[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2122":3,"related-2122":101},{"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":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":100},2122,"Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10448-0","Abstract Purpose Precision Agriculture technologies including satellite data, drones, field robots and automatic guidance, are increasingly promoted as tools to improve sustainability, competitiveness, and environmental efficiency in viticulture. However, the adoption of these practices among Italian winegrowers exhibits significant heterogeneity, influenced by a combination of structural and behavioural factors. This study aims to identify distinct groups of Italian winegrowers based on their intentions to adopt PATs, examining how these intentions relate to four behavioural constructs that capture cognitive, social, and risk-related influences. Method The present study integrates a profile-based perspective by estimating a latent-class generalised structural equation model on 272 Italian winegrowers. The model of class membership is predicated upon a multifaceted conceptualisation encompassing risk perception, risk tolerance, perceived ease of use, and subjective norms and the technology-specific five-year intentions are incorporated as class-specific probit intercepts. Results Three qualitatively distinct profiles emerge: risk-sensitive sceptics, selective pragmatists and usability-oriented high adopters with sharply different adoption patterns across technologies. Participation in rural development schemes is the strongest predictor of membership in the higher-propensity latent classes, namely the selective-pragmatist and usability-oriented high-adopter profiles. In addition, familiarity with PATs, male gender and participation in consortium are positively associated with the higher-propensity class, while age shows a weak negative correlation. Conclusion These results inform the design of extension and policy for specific segments, prioritising risk-mitigation trials for cautious growers, targeted information services for select profiles, and integrated PATs bundles for those with a high propensity.","摘要 目的 精准农业技术（Precision Agriculture Technologies，PATs），包括卫星数据、无人机、田间机器人和自动导航，日益被视为提升葡萄栽培可持续性、竞争力和环境效率的工具。然而，意大利葡萄种植者对上述技术的采纳呈现出显著异质性，受到结构性因素与行为因素的共同影响。本研究旨在基于意大利葡萄种植者采纳PATs的意愿识别不同群体，并考察这些意愿如何与四个行为构念相关联，这四个构念分别捕捉认知、社会和风险相关的影响。方法 本研究整合了基于剖面的视角，对272名意大利葡萄种植者估计了潜在类别广义结构方程模型。类别归属模型建立在涵盖风险感知、风险容忍度、感知易用性和主观规范的多维概念化基础之上，并将技术特定的五年意愿作为类别特定的probit截距纳入模型。结果 研究识别出三种性质不同的剖面：风险敏感型怀疑者、选择性实用主义者和以易用性为导向的高采纳者，其在不同技术上的采纳模式差异显著。参与农村发展计划是归属于较高倾向潜在类别（即选择性实用主义者和以易用性为导向的高采纳者剖面）的最强预测因素。此外，对PATs的熟悉程度、男性性别和参与合作社与较高倾向类别呈正相关，而年龄则表现出较弱的负相关。结论 上述结果为针对特定群体的推广和政策设计提供了依据，应优先为谨慎型种植者开展风险缓解试验，为特定剖面提供有针对性的信息服务，并为高倾向群体提供整合的PATs技术组合。",null,"Precision Agriculture","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"基于272户意大利葡萄种植者的潜类别分析，揭示精准农业采纳的异质性行为分群，对农户分类推广与政策设计有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农户采纳","精准农业","遥感监测","葡萄种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60,66,71],{"code":46,"score":17,"supported":47,"title":48,"abstract":50,"tags":51},"I1",true,[49],"precision agriculture",[29,49],[27,29],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"G",6,[],[57,58],"农村发展","rural development",[],{"code":33,"score":61,"supported":47,"title":62,"abstract":63,"tags":65},4,[],[64],"葡萄",[31],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"G2",[],[],[28],{"code":72,"score":22,"supported":14,"title":73,"abstract":74,"tags":75},"I3",[],[],[30],[77,78],"农户采纳 智慧农业 精准农业 葡萄种植","农户采纳 智慧农业","农户采纳智慧农业精准农业葡萄种植-2122",0,"10.1007\u002Fs11119-026-10448-0",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":80,"oa_url":6,"card":93,"direction":97,"ingested_from":99},"W7212151394",[85,87,90],{"name":86,"orcid":9},"Adriano Biondo",{"name":88,"orcid":89},"Antonino Galati","https:\u002F\u002Forcid.org\u002F0000-0003-0753-2934",{"name":91,"orcid":92},"Francesco Caracciolo","https:\u002F\u002Forcid.org\u002F0000-0001-9430-7529",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"基于272名意大利葡萄种植者，用潜类别分析识别精准农业技术采纳意向的异质性群体。","潜类别广义结构方程模型，纳入风险感知、风险容忍、易用性和主观规范。","分出风险敏感怀疑者、选择性实用主义者和易用性高采纳者三类，参与农村发展计划是最强预测因素。","数字乡村与农业信息化","可针对不同农户群体设计差异化推广策略，并研究政策参与如何通过行为路径影响技术采纳。","openalex","2026-09-11T23:30:02.943184Z",{"total":54,"page":22,"page_size":54,"items":102},[103,190,255,309,371,434],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":123,"classification":134,"search_phrases":162,"slug":165,"view_count":22,"doi":166,"paper":167,"created_at":189},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":17,"substance":113,"depth":19,"authority":114,"freshness":21,"relevant":22,"comment":115},21,13,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[117],{"name":109,"url":106},[27,119,120,30,121],"无人机","农业人工智能","病害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[124,130],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":72,"slug":131,"name":132,"path":133,"parent_code":129},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":135},{"candidates":136,"agricultural_evidence":47},[137,144,151,157],{"code":33,"score":138,"supported":47,"title":139,"abstract":141,"tags":143},17,[140],"apple",[142,64,140],"苹果",[],{"code":125,"score":145,"supported":47,"title":146,"abstract":148,"tags":150},15,[147],"deep learning",[149,147],"深度学习",[120],{"code":72,"score":152,"supported":47,"title":153,"abstract":155,"tags":156},12,[154],"multispectral",[154],[30],{"code":46,"score":158,"supported":47,"title":159,"abstract":160,"tags":161},7,[],[29,49],[27],[163,164],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":166,"openalex_id":168,"authors":169,"venue":109,"cited_by_count":80,"oa_url":181,"card":182,"direction":188,"ingested_from":99},"W7221558726",[170,173,176,178],{"name":171,"orcid":172},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":174,"orcid":175},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":177,"orcid":9},"Fatih Sinan Bir",{"name":179,"orcid":180},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:10.242467Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":202,"tags":204,"primary_topic":208,"secondary_topics":209,"classification":210,"search_phrases":234,"slug":237,"view_count":80,"doi":238,"paper":239,"created_at":254},4587,"Sentinel-2 NDVI as an indicator of apple orchard yield","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffhort.2026.1904481","Satellite data is increasingly being used as a basis for operational crop monitoring. However, satellite technologies for monitoring perennial crops remain underdeveloped. The aim of this study was to assess the relationship between apple yield and NDVI calculated using all available Sentinel-2 data for the growing seasons (March-November) from 2017 to 2024. Totally 81 cloud-free images were analyzed. The seasonal absolute maximum and area-weighted average NDVI were calculated based on analysis of all images for each growing season for each plot. Intensively irrigated, mixed-age apple orchards located in northern Azerbaijan served as the research site. Totally 30 plots with 6 apple tree varieties (Arian, Gala, Golden, Golden Parsi, Granny Smith, and Fuji) planted from 2007 to 2017 were analyzed. Apple yield for each plot for 2018–2024 were collected. The linear regression models between NDVI (the seasonal absolute maximum and area-weighted average) and apple yield was used as the relationship criterion. An analysis of the relationship was conducted for individual apple varieties, for orchard plots, and for plots of different ages. It was found that reliable relationships between apple yield and NDVI are only observed for specific plots with a single variety of the same age (R2cv higher than 0,8 with RPIQ more than 2,1). Grouping plots of the same variety or age does not demonstrate a reliable relationship between NDVI and yield. Only for groups of middle-aged orchards does such a relationship become stronger. These results should be considered when developing methods for satellite monitoring of apple orchards.","卫星数据正日益被用作业务化作物监测的基础。然而，用于监测多年生作物的卫星技术仍不发达。本研究旨在评估苹果产量与利用2017年至2024年生长季（3—11月）所有可用Sentinel-2数据计算的NDVI之间的关系。共分析了81幅无云图像。基于对每个生长季每块样地所有图像的分析，计算了季节绝对最大值NDVI和面积加权平均NDVI。研究地点位于阿塞拜疆北部集约化灌溉的混龄苹果园。共分析了30块样地，涵盖2007年至2017年种植的6个苹果品种（Arian、Gala、Golden、Golden Parsi、Granny Smith和Fuji）。收集了2018—2024年每块样地的苹果产量。以NDVI（季节绝对最大值和面积加权平均值）与苹果产量之间的线性回归模型作为关系评价标准。分别针对单个苹果品种、果园样地以及不同树龄样地进行了关系分析。研究发现，仅在特定同一品种、同树龄的样地中才能观察到苹果产量与NDVI之间可靠的关系（R2cv高于0.8，RPIQ大于2.1）。将同一品种或同一树龄的样地分组后，NDVI与产量之间并未表现出可靠的关系。仅对中年果园分组时，这种关系才变得更强。在开发苹果园卫星监测方法时，应考虑这些结果。","Frontiers in Horticulture","2026-10-09T00:00:00Z",71,{"impact":152,"substance":113,"depth":138,"authority":114,"freshness":200,"relevant":22,"comment":201},8,"基于2017—2024年81景Sentinel-2影像评估苹果园NDVI与产量的关系，方法扎实、结论有边界条件，对果园遥感监测有参考价值，但属细分领域研究，公共影响有限。",[203],{"name":196,"url":193},[27,205,206,30,207],"产量预测","Sentinel-2","苹果园",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":211},{"candidates":212,"agricultural_evidence":47},[213,219,226,230],{"code":33,"score":214,"supported":47,"title":215,"abstract":217,"tags":218},25,[216,140],"orchard",[142,216,140],[],{"code":220,"score":221,"supported":47,"title":222,"abstract":223,"tags":225},"H3",3,[],[224],"灌溉",[],{"code":72,"score":221,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[206,205,30],{"code":46,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[27],[235,236],"Sentinel-2 NDVI 苹果园 产量","阿塞拜疆 苹果园 遥感监测","Sentinel-2NDVI苹果园产量-4587","10.3389\u002Ffhort.2026.1904481",{"doi":238,"openalex_id":240,"authors":241,"venue":196,"cited_by_count":80,"oa_url":248,"card":249,"direction":186,"ingested_from":99},"W7221180616",[242,244,246],{"name":243,"orcid":9},"N. Gasimzade",{"name":245,"orcid":9},"A. Radzhabov",{"name":247,"orcid":9},"I. Savin","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fhorticulture\u002Farticles\u002F10.3389\u002Ffhort.2026.1904481\u002Fpdf",{"tldr":250,"method":251,"finding":252,"direction":186,"opportunity":253},"评估Sentinel-2 NDVI与苹果园产量的关系，发现仅单一品种同龄地块存在可靠相关。","用2017-2024年81景Sentinel-2影像计算NDVI，对30个苹果园","仅单一品种同龄地块NDVI与产量可靠相关（R²cv>0.8），按品种或树龄分组则关系不可靠。","多年生果园遥感估产需按品种、树龄精细分层建模，混合地块的普适性模型仍是研究空白。","2026-10-10T23:30:28.042882Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":9,"source_name":261,"source_url":258,"published_at":262,"category":12,"cover_url":9,"hotness":214,"is_selected":14,"score":263,"score_detail":264,"sources":266,"tags":270,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":290,"slug":293,"view_count":22,"doi":294,"paper":295,"created_at":308},4241,"Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142591","Plant diseases have major social, ecological and economic impact on agriculture and frequently results in large financial losses for farmers. Therefore early and precise disease detection is crucial for efficient crop management. The severity of leaf diseases and their direct impact on yield quality have been highlighted in recent studies. Both digital cameras and flatbed scanners can be used to take leaf images but they have certain drawbacks. Although scanners offer a more secure and regulated method of acquiring images they are comparatively slow and may cause edge shadows. This study uses a hybrid deep learning approach to identify diseases in apple plant leaves. To improve classification performance; the proposed approach combines the advantages of Residual Networks (ResNet), Genetic Algorithms (GA) and Convolutional Neural Networks (CNN). CNNs are used to extract features and classify diseases whereas ResNet enhances the learning efficiency of deeper networks. The feature set is optimized using a genetic algorithm which improves accuracy and lowers computational complexity. The model also calculates the percentage of the leaf area that is impacted. According to experimental results the proposed method detects leaf diseases with high accuracy of percentage and low computational cost making it a dependable and effective precision agriculture solution. The proposed model exhibited strong classification efficiency across various leaf conditions that pertaining an accuracy of 0.97 for healthy leaves, 0.994 for apple scab, 0.89 for rust, and 0.91 for powdery mildew.","植物病害对农业产生重大的社会、生态和经济影响，并经常给农民带来巨大的经济损失。因此，早期且精确的病害检测对于高效的作物管理至关重要。近期研究强调了叶部病害的严重程度及其对产量品质的直接影响。数码相机和平板扫描仪均可用于拍摄叶片图像，但它们各有一定缺陷。尽管扫描仪提供了更安全、更可控的图像获取方式，但其速度相对较慢，且可能产生边缘阴影。本研究采用混合深度学习方法识别苹果植株叶部病害。为提高分类性能，所提方法结合了残差网络（ResNet）、遗传算法（GA）和卷积神经网络（CNN）的优势。CNN用于提取特征并分类病害，而ResNet增强了更深网络的学习效率。利用遗传算法对特征集进行优化，从而提高准确率并降低计算复杂度。该模型还计算了受影响的叶面积百分比。实验结果表明，所提方法以高准确率和低计算成本检测叶部病害，使其成为可靠且有效的精准农业解决方案。所提模型在多种叶片条件下均表现出较强的分类效率，健康叶片准确率为0.97，苹果黑星病为0.994，锈病为0.89，白粉病为0.91。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-04T00:00:00Z",67,{"impact":152,"substance":19,"depth":17,"authority":152,"freshness":21,"relevant":22,"comment":265},"基于ResNet+GA+CNN的苹果叶病识别研究，准确率数据具体，方法有增量，但属实验室论文，产业影响有限。",[267,268],{"name":261,"url":258},{"name":261,"url":269},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142592",[27,120,149,29,271],"苹果病害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[274],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},{"status":40,"locked":14,"version":41,"method":42,"evidence":276},{"candidates":277,"agricultural_evidence":47},[278,282,286],{"code":33,"score":145,"supported":47,"title":279,"abstract":280,"tags":281},[140],[142,140],[271],{"code":46,"score":200,"supported":47,"title":283,"abstract":284,"tags":285},[],[29,49],[27,29],{"code":125,"score":200,"supported":47,"title":287,"abstract":288,"tags":289},[],[149,147],[120,149],[291,292],"苹果叶片病害 卷积神经网络","苹果锈病 白粉病 识别","苹果叶片病害卷积神经网络-4241","10.5281\u002Fzenodo.23142591",{"doi":294,"openalex_id":296,"authors":297,"venue":261,"cited_by_count":80,"oa_url":258,"card":302,"direction":97,"ingested_from":99},"W7219637192",[298,300],{"name":299,"orcid":9},"Mohd Arshad Raza",{"name":301,"orcid":9},"Kiran Pandey",{"tldr":303,"method":304,"finding":305,"direction":306,"opportunity":307},"提出ResNet+GA+CNN混合模型，用苹果叶片图像检测病害并计算病斑面积占比。","ResNet提取深层特征，遗传算法优化特征集，CNN分类，苹果叶片图像数据。","健康叶、黑星病、锈病、白粉病准确率分别达0.97、0.994、0.89、0.91，计算成本低。","农业人工智能与决策模型","可探索轻量化模型在田间移动端实时检测，并融合多病害严重度分级与产量损失预测。","2026-10-05T23:30:30.830366Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":317,"score_detail":318,"sources":322,"tags":324,"primary_topic":327,"secondary_topics":328,"classification":329,"search_phrases":348,"slug":351,"view_count":221,"doi":352,"paper":353,"created_at":370},3255,"Grape yield estimation using on-the-fly MIMO millimeter-wave radar at operational field speed: A variety-dependent proof of concept","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112395","Accurate pre-harvest grape yield estimation is a critical challenge in precision viticulture, yet current optical approaches (RGB cameras, LiDAR) are fundamentally limited by foliage occlusion and sensitivity to ambient lighting conditions. This paper presents a ground-based grape yield estimation system using a dual 77 GHz Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous-Wave (FM-CW) radar mounted on a rover operating at an average speed of 1.0 m\u002Fs (3.7 km\u002Fh), matching practical vineyard tractor speeds. Unlike prior radar-based studies conducted in static or slow-moving setups, the proposed pipeline achieves continuous 3D reconstruction of vinerows through digital beamforming in the elevation plane, combined with an IMU-fused Kalman filter for point cloud stabilization on irregular terrain. An experimental campaign was conducted on 136 km of scanned vinerows across seven grape varieties and three phenological growth stages (BBCH 75–89) in a commercial vineyard in Gascony, France. Statistical features extracted from 3D radar echo level images are used to train a Support Vector Regressor (SVR) evaluated in cross-validation. The primary out-of-sample result is a cross-validation Mean Absolute Error (CV MAE) of 31.2% on the full multi-variety dataset without any defoliation, achieved against certified wine-vat bulk weights as the ground truth reference. This figure should be contextualized against the ∼ 24–25% discrepancy observed between portable field weighing plates and vat-certified weights across the same dataset, and against manual pre-harvest estimation errors routinely exceeding 30% in viticultural practice. A sensitivity analysis over physical parameters reveals that grape variety is the dominant −and limiting- source of performance heterogeneity: CV MAE ranges from 7% for Tannat to 69% for Baco, demonstrating that a single global model is insufficient for multi-variety deployment and that variety-specific calibration is a necessary condition for operational use. These results are best understood as an architectural proof of concept: the primary contribution is the demonstration that MIMO digital beamforming removes the speed bottleneck of prior mechanically-scanned radar systems, enabling gapless 3D vinerow reconstruction at practical field speeds. The yield estimation results provide a first quantitative characterization of the system’s sensing capability and its variety-dependent limitations and establish a baseline for future variety-specific model development.","准确的采前葡萄产量估测是精准葡萄栽培中的一项关键挑战，然而当前的光学方法（RGB相机、LiDAR）从根本上受到冠层遮挡和对环境光照条件敏感性的限制。本文提出了一种地基葡萄产量估测系统，采用双77 GHz多输入多输出（MIMO）调频连续波（FM-CW）雷达，搭载于以平均速度1.0 m\u002Fs（3.7 km\u002Fh）行驶的移动平台上，与实际葡萄园拖拉机作业速度相匹配。与先前在静态或慢速移动装置中进行的雷达研究不同，所提出的处理流程通过仰角平面上的数字波束成形实现葡萄行连续三维重建，并结合IMU融合卡尔曼滤波器在不规则地形上实现点云稳定。在法国加斯科尼的一个商业葡萄园中，对七个葡萄品种和三个物候生长期（BBCH 75–89）的136 km扫描葡萄行进行了实验。从三维雷达回波强度图像中提取的统计特征用于训练支持向量回归器（SVR），并通过交叉验证进行评估。主要样本外结果为：在完整多品种数据集上、未进行任何去叶处理的情况下，交叉验证平均绝对误差（CV MAE）为31.2%，以认证酒罐散装重量作为地面真值参考。该数值应结合以下背景加以理解：在同一数据集上，便携式田间称重板与酒罐认证重量之间观察到约24–25%的差异，而葡萄栽培实践中人工采前估测误差通常超过30%。对物理参数的敏感性分析表明，葡萄品种是性能异质性的主要——也是限制性——来源：CV MAE从Tannat的7%到Baco的69%不等，表明单一全局模型不足以支持多品种部署，品种特异性校准是实际应用的必要条件。这些结果最好被理解为一种架构层面的概念验证：主要贡献在于证明了MIMO数字波束成形消除了先前机械扫描雷达系统的速度瓶颈，从而能够在实际田间速度下实现无间隙三维葡萄行重建。产量估测结果首次定量表征了该系统的感知能力及其品种依赖性局限，并为未来","Computers and Electronics in Agriculture","2026-09-23T00:00:00Z",84,{"impact":19,"substance":319,"depth":320,"authority":20,"freshness":13,"relevant":22,"comment":321},23,19,"首次在实用田间车速下用MIMO毫米波雷达实现葡萄产量预估，136公里商业园数据与品种依赖性结论具参考价值。",[323],{"name":315,"url":312},[27,29,64,325,326],"产量预估","毫米波雷达",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":330},{"candidates":331,"agricultural_evidence":47},[332,337,343],{"code":33,"score":145,"supported":47,"title":333,"abstract":335,"tags":336},[334],"grape",[64,334],[64],{"code":338,"score":221,"supported":47,"title":339,"abstract":340,"tags":342},"F5",[],[341],"tractor",[],{"code":46,"score":344,"supported":14,"title":345,"abstract":346,"tags":347},2,[],[],[27,29],[349,350],"MIMO 毫米波雷达 葡萄 产量预估","葡萄园 雷达 三维重建","MIMO毫米波雷达葡萄产量预估-3255","10.1016\u002Fj.compag.2026.112395",{"doi":352,"openalex_id":354,"authors":355,"venue":315,"cited_by_count":80,"oa_url":312,"card":365,"direction":186,"ingested_from":99},"W7214043753",[356,359,362],{"name":357,"orcid":358},"Etienne Dedic","https:\u002F\u002Forcid.org\u002F0000-0002-1414-019X",{"name":360,"orcid":361},"Dominique Henry","https:\u002F\u002Forcid.org\u002F0000-0002-6016-140X",{"name":363,"orcid":364},"H. Aubert","https:\u002F\u002Forcid.org\u002F0000-0002-6113-0648",{"tldr":366,"method":367,"finding":368,"direction":186,"opportunity":369},"用双77GHz MIMO雷达在1m\u002Fs车速下重建葡萄藤行并估算产量，验证品种依赖性。","双77GHz MIMO FMCW雷达、数字波束成形、IMU卡尔曼滤波、SVR，1","全品种交叉验证MAE为31.2%，但品种间差异大（Tannat 7%至Baco 69%），需按品种校","可研究品种自适应建模与迁移学习，解决多品种部署时单一模型失效问题，并融合光学与雷达数据。","2026-09-23T23:30:01.516727Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":9,"content":9,"source_name":376,"source_url":9,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":378,"sources":380,"tags":382,"primary_topic":386,"secondary_topics":387,"classification":394,"search_phrases":421,"slug":424,"view_count":344,"doi":9,"paper":425,"created_at":433},3245,"Shaanxi Kiwi Farmers Break Free from Yield-Security FoMO（陕西猕猴桃农户摆脱\"产量安全错失恐惧\"）——Frontiers Sustain Food Syst 试错经验可破除数字养分管理技术采纳障碍","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F19\u002Fshaanxi-kiwi-farmers-break-free-from-yield-security-fomo","Frontiers in Sustainable Food Systems 发表研究：马来西亚博特拉大学Xia Li等学者针对中国陕西省675户猕猴桃种植户开展调研，发现\"产量安全错失恐惧（Yield-Security FoMO）\"——农户对不使用每一种可用工具以最大化产量的焦虑——是数字养分管理技术（土壤检测仪、叶绿素仪、农技咨询App等）采纳的主要障碍。研究发现，虽然感知有用性和相对优势能促进采纳，但产量安全错失恐惧对三种技术的采纳意向均呈持续负面影响。然而，参与结构化\"动手试错\"体验的农户更可能采纳土壤检测仪和叶绿素仪，且该恐惧对决策的影响更小。对农技咨询App影响较小，说明试错体验的效应可能因技术而异。该研究为农技企业和政策制定者提供启示：让农户亲手尝试新技术是弥合怀疑与采纳之间鸿沟的关键。","Frontiers in Sustainable Food Systems","2026-09-19T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":114,"freshness":200,"relevant":22,"comment":379},"基于675户调研揭示FoMO阻碍数字养分管理技术采纳、试错体验可缓解，对农技推广有实证参考价值。",[381],{"name":376,"url":374},[27,28,383,384,385],"猕猴桃","陕西","数字农业技术",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[388],{"code":389,"slug":390,"name":391,"path":392,"parent_code":393},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":40,"locked":14,"version":41,"method":42,"evidence":395},{"candidates":396,"agricultural_evidence":47},[397,401,407,413,417],{"code":33,"score":152,"supported":47,"title":398,"abstract":399,"tags":400},[383],[383],[383],{"code":389,"score":402,"supported":47,"title":403,"abstract":405,"tags":406},11,[404],"养分管理",[404],[],{"code":408,"score":221,"supported":47,"title":409,"abstract":410,"tags":412},"H2",[],[411],"土壤",[],{"code":67,"score":22,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[28],{"code":46,"score":22,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[27],[422,423],"陕西 猕猴桃 数字养分管理","产量安全错失恐惧 农户采纳","陕西猕猴桃数字养分管理-3245",{"doi":9,"openalex_id":9,"authors":426,"venue":9,"cited_by_count":80,"oa_url":9,"card":427,"direction":97,"ingested_from":432},[],{"tldr":428,"method":429,"finding":430,"direction":97,"opportunity":431},"陕西675户猕猴桃种植户调研发现，产量安全错失恐惧阻碍数字养分管理技术采纳，试错体验可缓解。","对陕西675户猕猴桃种植户问卷调查，分析FoMO与三种数字技术采纳意向关系。","产量安全错失恐惧持续负向影响采纳；动手试错体验提升土壤检测仪和叶绿素仪采纳，对App影响小。","可探究不同数字农业技术中试错体验的差异化机制，设计降低农户FoMO的干预策略。","agent","2026-09-23T00:04:33.090863Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":440,"source_url":437,"published_at":441,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":442,"score_detail":443,"sources":445,"tags":447,"primary_topic":451,"secondary_topics":452,"classification":455,"search_phrases":483,"slug":486,"view_count":221,"doi":487,"paper":488,"created_at":512},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z",78,{"impact":20,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":444},"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[446],{"name":440,"url":437},[27,448,449,30,450],"机器学习","芒果","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[453,454],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},{"code":72,"slug":131,"name":132,"path":133,"parent_code":129},{"status":40,"locked":14,"version":41,"method":42,"evidence":456},{"candidates":457,"agricultural_evidence":47},[458,462,467,473,477],{"code":33,"score":145,"supported":47,"title":459,"abstract":460,"tags":461},[216],[449,216],[449],{"code":125,"score":145,"supported":47,"title":463,"abstract":465,"tags":466},[464],"machine learning",[448,464],[448],{"code":72,"score":200,"supported":47,"title":468,"abstract":469,"tags":472},[],[470,471],"遥感","remote sensing",[450,30],{"code":46,"score":61,"supported":47,"title":474,"abstract":475,"tags":476},[],[29],[27],{"code":478,"score":221,"supported":47,"title":479,"abstract":480,"tags":482},"J1",[],[481],"农业规划",[],[484,485],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":487,"openalex_id":489,"authors":490,"venue":440,"cited_by_count":80,"oa_url":437,"card":507,"direction":186,"ingested_from":99},"W7213920056",[491,493,496,498,500,503,505],{"name":492,"orcid":9},"V. Raju",{"name":494,"orcid":495},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":497,"orcid":9},"Dr. V. S. Thorat",{"name":499,"orcid":9},"V. T. Shinde",{"name":501,"orcid":502},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":504,"orcid":9},"Alok Shrivastava",{"name":506,"orcid":9},"A. P. Chaudhary",{"tldr":508,"method":509,"finding":510,"direction":186,"opportunity":511},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z"]