[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4305":3,"related-4305":91},{"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":24,"tags":28,"primary_topic":33,"secondary_topics":38,"classification":39,"search_phrases":73,"slug":76,"view_count":21,"doi":77,"paper":78,"created_at":90},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-31T00:00:00Z","论文",25,false,45,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},6,14,13,12,0,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155212",[29,30,31,32],"遥感监测","土地利用","区域发展","GIS",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"partial","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48,"reason":72},[45,54,60,67],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":53},"I3",7,true,[],[51,52],"遥感","remote sensing",[29],{"code":55,"score":47,"supported":48,"title":56,"abstract":57,"tags":59},"I4",[],[58,32],"地理信息",[32],{"code":61,"score":17,"supported":48,"title":62,"abstract":63,"tags":66},"H3",[],[64,65],"灌溉","irrigation",[],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"H6",[],[],[30],"一级领域明确，二级方向需要细分",[74,75],"Nandurbar 土地利用 对比","Akrani Nandurbar 遥感","Nandurbar土地利用对比-4305","10.5281\u002Fzenodo.23155213",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":21,"oa_url":6,"card":83,"direction":87,"ingested_from":89},"W7220403169",[81],{"name":82,"orcid":9},"Amol Rameshwarrao Bhuyar",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"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。","农业遥感与作物表型","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。","openalex","2026-10-06T23:30:19.626838Z",{"total":17,"page":22,"page_size":17,"items":92},[93,177,250,319,389,445],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":101,"is_selected":14,"score":102,"score_detail":103,"sources":109,"tags":111,"primary_topic":116,"secondary_topics":117,"classification":118,"search_phrases":151,"slug":154,"view_count":155,"doi":156,"paper":157,"created_at":176},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",10,70,{"impact":104,"substance":105,"depth":106,"authority":19,"freshness":107,"relevant":22,"comment":108},8,22,18,9,"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。",[110],{"name":99,"url":96},[112,113,29,30,114,115],"数字乡村","机器学习","洪涝风险","植被胁迫",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":119},{"candidates":120,"agricultural_evidence":48,"reason":72},[121,128,132,142,146],{"code":122,"score":123,"supported":48,"title":124,"abstract":126,"tags":127},"I2",15,[125],"machine learning",[113,125],[113],{"code":46,"score":123,"supported":48,"title":129,"abstract":130,"tags":131},[52],[51,52],[29],{"code":133,"score":107,"supported":48,"title":134,"abstract":135,"tags":141},"H7",[],[136,137,138,139,140],"气候变化","气候适应","干旱","climate change","drought",[],{"code":68,"score":22,"supported":14,"title":143,"abstract":144,"tags":145},[],[],[30],{"code":147,"score":22,"supported":14,"title":148,"abstract":149,"tags":150},"I6",[],[],[112],[152,153],"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":156,"openalex_id":158,"authors":159,"venue":99,"cited_by_count":21,"oa_url":96,"card":171,"direction":87,"ingested_from":89},"W7220850940",[160,162,165,168],{"name":161,"orcid":9},"Jiradech Majandang",{"name":163,"orcid":164},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":166,"orcid":167},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":169,"orcid":170},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":172,"method":173,"finding":174,"direction":87,"opportunity":175},"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":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":101,"is_selected":14,"score":185,"score_detail":186,"sources":190,"tags":192,"primary_topic":196,"secondary_topics":197,"classification":198,"search_phrases":226,"slug":229,"view_count":22,"doi":230,"paper":231,"created_at":249},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":20,"substance":187,"depth":188,"authority":20,"freshness":107,"relevant":22,"comment":189},20,16,"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。",[191],{"name":183,"url":180},[193,194,29,32,195],"可持续农业","耕地保护","土地整治",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":199},{"candidates":200,"agricultural_evidence":48,"reason":72},[201,206,211,216,221],{"code":46,"score":101,"supported":48,"title":202,"abstract":203,"tags":205},[],[51,52,204],"multispectral",[29],{"code":55,"score":101,"supported":48,"title":207,"abstract":208,"tags":210},[],[58,32,209],"geographic information",[32],{"code":212,"score":155,"supported":14,"title":213,"abstract":214,"tags":215},"H1",[],[],[195,194],{"code":217,"score":22,"supported":14,"title":218,"abstract":219,"tags":220},"G2",[],[],[195],{"code":222,"score":22,"supported":14,"title":223,"abstract":224,"tags":225},"H8",[],[],[193],[227,228],"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":230,"openalex_id":232,"authors":233,"venue":183,"cited_by_count":21,"oa_url":180,"card":244,"direction":87,"ingested_from":89},"W7220436664",[234,236,239,241],{"name":235,"orcid":9},"Ronak Meena",{"name":237,"orcid":238},"Chandan Goswami","https:\u002F\u002Forcid.org\u002F0000-0002-9487-5080",{"name":240,"orcid":9},"C. P. Suresh",{"name":242,"orcid":243},"Akmaul Hoque","https:\u002F\u002Forcid.org\u002F0009-0002-4475-566X",{"tldr":245,"method":246,"finding":247,"direction":87,"opportunity":248},"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":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":101,"is_selected":14,"score":21,"score_detail":258,"sources":260,"tags":262,"primary_topic":264,"secondary_topics":265,"classification":266,"search_phrases":288,"slug":291,"view_count":292,"doi":293,"paper":294,"created_at":318},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。","OpenAlex","2026-09-24T00:00:00Z",{"impact":21,"substance":21,"depth":21,"authority":21,"freshness":21,"relevant":21,"comment":259},"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。",[261],{"name":256,"url":253},[29,30,263],"城市化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":267},{"candidates":268,"agricultural_evidence":48,"reason":72},[269,273,277,284],{"code":46,"score":107,"supported":48,"title":270,"abstract":271,"tags":272},[52],[],[29],{"code":55,"score":107,"supported":48,"title":274,"abstract":275,"tags":276},[],[58,32,209],[],{"code":278,"score":17,"supported":48,"title":279,"abstract":280,"tags":283},"A8",[],[281,282],"粮食安全","food security",[],{"code":68,"score":22,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[30],[289,290],"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":293,"openalex_id":295,"authors":296,"venue":9,"cited_by_count":21,"oa_url":253,"card":313,"direction":87,"ingested_from":89},"W7214145360",[297,300,303,305,307,309,311],{"name":298,"orcid":299},"Mohammed Bakoji Yusuf","https:\u002F\u002Forcid.org\u002F0000-0002-8719-2396",{"name":301,"orcid":302},"Ambrose Audu Zemba","https:\u002F\u002Forcid.org\u002F0000-0002-7692-9911",{"name":304,"orcid":9},"Huzaima Abubakar Bakoji",{"name":306,"orcid":9},"AUWAL ADAMU BAKAKU",{"name":308,"orcid":9},"UMAR ABBA JAURO",{"name":310,"orcid":9},"Aliyu Mohammed Jingudo",{"name":312,"orcid":9},"Abubakar Isa",{"tldr":314,"method":315,"finding":316,"direction":87,"opportunity":317},"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。","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。","2026-09-25T23:30:30.441601Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":10,"source_url":322,"published_at":325,"category":12,"cover_url":9,"hotness":101,"is_selected":14,"score":326,"score_detail":327,"sources":329,"tags":331,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":371,"slug":374,"view_count":21,"doi":375,"paper":376,"created_at":388},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":20,"substance":188,"depth":19,"authority":17,"freshness":107,"relevant":22,"comment":328},"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。",[330],{"name":10,"url":322},[332,333,29,334,335],"智慧农业","农业人工智能","风险管理","病虫害监测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":339},{"candidates":340,"agricultural_evidence":48,"reason":72},[341,347,352,360,367],{"code":122,"score":101,"supported":48,"title":342,"abstract":343,"tags":346},[],[113,125,344,345],"artificial intelligence","decision support",[333],{"code":46,"score":101,"supported":48,"title":348,"abstract":349,"tags":351},[],[51,52,204,350],"hyperspectral",[29],{"code":353,"score":107,"supported":48,"title":354,"abstract":355,"tags":359},"I5",[],[356,357,358],"物联网","internet of things","IoT",[],{"code":361,"score":47,"supported":48,"title":362,"abstract":363,"tags":366},"I1",[],[364,365],"精准农业","precision agriculture",[332],{"code":133,"score":17,"supported":48,"title":368,"abstract":369,"tags":370},[],[136,139],[],[372,373],"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":375,"openalex_id":377,"authors":378,"venue":10,"cited_by_count":21,"oa_url":322,"card":381,"direction":387,"ingested_from":89},"W7223085400",[379],{"name":380,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":382,"method":383,"finding":384,"direction":385,"opportunity":386},"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":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":10,"source_url":392,"published_at":395,"category":12,"cover_url":9,"hotness":101,"is_selected":14,"score":102,"score_detail":396,"sources":399,"tags":401,"primary_topic":404,"secondary_topics":405,"classification":406,"search_phrases":425,"slug":428,"view_count":21,"doi":429,"paper":430,"created_at":444},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","2026-10-20T00:00:00Z",{"impact":20,"substance":187,"depth":397,"authority":20,"freshness":107,"relevant":22,"comment":398},17,"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[400],{"name":10,"url":392},[333,51,32,402,403],"野生动物保护","人兽冲突",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":407},{"candidates":408,"agricultural_evidence":48,"reason":72},[409,413,417,421],{"code":122,"score":101,"supported":48,"title":410,"abstract":411,"tags":412},[],[113,125,345],[333],{"code":55,"score":101,"supported":48,"title":414,"abstract":415,"tags":416},[],[58,32,209],[32],{"code":46,"score":292,"supported":48,"title":418,"abstract":419,"tags":420},[],[51],[51],{"code":68,"score":22,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[402],[426,427],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":429,"openalex_id":431,"authors":432,"venue":10,"cited_by_count":21,"oa_url":392,"card":439,"direction":385,"ingested_from":89},"W7222999966",[433,435,437],{"name":434,"orcid":9},"Bhutia Dechen Doma",{"name":436,"orcid":9},"Swarup Das",{"name":438,"orcid":9},"Mandal Rakesh Kumar",{"tldr":440,"method":441,"finding":442,"direction":87,"opportunity":443},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","2026-10-21T23:30:43.936453Z",{"id":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":9,"source_name":451,"source_url":448,"published_at":452,"category":12,"cover_url":9,"hotness":101,"is_selected":14,"score":453,"score_detail":454,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":464,"classification":465,"search_phrases":500,"slug":503,"view_count":21,"doi":504,"paper":505,"created_at":516},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":104,"substance":188,"depth":123,"authority":455,"freshness":104,"relevant":22,"comment":456},3,"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[458],{"name":451,"url":448},[460,51,32,461,462],"决策支持系统","水稻种植","土地评价",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":466},{"candidates":467,"agricultural_evidence":48,"reason":72},[468,472,476,485,489,496],{"code":122,"score":20,"supported":48,"title":469,"abstract":470,"tags":471},[345],[345],[460],{"code":55,"score":20,"supported":48,"title":473,"abstract":474,"tags":475},[32],[32],[32],{"code":477,"score":101,"supported":48,"title":478,"abstract":479,"tags":484},"A1",[],[480,481,482,483],"水稻","稻田","rice","paddy",[461],{"code":278,"score":17,"supported":48,"title":486,"abstract":487,"tags":488},[],[281,282],[],{"code":490,"score":17,"supported":48,"title":491,"abstract":492,"tags":495},"H2",[],[493,494],"土壤","soil",[],{"code":46,"score":22,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[51],[501,502],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":504,"openalex_id":506,"authors":507,"venue":451,"cited_by_count":21,"oa_url":448,"card":511,"direction":385,"ingested_from":89},"W7222963628",[508],{"name":509,"orcid":510},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":512,"method":513,"finding":514,"direction":87,"opportunity":515},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z"]