases":496,"slug":499,"view_count":118,"doi":9,"paper":500,"created_at":518},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":139,"substance":18,"depth":19,"authority":140,"freshness":21,"relevant":22,"comment":438},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[440],{"name":434,"url":431},[27,204,28,442,443],"温室环境","小气候预测",{"code":445,"slug":446,"name":447,"path":448,"parent_code":449},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[451,452],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":453,"slug":454,"name":455,"path":456,"parent_code":449},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":40,"locked":14,"version":41,"method":42,"evidence":458},{"candidates":459,"agricultural_evidence":46},[460,464,471,480,484,488,492],{"code":33,"score":153,"supported":46,"title":461,"abstract":462,"tags":463},[51],[49,51],[28],{"code":445,"score":465,"supported":46,"title":466,"abstract":468,"tags":470},14,[467],"greenhouse",[469,467],"温室",[],{"code":453,"score":140,"supported":46,"title":472,"abstract":473,"tags":479},[],[474,475,476,477,478],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":84,"score":55,"supported":46,"title":481,"abstract":482,"tags":483},[],[205,233],[27],{"code":77,"score":55,"supported":46,"title":485,"abstract":486,"tags":487},[],[223,227],[204],{"code":163,"score":63,"supported":46,"title":489,"abstract":490,"tags":491},[],[166,167],[],{"code":54,"score":63,"supported":46,"title":493,"abstract":494,"tags":495},[],[58,59],[],[497,498],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":501,"authors":502,"venue":434,"cited_by_count":118,"oa_url":9,"card":513,"direction":123,"ingested_from":125},"W7222909837",[503,505,507,509,511],{"name":504,"orcid":9},"Siman Emmanuel*",{"name":506,"orcid":9},"Edward Onyebueke Agu",{"name":508,"orcid":9},"Musa Gani",{"name":510,"orcid":9},"Oladunjoye John Abiodun",{"name":512,"orcid":9},"Anagu Emmanuel John",{"tldr":514,"method":515,"finding":516,"direction":123,"opportunity":517},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":520,"title":521,"url":522,"summary":523,"summary_zh":524,"content":9,"source_name":525,"source_url":526,"published_at":527,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":528,"score_detail":529,"sources":531,"tags":533,"primary_topic":536,"secondary_topics":537,"classification":538,"search_phrases":569,"slug":572,"view_count":78,"doi":573,"paper":574,"created_at":585},5085,"AgriSense: IoT-Driven Precision Agriculture for National Food Sustainability and Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23335305","Precision agriculture integrates digital technologies to improve agricultural resource management, productivity, and sustainability, while the Internet of Things (IoT) enables real-time monitoring of soil and environmental conditions through connected sensors. In Nigeria, inefficient water and fertilizer use, declining soil health, climate variability, and limited access to timely agricultural information constrain sustainable food production. This study presents AgriSense, an IoT-driven precision agriculture platform designed to support data-informed farm management under Nigerian agricultural conditions. The study adopts a mixed-method research framework incorporating descriptive, experimental, and analytical approaches, with proposed data sources including IoT sensor measurements, farmer surveys and interviews, field observations, and relevant weather information. The AgriSense platform integrates soil-moisture, nitrogen-phosphorus-potassium (N-P-K), soil-pH, temperature, and humidity monitoring with farm and field management, sensor-status tracking, automated alerts, agronomic recommendations, and reporting functions. The demonstrated interface represents agricultural fields and crops across Asaba Demonstration Farm, Jos Highland Farm, and Sokoto Smart Farm. Its threshold-based alerts identify conditions associated with low soil moisture, low nitrogen, acidic soil, high temperature, and sensor connectivity faults, linking selected conditions to recommended management actions. The available demonstration establishes the platform’s integrated monitoring and decision-support functionality. However, because the interface identifies its environment as a live simulation, the displayed information does not independently establish improvements in crop yield, input-use efficiency, soil health, or farmer income. The study provides a technological foundation for subsequent field validation of IoT-enabled precision agriculture and its potential contribution to sustainable food security in Nigeria.","精准农业（precision agriculture）融合数字技术，以改善农业资源管理、生产力和可持续性，而物联网（Internet of Things, IoT）则通过互联传感器实现对土壤和环境条件的实时监测。在尼日利亚，水资源和肥料利用效率低下、土壤健康状况下降、气候多变以及难以及时获取农业信息等因素制约了可持续粮食生产。本研究提出AgriSense——一个由物联网驱动的精准农业平台，旨在支持尼日利亚农业条件下的数据驱动型农场管理。本研究采用混合方法研究框架，融合描述性、实验性和分析性方法，拟采用的数据来源包括物联网传感器测量数据、农民问卷调查与访谈、田间观察以及相关天气信息。AgriSense平台将土壤湿度、氮磷钾（N-P-K）、土壤pH值、温度和湿度监测与农场及田块管理、传感器状态追踪、自动警报、农艺建议和报告功能集成在一起。所展示的界面呈现了阿萨巴示范农场（Asaba Demonstration Farm）、乔斯高原农场（Jos Highland Farm）和索科托智慧农场（Sokoto Smart Farm）的农田和作物。其基于阈值的警报可识别与土壤湿度低、氮含量低、土壤酸性、高温和传感器连接故障相关的情况，并将选定情况与建议的管理措施相关联。现有演示验证了该平台的集成监测与决策支持功能。然而，由于该界面将其环境标识为实时模拟，所显示的信息并不能独立证明作物产量、投入品使用效率、土壤健康或农民收入方面的改善。本研究为后续对物联网赋能精准农业及其对尼日利亚可持续粮食安全潜在贡献的田间验证提供了技术基础。","Tech-Sphere Journal for Pure and Applied Sciences","https:\u002F\u002Fstem.techspherejournals.com\u002Findex.php\u002Ftsjpas\u002Farticle\u002Fview\u002F80","2026-10-13T00:00:00Z",56,{"impact":21,"substance":231,"depth":465,"authority":140,"freshness":140,"relevant":22,"comment":530},"论文提出面向尼日利亚的IoT精准农业平台AgriSense，集成土壤水分、N-P-K、pH等监测与阈值预警，但界面为模拟环境，尚未验证增产增效，属技术原型阶段，参考价值有限。",[532],{"name":525,"url":526},[27,28,534,205,535],"粮食安全","土壤墒情监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":539},{"candidates":540,"agricultural_evidence":46},[541,545,549,555,561,565],{"code":33,"score":17,"supported":46,"title":542,"abstract":543,"tags":544},[51],[49,50,51],[28],{"code":84,"score":231,"supported":46,"title":546,"abstract":547,"tags":548},[233],[205,233],[27,205],{"code":550,"score":55,"supported":46,"title":551,"abstract":552,"tags":554},"A8",[],[534,553],"food security",[534],{"code":263,"score":63,"supported":46,"title":556,"abstract":557,"tags":560},[],[558,559],"肥料","fertilizer",[],{"code":163,"score":63,"supported":46,"title":562,"abstract":563,"tags":564},[],[166,167],[],{"code":54,"score":78,"supported":46,"title":566,"abstract":567,"tags":568},[],[172],[],[570,571],"AgriSense 物联网 精准农业","尼日利亚 智慧农业 平台","AgriSense物联网精准农业-5085","10.5281\u002Fzenodo.23335305",{"doi":573,"openalex_id":575,"authors":576,"venue":525,"cited_by_count":118,"oa_url":526,"card":580,"direction":123,"ingested_from":125},"W7221676154",[577],{"name":578,"orcid":579},"Innocent Uzougbo Onwuegbuzie","https:\u002F\u002Forcid.org\u002F0000-0002-1582-1739",{"tldr":581,"method":582,"finding":583,"direction":123,"opportunity":584},"提出AgriSense物联网精准农业平台，用于尼日利亚农田监测与决策支持。","混合方法框架，集成土壤水分、N-P-K、pH、温湿度传感器与阈值告警。","平台实现监测与决策支持功能，但界面为模拟，尚未验证产量与效益提升。","可开展真实田间验证，量化IoT精准农业对产量、投入效率和土壤健康的影响。","2026-10-16T23:30:14.088827Z"]