[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3493":3,"related-3493":100},{"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,"primary_topic":24,"secondary_topics":29,"classification":30,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":99},3493,"Urbanization and Land Use\u002FLand Cover Change in Jalingo Local Government Area, Taraba State, Nigeria: A Multi-Temporal Remote Sensing and Socio-Economic Assessment","https:\u002F\u002Fdoi.org\u002F10.54117\u002Fcanhm718","Rapid urbanization is a major driver of land use and land cover (LULC) transformation, particularly in rapidly developing cities of sub-Saharan Africa. This study examined the impact of urbanization on LULC in Jalingo Local Government Area of Taraba State, Nigeria, with particular emphasis on the period 1994–2024. The study integrated multi-temporal Landsat satellite imagery, Geographic Information Systems (GIS), post-classification change detection and questionnaire data to assess the spatial and temporal dynamics of urban growth and its environmental and socio-economic implications. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI\u002FTIRS imagery for 1994, 2004, 2014 and 2024 were classified into four major LULC categories: built-up area, vegetation, bare surface and water body. A questionnaire survey involving 384 respondents was also used to assess perceived drivers and consequences of urbanization. The results revealed substantial transformation of the Jalingo landscape. Vegetation declined from 170.56 km² (87.47%) in 1994 to 50.39 km² (25.84%) in 2024, representing a net loss of 120.17 km². In contrast, built-up land increased from 4.30 km² (2.20%) to 18.06 km² (9.26%), while bare surfaces expanded from 18.60 km² (9.54%) to 124.68 km² (63.94%). The most rapid transformation occurred between 2004 and 2014, when vegetation declined by 81.57 km² while bare surfaces increased by 71.79 km² and built-up land by 10.46 km². Survey results indicated that government policies and initiatives, economic opportunities, population growth and infrastructure development were important perceived drivers of urbanization. Most respondents also perceived significant negative effects on agricultural land, livelihoods, food security and access to essential natural resources. The study concludes that urban growth in Jalingo has generated important economic and infrastructural benefits but has also produced substantial environmental degradation and socio-economic pressures. Strengthened land-use planning, protection of agricultural and ecological areas, urban greening, sustainable livelihood programmes, community participation and continuous GIS-based monitoring are recommended.","快速城市化是土地利用与土地覆盖(LULC)变化的主要驱动力，在撒哈拉以南非洲快速发展的城市中尤为突出。本研究考察了尼日利亚塔拉巴州贾林戈地方政府辖区城市化对土地利用与土地覆盖的影响，重点关注1994—2024年这一时期。研究综合运用多时相Landsat卫星影像、地理信息系统(GIS)、分类后变化检测和问卷调查数据，评估城市增长的空间与时间动态及其环境和社会经济影响。将1994年、2004年、2014年和2024年的Landsat 5 TM、Landsat 7 ETM+和Landsat 8 OLI\u002FTIRS影像分为四个主要土地利用与土地覆盖类别：建设用地、植被、裸地表和水体。同时开展了一项涉及384名受访者的问卷调查，以评估城市化的感知驱动因素及其后果。结果表明，贾林戈景观发生了显著转变。植被从1994年的170.56 km²(87.47%)下降至2024年的50.39 km²(25.84%)，净损失120.17 km²。相比之下，建设用地从4.30 km²(2.20%)增至18.06 km²(9.26%)，而裸地表从18.60 km²(9.54%)扩展至124.68 km²(63.94%)。变化最快的时期为2004—2014年，期间植被减少81.57 km²，裸地表增加71.79 km²，建设用地增加10.46 km²。调查结果表明，政府政策和举措、经济机会、人口增长和基础设施建设是城市化的重要感知驱动因素。大多数受访者还认为，城市化对农业用地、生计、粮食安全和基本自然资源的获取产生了显著的负面影响。研究认为，贾林戈的城市增长带来了重要的经济和基础设施效益，但也造成了严重的环境退化和社会经济压力。建议加强土地利用规划、保护农业和生态区域、推进城市绿化、实施可持续生计项目、促进社区参与以及持续开展基于GIS的监测。",null,"OpenAlex","2026-09-24T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究对象为尼日利亚Jalingo地区城市化与土地利用变化，属国际区域地理研究，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感监测","土地利用","城市化",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":31,"locked":14,"version":32,"method":33,"evidence":34},"partial","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":39,"reason":66},[36,44,52,60],{"code":37,"score":38,"supported":39,"title":40,"abstract":42,"tags":43},"I3",9,true,[41],"remote sensing",[],[21],{"code":45,"score":38,"supported":39,"title":46,"abstract":47,"tags":51},"I4",[],[48,49,50],"地理信息","GIS","geographic information",[],{"code":53,"score":54,"supported":39,"title":55,"abstract":56,"tags":59},"A8",6,[],[57,58],"粮食安全","food security",[],{"code":61,"score":62,"supported":14,"title":63,"abstract":64,"tags":65},"H6",1,[],[],[22],"一级领域明确，二级方向需要细分",[68,69],"尼日利亚 Jalingo 土地利用","Landsat 遥感 城市化","尼日利亚Jalingo土地利用-3493",4,"10.54117\u002Fcanhm718",{"doi":72,"openalex_id":74,"authors":75,"venue":9,"cited_by_count":15,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7214145360",[76,79,82,84,86,88,90],{"name":77,"orcid":78},"Mohammed Bakoji Yusuf","https:\u002F\u002Forcid.org\u002F0000-0002-8719-2396",{"name":80,"orcid":81},"Ambrose Audu Zemba","https:\u002F\u002Forcid.org\u002F0000-0002-7692-9911",{"name":83,"orcid":9},"Huzaima Abubakar Bakoji",{"name":85,"orcid":9},"AUWAL ADAMU BAKAKU",{"name":87,"orcid":9},"UMAR ABBA JAURO",{"name":89,"orcid":9},"Aliyu Mohammed Jingudo",{"name":91,"orcid":9},"Abubakar Isa",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"用多时相遥感和问卷评估尼日利亚Jalingo 1994–2024年城市化驱动的土地利用变化。","Landsat 5\u002F7\u002F8影像分类、GIS变化检测与384份问卷。","植被减少120.17 km²，建设用地和裸地大幅扩张，2004–2014变化最快。","农业遥感与作物表型","可结合多源遥感与农户调查，研究西非城市扩张对农地流失和粮食安全的驱动机制。","openalex","2026-09-25T23:30:30.441601Z",{"total":54,"page":62,"page_size":54,"items":101},[102,186,247,319,378,448],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":117,"tags":119,"primary_topic":124,"secondary_topics":125,"classification":126,"search_phrases":160,"slug":163,"view_count":164,"doi":165,"paper":166,"created_at":185},4435,"Land Use Change, Flood Exposure, and Vegetation Stress in the Chi River Basin, Northeast Thailand: A Multi-Temporal Remote Sensing and Machine Learning Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910188","Climate change has intensified hydrological extremes in Southeast Asia, yet the relationships between land use change, flood exposure, and vegetation stress remain poorly understood in tropical floodplain environments. This study assessed land use dynamics, flood exposure, and vegetation stress in the Upper Chi River Basin, Northeast Thailand, using multi-temporal remote sensing datasets spanning 2015–2024, official land use data from the Land Development Department (LDD), and machine learning approaches, including Random Forest classification of binary flood occurrence, SHAP analysis, and spatial statistics. Agricultural land dominated the landscape (84.2% in 2023), with 11.0% land use change occurring between 2015 and 2023, primarily agricultural conversion from forest (38.6% of Forest Area converted) and water body (30.0% of Water Body converted). Flood frequency was highest in areas classified as Water Bodies (0.38 events) and Agricultural Areas (0.55 events), while Forest Areas showed the greatest resilience (0.05 events). Random Forest classification of flood occurrence achieved an overall accuracy of 0.920 and AUC-ROC of 0.905, with DEM emerging as the dominant predictor in both Random Forest importance (0.435) and mean absolute SHAP (0.308). Vegetation stress affected 21.1% of the basin, with 59.2% of points showing an NDVI decline greater than 0.02 and 33.3% falling below the VCI \u003C 0.4 drought threshold. Four sensitivity classes were identified: Low (66.4%), Moderate (19.2%), High (12.4%), and Very High (2.0%), with Very High Sensitivity concentrated along the Chi River corridor in low-lying areas (mean DEM = 146.0 m). Moran’s I confirmed strong spatial clustering of flood frequency (I = 0.787) and sensitivity class (I = 0.534), highlighting the need for spatial statistical approaches. The findings support elevation-based land use zoning, forest conservation, and climate-resilient agricultural practices to enhance flood resilience and contribute to Sustainable Development Goals 2 (Zero Hunger), 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land).","气候变化加剧了东南亚的水文极端事件，但在热带洪泛平原环境中，土地利用变化、洪水暴露与植被胁迫之间的关系仍缺乏深入认识。本研究利用2015—2024年多时相遥感数据集、泰国土地发展厅（LDD）的官方土地利用数据以及机器学习方法（包括对二元洪水发生事件的随机森林分类、SHAP分析和空间统计），评估了泰国东北部奇河上游流域的土地利用动态、洪水暴露和植被胁迫。农业用地在景观中占主导地位（2023年为84.2%），2015—2023年间土地利用变化率为11.0%，主要表现为农业用地扩张导致的林地转换（38.6%的林地面积被转换）和水体转换（30.0%的水体面积被转换）。洪水频率在水体分类区域（0.38次）和农业用地分类区域（0.55次）最高，而林地表现出最强的恢复力（0.05次）。洪水发生事件的随机森林分类总体精度达到0.920，AUC-ROC为0.905，其中DEM在随机森林重要性（0.435）和平均绝对SHAP值（0.308）中均为最主要的预测因子。植被胁迫影响了流域21.1%的区域，59.2%的样点NDVI下降幅度超过0.02，33.3%的样点低于VCI \u003C 0.4的干旱阈值。研究识别出四个敏感性等级：低敏感（66.4%）、中等敏感（19.2%）、高敏感（12.4%）和极高敏感（2.0%），其中极高敏感区集中在奇河沿岸低洼地带（平均DEM = 146.0 m）。Moran's I证实了洪水频率（I = 0.787）和敏感性等级（I = 0.534）具有强烈的空间聚集性，凸显了空间统计方法的必要性。研究结果支持基于海拔的土地利用分区、森林保护以及气候适应性农业实践，以增强洪水恢复力，并助力实现可持续发展目标2（零饥饿）、11（可持续城市）、13（气候行动）和15（陆地生物）。","Sustainability","2026-10-07T00:00:00Z",70,{"impact":112,"substance":113,"depth":114,"authority":115,"freshness":38,"relevant":62,"comment":116},8,22,18,13,"基于多时相遥感与机器学习的流域土地利用—洪涝—植被胁迫评估，方法扎实、数据翔实，对农业防灾与气候韧性有参考价值，但属区域性研究，公共影响有限。",[118],{"name":108,"url":105},[120,121,21,22,122,123],"数字乡村","机器学习","洪涝风险","植被胁迫",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},[],{"status":31,"locked":14,"version":32,"method":33,"evidence":127},{"candidates":128,"agricultural_evidence":39,"reason":66},[129,136,141,151,155],{"code":130,"score":131,"supported":39,"title":132,"abstract":134,"tags":135},"I2",15,[133],"machine learning",[121,133],[121],{"code":37,"score":131,"supported":39,"title":137,"abstract":138,"tags":140},[41],[139,41],"遥感",[21],{"code":142,"score":38,"supported":39,"title":143,"abstract":144,"tags":150},"H7",[],[145,146,147,148,149],"气候变化","气候适应","干旱","climate change","drought",[],{"code":61,"score":62,"supported":14,"title":152,"abstract":153,"tags":154},[],[],[22],{"code":156,"score":62,"supported":14,"title":157,"abstract":158,"tags":159},"I6",[],[],[120],[161,162],"泰国 Chi River 流域 遥感","土地利用变化 洪涝暴露 植被胁迫","泰国ChiRiver流域遥感-4435",2,"10.3390\u002Fsu181910188",{"doi":165,"openalex_id":167,"authors":168,"venue":108,"cited_by_count":15,"oa_url":105,"card":180,"direction":96,"ingested_from":98},"W7220850940",[169,171,174,177],{"name":170,"orcid":9},"Jiradech Majandang",{"name":172,"orcid":173},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":175,"orcid":176},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":178,"orcid":179},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":181,"method":182,"finding":183,"direction":96,"opportunity":184},"用多时相遥感和机器学习评估泰国栖河流域土地利用变化、洪水暴露与植被胁迫的关系。","2015–2024年多时相遥感、LDD土地利用数据、随机森林、SHAP与空间统计","农地占84.2%，洪水频发于农地和水体，21.1%流域植被受胁迫，DEM是洪水主导因子。","可结合高分辨率时序遥感与作物模型，量化洪水胁迫对热带平原农业产量的级联影响。","2026-10-08T23:30:29.519607Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":194,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":204,"primary_topic":206,"secondary_topics":207,"classification":208,"search_phrases":231,"slug":234,"view_count":15,"doi":235,"paper":236,"created_at":246},4305,"A Comparative Geographical Analysis of Land Use Patterns in Akrani and Nandurbar Tehsils of Nandurbar District","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155213","Abstract \"A Comparative Geographical Analysis of Land Use Patterns in Akrani and Nandurbar Tehsils in Nandurbar District,\" aims to analyze and contrast the features and spatial distribution of different land-use categories in the two tehsils. An key measure of how human activity and the physical environment interact is land use. Major land-use categories include agricultural land, forest land, fallow land, barren and uncultivable land, grazing land, built-up land, and water bodies are the focus of the study. Akrani Tehsil is distinguished by hilly and rugged terrain, a substantial amount of forest cover, and primarily rain-fed agriculture, whereas Nandurbar Tehsil has relatively favorable terrain, agricultural conditions, irrigation facilities, and a higher concentration of settlements and other non-agricultural activities. In addition to geographical methods like GIS and remote sensing when appropriate, the study makes use of secondary data from government reports, Census publications, statistical records, maps, and other pertinent sources. In order to determine the regional variances, similarities, and differences in land-use patterns, as well as to investigate the impact of socioeconomic and physical factors on land usage, a comparative study is conducted. By improving knowledge of regional differences in land use, the study is expected to support sustainable resource management, land-use planning, and balanced regional development in the Nandurbar District.","《阿克兰尼与南杜尔巴尔塔卢卡土地利用模式的比较地理分析——以楠杜尔巴尔县为例》旨在分析和对比这两个塔卢卡不同土地利用类别的特征与空间分布。土地利用是衡量人类活动与自然环境相互作用的重要指标。研究重点关注的主要土地利用类别包括农业用地、林地、休耕地、荒地和不可耕种地、放牧地、建设用地及水体。阿克兰尼塔卢卡以丘陵崎岖地形、大面积森林覆盖和以雨养农业为主为特征，而南杜尔巴尔塔卢卡则拥有相对有利的地形、农业条件、灌溉设施，以及更集中的居民点和其他非农业活动。本研究在适当情况下运用地理信息系统（GIS）和遥感等地理学方法，同时利用来自政府报告、人口普查出版物、统计记录、地图及其他相关来源的二手数据。为确定土地利用模式的区域差异、相似性与不同之处，并探究社会经济和自然因素对土地利用的影响，本研究进行了比较分析。通过增进对土地利用区域差异的认识，该研究有望为楠杜尔巴尔县的可持续资源管理、土地利用规划和区域均衡发展提供支持。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-31T00:00:00Z",25,45,{"impact":54,"substance":197,"depth":115,"authority":198,"freshness":15,"relevant":62,"comment":199},14,12,"印度地方性土地利用对比研究，方法常规、无全国性价值，且发布日期晚于今日，时效性为零，不建议进入每日精选。",[201,202],{"name":192,"url":189},{"name":192,"url":203},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155212",[21,22,205,49],"区域发展",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},[],{"status":31,"locked":14,"version":32,"method":33,"evidence":209},{"candidates":210,"agricultural_evidence":39,"reason":66},[211,216,220,227],{"code":37,"score":212,"supported":39,"title":213,"abstract":214,"tags":215},7,[],[139,41],[21],{"code":45,"score":212,"supported":39,"title":217,"abstract":218,"tags":219},[],[48,49],[49],{"code":221,"score":54,"supported":39,"title":222,"abstract":223,"tags":226},"H3",[],[224,225],"灌溉","irrigation",[],{"code":61,"score":62,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[22],[232,233],"Nandurbar 土地利用 对比","Akrani Nandurbar 遥感","Nandurbar土地利用对比-4305","10.5281\u002Fzenodo.23155213",{"doi":235,"openalex_id":237,"authors":238,"venue":192,"cited_by_count":15,"oa_url":189,"card":241,"direction":96,"ingested_from":98},"W7220403169",[239],{"name":240,"orcid":9},"Amol Rameshwarrao Bhuyar",{"tldr":242,"method":243,"finding":244,"direction":96,"opportunity":245},"对比分析印度Nandurbar县Akrani与Nandurbar两个乡的土地利用格局差异。","基于政府报告、普查等二手数据，结合GIS与遥感进行对比地理分析。","Akrani以林地、雨养农业为主，Nandurbar地形与灌溉条件更优、建设用地更集中。","可引入多时相遥感影像与农户调查，量化地形—灌溉差异对土地利用变化的驱动机制。","2026-10-06T23:30:19.626838Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":9,"source_name":192,"source_url":250,"published_at":253,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":258,"tags":260,"primary_topic":265,"secondary_topics":266,"classification":267,"search_phrases":301,"slug":304,"view_count":15,"doi":305,"paper":306,"created_at":318},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","2026-10-21T00:00:00Z",56,{"impact":198,"substance":256,"depth":115,"authority":54,"freshness":38,"relevant":62,"comment":257},16,"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。",[259],{"name":192,"url":250},[261,262,21,263,264],"智慧农业","农业人工智能","风险管理","病虫害监测",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},[],{"status":31,"locked":14,"version":32,"method":33,"evidence":268},{"candidates":269,"agricultural_evidence":39,"reason":66},[270,276,282,290,297],{"code":130,"score":13,"supported":39,"title":271,"abstract":272,"tags":275},[],[121,133,273,274],"artificial intelligence","decision support",[262],{"code":37,"score":13,"supported":39,"title":277,"abstract":278,"tags":281},[],[139,41,279,280],"multispectral","hyperspectral",[21],{"code":283,"score":38,"supported":39,"title":284,"abstract":285,"tags":289},"I5",[],[286,287,288],"物联网","internet of things","IoT",[],{"code":291,"score":212,"supported":39,"title":292,"abstract":293,"tags":296},"I1",[],[294,295],"精准农业","precision agriculture",[261],{"code":142,"score":54,"supported":39,"title":298,"abstract":299,"tags":300},[],[145,148],[],[302,303],"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":305,"openalex_id":307,"authors":308,"venue":192,"cited_by_count":15,"oa_url":250,"card":311,"direction":317,"ingested_from":98},"W7223085400",[309],{"name":310,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":312,"method":313,"finding":314,"direction":315,"opportunity":316},"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。","农业人工智能与决策模型","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":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":326,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":327,"score_detail":328,"sources":332,"tags":334,"primary_topic":338,"secondary_topics":339,"classification":340,"search_phrases":351,"slug":354,"view_count":62,"doi":355,"paper":356,"created_at":377},4586,"Advanced Assessment of the Agro-Silvo-Pastoral Coherence in Transboundary Transitional Water Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101916","Transitional waters play a vital role in the ecological health and sustainability of coastal areas. According to European Union legislation, transitional waters are coastal water bodies influenced by freshwater flows but are partly saline due to their proximity to the sea. This study focuses on agricultural and forestry regions adjacent to transboundary transitional waters—i.e., areas where two or more countries share an estuary, tidal inlet, delta, or lagoon and have a contiguous border in their territorial waters. The objective of the study is to determine the patterns of riparian agroforestry around the transboundary transitional waters in Europe and the dependence of the sustainability of these agro-silvo-pastoral systems under stress on the overall cross-border coherence of the countries sharing these waters. The innovative methodology uses GIS and geostatistical interpretation to quantitatively interpret multi-layered spatial data from remote sensing and other sources. The article’s main finding is that in the direct catchment areas of 12 transboundary transitional waters in three European bioregions—the Baltic Sea, the Adriatic and Black Seas, and the Northeast Atlantic—the Dissimilarity Index values vary from 1.5% to 8.2% in different agroforestry systems.","过渡水域对沿海地区的生态健康与可持续性具有至关重要的作用。根据欧盟立法，过渡水域是受淡水径流影响但因靠近海洋而具有一定盐度的沿海水体。本研究聚焦于毗邻跨界过渡水域的农业与林业区域，即两个或多个国家共享同一河口、潮汐入口、三角洲或潟湖，且其领海边界相连的区域。本研究旨在确定欧洲跨界过渡水域周边河岸农林复合系统的格局，以及这些处于压力下的农林牧系统可持续性对共享这些水域的国家之间整体跨境协调性的依赖程度。该创新性方法采用地理信息系统（GIS）和地统计解译技术，对来自遥感及其他来源的多层空间数据进行定量解译。本文的主要发现是：在欧洲三个生物区——波罗的海、亚得里亚海与黑海以及东北大西洋——的12处跨界过渡水域的直接集水区内，不同农林复合系统的相异指数值在1.5%至8.2%之间变化。","Land","2026-10-09T00:00:00Z",72,{"impact":198,"substance":329,"depth":330,"authority":115,"freshness":38,"relevant":62,"comment":331},21,17,"方法新颖、数据扎实的欧洲跨境滨海农林牧系统遥感评估论文，对农业空间信息化有参考价值，但属区域案例研究，公共影响有限。",[333],{"name":325,"url":322},[21,335,336,49,337],"跨境流域","农林牧复合系统","滨海湿地",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},[],{"status":31,"locked":14,"version":32,"method":33,"evidence":341},{"candidates":342,"agricultural_evidence":39,"reason":66},[343,347],{"code":37,"score":212,"supported":39,"title":344,"abstract":345,"tags":346},[],[139,41],[21],{"code":45,"score":212,"supported":39,"title":348,"abstract":349,"tags":350},[],[48,49],[49],[352,353],"欧洲 跨境过渡水域 农林复合","GIS 遥感 农林牧系统 一致性","欧洲跨境过渡水域农林复合-4586","10.3390\u002Fland15101916",{"doi":355,"openalex_id":357,"authors":358,"venue":325,"cited_by_count":15,"oa_url":322,"card":372,"direction":96,"ingested_from":98},"W7221179136",[359,361,363,366,369],{"name":360,"orcid":9},"Aistė Jurkienė",{"name":362,"orcid":9},"Egidijus Jurkus",{"name":364,"orcid":365},"Ramūnas Povilanskas","https:\u002F\u002Forcid.org\u002F0000-0003-0895-3917",{"name":367,"orcid":368},"Eduardas Spiriajevas","https:\u002F\u002Forcid.org\u002F0000-0002-4684-5979",{"name":370,"orcid":371},"Arvydas Urbis","https:\u002F\u002Forcid.org\u002F0000-0002-9641-9214",{"tldr":373,"method":374,"finding":375,"direction":96,"opportunity":376},"用GIS与地统计量化欧洲12处跨境过渡水域周边农林牧系统的一致性差异。","GIS与地统计解读遥感等多源多层空间数据，计算相异指数。","三大生物区12处水域集水区农林系统相异指数为1.5%至8.2%。","可拓展跨境流域农林牧系统一致性对生态可持续性的驱动机制与动态监测研究。","2026-10-10T23:30:27.974100Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":9,"source_name":384,"source_url":381,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":390,"tags":392,"primary_topic":396,"secondary_topics":397,"classification":398,"search_phrases":424,"slug":427,"view_count":62,"doi":428,"paper":429,"created_at":447},4312,"Identification of Cultivable Wastelands in Meghalaya, India using Geospatial Technology for Agricultural Reclamation","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i101131","Cultivable wastelands suitable for cultivation are a significant land resource that can be utilised to improve agricultural productivity and sustainable land management. The present study aimed to estimate the cultivable wastelands of Meghalaya using Remote Sensing and Geographic Information System (GIS) techniques and to assess the possibilities for reclaiming wastelands for agriculture. Multispectral satellite data from the LISS-IV sensor for the period 2025-2026 were processed to create a False Colour Composite (FCC), and Land Use\u002FLand Cover (LULC) classification was performed using visual image interpretation to distinguish the major cultivable wasteland categories: Dense Scrub Land, Sparse Scrub Land, Grasslands\u002FGrazing Lands, and Shifting Cultivation Area. About 340,673.36 ha, representing almost 15.2% of the total geographical area of Meghalaya, were identified as cultivable wastelands. Among the identified categories, Sparse Scrub Land occupied the largest area (206,595.18 ha), followed by Dense Scrub Land (99,488.89 ha), Shifting Cultivation Area (34,331.34 ha), and Grasslands\u002FGrazing Lands (257.94 ha). The distribution of these wastelands suggests considerable potential for reclamation through appropriate land restoration and sustainable management practices. The spatial database and thematic maps generated provide baseline information for land-use planning, resource management, and policy formulation aimed at enhancing agricultural productivity and the sustainable utilisation of wastelands in Meghalaya.","适宜耕作的 cultivable wasteland（可耕荒地）是一项重要的土地资源，可用于提高农业生产力和可持续土地管理。本研究旨在利用遥感（Remote Sensing）和地理信息系统（Geographic Information System, GIS）技术估算梅加拉亚邦的可耕荒地，并评估将荒地开垦用于农业的可能性。对2025—2026年LISS-IV传感器多光谱卫星数据进行处理，生成假彩色合成影像（False Colour Composite, FCC），并通过目视图像解译进行土地利用\u002F土地覆盖（Land Use\u002FLand Cover, LULC）分类，以区分主要可耕荒地类别：密灌丛地、疏灌丛地、草地\u002F放牧地以及轮垦区。约340,673.36公顷土地被识别为可耕荒地，占梅加拉亚邦总地理面积的近15.2%。在已识别类别中，疏灌丛地面积最大（206,595.18公顷），其次为密灌丛地（99,488.89公顷）、轮垦区（34,331.34公顷）和草地\u002F放牧地（257.94公顷）。这些荒地的分布表明，通过适当的土地恢复和可持续管理措施，具有相当大的开垦潜力。所生成的空间数据库和专题图件为土地利用规划、资源管理和政策制定提供了基础信息，旨在提高梅加拉亚邦的农业生产力和实现荒地的可持续利用。","Journal of Geography Environment and Earth Science International","2026-10-05T00:00:00Z",69,{"impact":198,"substance":388,"depth":256,"authority":198,"freshness":38,"relevant":62,"comment":389},20,"基于LISS-IV遥感与GIS的印度梅加拉亚邦可耕荒地清查，数据翔实、方法规范，对农业土地复垦与空间规划有参考价值，但属区域案例，公共影响有限。",[391],{"name":384,"url":381},[393,394,21,49,395],"可持续农业","耕地保护","土地整治",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},[],{"status":31,"locked":14,"version":32,"method":33,"evidence":399},{"candidates":400,"agricultural_evidence":39,"reason":66},[401,405,409,414,419],{"code":37,"score":13,"supported":39,"title":402,"abstract":403,"tags":404},[],[139,41,279],[21],{"code":45,"score":13,"supported":39,"title":406,"abstract":407,"tags":408},[],[48,49,50],[49],{"code":410,"score":164,"supported":14,"title":411,"abstract":412,"tags":413},"H1",[],[],[395,394],{"code":415,"score":62,"supported":14,"title":416,"abstract":417,"tags":418},"G2",[],[],[395],{"code":420,"score":62,"supported":14,"title":421,"abstract":422,"tags":423},"H8",[],[],[393],[425,426],"Meghalaya 可耕荒地 遥感","LISS-IV 土地利用 分类","Meghalaya可耕荒地遥感-4312","10.9734\u002Fjgeesi\u002F2026\u002Fv30i101131",{"doi":428,"openalex_id":430,"authors":431,"venue":384,"cited_by_count":15,"oa_url":381,"card":442,"direction":96,"ingested_from":98},"W7220436664",[432,434,437,439],{"name":433,"orcid":9},"Ronak Meena",{"name":435,"orcid":436},"Chandan Goswami","https:\u002F\u002Forcid.org\u002F0000-0002-9487-5080",{"name":438,"orcid":9},"C. P. Suresh",{"name":440,"orcid":441},"Akmaul Hoque","https:\u002F\u002Forcid.org\u002F0009-0002-4475-566X",{"tldr":443,"method":444,"finding":445,"direction":96,"opportunity":446},"利用遥感与GIS识别印度梅加拉亚邦可耕荒地并评估其农业复垦潜力。","LISS-IV多光谱影像、假彩色合成与目视解译的土地利用分类。","识别出约34万公顷可耕荒地，占全邦15.2%，以稀疏灌木地为主。","可结合机器学习自动分类与土壤、气候数据，构建荒地复垦适宜性评价模型。","2026-10-06T23:30:20.340575Z",{"id":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":455,"category":12,"cover_url":9,"hotness":194,"is_selected":14,"score":110,"score_detail":456,"sources":458,"tags":462,"primary_topic":464,"secondary_topics":465,"classification":466,"search_phrases":496,"slug":499,"view_count":164,"doi":500,"paper":501,"created_at":513},3616,"Integrated Machine Learning for Agriculture, Environment and Health-Adjacent Systems: Solar Cell Screening, Reservoir Monitoring and IoT-Driven Advisory Across Interdisciplinary Contexts","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991","This paper explores the convergence of precision agriculture and environmental monitoring by taking advantage of overlapping physical sensor infrastructure. The authors present three interdisciplinary machine learning studies built upon a shared edge-inference architecture optimized for low-latency execution on a Raspberry Pi 4. First, the framework accelerates agrivoltaic deployments by combining density functional theory with gradient boosting regressors to screen perovskite solar cell efficiencies without physical fabrication cycles. Second, it introduces a hybrid 1D-CNN and random forest model to detect water level anomalies in Bangladesh's Kaptai Reservoir, reducing RMSE by 35% over a 40-year validation window. Third, it details an IoT agricultural platform (\"AgriLM\") that pairs ResNet-50 pest detection with an instruction-tuned LLM layer to deliver natural language, climate-adaptive management advice to smallholder farmers, optimizing cross-system resource constraints.","本文探讨了通过利用重叠的物理传感器基础设施，实现精准农业与环境监测的融合。作者提出了三项跨学科机器学习研究，均构建于一个共享的边缘推理架构之上，该架构针对树莓派4（Raspberry Pi 4）上的低延迟执行进行了优化。首先，该框架将密度泛函理论与梯度提升回归器相结合，无需物理制造周期即可筛选钙钛矿太阳能电池效率，从而加速农业光伏部署。其次，该框架引入了一种混合一维卷积神经网络（1D-CNN）与随机森林模型，用于检测孟加拉国卡普泰水库（Kaptai Reservoir）的水位异常，在40年验证窗口内将均方根误差（RMSE）降低了35%。第三，该框架详细介绍了物联网农业平台（“AgriLM”），该平台将ResNet-50害虫检测与指令微调的大语言模型（LLM）层相结合，为小农户提供自然语言、气候适应性的管理建议，并优化跨系统资源约束。","Figshare","2026-09-26T00:00:00Z",{"impact":198,"substance":113,"depth":114,"authority":38,"freshness":38,"relevant":62,"comment":457},"跨学科机器学习论文，方法新颖、数据扎实，但偏学术预印本，产业落地影响有限。",[459,460],{"name":454,"url":451},{"name":454,"url":461},"https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991.v1",[261,262,286,21,463],"光伏农业",{"code":25,"slug":26,"name":27,"path":28,"parent_code":9},[],{"status":31,"locked":14,"version":32,"method":33,"evidence":467},{"candidates":468,"agricultural_evidence":39,"reason":66},[469,473,477,481,487,492],{"code":130,"score":131,"supported":39,"title":470,"abstract":471,"tags":472},[133],[121,133],[262],{"code":283,"score":131,"supported":39,"title":474,"abstract":475,"tags":476},[288],[286,288],[286],{"code":291,"score":212,"supported":39,"title":478,"abstract":479,"tags":480},[],[294,295],[261],{"code":415,"score":54,"supported":39,"title":482,"abstract":483,"tags":486},[],[484,485],"小农户","smallholder",[],{"code":142,"score":488,"supported":39,"title":489,"abstract":490,"tags":491},3,[],[146],[],{"code":37,"score":62,"supported":14,"title":493,"abstract":494,"tags":495},[],[],[21],[497,498],"Kaptai Reservoir 水位监测","AgriLM 物联网农业平台","KaptaiReservoir水位监测-3616","10.6084\u002Fm9.figshare.34001991",{"doi":500,"openalex_id":502,"authors":503,"venue":454,"cited_by_count":15,"oa_url":451,"card":508,"direction":317,"ingested_from":98},"W7214485392",[504,506],{"name":505,"orcid":9},"Alepera Rasaed",{"name":507,"orcid":9},"Minaya Aleguja",{"tldr":509,"method":510,"finding":511,"direction":317,"opportunity":512},"用共享边缘推理架构整合机器学习，实现太阳能电池筛选、水库监测与IoT农业咨询。","Raspberry Pi 4边缘推理，DFT+梯度提升、1D-CNN+随机森林、","混合模型将水库水位异常检测RMSE降低35%，AgriLM可提供自然语言气候适应建议。","可探索多任务边缘模型在农业-环境-健康跨域场景下的能效与泛化性权衡。","2026-09-27T23:30:24.718444Z"]