[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3452":3,"related-3452":62},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":61},3452,"Tree-level yield mapping for nut trees harvested with shake-catch harvesters – Part I: Yield monitoring and volume flow deconvolution","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104591","Tree-level yield mapping for nut trees harvested with shake-catch harvesters – Part I: Yield monitoring and volume flow deconvolution。Biosystems Engineering",null,"Biosystems Engineering","2026-09-23T00:00:00Z","论文",10,false,72,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,20,18,14,8,1,"核心期刊论文，提出摇振采收机树级产量监测与体积流量解卷积方法，专业价值高但受众较窄，适合作为智慧农业技术类精选。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","果园机械化","产量遥感","坚果采收",[31,32],"摇振采收机 坚果 产量监测","Biosystems Engineering 产量制图","摇振采收机坚果产量监测-3452",0,"10.1016\u002Fj.biosystemseng.2026.104591",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":34,"oa_url":8,"card":8,"direction":8,"ingested_from":60},"W7214033233",[39,42,45,48,51,54,57],{"name":40,"orcid":41},"Juan Villacrés","https:\u002F\u002Forcid.org\u002F0000-0001-7113-9497",{"name":43,"orcid":44},"Patrick H. Brown","https:\u002F\u002Forcid.org\u002F0000-0001-6857-8608",{"name":46,"orcid":47},"Yufang Jin","https:\u002F\u002Forcid.org\u002F0000-0002-9049-9807",{"name":49,"orcid":50},"José Luis Zárate Valdez","https:\u002F\u002Forcid.org\u002F0000-0002-6228-1552",{"name":52,"orcid":53},"Margherita A. Germani","https:\u002F\u002Forcid.org\u002F0009-0007-1775-3358",{"name":55,"orcid":56},"Ricardo de Camargo","https:\u002F\u002Forcid.org\u002F0000-0002-9425-5391",{"name":58,"orcid":59},"Stavros George Vougioukas","https:\u002F\u002Forcid.org\u002F0000-0003-2758-8900","openalex","2026-09-25T23:30:05.125062Z",{"total":63,"page":21,"page_size":63,"items":64},6,[65,98,137,182,221,260],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":8,"content":8,"source_name":70,"source_url":68,"published_at":71,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":72,"score_detail":73,"sources":76,"tags":78,"search_phrases":82,"slug":85,"view_count":34,"doi":86,"paper":87,"created_at":97},3518,"Soil physical data integration in commercial precision agriculture platforms: barriers to compaction-relevant decision support","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22005.74727","Soil physical data integration in commercial precision agriculture platforms: barriers to compaction-relevant decision support。OpenAlex","OpenAlex","2026-09-24T00:00:00Z",47,{"impact":20,"substance":63,"depth":16,"authority":16,"freshness":74,"relevant":21,"comment":75},9,"学术论文探讨商业精准农业平台土壤物理数据整合障碍，与农业信息化相关但偏学术、公共价值有限，时效新。",[77],{"name":70,"url":68},[26,79,80,81],"精准农业","决策支持","土壤数据",[83,84],"精准农业 土壤物理数据 平台","决策支持 土壤数据 智慧农业 精准农业","精准农业土壤物理数据平台-3518","10.13140\u002Frg.2.2.22005.74727",{"doi":86,"openalex_id":88,"authors":89,"venue":8,"cited_by_count":34,"oa_url":68,"card":8,"direction":96,"ingested_from":60},"W7214201555",[90,93],{"name":91,"orcid":92},"Hanna Radziuk","https:\u002F\u002Forcid.org\u002F0000-0001-5279-2175",{"name":94,"orcid":95},"Marcin Świtoniak","https:\u002F\u002Forcid.org\u002F0000-0002-9907-7088","农业人工智能与决策模型","2026-09-25T23:30:59.544316Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":104,"source_url":101,"published_at":71,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":105,"score_detail":106,"sources":111,"tags":113,"search_phrases":118,"slug":121,"view_count":34,"doi":122,"paper":123,"created_at":136},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":107,"substance":108,"depth":18,"authority":109,"freshness":74,"relevant":21,"comment":110},16,22,13,"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。",[112],{"name":104,"url":101},[26,114,115,116,117],"农业人工智能","病害识别","轻量化模型","豆类作物",[119,120],"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":122,"openalex_id":124,"authors":125,"venue":104,"cited_by_count":34,"oa_url":101,"card":131,"direction":96,"ingested_from":60},"W7154572440",[126,128],{"name":127,"orcid":8},"Hye Jin Rhee",{"name":129,"orcid":130},"Joseph Damilola Akinyemi","https:\u002F\u002Forcid.org\u002F0000-0003-3121-4231",{"tldr":132,"method":133,"finding":134,"direction":96,"opportunity":135},"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","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":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":8,"source_name":143,"source_url":140,"published_at":71,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":144,"score_detail":145,"sources":147,"tags":149,"search_phrases":153,"slug":156,"view_count":34,"doi":157,"paper":158,"created_at":181},3516,"Approaches to forecast soil nutrient dynamics for precision agriculture and sustainable fertiliser management: A review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16160","Predictive modelling of soil nutrient dynamics is an essential tool for promoting sustainable agricultural practices and environmentally responsible farming methods. The statistical and machine learning techniques used to forecast the availability and dynamics of soil nutrients are summarised in this review. The core frameworks for measuring spatio-temporal nutritional variability are established by traditional statistical approaches such as time-series models autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), multivariate techniques (Principal component analysis (PCA) and factor analysis) and geostatistical tools (kriging). By capturing intricate nonlinear interactions within heterogeneous agroecosystems, machine learning techniques like random forest, support vector machines and ensemble approaches (XGBoost, LightGBM and AdaBoost) provide higher prediction accuracy. Forecasting capabilities are further enhanced by hybrid frameworks [Autoregressive integrated moving average with exogenous variables–artificial neural network. (ARIMAX-ANN)] and deep learning architectures (Convolutional neural network (CNN), long short-term memory (LSTM), ANN). With R2 values above 0.93 and notable decreases in prediction errors, ensemble approaches routinely perform better than traditional linear models. Nevertheless, persistent challenges include data quality limitations, spatial sampling constraints, insufficient environmental covariates and reduced model transferability across diverse pedoclimatic regions. Integrating high-resolution soil properties, climatic variables, terrain attributes and spectral information with advanced modelling architectures remains crucial for enhancing predictive reliability, ultimately supporting precision nutrient management, improved fertiliser efficiency and environmentally responsible agricultural systems.","土壤养分动态的预测建模是推动可持续农业实践和环境友好型耕作方法的重要工具。本综述总结了用于预测土壤养分有效性及其动态变化的统计与机器学习技术。传统统计方法，如时间序列模型自回归积分滑动平均模型（ARIMA）、季节性自回归积分滑动平均模型（SARIMA）、多变量技术（主成分分析（PCA）和因子分析）以及地统计工具（克里金法），为量化养分的时空变异性奠定了核心框架。通过捕捉异质性农业生态系统中复杂的非线性相互作用，随机森林、支持向量机和集成方法（XGBoost、LightGBM和AdaBoost）等机器学习技术可实现更高的预测精度。混合框架[含外生变量的自回归积分滑动平均模型–人工神经网络（ARIMAX-ANN）]和深度学习架构（卷积神经网络（CNN）、长短期记忆网络（LSTM）、人工神经网络（ANN））进一步增强了预测能力。集成方法的R²值超过0.93，且预测误差显著降低，其表现通常优于传统线性模型。然而，持续存在的挑战包括数据质量限制、空间采样约束、环境协变量不足以及模型在不同土壤气候区域间可迁移性降低等问题。将高分辨率土壤属性、气候变量、地形属性和光谱信息与先进建模架构相结合，对于提高预测可靠性仍然至关重要，最终可为精准养分管理、提高肥料利用效率以及环境友好型农业系统提供支撑。","Plant Science Today",79,{"impact":18,"substance":108,"depth":18,"authority":109,"freshness":20,"relevant":21,"comment":146},"系统综述土壤养分动态预测的统计与机器学习方法，方法体系完整、结论有量化支撑，对精准施肥与农业信息化有参考价值，但属综述类论文，产业级影响有限。",[148],{"name":143,"url":140},[26,150,151,79,152],"变量施肥","机器学习","土壤养分",[154,155],"土壤养分 预测模型 精准农业","机器学习 施肥管理 可持续农业","土壤养分预测模型精准农业-3516","10.14719\u002Fpst.16160",{"doi":157,"openalex_id":159,"authors":160,"venue":143,"cited_by_count":34,"oa_url":140,"card":176,"direction":96,"ingested_from":60},"W7214167059",[161,164,167,170,173],{"name":162,"orcid":163},"R Rathna","https:\u002F\u002Forcid.org\u002F0009-0004-7797-2673",{"name":165,"orcid":166},"B Sivasankari","https:\u002F\u002Forcid.org\u002F0000-0001-9921-8170",{"name":168,"orcid":169},"R. Gangai Selvi","https:\u002F\u002Forcid.org\u002F0000-0002-4475-2293",{"name":171,"orcid":172},"J Prabhakaran","https:\u002F\u002Forcid.org\u002F0000-0001-7339-175X",{"name":174,"orcid":175},"K. G. Sabarinathan","https:\u002F\u002Forcid.org\u002F0000-0002-8659-6479",{"tldr":177,"method":178,"finding":179,"direction":96,"opportunity":180},"综述土壤养分动态预测的统计与机器学习方法，比较精度与局限。","综述ARIMA、地统计、随机森林、XGBoost、CNN\u002FLSTM及混合模型。","集成与深度学习模型精度更高（R²>0.93），但数据质量与跨区迁移性仍是瓶颈。","可研究多源遥感与气候数据融合的迁移学习模型，提升跨区域养分预测泛化能力。","2026-09-25T23:30:54.950445Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":8,"source_name":188,"source_url":185,"published_at":71,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":189,"score_detail":190,"sources":193,"tags":195,"search_phrases":199,"slug":202,"view_count":34,"doi":203,"paper":204,"created_at":220},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":16,"substance":19,"depth":191,"authority":109,"freshness":20,"relevant":21,"comment":192},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。",[194],{"name":188,"url":185},[26,114,196,197,198],"精准施肥","遥感","土壤制图",[200,201],"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":203,"openalex_id":205,"authors":206,"venue":188,"cited_by_count":34,"oa_url":8,"card":215,"direction":96,"ingested_from":60},"W7214144821",[207,210,212],{"name":208,"orcid":209},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":208,"orcid":211},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":213,"orcid":214},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":216,"method":217,"finding":218,"direction":96,"opportunity":219},"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":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":8,"source_name":70,"source_url":224,"published_at":227,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":228,"score_detail":229,"sources":233,"tags":235,"search_phrases":239,"slug":242,"view_count":34,"doi":243,"paper":244,"created_at":259},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。","2026-09-22T00:00:00Z",86,{"impact":108,"substance":230,"depth":231,"authority":109,"freshness":20,"relevant":21,"comment":232},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。",[234],{"name":70,"url":224},[26,114,236,237,238],"气候适应","遥感监测","早期预警",[240,241],"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":243,"openalex_id":245,"authors":246,"venue":8,"cited_by_count":34,"oa_url":252,"card":253,"direction":258,"ingested_from":60},"W7214097088",[247,249],{"name":248,"orcid":8},"H Heuristics",{"name":250,"orcid":251},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":254,"method":255,"finding":256,"direction":96,"opportunity":257},"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":261,"title":262,"url":263,"summary":264,"summary_zh":265,"content":8,"source_name":266,"source_url":263,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":267,"score_detail":268,"sources":270,"tags":272,"search_phrases":276,"slug":279,"view_count":34,"doi":280,"paper":281,"created_at":308},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",80,{"impact":18,"substance":108,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":269},"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。",[271],{"name":266,"url":263},[26,114,273,274,275],"计算机视觉","入侵物种监测","水生生物安全",[277,278],"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":280,"openalex_id":282,"authors":283,"venue":266,"cited_by_count":34,"oa_url":302,"card":303,"direction":258,"ingested_from":60},"W7214089404",[284,287,289,291,293,296,299],{"name":285,"orcid":286},"Rose A. Pearson","https:\u002F\u002Forcid.org\u002F0000-0002-4700-2113",{"name":288,"orcid":8},"Gareth Preston",{"name":290,"orcid":8},"Jeremy Bulleid",{"name":292,"orcid":8},"Svenja David",{"name":294,"orcid":295},"Felix Vaux","https:\u002F\u002Forcid.org\u002F0000-0002-2882-7996",{"name":297,"orcid":298},"Daniel Clements","https:\u002F\u002Forcid.org\u002F0000-0001-9319-5588",{"name":300,"orcid":301},"Leigh W. Tait","https:\u002F\u002Forcid.org\u002F0000-0001-9153-139X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72212-8_reference.pdf",{"tldr":304,"method":305,"finding":306,"direction":96,"opportunity":307},"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"]