[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3048":3,"related-3048":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":35,"created_at":44},3048,"基于双路径注意力与多尺度融合的作物病虫害识别网络DPMFNet","https:\u002F\u002Fwww.mdpi.com\u002F1099-4300\u002F28\u002F9\u002F1032","盐城工学院Hong Zhang、Fagen Song等联合江苏开放大学提出DPMFNet轻量级双路径网络，集成空间-通道双注意力（SCDA）与多尺度深度可分离卷积（MDSC）模块，构建AttMDSCBlock残差结构。在PlantVillage与AI Challenger 2018数据集上DPMFNet仅14.24M参数和2.55G FLOPs，跨注意力机制融合局部细节与全局上下文，轻量化金字塔策略自适应整合多分辨率特征，在复杂农田场景下兼顾精度与可部署性，为嵌入式田间设备提供高性价比方案。",null,"MDPI Entropy 28(9):1032","2026-09-20T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},16,22,18,13,1,"轻量级双路径注意力网络在两大公开数据集上兼顾精度与可部署性，对嵌入式田间设备落地有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","病虫害识别","作物监测","轻量化模型",[31,32],"盐城工学院 DPMFNet 病虫害识别","PlantVillage AI Challenger 作物病害","盐城工学院DPMFNet病虫害识别-3048",0,{"doi":8,"openalex_id":8,"authors":36,"venue":8,"cited_by_count":34,"oa_url":8,"card":37,"direction":41,"ingested_from":43},[],{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"提出轻量级双路径网络DPMFNet，实现复杂农田场景下的作物病虫害高精度识别。","空间-通道双注意力与多尺度深度可分离卷积，构建AttMDSCBlock残差结构。","仅14.24M参数、2.55G FLOPs，在PlantVillage与AI Challenger ","农业人工智能与决策模型","可探索真实田间多病虫害并发与边缘设备实时推理的轻量化自适应识别研究。","agent","2026-09-21T00:04:39.305757Z",{"total":46,"page":20,"page_size":46,"items":47},6,[48,90,119,175,197,242],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":53,"content":8,"source_name":54,"source_url":51,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":62,"tags":64,"search_phrases":66,"slug":69,"view_count":34,"doi":70,"paper":71,"created_at":89},1398,"AI-Enabled IoT for Precision Agriculture and Smart Crop Management","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1195","Agriculture is increasingly dependent on technologies that can improve productivity while reducing the consumption of water, fertilizers, pesticides, herbicides, labour and other resources. The five source chapters reviewed for this paper collectively describe the role of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), computer vision, cloud computing and embedded systems in smart farming. The summarized work focuses on continuous sensing of soil and environmental parameters, automated irrigation, crop and weed monitoring, disease identification, yield prediction and decision support. The sources identify Artificial Neural Networks (ANNs), Deep Learning, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as important approaches. A prototype architecture is also described using Arduino Mega 2560, Raspberry Pi, multiple sensors, Firebase and an Apache web server. In the disease-detection prototype, 295 leaf images were divided into training, validation and testing groups, and a CNN-based mobile application produced a reported confidence score of 0.97 for an example operation with an execution time of approximately 0.88 seconds. Overall, the reviewed material indicates that integrating IoT sensing with AI\u002FML can support real-time monitoring, resource optimization and faster agricultural decisions. However, Internet dependence, cybersecurity, system complexity, adoption cost, limited datasets and reduced accuracy for visually similar crop diseases remain important challenges.","农业日益依赖于能够在提高生产力的同时减少水、肥料、农药、除草剂、劳动力及其他资源消耗的技术。本文所综述的五篇源文献共同阐述了人工智能（AI）、机器学习（ML）、物联网（IoT）、计算机视觉、云计算及嵌入式系统在智慧农业中的作用。这些研究工作的重点集中于土壤与环境参数的连续感知、自动化灌溉、作物与杂草监测、病害识别、产量预测及决策支持。相关文献将人工神经网络（ANNs）、深度学习、支持向量机（SVMs）和卷积神经网络（CNNs）视为重要技术方法。此外，文中还描述了一种基于Arduino Mega 2560、Raspberry Pi、多种传感器、Firebase及Apache Web服务器的原型架构。在病害检测原型中，295张叶片图像被划分为训练集、验证集和测试集，基于CNN的移动应用在示例操作中报告的置信度得分为0.97，执行时间约为0.88秒。总体而言，所综述的材料表明，将物联网感知与人工智能\u002F机器学习相结合，可支持实时监测、资源优化及更快速的农业决策。然而，互联网依赖、网络安全、系统复杂性、采用成本、数据集有限以及视觉上相似的作物病害识别精度降低等问题仍是重要挑战。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-09-02T00:00:00Z",56,{"impact":58,"substance":18,"depth":16,"authority":59,"freshness":60,"relevant":20,"comment":61},12,8,2,"综述AI与IoT在精准农业中的应用，含原型与数据，但来源一般且时效性低。",[63],{"name":54,"url":51},[25,26,65,27,28],"物联网",[67,68],"农业人工智能 病虫害识别 作物监测 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物监测智慧农业-1398","10.38124\u002Fijisrt\u002F26aug1195",{"doi":70,"openalex_id":72,"authors":73,"venue":54,"cited_by_count":34,"oa_url":51,"card":82,"direction":86,"ingested_from":88},"W7204992523",[74,76,78,80],{"name":75,"orcid":8},"Tarun Badiwal",{"name":77,"orcid":8},"Manish Jain",{"name":79,"orcid":8},"Sandeep Jayswal",{"name":81,"orcid":8},"Suresh Meena",{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"综述AI与IoT在精准农业中的应用，涵盖传感、灌溉、病害检测等，并给出原型架构。","综述多篇文献，结合ANN、DL、SVM、CNN，并描述基于Arduino和树莓派","AI与IoT集成可支持实时监测和资源优化，但存在网络依赖、成本高、数据有限等挑战。","智慧农业 \u002F 农业物联网","针对视觉相似作物病害识别精度不足，可研究多模态数据融合或轻量化模型以提升鲁棒性。","openalex","2026-09-02T23:30:09.623547Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":8,"content":8,"source_name":95,"source_url":8,"published_at":96,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":97,"score_detail":98,"sources":102,"tags":104,"search_phrases":107,"slug":110,"view_count":34,"doi":8,"paper":111,"created_at":118},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",83,{"impact":18,"substance":99,"depth":18,"authority":100,"freshness":12,"relevant":20,"comment":101},23,14,"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。",[103],{"name":95,"url":93},[25,26,105,29,106],"花卉产业","鲜切花分级",[108,109],"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":8,"openalex_id":8,"authors":112,"venue":8,"cited_by_count":34,"oa_url":8,"card":113,"direction":41,"ingested_from":43},[],{"tldr":114,"method":115,"finding":116,"direction":41,"opportunity":117},"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。","2026-09-21T00:04:39.154232Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":8,"source_name":125,"source_url":122,"published_at":126,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":127,"score_detail":128,"sources":133,"tags":135,"search_phrases":138,"slug":141,"view_count":34,"doi":142,"paper":143,"created_at":174},3007,"Estimation of coffee leaf physiological indicators based on VAG-ViT: A lightweight framework fusing multi-view RGB images and dual-task learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112452","Estimation of coffee leaf physiological indicators based on VAG-ViT: A lightweight framework fusing multi-view RGB images and dual-task learning。Computers and Electronics in Agriculture","基于VAG-ViT的咖啡叶片生理指标估测：一种融合多视角RGB图像与双任务学习的轻量级框架。","Computers and Electronics in Agriculture","2026-09-19T00:00:00Z",77,{"impact":16,"substance":129,"depth":130,"authority":100,"freshness":131,"relevant":20,"comment":132},21,17,9,"提出融合多视图RGB与双任务学习的轻量化ViT框架，用于咖啡叶片生理指标估算，方法新颖且面向经济作物精准管理，具备行业参考价值。",[134],{"name":125,"url":122},[25,26,29,136,137],"咖啡种植","多视图图像",[139,140],"咖啡叶片 生理指标 多视图RGB","VAG-ViT 双任务学习","咖啡叶片生理指标多视图RGB-3007","10.1016\u002Fj.compag.2026.112452",{"doi":142,"openalex_id":144,"authors":145,"venue":125,"cited_by_count":34,"oa_url":122,"card":168,"direction":172,"ingested_from":88},"W7213674051",[146,148,150,153,155,157,159,161,163,165],{"name":147,"orcid":8},"Zongyuan Lv",{"name":149,"orcid":8},"Jianping Yang",{"name":151,"orcid":152},"Li Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7006-4443",{"name":154,"orcid":8},"Rongbiao Ji",{"name":156,"orcid":8},"Yadong Li",{"name":158,"orcid":8},"Mengyao Wu",{"name":160,"orcid":8},"Songling Huang",{"name":162,"orcid":8},"Jingyu Yuan",{"name":164,"orcid":8},"Zihao Jiang",{"name":166,"orcid":167},"Tong Li","https:\u002F\u002Forcid.org\u002F0000-0002-3257-213X",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"提出VAG-ViT轻量框架，融合多视角RGB图像与双任务学习，估算咖啡叶片生理指标。","多视角RGB图像融合、双任务学习、轻量级ViT框架。","VAG-ViT能有效估算咖啡叶片生理指标，兼顾精度与轻量化。","农业遥感与作物表型","可探索多视角RGB与双任务学习在更多作物生理指标估算中的泛化能力及田间部署。","2026-09-20T23:30:01.826242Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":8,"content":8,"source_name":180,"source_url":8,"published_at":181,"category":182,"cover_url":8,"hotness":12,"is_selected":13,"score":183,"score_detail":184,"sources":187,"tags":189,"search_phrases":193,"slug":195,"view_count":34,"doi":8,"paper":8,"created_at":196},2815,"WAFI2026人工智能与农业论坛:中国方案助力农民种得好、种得起、种得稳、种得赚","https:\u002F\u002Fbaike.baidu.com\u002Fitem\u002F2026%E4%B8%96%E7%95%8C%E5%86%9C%E4%B8%9A%E7%A7%91%E6%8A%80%E5%88%9B%E6%96%B0%E5%A4%A7%E4%BC%9A\u002F68651190","9月16日WAFI2026举行\"人工智能与农业论坛\",中国农业大学全球食物经济与政策研究院院长樊胜根提出\"人工智能如何造福农民\"议题。论坛介绍神农大模型(2023年1.0版到2025年3.0版,3.0版为\"小麦育种智能助手\",可识别70类、600余种病虫害,已在非洲落地)、农业食物经济与政策AI模型、北大荒\"未来农场\"平台(覆盖111个农场、接入8.4万台智能装备、为60万种植户服务)、北京市\"智京园\"智慧设施管控技术体系等案例。","百度百科 \u002F 中国农业大学","2026-09-16T00:00:00Z","报道",86,{"impact":185,"substance":129,"depth":130,"authority":100,"freshness":59,"relevant":20,"comment":186},26,"国际论坛上集中展示神农大模型、北大荒未来农场等中国AI农业方案，案例数据具体、信源权威，时效性强，值得进入每日精选。",[188],{"name":180,"url":178},[25,26,190,191,192,27],"未来农场","智能育种","大模型",[194,68],"农业人工智能 病虫害识别 智慧农业 智能育种","农业人工智能病虫害识别智慧农业智能育种-2815","2026-09-18T00:03:24.639818Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":8,"source_name":125,"source_url":200,"published_at":181,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":203,"score_detail":204,"sources":206,"tags":208,"search_phrases":211,"slug":214,"view_count":34,"doi":215,"paper":216,"created_at":241},2742,"LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112417","LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping。Computers and Electronics in Agriculture","LeafTrackNet：一种用于自上而下植物表型分析中稳健叶片追踪的深度学习框架。",70,{"impact":58,"substance":18,"depth":130,"authority":100,"freshness":131,"relevant":20,"comment":205},"核心期刊发表的叶片追踪深度学习框架，方法有创新但属细分技术进展，产业影响有限，可作为农业AI主题聚合素材。",[207],{"name":125,"url":200},[25,26,209,28,210],"深度学习","植物表型",[212,213],"农业人工智能 作物监测 智慧农业 植物表型","农业人工智能 作物监测","农业人工智能作物监测智慧农业植物表型-2742","10.1016\u002Fj.compag.2026.112417",{"doi":215,"openalex_id":217,"authors":218,"venue":125,"cited_by_count":34,"oa_url":200,"card":236,"direction":172,"ingested_from":88},"W7213430727",[219,222,225,227,229,231,234],{"name":220,"orcid":221},"Shanghua Liu","https:\u002F\u002Forcid.org\u002F0009-0009-9855-9040",{"name":223,"orcid":224},"Majharulislam Babor","https:\u002F\u002Forcid.org\u002F0000-0002-5440-7573",{"name":226,"orcid":8},"Christoph Verduyn",{"name":228,"orcid":8},"Breght Vandenberghe",{"name":230,"orcid":8},"Bruno Betoni Parodi",{"name":232,"orcid":233},"Cornelia Weltzien","https:\u002F\u002Forcid.org\u002F0000-0002-2951-3614",{"name":235,"orcid":8},"Marina M. -C. Höhne",{"tldr":237,"method":238,"finding":239,"direction":172,"opportunity":240},"提出LeafTrackNet深度学习框架，实现俯视植物表型中稳健的叶片追踪。","基于深度学习的叶片追踪框架，用于俯视植物表型图像序列。","该框架能稳健追踪叶片，提升植物表型分析的自动化与准确性。","可探索多物种、遮挡与生长形变下的长期叶片追踪及三维表型融合。","2026-09-17T23:30:01.562015Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":8,"source_name":248,"source_url":245,"published_at":181,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":249,"score_detail":250,"sources":252,"tags":254,"search_phrases":257,"slug":259,"view_count":34,"doi":260,"paper":261,"created_at":289},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。","Remote Sensing",82,{"impact":18,"substance":17,"depth":18,"authority":100,"freshness":12,"relevant":20,"comment":251},"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[253],{"name":248,"url":245},[25,26,255,28,256],"遥感","植被指数",[258,213],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能作物监测智慧农业植被指数-2660","10.3390\u002Frs18183180",{"doi":260,"openalex_id":262,"authors":263,"venue":248,"cited_by_count":34,"oa_url":245,"card":284,"direction":172,"ingested_from":88},"W7213344870",[264,267,269,272,275,278,281],{"name":265,"orcid":266},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":268,"orcid":8},"Nikolas Hoskin",{"name":270,"orcid":271},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":273,"orcid":274},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":276,"orcid":277},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":279,"orcid":280},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":282,"orcid":283},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":285,"method":286,"finding":287,"direction":172,"opportunity":288},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z"]