[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4571":3,"related-4571":119},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":101,"slug":104,"view_count":105,"doi":9,"paper":106,"created_at":118},4571,"SMART AGRICULTURE ROBOT","https:\u002F\u002Fijdim.com\u002Fjournal\u002Findex.php\u002Fijdim\u002Farticle\u002Fview\u002F1453","Agriculture continues to support a large part of the population, yet many field operations are still carried out by hand. Ploughing small plots, sowing seeds at regular spacing, watering, spraying pesticides, and checking crop health demand considerable labour and time, and farmers increasingly find it difficult to hire workers during peak seasons. Manual spraying also exposes workers to harmful chemicals. This paper presents a smart agriculture robot that performs several routine field tasks, monitors soil and crop conditions with IoT sensors, and reports to the farmer through a cloud dashboard and mobile application. The robot is built on a four-wheel chassis driven by high-torque DC motors and controlled by an ESP32 microcontroller. It carries a seed dispensing mechanism driven by a servo motor, a small tiller blade for loosening soil, a water and pesticide spraying unit with a pump and nozzle, and a soil probe fitted with moisture and temperature sensors. Additional sensors measure air temperature, humidity, and light intensity. A Raspberry Pi with a camera captures images of plants for disease and weed detection, and a solar panel helps charge the battery during operation. The robot can be driven manually from a mobile application or can move along crop rows automatically using ultrasonic sensors and a simple row-following method. As it moves, it drops seeds at set intervals, inserts the soil probe at sampling points, and records readings together with the position along the row. The camera images are processed by a lightweight convolutional neural network that classifies leaves as healthy or affected by common diseases and distinguishes weeds from crop plants, so that spraying can be applied only where it is required. All sensor readings, images, and task logs are sent to a cloud platform using the MQTT protocol over Wi-Fi or a GSM data connection. The dashboard shows soil moisture maps, crop health summaries, and the progress of each task, and the system sends alerts to the farmer's phone when soil moisture is low, disease is detected, or the battery is running out. Irrigation can be started from the application for dry patches identified by the robot.","农业仍然支撑着大部分人口，但许多田间作业仍由人工完成。小块土地的耕作、按固定间距播种、浇水、喷洒农药以及检查作物健康状况，都需要大量劳动力和时间，而农民在农忙季节越来越难以雇到工人。人工喷洒还会使工人接触有害化学物质。本文提出了一种智能农业机器人，可执行多项常规田间任务，利用物联网（IoT）传感器监测土壤和作物状况，并通过云端仪表板和移动应用程序向农民报告。该机器人基于四轮底盘构建，由高扭矩直流电机驱动，并由ESP32微控制器控制。它搭载了由伺服电机驱动的播种机构、用于松土的小型旋耕刀片、带有泵和喷嘴的水与农药喷洒单元，以及配备湿度和温度传感器的土壤探头。其他传感器用于测量气温、湿度和光照强度。配备摄像头的树莓派（Raspberry Pi）拍摄植物图像，用于病害和杂草检测，太阳能板则有助于在作业期间为电池充电。该机器人可通过移动应用程序手动驾驶，也可利用超声波传感器和简单的行跟随方法沿作物行自动行进。行进过程中，它按设定间隔播种，在采样点插入土壤探头，并将读数与沿行位置一并记录。摄像头图像由轻量级卷积神经网络处理，该网络将叶片分类为健康或受常见病害影响，并区分杂草与作物植株，从而仅在需要处进行喷洒。所有传感器读数、图像和任务日志均通过Wi-Fi或GSM数据连接，使用MQTT协议发送至云平台。仪表板显示土壤湿度图、作物健康摘要以及每项任务的进度，当土壤湿度低、检测到病害或电池电量耗尽时，系统会向农民的手机发送警报。对于机器人识别出的干旱地块，可从应用程序启动灌溉。",null,"International Journal of Data Science and IoT Management System","2026-10-08T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,15,8,9,1,"论文提出集耕作、播种、灌溉、喷药与IoT监测于一体的低成本农业机器人方案，方法完整但属实验室原型，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业机器人","农业物联网","病虫害识别","精准施药",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":100},[39,46,54,61,69,76,82,89,95],{"code":40,"score":17,"supported":41,"title":42,"abstract":44,"tags":45},"I1",true,[43],"smart agriculture",[43],[27],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":53},"F4",7,[],[51,52],"农药","pesticide",[31],{"code":55,"score":48,"supported":41,"title":56,"abstract":57,"tags":60},"I5",[],[58,59],"物联网","IoT",[29],{"code":62,"score":63,"supported":41,"title":64,"abstract":65,"tags":68},"H2",6,[],[66,67],"土壤","soil",[],{"code":70,"score":63,"supported":41,"title":71,"abstract":72,"tags":75},"H3",[],[73,74],"灌溉","irrigation",[],{"code":77,"score":78,"supported":41,"title":79,"abstract":80,"tags":81},"F6",4,[],[28],[28],{"code":83,"score":84,"supported":41,"title":85,"abstract":86,"tags":88},"A7",3,[],[87],"间作",[],{"code":90,"score":84,"supported":41,"title":91,"abstract":92,"tags":94},"H7",[],[93],"干旱",[],{"code":96,"score":22,"supported":14,"title":97,"abstract":98,"tags":99},"I2",[],[],[30],"多个产业证据相当，需要确认主归属",[102,103],"ESP32 农业机器人","Raspberry Pi 作物病害识别","ESP32农业机器人-4571",0,{"doi":9,"openalex_id":107,"authors":108,"venue":10,"cited_by_count":105,"oa_url":6,"card":111,"direction":115,"ingested_from":117},"W7221297032",[109],{"name":110,"orcid":9},"Mrs.Sunanda Kondapalli,Mohammed Tauseef,B Harsha Wardhan,MD Muddassir Ahmed,Mohd Afroz Khan,Ande Sathwik,C.Prany",{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"研发一款集成播种、喷药、土壤监测与病害识别的智能农业机器人，并通过云平台远程管理。","ESP32与树莓派控制，配多类传感器、摄像头、轻量CNN及MQTT云通信。","机器人可自动沿作物行作业，按需喷药并实时上传土壤与作物健康数据，低湿度或病害时报警。","智慧农业 \u002F 农业物联网","可探索多机协同、边缘端模型轻量化及基于历史数据的精准灌溉决策优化。","openalex","2026-10-10T23:30:12.573537Z",{"total":63,"page":22,"page_size":63,"items":120},[121,206,261,318,412,493],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":9,"source_name":127,"source_url":124,"published_at":128,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":129,"score_detail":130,"sources":134,"tags":136,"primary_topic":139,"secondary_topics":145,"classification":146,"search_phrases":169,"slug":172,"view_count":22,"doi":173,"paper":174,"created_at":205},4889,"A Distributed Multirobot Architecture for Decoupled Inspection and Localized Treatment in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faisy.70593","This work presents a distributed multirobot architecture for autonomous inspection and precision treatment in agriculture, validated under real vineyard conditions. The proposed system integrates a heterogeneous fleet of four specialized ground robots (two inspection and two treatment platforms), coordinated through a cloud‐based mission control center (MCC). The MCC acts as the cognitive layer of the system, integrating CNN‐based disease detection, mission planning, and fleet management, while the robotic platforms operate as autonomous agents responsible for sensing and actuation. Inspection and treatment processes are separated through an event‐driven, grid‐based coordination mechanism maintained by the MCC, enabling asynchronous and independent operation of both robot types without direct inter‐robot communication. Georeferenced images acquired by the inspection robots are transmitted in real time to the MCC, where the disease detection model identifies affected areas and updates the treatment grid accordingly. Experimental validation in a commercial vineyard demonstrated the feasibility of the approach: the model achieved a recall of 95.2% against expert annotations at a 16% infection prevalence, no data losses were recorded across communication streams, and 21.1% of acquired images generated treatment events. The proposed architecture is not tied to a specific task and can be extended to other precision agriculture scenarios.","本文提出了一种用于农业自主巡检与精准处理的分布式多机器人架构，并在真实葡萄园条件下进行了验证。所提出的系统集成了由四台专用地面机器人（两台巡检平台和两台处理平台）组成的异构机器人集群，并通过基于云的任务控制中心（MCC）进行协调。MCC作为系统的认知层，集成了基于CNN的病害检测、任务规划和集群管理，而机器人平台则作为负责感知与执行的自主智能体。巡检与处理过程通过MCC维护的事件驱动、基于网格的协调机制实现分离，使两类机器人能够异步独立运行，无需机器人之间的直接通信。巡检机器人采集的地理参考图像实时传输至MCC，由病害检测模型识别受影响区域并相应更新处理网格。在商业葡萄园中的实验验证表明了该方法的可行性：在16%感染率下，模型相对于专家标注达到了95.2%的召回率，各通信流中未记录到数据丢失，21.1%的采集图像触发了处理事件。所提出的架构不局限于特定任务，可扩展至其他精准农业场景。","Advanced Intelligent Systems","2026-10-12T00:00:00Z",79,{"impact":18,"substance":131,"depth":18,"authority":132,"freshness":20,"relevant":22,"comment":133},22,13,"多机器人分布式架构在真实葡萄园验证，检出召回率95.2%，对精准农业机器人协同有参考价值。",[135],{"name":127,"url":124},[27,28,31,137,138],"病害检测","多机器人协同",{"code":140,"slug":141,"name":142,"path":143,"parent_code":144},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":147,"locked":14,"version":35,"method":36,"evidence":148},"classified",{"candidates":149,"agricultural_evidence":41},[150,156,161,165],{"code":40,"score":19,"supported":41,"title":151,"abstract":153,"tags":155},[152],"precision agriculture",[154,152],"精准农业",[27],{"code":140,"score":84,"supported":41,"title":157,"abstract":158,"tags":160},[],[159],"葡萄",[],{"code":47,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[31],{"code":77,"score":22,"supported":14,"title":166,"abstract":167,"tags":168},[],[],[28],[170,171],"多机器人 葡萄园 病害检测","精准农业 机器人 架构","多机器人葡萄园病害检测-4889","10.1002\u002Faisy.70593",{"doi":173,"openalex_id":175,"authors":176,"venue":127,"cited_by_count":105,"oa_url":199,"card":200,"direction":115,"ingested_from":117},"W7221635124",[177,180,183,186,189,191,193,196],{"name":178,"orcid":179},"Jose Maria Bengochea-Guevara","https:\u002F\u002Forcid.org\u002F0000-0003-4081-7325",{"name":181,"orcid":182},"Héctor Montes","https:\u002F\u002Forcid.org\u002F0000-0001-8638-9966",{"name":184,"orcid":185},"María Nuria Conejero","https:\u002F\u002Forcid.org\u002F0000-0002-9372-3739",{"name":187,"orcid":188},"Laura Garrido-Rey","https:\u002F\u002Forcid.org\u002F0009-0002-1782-5175",{"name":190,"orcid":9},"Carlos Ranz",{"name":192,"orcid":9},"Angelo Vincenzo Barra",{"name":194,"orcid":195},"Dionisio Andújar","https:\u002F\u002Forcid.org\u002F0000-0002-5801-0944",{"name":197,"orcid":198},"Ángela Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0001-5807-8132","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faisy.70593",{"tldr":201,"method":202,"finding":203,"direction":115,"opportunity":204},"提出分布式多机器人架构，在葡萄园实现异步巡检与精准治疗。","四台异构地面机器人、云任务控制中心、CNN病害检测与网格协调。","病害检测召回率95.2%，通信无数据丢失，21.1%图像触发治疗事件。","可探索去中心化协同、多作物迁移及边缘计算下的实时病害决策优化。","2026-10-14T23:30:13.144306Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":9,"content":9,"source_name":211,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":218,"tags":220,"primary_topic":9,"secondary_topics":222,"classification":223,"search_phrases":247,"slug":250,"view_count":22,"doi":251,"paper":252,"created_at":260},4785,"Food's future: sustainability and agricultural robotics","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffrobt.2025.1696483\u002Ffull","Nleya等学者发表于Frontiers in Robotics and AI,围绕全球粮食系统面临的可持续挑战,系统梳理农业机器人如何助力农业可持续转型。文章提出五维分析框架:危机视角、技术分类、与精准农业IoT与AI与大数据的融合、环境经济社会可持续性评估,以及澳大利亚、美国、巴西、印度、中国等全球案例对比。文章指出机器人在降低化学品投入、节水、土壤改良、劳动力替代方面有显著效益。","Frontiers in Robotics and AI 2026",72,{"impact":18,"substance":214,"depth":215,"authority":216,"freshness":84,"relevant":22,"comment":217},20,17,14,"核心期刊综述，提出五维分析框架并含多国案例，对智慧农业与农业机器人方向有参考价值，但时效性偏弱。",[219],{"name":211,"url":209},[27,221,28,29,154],"农业人工智能",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":224},{"candidates":225,"agricultural_evidence":41,"reason":100},[226,231,235,239,243],{"code":40,"score":227,"supported":41,"title":228,"abstract":229,"tags":230},5,[],[154],[27,154],{"code":77,"score":78,"supported":41,"title":232,"abstract":233,"tags":234},[],[28],[28],{"code":55,"score":78,"supported":41,"title":236,"abstract":237,"tags":238},[],[59],[29],{"code":62,"score":84,"supported":41,"title":240,"abstract":241,"tags":242},[],[66],[],{"code":96,"score":22,"supported":14,"title":244,"abstract":245,"tags":246},[],[],[221],[248,249],"Frontiers in Robotics and AI 农业机器人","农业机器人 粮食系统 可持续","FrontiersinRoboticsandAI农业机器人-4785","10.3389\u002Ffrobt.2025.1696483\u002Ffull",{"doi":251,"openalex_id":9,"authors":253,"venue":9,"cited_by_count":105,"oa_url":9,"card":254,"direction":115,"ingested_from":259},[],{"tldr":255,"method":256,"finding":257,"direction":115,"opportunity":258},"系统梳理农业机器人助力粮食系统可持续转型的路径与全球案例。","文献综述与五维分析框架，结合多国案例对比。","农业机器人在减药、节水、改土和替代劳动力方面效益显著。","可量化评估机器人对减药节水贡献，并研究小农户采纳与成本效益。","agent","2026-10-13T00:08:38.520864Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":9,"content":9,"source_name":266,"source_url":9,"published_at":267,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":268,"sources":270,"tags":272,"primary_topic":275,"secondary_topics":280,"classification":285,"search_phrases":305,"slug":308,"view_count":309,"doi":9,"paper":310,"created_at":317},4648,"[预印本]面向农业机器人在多变天气与地形下基于视觉-语言模型的上下文自适应农药喷洒(Context-Aware Adaptive Pesticide Spraying for Agricultural Robots under Changing Weather and Terrain Using Vision-Language Models)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.08807","arXiv论文(2610.08807,2026年10月6日提交)提出基于视觉-语言模型(VLM)的上下文自适应喷洒框架,通过空间推理整合作物、农药、气象数据驱动喷洒决策,并结合MPPI轨迹跟踪控制。作物行检测精度至少提升30%,机器人可在两种环境中灵活调节喷洒量与行驶速度并减少农药漂移。[预印本未经同行评议,尚未实地部署验证]。","arXiv 2026-10-06 [预印本]","2026-10-06T00:00:00Z",{"impact":18,"substance":214,"depth":18,"authority":13,"freshness":63,"relevant":22,"comment":269},"预印本提出VLM驱动的自适应喷洒框架，方法新颖且减药减排指向明确，但未经同行评议与实地验证，宜作前沿动态收录而非重点精选。",[271],{"name":266,"url":264},[27,28,31,273,274],"视觉语言模型","农药减量",{"code":47,"slug":276,"name":277,"path":278,"parent_code":279},"crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[281],{"code":77,"slug":282,"name":283,"path":284,"parent_code":279},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots",{"status":147,"locked":14,"version":35,"method":36,"evidence":286},{"candidates":287,"agricultural_evidence":41},[288,293,297,301],{"code":47,"score":289,"supported":41,"title":290,"abstract":291,"tags":292},21,[51,52],[51],[274,31],{"code":77,"score":21,"supported":41,"title":294,"abstract":295,"tags":296},[28],[],[28],{"code":40,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[27],{"code":96,"score":22,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[273],[306,307],"农业机器人 视觉语言模型 农药喷洒","MPPI 轨迹跟踪 作物行检测","农业机器人视觉语言模型农药喷洒-4648",2,{"doi":9,"openalex_id":9,"authors":311,"venue":9,"cited_by_count":105,"oa_url":9,"card":312,"direction":115,"ingested_from":259},[],{"tldr":313,"method":314,"finding":315,"direction":115,"opportunity":316},"提出基于视觉-语言模型的上下文自适应农药喷洒框架，结合MPPI控制实现精准喷洒。","视觉-语言模型空间推理，融合作物、农药与气象数据，MPPI轨迹跟踪控制。","作物行检测精度至少提升30%，可灵活调节喷洒量与速度并减少农药漂移。","预印本未实地验证，可探索真实多变天气地形下的VLM鲁棒性与多模态数据融合优化。","2026-10-11T00:09:47.810289Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":9,"source_name":324,"source_url":321,"published_at":325,"category":12,"cover_url":9,"hotness":326,"is_selected":14,"score":327,"score_detail":328,"sources":330,"tags":335,"primary_topic":337,"secondary_topics":342,"classification":352,"search_phrases":396,"slug":399,"view_count":22,"doi":400,"paper":401,"created_at":411},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",25,81,{"impact":131,"substance":214,"depth":18,"authority":132,"freshness":20,"relevant":22,"comment":329},"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[331,332],{"name":324,"url":321},{"name":333,"url":334},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,221,28,29,154,336],"自动驾驶农机",{"code":96,"slug":338,"name":339,"path":340,"parent_code":341},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[343,348],{"code":344,"slug":345,"name":346,"path":347,"parent_code":279},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",{"code":55,"slug":349,"name":350,"path":351,"parent_code":341},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":147,"locked":14,"version":35,"method":36,"evidence":353},{"candidates":354,"agricultural_evidence":41},[355,364,371,376,383,387,392],{"code":96,"score":18,"supported":41,"title":356,"abstract":358,"tags":363},[357],"artificial intelligence",[359,360,361,357,362],"机器学习","计算机视觉","machine learning","computer vision",[221],{"code":344,"score":215,"supported":41,"title":365,"abstract":367,"tags":370},[366],"agricultural machinery",[368,369,366],"农业机械","收割机",[],{"code":55,"score":13,"supported":41,"title":372,"abstract":373,"tags":375},[],[58,374,59],"internet of things",[29],{"code":377,"score":63,"supported":41,"title":378,"abstract":379,"tags":382},"I3",[],[380,381],"遥感","remote sensing",[],{"code":40,"score":227,"supported":41,"title":384,"abstract":385,"tags":386},[],[154],[27,154],{"code":47,"score":84,"supported":41,"title":388,"abstract":389,"tags":391},[],[390],"除草",[],{"code":77,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[28],[397,398],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":400,"openalex_id":402,"authors":403,"venue":324,"cited_by_count":105,"oa_url":321,"card":406,"direction":115,"ingested_from":117},"W7221138004",[404],{"name":405,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":407,"method":408,"finding":409,"direction":115,"opportunity":410},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":324,"source_url":415,"published_at":418,"category":12,"cover_url":9,"hotness":326,"is_selected":14,"score":419,"score_detail":420,"sources":422,"tags":426,"primary_topic":429,"secondary_topics":433,"classification":435,"search_phrases":468,"slug":471,"view_count":22,"doi":472,"paper":473,"created_at":492},4323,"Intelligent Sprinkler: Design and Evaluation of a Low-Cost Mobile Agricultural Robot for Sensor-Driven Irrigation and Vision-Guided Targeted Spraying","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23151414","Uniform irrigation and blanket spraying can apply water or treatment liquid without accounting for local field conditions. This work presents the design of the Intelligent Sprinkler, a low-cost tracked mobile agricultural platform that combines soil and climate sensing, sensor-driven irrigation, camera-based target detection, targeted liquid delivery, obstacle-aware navigation, and feedback logging within a SENSE–PERCEIVE–DECIDE–NAVIGATE–ACTUATE–MEASURE–FEEDBACK loop. The platform uses a Raspberry Pi for high-level processing and a microcontroller for real-time sensing and actuation. Irrigation is implemented as a threshold-based closed-loop controller rather than as a machine-learning irrigation predictor, while YOLOv8n is specified for target detection and edge deployment. The evaluation protocol defines controlled irrigation, vision, spraying, navigation, and complete-cycle experiments with predefined metrics and statistical tests. This manuscript includes an explicitly labeled synthetic dataset to demonstrate the intended reporting structure. The numerical values are not experimental findings and are placeholders for verified measurements from the physical prototype.","均匀灌溉和全面喷施可以在不考虑田间局部条件的情况下施加水或处理液。本研究介绍了智能喷灌机（Intelligent Sprinkler）的设计，这是一种低成本履带式移动农业平台，在“感知—认知—决策—导航—执行—测量—反馈”闭环中集成了土壤与气候传感、传感器驱动灌溉、基于摄像头的目标检测、定向液体输送、障碍物感知导航以及反馈记录。该平台使用树莓派（Raspberry Pi）进行高层处理，并使用微控制器进行实时传感与执行。灌溉以基于阈值的闭环控制器实现，而非机器学习灌溉预测器，同时指定YOLOv8n用于目标检测和边缘部署。评估方案定义了受控灌溉、视觉、喷施、导航和完整周期实验，并预设了指标和统计检验。本手稿包含一个明确标注的合成数据集，以展示预期的报告结构。其中的数值并非实验发现，而是用于物理样机验证测量的占位符。","2026-10-05T00:00:00Z",52,{"impact":20,"substance":17,"depth":216,"authority":21,"freshness":21,"relevant":22,"comment":421},"低成本履带式农业机器人整合传感灌溉与视觉靶向施药，方法框架清晰，但数值均为合成占位数据，尚缺实测验证，暂不宜进入每日精选。",[423,424],{"name":324,"url":415},{"name":324,"url":425},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23151415",[27,28,427,31,428],"边缘计算","精准灌溉",{"code":70,"slug":74,"name":430,"path":431,"parent_code":432},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[434],{"code":77,"slug":282,"name":283,"path":284,"parent_code":279},{"status":147,"locked":14,"version":35,"method":36,"evidence":436},{"candidates":437,"agricultural_evidence":41},[438,442,447,451,456,460,464],{"code":70,"score":19,"supported":41,"title":439,"abstract":440,"tags":441},[74],[73,74],[428],{"code":77,"score":21,"supported":41,"title":443,"abstract":445,"tags":446},[444],"agricultural robot",[],[28],{"code":62,"score":63,"supported":41,"title":448,"abstract":449,"tags":450},[],[66,67],[],{"code":96,"score":63,"supported":41,"title":452,"abstract":453,"tags":455},[],[454,359],"目标检测",[],{"code":47,"score":22,"supported":14,"title":457,"abstract":458,"tags":459},[],[],[31],{"code":40,"score":22,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[27],{"code":55,"score":22,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[427],[469,470],"Intelligent Sprinkler 农业机器人","YOLOv8n 靶向施药","IntelligentSprinkler农业机器人-4323","10.5281\u002Fzenodo.23151414",{"doi":472,"openalex_id":474,"authors":475,"venue":324,"cited_by_count":105,"oa_url":415,"card":486,"direction":491,"ingested_from":117},"W7220424553",[476,478,480,482,484],{"name":477,"orcid":9},"Sampathkumar Nithyanantham",{"name":479,"orcid":9},"Doddi Sricharan Rao",{"name":481,"orcid":9},"Venkatesh Gurram",{"name":483,"orcid":9},"Satyanarayana Reddy Karri L O V E",{"name":485,"orcid":9},"Balaji Muram Reddy",{"tldr":487,"method":488,"finding":489,"direction":115,"opportunity":490},"设计并评估一款低成本履带式农业机器人，实现传感驱动灌溉与视觉引导靶向喷洒。","基于树莓派与微控制器，采用阈值闭环灌溉和YOLOv8n目标检测，含合成数据演示。","提出完整感知-决策-执行闭环架构，但数值为占位符，尚未有真实实验结果。","可开展真实田间实验验证性能，并探索机器学习灌溉预测替代阈值控制。","农业人工智能与决策模型","2026-10-06T23:30:38.487282Z",{"id":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":9,"source_name":324,"source_url":496,"published_at":499,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":500,"sources":502,"tags":504,"primary_topic":506,"secondary_topics":507,"classification":508,"search_phrases":523,"slug":526,"view_count":105,"doi":527,"paper":528,"created_at":538},5589,"Conformal-Risk Evaluation for Artificial Intelligence Agriculture Spray-Nozzle Fault Detection Under Label-Delay Feedback: A Computer Coverage Audit","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544107","Spray-nozzle fault detection in precision agriculture is a classification problem in which the label that confirms a fault arrives after the spray pass, so the detector must act on unlabelled evidence and its error rate must be certified before the label exists. Conformal prediction offers distribution-free coverage guarantees, but the guarantee is stated for exchangeable calibration and test data, an assumption that label-delayed feedback violates because the calibration set is always older than the deployment set. the present analysis formalises label-delay feedback as a distribution shift in the conformity score and derives a coverage bound for split-conformal fault detectors under that shift. The bound separates the nominal miscoverage rate into an exchangeable component and a delay-induced component that scales with the total variation distance between the calibration and deployment score distributions. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the study characterises the behaviour of the ordinary least squares estimator under the stated process. The estimator recovered the declared coefficients with estimated bias 0.01122 for the intercept and -0.0002 for exposure, root mean squared error 0.9217 and 0.01614 respectively, and empirical coverage of the nominal 95% intervals of 1.0 for both parameters across 600 replications. The coverage audit shows that the delay-induced term, not the exchangeable term, dominates the risk of a conformal spray-nozzle detector, and that the audit procedure can certify a detector before field labels return.","在精准农业中，喷雾喷嘴故障检测是一个分类问题，其中确认故障的标签在喷雾作业结束后才到达，因此检测器必须基于未标记的证据采取行动，并且其错误率必须在标签存在之前得到认证。保形预测（conformal prediction）提供了无分布覆盖保证，但该保证是针对可交换的校准数据和测试数据而言的，而标签延迟反馈违反了这一假设，因为校准集总是比部署集更旧。本分析将标签延迟反馈形式化为一致性分数中的分布偏移，并推导了在该偏移下分裂保形故障检测器的覆盖界。该界将名义错误覆盖 rate 分解为可交换分量和延迟诱导分量，后者随校准与部署分数分布之间的全变差距离而缩放。对 ['Intercept', 'exposure', 'group', 'severity'] 估计器进行了基于重复的评估：从声明的数据生成过程（截距 12.0，暴露 0.42，分组 -3.1，严重程度 1.35，误差标准差 4.2）中抽取了 600 个 N = 500 的数据集，研究刻画了普通最小二乘估计器在所述过程下的行为。该估计器恢复了声明的系数，截距的估计偏差为 0.01122，暴露为 -0.0002，均方根误差分别为 0.9217 和 0.01614，两个参数在 600 次重复中名义 95% 区间的经验覆盖率为 1.0。覆盖审计表明，延迟诱导项而非可交换项主导了保形喷雾喷嘴检测器的风险，并且该审计程序可以在田间标签返回之前对检测器进行认证。","2026-10-21T00:00:00Z",{"impact":20,"substance":18,"depth":215,"authority":21,"freshness":13,"relevant":22,"comment":501},"论文提出标签延迟下共形预测覆盖界，方法有新意但属预印本、应用面窄，可作为专业补充而非每日精选头条。",[503],{"name":324,"url":496},[27,221,31,505],"病虫害监测",{"code":96,"slug":338,"name":339,"path":340,"parent_code":341},[],{"status":147,"locked":14,"version":35,"method":36,"evidence":509},{"candidates":510,"agricultural_evidence":41},[511,515,519],{"code":96,"score":21,"supported":41,"title":512,"abstract":513,"tags":514},[357],[],[221],{"code":40,"score":48,"supported":41,"title":516,"abstract":517,"tags":518},[],[154,152],[27],{"code":47,"score":22,"supported":14,"title":520,"abstract":521,"tags":522},[],[],[31],[524,525],"农业人工智能 病虫害监测 智慧农业 精准施药","农业人工智能 病虫害监测","农业人工智能病虫害监测智慧农业精准施药-5589","10.5281\u002Fzenodo.23544107",{"doi":527,"openalex_id":529,"authors":530,"venue":324,"cited_by_count":105,"oa_url":496,"card":533,"direction":491,"ingested_from":117},"W7223116087",[531],{"name":532,"orcid":9},"Achieng' Atim Okello",{"tldr":534,"method":535,"finding":536,"direction":491,"opportunity":537},"将标签延迟反馈建模为共形分数分布偏移，推导覆盖率界并审计喷雾喷嘴故障检测器。","分裂共形预测、总变差距离、600次重复模拟（N=500）评估OLS估计。","延迟诱导项而非可交换项主导检测器风险，审计可在田间标签返回前认证检测器。","可将该覆盖率审计框架扩展到其他标签延迟的农业传感器故障检测，并研究延迟分布估计误差对认证的影响。","2026-10-22T11:21:02.845718Z"]