[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3467":3,"related-3467":79},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":78},3467,"Smart agriculture using digital holography and artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871510","In viticulture, direct protection against downy and powdery mildews relies on the preventive application of fungicides, requiring growers to anticipate infection events. Decision-making is mainly supported by forecasting models driven by weather predictions. However, these decisions are inherently uncertain, as some treatments ultimately prove unnecessary, although this information only becomes available retrospectively from observed conditions. This study explores the integration of an advanced spore detection device to better target fungicide applications in vineyards, aiming to reduce the use of phytosanitary products while maintaining high grape quality. The stand-alone device uses digital holography combined with artificial intelligence (AI) for detecting and classifying airborne spores of both downy and powdery mildew. It enables the tracking of disease dynamics as well as the assessment of environmental conditions and treatment effects on spore counts. Two case studies with real-time data access in Changins, Switzerland and Château le Puy, France, are presented and revealed promising strategies for substantial reductions in fungicide use while maintaining effective disease control.","在葡萄栽培中，对霜霉病和白粉病的直接防护依赖于杀菌剂的预防性施用，这要求种植者预判侵染事件的发生。决策主要依靠由天气预报驱动的预测模型来支持。然而，这些决策本质上具有不确定性，因为有些处理最终被证明是不必要的，尽管这一信息只能通过观测条件回顾性地获得。本研究探索了集成先进孢子检测装置以更精准地指导葡萄园杀菌剂施用的方法，旨在减少植物检疫产品的使用，同时保持葡萄的高品质。该独立装置利用数字全息术结合人工智能（AI）来检测和分类空气中的霜霉病和白粉病孢子。它能够追踪病害动态，并评估环境条件和处理措施对孢子数量的影响。本文介绍了在瑞士Changins和法国Château le Puy进行的两个可实时获取数据的案例研究，并揭示了在保持有效病害控制的同时大幅减少杀菌剂用量的有前景的策略。",null,"Frontiers in Plant Science","2026-09-24T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"数字全息结合AI检测葡萄病害孢子，为减少杀菌剂施用提供实证案例，方法新颖且具推广价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准施药","葡萄种植","病害预警",[32,33],"数字全息 孢子检测 葡萄","霜霉病 白粉病 人工智能","数字全息孢子检测葡萄-3467",0,"10.3389\u002Ffpls.2026.1871510",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":71,"direction":75,"ingested_from":77},"W7214152870",[40,42,44,47,50,53,56,58,60,63,66,69],{"name":41,"orcid":9},"Tessa Chiara Basso",{"name":43,"orcid":9},"Sara Leoni",{"name":45,"orcid":46},"Denis Ullmann","https:\u002F\u002Forcid.org\u002F0000-0002-7179-005X",{"name":48,"orcid":49},"Adimulya Kartiyasa","https:\u002F\u002Forcid.org\u002F0009-0003-3142-1174",{"name":51,"orcid":52},"Livio Ruzzante","https:\u002F\u002Forcid.org\u002F0000-0002-8693-8678",{"name":54,"orcid":55},"Sylvain Schnée","https:\u002F\u002Forcid.org\u002F0000-0002-1014-1961",{"name":57,"orcid":9},"Anne‐Lise Fabre",{"name":59,"orcid":9},"Steven Hewison",{"name":61,"orcid":62},"Jérôme Kasparian","https:\u002F\u002Forcid.org\u002F0000-0003-2398-3882",{"name":64,"orcid":65},"Nicolas Berti","https:\u002F\u002Forcid.org\u002F0000-0003-3769-3966",{"name":67,"orcid":68},"Pierre‐Henri Dubuis","https:\u002F\u002Forcid.org\u002F0000-0002-9624-3925",{"name":70,"orcid":9},"Jean-Pierre Wolf",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"利用数字全息与AI检测葡萄园空气中霜霉和白粉病菌孢子，优化杀菌剂施用。","数字全息成像结合AI分类孢子，在瑞士和法国葡萄园实时监测。","孢子检测可追踪病害动态，在保持防效下大幅减少杀菌剂使用。","智慧农业 \u002F 农业物联网","可探索孢子自动监测与气象预报融合的精准施药决策模型，并验证多作物适用性。","openalex","2026-09-25T23:30:09.459179Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,129,169,205,244,283],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":97,"tags":99,"search_phrases":102,"slug":105,"view_count":35,"doi":106,"paper":107,"created_at":128},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection","2026-09-19T00:00:00Z",77,{"impact":17,"substance":92,"depth":93,"authority":94,"freshness":95,"relevant":21,"comment":96},20,17,13,9,"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[98],{"name":88,"url":85},[26,27,100,101,30],"植物病害","遥感监测",[103,104],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":106,"openalex_id":108,"authors":109,"venue":88,"cited_by_count":35,"oa_url":9,"card":122,"direction":126,"ingested_from":77},"W7213649225",[110,112,114,116,119],{"name":111,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":113,"orcid":9},"C. L. Biji",{"name":115,"orcid":9},"Vanshika Arun Meda",{"name":117,"orcid":118},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":120,"orcid":121},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","农业遥感与作物表型","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":9,"content":9,"source_name":134,"source_url":132,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":141,"tags":143,"search_phrases":146,"slug":149,"view_count":35,"doi":150,"paper":151,"created_at":168},2620,"A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10444-4","A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation。Precision Agriculture","Precision Agriculture","2026-09-16T00:00:00Z",70,{"impact":138,"substance":17,"depth":139,"authority":19,"freshness":13,"relevant":21,"comment":140},12,16,"核心期刊论文，方法新颖且时效性强，但属细分作物技术进展，产业影响范围有限。",[142],{"name":134,"url":132},[26,27,28,144,145],"机器视觉","大白菜",[147,148],"农业人工智能 智慧农业 机器视觉 精准施药","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准施药-2620","10.1007\u002Fs11119-026-10444-4",{"doi":150,"openalex_id":152,"authors":153,"venue":134,"cited_by_count":35,"oa_url":9,"card":9,"direction":9,"ingested_from":77},"W7213275445",[154,156,158,160,163,166],{"name":155,"orcid":9},"Changxi Liu",{"name":157,"orcid":9},"Hang Shi",{"name":159,"orcid":9},"Hao Sun",{"name":161,"orcid":162},"Hui Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8843-7298",{"name":164,"orcid":165},"Qingda Li","https:\u002F\u002Forcid.org\u002F0000-0002-9046-2094",{"name":167,"orcid":9},"Jun Hu","2026-09-16T23:30:03.326664Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":179,"tags":181,"search_phrases":185,"slug":188,"view_count":35,"doi":189,"paper":190,"created_at":204},3517,"A Resource-Efficient Hybrid CNN-LSTM Network for Image-Based Bean Leaf Disease Classification","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjimaging12100468","Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial dependencies is often limited by standard pooling layers, and their high memory footprint hinders deployment on portable devices. This paper proposes a lightweight hybrid CNN-LSTM system for bean leaf disease classification. By integrating an LSTM layer to model the spatial–sequential relationships within feature maps, our hybrid architecture achieves a 94.36% accuracy and 94.38% F1 score while maintaining an exceptionally small footprint of 1.86 MB, a 70% reduction in size compared to traditional CNN-based systems. Furthermore, we provide a systematic evaluation of image augmentation strategies, demonstrating that tailored transformations are superior to generic combinations for maintaining the integrity of diagnostic patterns. Results on the ibean dataset confirm that the proposed system achieves state-of-the-art F1 scores of 99.22% with EfficientNet-B7+LSTM, providing a potentially robust and scalable framework for real-time agricultural decision support in resource-constrained environments. The code and augmented datasets used in this study are publicly available on this GitHub repo.","准确且资源高效的自动化诊断是现代农业专家系统的基石。尽管卷积神经网络（CNN）在植物病理学领域已确立了基准，但其捕捉长程空间依赖关系的能力常受限于标准池化层，且高内存占用阻碍了其在便携设备上的部署。本文提出了一种用于豆叶病害分类的轻量级混合CNN-LSTM系统。通过集成LSTM层来建模特征图内的空间-序列关系，我们的混合架构达到了94.36%的准确率和94.38%的F1分数，同时保持了仅1.86 MB的极小占用，相较于传统基于CNN的系统体积减少了70%。此外，我们系统评估了图像增强策略，表明定制化变换在保持诊断模式完整性方面优于通用组合。在ibean数据集上的结果证实，所提出的系统结合EfficientNet-B7+LSTM达到了99.22%的最先进F1分数，为资源受限环境中的实时农业决策支持提供了一个潜在稳健且可扩展的框架。本研究使用的代码和增强数据集已在此GitHub仓库公开。","Journal of Imaging",78,{"impact":139,"substance":18,"depth":17,"authority":94,"freshness":95,"relevant":21,"comment":178},"轻量级CNN-LSTM豆叶病害识别研究，方法新颖、数据可靠且代码开源，对资源受限场景下的农业智能诊断有实用价值。",[180],{"name":175,"url":172},[26,27,182,183,184],"病害识别","轻量化模型","豆类作物",[186,187],"ibean dataset 豆叶病害","农业人工智能 轻量化模型 智慧农业 病害识别","ibeandataset豆叶病害-3517","10.3390\u002Fjimaging12100468",{"doi":189,"openalex_id":191,"authors":192,"venue":175,"cited_by_count":35,"oa_url":172,"card":198,"direction":202,"ingested_from":77},"W7154572440",[193,195],{"name":194,"orcid":9},"Hye Jin Rhee",{"name":196,"orcid":197},"Joseph Damilola Akinyemi","https:\u002F\u002Forcid.org\u002F0000-0003-3121-4231",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"提出轻量级CNN-LSTM混合网络，用于豆叶病害分类，兼顾高精度与低资源占用。","CNN提取特征后接LSTM建模空间序列关系，在ibean数据集上评估并系统比较图","模型准确率94.36%、F1 94.38%，仅1.86MB，比传统CNN缩小70%，Efficien","农业人工智能与决策模型","可探索面向移动端\u002F边缘设备的超轻量病害诊断模型，并研究增强策略与模型结构的自适应协同优化。","2026-09-25T23:30:59.276381Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":9,"source_name":211,"source_url":208,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":216,"tags":218,"search_phrases":222,"slug":225,"view_count":35,"doi":226,"paper":227,"created_at":243},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management",62,{"impact":138,"substance":19,"depth":214,"authority":94,"freshness":20,"relevant":21,"comment":215},15,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[217],{"name":211,"url":208},[26,27,219,220,221],"精准施肥","遥感","土壤制图",[223,224],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":226,"openalex_id":228,"authors":229,"venue":211,"cited_by_count":35,"oa_url":9,"card":238,"direction":202,"ingested_from":77},"W7214144821",[230,233,235],{"name":231,"orcid":232},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":231,"orcid":234},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":236,"orcid":237},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":239,"method":240,"finding":241,"direction":202,"opportunity":242},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":257,"tags":259,"search_phrases":262,"slug":265,"view_count":35,"doi":266,"paper":267,"created_at":282},3514,"Artificial Intelligence for Climate Adaptation Decision Support in Data-Poor Developing Regions","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009304\u002Fv1","Climate adaptation is a sequence of decisions taken under uncertainty, and the regions where climate risk is rising fastest are those with the least information to guide them. Only about 10 per cent of deaths are registered in the WHO African Region; nearly 90 per cent of required surface weather observations are missing across least developed countries and small island states; and only 40 per cent of African countries have multi-hazard early warning systems. This report examines whether artificial intelligence — machine learning, remote sensing and predictive analytics — can close these information gaps and improve adaptation decisions in data-poor developing regions. The report organises the problem as a decision chain with three information gaps — observation, prediction and decision — followed by an action gap that AI cannot close. It finds that AI has advanced fastest on prediction: AI weather models became operational at ECMWF in 2025, AI flood forecasts now cover 100 countries and about 700 million people, satellite nowcasts reach a continent with little radar, and AI monsoon-onset forecasts reached 38 million Indian farmers in 2025. On observation, satellite machine learning explains around 70 per cent of the variation in village wealth but only up to about half of the variation in changes over time. On decision, evidence from Togo, Bangladesh and Kenya shows that AI-assisted targeting, forecast-based triggers and satellite index insurance can deliver assistance faster and better, within clear limits. The report's central argument is the ground-truth paradox: AI stretches scarce observations further, but every AI product must be trained and verified against ground truth, so reliance on AI raises the value of each remaining station, survey and label. The 2025 interruption of FEWS NET and termination of the DHS Program show how fragile that foundation is. Because the value of information is the product of skill, lead time, reach, trust and the means to act, the highest returns usually lie not in more skilful models but in dissemination, institutions and prearranged finance. The report sets out a risk register, a six-principle policy framework, actions by actor and a roadmap to 2030.","气候适应是在不确定性下做出的一系列决策，而气候风险上升最快的地区恰恰是指导信息最匮乏的地区。世卫组织非洲区域仅登记了约10%的死亡病例；最不发达国家和小岛屿国家缺失了近90%所需的地面天气观测数据；仅有40%的非洲国家拥有多灾种早期预警系统。本报告考察人工智能——机器学习、遥感和预测分析——能否弥合这些信息缺口，改善数据匮乏的发展中地区的适应决策。报告将这一问题组织为一条决策链，包含三个信息缺口——观测、预测和决策——以及一个人工智能无法弥合的行动缺口。报告发现，人工智能在预测方面进展最快：人工智能天气模型于2025年在欧洲中期天气预报中心（ECMWF）投入业务运行，人工智能洪水预报现已覆盖100个国家和约7亿人口，卫星临近预报覆盖了一个几乎没有雷达的大陆，人工智能季风爆发预报于2025年惠及3800万印度农民。在观测方面，卫星机器学习可解释村庄财富约70%的变异，但对时间变化的解释力仅约一半。在决策方面，来自多哥、孟加拉国和肯尼亚的证据表明，人工智能辅助的目标定位、基于预报的触发机制和卫星指数保险能够在明确限度内更快、更好地提供援助。报告的核心论点是地面真值悖论：人工智能能够将稀缺的观测数据发挥更大效用，但每个人工智能产品都必须依据地面真值进行训练和验证，因此对人工智能的依赖提升了每一个剩余站点、调查和标注数据的价值。2025年FEWS NET的中断和DHS项目的终止表明这一基础何等脆弱。由于信息的价值是技能、提前期、覆盖面、信任和行动手段的乘积，最高回报通常不在于更精密的模型，而在于传播、制度和预先安排的融资。报告提出了风险登记册、六项原则的政策框架、各行为主体的行动以及到2030年的路线图。","OpenAlex","2026-09-22T00:00:00Z",86,{"impact":18,"substance":254,"depth":255,"authority":94,"freshness":20,"relevant":21,"comment":256},24,19,"系统梳理AI在数据匮乏地区气候适应决策中的观测、预测与决策三类信息缺口，提出“地面真值悖论”，数据与结论扎实，对农业信息化与智慧农业有较强参考价值。",[258],{"name":250,"url":247},[26,27,260,101,261],"气候适应","早期预警",[263,264],"AI 气候适应 决策支持","数据匮乏地区 农业预警","AI气候适应决策支持-3514","10.22541\u002Fessoar.15009304\u002Fv1",{"doi":266,"openalex_id":268,"authors":269,"venue":9,"cited_by_count":35,"oa_url":275,"card":276,"direction":281,"ingested_from":77},"W7214097088",[270,272],{"name":271,"orcid":9},"H Heuristics",{"name":273,"orcid":274},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":277,"method":278,"finding":279,"direction":202,"opportunity":280},"评估AI能否弥补数据匮乏地区气候适应决策的信息缺口，并提出地面真值悖论。","梳理观测、预测、决策三环节，结合AI天气模型、卫星ML与多国案例证据。","AI预测进展最快，但依赖地面真值；最高回报常在传播、制度与预置资金而非模型。","可研究AI辅助农业气候适应中地面真值稀缺下的验证与信任机制，及预置资金触发设计。","数字乡村与农业信息化","2026-09-25T23:30:46.008325Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":291,"sources":293,"tags":295,"search_phrases":299,"slug":302,"view_count":35,"doi":303,"paper":304,"created_at":331},3513,"Accelerated development and deployment of computer vision models for invasive aquatic pests","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72212-8","Abstract Aquatic non-indigenous species (NIS) incur significant cultural, environmental and economic costs worldwide, and human surveillance is expensive and impractical at large scales. Computer vision models (CVMs) deployed on remote and autonomous vehicles can reduce the burden on trained human observers, but aquatic environments present unique challenges and lack frameworks for biosecurity-focused development and deployment. We present a practical framework for developing and deploying new species-specific CVMs for real-time use across surface vessel and remote vehicle platforms. We demonstrate the utility of our framework through its application to several taxonomically and ecologically diverse NIS in New Zealand: Mediterranean fanworm Sabella spallanzanii , South African oxygen weed Lagarosiphon major, and exotic Caulerpa ( Caulerpa brachypus and C. parvifolia ). For S. spallanzanii , we demonstrate efficient training from small datasets and deployment for real-time detection. Using exotic Caulerpa , we show how CVMs can be rapidly improved during an early incursion response. For L. major , standardised field validation methods enable the comparison of CVM and human detection rates across diverse locations and operating conditions. Collectively, these case studies demonstrate that our framework enables accurate detection and the robust assessment of model effectiveness under realistic field conditions and can be effectively applied to imagery from multiple platforms.","摘要 水生外来非本土物种（NIS）在全球范围内造成显著的文化、环境和经济损失，而人工监测成本高昂且难以大规模实施。部署在远程和自主载具上的计算机视觉模型（CVM）可减轻对训练有素的人类观察者的依赖，但水生环境面临独特挑战，且缺乏以生物安全为重点的开发和部署框架。我们提出了一个实用框架，用于开发和部署新的物种特异性CVM，以在水面船只和远程载具平台上实时使用。我们通过将该框架应用于新西兰多个在分类学和生态学上具有多样性的NIS来展示其效用：地中海缨鳃虫 Sabella spallanzanii、南非氧草 Lagarosiphon major 以及外来Caulerpa（Caulerpa brachypus 和 C. parvifolia）。对于 S. spallanzanii，我们展示了从小型数据集进行高效训练并部署用于实时检测。利用外来Caulerpa，我们展示了CVM如何在入侵早期响应期间快速改进。对于 L. major，标准化野外验证方法使得能够在不同地点和操作条件下比较CVM与人类检测率。总体而言，这些案例研究表明，我们的框架能够在现实野外条件下实现准确检测和对模型有效性的稳健评估，并可有效应用于来自多个平台的图像。","Scientific Reports","2026-09-23T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":292},"提出面向水生入侵物种的计算机视觉模型开发与部署框架，含多物种实地验证，方法新颖且具生物安全应用价值，但属新西兰案例，对国内农业信息化影响有限。",[294],{"name":289,"url":286},[26,27,296,297,298],"计算机视觉","入侵物种监测","水生生物安全",[300,301],"新西兰 入侵水生物种 计算机视觉","Caulerpa 实时检测 模型","新西兰入侵水生物种计算机视觉-3513","10.1038\u002Fs41598-026-72212-8",{"doi":303,"openalex_id":305,"authors":306,"venue":289,"cited_by_count":35,"oa_url":325,"card":326,"direction":281,"ingested_from":77},"W7214089404",[307,310,312,314,316,319,322],{"name":308,"orcid":309},"Rose A. Pearson","https:\u002F\u002Forcid.org\u002F0000-0002-4700-2113",{"name":311,"orcid":9},"Gareth Preston",{"name":313,"orcid":9},"Jeremy Bulleid",{"name":315,"orcid":9},"Svenja David",{"name":317,"orcid":318},"Felix Vaux","https:\u002F\u002Forcid.org\u002F0000-0002-2882-7996",{"name":320,"orcid":321},"Daniel Clements","https:\u002F\u002Forcid.org\u002F0000-0001-9319-5588",{"name":323,"orcid":324},"Leigh W. Tait","https:\u002F\u002Forcid.org\u002F0000-0001-9153-139X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72212-8_reference.pdf",{"tldr":327,"method":328,"finding":329,"direction":202,"opportunity":330},"提出一套开发与部署水生入侵物种计算机视觉模型的实用框架，并在新西兰三种入侵物种上验证。","基于水面船与遥控平台图像，小数据集训练、实时检测与标准化野外验证。","框架能实现实时准确检测，并在真实野外条件下稳健评估模型效果。","可迁移至农业入侵生物监测，探索小样本跨平台模型与野外标准化评估体系。","2026-09-25T23:30:42.003495Z"]