[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3471":3,"related-3471":54},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":53},3471,"A secure federated learning framework with blockchain-based authentication for anomaly detection in cooperative smart farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116072","Agriculture is still a crucial global pillar of the world economy, ensuring food security and livelihood. But changing climate conditions and increasing reliance on the Internet of Things (IoT) based precision agriculture have brought new challenges, mainly cybersecurity. Cooperative Smart Farming (CSF) schemes promote resource pooling among small farmers and thus, the adoption of cutting-edge technologies. Although democratizing precision farming also makes it more accessible to cyber threats. To sort out all these problems, the proposed method introduces a secure federated learning framework along with blockchain-based authentication for anomaly detection in CSFs. The proposed method employs a federated transfer learning-based shadow attention mechanism, in conjunction with the Zeiler and Fergus network, to amplify the performance of the anomaly detection technique. This allows farms to detect threats in premises while protecting data privacy, as only model updates that are encrypted are transmitted. A blockchain-backed decentralized authentication scheme based on the proof of authentication consensus protocol is built to provide trust and resistance to tampering. Moreover, the random k-sparsification technique with changing rank adjustment has a positive impact on communication efficiency, which results in the same level of accuracy but with less communication overhead. The proposed method is executed on four datasets, CSE-CIS-IDS2018, MQTTset, ToN-IoT, and Edge IoT, achieving classification accuracies of 99.99%,99.93%,99.94%, and 99.91%, respectively, and it performs better than the current techniques. This comprehensive framework provides a scalable and trusted method for anomaly detection in CSFs, enabling resilient, privacy-preserving, and intelligent smart farming ecosystems.","农业仍然是全球经济的重要支柱，保障着粮食安全和生计。然而，不断变化的气候条件以及日益依赖基于物联网（IoT）的精准农业，带来了新的挑战，主要是网络安全问题。合作智慧农业（Cooperative Smart Farming, CSF）方案促进了小农户之间的资源共享，从而推动了前沿技术的采用。尽管精准农业的民主化使其更加普及，但也使其更容易受到网络威胁。为解决上述问题，所提出的方法引入了一种安全的联邦学习框架，并结合基于区块链的身份验证，用于CSF中的异常检测。所提出的方法采用基于联邦迁移学习的阴影注意力机制，结合Zeiler和Fergus网络，以提升异常检测技术的性能。这使得农场能够在本地检测威胁，同时保护数据隐私，因为仅传输加密的模型更新。构建了一种基于认证证明共识协议的区块链支持的去中心化身份验证方案，以提供信任和防篡改能力。此外，具有变化秩调整的随机k稀疏化技术对通信效率产生了积极影响，在保持相同精度的同时减少了通信开销。所提出的方法在四个数据集CSE-CIS-IDS2018、MQTTset、ToN-IoT和Edge IoT上执行，分别达到了99.99%、99.93%、99.94%和99.91%的分类准确率，且性能优于当前技术。该综合框架为CSF中的异常检测提供了一种可扩展且可信的方法，实现了具有韧性、隐私保护和智能化的智慧农业生态系统。",null,"Engineering Applications of Artificial Intelligence","2026-09-24T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出联邦学习与区块链认证结合的农业异常检测框架，在四个数据集上验证高精度，方法新颖且具产业应用潜力，值得入选每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","联邦学习","区块链","物联网安全",[32,33],"联邦学习 区块链 异常检测","协同智慧农业 物联网安全","联邦学习区块链异常检测-3471",0,"10.1016\u002Fj.engappai.2026.116072",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":46,"direction":50,"ingested_from":52},"W7214230618",[40,42,44],{"name":41,"orcid":9},"Ramesh Pandharinath Daund",{"name":43,"orcid":9},"Mohammad Junedul Haque",{"name":45,"orcid":9},"Prof. Umesh B. Pawar",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"提出融合联邦迁移学习与区块链认证的异常检测框架，保障协作智慧农业数据隐私与安全。","联邦迁移学习、影子注意力机制、Zeiler-Fergus网络、区块链认证、随机k","在四个IoT数据集上分类准确率均超99.9%，且通信开销更低，优于现有方法。","智慧农业 \u002F 农业物联网","可探索轻量级区块链共识与联邦学习在资源受限农业边缘设备上的能效与实时性优化。","openalex","2026-09-25T23:30:09.960528Z",{"total":55,"page":21,"page_size":55,"items":56},6,[57,105,147,178,215,254],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":64,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":65,"sources":67,"tags":69,"search_phrases":72,"slug":75,"view_count":35,"doi":76,"paper":77,"created_at":104},2279,"Communication-efficient Federated Transfer Learning for real-time intrusion detection in agricultural IoT systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112411","The rapid deployment of Agricultural Internet of Things (Ag-IoT) networks has introduced significant security challenges, mainly due to their limited computational resources and operation in remote environments. These constraints, combined with the critical nature of agricultural data and operations, make Ag-IoT systems particularly vulnerable to cyber threats, thus requiring robust, efficient security mechanisms. In this context, Intrusion Detection Systems (IDS) play a crucial role in monitoring network activities and detecting malicious behavior. However, conventional IDS solutions often suffer from high computational overhead, increased latency, and substantial communication costs. Although Federated Learning (FL) enhances data privacy by enabling distributed model training, its efficiency remains constrained in resource-limited Ag-IoT scenarios. To overcome these limitations, we propose a novel IDS based on Federated Transfer Learning (FTL) for multiclass network intrusion classification, which integrates FL with lightweight pretrained image models. The key idea of our approach is to transform network traffic data into grayscale images, allowing the reuse of efficient computer vision models. This transformation reduces data complexity, which is expected to support low-resource training and inference on edge devices. The FTL framework ensures that only model updates are exchanged, minimizing communication overhead while preserving data privacy. Experimental results, validated over three independent runs (Mean ± SD), demonstrate that our approach achieves 99.96% ± 0.00% accuracy on the Edge-IIoTset and 99.96% ± 0.01% accuracy on the Farm-Flow dataset. These findings highlight the strong theoretical potential of our FTL-based IDS as a promising candidate to enable intrusion detection in real-time, resource-efficient, and privacy-preserving Ag-IoT networks.","农业物联网(Ag-IoT)网络的快速部署带来了显著的安全挑战，这主要源于其有限的计算资源和在偏远环境中的运行条件。这些限制，加之农业数据和操作的关键性，使Ag-IoT系统特别容易受到网络威胁，因此需要稳健、高效的安全机制。在此背景下，入侵检测系统(IDS)在监控网络活动和检测恶意行为方面发挥着至关重要的作用。然而，传统IDS解决方案往往面临高计算开销、延迟增加和大量通信成本的问题。尽管联邦学习(FL)通过支持分布式模型训练增强了数据隐私，但其效率在资源受限的Ag-IoT场景中仍然受到制约。为克服这些局限，我们提出了一种基于联邦迁移学习(FTL)的新型IDS，用于多类网络入侵分类，该方案将FL与轻量级预训练图像模型相结合。我们方法的核心思想是将网络流量数据转换为灰度图像，从而能够复用高效的计算机视觉模型。这种转换降低了数据复杂度，有望支持边缘设备上的低资源训练和推理。FTL框架确保仅交换模型更新，在保持数据隐私的同时最大限度地减少通信开销。实验结果表明，在三次独立运行验证下(均值±标准差)，我们的方法在Edge-IIoTset上达到99.96%±0.00%的准确率，在Farm-Flow数据集上达到99.96%±0.01%的准确率。这些发现凸显了基于FTL的IDS在实现实时、资源高效且隐私保护的Ag-IoT网络入侵检测方面具有强大的理论潜力。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":66},"提出联邦迁移学习入侵检测方法，将流量转为灰度图复用轻量视觉模型，在Edge-IIoTset与Farm-Flow上达99.96%精度，兼顾实时性、低通信开销与隐私保护，对农业物联网安全有较强参考价值。",[68],{"name":63,"url":60},[26,27,70,28,71],"农业物联网","网络安全",[73,74],"农业人工智能 农业物联网 智慧农业 网络安全","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业网络安全-2279","10.1016\u002Fj.compag.2026.112411",{"doi":76,"openalex_id":78,"authors":79,"venue":63,"cited_by_count":35,"oa_url":60,"card":99,"direction":50,"ingested_from":52},"W7212387370",[80,83,86,89,92,94,96],{"name":81,"orcid":82},"Amina Khacha","https:\u002F\u002Forcid.org\u002F0009-0009-3299-8622",{"name":84,"orcid":85},"Zibouda Aliouat","https:\u002F\u002Forcid.org\u002F0000-0002-7007-7607",{"name":87,"orcid":88},"Yasmine Harbi","https:\u002F\u002Forcid.org\u002F0000-0001-6731-7895",{"name":90,"orcid":91},"Chirihane Gherbi","https:\u002F\u002Forcid.org\u002F0000-0002-0551-3978",{"name":93,"orcid":9},"Rafika Saadouni",{"name":95,"orcid":9},"Ado Adamou ABBA ARI",{"name":97,"orcid":98},"Hakim Mabed","https:\u002F\u002Forcid.org\u002F0000-0001-8358-4029",{"tldr":100,"method":101,"finding":102,"direction":50,"opportunity":103},"提出联邦迁移学习入侵检测方法，将流量转灰度图复用轻量视觉模型，实现农业物联网实时检测。","联邦迁移学习+轻量预训练图像模型，流量转灰度图，Edge-IIoTset与Far","在Edge-IIoTset和Farm-Flow上均达99.96%准确率，通信开销低且保护隐私。","可探索真实Ag-IoT边缘设备部署与动态异构流量下的联邦迁移学习鲁棒性及通信压缩优化。","2026-09-13T23:30:01.631534Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":120,"tags":122,"search_phrases":125,"slug":128,"view_count":35,"doi":129,"paper":130,"created_at":146},1723,"Bridging challenges with technology: A scientometric and systematic study on sustainability in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.59953\u002Fpaperasia.v42i4b.1489","The rapid evolution of agricultural practices driven by population growth, climate change, and technological advancements has necessitated the integration of smart technologies to enhance productivity and sustainability. This study aims to achieve two research objectives: (1) to provide a comprehensive overview of research patterns and key contributors in the domain of technology integration in smart agriculture, and (2) to evaluate the advantages of integrating technology in smart agriculture to address existing challenges. A scientometric analysis and a systematic literature review (SLR) were employed to examine the research landscape and synthesize current evidence. Following the PRISMA screening process, 18 articles were retained for the final systematic review. The findings reveal the transformative role of the Internet of Things (IoT), artificial intelligence (AI), blockchain, and unmanned aerial vehicles (UAVs) in improving resource management, reducing labour costs, enhancing real-time monitoring, and strengthening farm safety. The scientometric analysis further highlights global research collaborations, influential contributors, and emerging technological trends shaping the field of smart agriculture. These findings demonstrate the significant potential of advanced technologies to address contemporary agricultural challenges while promoting sustainable and resilient farming practices worldwide.","人口增长、气候变化和技术进步推动农业实践快速演变，这要求整合智能技术以提升生产力和可持续性。本研究旨在实现两个研究目标：（1）全面概述智能农业技术整合领域的研究模式及主要贡献者；（2）评估智能农业中技术整合的优势，以应对现有挑战。采用科学计量分析和系统文献综述（SLR）来考察研究格局并综合现有证据。经过PRISMA筛选流程，最终纳入18篇文章进行系统综述。研究结果揭示了物联网（IoT）、人工智能（AI）、区块链和无人机（UAVs）在改善资源管理、降低劳动力成本、增强实时监测及强化农场安全方面的变革性作用。科学计量分析进一步凸显了全球研究合作、有影响力的贡献者以及塑造智能农业领域的新兴技术趋势。这些发现表明，先进技术在应对当代农业挑战、同时促进全球可持续和韧性农业实践方面具有巨大潜力。","PaperAsia","2026-09-04T00:00:00Z",56,{"impact":115,"substance":17,"depth":116,"authority":117,"freshness":118,"relevant":21,"comment":119},12,16,8,2,"系统综述与科学计量分析，梳理智慧农业技术应用与挑战，但时效性较低。",[121],{"name":111,"url":108},[26,123,27,124,29],"无人机","物联网",[126,127],"农业人工智能 智慧农业 区块链 无人机","农业人工智能 智慧农业","农业人工智能智慧农业区块链无人机-1723","10.59953\u002Fpaperasia.v42i4b.1489",{"doi":129,"openalex_id":131,"authors":132,"venue":111,"cited_by_count":35,"oa_url":108,"card":141,"direction":50,"ingested_from":52},"W7208757733",[133,135,137,139],{"name":134,"orcid":9},"Nor Suzylah Sohaimi",{"name":136,"orcid":9},"Nor Syahidah Ishak",{"name":138,"orcid":9},"Rozaimi Majid",{"name":140,"orcid":9},"Siti Noor Zilawati Mingat@Minhad",{"tldr":142,"method":143,"finding":144,"direction":50,"opportunity":145},"通过科学计量和系统综述，评估智能农业中技术整合的研究格局与优势。","科学计量分析结合PRISMA系统文献综述，筛选18篇文章。","IoT、AI、区块链和无人机提升资源管理、降低成本、增强监测与安全。","可深入探究区块链与AI在农业中的协同应用，或针对特定作物\u002F区域的实证研究。","2026-09-05T23:30:09.377361Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":159,"tags":161,"search_phrases":162,"slug":164,"view_count":35,"doi":165,"paper":166,"created_at":177},1419,"Smart Agriculture with Quantum Intelligence and Blockchain Security","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84736","Selecting energy-efficient, trustworthy cluster heads (CHs) remains a key bottleneck in agricultural wireless sensor and Internet of Things (IoT) networks, where heuristic protocols such as LEACH often converge to unstable configurations and cannot verify whether a selected node is trustworthy. This paper proposes a framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) for cluster head selection with a permissioned blockchain trust-verification layer. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed method encodes candidate cluster head assignments as qubit chromosomes and evolves them through quantum rotation-gate updates, treating cluster formation as a combinatorial optimization problem rather than a classification task. Verified assignments and node reputation scores are recorded as immutable blockchain transactions validated through a lightweight Byzantine fault-tolerant consensus, enabling automatic exclusion of low-trust nodes without a central authority. A discrete-event simulation comparing the proposed framework against LEACH and a classical genetic algorithm baseline shows comparable or improved energy retention and reliable detection of malicious nodes across a range of attack ratios. The results indicate that combining quantum-inspired combinatorial optimization with blockchain-verified trust offers a practical, hardware-independent pathway toward energy-aware, tamperresistant clustering for precision agriculture","在农业无线传感器与物联网（IoT）网络中，选择节能且可信的簇头（CH）仍是关键瓶颈，诸如LEACH等启发式协议往往收敛于不稳定配置，且无法验证所选节点是否可信。本文提出一个框架，将量子启发遗传算法（QIGA）用于簇头选择，并融合许可型区块链信任验证层。与以往将量子分类器应用于传感器数据分类的研究不同，本方法将候选簇头分配编码为量子比特染色体，并通过量子旋转门更新进行演化，将簇形成视为组合优化问题而非分类任务。验证后的分配结果与节点信誉评分被记录为不可篡改的区块链交易，并通过轻量级拜占庭容错共识机制加以验证，从而在无中心权威的情况下自动排除低信任节点。将所提框架与LEACH及经典遗传算法基线进行离散事件仿真对比，结果表明，在不同攻击比率下，该框架在能量保持方面表现相当或更优，并能可靠检测恶意节点。研究结果表明，将量子启发组合优化与区块链验证信任相结合，为实现精准农业中能量感知、防篡改的分簇提供了一条实用且不依赖硬件的路径。","International Journal for Research in Applied Science and Engineering Technology","2026-08-31T00:00:00Z",59,{"impact":115,"substance":17,"depth":116,"authority":13,"freshness":157,"relevant":21,"comment":158},3,"提出量子启发遗传算法与区块链结合的簇头选择框架，方法新颖，但尚处仿真阶段，影响有限。",[160],{"name":153,"url":150},[26,27,124,29],[163,127],"农业人工智能 智慧农业 区块链 物联网","农业人工智能智慧农业区块链物联网-1419","10.22214\u002Fijraset.2026.84736",{"doi":165,"openalex_id":167,"authors":168,"venue":153,"cited_by_count":35,"oa_url":150,"card":171,"direction":50,"ingested_from":52},"W7204875790",[169],{"name":170,"orcid":9},"Munta Padmavathi",{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"提出量子启发遗传算法与区块链结合，用于农业物联网中可信且节能的簇头选择。","量子启发遗传算法（QIGA）优化簇头选择，结合许可区块链和拜占庭容错共识验证信任","相比LEACH和经典遗传算法，QIGA+区块链在能量保持和恶意节点检测上相当或更优。","其他","可探索将量子启发优化与区块链用于动态作物监测场景，或扩展到边缘计算与真实硬件部署验证。","2026-09-02T23:30:17.512521Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"search_phrases":195,"slug":198,"view_count":35,"doi":199,"paper":200,"created_at":214},3517,"A Resource-Efficient Hybrid CNN-LSTM Network for Image-Based Bean Leaf Disease Classification","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjimaging12100468","Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial dependencies is often limited by standard pooling layers, and their high memory footprint hinders deployment on portable devices. This paper proposes a lightweight hybrid CNN-LSTM system for bean leaf disease classification. By integrating an LSTM layer to model the spatial–sequential relationships within feature maps, our hybrid architecture achieves a 94.36% accuracy and 94.38% F1 score while maintaining an exceptionally small footprint of 1.86 MB, a 70% reduction in size compared to traditional CNN-based systems. Furthermore, we provide a systematic evaluation of image augmentation strategies, demonstrating that tailored transformations are superior to generic combinations for maintaining the integrity of diagnostic patterns. Results on the ibean dataset confirm that the proposed system achieves state-of-the-art F1 scores of 99.22% with EfficientNet-B7+LSTM, providing a potentially robust and scalable framework for real-time agricultural decision support in resource-constrained environments. The code and augmented datasets used in this study are publicly available on this GitHub repo.","准确且资源高效的自动化诊断是现代农业专家系统的基石。尽管卷积神经网络（CNN）在植物病理学领域已确立了基准，但其捕捉长程空间依赖关系的能力常受限于标准池化层，且高内存占用阻碍了其在便携设备上的部署。本文提出了一种用于豆叶病害分类的轻量级混合CNN-LSTM系统。通过集成LSTM层来建模特征图内的空间-序列关系，我们的混合架构达到了94.36%的准确率和94.38%的F1分数，同时保持了仅1.86 MB的极小占用，相较于传统基于CNN的系统体积减少了70%。此外，我们系统评估了图像增强策略，表明定制化变换在保持诊断模式完整性方面优于通用组合。在ibean数据集上的结果证实，所提出的系统结合EfficientNet-B7+LSTM达到了99.22%的最先进F1分数，为资源受限环境中的实时农业决策支持提供了一个潜在稳健且可扩展的框架。本研究使用的代码和增强数据集已在此GitHub仓库公开。","Journal of Imaging",78,{"impact":116,"substance":18,"depth":17,"authority":187,"freshness":20,"relevant":21,"comment":188},13,"轻量级CNN-LSTM豆叶病害识别研究，方法新颖、数据可靠且代码开源，对资源受限场景下的农业智能诊断有实用价值。",[190],{"name":184,"url":181},[26,27,192,193,194],"病害识别","轻量化模型","豆类作物",[196,197],"ibean dataset 豆叶病害","农业人工智能 轻量化模型 智慧农业 病害识别","ibeandataset豆叶病害-3517","10.3390\u002Fjimaging12100468",{"doi":199,"openalex_id":201,"authors":202,"venue":184,"cited_by_count":35,"oa_url":181,"card":208,"direction":212,"ingested_from":52},"W7154572440",[203,205],{"name":204,"orcid":9},"Hye Jin Rhee",{"name":206,"orcid":207},"Joseph Damilola Akinyemi","https:\u002F\u002Forcid.org\u002F0000-0003-3121-4231",{"tldr":209,"method":210,"finding":211,"direction":212,"opportunity":213},"提出轻量级CNN-LSTM混合网络，用于豆叶病害分类，兼顾高精度与低资源占用。","CNN提取特征后接LSTM建模空间序列关系，在ibean数据集上评估并系统比较图","模型准确率94.36%、F1 94.38%，仅1.86MB，比传统CNN缩小70%，Efficien","农业人工智能与决策模型","可探索面向移动端\u002F边缘设备的超轻量病害诊断模型，并研究增强策略与模型结构的自适应协同优化。","2026-09-25T23:30:59.276381Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":222,"score_detail":223,"sources":226,"tags":228,"search_phrases":232,"slug":235,"view_count":35,"doi":236,"paper":237,"created_at":253},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management",62,{"impact":115,"substance":19,"depth":224,"authority":187,"freshness":117,"relevant":21,"comment":225},15,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[227],{"name":221,"url":218},[26,27,229,230,231],"精准施肥","遥感","土壤制图",[233,234],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":236,"openalex_id":238,"authors":239,"venue":221,"cited_by_count":35,"oa_url":9,"card":248,"direction":212,"ingested_from":52},"W7214144821",[240,243,245],{"name":241,"orcid":242},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":241,"orcid":244},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":246,"orcid":247},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":249,"method":250,"finding":251,"direction":212,"opportunity":252},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":267,"tags":269,"search_phrases":273,"slug":276,"view_count":35,"doi":277,"paper":278,"created_at":293},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。","OpenAlex","2026-09-22T00:00:00Z",86,{"impact":18,"substance":264,"depth":265,"authority":187,"freshness":117,"relevant":21,"comment":266},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。",[268],{"name":260,"url":257},[26,27,270,271,272],"气候适应","遥感监测","早期预警",[274,275],"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":277,"openalex_id":279,"authors":280,"venue":9,"cited_by_count":35,"oa_url":286,"card":287,"direction":292,"ingested_from":52},"W7214097088",[281,283],{"name":282,"orcid":9},"H Heuristics",{"name":284,"orcid":285},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":288,"method":289,"finding":290,"direction":212,"opportunity":291},"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。","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"]