[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2867":3,"related-2867":58},{"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":18,"tags":20,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":57},2867,"From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpubh.2026.1922265","Background Antimicrobial resistance (AMR) is a leading global health threat requiring coordinated surveillance across human, animal, environmental, and genomic systems. Machine learning is increasingly applied to AMR data, yet its contribution to actionable surveillance intelligence, rather than prediction alone, remains poorly defined. Objective To map how machine-learning approaches generate AMR surveillance intelligence, to characterise their validation and implementation maturity, and to propose a framework distinguishing technical prediction from actionable surveillance intelligence. Methods We conducted a scoping review following JBI methodology and PRISMA-ScR reporting. PubMed\u002FMEDLINE, Scopus, and Web of Science were searched from January 2015 to May 2026 for studies applying machine learning or related methods to AMR surveillance intelligence. Two reviewers independently screened and charted records. Of 1,985 records, 66 met eligibility and formed the working evidence base; 41 studies (40 core empirical and one supporting preprint) were appraised against TRIPOD+AI- and PROBAST-aligned reporting, validation, and implementation-readiness domains. Results Machine learning was applied across five clusters: clinical and electronic-health-record risk prediction and decision support; genomic and whole-genome-sequencing prediction; MALDI-TOF-based rapid resistance prediction; wastewater and metagenomic surveillance; and environmental, animal, food-chain, and One Health early warning. Prediction and risk stratification predominated, but validation maturity was limited: most studies were retrospective or internally validated, with few using external, cross-country, temporal, prospective, or drift-focused evaluation. On appraisal, discrimination was reported in 31 of 41 studies (76%) and explainability in 26 (63%); by contrast, external or temporal validation was present in only 15 (37%), calibration in 5 (12%), prospective evaluation in 1 (2%), and operational deployment with measured clinical or public-health impact in a single study (2%). Conclusion Machine learning can support AMR surveillance intelligence across clinical, genomic, diagnostic, environmental, and One Health settings, but the evidence demonstrates technical feasibility far more convincingly than operational readiness. Realising this transition will require external and prospective validation, calibration and drift monitoring, transparent and equitable reporting, workflow integration, and explicit linkage of model outputs to clinical and public-health action. We propose a One Health AMR Surveillance Intelligence Framework to organise this shift from data generation toward actionable, adaptive surveillance intelligence.","背景 抗微生物药物耐药性（AMR）是主要的全球健康威胁，需要在人类、动物、环境和基因组系统之间开展协调监测。机器学习正越来越多地应用于AMR数据，但其对可操作监测情报的贡献，而非仅用于预测，仍界定不清。目的 梳理机器学习方法如何生成AMR监测情报，描述其验证和实施成熟度，并提出一个区分技术预测与可操作监测情报的框架。方法 我们按照JBI方法学和PRISMA-ScR报告规范开展了一项范围综述。检索PubMed\u002FMEDLINE、Scopus和Web of Science，时间范围为2015年1月至2026年5月，纳入将机器学习或相关方法应用于AMR监测情报的研究。两名综述者独立筛选并提取记录。在1，985条记录中，66项符合纳入标准并构成工作证据基础；41项研究（40项核心实证研究和1项支持性预印本）依据与TRIPOD+AI和PROBAST一致的报告、验证和实施准备度领域进行了评价。结果 机器学习应用于五个集群：临床和电子健康记录风险预测与决策支持；基因组和全基因组测序预测；基于MALDI-TOF的快速耐药预测；废水和宏基因组监测；以及环境、动物、食物链和“同一健康”早期预警。预测和风险分层占主导，但验证成熟度有限：大多数研究为回顾性或内部验证，少数采用外部、跨国、时间、前瞻性或聚焦漂移的评估。评价中，41项研究有31项（76%）报告了区分度，26项（63%）报告了可解释性；相比之下，仅15项（37%）进行了外部或时间验证，5项（12%）进行了校准，1项（2%）进行了前瞻性评价，仅1项研究（2%）进行了实际部署并测量了临床或公共卫生影响。结论 机器学习可在临床、基因组、诊断、环境和“同一健康”背景下支持AMR监测情报，但证据在技术可行性方面远比在操作准备度方面更具说服力。实现这一转变需要外部和前瞻性验证、校准和漂移监测、透明且公平的报告、工作流程整合，以及ex",null,"Frontiers in Public Health","2026-09-17T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为公共卫生领域抗微生物耐药监测的机器学习综述，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"机器学习","One Health","抗微生物耐药","公共卫生监测",[26,27],"公共卫生监测 抗微生物耐药 机器学习 One Health","公共卫生监测 抗微生物耐药","公共卫生监测抗微生物耐药机器学习OneHealth-2867","10.3389\u002Ffpubh.2026.1922265",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":48,"card":49,"direction":55,"ingested_from":56},"W7213443344",[33,36,38,40,43,45],{"name":34,"orcid":35},"Syed Arman Rabbani","https:\u002F\u002Forcid.org\u002F0000-0002-8454-8158",{"name":37,"orcid":9},"Mohamed El-Tanani",{"name":39,"orcid":9},"Ismail I. Matalka",{"name":41,"orcid":42},"Shrestha Sharma","https:\u002F\u002Forcid.org\u002F0000-0001-8527-3419",{"name":44,"orcid":9},"Manita saini",{"name":46,"orcid":47},"Rakesh Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-8807-8421","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fpublic-health\u002Farticles\u002F10.3389\u002Ffpubh.2026.1922265\u002Fpdf",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"综述机器学习在One Health抗微生物耐药监测情报中的应用与成熟度。","遵循JBI与PRISMA-ScR的范围综述，检索三大数据库并评估66项研究。","ML多用于预测与风险分层，但外部验证、校准与落地应用严重不足。","农业人工智能与决策模型","可探索动物-环境-食品链AMR数据的跨域外部验证与漂移监测框架。","农业遥感与作物表型","openalex","2026-09-18T23:30:21.367014Z",{"total":59,"page":60,"page_size":59,"items":61},6,1,[62,112,146,193,239,279],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":67,"content":9,"source_name":68,"source_url":65,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":77,"tags":79,"search_phrases":84,"slug":87,"view_count":15,"doi":88,"paper":89,"created_at":111},2871,"Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1948848","Introduction Systematically clarifying the spatiotemporal evolution of vegetation coverage and elucidating its response mechanisms to climatic fluctuations and human activities carries substantial practical implications for advancing the Dual Carbon Strategy and optimizing the pattern of territorial spatial development and conservation across Shanxi Province, China. Methods Based on kernel Normalized Difference Vegetation Index (kNDVI) datasets, multi-source meteorological, topographic and socioeconomic datasets, this study integrated Theil-Sen slope estimation, Mann-Kendall significance test, Hurst exponent, standard deviational ellipse and gravity center migration model to systematically characterize the spatiotemporal patterns of vegetation coverage for the period 2000 to 2024 and predict the potential persistence of its future evolution. Meanwhile, an interpretable XGBoost-SHAP machine learning framework was constructed to quantify the independent contributions, nonlinear marginal responses, and temporal evolutionary characteristics of seven driving factors: elevation, slope, aspect, annual mean temperature, annual mean precipitation, population density and nighttime light intensity. Results (1) Temporally, the multi-year average kNDVI of the whole province reached approximately 0.1699, presenting an extremely significant fluctuating upward trend with an annual growth rate of 0.0041. Annual kNDVI ranges from 0.1132 recorded in 2001 to a peak value of 0.2227 in 2024. (2) Spatially, vegetation coverage exhibited a remarkable differentiation pattern of “low values in the north and high values in the south”. Higher kNDVI values were predominantly distributed within the forest-covered mountainous regions of the Taihang, Lüliang, Zhongtiao, and Taiyue Mountains, whereas relatively low values were distributed across the sandy-hilly regions of northern Shanxi and urban agglomerations along the Fen River Valley. Vegetation restoration was observed across 98.02% of the study area, among which roughly 92.18% exhibited an extremely significant improvement. (3) Over 2000–2024, the major axis of vegetation spatial distribution maintained a stable northeast-southwest orientation, accompanied by moderate outward expansion of the standard deviational ellipse, and a general northwestward shift of the vegetation gravity centre. (4) The Hurst exponent indicated that the vegetation improvement trend in 98.03% of the region would persist, while scattered patches of industrial, mining and urban land (accounting for 1.97%) faced the potential vegetation degradation risk. (5) The regional vegetation driving system experienced three evolutionary stages: a single precipitation-dominated natural driving stage (2000–2005), a climate-human coupled transitional stage (2005–2010), and a multi-factor synergistic balanced stage (2010–2020). Precipitation and temperature acted as core climatic drivers, and elevation largely determined the vertical differentiation baseline of vegetation distribution. Population density and nighttime light intensity exerted persistent suppressive effects on vegetation growth. Empirically derived tentative thresholds are identified for major predictors: elevation ∼1,200 m, slope ∼8°, annual mean temperature ∼7.5 °C, annual mean precipitation ∼500 mm, population density 500 persons\u002Fkm 2 , and nighttime light intensity ∼5. These values can serve as reference boundaries for differentiating ecological conservation zones from human-disturbed zones. Conclusion This research identified the intertemporal differentiation and nonlinear coupling laws governing vegetation dynamics in Shanxi Province, a temperate transition zone on the Loess Plateau. Differentiated ecological governance strategies targeting climate adaptation, topographic zoning, and anthropogenic-pressure regulation are proposed, which provide observational scientific support for the construction of ecological security barriers on China’s Loess Plateau.","引言 系统厘清植被覆盖的时空演变规律并阐明其对气候波动与人类活动的响应机制，对推进双碳战略、优化山西省国土空间开发保护格局具有重要现实意义。方法 基于核归一化植被指数（kNDVI）数据集及多源气象、地形和社会经济数据，本研究综合运用Theil-Sen斜率估计、Mann-Kendall显著性检验、Hurst指数、标准差椭圆和重心迁移模型，系统刻画了2000—2024年植被覆盖的时空格局，并预测其未来演变的潜在持续性。同时，构建了可解释的XGBoost-SHAP机器学习框架，量化了高程、坡度、坡向、年平均气温、年平均降水量、人口密度和夜间灯光强度7个驱动因子的独立贡献、非线性边际响应及时间演变特征。结果 （1）时间上，全省多年平均kNDVI约为0.1699，呈极显著波动上升趋势，年增长率为0.0041。年kNDVI从2001年的0.1132变化至2024年的峰值0.2227。（2）空间上，植被覆盖呈现“北低南高”的显著分异格局。较高kNDVI值主要分布于太行山、吕梁山、中条山和太岳山等森林覆盖山区，而较低值分布于晋北沙丘丘陵区和汾河谷地城市群。研究区98.02%的区域植被呈恢复态势，其中约92.18%表现为极显著改善。（3）2000—2024年间，植被空间分布的主轴保持稳定的东北—西南走向，标准差椭圆呈中度向外扩张，植被重心总体向西北方向迁移。（4）Hurst指数表明，98.03%区域的植被改善趋势将持续，而零星斑块区域的工业","Frontiers in Environmental Science",82,{"impact":71,"substance":72,"depth":73,"authority":74,"freshness":75,"relevant":60,"comment":76},18,23,19,13,9,"基于kNDVI与XGBoost-SHAP的山西植被时空演变与驱动力研究，方法新颖、数据扎实，对黄土高原生态治理有参考价值。",[78],{"name":68,"url":65},[21,80,81,82,83],"生态保护","遥感监测","植被覆盖","黄土高原",[85,86],"山西 kNDVI 植被覆盖","XGBoost-SHAP 植被驱动","山西kNDVI植被覆盖-2871","10.3389\u002Ffenvs.2026.1948848",{"doi":88,"openalex_id":90,"authors":91,"venue":68,"cited_by_count":15,"oa_url":105,"card":106,"direction":55,"ingested_from":56},"W7213469060",[92,94,96,98,100,103],{"name":93,"orcid":9},"Jie Chen",{"name":95,"orcid":9},"Yi Hou",{"name":97,"orcid":9},"Jianhua Xue",{"name":99,"orcid":9},"Jianhua Ni",{"name":101,"orcid":102},"Hao Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8903-0983",{"name":104,"orcid":9},"Pengxiang Gao","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1948848\u002Fpdf",{"tldr":107,"method":108,"finding":109,"direction":55,"opportunity":110},"基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。","kNDVI数据结合Theil-Sen、Mann-Kendall、Hurst指数与","山西植被呈显著上升趋势，98.02%区域改善，驱动因子具非线性与时空差异。","可引入多源遥感与作物物候数据，将kNDVI驱动分析拓展至农田尺度精准管理与碳汇评估。","2026-09-18T23:30:29.212735Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":9,"content":9,"source_name":117,"source_url":9,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":126,"tags":128,"search_phrases":133,"slug":136,"view_count":15,"doi":9,"paper":137,"created_at":145},2855,"UAV无人机高光谱图像土壤盐度制图(湿度校正)——MDPI Agronomy","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1812","研究评估了6种光谱变换方法(原始反射率Ref、一阶导数FDR、PDS、OSC、FDR+PDS、FDR+OSC),结合3种机器学习算法(KNN、SVR、MLP)。进一步开发了集成这些基础学习者的Stacking集成模型,以提高湿度干扰下土壤盐度反演的精度。结果表明,Stacking模型在评估模型中达到最高的精度和稳定性。FDR+OSC-Stacking组合实现最佳验证性能,R²p=0.87,RMSEP=0.67 mS·cm⁻¹,RPD=2.93。FDR+OSC-Stacking组合成功应用于UAV高光谱图像,用于EC1:5的空间制图。来自吉林大学。","MDPI Agronomy","2026-09-15T00:00:00Z",75,{"impact":121,"substance":122,"depth":123,"authority":74,"freshness":124,"relevant":60,"comment":125},16,21,17,8,"方法组合新颖、验证指标扎实的无人机高光谱盐分制图研究，属细分领域实质进展，值得精选。",[127],{"name":117,"url":115},[129,130,21,131,132],"智慧农业","无人机","遥感","土壤盐渍化",[134,135],"土壤盐渍化 智慧农业 机器学习 无人机","土壤盐渍化 智慧农业","土壤盐渍化智慧农业机器学习无人机-2855",{"doi":9,"openalex_id":9,"authors":138,"venue":9,"cited_by_count":15,"oa_url":9,"card":139,"direction":55,"ingested_from":144},[],{"tldr":140,"method":141,"finding":142,"direction":55,"opportunity":143},"用无人机高光谱结合Stacking集成模型实现湿度干扰下的土壤盐度制图。","6种光谱变换与KNN、SVR、MLP及Stacking集成，基于UAV高光谱数据","FDR+OSC-Stacking最优，R²p=0.87、RMSEP=0.67 mS·cm⁻¹、RPD","可探索多时相\u002F多传感器融合与迁移学习，提升不同湿度与区域下盐度反演泛化性。","agent","2026-09-18T00:03:30.822732Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":9,"source_name":152,"source_url":149,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":153,"score_detail":154,"sources":158,"tags":160,"search_phrases":164,"slug":167,"view_count":15,"doi":168,"paper":169,"created_at":192},2804,"A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02584-x","A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning。Journal of the Indian Society of Remote Sensing","一种基于NASA FIRMS和机器学习的新型数据驱动秸秆焚烧检测框架。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing",76,{"impact":155,"substance":156,"depth":71,"authority":74,"freshness":13,"relevant":60,"comment":157},15,20,"该论文提出基于NASA FIRMS卫星数据与机器学习的秸秆焚烧检测新框架，方法新颖、数据规模明确，对农业遥感监测有实质参考价值，但属细分领域学术进展，影响力限于专业圈层。",[159],{"name":152,"url":149},[161,21,81,162,163],"农业人工智能","秸秆焚烧","卫星数据",[165,166],"农业人工智能 卫星数据 机器学习 秸秆焚烧","农业人工智能 卫星数据","农业人工智能卫星数据机器学习秸秆焚烧-2804","10.1007\u002Fs12524-026-02584-x",{"doi":168,"openalex_id":170,"authors":171,"venue":152,"cited_by_count":15,"oa_url":9,"card":187,"direction":53,"ingested_from":56},"W7213455429",[172,175,177,180,182,185],{"name":173,"orcid":174},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":176,"orcid":9},"Oshin Rastogi",{"name":178,"orcid":179},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":181,"orcid":9},"Raviya",{"name":183,"orcid":184},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":186,"orcid":9},"Shelza Dua",{"tldr":188,"method":189,"finding":190,"direction":55,"opportunity":191},"提出基于NASA FIRMS与机器学习的数据驱动框架，用于检测秸秆焚烧。","使用NASA FIRMS火点数据结合机器学习分类算法。","该框架能有效识别秸秆焚烧事件，提升检测精度。","可结合多源遥感与深度学习，提升小尺度焚烧检测与实时预警能力。","2026-09-17T23:30:59.313408Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":204,"tags":206,"search_phrases":210,"slug":213,"view_count":15,"doi":214,"paper":215,"created_at":238},2803,"Volatile Fingerprinting Empowers Salinity Monitoring in Peppermint Using MOS Sensors and Feature-Optimized Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184233","Early detection of salinity stress is essential for precision agriculture, particularly in scalable, resource-constrained monitoring systems. This study presents a portable sensing module integrating a low-cost metal oxide semiconductor (MOS) sensor array with potential application for edge deployment to detect salinity stress in peppermint. Salinity significantly reduced plant biomass, confirming physiological stress induction. Volatile organic compound (VOC) fingerprints were collected over eleven consecutive days in a controlled enclosure. Sensor signals underwent outlier filtering, normalization, and smoothing, while treatment discrimination was verified using the Kruskal–Wallis test. Thirty-three machine learning models were evaluated using a 75:25 train–test split with five-fold cross-validation. Wide neural network models achieved the highest predictive performance, exceeding 98% test accuracy and a 97% macro F1 score. Feature adequacy analysis showed that six sensors captured the dominant variance required for reliable classification. Considering computational constraints, a bilayered neural network using only six features maintained over 97% accuracy with a memory footprint of 0.008 MB while remaining Pareto optimal. These findings support the feasibility of a compact, computationally efficient, and edge-compatible VOC sensing framework for salinity stress detection in precision agriculture and intelligent crop monitoring.","盐胁迫的早期检测对精准农业至关重要，尤其是在可扩展、资源受限的监测系统中。本研究提出了一种便携式传感模块，将低成本金属氧化物半导体（MOS）传感器阵列集成其中，具备边缘部署的应用潜力，用于检测薄荷中的盐胁迫。盐胁迫显著降低了植物生物量，证实了生理胁迫的诱导作用。在受控密闭环境中连续十一天采集了挥发性有机化合物（VOC）指纹图谱。对传感器信号进行了异常值过滤、归一化和平滑处理，并使用Kruskal–Wallis检验验证了处理组间的区分度。采用75:25的训练-测试划分和五折交叉验证评估了三十三种机器学习模型。宽神经网络模型取得了最高的预测性能，测试准确率超过98%，宏F1分数达到97%。特征充分性分析表明，六个传感器即可捕获可靠分类所需的主要方差。考虑到计算约束，仅使用六个特征的双层神经网络在保持超过97%准确率的同时，内存占用仅为0.008 MB，且仍处于帕累托最优。这些发现支持了一种紧凑、计算高效且兼容边缘计算的VOC传感框架用于精准农业和智能作物监测中盐胁迫检测的可行性。","Electronics",78,{"impact":155,"substance":202,"depth":71,"authority":74,"freshness":13,"relevant":60,"comment":203},22,"低成本MOS传感器阵列结合特征优化机器学习实现薄荷盐胁迫早期无损检测，方法新颖、数据扎实，对边缘部署式作物监测有参考价值。",[205],{"name":199,"url":196},[129,21,207,208,209],"精准农业","农业传感器","盐胁迫监测",[211,212],"农业传感器 盐胁迫监测 智慧农业 机器学习","农业传感器 盐胁迫监测","农业传感器盐胁迫监测智慧农业机器学习-2803","10.3390\u002Felectronics15184233",{"doi":214,"openalex_id":216,"authors":217,"venue":199,"cited_by_count":15,"oa_url":196,"card":232,"direction":53,"ingested_from":56},"W7213452763",[218,221,224,226,229],{"name":219,"orcid":220},"Ahmad Ali","https:\u002F\u002Forcid.org\u002F0000-0001-5530-7374",{"name":222,"orcid":223},"Vinie Lee Silva Alvarado","https:\u002F\u002Forcid.org\u002F0009-0000-5857-3248",{"name":225,"orcid":9},"Arman Heydari",{"name":227,"orcid":228},"Sandra Sendra","https:\u002F\u002Forcid.org\u002F0000-0001-9556-9088",{"name":230,"orcid":231},"Jaime Lloret","https:\u002F\u002Forcid.org\u002F0000-0002-0862-0533",{"tldr":233,"method":234,"finding":235,"direction":236,"opportunity":237},"用低成本MOS传感器阵列采集薄荷VOC指纹，结合特征优化机器学习实现盐胁迫检测。","11天VOC指纹采集，33种机器学习模型，五折交叉验证，特征充分性分析。","宽神经网络准确率超98%，仅用6个特征的双层网络保持97%以上且内存仅0.008MB。","智慧农业 \u002F 农业物联网","可探索多作物VOC指纹迁移学习与田间边缘设备长期稳定性验证。","2026-09-17T23:30:59.249847Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":9,"source_name":245,"source_url":242,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":251,"tags":253,"search_phrases":255,"slug":258,"view_count":15,"doi":259,"paper":260,"created_at":278},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports",73,{"impact":248,"substance":156,"depth":123,"authority":249,"freshness":13,"relevant":60,"comment":250},12,14,"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[252],{"name":245,"url":242},[129,161,21,207,254],"表型分析",[256,257],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器学习精准农业-2802","10.1038\u002Fs41598-026-71390-9",{"doi":259,"openalex_id":261,"authors":262,"venue":245,"cited_by_count":15,"oa_url":242,"card":273,"direction":53,"ingested_from":56},"W7213449017",[263,266,268,270],{"name":264,"orcid":265},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":267,"orcid":9},"Hossein Sabouri",{"name":269,"orcid":9},"Ali Tanhaei",{"name":271,"orcid":272},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":274,"method":275,"finding":276,"direction":55,"opportunity":277},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","2026-09-17T23:30:59.169744Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":290,"tags":292,"search_phrases":296,"slug":299,"view_count":15,"doi":300,"paper":301,"created_at":326},2791,"Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101524","Land degradation remains a major environmental challenge, particularly in semi-arid and heterogeneous landscapes, where interactions between vegetation loss and soil exposure are complex and spatially dynamic. This study, therefore, seeks to evaluate the spatial extent of land degradation and drivers over time (2005 - 2025) using Landsat data series and support vector machine (SVM) in the Keiskamma Catchment of South Africa. Degraded land followed a non-monotonic trajectory: it declined from ∼197 km 2 in 2005 to ∼157 km 2 in 2015 (a temporary contraction of 20.3%, consistent with short-term restoration and land-use shifts), before rising sharply and unsustainably to ∼328 km 2 by 2025 (a 108.9% increase relative to 2015, and a net increase of 66.5% over the full two-decade period), largely at the expense of grassland and agricultural land. Furthermore, the findings show that soil-sensitive indicators, particularly BSI and SWIR spectral bands, play a crucial role in determining degraded land. In contrast, vegetation indices such as NDVI contribute less under degraded conditions because degraded areas were severely dominated by exposed soil rather than vegetation. Correlation matrix analysis further reveals a temporal shift from mixed soil–vegetation spectral relationships toward strong soil-dominated reflectance patterns by 2025, indicating advanced degradation stages. Overall, the integration of SVM classification with VIF and SHAP provides a transparent, reliable, and spatially explicit framework for monitoring land degradation. The findings support land degradation neutrality monitoring and provide critical insights for sustainable land-management planning in support of Sustainable Development Goal (SDG) 15.3.","土地退化仍然是一项重大环境挑战，尤其是在半干旱和异质性景观中，植被丧失与土壤裸露之间的相互作用复杂且具有空间动态性。因此，本研究旨在利用Landsat数据序列和支持向量机（SVM），评估南非Keiskamma集水区2005—2025年间土地退化的空间范围及其驱动因素。退化土地呈非单调变化轨迹：从2005年的约197 km²下降至2015年的约157 km²（暂时收缩20.3%，与短期恢复和土地利用变化相一致），随后急剧且不可持续地上升至2025年的约328 km²（较2015年增加108.9%，在整个二十年期间净增加66.5%），且主要以草地和农用地为代价。此外，研究结果表明，土壤敏感指标，尤其是BSI和SWIR光谱波段，在判定退化土地方面发挥着关键作用。相比之下，NDVI等植被指数在退化条件下贡献较小，因为退化区域严重以裸露土壤为主，而非植被。相关矩阵分析进一步揭示，到2025年，光谱关系由土壤—植被混合关系向强烈的土壤主导反射模式发生时间转变，表明退化已进入后期阶段。总体而言，将SVM分类与VIF和SHAP相结合，为监测土地退化提供了一个透明、可靠且具有空间显式性的框架。研究结果支持土地退化零增长监测，并为支持可持续发展目标（SDG）15.3的可持续土地管理规划提供了关键见解。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",77,{"impact":155,"substance":202,"depth":71,"authority":74,"freshness":75,"relevant":60,"comment":289},"基于Landsat时序与SVM\u002FSHAP的南非土地退化监测研究，方法透明可复现，对农业遥感与土地退化中性监测有参考价值，但属区域案例、非国内三农直接政策信息。",[291],{"name":285,"url":282},[293,21,294,295,81],"农业遥感","可持续发展","土地退化",[297,298],"可持续发展 农业遥感 土地退化 机器学习","可持续发展 农业遥感","可持续发展农业遥感土地退化机器学习-2791","10.1016\u002Fj.indic.2026.101524",{"doi":300,"openalex_id":302,"authors":303,"venue":285,"cited_by_count":15,"oa_url":320,"card":321,"direction":55,"ingested_from":56},"W7213298151",[304,306,309,312,314,317],{"name":305,"orcid":9},"Mandisa Zameko",{"name":307,"orcid":308},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":310,"orcid":311},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":313,"orcid":9},"Matthieu Tshanga",{"name":315,"orcid":316},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":318,"orcid":319},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":322,"method":323,"finding":324,"direction":55,"opportunity":325},"用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。","Landsat时序数据、SVM分类，结合VIF与SHAP做特征解释。","退化面积先降后升，2025年达328km²，土壤光谱指标比NDVI更关键。","可将该SVM-SHAP框架迁移到其他半干旱区，并耦合气候与土地利用驱动做退化预警。","2026-09-17T23:30:36.054539Z"]