[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3609":3,"related-3609":51},{"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":27,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":50},3609,"EXPLAINABLE EDGE AI FOR EARLY DETECTION OF PLANT DISEASES IN SMART AGRICULTURE USING LIGHTWEIGHT MOBILENETV2 WITH GRADIENT SALIENCY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22972972","EXPLAINABLE EDGE AI FOR EARLY DETECTION OF PLANT DISEASES IN SMART AGRICULTURE USING LIGHTWEIGHT MOBILENETV2 WITH GRADIENT SALIENCY。Zenodo (CERN European Organization for Nuclear Research)","面向智慧农业中植物病害早期检测的可解释边缘人工智能：采用带梯度显著性的轻量级MobileNetV2。Zenodo（CERN欧洲核子研究组织）。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-26T00:00:00Z","论文",25,false,66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,8,1,"面向智慧农业的轻量可解释边缘AI病害检测论文，方法有创新但属细分技术进展，影响力有限。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22972971",[28,29,30,31],"智慧农业","农业人工智能","边缘计算","植物病害检测",[33,34],"MobileNetV2 植物病害 检测","边缘AI 智慧农业 病害","MobileNetV2植物病害检测-3609",0,"10.5281\u002Fzenodo.22972972",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":43,"direction":47,"ingested_from":49},"W7214432284",[41],{"name":42,"orcid":9},"Yasir Javaid,Sana Cheema,Akkasha Latif,Atiqa Faiz ur Rehman,Aisha Tariq Khan,Qandeel Nasir,Hafiz Farrukh Abbas",{"tldr":44,"method":45,"finding":46,"direction":47,"opportunity":48},"提出轻量MobileNetV2结合梯度显著性实现边缘端植物病害早期可解释检测。","MobileNetV2轻量网络+梯度显著性可视化，部署于边缘设备。","在保证精度的同时实现可解释的早期病害识别，适合资源受限场景。","智慧农业 \u002F 农业物联网","可探索多作物多病害的轻量可解释模型，并优化田间边缘部署的实时性与鲁棒性。","openalex","2026-09-27T23:30:13.798543Z",{"total":52,"page":21,"page_size":52,"items":53},6,[54,109,143,178,209,249],{"id":55,"title":56,"url":57,"summary":58,"summary_zh":59,"content":9,"source_name":60,"source_url":57,"published_at":61,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":63,"score_detail":64,"sources":68,"tags":70,"search_phrases":73,"slug":76,"view_count":36,"doi":77,"paper":78,"created_at":108},3254,"End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112412","End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation。Computers and Electronics in Agriculture","端边云协同的水稻秧苗群体表型识别与诊断及其在立体水稻育秧中的应用。","Computers and Electronics in Agriculture","2026-09-23T00:00:00Z",10,80,{"impact":18,"substance":65,"depth":18,"authority":66,"freshness":62,"relevant":21,"comment":67},20,14,"核心期刊论文，提出端边云协同的水稻育苗群体表型识别诊断方法，方法新颖、时效性强，对智慧育苗有参考价值。",[69],{"name":60,"url":57},[28,29,30,71,72],"水稻育苗","表型识别",[74,75],"水稻育苗 群体表型 识别","垂直水稻育苗 端边云协同","水稻育苗群体表型识别-3254","10.1016\u002Fj.compag.2026.112412",{"doi":77,"openalex_id":79,"authors":80,"venue":60,"cited_by_count":36,"oa_url":9,"card":102,"direction":106,"ingested_from":49},"W7214024203",[81,83,86,88,90,92,94,96,99],{"name":82,"orcid":9},"Zheng Ma",{"name":84,"orcid":85},"Ruohan Wang","https:\u002F\u002Forcid.org\u002F0009-0009-7656-5132",{"name":87,"orcid":9},"Yuxuan Cai",{"name":89,"orcid":9},"Donghong Wen",{"name":91,"orcid":9},"Long Zhou",{"name":93,"orcid":9},"Yunzhe Huang",{"name":95,"orcid":9},"Yihua Liu",{"name":97,"orcid":98},"Zhongbin Su","https:\u002F\u002Forcid.org\u002F0000-0002-8966-8933",{"name":100,"orcid":101},"Hongbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7711-5744",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"提出端边云协同的水稻秧苗群体表型识别与诊断方法，用于垂直育秧场景。","端边云协同架构结合深度学习，对垂直育秧水稻秧苗群体图像进行表型识别与诊断。","端边云协同可实现水稻秧苗群体表型高效识别与诊断，支撑垂直育秧精准管理。","农业遥感与作物表型","可探索端边云协同下多尺度群体表型实时诊断与育秧环境调控闭环，弥补边缘算力与模型轻量化研究空白。","2026-09-23T23:30:01.424596Z",{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":117,"score_detail":118,"sources":121,"tags":123,"search_phrases":126,"slug":129,"view_count":36,"doi":130,"paper":131,"created_at":142},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。","International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z",59,{"impact":17,"substance":66,"depth":119,"authority":62,"freshness":20,"relevant":21,"comment":120},15,"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。",[122],{"name":115,"url":112},[28,29,124,125,31],"农业物联网","精准农业",[127,128],"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":130,"openalex_id":132,"authors":133,"venue":115,"cited_by_count":36,"oa_url":9,"card":136,"direction":47,"ingested_from":49},"W7213950095",[134],{"name":135,"orcid":9},"Jamuna Ratcha",{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"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。","2026-09-22T23:30:11.209653Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":9,"source_name":149,"source_url":146,"published_at":150,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":151,"score_detail":152,"sources":157,"tags":159,"search_phrases":162,"slug":165,"view_count":36,"doi":166,"paper":167,"created_at":177},2929,"A REAL-TİME AI-DRİVEN AGRİCULTURAL ROVER INTEGRATİNG PLANT DİSEASE DETECTİON AND GEO-REFERENCED SOİL MOİSTURE ANALYSİS","https:\u002F\u002Fdoi.org\u002F10.30546\u002Femnaa.2026.02.28.123","This paper presents an autonomous agricultural ground robot for real-time monitoring of plant health and soil moisture in large-scale crop fields.The proposed system integrates a deep learning-based perception subsystem with autonomous navigation and a custom ground control station (GCS) to enable continuous and geo-referenced field analysis.Visual data are acquired using an onboard camera and processed in real time on a Raspberry Pi using an object detection model (ODM) to identify disease-related visual symptoms such as discoloration, deformation and leaf degradation.Each detected instance is associated with a confidence score and accurately geo-tagged using GPS data.In parallel, a contact-based soil moisture sensor performs localized measurements at fixed spatial intervals along a grid-based coverage trajectory.All perception outputs are synchronized with navigation data and transmitted via a telemetry link to the GCS where live video with detection overlays, rover trajectory, mission status and sensor telemetry are visualized and logged for postmission analysis.Field experiments conducted under real operating conditions demonstrate stable autonomous operation and achieve an overall plant disease detection accuracy of 85-90%, confirming the effectiveness of the proposed system for precision agriculture applications.","本文提出了一种用于大规模农田植物健康和土壤湿度实时监测的自主农业地面机器人。该系统将基于深度学习的感知子系统与自主导航及自定义地面控制站（GCS）相结合，实现连续且带地理参考的田间分析。视觉数据通过机载相机采集，并利用目标检测模型（ODM）在树莓派上实时处理，以识别与病害相关的视觉症状，如变色、变形和叶片退化。每个检测到的实例均关联置信度分数，并通过GPS数据精确标注地理位置。与此同时，基于接触式的土壤湿度传感器沿网格化覆盖轨迹以固定空间间隔进行局部测量。所有感知输出与导航数据同步，并通过遥测链路传输至地面控制站，在此可视化并记录带有检测叠加层的实时视频、漫游车轨迹、任务状态和传感器遥测数据，以供任务后分析。在实际运行条件下进行的田间实验证明了稳定的自主运行能力，并实现了85-90%的植物病害整体检测准确率，验证了所提系统在精准农业应用中的有效性。","Scientific Journal","2026-09-18T00:00:00Z",77,{"impact":18,"substance":153,"depth":154,"authority":155,"freshness":20,"relevant":21,"comment":156},21,17,13,"集成深度学习病害识别与地理参考土壤墒情监测的自主农业机器人论文，田间实测准确率85-90%，方法新颖且数据可靠，对精准农业有参考价值。",[158],{"name":149,"url":146},[28,29,160,31,161],"农业机器人","土壤墒情监测",[163,164],"农业机器人 病害检测 土壤墒情","Raspberry Pi 植物病害识别","农业机器人病害检测土壤墒情-2929","10.30546\u002Femnaa.2026.02.28.123",{"doi":166,"openalex_id":168,"authors":169,"venue":149,"cited_by_count":36,"oa_url":146,"card":172,"direction":47,"ingested_from":49},"W7213559917",[170],{"name":171,"orcid":9},"Gasimov V.A., Dadashov F.H., Hasanov H.B., Huseynov N.E",{"tldr":173,"method":174,"finding":175,"direction":47,"opportunity":176},"开发实时AI农业机器人，集成植物病害检测与地理参考土壤湿度分析。","Raspberry Pi上部署目标检测模型，结合GPS和接触式土壤湿度传感器。","田间试验实现85-90%的植物病害检测准确率，并稳定自主运行。","可探索多模态传感器融合与边缘计算优化，提升复杂田间环境下的实时检测鲁棒性。","2026-09-19T23:30:11.232025Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":9,"content":9,"source_name":183,"source_url":9,"published_at":184,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":185,"score_detail":186,"sources":190,"tags":192,"search_phrases":195,"slug":198,"view_count":36,"doi":199,"paper":200,"created_at":208},2852,"面向边缘部署的温室串番茄采摘机器人:YOLOv8n-BiFPN-WIoU+ROS分布式控制——Frontiers in Plant Science","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1926482\u002Ffull","论文提出了一种可在边缘部署的温室串番茄采摘机器人,集成YOLOv8n-BiFPN-WIoU视觉检测模型与基于ROS的分布式控制架构。BiFPN加权特征融合结构增强了跨尺度特征表示,WIoU改进了部分遮挡和重叠目标的定位稳健性。在独立测试集上,该模型Precision达88.534%、Recall 89.377%、F1 88.954%、mAP@0.5为92.338%、mAP@0.5:0.95为71.368%,相比基线YOLOv8n分别提升2.143、2.779、2.460、4.587和5.675个百分点。视觉检测器与ROS机器人系统集成,协调目标感知、轨道站点间运动和机械臂操作。","Frontiers in Plant Science","2026-09-14T00:00:00Z",81,{"impact":18,"substance":187,"depth":18,"authority":66,"freshness":188,"relevant":21,"comment":189},22,9,"方法改进与实测指标扎实、面向边缘部署的温室串番茄采摘机器人研究，对智慧农业具参考价值，但属细分技术进展，未达产业级突破。",[191],{"name":183,"url":181},[28,29,30,193,194],"串番茄","采摘机器人",[196,197],"农业人工智能 采摘机器人 智慧农业 边缘计算","农业人工智能 采摘机器人","农业人工智能采摘机器人智慧农业边缘计算-2852","10.3389\u002Ffpls.2026.1926482\u002Ffull",{"doi":199,"openalex_id":9,"authors":201,"venue":9,"cited_by_count":36,"oa_url":9,"card":202,"direction":47,"ingested_from":207},[],{"tldr":203,"method":204,"finding":205,"direction":47,"opportunity":206},"提出边缘部署的温室串番茄采摘机器人，融合改进YOLOv8n检测与ROS分布式控制。","YOLOv8n结合BiFPN加权特征融合与WIoU损失，集成ROS分布式控制架构","模型mAP@0.5达92.338%，较基线YOLOv8n提升4.587个百分点，可完成采摘。","可探索轻量化模型在更多边缘设备上的泛化部署，及多机器人协同采摘调度优化。","agent","2026-09-18T00:03:30.570025Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":183,"source_url":212,"published_at":215,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":151,"score_detail":216,"sources":218,"tags":220,"search_phrases":223,"slug":226,"view_count":36,"doi":227,"paper":228,"created_at":248},2774,"SpatioFormer: spatial perception enhancement for lightweight agricultural pest and disease detection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1925867","Introduction In precision agriculture, accurate and efficient detection of crop pests and diseases is crucial. However, existing models in complex environments are prone to insufficient spatial perception and attenuation of disease texture features, making it difficult to balance recognition accuracy and lightweighting. Methods To address this, this study proposes a lightweight spatial perception enhancement hybrid architecture, SpatioFormer. First, a Pixel-level Detail Retrieval (PDR) mechanism is designed. This mechanism leverages cross-layer dynamic routing to facilitate the fusion of deep semantic features with shallow texture features, significantly enhancing the capability to capture disease features. Second, we design a Spatially Adaptive Modulation Attention (SA-SHMA) mechanism, which utilizes large-kernel depthwise convolution to capture contextual information and combines dynamic modulation maps for fine-grained focusing, efficiently recovering spatial details, and suppressing background noise. Furthermore, this paper introduces a Context-Guided Asymmetric Gated Linear Unit (CGA-GLU), which utilizes an asymmetric design focusing on the gating branch and incorporates contextual information for guidance, enhancing the inter-channel representation capability with minimal computational overhead. Results Finally, extensive experiments on the PDDD and Tomato-Village datasets validated the effectiveness of the proposed model. The proposed model achieves a Top-1 accuracy of 81.05% on the PDDD dataset and an AP 50 of 61.53% on the Tomato-Village dataset, with testing latency on edge devices being highly competitive among existing models. Discussion Compared to existing lightweight hybrid models, SpatioFormer effectively recovers shallow spatial details and precisely suppresses complex background noise under an extremely low parameter budget. Consequently, it achieves a superior balance between practical disease localization capability and inference latency on resource-constrained edge devices.","引言 在精准农业中，准确高效地检测作物病虫害至关重要。然而，复杂环境下的现有模型容易出现空间感知不足和病害纹理特征衰减的问题，难以兼顾识别精度与轻量化。方法 为解决这一问题，本研究提出了一种轻量级空间感知增强混合架构——SpatioFormer。首先，设计了像素级细节检索（Pixel-level Detail Retrieval，PDR）机制。该机制利用跨层动态路由，促进深层语义特征与浅层纹理特征的融合，显著增强了对病害特征的捕捉能力。其次，设计了空间自适应调制注意力（Spatially Adaptive Modulation Attention，SA-SHMA）机制，该机制利用大核深度卷积捕获上下文信息，并结合动态调制图进行细粒度聚焦，高效恢复空间细节并抑制背景噪声。此外，本文引入了上下文引导非对称门控线性单元（Context-Guided Asymmetric Gated Linear Unit，CGA-GLU），其采用聚焦门控分支的非对称设计，并融入上下文信息进行引导，以极小的计算开销增强了通道间表征能力。结果 最后，在PDDD和Tomato-Village数据集上的大量实验验证了所提模型的有效性。所提模型在PDDD数据集上取得了81.05%的Top-1准确率，在Tomato-Village数据集上取得了61.53%的AP 50，其在边缘设备上的测试延迟在现有模型中极具竞争力。讨论 与现有轻量级混合模型相比，SpatioFormer在极低的参数预算下有效恢复了浅层空间细节，并精确抑制了复杂背景噪声。因此，它在实际病害定位能力与资源受限边缘设备上的推理延迟之间实现了更优的平衡。","2026-09-16T00:00:00Z",{"impact":19,"substance":153,"depth":18,"authority":155,"freshness":188,"relevant":21,"comment":217},"提出轻量化空间感知增强架构，在边缘设备上兼顾检测精度与推理延迟，方法新颖、实验扎实，对农业病虫害智能识别有参考价值。",[219],{"name":183,"url":212},[28,29,30,221,222],"番茄","病虫害检测",[224,225],"农业人工智能 病虫害检测 智慧农业 边缘计算","农业人工智能 病虫害检测","农业人工智能病虫害检测智慧农业边缘计算-2774","10.3389\u002Ffpls.2026.1925867",{"doi":227,"openalex_id":229,"authors":230,"venue":183,"cited_by_count":36,"oa_url":242,"card":243,"direction":47,"ingested_from":49},"W7213437661",[231,233,236,238,240],{"name":232,"orcid":9},"Wenbo Ma",{"name":234,"orcid":235},"Hao Sun","https:\u002F\u002Forcid.org\u002F0000-0002-6983-8149",{"name":237,"orcid":9},"Kun Zhou",{"name":239,"orcid":9},"Meichun Wang",{"name":241,"orcid":9},"Rui Fu","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925867\u002Fpdf",{"tldr":244,"method":245,"finding":246,"direction":140,"opportunity":247},"提出轻量混合架构SpatioFormer，提升复杂环境下农作物病虫害检测的空间感知能力。","设计PDR跨层动态路由、SA-SHMA大核注意力与CGA-GLU门控，在PDDD","在极低参数量下恢复浅层空间细节并抑制背景噪声，边缘设备延迟具竞争力。","可探索将空间感知增强机制迁移至多作物多病害场景，并研究边缘端实时部署的能效优化。","2026-09-17T23:30:14.148727Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":215,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":256,"score_detail":257,"sources":260,"tags":262,"search_phrases":265,"slug":268,"view_count":21,"doi":269,"paper":270,"created_at":289},2771,"Edge-AI Based Smart Pet Monitoring Framework: A Rabbit Case Study","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fijirt.208534-459","Continuous Monitoring of animals is hard when the people who take care of them are not around.Changes in how they are eaten, how much they drink, the way they stand and what they do during the day can show if their health is changing or if their normal routine is different.This paper talks about an Edge AI-based Smart Pet Monitoring Framework.The goal is watching the pet's behaviour and the environment around them in time.The system keeps privacy in mind.It uses computer vision, IoT, sensors and computing that happens close to the data not in the cloud This reduces the need for internet access.A Raspberry Pi serves as the edge device running a Python and OpenCV image processing chain that uses a YOLOv9 detector to spot feeding, drinking, posture and activity states.Temperature, humidity, ultrasonic distance and water-level sensors give environment data while an Arduino control layer handles alerts using an LCD, buzzer, LEDs and a servo.Local inference gives real-time processing and improves privacy, data ownership and response time.We tested than 200 images in a controlled setting and found it works but the test is still early.This framework offers a flexible base for smart pet monitoring, which could help at home in shelters and, for vets once more tests are done.","当照顾动物的人不在身边时，对动物进行持续监测是很困难的。它们进食方式、饮水量、站立姿态以及白天活动的变化，可以反映其健康状况是否发生变化或日常规律是否出现异常。本文讨论了一种基于边缘人工智能的智能宠物监测框架。其目标是及时观察宠物的行为及其周围环境。该系统注重隐私保护。它使用计算机视觉、物联网、传感器以及靠近数据端而非云端进行的计算，这减少了对互联网接入的需求。树莓派作为边缘设备，运行Python和OpenCV图像处理流程，并使用YOLOv9检测器识别进食、饮水、姿态和活动状态。温度、湿度、超声波距离和水位传感器提供环境数据，而Arduino控制层通过LCD、蜂鸣器、LED和舵机处理警报。本地推理实现了实时处理，并改善了隐私、数据所有权和响应时间。我们在受控环境中测试了200多张图像，发现系统可以工作，但测试仍处于早期阶段。该框架为智能宠物监测提供了一个灵活的基础，在完成更多测试后，可能有助于家庭、收容所以及兽医使用。","International Journal of Innovative Research in Technology",49,{"impact":20,"substance":66,"depth":155,"authority":258,"freshness":188,"relevant":21,"comment":259},5,"边缘AI宠物监测框架，与农业信息化关联偏弱且测试样本仅200张，属早期探索性论文，不宜进入每日精选。",[261],{"name":255,"url":252},[28,29,30,263,264],"物联网","动物监测",[266,267],"农业人工智能 动物监测 智慧农业 边缘计算","农业人工智能 动物监测","农业人工智能动物监测智慧农业边缘计算-2771","10.64643\u002Fijirt.208534-459",{"doi":269,"openalex_id":271,"authors":272,"venue":255,"cited_by_count":36,"oa_url":283,"card":284,"direction":47,"ingested_from":49},"W7213273522",[273,275,277,279,281],{"name":274,"orcid":9},"Vinit Masale",{"name":276,"orcid":9},"Suyog Mamankar",{"name":278,"orcid":9},"Prathamesh Sawant",{"name":280,"orcid":9},"Mhaboob Ali",{"name":282,"orcid":9},"Prof. Jyoti Shrote","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208534_PAPER.pdf",{"tldr":285,"method":286,"finding":287,"direction":47,"opportunity":288},"提出基于边缘AI的宠物监测框架，用树莓派和YOLOv9识别兔子行为与环境。","树莓派边缘计算、YOLOv9、OpenCV、Arduino及温湿度超声波传感器。","200张图像测试验证了框架可行性，但需更多测试才能实际应用。","可扩展至畜禽行为健康监测，解决边缘设备算力与多目标识别精度问题。","2026-09-17T23:30:10.728310Z"]