[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-975":3,"related-975":90},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":36,"secondary_topics":42,"classification":43,"search_phrases":84,"slug":87,"view_count":88,"doi":8,"paper":8,"created_at":89},975,"农业法修订草案二审稿8月25日提请全国人大常委会审议 拟增加鼓励物联网、人工智能等新技术与农业生产经营管理相融合条款","http:\u002F\u002Fwww.npc.gov.cn\u002Fnpc\u002Fc2\u002Fc30834\u002F202608\u002Ft20260825_456878.html","新华社北京8月25日电：农业法修订草案二审稿25日提请十四届全国人大常委会第二十四次会议审议。草案二审稿完善了支持农业和农村经济发展的措施，增加统筹发展科技农业、绿色农业、质量农业、品牌农业，维护种业安全，提升种业现代化水平，提高农业保险保障水平和服务质量，鼓励『物联网、人工智能』等新技术与农业生产、经营、管理相融合等规定。同时加强乡村建设和治理，推进平安乡村建设。草案落实整治形式主义为基层减负要求，并规定国家支持开展农业领域国际交流合作。全国政协委员、中国社会科学院农村发展研究所杜志雄研究员建议在总则部分增加农业安全协同保障制度的规定。",null,"新华社北京8月25日电（记者胡璐、韩佳诺）农业法修订草案二审稿25日提请十四届全国人大常委会第二十四次会议审议。草案二审稿拟完善支持农业和农村经济发展措施。\n\n农业法修订草案今年4月首次提请全国人大常委会会议审议，会后征求社会公众意见。经征求多方意见建议，形成了此次提请审议的草案二审稿。\n\n有的常委会组成人员和地方、部门建议，完善支持农业和农村经济发展的措施。对此，草案二审稿增加规定，包括统筹发展科技农业、绿色农业、质量农业、品牌农业；维护种业安全，提升种业现代化水平；提高农业保险保障水平和服务质量；鼓励物联网、人工智能等新技术与农业生产、经营、管理相融合。\n\n加强乡村建设和治理，对农业和农村经济发展至关重要。草案二审稿增加规定，国家加强乡村治理，推进平安乡村建设，构建和谐有序的乡村社会，不断增强广大农民的获得感、幸福感、安全感。\n\n为落实整治形式主义为基层减负的要求，草案二审稿增加各级人民政府应当建立健全整治形式主义为基层减负长效机制的规定。此外，草案二审稿还规定国家坚持平等互利、合作共赢的方针，支持开展农业领域的国际交流与合作，积极参与农业可持续发展与粮食安全国际治理。","中国人大网 2026-08-25 · 2026-08-25","2026-08-25T00:00:00Z","政策",10,true,92,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},28,24,17,15,8,1,"全国性农业法修订草案，明确鼓励物联网、人工智能与农业融合，政策导向性强，信息权威且时效性佳。",[25],{"name":10,"url":6},[27,28,29,30,31,32,33,34,35],"智慧农业","物联网","人工智能","农业法修订草案","二审","农业","十四届人大常委会","种业安全","法治",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":44,"locked":45,"version":46,"method":47,"evidence":48},"classified",false,"2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":14},[50,55,62,68,74,79],{"code":37,"score":51,"supported":14,"title":52,"abstract":53,"tags":54},12,[28],[28],[28],{"code":56,"score":57,"supported":14,"title":58,"abstract":59,"tags":61},"F1",3,[],[60],"种业",[],{"code":63,"score":57,"supported":14,"title":64,"abstract":65,"tags":67},"G",[],[66],"农村发展",[],{"code":69,"score":57,"supported":14,"title":70,"abstract":71,"tags":73},"G3",[],[72],"农业保险",[],{"code":75,"score":22,"supported":45,"title":76,"abstract":77,"tags":78},"I1",[],[],[27],{"code":80,"score":22,"supported":45,"title":81,"abstract":82,"tags":83},"I2",[],[],[29],[85,86],"十四届人大常委会 农业法修订草案 人工智能 智慧农业","十四届人大常委会 农业法修订草案","十四届人大常委会农业法修订草案人工智能智慧农业-975",7,"2026-08-27T00:05:17.098088Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,214,278,337,403,468],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":8,"source_name":99,"source_url":96,"published_at":100,"category":101,"cover_url":8,"hotness":13,"is_selected":45,"score":102,"score_detail":103,"sources":109,"tags":111,"primary_topic":115,"secondary_topics":119,"classification":127,"search_phrases":186,"slug":189,"view_count":190,"doi":191,"paper":192,"created_at":213},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)","2026-10-21T00:00:00Z","论文",80,{"impact":104,"substance":105,"depth":104,"authority":106,"freshness":107,"relevant":22,"comment":108},18,22,13,9,"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。",[110],{"name":99,"url":96},[27,112,28,113,114],"农业人工智能","精准农业","气候韧性",{"code":80,"slug":116,"name":117,"path":118,"parent_code":41},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[120,121],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":44,"locked":45,"version":46,"method":47,"evidence":128},{"candidates":129,"agricultural_evidence":14},[130,141,147,154,160,167,174,180],{"code":80,"score":104,"supported":14,"title":131,"abstract":133,"tags":140},[132],"artificial intelligence",[134,135,136,137,138,139],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[112],{"code":75,"score":142,"supported":14,"title":143,"abstract":145,"tags":146},16,[144],"precision agriculture",[113,144],[27,113],{"code":37,"score":13,"supported":14,"title":148,"abstract":149,"tags":153},[],[28,150,151,152],"边缘计算","IoT","edge computing",[28],{"code":122,"score":107,"supported":14,"title":155,"abstract":156,"tags":159},[],[157,158,123],"灌溉","水资源",[],{"code":161,"score":88,"supported":14,"title":162,"abstract":163,"tags":166},"H7",[],[164,165],"气候变化","climate change",[114],{"code":168,"score":91,"supported":14,"title":169,"abstract":170,"tags":173},"H2",[],[171,172],"土壤","soil",[],{"code":175,"score":57,"supported":14,"title":176,"abstract":177,"tags":179},"A7",[],[178],"间作",[],{"code":181,"score":57,"supported":14,"title":182,"abstract":183,"tags":185},"F3",[],[184],"养分管理",[],[187,188],"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",0,"10.53982\u002Fajerd.2026.01.14-j",{"doi":191,"openalex_id":193,"authors":194,"venue":99,"cited_by_count":190,"oa_url":96,"card":205,"direction":211,"ingested_from":212},"W7223121456",[195,198,200,202],{"name":196,"orcid":197},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":199,"orcid":8},"Oluwadare OYEBODE",{"name":201,"orcid":8},"Iretiayo Adelaiye",{"name":203,"orcid":204},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":206,"method":207,"finding":208,"direction":209,"opportunity":210},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.151376Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":8,"source_name":220,"source_url":217,"published_at":221,"category":101,"cover_url":8,"hotness":13,"is_selected":45,"score":222,"score_detail":223,"sources":225,"tags":227,"primary_topic":230,"secondary_topics":231,"classification":233,"search_phrases":254,"slug":257,"view_count":190,"doi":258,"paper":259,"created_at":277},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。","Electronics","2026-10-19T00:00:00Z",67,{"impact":51,"substance":104,"depth":142,"authority":106,"freshness":21,"relevant":22,"comment":224},"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[226],{"name":220,"url":217},[27,28,113,228,229],"节水灌溉","LoRa",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},[232],{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},{"status":44,"locked":45,"version":46,"method":47,"evidence":234},{"candidates":235,"agricultural_evidence":14},[236,241,245,249],{"code":37,"score":104,"supported":14,"title":237,"abstract":238,"tags":240},[151],[28,239,151],"internet of things",[28],{"code":122,"score":20,"supported":14,"title":242,"abstract":243,"tags":244},[123],[157,123],[228],{"code":168,"score":91,"supported":14,"title":246,"abstract":247,"tags":248},[],[171,172],[],{"code":75,"score":250,"supported":45,"title":251,"abstract":252,"tags":253},2,[],[],[27,113],[255,256],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":258,"openalex_id":260,"authors":261,"venue":220,"cited_by_count":190,"oa_url":217,"card":272,"direction":211,"ingested_from":212},"W7222959710",[262,264,266,269],{"name":263,"orcid":8},"Hanfoug Salah",{"name":265,"orcid":8},"Riad Saidi",{"name":267,"orcid":268},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":270,"orcid":271},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":273,"method":274,"finding":275,"direction":211,"opportunity":276},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":8,"source_name":284,"source_url":281,"published_at":285,"category":101,"cover_url":8,"hotness":13,"is_selected":45,"score":286,"score_detail":287,"sources":290,"tags":292,"primary_topic":296,"secondary_topics":297,"classification":298,"search_phrases":317,"slug":320,"view_count":190,"doi":321,"paper":322,"created_at":336},5309,"A Multi-Layer Contextual Model for Managing Data Overload in Smart Hydroponics","https:\u002F\u002Fdoi.org\u002F10.32890\u002Fjict2026.25.4.4","The widespread use of the Internet of Things (IoT) in hydroponic agriculture allows continuous monitoring of environmental conditions and provides real-time information to support crop management. However, the increasing amount of sensor data generated makes the monitoring process more complex as users need to interpret multiple environmental parameters simultaneously. Existing hydroponic monitoring systems focus on data collection and visualization, while the management and information delivery aspects to reduce data overload are still limited. This study proposes a multi-layer contextual model to manage environmental information in an intelligent hydroponic monitoring system. The model consists of five interconnected layers (Power, Sensor, Communication, Database and Visualization), which support the process of acquisition, validation, management and contextual data delivery. To validate the proposed model, a solar-powered IoT hydroponic monitoring system integrated with an Augmented Reality (AR) application was developed based on Situation Awareness Theory. Using the System Usability Scale (SUS), the prototype was assessed by three domain experts. The results achieved an average usability score of 85.83, which reflects excellent usability. The results indicate that the proposed model improves organisation and presentation of environmental information by prioritising contextually relevant data rather than displaying all available sensor readings at once. This enables users to identify important environmental conditions efficiently and supports more effective monitoring. This approach helps to reduce data overload and provides a practical framework that can support the development of IoT-based smart agriculture applications in the future through the integration of anomaly detection techniques and predictive analytics.","物联网（IoT）在水培农业中的广泛使用使得环境条件的持续监测成为可能，并提供实时信息以支持作物管理。然而，传感器数据量的不断增加使监测过程更加复杂，因为用户需要同时解读多个环境参数。现有的水培监测系统侧重于数据采集与可视化，而在减少数据过载的管理与信息传递方面仍然有限。本研究提出一种多层上下文模型，用于管理智能水培监测系统中的环境信息。该模型由五个相互关联的层级（供电、传感器、通信、数据库和可视化）组成，支持数据的采集、验证、管理和上下文传递过程。为验证所提出的模型，基于态势感知理论开发了一套集成了增强现实（AR）应用的太阳能物联网水培监测系统。采用系统可用性量表（SUS）由三位领域专家对原型进行评估，结果获得平均可用性得分85.83，反映出优异的可用性。结果表明，所提出的模型通过优先呈现上下文相关的数据而非一次性显示所有可用的传感器读数，改善了环境信息的组织与呈现方式。这使用户能够高效识别重要的环境条件，并支持更有效的监测。该方法有助于减少数据过载，并提供了一个实用框架，未来可通过集成异常检测技术和预测分析来支持基于物联网的智慧农业应用的开发。","Journal of Information and Communication Technology","2026-10-18T00:00:00Z",70,{"impact":51,"substance":288,"depth":142,"authority":106,"freshness":107,"relevant":22,"comment":289},20,"提出五层情境化模型，并用太阳能物联网与AR原型验证，SUS 85.83，方法新颖、结论可靠，但属单点原型研究，产业影响有限。",[291],{"name":284,"url":281},[27,28,293,294,295],"水培","增强现实","数据过载",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},[],{"status":44,"locked":45,"version":46,"method":47,"evidence":299},{"candidates":300,"agricultural_evidence":14},[301,305,310],{"code":37,"score":13,"supported":14,"title":302,"abstract":303,"tags":304},[],[28,239,151],[28],{"code":75,"score":88,"supported":14,"title":306,"abstract":307,"tags":309},[],[27,308],"smart agriculture",[27],{"code":311,"score":312,"supported":14,"title":313,"abstract":314,"tags":316},"B7",4,[],[315],"hydroponic",[293],[318,319],"智能水培 物联网 监测系统","增强现实 水培 数据过载","智能水培物联网监测系统-5309","10.32890\u002Fjict2026.25.4.4",{"doi":321,"openalex_id":323,"authors":324,"venue":284,"cited_by_count":190,"oa_url":281,"card":331,"direction":211,"ingested_from":212},"W7222865525",[325,328],{"name":326,"orcid":327},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":329,"orcid":330},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":332,"method":333,"finding":334,"direction":211,"opportunity":335},"提出五层情境模型管理水培IoT环境数据，结合AR应用减少数据过载。","五层情境模型+太阳能IoT水培监测系统+AR应用，SUS量表由3位专家评估。","原型SUS均分85.83，情境化优先展示关键数据可有效缓解数据过载。","可延伸至异常检测与预测分析融合，并开展多用户大规模实证验证。","2026-10-19T23:30:07.917393Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":8,"source_name":343,"source_url":340,"published_at":344,"category":101,"cover_url":8,"hotness":13,"is_selected":45,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":353,"secondary_topics":358,"classification":360,"search_phrases":378,"slug":381,"view_count":190,"doi":382,"paper":383,"created_at":402},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,{"impact":21,"substance":104,"depth":142,"authority":91,"freshness":21,"relevant":22,"comment":347},"论文将Laravel与Android集成用于水培温室多参数监测，方法具体、数据连续24小时完整，但属单点应用研究，公共影响有限，适合作为技术案例而非每日精选头条。",[349],{"name":343,"url":340},[27,28,113,351,352],"温室大棚","水培蔬菜",{"code":311,"slug":354,"name":355,"path":356,"parent_code":357},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[359],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":44,"locked":45,"version":46,"method":47,"evidence":361},{"candidates":362,"agricultural_evidence":14},[363,367,374],{"code":37,"score":104,"supported":14,"title":364,"abstract":365,"tags":366},[151],[28,239,151],[28],{"code":311,"score":368,"supported":14,"title":369,"abstract":371,"tags":373},14,[370],"greenhouse",[372,370],"温室",[],{"code":75,"score":21,"supported":14,"title":375,"abstract":376,"tags":377},[],[113,144],[27,113],[379,380],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":382,"openalex_id":384,"authors":385,"venue":343,"cited_by_count":190,"oa_url":396,"card":397,"direction":211,"ingested_from":212},"W7222850582",[386,388,391,394],{"name":387,"orcid":8},"Ramadanzah Ramadanzah",{"name":389,"orcid":390},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":392,"orcid":393},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":395,"orcid":8},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":398,"method":399,"finding":400,"direction":211,"opportunity":401},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":8,"source_name":409,"source_url":406,"published_at":410,"category":101,"cover_url":8,"hotness":13,"is_selected":45,"score":411,"score_detail":412,"sources":415,"tags":417,"primary_topic":420,"secondary_topics":421,"classification":422,"search_phrases":452,"slug":455,"view_count":190,"doi":456,"paper":457,"created_at":467},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",75,{"impact":142,"substance":413,"depth":19,"authority":51,"freshness":107,"relevant":22,"comment":414},21,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[416],{"name":409,"url":406},[27,112,28,418,419],"作物胁迫","遥感监测",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},[],{"status":44,"locked":45,"version":46,"method":47,"evidence":423},{"candidates":424,"agricultural_evidence":14},[425,429,436,440,444,448],{"code":37,"score":104,"supported":14,"title":426,"abstract":427,"tags":428},[151],[28,239,151],[28],{"code":430,"score":88,"supported":14,"title":431,"abstract":432,"tags":435},"I3",[],[433,434],"遥感","remote sensing",[419],{"code":168,"score":91,"supported":14,"title":437,"abstract":438,"tags":439},[],[171,172],[],{"code":122,"score":91,"supported":14,"title":441,"abstract":442,"tags":443},[],[157,123],[],{"code":75,"score":22,"supported":45,"title":445,"abstract":446,"tags":447},[],[],[27],{"code":80,"score":22,"supported":45,"title":449,"abstract":450,"tags":451},[],[],[112],[453,454],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":456,"openalex_id":458,"authors":459,"venue":409,"cited_by_count":190,"oa_url":406,"card":462,"direction":211,"ingested_from":212},"W7222730658",[460],{"name":461,"orcid":8},"Sunitha Thankappan",{"tldr":463,"method":464,"finding":465,"direction":211,"opportunity":466},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":8,"source_name":474,"source_url":471,"published_at":475,"category":101,"cover_url":8,"hotness":13,"is_selected":45,"score":286,"score_detail":476,"sources":478,"tags":480,"primary_topic":483,"secondary_topics":484,"classification":485,"search_phrases":519,"slug":522,"view_count":190,"doi":8,"paper":523,"created_at":540},4890,"ระบบพยากรณและเฝาระวงคณภาพนำอจฉรยะตามดชนคณภาพนำ ดวยเทคนคการเรยนรของเครองและอนเทอรเนตของสรรพสง","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FJSSE\u002Farticle\u002Fview\u002F289948","This research utilizes Internet of Things (IoT) technology and Machine Learning (ML) to develop a prototype for a smart water quality forecasting and monitoring system. The study focuses on analyzing the Reduced Water Quality Index (rWQI) based on water temperature, pH, and Total Dissolved Solids (TDS). To forecast water quality 1 to 6 hours in advance, the performance of four predictive models was evaluated and compared: Extremely Randomized Trees (ET), Random Forest (RF), Simple Linear Regression (LR), and Multiple Linear Regression (MLR). The efficiency of these algorithms was validated using four key evaluation metrics: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE). The experimental results indicated that the predictive performance of the models varied significantly depending on the forecasting horizon. In the short-term horizon (1–3 hours ahead), the RF model achieved the highest performance, with an R² of 0.566 and an MAE of 5.215 in the first hour. Conversely, for long-term forecasting (4–6 hours ahead), all models exhibited a significant drop in performance (R² \u003C 0), yielding higher prediction errors than the Mean Baseline. This long-term limitation stems from high water quality volatility in real-world environments combined with error accumulation in multi-step ahead forecasting. In conclusion, these findings emphasize that aligning model selection with the specific forecasting horizon is critical for enhancing system efficiency and stability. The developed model demonstrates high accuracy and suitability for short-term early warnings within 3 hours. This knowledge enables operators to plan water quality management with precision and speed, thereby reducing operational costs and the risk of aquatic livestock loss. Furthermore, this system serves as a technological prototype in digital agriculture, elevating conventional water quality monitoring into proactive management through intelligent technology.","本研究利用物联网（IoT）技术与机器学习（ML），开发了一套智能水质预测与监测系统原型。研究聚焦于基于水温、pH值和总溶解固体（TDS）的简化水质指数（rWQI）分析。为提前1至6小时预测水质，本研究评估并比较了四种预测模型的性能：极端随机树（ET）、随机森林（RF）、简单线性回归（LR）和多元线性回归（MLR）。采用四项关键评价指标验证了这些算法的有效性：决定系数（R²）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和对称平均绝对百分比误差（sMAPE）。实验结果表明，各模型的预测性能因预测时间跨度不同而存在显著差异。在短期预测（提前1至3小时）中，RF模型表现最佳，第一小时的R²为0.566，MAE为5.215。相反，在长期预测（提前4至6小时）中，所有模型性能均显著下降（R² \u003C 0），预测误差高于均值基线。这一长期局限性源于真实环境中水质的高波动性以及多步超前预测中的误差累积。综上所述，这些发现强调，将模型选择与特定预测时间跨度相匹配对于提升系统效率与稳定性至关重要。所开发的模型具有较高精度，适用于3小时内的短期预警。这一认识使操作人员能够精准、快速地规划水质管理，从而降低运营成本和水产养殖损失风险。此外，该系统可作为数字农业中的技术原型，通过智能技术将传统水质监测提升为主动管理。","Journal of Science and Science Education (JSSE)","2026-10-12T00:00:00Z",{"impact":51,"substance":288,"depth":19,"authority":106,"freshness":21,"relevant":22,"comment":477},"论文将物联网与机器学习结合用于水产养殖水质短期预警，方法对比扎实、结论明确，但属细分技术原型，影响范围有限。",[479],{"name":474,"url":471},[27,135,28,481,482],"水产养殖","水质监测",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},[],{"status":44,"locked":45,"version":46,"method":47,"evidence":486},{"candidates":487,"agricultural_evidence":14},[488,492,498,502,508,514],{"code":37,"score":13,"supported":14,"title":489,"abstract":490,"tags":491},[],[28,239,151],[28],{"code":75,"score":88,"supported":14,"title":493,"abstract":494,"tags":497},[],[495,496],"数字农业","digital agriculture",[27],{"code":80,"score":88,"supported":14,"title":499,"abstract":500,"tags":501},[],[135,137],[135],{"code":503,"score":312,"supported":14,"title":504,"abstract":505,"tags":507},"D",[],[506],"水产",[481],{"code":509,"score":57,"supported":14,"title":510,"abstract":511,"tags":513},"C",[],[512],"livestock",[],{"code":515,"score":22,"supported":45,"title":516,"abstract":517,"tags":518},"D4",[],[],[482],[520,521],"物联网 机器学习 水质预测","rWQI 水质监测 水产养殖","物联网机器学习水质预测-4890",{"doi":8,"openalex_id":524,"authors":525,"venue":474,"cited_by_count":190,"oa_url":471,"card":535,"direction":211,"ingested_from":212},"W7221732805",[526,528,530,532],{"name":527,"orcid":8},"Wanarase Sinhashthita",{"name":529,"orcid":8},"Tossaporn Alherbe",{"name":531,"orcid":8},"Kamol Chareonsri",{"name":533,"orcid":534},"Chayaporn Kaensar","https:\u002F\u002Forcid.org\u002F0009-0005-3330-8532",{"tldr":536,"method":537,"finding":538,"direction":211,"opportunity":539},"利用物联网与机器学习构建水质预测监测系统，提前1-6小时预测rWQI。","基于水温、pH、TDS数据，比较ET、RF、LR、MLR四种模型预测性能。","短期（1-3小时）RF最优，长期（4-6小时）所有模型性能显著下降。","可探索融合时序深度学习与误差校正，提升长期水质预测精度与鲁棒性。","2026-10-14T23:30:13.314647Z"]