[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1627":3,"related-1627":122},{"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":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":121},1627,"Autonomous Multi-Crop Weeding and Nutrient Management","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i9.016","Agriculture faces major challenges such as weed growth, labor shortages, excessive fertilizer usage, and lack of real-time monitoring. This paper proposes an Autonomous Multi-Crop Weeding and Nutrient Management Machine using Artificial Intelligence (AI), Internet of Things (IoT), and embedded systems. The system uses AI-based image processing for weed detection and activates a mechanical cutter to remove weeds automatically. Soil pH and NPK sensors monitor nutrient levels and enable precision nutrient spraying only when required. An ESP32 microcontroller controls navigation, sensor interfacing, motor operation, and IoT communication. GPS, ultrasonic, and IMU sensors enable autonomous navigation and obstacle detection. Real-time data is transmitted to farmers through Wi-Fi or Bluetooth. The proposed system reduces labor dependency, minimizes chemical usage, improves crop productivity, and supports sustainable precision agriculture. The proposed machine is designed to provide an integrated approach to precision agriculture by combining intelligent weed management, soil monitoring, nutrient management, and autonomous field operation in a single system. The camera-based detection mechanism continuously observes the crop area and identifies weeds, after which the mechanical weeding unit is activated for selective removal. Simultaneously, soil pH and NPK sensors provide continuous information about soil conditions, allowing the system to determine nutrient requirements and operate the spraying mechanism accordingly. The integration of GPS, ultrasonic, and IMU sensors supports autonomous movement, obstacle detection, and directional stability during field operations. The ESP32 coordinates the different subsystems and enables communication of important field and machine parameters through Wi-Fi or Bluetooth. This integrated operation reduces unnecessary human intervention and promotes efficient utilization of agricultural resources. By combining AI-based decision-making with embedded control, sensing, robotics, and IoT connectivity, the system provides a practical approach for reducing labor requirements, minimizing input wastage, improving crop management, and supporting sustainable and data-driven agricultural practices. Keywords— Autonomous agriculture, ESP32, AI in farming, IoT, weed detection, precision farming, nutrient monitoring.","农业面临着杂草生长、劳动力短缺、化肥过量使用以及缺乏实时监测等重大挑战。本文提出了一种基于人工智能（AI）、物联网（IoT）和嵌入式系统的自主多作物除草与养分管理机器。该系统利用基于AI的图像处理进行杂草检测，并自动激活机械切割器以清除杂草。土壤pH值和氮磷钾（NPK）传感器监测养分水平，并仅在需要时实现精准养分喷洒。ESP32微控制器控制导航、传感器接口、电机运行和物联网通信。GPS、超声波和惯性测量单元（IMU）传感器实现自主导航和障碍物检测。实时数据通过Wi-Fi或蓝牙传输给农民。该系统减少了劳动力依赖，最小化化学品使用，提高作物生产力，并支持可持续的精准农业。所提出的机器旨在通过将智能杂草管理、土壤监测、养分管理和自主田间作业集成于单一系统中，提供精准农业的综合方法。基于摄像头的检测机制持续观察作物区域并识别杂草，随后激活机械除草单元进行选择性清除。同时，土壤pH值和NPK传感器提供关于土壤状况的连续信息，使系统能够确定养分需求并相应操作喷洒机制。GPS、超声波和IMU传感器的集成支持田间作业中的自主移动、障碍物检测和方向稳定性。ESP32协调各子系统，并通过Wi-Fi或蓝牙实现重要田间和机器参数的通信。这种集成操作减少了不必要的人为干预，促进了农业资源的高效利用。通过将基于AI的决策与嵌入式控制、传感、机器人技术和物联网连接相结合，该系统为减少劳动力需求、最小化投入浪费、改善作物管理以及支持可持续和数据驱动的农业实践提供了一种实用方法。\n\n关键词——自主农业，ESP32，农业中的AI，物联网，杂草检测，精准农业，养分监测。",null,"International Journal of Creative and Open Research in Engineering and Management","2026-09-03T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,15,14,8,7,1,"提出集成AI与IoT的自主除草与养分管理机器，系统设计较完整，但缺乏实验数据验证，影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","杂草识别","精准农业","养分管理",{"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":47},[45,54,60,66,74,81,87],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":53},9,true,[],[50,51,52],"物联网","internet of things","IoT",[],{"code":55,"score":20,"supported":47,"title":56,"abstract":57,"tags":59},"I1",[],[30,58],"precision agriculture",[27,30],{"code":61,"score":21,"supported":47,"title":62,"abstract":63,"tags":65},"F3",[],[31,64],"fertilizer",[31],{"code":67,"score":68,"supported":47,"title":69,"abstract":70,"tags":73},"H2",6,[],[71,72],"土壤","soil",[],{"code":75,"score":76,"supported":47,"title":77,"abstract":78,"tags":80},"F4",4,[],[79],"除草",[29],{"code":82,"score":76,"supported":47,"title":83,"abstract":84,"tags":86},"I2",[],[85],"artificial intelligence",[28],{"code":88,"score":89,"supported":47,"title":90,"abstract":91,"tags":93},"A7",3,[],[92],"间作",[],[95,96],"农业人工智能 养分管理 智慧农业 杂草识别","农业人工智能 养分管理","农业人工智能养分管理智慧农业杂草识别-1627",2,"10.55041\u002Fijcope.v2i9.016",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":113,"oa_url":9,"card":114,"direction":118,"ingested_from":120},"W7207814982",[103,105,107,109,111],{"name":104,"orcid":9},"D.C Likitha",{"name":106,"orcid":9},"Ganavi N",{"name":108,"orcid":9},"Keerthana C",{"name":110,"orcid":9},"Manushree V",{"name":112,"orcid":9},"Yeshwini Yeshwini",0,{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"提出一种集成AI、IoT和嵌入式系统的自主多作物除草与养分管理机器，实现精准农业。","AI图像处理识别杂草，机械切割；土壤pH和NPK传感器监测养分，精准喷洒；ESP","系统减少劳动力依赖、化学品使用，提高作物生产力，支持可持续精准农业。","智慧农业 \u002F 农业物联网","可延伸研究多作物场景下杂草识别模型的泛化能力，以及基于边缘计算的实时决策优化。","openalex","2026-09-04T23:30:14.890290Z",{"total":68,"page":22,"page_size":68,"items":123},[124,228,299,357,433,491],{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":138,"tags":140,"primary_topic":142,"secondary_topics":146,"classification":154,"search_phrases":203,"slug":206,"view_count":113,"doi":207,"paper":208,"created_at":227},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":134,"substance":135,"depth":134,"authority":136,"freshness":46,"relevant":22,"comment":137},18,22,13,"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。",[139],{"name":130,"url":127},[27,28,50,30,141],"气候韧性",{"code":82,"slug":143,"name":144,"path":145,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[147,148],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":149,"slug":150,"name":151,"path":152,"parent_code":153},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":155},{"candidates":156,"agricultural_evidence":47},[157,167,172,178,184,191,195,199],{"code":82,"score":134,"supported":47,"title":158,"abstract":159,"tags":166},[85],[160,161,162,163,164,165],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[28],{"code":55,"score":168,"supported":47,"title":169,"abstract":170,"tags":171},16,[58],[30,58],[27,30],{"code":33,"score":13,"supported":47,"title":173,"abstract":174,"tags":177},[],[50,175,52,176],"边缘计算","edge computing",[50],{"code":149,"score":46,"supported":47,"title":179,"abstract":180,"tags":183},[],[181,182,150],"灌溉","水资源",[],{"code":185,"score":21,"supported":47,"title":186,"abstract":187,"tags":190},"H7",[],[188,189],"气候变化","climate change",[141],{"code":67,"score":68,"supported":47,"title":192,"abstract":193,"tags":194},[],[71,72],[],{"code":88,"score":89,"supported":47,"title":196,"abstract":197,"tags":198},[],[92],[],{"code":61,"score":89,"supported":47,"title":200,"abstract":201,"tags":202},[],[31],[],[204,205],"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":207,"openalex_id":209,"authors":210,"venue":130,"cited_by_count":113,"oa_url":127,"card":221,"direction":118,"ingested_from":120},"W7223121456",[211,214,216,218],{"name":212,"orcid":213},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":215,"orcid":9},"Oluwadare OYEBODE",{"name":217,"orcid":9},"Iretiayo Adelaiye",{"name":219,"orcid":220},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":222,"method":223,"finding":224,"direction":225,"opportunity":226},"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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":234,"source_url":231,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":243,"secondary_topics":244,"classification":245,"search_phrases":275,"slug":278,"view_count":89,"doi":279,"paper":280,"created_at":298},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":20,"substance":19,"depth":168,"authority":68,"freshness":20,"relevant":22,"comment":238},"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。",[240],{"name":234,"url":231},[27,28,242,50,30],"农业机器人",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":246},{"candidates":247,"agricultural_evidence":47},[248,252,257,262,266,270],{"code":33,"score":134,"supported":47,"title":249,"abstract":250,"tags":251},[52],[50,51,52],[50],{"code":55,"score":253,"supported":47,"title":254,"abstract":255,"tags":256},11,[],[27,30,58],[27,30],{"code":82,"score":21,"supported":47,"title":258,"abstract":259,"tags":261},[],[85,260],"decision support",[28],{"code":67,"score":68,"supported":47,"title":263,"abstract":264,"tags":265},[],[71,72],[],{"code":149,"score":68,"supported":47,"title":267,"abstract":268,"tags":269},[],[181,150],[],{"code":271,"score":22,"supported":14,"title":272,"abstract":273,"tags":274},"F6",[],[],[242],[276,277],"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","10.22214\u002Fijraset.2026.85108",{"doi":279,"openalex_id":281,"authors":282,"venue":234,"cited_by_count":113,"oa_url":231,"card":293,"direction":118,"ingested_from":120},"W7221682750",[283,285,287,289,291],{"name":284,"orcid":9},"Chaitra A S",{"name":286,"orcid":9},"Venugopal Gowda V",{"name":288,"orcid":9},"Vinay H",{"name":290,"orcid":9},"Jeevan T",{"name":292,"orcid":9},"Puneeth M",{"tldr":294,"method":295,"finding":296,"direction":118,"opportunity":297},"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":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":305,"source_url":302,"published_at":306,"category":12,"cover_url":9,"hotness":307,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":321,"search_phrases":336,"slug":339,"view_count":22,"doi":9,"paper":340,"created_at":356},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,47,{"impact":20,"substance":19,"depth":136,"authority":68,"freshness":68,"relevant":22,"comment":310},"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[312,313],{"name":305,"url":302},{"name":305,"url":314},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[27,28,316,30,317],"农业物联网","低功耗通信",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[320],{"code":82,"slug":143,"name":144,"path":145,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":322},{"candidates":323,"agricultural_evidence":47},[324,328,332],{"code":33,"score":134,"supported":47,"title":325,"abstract":326,"tags":327},[52],[50,175,51,52,176],[316],{"code":82,"score":13,"supported":47,"title":329,"abstract":330,"tags":331},[],[161,163,85],[28],{"code":55,"score":20,"supported":47,"title":333,"abstract":334,"tags":335},[],[30,58],[27,30],[337,338],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":9,"openalex_id":341,"authors":342,"venue":305,"cited_by_count":113,"oa_url":302,"card":351,"direction":118,"ingested_from":120},"W7221414266",[343,345,347,349],{"name":344,"orcid":9},"Shital Chavan",{"name":346,"orcid":9},"Anam Bedrekar",{"name":348,"orcid":9},"Sanika Babar",{"name":350,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":352,"method":353,"finding":354,"direction":118,"opportunity":355},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z",{"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":365,"score_detail":366,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":380,"classification":383,"search_phrases":414,"slug":417,"view_count":22,"doi":418,"paper":419,"created_at":432},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":168,"substance":367,"depth":368,"authority":17,"freshness":46,"relevant":22,"comment":369},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。",[371],{"name":363,"url":360},[27,28,50,30,373],"棉花病害",{"code":375,"slug":376,"name":377,"path":378,"parent_code":379},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[381,382],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":82,"slug":143,"name":144,"path":145,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":384},{"candidates":385,"agricultural_evidence":47},[386,390,396,400,404,410],{"code":33,"score":18,"supported":47,"title":387,"abstract":388,"tags":389},[52],[50,52],[50],{"code":375,"score":19,"supported":47,"title":391,"abstract":393,"tags":395},[392],"cotton",[394,392],"棉花",[],{"code":55,"score":253,"supported":47,"title":397,"abstract":398,"tags":399},[],[27,30,58],[27,30],{"code":82,"score":13,"supported":47,"title":401,"abstract":402,"tags":403},[],[160,162,164,85,165],[28],{"code":75,"score":68,"supported":47,"title":405,"abstract":406,"tags":409},[],[407,408],"农药","pesticide",[],{"code":67,"score":68,"supported":47,"title":411,"abstract":412,"tags":413},[],[71,72],[],[415,416],"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":418,"openalex_id":420,"authors":421,"venue":363,"cited_by_count":113,"oa_url":360,"card":427,"direction":118,"ingested_from":120},"W7221340722",[422,424],{"name":423,"orcid":9},"Deepak R Khadse",{"name":425,"orcid":426},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":428,"method":429,"finding":430,"direction":118,"opportunity":431},"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":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":9,"source_name":439,"source_url":436,"published_at":306,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":447,"secondary_topics":448,"classification":449,"search_phrases":468,"slug":471,"view_count":22,"doi":472,"paper":473,"created_at":490},4576,"Compressing Semantic Segmentation Models for Agricultural IoT: A Systematic Study of Pruning Trade-Offs for Crop–Weed Segmentation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjimaging12100495","Semantic segmentation of crops and weeds is an important component of selective weeding systems, but accurate segmentation networks can impose substantial computational and memory demands on resource-constrained agricultural platforms. This paper presents a systematic study of the accuracy–efficiency trade-offs involved in compressing semantic segmentation networks for agricultural edge computing. Six architectures spanning conventional CNN encoder–decoders, atrous and pyramid-pooling decoders, a mobile-oriented CNN, and two transformer scales are evaluated on the Crop\u002FWeed Field Image Dataset (CWFID), with three random seeds per architecture. The most favourable compact model is subsequently evaluated under unstructured global magnitude pruning and dependency-aware structured channel pruning, using one-shot and iterative prune-and-fine-tune schedules at sparsity levels from 10% to 80%, with three seeds per configuration. Among the dense baselines, U-Net with a MobileNetV3-Large encoder matches the accuracy of the larger SegFormer-B2 model (88.85% versus 88.67% mIoU, a difference of 0.18% across three seeds, p = 0.58) with 3.7× fewer parameters than SegFormer-B2. Iterative unstructured pruning reaches 80% weight sparsity while retaining 89.04% mIoU; however, because the dense tensor dimensions are unchanged, this sparsity does not reduce the parameter count, GFLOPs, or model size on conventional hardware. Structured channel pruning, which does reduce all three, remains viable only up to 30% sparsity, reducing GFLOPs by 2.1× at a cost of 4.21 mIoU points and collapsing beyond 40%. Without fine-tuning, structured pruning causes severe degradation even at 10% channel sparsity. The results show that architecture selection has a major influence on the accuracy–efficiency trade-off, while the effects of pruning depend strongly on the pruning structure and fine-tuning strategy. Crop segmentation consistently degrades before the aggregate mIoU indicates severe failure, highlighting the need for class-wise evaluation when compressing crop–weed segmentation models.","作物与杂草的语义分割是选择性除草系统的重要组成部分，但精确的分割网络会对资源受限的农业平台造成大量的计算和内存需求。本文针对农业边缘计算中语义分割网络压缩所涉及的精度-效率权衡进行了系统性研究。在Crop\u002FWeed Field Image Dataset（CWFID）数据集上评估了六种架构，涵盖传统CNN编码器-解码器、空洞卷积与金字塔池化解码器、面向移动端的CNN以及两种规模的Transformer，每种架构使用三个随机种子。随后对最优紧凑模型在非结构化全局幅度剪枝和依赖感知结构化通道剪枝下进行评估，采用一次性及迭代式剪枝-微调方案，稀疏度从10%到80%，每种配置使用三个种子。在稠密基线中，采用MobileNetV3-Large编码器的U-Net在精度上匹配了更大的SegFormer-B2模型（88.85%对88.67% mIoU，三个种子间差异为0.18%，p = 0.58），而参数量比SegFormer-B2少3.7倍。迭代式非结构化剪枝在保持89.04% mIoU的同时达到80%权重稀疏度；然而，由于稠密张量维度不变，该稀疏度在常规硬件上并不减少参数量、GFLOPs或模型大小。结构化通道剪枝虽能同时降低这三者，但仅在30%稀疏度以内可行，此时GFLOPs降低2.1倍，代价是损失4.21个mIoU点，超过40%后性能崩溃。在不进行微调的情况下，结构化剪枝即使在10%通道稀疏度下也会导致严重退化。结果表明，架构选择对精度-效率权衡具有重大影响，而剪枝的效果则强烈依赖于剪枝结构和微调策略。作物分割始终在总体mIoU显示严重失败之前就已退化，凸显了在压缩作物-杂草分割模型时进行逐类评估的必要性。","Journal of Imaging",78,{"impact":168,"substance":135,"depth":134,"authority":136,"freshness":46,"relevant":22,"comment":442},"系统评估语义分割模型剪枝精度-效率权衡，含多架构多稀疏度对比数据，对农业边缘部署有实质参考价值。",[444],{"name":439,"url":436},[27,28,316,29,446],"模型轻量化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":450},{"candidates":451,"agricultural_evidence":47},[452,456,460,464],{"code":33,"score":18,"supported":47,"title":453,"abstract":454,"tags":455},[52],[175,176],[316],{"code":75,"score":76,"supported":47,"title":457,"abstract":458,"tags":459},[],[79],[29],{"code":82,"score":98,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[28,446],{"code":55,"score":22,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[27],[469,470],"CWFID 作物 杂草 分割","MobileNetV3 U-Net 剪枝","CWFID作物杂草分割-4576","10.3390\u002Fjimaging12100495",{"doi":472,"openalex_id":474,"authors":475,"venue":439,"cited_by_count":113,"oa_url":436,"card":485,"direction":118,"ingested_from":120},"W7221103527",[476,479,482],{"name":477,"orcid":478},"Flora Amato","https:\u002F\u002Forcid.org\u002F0000-0002-5128-5558",{"name":480,"orcid":481},"Rajib Chandra Ghosh","https:\u002F\u002Forcid.org\u002F0009-0001-1137-3465",{"name":483,"orcid":484},"Semanto Mondal","https:\u002F\u002Forcid.org\u002F0009-0000-2306-4478",{"tldr":486,"method":487,"finding":488,"direction":225,"opportunity":489},"系统评估六种语义分割模型在农业IoT上的压缩精度-效率权衡，重点分析剪枝策略。","在CWFID数据集上对比CNN与Transformer模型，采用非结构化与结构化","MobileNetV3-Large U-Net精度接近SegFormer-B2且参数少3.7倍；结构","需开发类别感知的压缩评估与自适应剪枝方法，以在资源受限下保持作物分割精度。","2026-10-10T23:30:15.016722Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":496,"content":9,"source_name":363,"source_url":494,"published_at":497,"category":12,"cover_url":9,"hotness":307,"is_selected":14,"score":498,"score_detail":499,"sources":501,"tags":506,"primary_topic":508,"secondary_topics":509,"classification":517,"search_phrases":555,"slug":558,"view_count":22,"doi":559,"paper":560,"created_at":570},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。","2026-10-09T00:00:00Z",81,{"impact":135,"substance":367,"depth":134,"authority":136,"freshness":20,"relevant":22,"comment":500},"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。",[502,503],{"name":363,"url":494},{"name":504,"url":505},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,28,242,316,30,507],"自动驾驶农机",{"code":82,"slug":143,"name":144,"path":145,"parent_code":37},[510,516],{"code":511,"slug":512,"name":513,"path":514,"parent_code":515},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":518},{"candidates":519,"agricultural_evidence":47},[520,524,531,535,542,547,551],{"code":82,"score":134,"supported":47,"title":521,"abstract":522,"tags":523},[85],[161,162,163,85,165],[28],{"code":511,"score":368,"supported":47,"title":525,"abstract":527,"tags":530},[526],"agricultural machinery",[528,529,526],"农业机械","收割机",[],{"code":33,"score":13,"supported":47,"title":532,"abstract":533,"tags":534},[],[50,51,52],[316],{"code":536,"score":68,"supported":47,"title":537,"abstract":538,"tags":541},"I3",[],[539,540],"遥感","remote sensing",[],{"code":55,"score":543,"supported":47,"title":544,"abstract":545,"tags":546},5,[],[30],[27,30],{"code":75,"score":89,"supported":47,"title":548,"abstract":549,"tags":550},[],[79],[],{"code":271,"score":22,"supported":14,"title":552,"abstract":553,"tags":554},[],[],[242],[556,557],"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":559,"openalex_id":561,"authors":562,"venue":363,"cited_by_count":113,"oa_url":494,"card":565,"direction":118,"ingested_from":120},"W7221138004",[563],{"name":564,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":566,"method":567,"finding":568,"direction":118,"opportunity":569},"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"]