[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2908":3,"related-2908":92},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":91},2908,"An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112447","An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization。Computers and Electronics in Agriculture","一种信息驱动的空地协同框架，用于基于无人机耕作缺陷识别与再耕作路径优化。《农业计算机与电子》",null,"Computers and Electronics in Agriculture","2026-09-18T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,14,8,1,"发表于农业信息领域核心期刊，提出空地协同的无人机耕地缺陷识别与再耕路径优化框架，方法新颖且面向精准农业实际需求，具备较高参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业无人机","精准农业","遥感",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":54,"reason":55},[38,44,49],{"code":39,"score":40,"supported":14,"title":41,"abstract":42,"tags":43},"I1",2,[],[],[26,29],{"code":45,"score":21,"supported":14,"title":46,"abstract":47,"tags":48},"I2",[],[],[27],{"code":50,"score":21,"supported":14,"title":51,"abstract":52,"tags":53},"I3",[],[],[30],true,"缺少足够的主题证据",[57,58],"无人机 耕地缺陷 识别","再耕路径 优化","无人机耕地缺陷识别-2908",3,"10.1016\u002Fj.compag.2026.112447",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":83,"oa_url":9,"card":84,"direction":88,"ingested_from":90},"W7213547466",[65,67,69,71,74,76,78,81],{"name":66,"orcid":9},"Chenshuo Xie",{"name":68,"orcid":9},"Yejun Zhu",{"name":70,"orcid":9},"Dongfang Li",{"name":72,"orcid":73},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":75,"orcid":9},"Le Yang",{"name":77,"orcid":9},"Yuxuan Wan",{"name":79,"orcid":80},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":82,"orcid":9},"Mingfeng Wang",0,{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。","无人机遥感影像与信息驱动算法，识别缺陷并规划重耕路径。","框架能有效识别耕整缺陷并生成优化补耕路径，提升作业质量。","智慧农业 \u002F 农业物联网","可探索多机协同与实时动态重规划，结合土壤传感器提升缺陷识别精度。","openalex","2026-09-19T23:30:02.081036Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,188,254,325,390,458],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":54,"score":103,"score_detail":104,"sources":111,"tags":113,"primary_topic":115,"secondary_topics":120,"classification":121,"search_phrases":156,"slug":159,"view_count":146,"doi":160,"paper":161,"created_at":187},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":105,"substance":106,"depth":107,"authority":108,"freshness":109,"relevant":21,"comment":110},26,24,19,15,9,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[112],{"name":101,"url":98},[26,27,29,30,114],"全球评估",{"code":45,"slug":116,"name":117,"path":118,"parent_code":119},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":122,"locked":14,"version":34,"method":35,"evidence":123},"classified",{"candidates":124,"agricultural_evidence":54},[125,131,138,145,150],{"code":39,"score":126,"supported":54,"title":127,"abstract":128,"tags":130},11,[],[26,29,129],"precision agriculture",[26,29],{"code":45,"score":132,"supported":54,"title":133,"abstract":134,"tags":137},7,[],[135,136],"机器学习","machine learning",[27],{"code":139,"score":93,"supported":54,"title":140,"abstract":141,"tags":144},"H3",[],[142,143],"灌溉","irrigation",[],{"code":50,"score":146,"supported":54,"title":147,"abstract":148,"tags":149},4,[],[30],[30],{"code":151,"score":60,"supported":54,"title":152,"abstract":153,"tags":155},"F4",[],[154],"除草",[],[157,158],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":160,"openalex_id":162,"authors":163,"venue":101,"cited_by_count":83,"oa_url":98,"card":182,"direction":88,"ingested_from":90},"W7222974188",[164,167,170,173,176,179],{"name":165,"orcid":166},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":168,"orcid":169},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":171,"orcid":172},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":174,"orcid":175},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":177,"orcid":178},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":180,"orcid":181},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":183,"method":184,"finding":185,"direction":88,"opportunity":186},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":205,"secondary_topics":206,"classification":207,"search_phrases":218,"slug":221,"view_count":83,"doi":222,"paper":223,"created_at":253},5153,"Real-Time UAV scheduling under hazardous weather conditions using an integrated fuzzy logic and machine learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00489-2","The use of Unmanned Aerial Vehicles (UAVs) in precision agriculture, specifically palm farm management, is perpetually challenged by dynamic and extreme weather. From sudden wind gusts and thermal stress to destructive regional events like sandstorms and flash floods, these hazards severely constrict operational windows. This paper proposes a robust, hybrid scheduling framework that integrates fuzzy logic inference with an ensemble of machine learning classifiers to enhance real-time UAV flight reliability. The primary novelty of this research lies not in a new mathematical paradigm, but in its specialized application: the holistic integration of region-specific agricultural hazards into a real-time, decision-fusion architecture. The framework classifies takeoff viability into three categories (SAFE, CAUTION, and UNSAFE) automatically issuing alerts to trigger preventative measures and preserve hardware. By leveraging both short-term farm datasets and a decade of historical weather records, the fuzzy logic component reacts to rapid environmental shifts while establishing safety baselines. Simultaneously, tree-based ensemble models (Gradient Boosting, XGBoost, LightGBM, and CatBoost) analyze non-linear atmospheric interactions, achieving up to 99.67% accuracy in predicting safe windows. Comprehensive sensitivity analyses, including evaluations without SMOTE and seasonal temporal holdout validations, confirm the model’s robustness against class imbalance and unseen extremes. Empirical benchmarking demonstrates the framework's superiority over existing hybrid approaches, highlighting the necessity of combining adaptive fuzzy reasoning with deep historical learning for smart farming.","无人机（UAV）在精准农业尤其是棕榈农场管理中的应用，始终受到动态极端天气的困扰。从突发阵风和热应激到沙尘暴和山洪等破坏性区域事件，这些灾害严重压缩了作业窗口。本文提出了一种稳健的混合调度框架，将模糊逻辑推理与机器学习分类器集成相结合，以提升无人机实时飞行的可靠性。本研究的主要创新点不在于新的数学范式，而在于其专门化应用：将区域特定的农业灾害整体集成到实时决策融合架构中。该框架将起飞可行性分为三类（安全、 caution、不安全），自动发出警报以触发预防措施并保护硬件。通过利用短期农场数据集和十年历史天气记录，模糊逻辑组件能够对快速环境变化作出反应，同时建立安全基线。与此同时，基于树的集成模型（梯度提升、XGBoost、LightGBM和CatBoost）分析非线性大气相互作用，在预测安全窗口方面达到高达99.67%的准确率。全面的敏感性分析，包括无SMOTE的评估和季节性时间留出验证，证实了模型对类别不平衡和未见极端情况的稳健性。实证基准测试表明，该框架优于现有的混合方法，凸显了将自适应模糊推理与深度历史学习相结合对于智慧农业的必要性。","Discover Internet of Things","2026-10-15T00:00:00Z",76,{"impact":198,"substance":18,"depth":17,"authority":199,"freshness":20,"relevant":21,"comment":200},16,13,"将模糊逻辑与集成学习融合用于极端天气下的农业无人机实时调度，方法扎实、验证充分，对智慧农业作业安全有实际参考价值。",[202],{"name":194,"url":191},[26,27,28,29,204],"气象预警",{"code":45,"slug":116,"name":117,"path":118,"parent_code":119},[],{"status":122,"locked":14,"version":34,"method":35,"evidence":208},{"candidates":209,"agricultural_evidence":54},[210,214],{"code":45,"score":108,"supported":54,"title":211,"abstract":212,"tags":213},[136],[135,136],[27],{"code":39,"score":126,"supported":54,"title":215,"abstract":216,"tags":217},[],[26,29,129],[26,29],[219,220],"无人机 精准农业 极端天气","UAV 棕榈园 调度","无人机精准农业极端天气-5153","10.1007\u002Fs43926-026-00489-2",{"doi":222,"openalex_id":224,"authors":225,"venue":194,"cited_by_count":83,"oa_url":247,"card":248,"direction":88,"ingested_from":90},"W7166891579",[226,229,232,234,236,239,241,244],{"name":227,"orcid":228},"Ahmad B. Alkhodre","https:\u002F\u002Forcid.org\u002F0000-0001-6168-3552",{"name":230,"orcid":231},"Adnan Ahmed Abi Sen","https:\u002F\u002Forcid.org\u002F0000-0003-2960-5807",{"name":233,"orcid":9},"Abdelouahab Abid",{"name":235,"orcid":9},"Nour Mahmoud Bahbouh",{"name":237,"orcid":238},"Adel Ben Mnaouer","https:\u002F\u002Forcid.org\u002F0000-0003-3617-7636",{"name":240,"orcid":9},"Mohamed Benaida",{"name":242,"orcid":243},"Yazed Alsaawy","https:\u002F\u002Forcid.org\u002F0000-0001-5031-3388",{"name":245,"orcid":246},"Hani Almoamari","https:\u002F\u002Forcid.org\u002F0000-0002-3550-8977","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43926-026-00489-2.pdf",{"tldr":249,"method":250,"finding":251,"direction":88,"opportunity":252},"提出融合模糊逻辑与机器学习分类器的无人机实时调度框架，在恶劣天气下保障精准农业飞行安全。","模糊逻辑推理结合梯度提升、XGBoost、LightGBM、CatBoost集成","框架将起飞可行性分为安全、警告、不安全三级，预测安全窗口准确率最高达99.67%，优于现有混合方法。","可将区域特定灾害（沙尘暴、山洪）纳入实时决策融合，并扩展到多机协同调度与边缘端轻量化部署研究。","2026-10-17T23:30:08.889571Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":266,"tags":268,"primary_topic":270,"secondary_topics":274,"classification":275,"search_phrases":302,"slug":305,"view_count":21,"doi":306,"paper":307,"created_at":324},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,{"impact":17,"substance":264,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":265},20,"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[267],{"name":260,"url":257},[26,27,29,30,269],"多源数据融合",{"code":119,"slug":271,"name":272,"path":273,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":276,"locked":14,"version":34,"method":35,"evidence":277},"partial",{"candidates":278,"agricultural_evidence":54,"reason":301},[279,283,289,294],{"code":39,"score":198,"supported":54,"title":280,"abstract":281,"tags":282},[129],[29,129],[26,29],{"code":45,"score":108,"supported":54,"title":284,"abstract":286,"tags":288},[285],"deep learning",[287,285],"深度学习",[27],{"code":50,"score":108,"supported":54,"title":290,"abstract":292,"tags":293},[291],"remote sensing",[30,291],[30],{"code":295,"score":132,"supported":54,"title":296,"abstract":297,"tags":300},"I5",[],[298,299],"边缘计算","edge computing",[269],"一级领域明确，二级方向需要细分",[303,304],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":306,"openalex_id":308,"authors":309,"venue":260,"cited_by_count":83,"oa_url":257,"card":318,"direction":322,"ingested_from":90},"W7221565940",[310,313,316],{"name":311,"orcid":312},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":314,"orcid":315},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":317,"orcid":9},"Cundeng Wang",{"tldr":319,"method":320,"finding":321,"direction":322,"opportunity":323},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","农业遥感与作物表型","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":9,"source_name":331,"source_url":328,"published_at":332,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":333,"score_detail":334,"sources":337,"tags":339,"primary_topic":9,"secondary_topics":341,"classification":342,"search_phrases":364,"slug":367,"view_count":21,"doi":368,"paper":369,"created_at":389},4322,"Ensemble Machine Learning for Farm-Level Rainfall Forecasting Based on Satellite Observations","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41976-026-00312-0","Ensemble Machine Learning for Farm-Level Rainfall Forecasting Based on Satellite Observations。Remote Sensing in Earth Systems Sciences","基于卫星观测的农场级降雨预测的集成机器学习。《地球系统科学中的遥感》","Remote Sensing in Earth Systems Sciences","2026-10-05T00:00:00Z",67,{"impact":335,"substance":17,"depth":198,"authority":199,"freshness":20,"relevant":21,"comment":336},12,"该论文提出基于卫星观测的集成机器学习农场级降雨预报方法，方法新颖且对精准农业有实用价值，但属细分领域研究，影响范围有限。",[338],{"name":331,"url":328},[26,27,29,30,340],"农业气象",[],{"status":343,"locked":14,"version":34,"method":35,"evidence":344},"needs_review",{"candidates":345,"agricultural_evidence":14,"reason":363},[346,350,354,358],{"code":45,"score":108,"supported":54,"title":347,"abstract":348,"tags":349},[136],[135,136],[27],{"code":50,"score":132,"supported":54,"title":351,"abstract":352,"tags":353},[],[30,291],[30],{"code":39,"score":40,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[26,29],{"code":359,"score":21,"supported":14,"title":360,"abstract":361,"tags":362},"H7",[],[],[340],"农业相关性需要复核",[365,366],"卫星遥感 降雨预测 农场","集成机器学习 降雨预报","卫星遥感降雨预测农场-4322","10.1007\u002Fs41976-026-00312-0",{"doi":368,"openalex_id":370,"authors":371,"venue":331,"cited_by_count":83,"oa_url":9,"card":383,"direction":388,"ingested_from":90},"W7220415700",[372,374,376,378,380],{"name":373,"orcid":9},"Yash Pawar",{"name":375,"orcid":9},"Saee Mane",{"name":377,"orcid":9},"Sachi Bote",{"name":379,"orcid":9},"Vineet Yelleswarapu",{"name":381,"orcid":382},"Shalaka Prasad Deore","https:\u002F\u002Forcid.org\u002F0000-0002-5322-779X",{"tldr":384,"method":385,"finding":386,"direction":322,"opportunity":387},"基于卫星观测，用集成机器学习为农场尺度做降雨预报。","集成机器学习，结合卫星遥感观测数据。","集成方法可提升农场尺度降雨预报精度。","可探索多源卫星融合与作物物候耦合的田块级降雨预报。","农业人工智能与决策模型","2026-10-06T23:30:38.412053Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":398,"sources":401,"tags":403,"primary_topic":405,"secondary_topics":409,"classification":410,"search_phrases":441,"slug":444,"view_count":40,"doi":9,"paper":445,"created_at":457},4072,"Безпілотні літальні апарати та Інтернет речей у сільському господарстві: систематичний огляд технологічних напрямів і перспектив розвитку","https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fview\u002F372716","Relevance. Modern agriculture is undergoing a profound cyber-physical transformation driven by the need to shift from extensive to precision, high-accuracy, and environmentally balanced production methods. The technological foundation of this transition is the integration of unmanned aerial vehicles (UAVs) and wireless Internet of Things (IoT) sensor networks, which together form the concept of Agriculture 5.0. Despite the rapid growth of publications on this topic, the accumulated body of research remains fragmented across directions (computer vision, network technologies, robotics, adoption economics), which complicates the formation of a holistic picture of the field and of priority directions for further research. The subject of this study are methods and technologies of applying UAVs and IoT networks to precision-agriculture tasks. The purpose of the article to systematize and classify current scientific research on the use of UAVs and IoT in agriculture, and to determine the leading technological directions, their quantitative proportions, and promising development vectors. The following results were obtained. A total of 198 scientific publications from 2024-2026 were processed and classified into seven thematic classes: crop health and biological threat monitoring, hardware engineering, edge computing and spraying, navigation, swarm intelligence and data collection, phenotyping, biomass and yield forecasting, soil science, water balance and precision irrigation, economics, ecology and socio-cultural perception, and specialized agrotechnical operations. For each class, consolidated tables of applied methods and results, as well as diagrams revealing the internal structure of the direction, were constructed. The dominance of navigation\u002Fswarm-control tasks and biological-threat monitoring was identified, along with key technical barriers (limited battery energy density, beyond-visual-line-of-sight flight regulations, and fragmentation of data-exchange standards). Conclusion. The obtained results provide a holistic assessment of the state and structure of the research field on UAV and IoT applications in agriculture and can serve as a basis for shaping further research programs and educational courses in precision agriculture. Future research directions concern improving the energy autonomy of platforms, standardizing data-exchange protocols, and developing edge artificial-intelligence models resilient to complex field conditions.","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自于从粗放型向精准、高精度和环境均衡型生产方式转变的需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采用经济学），这使得形成该领域的整体图景及未来优先研究方向变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化和分类当前关于无人机和物联网在农业中应用的科学研 究，并确定主导技术方向、其定量比例及有前景的发展路径。获得了以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测、硬件工程、边缘计算与喷洒、导航、群体智能与数据采集、表型分析、生物量与产量预测、土壤科学、水分平衡与精准灌溉、经济学、生态学与社会文化感知，以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务和生物威胁监测的主导地位，以及关键技术障碍（电池能量密度有限、超视距飞行法规、数据交换标准的碎片化）。结论。所获得的结果对无人机和物联网在农业中应用的研究领域现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教学课程的基础。未来研究方向涉及提高平台的能量自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","2026-10-02T00:00:00Z",{"impact":17,"substance":399,"depth":17,"authority":335,"freshness":20,"relevant":21,"comment":400},22,"系统梳理2024-2026年198篇无人机与物联网农业应用文献，分类清晰、结论可靠，对智慧农业研究方向有实质参考价值。",[402],{"name":396,"url":393},[26,27,404,28,29],"农业物联网",{"code":295,"slug":406,"name":407,"path":408,"parent_code":119},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":122,"locked":14,"version":34,"method":35,"evidence":411},{"candidates":412,"agricultural_evidence":54},[413,420,424,430,437],{"code":295,"score":13,"supported":54,"title":414,"abstract":415,"tags":419},[],[416,298,417,418,299],"物联网","internet of things","IoT",[404],{"code":39,"score":20,"supported":54,"title":421,"abstract":422,"tags":423},[],[29,129],[26,29],{"code":45,"score":132,"supported":54,"title":425,"abstract":426,"tags":429},[],[427,428],"计算机视觉","computer vision",[27],{"code":431,"score":93,"supported":54,"title":432,"abstract":433,"tags":436},"H2",[],[434,435],"土壤","soil",[],{"code":139,"score":93,"supported":54,"title":438,"abstract":439,"tags":440},[],[142,143],[],[442,443],"农业人工智能 农业无人机 农业物联网 智慧农业","农业人工智能 农业无人机","农业人工智能农业无人机农业物联网智慧农业-4072",{"doi":9,"openalex_id":446,"authors":447,"venue":396,"cited_by_count":83,"oa_url":393,"card":452,"direction":88,"ingested_from":90},"W7218223913",[448,450],{"name":449,"orcid":9},"Володимир Панченко",{"name":451,"orcid":9},"Георгій Кучук",{"tldr":453,"method":454,"finding":455,"direction":88,"opportunity":456},"系统综述2024-2026年198篇无人机与物联网农业应用文献，划分七类技术方向并指出发展瓶颈。","文献计量与主题分类，分析198篇论文的方法、结果与内部结构。","导航\u002F集群控制与生物威胁监测占主导，瓶颈为电池能量密度、超视距法规与数据标准碎片化。","可聚焦边缘AI模型在复杂田间条件下的鲁棒性、平台能量自治与跨厂商数据交换协议标准化研究。","2026-10-03T23:30:16.357874Z",{"id":459,"title":460,"url":461,"summary":394,"summary_zh":462,"content":9,"source_name":463,"source_url":461,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":464,"sources":466,"tags":468,"primary_topic":469,"secondary_topics":470,"classification":471,"search_phrases":494,"slug":495,"view_count":21,"doi":496,"paper":497,"created_at":508},4071,"UNMANNED AERIAL VEHICLES AND THE INTERNET OF THINGS IN AGRICULTURE: A SYSTEMATIC REVIEW OF TECHNOLOGICAL DIRECTIONS AND DEVELOPMENT PROSPECTS","https:\u002F\u002Fdoi.org\u002F10.20998\u002F2522-9052.2026.4.06","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自从粗放型生产向精准、高精度和环境均衡型生产方式的转变需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采纳经济学），这使得形成对该领域整体图景及未来优先研究方向的把握变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化梳理和分类当前关于无人机与物联网在农业中应用的科学研 究，并确定主导技术方向、其数量比例及有前景的发展路径。获得以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测；硬件工程；边缘计算与喷洒；导航、群体智能与数据采集；表型分析、生物量与产量预测；土壤科学、水分平衡与精准灌溉；经济、生态与社会文化感知；以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务与生物威胁监测的主导地位，以及关键技术瓶颈（电池能量密度有限、超视距飞行法规、数据交换标准碎片化）。结论。所获结果对无人机与物联网在农业中应用研究领域的现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教育课程的基础。未来研究方向涉及提升平台能源自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","Advanced Information Systems",{"impact":17,"substance":399,"depth":17,"authority":335,"freshness":20,"relevant":21,"comment":465},"系统综述198篇2024-2026年文献，梳理无人机与物联网在农业中的七大技术方向及瓶颈，方法学扎实、信息增量高，对智慧农业研究布局有参考价值。",[467],{"name":463,"url":461},[26,27,404,28,29],{"code":295,"slug":406,"name":407,"path":408,"parent_code":119},[],{"status":122,"locked":14,"version":34,"method":35,"evidence":472},{"candidates":473,"agricultural_evidence":54},[474,478,482,486,490],{"code":295,"score":17,"supported":54,"title":475,"abstract":476,"tags":477},[417],[416,298,417,418,299],[404],{"code":39,"score":20,"supported":54,"title":479,"abstract":480,"tags":481},[],[29,129],[26,29],{"code":45,"score":132,"supported":54,"title":483,"abstract":484,"tags":485},[],[427,428],[27],{"code":431,"score":93,"supported":54,"title":487,"abstract":488,"tags":489},[],[434,435],[],{"code":139,"score":93,"supported":54,"title":491,"abstract":492,"tags":493},[],[142,143],[],[442,443],"农业人工智能农业无人机农业物联网智慧农业-4071","10.20998\u002F2522-9052.2026.4.06",{"doi":496,"openalex_id":498,"authors":499,"venue":463,"cited_by_count":83,"oa_url":461,"card":503,"direction":88,"ingested_from":90},"W7216591198",[500,502],{"name":501,"orcid":9},"Volodymyr Panchenko",{"name":451,"orcid":9},{"tldr":504,"method":505,"finding":506,"direction":88,"opportunity":507},"系统综述2024-2026年198篇无人机与物联网农业应用文献，划分七类技术方向并指出发展重点。","文献计量与主题分类，分析198篇论文的方法、成果及技术瓶颈。","导航\u002F蜂群控制与生物威胁监测占主导，瓶颈为电池能量、超视距法规与数据标准碎片化。","可聚焦边缘AI模型在复杂田间条件下的鲁棒性、平台能量自治与数据交换协议标准化研究。","2026-10-03T23:30:16.292508Z"]