[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3549":3,"related-3549":53},{"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":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":52},3549,"Artificial Intelligence-Based Innovations for Environmental Management in Uganda: A Scoping Review of Current Innovations","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fjatir.141025","Uganda's extraordinary biodiversity, encompassing the Albertine Rift's endangered primate populations, East Africa's most significant freshwater wetland systems, and montane forest corridors of global conservation significance, is under compound and steadily accelerating environmental stress from widespread deforestation, wetland encroachment, agricultural expansion, climate variability, and persistently inadequate environmental governance capacity.Simultaneously, artificial intelligence technologies are undergoing rapid global deployment across environmental monitoring, predictive modelling, natural resource management, and climate adaptation domains, raising the question of whether these innovations are reaching contexts such as Uganda.This scoping review systematically maps the emerging landscape of artificial intelligence applications in environmental management in Uganda, examining which specific technologies have been deployed or piloted, across which environmental domains, by which actor types, and with what documented outcomes or limitations.A systematic search of peer-reviewed literature, grey literature, policy documents, and institutional reports published between 2020 and 2026 was conducted across five databases, ultimately yielding just 18 studies and reports meeting inclusion criteria after careful screening of 247 initial records identified.Findings reveal that artificial intelligence applications in Ugandan environmental management remain genuinely promising yet geographically and thematically concentrated, with remote sensing and machine learning applications for land cover change detection and forest monitoring constituting the single dominant application cluster identified.Applications in wetland management, water quality monitoring, wildlife protection, and climate adaptation do exist but remain isolated, under-resourced, and poorly integrated into government environmental governance frameworks.Critical cross-cutting barriers, including persistent data infrastructure deficits, digital skills shortages, electricity access constraints, and an underdeveloped AI policy ecosystem, systematically limit the effective translation of global AI innovations into practical, Uganda-specific environmental management solutions.The review concludes by proposing an AI-Environmental Governance Integration Framework","乌干达拥有非凡的生物多样性，涵盖艾伯丁裂谷（Albertine Rift）的濒危灵长类动物种群、东非最重要的淡水湿地系统以及具有全球保护意义的山地森林廊道，然而这些生态系统正面临来自大面积森林砍伐、湿地侵占、农业扩张、气候变率以及长期不足的环境治理能力的复合且持续加速的环境压力。与此同时，人工智能技术正在全球范围内迅速部署于环境监测、预测建模、自然资源管理和气候适应等领域，这引发了一个问题：这些创新是否正在惠及乌干达等地区。本范围综述（scoping review）系统梳理了乌干达环境管理中人工智能应用的新兴图景，考察了哪些具体技术已被部署或试点、涉及哪些环境领域、由哪些类型的行动者实施，以及有哪些已记录的结果或局限。研究对2020年至2026年间发表的同行评审文献、灰色文献、政策文件和机构报告在五个数据库中进行了系统检索，在仔细筛选初步识别的247条记录后，最终仅有18项研究和报告符合纳入标准。研究结果表明，人工智能在乌干达环境管理中的应用确实具有前景，但在地理和主题上高度集中，其中用于土地覆盖变化检测和森林监测的遥感与机器学习应用构成了唯一占主导地位的应用集群。湿地管理、水质监测、野生动物保护和气候适应方面的应用确实存在，但仍然孤立、资源不足，且未能有效纳入政府环境治理框架。关键的跨领域障碍，包括持续存在的数据基础设施赤字、数字技能短缺、电力获取限制以及欠发达的人工智能政策生态系统，系统性地限制了全球人工智能创新向乌干达本土化环境管理解决方案的有效转化。综述最后提出了一个人工智能-环境治理整合框架（AI-Environmental Governance Integration Framework）。",null,"OpenAlex","2026-09-24T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,9,1,"系统梳理乌干达AI环境管理应用的综述论文，方法规范、结论可靠，但属区域性研究且与三农信息化关联偏间接，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","环境治理","遥感监测","乌干达",[33,34],"乌干达 人工智能 环境管理","AI 遥感 森林监测","乌干达人工智能环境管理-3549",0,"10.64643\u002Fjatir.141025",{"doi":37,"openalex_id":39,"authors":40,"venue":9,"cited_by_count":36,"oa_url":43,"card":44,"direction":50,"ingested_from":51},"W7214195868",[41],{"name":42,"orcid":9},"Henry Omara","https:\u002F\u002Fjatir.org\u002Fpublishedpapers\u002F141025_PAPER.pdf",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"综述乌干达环境管理中AI应用现状，发现应用集中于遥感与森林监测，整体零散且受基础设施制约。","系统检索2020-2026年五个数据库的文献与政策报告，筛选出18项研究做范围综","AI应用有前景但地理与主题集中，湿地、水质、野生动物等领域应用孤立，数据与政策短板明显。","农业遥感与作物表型","可研究低成本遥感与机器学习在乌干达湿地、水质及小农农业环境监测中的落地路径与治理整合。","智慧农业 \u002F 农业物联网","openalex","2026-09-26T23:30:15.512560Z",{"total":54,"page":22,"page_size":54,"items":55},6,[56,100,139,176,205,231],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":75,"slug":78,"view_count":36,"doi":79,"paper":80,"created_at":99},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry","2026-09-25T00:00:00Z",70,{"impact":17,"substance":66,"depth":67,"authority":68,"freshness":21,"relevant":22,"comment":69},22,18,13,"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[71],{"name":62,"url":59},[27,28,30,73,74],"洪涝灾害","植被恢复",[76,77],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":79,"openalex_id":81,"authors":82,"venue":62,"cited_by_count":36,"oa_url":59,"card":94,"direction":48,"ingested_from":51},"W7214283372",[83,85,88,91],{"name":84,"orcid":9},"Jiradech Majandang",{"name":86,"orcid":87},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":89,"orcid":90},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":92,"orcid":93},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":95,"method":96,"finding":97,"direction":48,"opportunity":98},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","2026-09-26T23:30:29.514361Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":10,"source_url":103,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":112,"tags":114,"search_phrases":117,"slug":120,"view_count":36,"doi":121,"paper":122,"created_at":138},3514,"Artificial Intelligence for Climate Adaptation Decision Support in Data-Poor Developing Regions","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009304\u002Fv1","Climate adaptation is a sequence of decisions taken under uncertainty, and the regions where climate risk is rising fastest are those with the least information to guide them. Only about 10 per cent of deaths are registered in the WHO African Region; nearly 90 per cent of required surface weather observations are missing across least developed countries and small island states; and only 40 per cent of African countries have multi-hazard early warning systems. This report examines whether artificial intelligence — machine learning, remote sensing and predictive analytics — can close these information gaps and improve adaptation decisions in data-poor developing regions. The report organises the problem as a decision chain with three information gaps — observation, prediction and decision — followed by an action gap that AI cannot close. It finds that AI has advanced fastest on prediction: AI weather models became operational at ECMWF in 2025, AI flood forecasts now cover 100 countries and about 700 million people, satellite nowcasts reach a continent with little radar, and AI monsoon-onset forecasts reached 38 million Indian farmers in 2025. On observation, satellite machine learning explains around 70 per cent of the variation in village wealth but only up to about half of the variation in changes over time. On decision, evidence from Togo, Bangladesh and Kenya shows that AI-assisted targeting, forecast-based triggers and satellite index insurance can deliver assistance faster and better, within clear limits. The report's central argument is the ground-truth paradox: AI stretches scarce observations further, but every AI product must be trained and verified against ground truth, so reliance on AI raises the value of each remaining station, survey and label. The 2025 interruption of FEWS NET and termination of the DHS Program show how fragile that foundation is. Because the value of information is the product of skill, lead time, reach, trust and the means to act, the highest returns usually lie not in more skilful models but in dissemination, institutions and prearranged finance. The report sets out a risk register, a six-principle policy framework, actions by actor and a roadmap to 2030.","气候适应是在不确定性下做出的一系列决策，而气候风险上升最快的地区恰恰是指导信息最匮乏的地区。世卫组织非洲区域仅登记了约10%的死亡病例；最不发达国家和小岛屿国家缺失了近90%所需的地面天气观测数据；仅有40%的非洲国家拥有多灾种早期预警系统。本报告考察人工智能——机器学习、遥感和预测分析——能否弥合这些信息缺口，改善数据匮乏的发展中地区的适应决策。报告将这一问题组织为一条决策链，包含三个信息缺口——观测、预测和决策——以及一个人工智能无法弥合的行动缺口。报告发现，人工智能在预测方面进展最快：人工智能天气模型于2025年在欧洲中期天气预报中心（ECMWF）投入业务运行，人工智能洪水预报现已覆盖100个国家和约7亿人口，卫星临近预报覆盖了一个几乎没有雷达的大陆，人工智能季风爆发预报于2025年惠及3800万印度农民。在观测方面，卫星机器学习可解释村庄财富约70%的变异，但对时间变化的解释力仅约一半。在决策方面，来自多哥、孟加拉国和肯尼亚的证据表明，人工智能辅助的目标定位、基于预报的触发机制和卫星指数保险能够在明确限度内更快、更好地提供援助。报告的核心论点是地面真值悖论：人工智能能够将稀缺的观测数据发挥更大效用，但每个人工智能产品都必须依据地面真值进行训练和验证，因此对人工智能的依赖提升了每一个剩余站点、调查和标注数据的价值。2025年FEWS NET的中断和DHS项目的终止表明这一基础何等脆弱。由于信息的价值是技能、提前期、覆盖面、信任和行动手段的乘积，最高回报通常不在于更精密的模型，而在于传播、制度和预先安排的融资。报告提出了风险登记册、六项原则的政策框架、各行为主体的行动以及到2030年的路线图。","2026-09-22T00:00:00Z",86,{"impact":66,"substance":109,"depth":110,"authority":68,"freshness":17,"relevant":22,"comment":111},24,19,"系统梳理AI在数据匮乏地区气候适应决策中的观测、预测与决策三类信息缺口，提出“地面真值悖论”，数据与结论扎实，对农业信息化与智慧农业有较强参考价值。",[113],{"name":10,"url":103},[27,28,115,30,116],"气候适应","早期预警",[118,119],"AI 气候适应 决策支持","数据匮乏地区 农业预警","AI气候适应决策支持-3514","10.22541\u002Fessoar.15009304\u002Fv1",{"doi":121,"openalex_id":123,"authors":124,"venue":9,"cited_by_count":36,"oa_url":130,"card":131,"direction":137,"ingested_from":51},"W7214097088",[125,127],{"name":126,"orcid":9},"H Heuristics",{"name":128,"orcid":129},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":132,"method":133,"finding":134,"direction":135,"opportunity":136},"评估AI能否弥补数据匮乏地区气候适应决策的信息缺口，并提出地面真值悖论。","梳理观测、预测、决策三环节，结合AI天气模型、卫星ML与多国案例证据。","AI预测进展最快，但依赖地面真值；最高回报常在传播、制度与预置资金而非模型。","农业人工智能与决策模型","可研究AI辅助农业气候适应中地面真值稀缺下的验证与信任机制，及预置资金触发设计。","数字乡村与农业信息化","2026-09-25T23:30:46.008325Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":9,"source_name":145,"source_url":142,"published_at":146,"category":12,"cover_url":9,"hotness":147,"is_selected":14,"score":148,"score_detail":149,"sources":152,"tags":156,"search_phrases":160,"slug":163,"view_count":36,"doi":164,"paper":165,"created_at":175},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,69,{"impact":66,"substance":67,"depth":150,"authority":68,"freshness":36,"relevant":22,"comment":151},16,"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[153,154],{"name":145,"url":142},{"name":145,"url":155},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[157,27,28,158,159,30],"数字乡村","农业物联网","气候韧性",[161,162],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":164,"openalex_id":166,"authors":167,"venue":145,"cited_by_count":36,"oa_url":142,"card":170,"direction":50,"ingested_from":51},"W7214083098",[168],{"name":169,"orcid":9},"Twinkal Prakash Sawant",{"tldr":171,"method":172,"finding":173,"direction":50,"opportunity":174},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":9,"content":9,"source_name":181,"source_url":9,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":186,"tags":188,"search_phrases":192,"slug":195,"view_count":36,"doi":9,"paper":196,"created_at":204},3325,"MBF-HybridNet：在极端气候下仍可提前一月预测冬小麦产量的多分支AI模型——Qingdao六县R² 0.756-0.765 MAPE 4.2%","https:\u002F\u002Fbioengineer.org\u002Fnew-multi-branch-ai-model-predicts-winter-wheat-yields-weeks-before-harvest-even-under-extreme-weather\u002F","MBF-HybridNet由青岛六县研究团队开发和测试：采用多分支并行架构，包括处理日常遥感和气象数据的动态变量模块、处理年度尺度极端气候指数数据的动态ECI模块以及处理土壤属性的静态变量模块。动态模块堆叠三个二维卷积层，插入自注意力机制；静态模块独立处理土壤有机碳、阳离子交换容量、pH、砂和粘土含量。研究团队计算了九个极端气候指数（热日、热应力强度、连续热日、霜日、冷应力强度、连续冷日、强降水日、连续湿日和连续干日）用于每个生长阶段。2004至2019年留一年交叉验证，MBF-HybridNet在三个累积生长阶段的R²达到0.756-0.765，平均绝对百分比误差约为4.2%。","MBF-HybridNet 2026 (Qingdao)","2026-09-17T00:00:00Z",78,{"impact":67,"substance":66,"depth":67,"authority":20,"freshness":17,"relevant":22,"comment":185},"多分支AI融合遥感气象与极端气候指数，提前一月预测冬小麦产量且精度可靠，方法新颖、数据扎实，对农业信息化与智慧农业有较高参考价值。",[187],{"name":181,"url":179},[27,28,189,30,190,191],"产量预测","冬小麦","极端气候",[193,194],"MBF-HybridNet 冬小麦 产量预测","青岛 冬小麦 遥感 极端气候","MBF-HybridNet冬小麦产量预测-3325",{"doi":9,"openalex_id":9,"authors":197,"venue":9,"cited_by_count":36,"oa_url":9,"card":198,"direction":135,"ingested_from":203},[],{"tldr":199,"method":200,"finding":201,"direction":135,"opportunity":202},"提出多分支AI模型MBF-HybridNet，融合遥感、气象与土壤数据，提前一月预测冬小麦产量。","多分支并行架构，含2D卷积、自注意力与极端气候指数，2004-2019年留一年交","在青岛六县三个累积生长阶段R²达0.756-0.765，MAPE约4.2%，极端气候下仍可提前一月预","可探索极端气候指数与深度学习结合在其他作物或区域的泛化能力，并提升可解释性。","agent","2026-09-24T00:04:02.748442Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":9,"content":9,"source_name":210,"source_url":9,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":211,"score_detail":212,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":36,"doi":9,"paper":223,"created_at":230},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",81,{"impact":67,"substance":66,"depth":67,"authority":68,"freshness":13,"relevant":22,"comment":213},"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。",[215],{"name":210,"url":208},[27,28,217,218,30],"机器学习","作物推荐",[220,221],"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":224,"venue":9,"cited_by_count":36,"oa_url":9,"card":225,"direction":135,"ingested_from":203},[],{"tldr":226,"method":227,"finding":228,"direction":135,"opportunity":229},"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。","2026-09-23T00:04:33.331160Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":242,"tags":244,"search_phrases":247,"slug":250,"view_count":36,"doi":251,"paper":252,"created_at":272},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection","2026-09-19T00:00:00Z",77,{"impact":67,"substance":18,"depth":19,"authority":68,"freshness":21,"relevant":22,"comment":241},"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[243],{"name":237,"url":234},[27,28,245,30,246],"植物病害","病害预警",[248,249],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":251,"openalex_id":253,"authors":254,"venue":237,"cited_by_count":36,"oa_url":9,"card":267,"direction":48,"ingested_from":51},"W7213649225",[255,257,259,261,264],{"name":256,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":258,"orcid":9},"C. L. Biji",{"name":260,"orcid":9},"Vanshika Arun Meda",{"name":262,"orcid":263},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":265,"orcid":266},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":268,"method":269,"finding":270,"direction":48,"opportunity":271},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z"]