[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3161":3,"related-3161":115},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":95,"slug":98,"view_count":84,"doi":99,"paper":100,"created_at":114},3161,"Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.59256\u002Fijire.20260705005","Plant diseases are a major challenge in modern agriculture, as they can significantly reduce crop yield, crop quality, and economic productivity. Traditional plant disease detection methods mainly depend on visual inspection and expert knowledge, which can be time-consuming, subjective, and difficult to apply across large agricultural fields. The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), and Computer Vision, has created new opportunities for automated and efficient crop disease detection and crop health monitoring. AI-based plant disease detection systems can analyze plant and leaf images to identify disease-related characteristics such as leaf discoloration, spots, lesions, texture variations, and abnormal growth patterns. Advanced techniques, including Convolutional Neural Networks (CNNs), transfer learning, image processing, image segmentation, and object detection, can be used for plant disease classification and identification of affected regions with high accuracy. This chapter introduces the fundamental concepts of AI-based plant disease detection, covering image acquisition, image preprocessing, feature extraction, model development, disease classification, and performance evaluation. It also examines the applications of AI in precision agriculture, smart agriculture, mobile-based plant disease diagnosis, drone-assisted crop monitoring, IoT-enabled farming, and edge-based agricultural systems. Furthermore, the chapter discusses important challenges such as limited and imbalanced datasets, environmental variations, similar disease symptoms, model generalization, computational requirements, and the need for explainable AI in agricultural applications. Finally, emerging trends and future opportunities are discussed, with emphasis on integrating AI with IoT, remote sensing, agricultural robotics, and multimodal agricultural data. The chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.","植物病害是现代农业面临的一项重大挑战，因为它们会显著降低作物产量、作物品质和经济生产力。传统的植物病害检测方法主要依赖视觉检查和专家知识，这种方式耗时、主观性强，且难以在大规模农田中应用。人工智能（AI）的快速发展，尤其是机器学习（ML）、深度学习（DL）和计算机视觉，为自动化、高效的作物病害检测和作物健康监测创造了新的机遇。基于AI的植物病害检测系统可以分析植物和叶片图像，以识别与病害相关的特征，如叶片变色、斑点、病斑、纹理变化和异常生长模式。包括卷积神经网络（CNN）、迁移学习、图像处理、图像分割和目标检测在内的先进技术，可用于植物病害分类和受影响区域的高精度识别。本章介绍了基于AI的植物病害检测的基本概念，涵盖图像采集、图像预处理、特征提取、模型开发、病害分类和性能评估。本章还探讨了AI在精准农业、智慧农业、基于移动端的植物病害诊断、无人机辅助作物监测、物联网（IoT）赋能农业和边缘农业系统中的应用。此外，本章讨论了重要挑战，如数据集有限且不平衡、环境变化、相似病害症状、模型泛化、计算需求，以及农业应用中可解释AI的需求。最后，讨论了新兴趋势和未来机遇，重点强调将AI与物联网、遥感、农业机器人和多模态农业数据相结合。本章为理解人工智能用于植物病害检测如何支持早期病害识别、减少作物损失、优化农业资源，并促进可持续和智能农业实践提供了基础。",null,"International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z","论文",10,false,59,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,14,15,8,1,"系统综述AI在植物病害检测中的应用，内容全面但属教科书式介绍，方法新颖性与数据规模有限，可作为智慧农业主题聚合素材。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业物联网","精准农业","植物病害检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,58,66,74,82,89],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":57},18,true,[48],"artificial intelligence",[50,51,52,53,54,55,48,56],"目标检测","深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[27],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":65},"I1",11,[],[26,29,63,64],"smart agriculture","precision agriculture",[26,29],{"code":67,"score":68,"supported":46,"title":69,"abstract":70,"tags":73},"I5",7,[],[71,72],"物联网","IoT",[28],{"code":75,"score":76,"supported":46,"title":77,"abstract":78,"tags":81},"I3",6,[],[79,80],"遥感","remote sensing",[],{"code":83,"score":84,"supported":46,"title":85,"abstract":86,"tags":88},"F6",3,[],[87],"农业机器人",[],{"code":90,"score":84,"supported":46,"title":91,"abstract":92,"tags":94},"I6",[],[93],"agricultural data",[],[96,97],"AI 植物病害检测","无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":105,"oa_url":9,"card":106,"direction":112,"ingested_from":113},"W7213950095",[103],{"name":104,"orcid":9},"Jamuna Ratcha",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"综述AI在植物病害检测中的应用，涵盖图像采集到模型评估全流程及未来趋势。","综述CNN、迁移学习、图像分割与目标检测在叶片病害识别中的应用。","AI可高精度识别病害，但受限于数据集不足、环境变化与模型泛化能力。","农业人工智能与决策模型","可探索多模态数据融合与可解释AI，提升复杂田间环境下病害检测的泛化能力。","智慧农业 \u002F 农业物联网","openalex","2026-09-22T23:30:11.209653Z",{"total":76,"page":21,"page_size":76,"items":116},[117,178,242,323,392,464],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":160,"slug":163,"view_count":164,"doi":165,"paper":166,"created_at":177},4912,"Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.11.no12.2025.pg220.231","Plant diseases pose a significant threat to global food security, crop yield, and agricultural sustainability. Traditional disease diagnosis methods, relying on expert visual inspection and laboratory analyses, are often time-consuming, labor-intensive, and costly, limiting their scalability. Recent advances in artificial intelligence, particularly deep learning, offer automated and accurate solutions for plant disease detection. This review synthesizes current research on deep learning–based approaches, focusing on convolutional neural networks (CNNs), hybrid CNN-transformer models, attention mechanisms, and generative data augmentation techniques. CNNs have demonstrated strong capabilities in extracting hierarchical features from leaf images, capturing complex patterns such as lesion shapes, color variations, and textures, thereby outperforming conventional machine learning and rule-based methods. Hybrid architectures and transformer-based models enhance feature extraction, particularly for subtle or visually similar disease symptoms. Data augmentation strategies, including generative adversarial networks (GANs), help address class imbalance and improve model generalization. Despite high reported accuracies, challenges remain, including limited dataset diversity, environmental variability, overfitting, lack of explainable AI, and computational constraints for mobile deployment. Future research should prioritize explainable models, multi-crop and multi-disease detection, lightweight architectures suitable for field applications, and integration with precision agriculture systems. Overall, deep learning–based plant disease detection presents a transformative approach for early diagnosis, timely intervention, and sustainable agricultural practices, offering the potential to reduce crop losses, enhance productivity, and contribute to global food security.","植物病害对全球粮食安全、作物产量和农业可持续性构成重大威胁。传统的病害诊断方法依赖专家视觉检查和实验室分析，往往耗时、费力且成本高昂，限制了其可扩展性。人工智能，特别是深度学习的最新进展，为植物病害检测提供了自动化且准确的解决方案。本文综述了基于深度学习方法的最新研究，重点关注卷积神经网络（CNN）、CNN-Transformer混合模型、注意力机制和生成式数据增强技术。CNN在从叶片图像中提取层次化特征方面展现出强大能力，能够捕捉病变形状、颜色变化和纹理等复杂模式，从而优于传统机器学习和基于规则的方法。混合架构和基于Transformer的模型增强了特征提取能力，尤其针对细微或视觉上相似的病害症状。数据增强策略，包括生成对抗网络（GAN），有助于解决类别不平衡问题并提高模型泛化能力。尽管已有研究报告了较高的准确率，但仍存在诸多挑战，包括数据集多样性有限、环境变异性、过拟合、缺乏可解释的人工智能以及移动端部署的计算约束。未来研究应优先关注可解释模型、多作物和多病害检测、适用于田间应用的轻量级架构以及与精准农业系统的集成。总体而言，基于深度学习的植物病害检测为早期诊断、及时干预和可持续农业实践提供了一种变革性方法，具有减少作物损失、提高生产力并促进全球粮食安全的潜力。","INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-10-12T00:00:00Z",75,{"impact":45,"substance":127,"depth":128,"authority":17,"freshness":20,"relevant":21,"comment":129},20,17,"系统综述深度学习植物病害检测的方法进展与挑战，学术价值较高但偏理论，对产业落地有参考意义。",[131],{"name":123,"url":120},[26,27,51,29,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":136},{"candidates":137,"agricultural_evidence":46},[138,143,147,154],{"code":32,"score":139,"supported":46,"title":140,"abstract":141,"tags":142},19,[55],[51,52,54,55,48],[27,51],{"code":59,"score":20,"supported":46,"title":144,"abstract":145,"tags":146},[],[29,64],[26,29],{"code":148,"score":76,"supported":46,"title":149,"abstract":150,"tags":153},"A8",[],[151,152],"粮食安全","food security",[],{"code":155,"score":84,"supported":46,"title":156,"abstract":157,"tags":159},"H8",[],[158],"可持续农业",[],[161,162],"植物病害检测 深度学习","卷积神经网络 作物病害识别","植物病害检测深度学习-4912",2,"10.56201\u002Fijcsmt.vol.11.no12.2025.pg220.231",{"doi":165,"openalex_id":167,"authors":168,"venue":123,"cited_by_count":105,"oa_url":171,"card":172,"direction":110,"ingested_from":113},"W7221637766",[169],{"name":170,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 12 2025\u002FDeep Learning Approaches for Plant 220-231.pdf",{"tldr":173,"method":174,"finding":175,"direction":110,"opportunity":176},"综述深度学习在植物病害检测中的应用，涵盖CNN、混合Transformer、注意力机制与生成式数据增","综述CNN、CNN-Transformer混合模型、注意力机制及GAN数据增强在","深度学习检测精度高，但仍受数据集多样性、环境变异、可解释性和移动部署算力限制。","可探索轻量化可解释模型、多作物多病害检测及与精准农业系统集成，解决田间部署难题。","2026-10-14T23:30:54.687367Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":186,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":195,"primary_topic":197,"secondary_topics":201,"classification":203,"search_phrases":221,"slug":224,"view_count":21,"doi":9,"paper":225,"created_at":241},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":18,"depth":189,"authority":76,"freshness":76,"relevant":21,"comment":190},13,"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。",[192,193],{"name":184,"url":181},{"name":184,"url":194},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[26,27,28,29,196],"低功耗通信",{"code":67,"slug":198,"name":199,"path":200,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[202],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,213,217],{"code":67,"score":45,"supported":46,"title":207,"abstract":208,"tags":212},[72],[71,209,210,72,211],"边缘计算","internet of things","edge computing",[28],{"code":32,"score":13,"supported":46,"title":214,"abstract":215,"tags":216},[],[52,54,48],[27],{"code":59,"score":20,"supported":46,"title":218,"abstract":219,"tags":220},[],[29,64],[26,29],[222,223],"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":226,"authors":227,"venue":184,"cited_by_count":105,"oa_url":181,"card":236,"direction":112,"ingested_from":113},"W7221414266",[228,230,232,234],{"name":229,"orcid":9},"Shital Chavan",{"name":231,"orcid":9},"Anam Bedrekar",{"name":233,"orcid":9},"Sanika Babar",{"name":235,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":237,"method":238,"finding":239,"direction":112,"opportunity":240},"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":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":249,"category":12,"cover_url":9,"hotness":186,"is_selected":14,"score":250,"score_detail":251,"sources":254,"tags":259,"primary_topic":261,"secondary_topics":262,"classification":270,"search_phrases":307,"slug":310,"view_count":21,"doi":311,"paper":312,"created_at":322},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",81,{"impact":252,"substance":127,"depth":45,"authority":189,"freshness":20,"relevant":21,"comment":253},22,"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。",[255,256],{"name":248,"url":245},{"name":257,"url":258},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[26,27,87,28,29,260],"自动驾驶农机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[263,269],{"code":264,"slug":265,"name":266,"path":267,"parent_code":268},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":67,"slug":198,"name":199,"path":200,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":271},{"candidates":272,"agricultural_evidence":46},[273,277,284,288,292,297,303],{"code":32,"score":45,"supported":46,"title":274,"abstract":275,"tags":276},[48],[52,53,54,48,56],[27],{"code":264,"score":128,"supported":46,"title":278,"abstract":280,"tags":283},[279],"agricultural machinery",[281,282,279],"农业机械","收割机",[],{"code":67,"score":13,"supported":46,"title":285,"abstract":286,"tags":287},[],[71,210,72],[28],{"code":75,"score":76,"supported":46,"title":289,"abstract":290,"tags":291},[],[79,80],[],{"code":59,"score":293,"supported":46,"title":294,"abstract":295,"tags":296},5,[],[29],[26,29],{"code":298,"score":84,"supported":46,"title":299,"abstract":300,"tags":302},"F4",[],[301],"除草",[],{"code":83,"score":21,"supported":14,"title":304,"abstract":305,"tags":306},[],[],[87],[308,309],"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":311,"openalex_id":313,"authors":314,"venue":248,"cited_by_count":105,"oa_url":245,"card":317,"direction":112,"ingested_from":113},"W7221138004",[315],{"name":316,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":318,"method":319,"finding":320,"direction":112,"opportunity":321},"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":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":331,"score_detail":332,"sources":335,"tags":337,"primary_topic":339,"secondary_topics":340,"classification":341,"search_phrases":366,"slug":368,"view_count":84,"doi":369,"paper":370,"created_at":391},4177,"The Changing Landscape of Plant Disease Detection: The Role of Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i104569","Plant diseases constrain agricultural productivity and food security, while conventional diagnosis depends heavily on visible symptoms and specialist expertise, limiting rapid and scalable assessment across heterogeneous agricultural environments. Artificial intelligence (AI), particularly machine learning, deep learning, and computer vision, offers complementary approaches for automated and non-invasive disease detection. This review examines AI-based methods for image classification, object detection, image segmentation, severity assessment, and early or pre-symptomatic disease detection. It also considers the integration of AI with hyperspectral, multispectral, and thermal imaging and unmanned aerial vehicles for crop-scale monitoring and precision management. Reported studies demonstrate high performance under many experimental conditions; however, reliable field deployment remains constrained by limited or imbalanced datasets, symptom similarity, environmental variability, domain shift, computational requirements, and insufficient independent validation. The review further considers explainable artificial intelligence, edge-based computing, multimodal sensing, foundation models, vision-language models, and federated learning as emerging directions. These approaches may improve robustness, interpretability, accessibility, and scalability, but their value depends on representative field data, biological validity, rigorous external validation, and integration with plant-pathology expertise across diverse crops, cultivars, locations, seasons, and imaging conditions. AI should therefore be regarded as a complementary decision-support tool whose practical contribution depends on combining computational methods with biological knowledge and real-world agricultural observations.","植物病害制约农业生产力和粮食安全，而传统诊断高度依赖可见症状和专家经验，限制了在异质性农业环境中进行快速、可扩展的评估。人工智能（AI），特别是机器学习、深度学习和计算机视觉，为自动化、非侵入式病害检测提供了互补性方法。本文综述了基于AI的图像分类、目标检测、图像分割、严重度评估以及早期或症状前病害检测方法，并探讨了AI与高光谱、多光谱和热成像及无人机相结合用于作物尺度监测和精准管理的集成应用。已报道的研究在许多实验条件下表现出高性能；然而，可靠的田间部署仍受限于数据集有限或不平衡、症状相似性、环境变异性、域偏移、计算需求以及独立验证不足等问题。本文进一步探讨了可解释人工智能、边缘计算、多模态传感、基础模型、视觉语言模型和联邦学习等新兴方向。这些方法有望提升鲁棒性、可解释性、可及性和可扩展性，但其价值取决于具有代表性的田间数据、生物学有效性、严格的外部验证，以及与植物病理学专业知识在多样化作物、品种、地点、季节和成像条件下的整合。因此，AI应被视为一种互补性决策支持工具，其实际贡献取决于将计算方法与生物学知识和真实农业观测相结合。","Journal of Scientific Research and Reports","2026-10-03T00:00:00Z",78,{"impact":45,"substance":333,"depth":45,"authority":189,"freshness":20,"relevant":21,"comment":334},21,"系统综述AI植物病害检测的方法、瓶颈与前沿方向，学术价值高但属综述性论文，产业落地影响有限。",[336],{"name":329,"url":326},[26,27,29,338,30],"遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":342},{"candidates":343,"agricultural_evidence":46},[344,348,354,358,362],{"code":32,"score":45,"supported":46,"title":345,"abstract":346,"tags":347},[48],[50,51,52,53,54,55,48,56],[27],{"code":75,"score":68,"supported":46,"title":349,"abstract":350,"tags":353},[],[351,352],"multispectral","hyperspectral",[338],{"code":148,"score":76,"supported":46,"title":355,"abstract":356,"tags":357},[],[151,152],[],{"code":67,"score":84,"supported":46,"title":359,"abstract":360,"tags":361},[],[209],[],{"code":59,"score":164,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[26,29],[96,367],"无人机 高光谱 作物监测","AI植物病害检测-4177","10.9734\u002Fjsrr\u002F2026\u002Fv32i104569",{"doi":369,"openalex_id":371,"authors":372,"venue":329,"cited_by_count":105,"oa_url":326,"card":386,"direction":110,"ingested_from":113},"W7218426608",[373,375,377,380,382,384],{"name":374,"orcid":9},"Aksa Anna Shajan",{"name":376,"orcid":9},"Sumiya K. V.",{"name":378,"orcid":379},"Sainamole Kurian P.","https:\u002F\u002Forcid.org\u002F0009-0004-1992-3582",{"name":381,"orcid":9},"Pooja Naduvalath",{"name":383,"orcid":9},"Athira Suresh",{"name":385,"orcid":9},"Aswathi P. R.",{"tldr":387,"method":388,"finding":389,"direction":110,"opportunity":390},"综述AI在植物病害检测中的应用，涵盖分类、检测、分割及早期诊断，并指出田间部署的瓶颈与新兴方向。","综述机器学习、深度学习、计算机视觉与高光谱\u002F多光谱\u002F热成像及无人机结合的研究。","AI实验表现优异，但受数据、环境变异和验证不足限制，田间可靠部署仍困难。","可探索多模态基础模型与联邦学习在跨域田间病害检测中的鲁棒性和可解释性验证。","2026-10-04T23:30:40.159648Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":399,"category":12,"cover_url":9,"hotness":186,"is_selected":14,"score":400,"score_detail":401,"sources":406,"tags":411,"primary_topic":413,"secondary_topics":414,"classification":416,"search_phrases":431,"slug":434,"view_count":403,"doi":435,"paper":436,"created_at":463},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。","Preprints.org","2026-09-23T00:00:00Z",50,{"impact":76,"substance":402,"depth":19,"authority":403,"freshness":404,"relevant":21,"comment":405},16,4,9,"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[407,408],{"name":398,"url":395},{"name":409,"url":410},"Computers","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15100663",[26,27,28,29,412],"农业教育",{"code":67,"slug":198,"name":199,"path":200,"parent_code":36},[415],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":417},{"candidates":418,"agricultural_evidence":46},[419,423,427],{"code":67,"score":45,"supported":46,"title":420,"abstract":421,"tags":422},[72],[71,210,72],[28],{"code":32,"score":17,"supported":46,"title":424,"abstract":425,"tags":426},[48],[48],[27],{"code":59,"score":20,"supported":46,"title":428,"abstract":429,"tags":430},[],[29,64],[26,29],[432,433],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":435,"openalex_id":437,"authors":438,"venue":398,"cited_by_count":105,"oa_url":395,"card":457,"direction":112,"ingested_from":113},"W7214071608",[439,442,445,448,451,454],{"name":440,"orcid":441},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":443,"orcid":444},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":446,"orcid":447},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":449,"orcid":450},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":452,"orcid":453},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":455,"orcid":456},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":458,"method":459,"finding":460,"direction":461,"opportunity":462},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","2026-09-24T23:30:13.353443Z",{"id":465,"title":466,"url":467,"summary":468,"summary_zh":469,"content":9,"source_name":470,"source_url":467,"published_at":471,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":472,"score_detail":473,"sources":475,"tags":477,"primary_topic":479,"secondary_topics":480,"classification":488,"search_phrases":535,"slug":538,"view_count":105,"doi":539,"paper":540,"created_at":558},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":45,"substance":252,"depth":45,"authority":189,"freshness":404,"relevant":21,"comment":474},"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。",[476],{"name":470,"url":467},[26,27,71,29,478],"气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[481,482],{"code":67,"slug":198,"name":199,"path":200,"parent_code":36},{"code":483,"slug":484,"name":485,"path":486,"parent_code":487},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":489},{"candidates":490,"agricultural_evidence":46},[491,495,499,503,509,516,523,529],{"code":32,"score":45,"supported":46,"title":492,"abstract":493,"tags":494},[48],[51,52,53,54,55,56],[27],{"code":59,"score":402,"supported":46,"title":496,"abstract":497,"tags":498},[64],[29,64],[26,29],{"code":67,"score":13,"supported":46,"title":500,"abstract":501,"tags":502},[],[71,209,72,211],[71],{"code":483,"score":404,"supported":46,"title":504,"abstract":505,"tags":508},[],[506,507,484],"灌溉","水资源",[],{"code":510,"score":68,"supported":46,"title":511,"abstract":512,"tags":515},"H7",[],[513,514],"气候变化","climate change",[478],{"code":517,"score":76,"supported":46,"title":518,"abstract":519,"tags":522},"H2",[],[520,521],"土壤","soil",[],{"code":524,"score":84,"supported":46,"title":525,"abstract":526,"tags":528},"A7",[],[527],"间作",[],{"code":530,"score":84,"supported":46,"title":531,"abstract":532,"tags":534},"F3",[],[533],"养分管理",[],[536,537],"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":539,"openalex_id":541,"authors":542,"venue":470,"cited_by_count":105,"oa_url":467,"card":553,"direction":112,"ingested_from":113},"W7223121456",[543,546,548,550],{"name":544,"orcid":545},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":547,"orcid":9},"Oluwadare OYEBODE",{"name":549,"orcid":9},"Iretiayo Adelaiye",{"name":551,"orcid":552},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":554,"method":555,"finding":556,"direction":110,"opportunity":557},"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。","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"]