[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4572":3,"related-4572":113},{"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":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":112},4572,"Design and development of IoT based solar power agriculture robot","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44430-026-00040-6","Agriculture in developing economies such as Pakistan remains heavily dependent on manual labor and single-purpose, grid or fuel-powered machinery that is often unaffordable for smallholder farmers. This paper presents the design, fabrication, and experimental evaluation of a multifunctional, solar-powered, Internet of Things (IoT)-enabled agricultural robot capable of performing soil cultivation, seed placement, irrigation, and crop cutting within a single integrated platform. The system architecture is built around an ESP32 microcontroller that interfaces with a DHT11 temperature\u002Fhumidity sensor, an HC-SR04 ultrasonic obstacle sensor, an ESP32-CAM module for visual field monitoring, an L298N dual H-bridge motor driver, and relay-controlled actuators, with energy supplied by a 20 W photovoltaic panel, a 12 V\u002F7.2 Ah battery, and a battery management system (BMS). Mechanical subsystems comprising a tooth-type cultivator, a soil-leveling roller, a servo-actuated seed dispenser, a DC pump-based irrigation unit, and a blade-type cutter were designed in CAD, fabricated, and integrated onto a four-wheel-drive chassis. Engineering calculations were used to size the power budget, battery backup, drive-motor torque, structural factor of safety, seeding rate, and irrigation flow. Field trials show a typical operating power demand of 24.4 W against an average usable solar output of 11–12 W, a battery backup of approximately 3.5 h, The experimental results demonstrated satisfactory performance of the proposed agricultural robot, achieving an average seed-placement accuracy of 74.45% over 11 experimental trials. The developed system also successfully performed soil digging, irrigation, crop cutting, obstacle detection, and real-time IoT-based remote monitoring, demonstrating its suitability for sustainable precision agriculture.","巴基斯坦等发展中经济体的农业仍然高度依赖人工劳动以及单一用途的电网或燃料驱动机械，而这类机械往往是小农户无力承担的。本文介绍了一种多功能、太阳能供电、支持物联网（IoT）的农业机器人的设计、制造与实验评估，该机器人能够在单一集成平台上完成土壤耕作、播种、灌溉和作物收割。系统架构以ESP32微控制器为核心，连接DHT11温湿度传感器、HC-SR04超声波避障传感器、用于田间视觉监测的ESP32-CAM模块、L298N双H桥电机驱动器以及继电器控制执行器，能源由20 W光伏板、12 V\u002F7.2 Ah电池和电池管理系统（BMS）提供。机械子系统包括齿式耕作器、平土辊、舵机驱动排种器、基于直流泵的灌溉单元和刀片式切割器，经CAD设计、制造并集成到四轮驱动底盘上。通过工程计算确定了功率预算、电池续航、驱动电机扭矩、结构安全系数、播种速率和灌溉流量。田间试验表明，典型工作功率需求为24.4 W，而平均可用太阳能输出为11–12 W，电池续航约为3.5 h。实验结果表明，所提出的农业机器人性能令人满意，在11次实验 trials 中实现了74.45%的平均播种精度。所开发的系统还成功完成了挖土、灌溉、作物切割、障碍物检测以及基于物联网的实时远程监测，表明其适用于可持续精准农业。",null,"Discover Robotics","2026-10-09T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,6,9,1,"面向小农户的多功能太阳能物联网农业机器人论文，方法集成度高、有实测数据，但属单篇学术成果，公共影响有限，可作为智慧农业技术前沿收录。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业机器人","物联网","精准农业","光伏农业",{"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},[45,52,58,65,72,79],{"code":33,"score":18,"supported":46,"title":47,"abstract":49,"tags":51},true,[48],"IoT",[29,50,48],"internet of things",[29],{"code":53,"score":17,"supported":46,"title":54,"abstract":55,"tags":57},"I1",[],[30,56],"precision agriculture",[27,30],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":64},"F6",7,[],[28,63],"agricultural robot",[28],{"code":66,"score":20,"supported":46,"title":67,"abstract":68,"tags":71},"G2",[],[69,70],"小农户","smallholder",[],{"code":73,"score":20,"supported":46,"title":74,"abstract":75,"tags":78},"H2",[],[76,77],"土壤","soil",[],{"code":80,"score":20,"supported":46,"title":81,"abstract":82,"tags":85},"H3",[],[83,84],"灌溉","irrigation",[],[87,88],"太阳能农业机器人 ESP32","IoT 农业机器人 播种精度","太阳能农业机器人ESP32-4572",0,"10.1007\u002Fs44430-026-00040-6",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":90,"oa_url":6,"card":105,"direction":109,"ingested_from":111},"W7221126513",[95,97,99,101,103],{"name":96,"orcid":9},"Mohammad Ali",{"name":98,"orcid":9},"Sana Ullah",{"name":100,"orcid":9},"Asad Ullah",{"name":102,"orcid":9},"M. Danyal Khan",{"name":104,"orcid":9},"Mubasher Hassan",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"设计并试验了一款太阳能物联网多功能农业机器人，可耕地、播种、灌溉和收割。","基于ESP32、DHT11、超声波、ESP32-CAM和20W光伏板，CAD制造","作业功耗24.4W，电池续航约3.5小时，11次试验平均播种精度74.45%。","智慧农业 \u002F 农业物联网","太阳能供电与作业功耗不匹配，可研究能量优化调度或混合供电提升续航与播种精度。","openalex","2026-10-10T23:30:13.752841Z",{"total":20,"page":22,"page_size":20,"items":114},[115,190,293,357,422,499],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":132,"secondary_topics":133,"classification":134,"search_phrases":165,"slug":168,"view_count":169,"doi":170,"paper":171,"created_at":189},4877,"IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85108","The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.","物联网（IoT）、自主机器人、人工智能（AI）、嵌入式系统、无线通信和精准农业的快速发展，为提升农业生产率和资源利用率创造了新的机遇。传统农业监测在很大程度上依赖人工田间巡检，这种方式耗时、费力，且往往无法持续提供作物和土壤状况的信息。自主农业巡田机器人通过实现移动传感、实时环境监测、作物观测、导航和精准农业作业，提供了一种替代方案。本文对面向智慧农业的物联网赋能自主农业巡田机器人进行了全面的文献综述，特别关注IEEE文献中报道的最新进展。现有研究按照巡田机器人架构、传感技术、导航技术、通信技术、物联网平台、农业应用、人工智能集成和自主性水平进行了分析。近期研究已证明基于ESP32的物联网巡田机器人、基于ROS的农业监测机器人、自主导航系统、精准灌溉平台、土壤监测机器人和人工智能赋能农业巡田机器人的可行性。然而，仍存在若干局限，包括非结构化环境中的自主导航受限、多参数传感能力有限、农村地区通信可靠性不足、人工智能的高计算需求、能量限制，以及监测与自主决策的集成不充分。基于所综述的文献，本文识别出一个研究空白，即缺乏一种低成本、模块化、物联网赋能的自主农业监测巡田机器人，能够同时执行土壤与环境传感、作物监测、避障、实时无线通信、位置感知作业和智能决策支持。","International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z",52,{"impact":17,"substance":125,"depth":126,"authority":20,"freshness":17,"relevant":22,"comment":127},14,16,"综述性论文，系统梳理IoT自主农业巡检机器人架构与瓶颈，但来源期刊影响力有限，属细分领域参考价值。",[129],{"name":121,"url":118},[27,131,28,29,30],"农业人工智能",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":135},{"candidates":136,"agricultural_evidence":46},[137,141,146,153,157,161],{"code":33,"score":18,"supported":46,"title":138,"abstract":139,"tags":140},[48],[29,50,48],[29],{"code":53,"score":142,"supported":46,"title":143,"abstract":144,"tags":145},11,[],[27,30,56],[27,30],{"code":147,"score":60,"supported":46,"title":148,"abstract":149,"tags":152},"I2",[],[150,151],"artificial intelligence","decision support",[131],{"code":73,"score":20,"supported":46,"title":154,"abstract":155,"tags":156},[],[76,77],[],{"code":80,"score":20,"supported":46,"title":158,"abstract":159,"tags":160},[],[83,84],[],{"code":59,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[28],[166,167],"ESP32 ROS 农业机器人","农业人工智能 农业机器人 智慧农业 精准农业","ESP32ROS农业机器人-4877",3,"10.22214\u002Fijraset.2026.85108",{"doi":170,"openalex_id":172,"authors":173,"venue":121,"cited_by_count":90,"oa_url":118,"card":184,"direction":109,"ingested_from":111},"W7221682750",[174,176,178,180,182],{"name":175,"orcid":9},"Chaitra A S",{"name":177,"orcid":9},"Venugopal Gowda V",{"name":179,"orcid":9},"Vinay H",{"name":181,"orcid":9},"Jeevan T",{"name":183,"orcid":9},"Puneeth M",{"tldr":185,"method":186,"finding":187,"direction":109,"opportunity":188},"综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白","文献综述，聚焦IEEE近期研究，按架构、传感、导航、通信、AI与自主等级分类分析","现有系统在非结构化导航、多参数传感、农村通信、AI算力与能耗上存在局限，缺乏监测与决策一体化。","可研发低成本模块化IoT自主巡田机器人，融合多传感、避障、实时通信与智能决策，填补集成空白。","2026-10-14T23:30:08.717386Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":207,"secondary_topics":211,"classification":217,"search_phrases":268,"slug":271,"view_count":90,"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)","2026-10-21T00:00:00Z",80,{"impact":18,"substance":200,"depth":18,"authority":201,"freshness":21,"relevant":22,"comment":202},22,13,"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。",[204],{"name":196,"url":193},[27,131,29,30,206],"气候韧性",{"code":147,"slug":208,"name":209,"path":210,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[212,213],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":80,"slug":84,"name":214,"path":215,"parent_code":216},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,230,234,240,245,252,256,262],{"code":147,"score":18,"supported":46,"title":221,"abstract":222,"tags":229},[150],[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",[131],{"code":53,"score":126,"supported":46,"title":231,"abstract":232,"tags":233},[56],[30,56],[27,30],{"code":33,"score":13,"supported":46,"title":235,"abstract":236,"tags":239},[],[29,237,48,238],"边缘计算","edge computing",[29],{"code":80,"score":21,"supported":46,"title":241,"abstract":242,"tags":244},[],[83,243,84],"水资源",[],{"code":246,"score":60,"supported":46,"title":247,"abstract":248,"tags":251},"H7",[],[249,250],"气候变化","climate change",[206],{"code":73,"score":20,"supported":46,"title":253,"abstract":254,"tags":255},[],[76,77],[],{"code":257,"score":169,"supported":46,"title":258,"abstract":259,"tags":261},"A7",[],[260],"间作",[],{"code":263,"score":169,"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":90,"oa_url":193,"card":286,"direction":109,"ingested_from":111},"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":299,"source_url":296,"published_at":300,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":301,"score_detail":302,"sources":305,"tags":307,"primary_topic":310,"secondary_topics":311,"classification":313,"search_phrases":333,"slug":336,"view_count":90,"doi":337,"paper":338,"created_at":356},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":303,"substance":18,"depth":126,"authority":201,"freshness":17,"relevant":22,"comment":304},12,"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。",[306],{"name":299,"url":296},[27,29,30,308,309],"节水灌溉","LoRa",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[312],{"code":80,"slug":84,"name":214,"path":215,"parent_code":216},{"status":40,"locked":14,"version":41,"method":42,"evidence":314},{"candidates":315,"agricultural_evidence":46},[316,320,324,328],{"code":33,"score":18,"supported":46,"title":317,"abstract":318,"tags":319},[48],[29,50,48],[29],{"code":80,"score":19,"supported":46,"title":321,"abstract":322,"tags":323},[84],[83,84],[308],{"code":73,"score":20,"supported":46,"title":325,"abstract":326,"tags":327},[],[76,77],[],{"code":53,"score":329,"supported":14,"title":330,"abstract":331,"tags":332},2,[],[],[27,30],[334,335],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":337,"openalex_id":339,"authors":340,"venue":299,"cited_by_count":90,"oa_url":296,"card":351,"direction":109,"ingested_from":111},"W7222959710",[341,343,345,348],{"name":342,"orcid":9},"Hanfoug Salah",{"name":344,"orcid":9},"Riad Saidi",{"name":346,"orcid":347},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":349,"orcid":350},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":352,"method":353,"finding":354,"direction":109,"opportunity":355},"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":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":363,"source_url":360,"published_at":364,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":365,"sources":367,"tags":369,"primary_topic":372,"secondary_topics":378,"classification":380,"search_phrases":397,"slug":400,"view_count":90,"doi":401,"paper":402,"created_at":421},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",{"impact":17,"substance":18,"depth":126,"authority":20,"freshness":17,"relevant":22,"comment":366},"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。",[368],{"name":363,"url":360},[27,29,30,370,371],"温室大棚","水培蔬菜",{"code":373,"slug":374,"name":375,"path":376,"parent_code":377},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[379],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":381},{"candidates":382,"agricultural_evidence":46},[383,387,393],{"code":33,"score":18,"supported":46,"title":384,"abstract":385,"tags":386},[48],[29,50,48],[29],{"code":373,"score":125,"supported":46,"title":388,"abstract":390,"tags":392},[389],"greenhouse",[391,389],"温室",[],{"code":53,"score":17,"supported":46,"title":394,"abstract":395,"tags":396},[],[30,56],[27,30],[398,399],"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":401,"openalex_id":403,"authors":404,"venue":363,"cited_by_count":90,"oa_url":415,"card":416,"direction":109,"ingested_from":111},"W7222850582",[405,407,410,413],{"name":406,"orcid":9},"Ramadanzah Ramadanzah",{"name":408,"orcid":409},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":411,"orcid":412},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":414,"orcid":9},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":417,"method":418,"finding":419,"direction":109,"opportunity":420},"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":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":435,"tags":437,"primary_topic":439,"secondary_topics":445,"classification":448,"search_phrases":480,"slug":483,"view_count":22,"doi":484,"paper":485,"created_at":498},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",74,{"impact":126,"substance":432,"depth":433,"authority":303,"freshness":21,"relevant":22,"comment":434},20,17,"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[436],{"name":428,"url":425},[27,131,29,30,438],"棉花病害",{"code":440,"slug":441,"name":442,"path":443,"parent_code":444},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[446,447],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":147,"slug":208,"name":209,"path":210,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":449},{"candidates":450,"agricultural_evidence":46},[451,455,461,465,469,476],{"code":33,"score":19,"supported":46,"title":452,"abstract":453,"tags":454},[48],[29,48],[29],{"code":440,"score":125,"supported":46,"title":456,"abstract":458,"tags":460},[457],"cotton",[459,457],"棉花",[],{"code":53,"score":142,"supported":46,"title":462,"abstract":463,"tags":464},[],[27,30,56],[27,30],{"code":147,"score":13,"supported":46,"title":466,"abstract":467,"tags":468},[],[223,225,227,150,228],[131],{"code":470,"score":20,"supported":46,"title":471,"abstract":472,"tags":475},"F4",[],[473,474],"农药","pesticide",[],{"code":73,"score":20,"supported":46,"title":477,"abstract":478,"tags":479},[],[76,77],[],[481,482],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":484,"openalex_id":486,"authors":487,"venue":428,"cited_by_count":90,"oa_url":425,"card":493,"direction":109,"ingested_from":111},"W7221340722",[488,490],{"name":489,"orcid":9},"Deepak R Khadse",{"name":491,"orcid":492},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":494,"method":495,"finding":496,"direction":109,"opportunity":497},"基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":500,"title":501,"url":502,"summary":503,"summary_zh":504,"content":9,"source_name":505,"source_url":502,"published_at":506,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":507,"score_detail":508,"sources":510,"tags":512,"primary_topic":515,"secondary_topics":516,"classification":518,"search_phrases":547,"slug":550,"view_count":169,"doi":9,"paper":551,"created_at":561},4577,"Automatic irrigation system-2","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4719","Agriculture is one of the major consumers of freshwater, and inefficient irrigation can result in significant water loss. Traditional irrigation methods often depend on manual observation and fixed watering schedules, which may lead to over-irrigation or under-irrigation. An automatic irrigation system provides a technological solution by monitoring soil moisture and automatically supplying water according to the requirements of the crop. This research paper presents the design and working principle of an automatic irrigation system based on soilmoisture sensing, a microcontroller, a relay, and a water pump. The proposed system continuously measures the moisture level of the soil. When the moisture falls below a predefined threshold, the controller activates the water pump. Once sufficient moisture is restored, the pump is automatically switched off. The system can additionally incorporate Internet of Things (IoT) technology, allowing farmers to monitor soil and environmental conditions remotely through a mobile or web interface. Previous research has demonstrated the potential of sensor-based irrigation automation to improve water-use efficiency. For example, a soil-moisture-based automated basin irrigation system reported irrigation application efficiency of up to 86.6%. I More recent systems combine soil moisture, temperature, humidity, weather information, IoT connectivity, and machine learning to improve irrigation scheduling. Wiley Online Library+1 The proposed system is therefore a low-cost and scalable approach toward precision agriculture, water conservation, reduced labor requirements, and sustainable agricultural production.","农业是淡水的主要消耗领域之一，低效灌溉会造成大量水资源浪费。传统灌溉方法往往依赖人工观察和固定灌溉计划，可能导致过度灌溉或灌溉不足。自动灌溉系统通过监测土壤湿度并根据作物需求自动供水，提供了一种技术解决方案。本文介绍了一种基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统的设计和工作原理。该系统持续测量土壤湿度水平。当湿度低于预设阈值时，控制器启动水泵。当湿度恢复到足够水平后，水泵自动关闭。该系统还可集成物联网（IoT）技术，使农民能够通过移动端或网页界面远程监测土壤和环境状况。已有研究表明，基于传感器的灌溉自动化具有提高水资源利用效率的潜力。例如，一项基于土壤湿度的自动化畦灌系统报告称，其灌溉施用效率可达86.6%。近期系统则结合土壤湿度、温度、湿度、天气信息、物联网连接和机器学习来优化灌溉调度。因此，所提出的系统是一种低成本、可扩展的方案，有助于实现精准农业、水资源保护、减少劳动力需求以及可持续农业生产。","International Journal of Engineering Research and Science & Technology","2026-10-07T00:00:00Z",42,{"impact":17,"substance":13,"depth":303,"authority":20,"freshness":20,"relevant":22,"comment":509},"主题契合智慧农业，但属通用性论文介绍，方法无新意、来源权威性一般，不建议进入每日精选。",[511],{"name":505,"url":502},[27,29,30,513,514],"土壤墒情","自动灌溉",{"code":80,"slug":84,"name":214,"path":215,"parent_code":216},[517],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":519},{"candidates":520,"agricultural_evidence":46},[521,525,529,533,537,541],{"code":80,"score":433,"supported":46,"title":522,"abstract":523,"tags":524},[84],[83,243,84],[],{"code":33,"score":13,"supported":46,"title":526,"abstract":527,"tags":528},[],[29,50,48],[29],{"code":53,"score":17,"supported":46,"title":530,"abstract":531,"tags":532},[],[30,56],[27,30],{"code":73,"score":60,"supported":46,"title":534,"abstract":535,"tags":536},[],[76,77],[513],{"code":147,"score":20,"supported":46,"title":538,"abstract":539,"tags":540},[],[224,226],[],{"code":542,"score":169,"supported":46,"title":543,"abstract":544,"tags":546},"H8",[],[545],"可持续农业",[],[548,549],"自动灌溉系统 土壤湿度 物联网","土壤墒情 智慧农业 精准农业 自动灌溉","自动灌溉系统土壤湿度物联网-4577",{"doi":9,"openalex_id":552,"authors":553,"venue":505,"cited_by_count":90,"oa_url":502,"card":556,"direction":109,"ingested_from":111},"W7221285464",[554],{"name":555,"orcid":9},"Pooja Rajendra Sangole,Shravani Datta Dhomane ,Shreyas nivrutti thete ,amol Dinakar laware,Sumit sudan dalu,Kalpesh Gopal Agrawal,Piyush Sitaram Ghore",{"tldr":557,"method":558,"finding":559,"direction":109,"opportunity":560},"设计基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统，实现按需供水。","土壤湿度传感器、微控制器、继电器、水泵，可选IoT远程监控。","系统在湿度低于阈值时自动启泵，恢复后停泵，低成本可扩展，提升水分利用效率。","可结合机器学习与气象数据优化灌溉阈值，并开展多作物田间验证与能耗优化。","2026-10-10T23:30:15.132034Z"]