[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3839":3,"related-3839":102},{"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":48,"search_phrases":79,"slug":82,"view_count":83,"doi":84,"paper":85,"created_at":101},3839,"A remote sensing image processing method based on edge computing and deep learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00508-2","The rapid advancement of Artificial Intelligence (AI) and edge computing has increased the demand for accurate and low-latency remote sensing image classification; however, conventional cloud-centric approaches face challenges related to processing latency, communication overhead, and efficient analysis of diverse land-cover scenes. This research proposes a remote sensing image processing method based on edge computing and deep learning (DL), integrating an Adaptive Chameleon Swarm-tuned Efficient Three-Dimensional Convolutional Neural Network (ACS-E3DCNN). The Remote Sensing Image Dataset obtained from Kaggle contains 5,631 RGB images categorized into five land-cover classes, namely agriculture, barren land, urban, vegetation, and water. During preprocessing, image normalization, adaptive histogram equalization, and non-local means filtering are applied to reduce noise, enhance contrast, and improve image quality. Subsequently, Principal Component Analysis (PCA) is employed for dimensionality refinement. The Efficient Three-Dimensional Convolutional Neural Network captures discriminative image representations with reduced parameter complexity, while the Adaptive Chameleon Swarm algorithm optimizes critical hyperparameters, including learning rate, kernel depth, and filter dimensions, to improve convergence and classification performance. Experimental results implemented using Python 3.10.1 demonstrate that the proposed model outperforms existing approaches, achieving an accuracy of 98.10%, F1-score of 96.85%, PSNR of 38.05, SSIM of 0.97, and latency of 85 ms under the 80:20 data split, with a statistically significant paired t-test result ( p \u003C 0.05). The proposed model provides an intelligent, reliable, and efficient approach for remote sensing image classification, supporting accurate land-cover mapping, environmental monitoring, and low-latency edge-enabled image processing.","人工智能（AI）与边缘计算的快速发展，提升了对高精度、低延迟遥感图像分类的需求；然而，传统的以云为中心的方法在处理延迟、通信开销以及高效分析多样化土地覆盖场景方面面临挑战。本研究提出了一种基于边缘计算与深度学习（DL）的遥感图像处理方法，集成了自适应变色龙群优化高效三维卷积神经网络（ACS-E3DCNN）。从Kaggle获取的遥感图像数据集包含5，631幅RGB图像，分为农业、裸地、城市、植被和水体五个土地覆盖类别。在预处理阶段，采用图像归一化、自适应直方图均衡化和非局部均值滤波来降低噪声、增强对比度并提升图像质量。随后，采用主成分分析（PCA）进行降维优化。高效三维卷积神经网络以较低的参数复杂度捕获具有判别性的图像表征，而自适应变色龙群算法则优化关键超参数，包括学习率、卷积核深度和滤波器尺寸，以改善收敛性和分类性能。使用Python 3.10.1实现的实验结果表明，所提模型优于现有方法，在80：20数据划分下达到了98.10%的准确率、96.85%的F1分数、38.05的PSNR、0.97的SSIM以及85 ms的延迟，配对t检验结果具有统计学显著性（p \u003C 0.05）。所提模型为遥感图像分类提供了一种智能、可靠且高效的方法，支持精确的土地覆盖制图、环境监测以及低延迟的边缘图像处理。",null,"Discover Internet of Things","2026-09-29T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,9,1,"该论文提出边缘计算与深度学习结合的遥感图像分类方法，在农业土地覆盖识别上精度高、延迟低，对智慧农业遥感应用有参考价值，但属方法类研究，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业遥感","边缘计算","土地覆盖分类",{"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",[38,43],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":44,"slug":45,"name":46,"path":47,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":49,"locked":14,"version":50,"method":51,"evidence":52},"classified","2026-10-22.2","rules",{"candidates":53,"agricultural_evidence":55},[54,62,69,74],{"code":32,"score":18,"supported":55,"title":56,"abstract":58,"tags":61},true,[57],"deep learning",[59,57,60],"深度学习","artificial intelligence",[27],{"code":39,"score":63,"supported":55,"title":64,"abstract":66,"tags":68},15,[65],"remote sensing",[67,65],"遥感",[28],{"code":44,"score":63,"supported":55,"title":70,"abstract":72,"tags":73},[71],"edge computing",[29,71],[29],{"code":75,"score":21,"supported":14,"title":76,"abstract":77,"tags":78},"I1",[],[],[26],[80,81],"遥感图像 土地覆盖 分类","边缘计算 深度学习 遥感","遥感图像土地覆盖分类-3839",3,"10.1007\u002Fs43926-026-00508-2",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":93,"oa_url":6,"card":94,"direction":98,"ingested_from":100},"W7214769118",[88,90],{"name":89,"orcid":9},"Yuli Song",{"name":91,"orcid":92},"Hong Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3176-3994",0,{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"提出边缘计算与深度学习结合的遥感图像分类方法，用改进3D CNN实现低延迟高精度土地覆盖识别。","ACS-E3DCNN模型，结合自适应变色龙群优化超参数，用Kaggle 5631","准确率98.10%、F1为96.85%、延迟85ms，优于现有方法，支持低延迟边缘处理。","农业遥感与作物表型","可探索边缘端轻量化模型在农田实时监测中的部署，结合多光谱与时序数据提升作物分类泛化性。","openalex","2026-09-30T23:30:27.868009Z",{"total":103,"page":21,"page_size":103,"items":104},6,[105,205,277,352,430,526],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":124,"secondary_topics":125,"classification":127,"search_phrases":166,"slug":169,"view_count":93,"doi":170,"paper":171,"created_at":204},4489,"Artificial Intelligence and Internet of Things in Smart Agriculture: A Bibliometric and Thematic Review of Data Infrastructures and Sustainability Implications","https:\u002F\u002Fdoi.org\u002F10.54175\u002Fhsustain5040036","Artificial intelligence (AI) is reshaping smart agriculture through prediction, automation, resource optimization, and data-driven decision-making. However, its effective and sustainable deployment depends on reliable Internet of Things (IoT) data infrastructures supporting sensing, connectivity, data acquisition, transmission, and distributed processing. This review maps research at the intersection of AI, IoT, and smart agriculture and examines its data-infrastructure requirements and sustainability implications. Drawing on 441 Scopus-indexed, peer-reviewed journal articles published between 2017 and 2025, the study combines bibliometric performance analysis, science mapping, and content-based thematic interpretation to identify publication trends, intellectual foundations, influential research fronts, emerging themes, and deployment challenges. The bibliometric analyses characterize AI–IoT research in smart agriculture, while IoT data infrastructure provides the principal analytical lens, and sustainability and responsible deployment are examined through interpretive synthesis. The findings reveal rapid publication growth and prominent themes involving connected sensing, predictive analytics, edge intelligence, remote sensing, smart irrigation, cybersecurity, blockchain, and edge–cloud processing. Persistent barriers include limited interoperability, inconsistent data quality, energy requirements, scalability constraints, cybersecurity and governance risks, affordability, and unequal adoption in resource-constrained contexts. The review concludes that sustainable AI-enabled agriculture requires reliable, secure, interoperable, energy-efficient, inclusive, and context-appropriate digital infrastructures.","人工智能（AI）正通过预测、自动化、资源优化和数据驱动决策重塑智慧农业。然而，其有效且可持续的部署依赖于可靠的物联网（IoT）数据基础设施，以支持感知、连接、数据采集、传输和分布式处理。本综述梳理了人工智能、物联网与智慧农业交叉领域的研究，并考察其数据基础设施需求及可持续性影响。基于2017年至2025年间发表的441篇Scopus索引同行评审期刊论文，本研究结合文献计量绩效分析、科学知识图谱和基于内容的主题解读，以识别发文趋势、知识基础、有影响力的研究前沿、新兴主题及部署挑战。文献计量分析刻画了智慧农业中人工智能—物联网研究的特征，物联网数据基础设施则作为主要分析视角，可持续性与负责任部署则通过解释性综合加以考察。研究发现发文量快速增长，突出主题涉及互联感知、预测分析、边缘智能、遥感、智能灌溉、网络安全、区块链及边云处理。持续存在的障碍包括互操作性有限、数据质量不一致、能源需求、可扩展性约束、网络安全与治理风险、可负担性以及在资源受限情境中的不均衡采用。本综述得出结论：可持续的人工智能赋能农业需要可靠、安全、可互操作、节能、包容且情境适宜的数字基础设施。","Highlights of Sustainability","2026-10-08T00:00:00Z",79,{"impact":18,"substance":115,"depth":18,"authority":116,"freshness":117,"relevant":21,"comment":118},22,13,8,"基于441篇文献的AI-IoT智慧农业文献计量综述，系统梳理数据基础设施与可持续部署瓶颈，学术参考价值较高。",[120],{"name":111,"url":108},[26,27,122,29,123],"农业物联网","数据基础设施",{"code":44,"slug":45,"name":46,"path":47,"parent_code":36},[126],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":49,"locked":14,"version":50,"method":51,"evidence":128},{"candidates":129,"agricultural_evidence":55},[130,138,143,151,155,162],{"code":44,"score":131,"supported":55,"title":132,"abstract":134,"tags":137},19,[133],"internet of things",[135,133,136],"物联网","IoT",[122,29],{"code":75,"score":63,"supported":55,"title":139,"abstract":141,"tags":142},[140],"smart agriculture",[26,140],[26],{"code":144,"score":145,"supported":55,"title":146,"abstract":148,"tags":150},"I7",14,[147],"bibliometric",[149,147],"文献计量",[],{"code":32,"score":17,"supported":55,"title":152,"abstract":153,"tags":154},[60],[60],[27],{"code":156,"score":103,"supported":55,"title":157,"abstract":158,"tags":161},"H3",[],[159,160],"灌溉","irrigation",[],{"code":39,"score":103,"supported":55,"title":163,"abstract":164,"tags":165},[],[67,65],[],[167,168],"AI IoT 智慧农业 文献计量","农业物联网 数据基础设施","AIIoT智慧农业文献计量-4489","10.54175\u002Fhsustain5040036",{"doi":170,"openalex_id":172,"authors":173,"venue":111,"cited_by_count":93,"oa_url":197,"card":198,"direction":202,"ingested_from":100},"W7220924310",[174,177,180,182,185,188,191,194],{"name":175,"orcid":176},"Altyeb Ali Abaker Omer","https:\u002F\u002Forcid.org\u002F0000-0001-9420-9910",{"name":178,"orcid":179},"Yajie Dong","https:\u002F\u002Forcid.org\u002F0009-0001-2843-6135",{"name":181,"orcid":9},"Ibrahim Issa Mohamed Issa",{"name":183,"orcid":184},"Yousif Yahia Ahmed Abuker","https:\u002F\u002Forcid.org\u002F0009-0002-8111-9283",{"name":186,"orcid":187},"Mohamed Ibrahim Abdallh Babeker","https:\u002F\u002Forcid.org\u002F0009-0008-0904-9501",{"name":189,"orcid":190},"Alfadil Aboud Alfadil Hassan","https:\u002F\u002Forcid.org\u002F0009-0005-0656-1903",{"name":192,"orcid":193},"Zhiguo Shan","https:\u002F\u002Forcid.org\u002F0000-0002-6165-8258",{"name":195,"orcid":196},"Yinxue Shi","https:\u002F\u002Forcid.org\u002F0009-0002-1465-8666","https:\u002F\u002Fwww.hos.pub\u002Farticles\u002Fhsustain5040036\u002Fpdf",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"综述2017-2025年441篇文献，梳理AI与IoT在智慧农业中的数据基础设施与可持续性。","Scopus文献计量、科学映射与主题内容分析，以IoT数据基础设施为分析视角。","研究快速增长，但互操作性、数据质量、能耗、安全与公平采用仍是主要障碍。","智慧农业 \u002F 农业物联网","可针对资源受限小农户场景，研究低能耗、可互操作且包容的农业IoT数据基础设施与治理机制。","2026-10-09T23:30:07.811940Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":9,"source_name":211,"source_url":208,"published_at":212,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":213,"score_detail":214,"sources":216,"tags":218,"primary_topic":220,"secondary_topics":221,"classification":223,"search_phrases":256,"slug":259,"view_count":21,"doi":260,"paper":261,"created_at":276},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",55,{"impact":117,"substance":18,"depth":63,"authority":103,"freshness":117,"relevant":21,"comment":215},"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[217],{"name":211,"url":208},[26,27,29,135,219],"精准灌溉",{"code":44,"slug":45,"name":46,"path":47,"parent_code":36},[222],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":49,"locked":14,"version":50,"method":51,"evidence":224},{"candidates":225,"agricultural_evidence":55},[226,231,238,243,249],{"code":44,"score":227,"supported":55,"title":228,"abstract":229,"tags":230},11,[],[135,133,136],[135,29],{"code":32,"score":13,"supported":55,"title":232,"abstract":233,"tags":237},[],[59,234,57,235,236],"计算机视觉","computer vision","decision support",[27],{"code":156,"score":239,"supported":55,"title":240,"abstract":241,"tags":242},7,[],[159,160],[219],{"code":75,"score":239,"supported":55,"title":244,"abstract":245,"tags":248},[],[246,247],"精准农业","precision agriculture",[26],{"code":250,"score":103,"supported":55,"title":251,"abstract":252,"tags":255},"H2",[],[253,254],"土壤","soil",[],[257,258],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":260,"openalex_id":262,"authors":263,"venue":211,"cited_by_count":93,"oa_url":208,"card":271,"direction":202,"ingested_from":100},"W7220855887",[264,267,269],{"name":265,"orcid":266},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":268,"orcid":9},"Shetty Chirag",{"name":270,"orcid":9},"Sunay Kanade",{"tldr":272,"method":273,"finding":274,"direction":202,"opportunity":275},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":285,"score_detail":286,"sources":288,"tags":290,"primary_topic":293,"secondary_topics":294,"classification":296,"search_phrases":329,"slug":332,"view_count":333,"doi":334,"paper":335,"created_at":351},2906,"UAV remote sensing for crop lodging monitoring: A comprehensive review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112446","Global agriculture is facing increasing pressure to improve productivity while minimizing losses caused by extreme climate events. Crop lodging poses a major threat to food security by reducing crop yield, deteriorating grain quality, and increasing harvesting costs. As a flexible, high-resolution remote sensing platform, unmanned aerial vehicles (UAVs) have emerged as an effective solution for eliminating the gap between labor-intensive ground surveys and low-resolution satellite observations. This review systematically synthesizes the current state of research on UAV-based crop lodging monitoring through a comprehensive analysis of peer-reviewed literature. It covers the complete technical workflow, from lodging mechanisms to lodging quantification. The capabilities of diverse sensing modalities, including RGB, multispectral imagery (MSI), hyperspectral imagery (HSI), thermal infrared (TIR), light detection and ranging (LiDAR), and synthetic aperture radar (SAR), are systematically evaluated for detecting lodging-induced variations in color, texture, spectral characteristics, canopy temperature, structural attributes, and radar backscatter. In addition, recent advances in analytical approaches are reviewed, encompassing traditional statistical analyses, machine learning techniques, and state-of-the-art deep learning models. Furthermore, the review summarizes UAV-based lodging assessments across a wide range of crop species and examines methods for quantifying key lodging parameters, including lodging severity, lodging type, and affected area. By identifying current research gaps and emerging opportunities, this review outlines future research directions and provides valuable guidance for advancing accurate, efficient, and real-time crop lodging monitoring in smart agriculture.","全球农业正面临日益增大的压力，既要提高生产力，又要尽量减少极端气候事件造成的损失。作物倒伏通过降低作物产量、恶化籽粒品质并增加收获成本，对粮食安全构成重大威胁。作为一种灵活的高分辨率遥感平台，无人机（UAV）已成为弥合劳动密集型地面调查与低分辨率卫星观测之间差距的有效解决方案。本文通过系统分析同行评议文献，全面综述了基于无人机的作物倒伏监测研究现状，涵盖从倒伏机理到倒伏量化的完整技术流程。系统评估了多种传感模态的能力，包括RGB影像、多光谱影像（MSI）、高光谱影像（HSI）、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR），用于检测倒伏引起的颜色、纹理、光谱特征、冠层温度、结构属性和雷达后向散射变化。此外，综述了分析方法的最新进展，涵盖传统统计分析、机器学习技术和前沿深度学习模型。同时，本文总结了针对多种作物类型的基于无人机的倒伏评估研究，并探讨了关键倒伏参数的量化方法，包括倒伏严重程度、倒伏类型和受影响面积。通过识别当前研究空白和新兴机遇，本文展望了未来研究方向，为推进智慧农业中准确、高效、实时的作物倒伏监测提供了有价值的指导。","Computers and Electronics in Agriculture","2026-09-18T00:00:00Z",81,{"impact":18,"substance":115,"depth":18,"authority":63,"freshness":117,"relevant":21,"comment":287},"核心期刊发表的无人机遥感作物倒伏监测综述，系统梳理多模态传感与深度学习分析路径，对智慧农业精准监测有实质参考价值。",[289],{"name":283,"url":280},[26,291,27,28,292],"无人机","作物倒伏",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[295],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":49,"locked":14,"version":50,"method":51,"evidence":297},{"candidates":298,"agricultural_evidence":55},[299,305,311,315,322],{"code":39,"score":18,"supported":55,"title":300,"abstract":301,"tags":304},[65],[67,65,302,303],"multispectral","hyperspectral",[28],{"code":32,"score":13,"supported":55,"title":306,"abstract":307,"tags":310},[],[59,308,309,57],"机器学习","machine learning",[27],{"code":75,"score":239,"supported":55,"title":312,"abstract":313,"tags":314},[],[26,140],[26],{"code":316,"score":103,"supported":55,"title":317,"abstract":318,"tags":321},"A8",[],[319,320],"粮食安全","food security",[],{"code":323,"score":103,"supported":55,"title":324,"abstract":325,"tags":328},"J3",[],[326,327],"全球农业","global agriculture",[],[330,331],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906",4,"10.1016\u002Fj.compag.2026.112446",{"doi":334,"openalex_id":336,"authors":337,"venue":283,"cited_by_count":93,"oa_url":280,"card":346,"direction":202,"ingested_from":100},"W7213542473",[338,341,343],{"name":339,"orcid":340},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":342,"orcid":9},"Changxing Geng",{"name":344,"orcid":345},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":347,"method":348,"finding":349,"direction":98,"opportunity":350},"综述无人机遥感在作物倒伏监测中的技术流程、传感器与分析模型。","系统综述RGB、多光谱、高光谱、热红外、LiDAR、SAR及机器学习深度学习方法","多模态遥感结合深度学习可实现倒伏程度、类型与面积的精准量化。","多源数据融合与实时轻量化模型是倒伏监测的潜在突破方向。","2026-09-19T23:30:01.740139Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":9,"content":9,"source_name":357,"source_url":355,"published_at":358,"category":12,"cover_url":9,"hotness":213,"is_selected":14,"score":359,"score_detail":360,"sources":362,"tags":372,"primary_topic":374,"secondary_topics":375,"classification":381,"search_phrases":409,"slug":412,"view_count":333,"doi":413,"paper":414,"created_at":429},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",74,{"impact":18,"substance":115,"depth":18,"authority":116,"freshness":83,"relevant":21,"comment":361},"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[363,364,367,369],{"name":357,"url":355},{"name":365,"url":366},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":365,"url":368},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":370,"url":371},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[26,27,29,135,373],"智能灌溉",{"code":44,"slug":45,"name":46,"path":47,"parent_code":36},[376,380],{"code":156,"slug":160,"name":377,"path":378,"parent_code":379},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":49,"locked":14,"version":50,"method":51,"evidence":382},{"candidates":383,"agricultural_evidence":55},[384,389,393,397,401,405],{"code":44,"score":385,"supported":55,"title":386,"abstract":387,"tags":388},27,[136,71],[133,136,71],[135,29],{"code":156,"score":17,"supported":55,"title":390,"abstract":391,"tags":392},[160],[160],[373],{"code":32,"score":17,"supported":55,"title":394,"abstract":395,"tags":396},[309],[309],[27],{"code":316,"score":83,"supported":55,"title":398,"abstract":399,"tags":400},[],[320],[],{"code":250,"score":83,"supported":55,"title":402,"abstract":403,"tags":404},[],[254],[],{"code":75,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[26],[410,411],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":413,"openalex_id":415,"authors":416,"venue":357,"cited_by_count":93,"oa_url":423,"card":424,"direction":202,"ingested_from":100},"W7204189894",[417,420],{"name":418,"orcid":419},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":421,"orcid":422},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":425,"method":426,"finding":427,"direction":202,"opportunity":428},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":211,"source_url":433,"published_at":436,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":440,"tags":442,"primary_topic":446,"secondary_topics":447,"classification":449,"search_phrases":498,"slug":501,"view_count":93,"doi":502,"paper":503,"created_at":525},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","2026-10-21T00:00:00Z",65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":439},"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[441],{"name":211,"url":433},[26,27,443,444,445],"无损检测","多光谱成像","食品质量检测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[448],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":49,"locked":14,"version":50,"method":51,"evidence":450},{"candidates":451,"agricultural_evidence":55},[452,456,460,467,474,481,488,494],{"code":39,"score":17,"supported":55,"title":453,"abstract":454,"tags":455},[302],[302],[444],{"code":32,"score":13,"supported":55,"title":457,"abstract":458,"tags":459},[],[59,308,234,309,57,60,235],[27],{"code":461,"score":239,"supported":55,"title":462,"abstract":463,"tags":466},"E4",[],[464,465],"食品安全","food safety",[443],{"code":468,"score":103,"supported":55,"title":469,"abstract":470,"tags":473},"A1",[],[471,472],"稻米","rice",[],{"code":475,"score":103,"supported":55,"title":476,"abstract":477,"tags":480},"B1",[],[478,479],"番茄","tomato",[],{"code":482,"score":103,"supported":55,"title":483,"abstract":484,"tags":487},"B2",[],[485,486],"苹果","apple",[],{"code":489,"score":83,"supported":55,"title":490,"abstract":491,"tags":493},"E1",[],[492],"农产品加工",[],{"code":75,"score":21,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[26],[499,500],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":502,"openalex_id":504,"authors":505,"venue":211,"cited_by_count":93,"oa_url":433,"card":518,"direction":524,"ingested_from":100},"W7223139905",[506,508,510,512,514,516],{"name":507,"orcid":9},"Eshwari Take",{"name":509,"orcid":9},"Avanish Raut",{"name":511,"orcid":9},"Vedant Joshi",{"name":513,"orcid":9},"Shravani Akolkar",{"name":515,"orcid":9},"R. M. Gharat",{"name":517,"orcid":9},"Shruti Dive",{"tldr":519,"method":520,"finding":521,"direction":522,"opportunity":523},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":365,"source_url":529,"published_at":436,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":532,"sources":536,"tags":538,"primary_topic":541,"secondary_topics":542,"classification":544,"search_phrases":559,"slug":562,"view_count":93,"doi":563,"paper":564,"created_at":574},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",{"impact":117,"substance":533,"depth":534,"authority":17,"freshness":13,"relevant":21,"comment":535},20,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[537],{"name":365,"url":529},[26,27,539,67,540],"产量预测","可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[543],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":49,"locked":14,"version":50,"method":51,"evidence":545},{"candidates":546,"agricultural_evidence":55},[547,551,555],{"code":32,"score":17,"supported":55,"title":548,"abstract":549,"tags":550},[235],[234],[27],{"code":39,"score":117,"supported":55,"title":552,"abstract":553,"tags":554},[],[67,65],[539,67],{"code":75,"score":21,"supported":14,"title":556,"abstract":557,"tags":558},[],[],[26],[560,561],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":563,"openalex_id":565,"authors":566,"venue":365,"cited_by_count":93,"oa_url":529,"card":569,"direction":98,"ingested_from":100},"W7223114002",[567],{"name":568,"orcid":9},"Kadiatou Barry",{"tldr":570,"method":571,"finding":572,"direction":98,"opportunity":573},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z"]