[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2285":3,"related-2285":99},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":62,"slug":65,"view_count":46,"doi":66,"paper":67,"created_at":98},2285,"PetalSpot: an open-source two-stage deep-learning workflow for pixel-level quantification of petal lesions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112421","PetalSpot: an open-source two-stage deep-learning workflow for pixel-level quantification of petal lesions。Computers and Electronics in Agriculture","PetalSpot：一种用于花瓣病斑像素级量化的开源两阶段深度学习工作流程。",null,"Computers and Electronics in Agriculture","2026-09-11T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,14,8,1,"开源两阶段深度学习流程实现花瓣病斑像素级量化，方法新颖且可复用，对花卉病害智能监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","花卉产业","开源工具","植物病害检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,57],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},4,true,[],[50],"深度学习",[28],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"B4",[],[],[29],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[27],[63,64],"农业人工智能 植物病害检测 开源工具 智慧农业","农业人工智能 植物病害检测","农业人工智能植物病害检测开源工具智慧农业-2285","10.1016\u002Fj.compag.2026.112421",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":90,"oa_url":9,"card":91,"direction":95,"ingested_from":97},"W7212311236",[70,72,74,76,78,80,82,85,87],{"name":71,"orcid":9},"Yao Huang",{"name":73,"orcid":9},"Xiaoqian Cao",{"name":75,"orcid":9},"Fangqi Liu",{"name":77,"orcid":9},"Hong Sha",{"name":79,"orcid":9},"Phurisorn Watcharatpong",{"name":81,"orcid":9},"Ruoheng Jian",{"name":83,"orcid":84},"Wen Chen","https:\u002F\u002Forcid.org\u002F0000-0001-8493-0157",{"name":86,"orcid":9},"Yiqian Fu",{"name":88,"orcid":89},"Zhao Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-7323-5500",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"提出开源两阶段深度学习流程PetalSpot，实现花瓣病斑像素级量化。","两阶段深度学习工作流，开源代码，用于花瓣病斑分割与量化。","该流程能对花瓣病斑进行像素级精确量化，且开源可复现。","农业人工智能与决策模型","可迁移至其他作物器官病斑量化，并融合多模态数据提升泛化性。","openalex","2026-09-13T23:30:02.086637Z",{"total":100,"page":22,"page_size":100,"items":101},6,[102,169,247,318,369,424],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":110,"sources":114,"tags":116,"primary_topic":118,"secondary_topics":119,"classification":120,"search_phrases":151,"slug":154,"view_count":155,"doi":156,"paper":157,"created_at":168},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",{"impact":111,"substance":18,"depth":19,"authority":112,"freshness":21,"relevant":22,"comment":113},18,12,"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。",[115],{"name":108,"url":105},[27,28,50,117,31],"精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":121},{"candidates":122,"agricultural_evidence":47},[123,132,137,144],{"code":33,"score":124,"supported":47,"title":125,"abstract":127,"tags":131},19,[126],"deep learning",[50,128,129,126,130],"机器学习","machine learning","artificial intelligence",[28,50],{"code":58,"score":21,"supported":47,"title":133,"abstract":134,"tags":136},[],[117,135],"precision agriculture",[27,117],{"code":138,"score":100,"supported":47,"title":139,"abstract":140,"tags":143},"A8",[],[141,142],"粮食安全","food security",[],{"code":145,"score":146,"supported":47,"title":147,"abstract":148,"tags":150},"H8",3,[],[149],"可持续农业",[],[152,153],"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":156,"openalex_id":158,"authors":159,"venue":108,"cited_by_count":90,"oa_url":162,"card":163,"direction":95,"ingested_from":97},"W7221637766",[160],{"name":161,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 12 2025\u002FDeep Learning Approaches for Plant 220-231.pdf",{"tldr":164,"method":165,"finding":166,"direction":95,"opportunity":167},"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":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":182,"tags":184,"primary_topic":186,"secondary_topics":187,"classification":188,"search_phrases":220,"slug":223,"view_count":146,"doi":224,"paper":225,"created_at":246},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":111,"substance":179,"depth":111,"authority":180,"freshness":21,"relevant":22,"comment":181},21,13,"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。",[183],{"name":175,"url":172},[27,28,117,185,31],"遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":189},{"candidates":190,"agricultural_evidence":47},[191,198,206,210,216],{"code":33,"score":111,"supported":47,"title":192,"abstract":193,"tags":197},[130],[194,50,128,195,129,126,130,196],"目标检测","计算机视觉","computer vision",[28],{"code":199,"score":200,"supported":47,"title":201,"abstract":202,"tags":205},"I3",7,[],[203,204],"multispectral","hyperspectral",[185],{"code":138,"score":100,"supported":47,"title":207,"abstract":208,"tags":209},[],[141,142],[],{"code":211,"score":146,"supported":47,"title":212,"abstract":213,"tags":215},"I5",[],[214],"边缘计算",[],{"code":58,"score":155,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[27,117],[221,222],"AI 植物病害检测","无人机 高光谱 作物监测","AI植物病害检测-4177","10.9734\u002Fjsrr\u002F2026\u002Fv32i104569",{"doi":224,"openalex_id":226,"authors":227,"venue":175,"cited_by_count":90,"oa_url":172,"card":241,"direction":95,"ingested_from":97},"W7218426608",[228,230,232,235,237,239],{"name":229,"orcid":9},"Aksa Anna Shajan",{"name":231,"orcid":9},"Sumiya K. V.",{"name":233,"orcid":234},"Sainamole Kurian P.","https:\u002F\u002Forcid.org\u002F0009-0004-1992-3582",{"name":236,"orcid":9},"Pooja Naduvalath",{"name":238,"orcid":9},"Athira Suresh",{"name":240,"orcid":9},"Aswathi P. R.",{"tldr":242,"method":243,"finding":244,"direction":95,"opportunity":245},"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":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":9,"source_name":253,"source_url":250,"published_at":254,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":255,"score_detail":256,"sources":259,"tags":261,"primary_topic":263,"secondary_topics":264,"classification":265,"search_phrases":302,"slug":304,"view_count":146,"doi":305,"paper":306,"created_at":317},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":112,"substance":20,"depth":257,"authority":13,"freshness":21,"relevant":22,"comment":258},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。",[260],{"name":253,"url":250},[27,28,262,117,31],"农业物联网",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":266},{"candidates":267,"agricultural_evidence":47},[268,272,278,284,290,296],{"code":33,"score":111,"supported":47,"title":269,"abstract":270,"tags":271},[130],[194,50,128,195,129,126,130,196],[28],{"code":58,"score":273,"supported":47,"title":274,"abstract":275,"tags":277},11,[],[27,117,276,135],"smart agriculture",[27,117],{"code":211,"score":200,"supported":47,"title":279,"abstract":280,"tags":283},[],[281,282],"物联网","IoT",[262],{"code":199,"score":100,"supported":47,"title":285,"abstract":286,"tags":289},[],[287,288],"遥感","remote sensing",[],{"code":291,"score":146,"supported":47,"title":292,"abstract":293,"tags":295},"F6",[],[294],"农业机器人",[],{"code":297,"score":146,"supported":47,"title":298,"abstract":299,"tags":301},"I6",[],[300],"agricultural data",[],[221,303],"无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":305,"openalex_id":307,"authors":308,"venue":253,"cited_by_count":90,"oa_url":9,"card":311,"direction":316,"ingested_from":97},"W7213950095",[309],{"name":310,"orcid":9},"Jamuna Ratcha",{"tldr":312,"method":313,"finding":314,"direction":95,"opportunity":315},"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。","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","2026-09-22T23:30:11.209653Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":9,"content":9,"source_name":323,"source_url":9,"published_at":324,"category":12,"cover_url":9,"hotness":325,"is_selected":14,"score":326,"score_detail":327,"sources":330,"tags":335,"primary_topic":338,"secondary_topics":339,"classification":340,"search_phrases":356,"slug":359,"view_count":146,"doi":9,"paper":360,"created_at":368},3046,"基于Flor-YOLO的香石竹鲜切花分级轻量化检测方法","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-09-20\u002Fdoc-inisnssw6528969.shtml","西南林业大学李传孟、杨洁副教授、张晓宇在《智慧农业（中英文）》2026,8(4):85-99发表Flor-YOLO模型，针对香石竹鲜切花开放度人工分级主观性强、效率低问题，以YOLO11n为基线进行骨干网络、下采样方式、检测头结构针对性改进。Flor-YOLO在自建香石竹数据集上mAP@50达到96.10%，较基准模型提升3.25个百分点；模型参数量与计算量分别为1.26M和1.1GFLOPs，同比降低51.2%和82.5%。","智慧农业(中英文)2026,8(4):85-99","2026-09-20T12:26:00Z",25,83,{"impact":111,"substance":328,"depth":111,"authority":20,"freshness":13,"relevant":22,"comment":329},23,"核心期刊论文，提出轻量化Flor-YOLO模型实现香石竹鲜切花自动分级，数据翔实、方法有创新，对花卉产业智能化有参考价值。",[331,332],{"name":323,"url":321},{"name":333,"url":334},"《智慧农业（中英文）》2026年第4期","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7687597315586818611",[27,28,29,336,337],"轻量化模型","鲜切花分级",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":341},{"candidates":342,"agricultural_evidence":47},[343,348,352],{"code":33,"score":180,"supported":47,"title":344,"abstract":346,"tags":347},[345],"YOLO",[345],[28,336],{"code":58,"score":46,"supported":47,"title":349,"abstract":350,"tags":351},[],[27],[27],{"code":53,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[29],[357,358],"西南林业大学 香石竹 鲜切花分级","Flor-YOLO 香石竹 检测","西南林业大学香石竹鲜切花分级-3046",{"doi":9,"openalex_id":9,"authors":361,"venue":9,"cited_by_count":90,"oa_url":9,"card":362,"direction":95,"ingested_from":367},[],{"tldr":363,"method":364,"finding":365,"direction":95,"opportunity":366},"提出轻量化Flor-YOLO模型，实现香石竹鲜切花开放度自动分级检测。","以YOLO11n为基线，改进骨干网络、下采样方式与检测头，自建香石竹数据集。","mAP@50达96.10%，较基准提升3.25个百分点，参数量与计算量分别降低51.2%和82.5%","可迁移至其他花卉或果蔬的轻量化分级，并探索边缘设备实时部署与多任务联合检测。","agent","2026-09-21T00:04:39.154232Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":384,"secondary_topics":385,"classification":386,"search_phrases":408,"slug":411,"view_count":46,"doi":412,"paper":413,"created_at":423},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":111,"substance":179,"depth":19,"authority":180,"freshness":21,"relevant":22,"comment":379},"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。",[381],{"name":375,"url":372},[27,28,294,31,383],"土壤墒情监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":387},{"candidates":388,"agricultural_evidence":47},[389,393,397,404],{"code":33,"score":13,"supported":47,"title":390,"abstract":391,"tags":392},[],[194,50,126],[28],{"code":58,"score":200,"supported":47,"title":394,"abstract":395,"tags":396},[],[117,135],[27],{"code":398,"score":100,"supported":47,"title":399,"abstract":400,"tags":403},"H2",[],[401,402],"土壤","soil",[],{"code":291,"score":22,"supported":14,"title":405,"abstract":406,"tags":407},[],[],[294],[409,410],"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":412,"openalex_id":414,"authors":415,"venue":375,"cited_by_count":90,"oa_url":372,"card":418,"direction":316,"ingested_from":97},"W7213559917",[416],{"name":417,"orcid":9},"Gasimov V.A., Dadashov F.H., Hasanov H.B., Huseynov N.E",{"tldr":419,"method":420,"finding":421,"direction":316,"opportunity":422},"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":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":431,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":432,"score_detail":433,"sources":437,"tags":439,"primary_topic":442,"secondary_topics":443,"classification":444,"search_phrases":455,"slug":458,"view_count":46,"doi":459,"paper":460,"created_at":493},2783,"Toward scalable organ-level 3D plant segmentation: A systematic and quantitative review through the lens of the data-algorithm-computing triangle","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isprsjprs.2026.09.003","The precise characterization of plant morphology provides valuable insights into plant-environment interactions and genetic evolution. A key technology for extracting this information is 3D segmentation, which delineates individual plant organs from complex point clouds. Despite significant progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges: (i) the scarcity of large-scale annotated datasets, (ii) technical difficulties in adapting advanced deep neural networks to plant point clouds, and (iii) the lack of standardized benchmarks and evaluation protocols tailored to plant science. This review systematically addresses these barriers by: (i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, (ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, (iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and (iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning strategies. Our findings highlight the efficacy of sparse-convolution and serialization-based backbones, as well as transformer-based instance segmentation networks, while also emphasizing the complementary role of modeling-based and augmentation-based synthetic data generation for sim-to-real learning in reducing annotation demands. Overall, this study bridges the gap between algorithmic advances and practical deployment, providing immediate tools for researchers and a roadmap for developing data-efficient and generalizable deep learning solutions in 3D plant phenotyping. Data and code are available at: https:\u002F\u002Fgithub.com\u002Fperrydoremi\u002FPlantSegStudio .","植物形态的精确表征为理解植物-环境相互作用和遗传演化提供了宝贵见解。提取此类信息的一项关键技术是三维分割，它能够从复杂点云中勾勒出植物各器官。尽管通用三维计算机视觉领域已取得显著进展，三维分割在植物表型分析中的应用仍受限于三大挑战：(i) 大规模标注数据集的稀缺，(ii) 将先进深度神经网络适配于植物点云的技术困难，以及(iii) 缺乏面向植物科学量身定制的标准化基准与评估协议。本综述通过以下方式系统性地应对这些障碍：(i) 在通用三维分割领域的背景下概述现有三维植物数据集，(ii) 系统总结基于深度学习的点云语义与实例分割方法，(iii) 介绍植物分割工作室（Plant Segmentation Studio, PSS）——一个用于可重复基准测试的开源框架，以及(iv) 开展大量定量实验以评估代表性网络和仿真到现实（sim-to-real）学习策略。我们的研究结果凸显了稀疏卷积与基于序列化的主干网络以及基于Transformer的实例分割网络的有效性，同时强调了基于建模和基于增强的合成数据生成在仿真到现实学习中对降低标注需求的互补作用。总体而言，本研究弥合了算法进展与实际部署之间的差距，为研究人员提供了即时可用的工具，并为在三维植物表型分析中开发数据高效且具有泛化能力的深度学习解决方案提供了路线图。数据与代码见：https:\u002F\u002Fgithub.com\u002Fperrydoremi\u002FPlantSegStudio 。","ISPRS Journal of Photogrammetry and Remote Sensing","2026-09-16T00:00:00Z",81,{"impact":111,"substance":434,"depth":111,"authority":20,"freshness":435,"relevant":22,"comment":436},22,9,"ISPRS顶刊综述，系统梳理器官级3D植物分割的数据-算法-算力瓶颈，并开源Plant Segmentation Studio基准框架，对作物表型与智慧育种有直接工具价值。",[438],{"name":430,"url":427},[27,28,30,440,441],"植物表型","三维点云",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":445},{"candidates":446,"agricultural_evidence":47},[447,451],{"code":33,"score":273,"supported":47,"title":448,"abstract":449,"tags":450},[],[50,195,126,196],[441,28],{"code":58,"score":22,"supported":14,"title":452,"abstract":453,"tags":454},[],[],[27],[456,457],"农业人工智能 三维点云 开源工具 智慧农业","农业人工智能 三维点云","农业人工智能三维点云开源工具智慧农业-2783","10.1016\u002Fj.isprsjprs.2026.09.003",{"doi":459,"openalex_id":461,"authors":462,"venue":430,"cited_by_count":90,"oa_url":427,"card":487,"direction":491,"ingested_from":97},"W7213280479",[463,465,467,469,472,475,477,479,481,484],{"name":464,"orcid":9},"Ruiming Du",{"name":466,"orcid":9},"Guangxun Zhai",{"name":468,"orcid":9},"Tian Qiu",{"name":470,"orcid":471},"Shichao Jin","https:\u002F\u002Forcid.org\u002F0000-0003-1150-336X",{"name":473,"orcid":474},"Dawei Li","https:\u002F\u002Forcid.org\u002F0000-0002-9702-8848",{"name":476,"orcid":9},"Zhihong Ma",{"name":478,"orcid":9},"Yongliang Qiao",{"name":480,"orcid":9},"Junfeng Gao",{"name":482,"orcid":483},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",{"name":485,"orcid":486},"Yu Jiang","https:\u002F\u002Forcid.org\u002F0000-0003-4495-3033",{"tldr":488,"method":489,"finding":490,"direction":491,"opportunity":492},"系统综述植物器官级3D分割，提出数据-算法-算力三角框架并开源基准平台PSS。","综述3D植物数据集与深度学习分割方法，构建PSS开源基准并做定量实验。","稀疏卷积与序列化骨干及Transformer实例分割有效，合成数据可降低标注需求。","农业遥感与作物表型","可探索数据高效、跨物种泛化的3D植物分割模型与标准化评测协议。","2026-09-17T23:30:30.425366Z"]