[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2118":3,"related-2118":99},{"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":69,"slug":72,"view_count":22,"doi":73,"paper":74,"created_at":98},2118,"IoT-Enhanced forecasting and early warning system for whitefly incidence in greenhouses","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112420","IoT-Enhanced forecasting and early warning system for whitefly incidence in greenhouses。Computers and Electronics in Agriculture","物联网增强的温室烟粉虱发生预测与预警系统",null,"Computers and Electronics in Agriculture","2026-09-10T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,17,14,8,1,"核心期刊发表的温室白粉虱IoT预测预警研究，方法新颖且面向设施农业实际防控需求，具备专业参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","设施农业","物联网","病虫害预警",{"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},[45,52,59,64],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},15,true,[49],"IoT",[30,49],[30],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"B7",3,[],[57],"温室",[],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"I1",[],[],[27],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I2",[],[],[28],[70,71],"农业人工智能 病虫害预警 智慧农业 设施农业","农业人工智能 病虫害预警","农业人工智能病虫害预警智慧农业设施农业-2118","10.1016\u002Fj.compag.2026.112420",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":90,"oa_url":6,"card":91,"direction":95,"ingested_from":97},"W7212169080",[77,80,83,85,87],{"name":78,"orcid":79},"Lin‐Ya Chiu","https:\u002F\u002Forcid.org\u002F0000-0003-1691-5582",{"name":81,"orcid":82},"Dan Jeric Arcega Rustia","https:\u002F\u002Forcid.org\u002F0000-0002-5855-8109",{"name":84,"orcid":9},"Ya‐Fang Wu",{"name":86,"orcid":9},"Jui‐Yung Chung",{"name":88,"orcid":89},"Ta‐Te Lin","https:\u002F\u002Forcid.org\u002F0000-0003-0852-1372",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"构建物联网增强的温室白粉虱发生预测预警系统。","物联网传感器采集温室环境数据，结合预测模型实现白粉虱预警。","物联网数据可提升温室白粉虱发生预测与早期预警能力。","智慧农业 \u002F 农业物联网","可探索多模态数据融合与迁移学习，提升不同温室场景下虫害预警泛化性。","openalex","2026-09-11T23:30:01.850931Z",{"total":100,"page":22,"page_size":100,"items":101},6,[102,216,283,354,430,489],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":125,"classification":133,"search_phrases":191,"slug":194,"view_count":90,"doi":195,"paper":196,"created_at":215},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":112,"depth":18,"authority":113,"freshness":114,"relevant":22,"comment":115},22,13,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[117],{"name":108,"url":105},[27,28,30,119,120],"精准农业","气候韧性",{"code":65,"slug":122,"name":123,"path":124,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[126,127],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":128,"slug":129,"name":130,"path":131,"parent_code":132},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":134},{"candidates":135,"agricultural_evidence":47},[136,147,152,158,164,172,179,185],{"code":65,"score":18,"supported":47,"title":137,"abstract":139,"tags":146},[138],"artificial intelligence",[140,141,142,143,144,145],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[28],{"code":60,"score":17,"supported":47,"title":148,"abstract":150,"tags":151},[149],"precision agriculture",[119,149],[27,119],{"code":33,"score":13,"supported":47,"title":153,"abstract":154,"tags":157},[],[30,155,49,156],"边缘计算","edge computing",[30],{"code":128,"score":114,"supported":47,"title":159,"abstract":160,"tags":163},[],[161,162,129],"灌溉","水资源",[],{"code":165,"score":166,"supported":47,"title":167,"abstract":168,"tags":171},"H7",7,[],[169,170],"气候变化","climate change",[120],{"code":173,"score":100,"supported":47,"title":174,"abstract":175,"tags":178},"H2",[],[176,177],"土壤","soil",[],{"code":180,"score":54,"supported":47,"title":181,"abstract":182,"tags":184},"A7",[],[183],"间作",[],{"code":186,"score":54,"supported":47,"title":187,"abstract":188,"tags":190},"F3",[],[189],"养分管理",[],[192,193],"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":195,"openalex_id":197,"authors":198,"venue":108,"cited_by_count":90,"oa_url":105,"card":209,"direction":95,"ingested_from":97},"W7223121456",[199,202,204,206],{"name":200,"orcid":201},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":203,"orcid":9},"Oluwadare OYEBODE",{"name":205,"orcid":9},"Iretiayo Adelaiye",{"name":207,"orcid":208},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":210,"method":211,"finding":212,"direction":213,"opportunity":214},"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":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":9,"source_name":222,"source_url":219,"published_at":223,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":225,"sources":229,"tags":231,"primary_topic":234,"secondary_topics":235,"classification":236,"search_phrases":267,"slug":270,"view_count":90,"doi":271,"paper":272,"created_at":282},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",75,{"impact":17,"substance":226,"depth":19,"authority":227,"freshness":114,"relevant":22,"comment":228},21,12,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[230],{"name":222,"url":219},[27,28,30,232,233],"作物胁迫","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":237},{"candidates":238,"agricultural_evidence":47},[239,244,251,255,259,263],{"code":33,"score":18,"supported":47,"title":240,"abstract":241,"tags":243},[49],[30,242,49],"internet of things",[30],{"code":245,"score":166,"supported":47,"title":246,"abstract":247,"tags":250},"I3",[],[248,249],"遥感","remote sensing",[233],{"code":173,"score":100,"supported":47,"title":252,"abstract":253,"tags":254},[],[176,177],[],{"code":128,"score":100,"supported":47,"title":256,"abstract":257,"tags":258},[],[161,129],[],{"code":60,"score":22,"supported":14,"title":260,"abstract":261,"tags":262},[],[],[27],{"code":65,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[28],[268,269],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":271,"openalex_id":273,"authors":274,"venue":222,"cited_by_count":90,"oa_url":219,"card":277,"direction":95,"ingested_from":97},"W7222730658",[275],{"name":276,"orcid":9},"Sunitha Thankappan",{"tldr":278,"method":279,"finding":280,"direction":95,"opportunity":281},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":294,"tags":296,"primary_topic":298,"secondary_topics":299,"classification":300,"search_phrases":330,"slug":333,"view_count":54,"doi":334,"paper":335,"created_at":353},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":21,"substance":20,"depth":17,"authority":100,"freshness":21,"relevant":22,"comment":293},"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。",[295],{"name":289,"url":286},[27,28,297,30,119],"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":301},{"candidates":302,"agricultural_evidence":47},[303,307,312,317,321,325],{"code":33,"score":18,"supported":47,"title":304,"abstract":305,"tags":306},[49],[30,242,49],[30],{"code":60,"score":308,"supported":47,"title":309,"abstract":310,"tags":311},11,[],[27,119,149],[27,119],{"code":65,"score":166,"supported":47,"title":313,"abstract":314,"tags":316},[],[138,315],"decision support",[28],{"code":173,"score":100,"supported":47,"title":318,"abstract":319,"tags":320},[],[176,177],[],{"code":128,"score":100,"supported":47,"title":322,"abstract":323,"tags":324},[],[161,129],[],{"code":326,"score":22,"supported":14,"title":327,"abstract":328,"tags":329},"F6",[],[],[297],[331,332],"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":334,"openalex_id":336,"authors":337,"venue":289,"cited_by_count":90,"oa_url":286,"card":348,"direction":95,"ingested_from":97},"W7221682750",[338,340,342,344,346],{"name":339,"orcid":9},"Chaitra A S",{"name":341,"orcid":9},"Venugopal Gowda V",{"name":343,"orcid":9},"Vinay H",{"name":345,"orcid":9},"Jeevan T",{"name":347,"orcid":9},"Puneeth M",{"tldr":349,"method":350,"finding":351,"direction":95,"opportunity":352},"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":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":362,"score_detail":363,"sources":366,"tags":368,"primary_topic":370,"secondary_topics":376,"classification":379,"search_phrases":411,"slug":414,"view_count":22,"doi":415,"paper":416,"created_at":429},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":17,"substance":364,"depth":19,"authority":227,"freshness":114,"relevant":22,"comment":365},20,"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。",[367],{"name":360,"url":357},[27,28,30,119,369],"棉花病害",{"code":371,"slug":372,"name":373,"path":374,"parent_code":375},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[377,378],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":65,"slug":122,"name":123,"path":124,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":380},{"candidates":381,"agricultural_evidence":47},[382,386,392,396,400,407],{"code":33,"score":46,"supported":47,"title":383,"abstract":384,"tags":385},[49],[30,49],[30],{"code":371,"score":20,"supported":47,"title":387,"abstract":389,"tags":391},[388],"cotton",[390,388],"棉花",[],{"code":60,"score":308,"supported":47,"title":393,"abstract":394,"tags":395},[],[27,119,149],[27,119],{"code":65,"score":13,"supported":47,"title":397,"abstract":398,"tags":399},[],[140,142,144,138,145],[28],{"code":401,"score":100,"supported":47,"title":402,"abstract":403,"tags":406},"F4",[],[404,405],"农药","pesticide",[],{"code":173,"score":100,"supported":47,"title":408,"abstract":409,"tags":410},[],[176,177],[],[412,413],"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":415,"openalex_id":417,"authors":418,"venue":360,"cited_by_count":90,"oa_url":357,"card":424,"direction":95,"ingested_from":97},"W7221340722",[419,421],{"name":420,"orcid":9},"Deepak R Khadse",{"name":422,"orcid":423},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":425,"method":426,"finding":427,"direction":95,"opportunity":428},"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":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":437,"sources":439,"tags":441,"primary_topic":444,"secondary_topics":445,"classification":447,"search_phrases":475,"slug":478,"view_count":90,"doi":479,"paper":480,"created_at":488},4658,"Energy-Efficient Smart Agriculture Management Framework Using IoT and Ensemble Machine Learning for Intelligent Crop Health Monitoring and Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.62s.32","Context: Smart agriculture plays an important role in addressing the challenges of food security, environmental sustainability, and sustainable farming through IoT.Farmers are facing many issues in traditional farming, such as inefficient monitoring and poor crop health detection.Traditional farming has a negative impact on crop production and sustainability.In this regard, the development of intelligent and energy-efficient agricultural monitoring systems has become crucial for contemporary precision farming applications.Objective: This study aims to monitor crop health and optimize resource use using machine learning techniques.Our proposed system intends to monitor crop conditions in real time, enhance agricultural management, and minimize unnecessary sensor utilization and communication overhead.Material\u002Fmethods: In this paper, we used five machine learning classifiers to monitor crop health using sensor data.The agricultural sensor data (soil moisture, soil temperature, soil pH, humidity, air temperature, solar radiation, wind speed, NDVI index, and 5G latency) were used for crop health monitoring.The models, such as RF, XGBoost, LightGBM, and CatBoost, and soft voting were used for crop health classification into the Healthy, High_Stress, and Moderate_Stress categories.The experimental observation revealed that soft voting achieved the highest accuracy of 94.5%, macro-F1 score of 0.945, weighted F1 score of 0.945, and AUC-ROC score of 0.988 compared to the other models.The cross-validation results further validated the robustness and stability of the proposed framework.The IoTenabled intelligent agriculture management system offers an efficient, scalable solution for real-time crop health monitoring, energy-efficient resource optimization, and sustainable smart farming.","背景：智慧农业在借助物联网（IoT）应对粮食安全、环境可持续性和可持续耕作等挑战方面发挥着重要作用。农民在传统耕作中面临诸多问题，例如监测效率低下和作物健康检测不佳。传统耕作对作物产量和可持续性产生负面影响。在此背景下，开发智能且节能的农业监测系统对于当代精准农业应用已变得至关重要。目标：本研究旨在利用机器学习技术监测作物健康并优化资源利用。我们提出的系统旨在实时监测作物状况、增强农业管理并最大限度减少不必要的传感器使用和通信开销。材料\u002F方法：在本文中，我们使用了五种机器学习分类器，利用传感器数据监测作物健康。农业传感器数据（土壤湿度、土壤温度、土壤pH值、湿度、空气温度、太阳辐射、风速、NDVI指数和5G延迟）被用于作物健康监测。RF、XGBoost、LightGBM和CatBoost等模型以及软投票被用于将作物健康分类为健康、高胁迫和中等胁迫类别。实验观察表明，与其他模型相比，软投票达到了最高的准确率94.5%、宏F1分数0.945、加权F1分数0.945和AUC-ROC分数0.988。交叉验证结果进一步验证了所提出框架的鲁棒性和稳定性。该物联网赋能的智能农业管理系统为实时作物健康监测、节能资源优化和可持续智慧农业提供了一种高效、可扩展的解决方案。","International Journal of Drug Delivery Technology",{"impact":21,"substance":17,"depth":20,"authority":100,"freshness":21,"relevant":22,"comment":438},"方法组合与多源传感器数据有一定新意，但期刊权威性弱、属实验室验证，公共价值有限，不宜进入每日精选。",[440],{"name":436,"url":433},[27,28,30,442,443],"作物健康监测","资源优化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[446],{"code":65,"slug":122,"name":123,"path":124,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":448},{"candidates":449,"agricultural_evidence":47},[450,455,460,464,471],{"code":60,"score":18,"supported":47,"title":451,"abstract":453,"tags":454},[452],"smart agriculture",[27,119,452],[27],{"code":33,"score":18,"supported":47,"title":456,"abstract":457,"tags":459},[49],[30,458,49],"农业传感",[30],{"code":65,"score":46,"supported":47,"title":461,"abstract":462,"tags":463},[143],[141,143],[28],{"code":465,"score":100,"supported":47,"title":466,"abstract":467,"tags":470},"A8",[],[468,469],"粮食安全","food security",[],{"code":173,"score":100,"supported":47,"title":472,"abstract":473,"tags":474},[],[176,177],[],[476,477],"IoT 作物健康监测","集成学习 精准农业","IoT作物健康监测-4658","10.25258\u002Fijddt.16.62s.32",{"doi":479,"openalex_id":481,"authors":482,"venue":436,"cited_by_count":90,"oa_url":433,"card":483,"direction":95,"ingested_from":97},"W7221334384",[],{"tldr":484,"method":485,"finding":486,"direction":95,"opportunity":487},"用IoT传感器数据与集成机器学习实现作物健康分类与资源优化。","五种分类器（RF、XGBoost、LightGBM、CatBoost、软投票）结","软投票集成模型准确率达94.5%，AUC-ROC为0.988，优于单一模型。","可探索边缘端轻量化集成模型与能耗-精度联合优化，降低传感器通信开销。","2026-10-11T23:30:07.636015Z",{"id":490,"title":491,"url":492,"summary":493,"summary_zh":494,"content":9,"source_name":360,"source_url":492,"published_at":495,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":503,"secondary_topics":504,"classification":510,"search_phrases":537,"slug":540,"view_count":22,"doi":541,"paper":542,"created_at":553},4491,"INTEGRATING \"SMART AGRICULTURE\" TECHNOLOGIES INTO THE EDUCATIONAL PROCESS IN AGRICULTURAL EDUCATION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23242369","This thesis discusses the pedagogical potential of integrating “Smart Agriculture” technologies into the teaching of specialized subjects at higher agricultural education institutions. Internet of Things (IoT), sensors, digital monitoring, Geographic Information Systems (GIS), remote sensing, and artificial intelligence tools enable students to connect theoretical knowledge with real-world agrotechnological tasks. It is proposed that these technologies should be used in lessons not merely as demonstration tools, but as means of identifying problems, collecting and analyzing data, and making practical decisions.","本论文探讨了将“智慧农业”技术融入高等农业院校专业课程教学的教学潜力。物联网（IoT）、传感器、数字监测、地理信息系统（GIS）、遥感以及人工智能工具能够帮助学生将理论知识与现实农业技术任务相联系。本文提出，这些技术不应仅在课堂上作为演示工具使用，而应作为发现问题、收集与分析数据以及做出实际决策的手段。","2026-10-08T00:00:00Z",64,{"impact":227,"substance":17,"depth":46,"authority":227,"freshness":114,"relevant":22,"comment":498},"探讨将智慧农业技术融入高等农业教学的论文，属细分领域教育研究，有方法性内容但影响面有限。",[500],{"name":360,"url":492},[27,28,30,248,502],"农业教育",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[505],{"code":506,"slug":507,"name":508,"path":509,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":511},{"candidates":512,"agricultural_evidence":47},[513,517,521,528,532],{"code":60,"score":46,"supported":47,"title":514,"abstract":515,"tags":516},[452],[27,452],[27],{"code":33,"score":13,"supported":47,"title":518,"abstract":519,"tags":520},[],[30,242,49],[30],{"code":506,"score":114,"supported":47,"title":522,"abstract":523,"tags":527},[],[524,525,526],"地理信息","GIS","geographic information",[],{"code":245,"score":166,"supported":47,"title":529,"abstract":530,"tags":531},[],[248,249],[248],{"code":65,"score":533,"supported":47,"title":534,"abstract":535,"tags":536},4,[],[138],[28],[538,539],"智慧农业 农业教育 课程","农业人工智能 农业教育 智慧农业 物联网","智慧农业农业教育课程-4491","10.5281\u002Fzenodo.23242369",{"doi":541,"openalex_id":543,"authors":544,"venue":360,"cited_by_count":90,"oa_url":492,"card":547,"direction":95,"ingested_from":97},"W7221022747",[545],{"name":546,"orcid":9},"Shamsiyeva Ogiloy",{"tldr":548,"method":549,"finding":550,"direction":551,"opportunity":552},"探讨将智慧农业技术融入高等农业教育，作为学生解决实际农艺问题的教学工具。","整合物联网、传感器、GIS、遥感与AI工具于专业课程教学。","技术不应仅作演示，而应成为发现问题、采集分析数据并决策的手段。","数字乡村与农业信息化","可研究智慧农业教学对学生数据决策能力的量化影响及课程设计模式。","2026-10-09T23:30:08.054996Z"]