[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2804":3,"related-2804":94},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":93},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》",null,"Journal of the Indian Society of Remote Sensing","2026-09-17T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},15,20,18,13,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","机器学习","遥感监测","秸秆焚烧","卫星数据",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":59},[38,46,54],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"I2",16,true,[43],"machine learning",[27,43],[26,27],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":53},"I3",7,[],[51,52],"遥感","remote sensing",[28],{"code":55,"score":21,"supported":14,"title":56,"abstract":57,"tags":58},"H5",[],[],[29],"农业相关性需要复核",[61,62],"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",2,"10.1007\u002Fs12524-026-02584-x",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":84,"oa_url":9,"card":85,"direction":91,"ingested_from":92},"W7213455429",[69,72,74,77,79,82],{"name":70,"orcid":71},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":73,"orcid":9},"Oshin Rastogi",{"name":75,"orcid":76},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":78,"orcid":9},"Raviya",{"name":80,"orcid":81},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":83,"orcid":9},"Shelza Dua",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"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。","农业遥感与作物表型","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。","农业人工智能与决策模型","openalex","2026-09-17T23:30:59.313408Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,171,227,301,388,456],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":116,"secondary_topics":121,"classification":122,"search_phrases":146,"slug":149,"view_count":21,"doi":150,"paper":151,"created_at":170},4599,"Reducing redundancy in multi-temporal analysis to improve field-scale detection of crop residue burning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rsase.2026.102310","Accurate field-scale mapping of paddy stubble burning is essential for reliable emission estimation and effective policy intervention. However, current remote sensing approaches often suffer from significant inaccuracies due to the repeated detection of the same field-scale across multi-temporal observations. This leads to systematic overestimation of field-scale and remains insufficiently addressed in existing studies. This study addresses the challenge using multi-temporal remote sensing data and a fuzzy machine-learning technique using the ‘Individual Sample as Mean (ISM)’ training approach. The study was implemented in the Nanakmatta area of Udham Singh Nagar District, Uttarakhand, India. PlanetScope satellite imagery, covering both harvesting and burnt-stubble conditions, was utilised to reduce redundancy. A Modified Possibilistic c -Means (MPCM) classifier, integrated with the ISM approach, was employed to map stubble burning across 13 observation dates during October-November 2025. Peak burning activity was recorded around 23 October 2025, followed by a gradual decline, with no detectable burning on 16 November 2025. The combination of MPCM and ISM effectively minimises inflated estimates of burning events caused by repeated detections of the same burnt areas. Specifically, the ISM training approach handles variability in burning patterns caused by partial combustion, residue mixing, and delayed field preparation. It accounts for class diversity rather than forcing all instances into a single generalised representation. Accuracy assessment using both Mean Membership Difference (MMD) and conventional classification metrics demonstrated the robustness of the proposed framework. A within-class MMD of 0 and a between-class MMD of 0.66 indicated strong class separability, with precision, recall, and F1-score values of 0.775, 0.688, and 0.729, respectively. The proposed framework provides a more reliable and quantitatively validated approach for addressing temporal redundancy and monitoring residue-burning practices across diverse agricultural environments.","准确的田块尺度水稻秸秆焚烧制图对于可靠的排放估算和有效的政策干预至关重要。然而，当前遥感方法常因在多时相观测中重复检测同一田块尺度而导致显著不准确，造成田块尺度的系统性高估，现有研究对此仍未充分解决。本研究利用多时相遥感数据和模糊机器学习技术，采用“个体样本作为均值”（Individual Sample as Mean, ISM）训练方法应对这一挑战。研究在印度北阿坎德邦乌达姆·辛格·纳加尔县的纳纳克马塔地区实施。利用覆盖收割和焚烧后秸秆状况的PlanetScope卫星影像以减少冗余。采用改进的可能性c均值（Modified Possibilistic c-Means, MPCM）分类器，结合ISM方法，对2025年10月至11月期间的13个观测日期进行秸秆焚烧制图。焚烧活动高峰出现在2025年10月23日前后，随后逐渐下降，至2025年11月16日未检测到焚烧。MPCM与ISM的结合有效减少了因重复检测同一焚烧区域而导致的焚烧事件高估。具体而言，ISM训练方法能够处理由不完全燃烧、残茬混合和田间准备延迟引起的焚烧模式变异性，它考虑了类别多样性，而非将所有实例强制归入单一泛化表示。使用平均隶属度差异（Mean Membership Difference, MMD）和传统分类指标进行精度评估，证明了所提框架的稳健性。类内MMD为0，类间MMD为0.66，表明类别可分性强，精确率、召回率和F1分数分别为0.775、0.688和0.729。所提框架为解决时间冗余和监测多样化农业环境中的残茬焚烧行为提供了一种更可靠且经定量验证的方法。","Remote Sensing Applications Society and Environment","2026-10-08T00:00:00Z",73,{"impact":107,"substance":108,"depth":19,"authority":20,"freshness":109,"relevant":21,"comment":110},12,21,9,"方法新颖、数据扎实的遥感论文，对秸秆焚烧监测与排放估算有参考价值，但属细分领域技术进展，公共影响有限。",[112],{"name":103,"url":100},[26,114,115,28,29],"农业面源污染","作物识别",{"code":47,"slug":117,"name":118,"path":119,"parent_code":120},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":123,"locked":14,"version":34,"method":35,"evidence":124},"classified",{"candidates":125,"agricultural_evidence":41},[126,130,137,142],{"code":47,"score":48,"supported":41,"title":127,"abstract":128,"tags":129},[],[51,52],[28],{"code":131,"score":95,"supported":41,"title":132,"abstract":133,"tags":136},"A1",[],[134,135],"水稻","paddy",[],{"code":39,"score":138,"supported":41,"title":139,"abstract":140,"tags":141},4,[],[27],[26],{"code":55,"score":64,"supported":14,"title":143,"abstract":144,"tags":145},[],[],[114,29],[147,148],"PlanetScope 秸秆焚烧 遥感","Uttarakhand 稻田 秸秆焚烧","PlanetScope秸秆焚烧遥感-4599","10.1016\u002Fj.rsase.2026.102310",{"doi":150,"openalex_id":152,"authors":153,"venue":103,"cited_by_count":84,"oa_url":100,"card":165,"direction":89,"ingested_from":92},"W7221040421",[154,157,160,163],{"name":155,"orcid":156},"Anil Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-7177-0817",{"name":158,"orcid":159},"Anamika Palavesam Sarathamani","https:\u002F\u002Forcid.org\u002F0009-0007-4223-7443",{"name":161,"orcid":162},"Ishwari Datt","https:\u002F\u002Forcid.org\u002F0000-0001-9936-2367",{"name":164,"orcid":9},"R.P. Singh",{"tldr":166,"method":167,"finding":168,"direction":89,"opportunity":169},"用多时相遥感与模糊机器学习减少重复检测，提升稻田秸秆焚烧田块尺度制图精度。","PlanetScope多时相影像，MPCM分类器结合ISM训练，13期观测。","MPCM+ISM有效抑制重复检测导致的焚烧面积高估，F1达0.729。","可将去冗余框架迁移至其他作物残茬焚烧监测，并融合Sentinel-2提升时空覆盖。","2026-10-10T23:30:31.208858Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":9,"content":9,"source_name":176,"source_url":9,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"primary_topic":187,"secondary_topics":188,"classification":189,"search_phrases":214,"slug":217,"view_count":138,"doi":9,"paper":218,"created_at":226},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。","MDPI","2026-09-22T00:00:00Z",81,{"impact":19,"substance":180,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":181},22,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[183],{"name":176,"url":174},[185,26,27,186,28],"智慧农业","作物推荐",{"code":47,"slug":117,"name":118,"path":119,"parent_code":120},[],{"status":123,"locked":14,"version":34,"method":35,"evidence":190},{"candidates":191,"agricultural_evidence":41},[192,199,203,210],{"code":193,"score":18,"supported":41,"title":194,"abstract":197,"tags":198},"I1",[195,196],"精准农业","precision agriculture",[195],[185],{"code":47,"score":138,"supported":41,"title":200,"abstract":201,"tags":202},[],[51],[28],{"code":204,"score":205,"supported":41,"title":206,"abstract":207,"tags":209},"H2",3,[],[208],"土壤",[],{"code":39,"score":64,"supported":14,"title":211,"abstract":212,"tags":213},[],[],[26,27],[215,216],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":9,"openalex_id":9,"authors":219,"venue":9,"cited_by_count":84,"oa_url":9,"card":220,"direction":91,"ingested_from":225},[],{"tldr":221,"method":222,"finding":223,"direction":91,"opportunity":224},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":238,"tags":240,"primary_topic":243,"secondary_topics":247,"classification":248,"search_phrases":284,"slug":287,"view_count":84,"doi":288,"paper":289,"created_at":300},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",56,{"impact":107,"substance":40,"depth":20,"authority":95,"freshness":109,"relevant":21,"comment":237},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[239],{"name":233,"url":230},[185,26,28,241,242],"风险管理","病虫害监测",{"code":120,"slug":244,"name":245,"path":246,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":249,"locked":14,"version":34,"method":35,"evidence":250},"partial",{"candidates":251,"agricultural_evidence":41,"reason":283},[252,258,264,272,276],{"code":39,"score":13,"supported":41,"title":253,"abstract":254,"tags":257},[],[27,43,255,256],"artificial intelligence","decision support",[26],{"code":47,"score":13,"supported":41,"title":259,"abstract":260,"tags":263},[],[51,52,261,262],"multispectral","hyperspectral",[28],{"code":265,"score":109,"supported":41,"title":266,"abstract":267,"tags":271},"I5",[],[268,269,270],"物联网","internet of things","IoT",[],{"code":193,"score":48,"supported":41,"title":273,"abstract":274,"tags":275},[],[195,196],[185],{"code":277,"score":95,"supported":41,"title":278,"abstract":279,"tags":282},"H7",[],[280,281],"气候变化","climate change",[],"一级领域明确，二级方向需要细分",[285,286],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":288,"openalex_id":290,"authors":291,"venue":233,"cited_by_count":84,"oa_url":230,"card":294,"direction":299,"ingested_from":92},"W7223085400",[292],{"name":293,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":295,"method":296,"finding":297,"direction":91,"opportunity":298},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":312,"tags":314,"primary_topic":317,"secondary_topics":323,"classification":333,"search_phrases":365,"slug":368,"view_count":84,"doi":369,"paper":370,"created_at":387},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":40,"substance":108,"depth":310,"authority":20,"freshness":13,"relevant":21,"comment":311},17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[313],{"name":307,"url":304},[185,27,28,315,316],"土壤墒情","冬小麦",{"code":318,"slug":319,"name":320,"path":321,"parent_code":322},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[324,329],{"code":204,"slug":325,"name":326,"path":327,"parent_code":328},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":330,"name":331,"path":332,"parent_code":120},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":123,"locked":14,"version":34,"method":35,"evidence":334},{"candidates":335,"agricultural_evidence":41},[336,342,346,350,354,361],{"code":318,"score":337,"supported":41,"title":338,"abstract":340,"tags":341},26,[339,319],"winter wheat",[320,339,319],[316],{"code":204,"score":17,"supported":41,"title":343,"abstract":344,"tags":345},[325],[208,325],[315],{"code":39,"score":17,"supported":41,"title":347,"abstract":348,"tags":349},[43],[27,43],[27],{"code":47,"score":13,"supported":41,"title":351,"abstract":352,"tags":353},[],[51,52,261],[28],{"code":355,"score":95,"supported":41,"title":356,"abstract":357,"tags":360},"H3",[],[358,359],"灌溉","irrigation",[],{"code":193,"score":138,"supported":41,"title":362,"abstract":363,"tags":364},[],[185],[185],[366,367],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":369,"openalex_id":371,"authors":372,"venue":307,"cited_by_count":84,"oa_url":304,"card":382,"direction":299,"ingested_from":92},"W7223156204",[373,376,378,380],{"name":374,"orcid":375},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":377,"orcid":9},"Yi Zhang",{"name":379,"orcid":9},"WEI Lei",{"name":381,"orcid":9},"Chengcai Zhang",{"tldr":383,"method":384,"finding":385,"direction":89,"opportunity":386},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":410,"search_phrases":433,"slug":436,"view_count":84,"doi":437,"paper":438,"created_at":455},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":84,"substance":84,"depth":84,"authority":84,"freshness":84,"relevant":84,"comment":397},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[399],{"name":394,"url":391},[27,28,401,402],"土地利用","城市扩张",{"code":39,"slug":330,"name":331,"path":332,"parent_code":120},[405],{"code":406,"slug":407,"name":408,"path":409,"parent_code":120},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":123,"locked":14,"version":34,"method":35,"evidence":411},{"candidates":412,"agricultural_evidence":41},[413,417,424,428],{"code":39,"score":17,"supported":41,"title":414,"abstract":415,"tags":416},[43],[27,43],[27],{"code":406,"score":109,"supported":41,"title":418,"abstract":419,"tags":423},[],[420,421,422],"空间分析","GIS","spatial analysis",[],{"code":47,"score":48,"supported":41,"title":425,"abstract":426,"tags":427},[],[51,52],[28],{"code":429,"score":21,"supported":14,"title":430,"abstract":431,"tags":432},"H6",[],[],[401],[434,435],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":437,"openalex_id":439,"authors":440,"venue":394,"cited_by_count":84,"oa_url":391,"card":450,"direction":89,"ingested_from":92},"W7223022155",[441,444,446,448],{"name":442,"orcid":443},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":445,"orcid":9},"Goanar Jal Dup",{"name":447,"orcid":9},"Weyesa Merga Chemeda",{"name":449,"orcid":9},"Shankar Karuppannan",{"tldr":451,"method":452,"finding":453,"direction":89,"opportunity":454},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":9,"content":9,"source_name":461,"source_url":9,"published_at":462,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":463,"score_detail":464,"sources":467,"tags":469,"primary_topic":472,"secondary_topics":473,"classification":474,"search_phrases":493,"slug":496,"view_count":84,"doi":9,"paper":497,"created_at":504},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",70,{"impact":107,"substance":18,"depth":310,"authority":20,"freshness":465,"relevant":21,"comment":466},8,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[468],{"name":461,"url":459},[26,27,195,470,471],"土壤数据","作物分类",{"code":204,"slug":325,"name":326,"path":327,"parent_code":328},[],{"status":123,"locked":14,"version":34,"method":35,"evidence":475},{"candidates":476,"agricultural_evidence":41},[477,481,485,489],{"code":204,"score":18,"supported":41,"title":478,"abstract":479,"tags":480},[208,325],[208],[470],{"code":39,"score":64,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[26,27],{"code":193,"score":21,"supported":14,"title":486,"abstract":487,"tags":488},[],[],[195],{"code":47,"score":21,"supported":14,"title":490,"abstract":491,"tags":492},[],[],[471],[494,495],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":9,"openalex_id":9,"authors":498,"venue":9,"cited_by_count":84,"oa_url":9,"card":499,"direction":91,"ingested_from":225},[],{"tldr":500,"method":501,"finding":502,"direction":91,"opportunity":503},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","2026-10-21T00:10:08.428584Z"]