[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2351":3,"related-2351":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":71,"doi":8,"paper":8,"created_at":72},2351,"'AI+'助力农林产业智变:2026服贸会集中展示智慧农业落地应用","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F13\u002F991028309.html","2026年中国国际服务贸易交易会上，中粮集团展示玉米质量智能检验应用入选国资委首批央企人工智能战略性高价值场景；广西展示已在马来西亚榴莲主产区规模化落地的榴莲大模型；中国铁塔在北京西山试验林场构建空地一体林草防火监测体系，林上火情监控覆盖率提升至40%以上、重点林下区域达95%。",null,"中国农网 | 2026-09-13","2026-09-13T00:00:00Z","报道",10,false,82,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},24,20,16,13,9,1,"服贸会集中展示AI在玉米质检、榴莲大模型、林草防火等场景的落地应用，案例具体、数据明确，具备产业级参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业遥感","大模型","林草防火",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,55,62],{"code":45,"score":20,"supported":46,"title":47,"abstract":48,"tags":49},"I1",true,[26],[],[26],{"code":32,"score":51,"supported":46,"title":52,"abstract":53,"tags":54},5,[],[29],[27,29],{"code":56,"score":57,"supported":46,"title":58,"abstract":59,"tags":61},"A3",3,[],[60],"玉米",[],{"code":63,"score":21,"supported":13,"title":64,"abstract":65,"tags":66},"I3",[],[],[28],[68,69],"农业人工智能 农业遥感 智慧农业 林草防火","农业人工智能 农业遥感","农业人工智能农业遥感智慧农业林草防火-2351",2,"2026-09-14T00:06:25.997866Z",{"total":74,"page":21,"page_size":74,"items":75},6,[76,167,224,296,363,416],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":84,"cover_url":8,"hotness":12,"is_selected":13,"score":85,"score_detail":86,"sources":90,"tags":92,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":138,"slug":141,"view_count":142,"doi":143,"paper":144,"created_at":166},4985,"Hybrid deep ensemble learning for crop disease prediction and climate-resilient disaster management in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10579-y","Abstract Accurate crop disease prediction and climate-resilient disaster management are essential for improving agricultural productivity and ensuring food security under increasingly dynamic environmental conditions. Existing deep learning approaches typically perform crop disease detection and climate forecasting independently and often rely on deterministic CNN–LSTM architectures without effective multimodal feature fusion, predictive uncertainty estimation, or comprehensive statistical validation. Consequently, their ability to provide reliable decision support under changing climatic conditions remains limited. To address these challenges, this paper proposes a novel hybrid deep ensemble learning framework for integrated crop disease prediction and climate-resilient disaster management in smart agriculture. The proposed framework combines convolutional neural networks (CNNs) for spatial crop image feature extraction with long short-term memory (LSTM) networks for temporal climate forecasting. An attention-based multimodal feature fusion mechanism is employed to integrate heterogeneous crop and climate information, while Bayesian uncertainty estimation and deep ensemble learning are incorporated to improve prediction reliability, robustness, and confidence-aware decision support. Experimental evaluation is performed using multimodal datasets comprising crop images and climate observations collected from the PlantVillage, NOAA, and NASA EOS datasets. The dataset is divided into training, validation, and testing subsets using a 70:15:15 ratio. The proposed framework is evaluated using classification and regression metrics, including Accuracy, Precision, Recall, F1-score, RMSE, MAE, MAPE, and AUC-ROC. To ensure reproducibility, all experiments are repeated five times using different random seeds, and the results are reported with mean $$\\pm$$ standard deviation, 95% confidence intervals, and paired Student’s $$t$$ -tests. Experimental results demonstrate that the proposed framework consistently outperforms conventional CNN, LSTM, and CNN–LSTM hybrid models by achieving higher classification accuracy, lower climate forecasting error, improved robustness under varying climate conditions, and statistically significant performance improvements. The proposed framework provides a reliable, scalable, and uncertainty-aware solution for intelligent climate-resilient smart agriculture and early agricultural disaster management.","摘要：准确的作物病害预测和气候适应性灾害管理对于在日益动态的环境条件下提高农业生产力和保障粮食安全至关重要。现有深度学习方法通常独立执行作物病害检测和气候预测，且往往依赖确定性CNN–LSTM架构，缺乏有效的多模态特征融合、预测不确定性估计或全面的统计验证。因此，其在气候变化条件下提供可靠决策支持的能力仍然有限。为应对这些挑战，本文提出了一种新颖的混合深度集成学习框架，用于智慧农业中的作物病害预测与气候适应性灾害管理一体化。该框架将用于空间作物图像特征提取的卷积神经网络（CNN）与用于时间气候预测的长短期记忆（LSTM）网络相结合。采用基于注意力的多模态特征融合机制整合异构的作物与气候信息，同时引入贝叶斯不确定性估计和深度集成学习，以提高预测可靠性、鲁棒性和置信感知决策支持能力。实验评估使用包含作物图像和气候观测数据的多模态数据集，数据来源于PlantVillage、NOAA和NASA EOS数据集。数据集按70：15：15的比例划分为训练集、验证集和测试集。采用分类和回归指标对所提框架进行评估，包括准确率、精确率、召回率、F1分数、RMSE、MAE、MAPE和AUC-ROC。为确保可复现性，所有实验使用不同随机种子重复五次，结果以均值$$\\pm$$标准差、95%置信区间和配对Student's $$t$$检验报告。实验结果表明，所提框架在分类准确率、气候预测误差、不同气候条件下的鲁棒性以及统计显著性性能提升方面均持续优于传统CNN、LSTM和CNN–LSTM混合模型。该框架为智能气候适应性智慧农业和早期农业灾害管理提供了一种可靠、可扩展且具有不确定性感知能力的解决方案。","Discover Computing","2026-10-14T00:00:00Z","论文",71,{"impact":87,"substance":17,"depth":88,"authority":19,"freshness":20,"relevant":21,"comment":89},12,17,"提出CNN-LSTM注意力融合与贝叶斯不确定性估计的混合深度集成框架，多源数据与统计验证扎实，但属方法类论文，产业落地影响有限。",[91],{"name":82,"url":79},[26,27,28,93,94],"防灾减灾","作物病害预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":98},{"candidates":99,"agricultural_evidence":46},[100,106,113,121,128,134],{"code":45,"score":101,"supported":46,"title":102,"abstract":104,"tags":105},15,[103],"smart agriculture",[26,103],[26],{"code":32,"score":12,"supported":46,"title":107,"abstract":108,"tags":112},[],[109,110,111],"深度学习","deep learning","decision support",[27],{"code":114,"score":115,"supported":46,"title":116,"abstract":117,"tags":120},"H7",7,[],[118,119],"气候变化","气候适应",[93],{"code":122,"score":74,"supported":46,"title":123,"abstract":124,"tags":127},"A8",[],[125,126],"粮食安全","food security",[],{"code":129,"score":57,"supported":46,"title":130,"abstract":131,"tags":133},"A7",[],[132],"间作",[],{"code":63,"score":21,"supported":13,"title":135,"abstract":136,"tags":137},[],[],[28],[139,140],"PlantVillage NOAA NASA 作物病害预测","CNN LSTM 气候韧性 智慧农业","PlantVillageNOAANASA作物病害预测-4985",0,"10.1007\u002Fs10791-026-10579-y",{"doi":143,"openalex_id":145,"authors":146,"venue":82,"cited_by_count":142,"oa_url":79,"card":158,"direction":164,"ingested_from":165},"W7221778141",[147,150,153,155],{"name":148,"orcid":149},"Deepak Motwani","https:\u002F\u002Forcid.org\u002F0000-0002-0217-7155",{"name":151,"orcid":152},"Sellappan Palaniappan","https:\u002F\u002Forcid.org\u002F0009-0009-1168-2864",{"name":154,"orcid":8},"A. Daniel",{"name":156,"orcid":157},"Azween B. Abdullah","https:\u002F\u002Forcid.org\u002F0000-0003-4425-8604",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"提出混合深度集成框架，融合CNN与LSTM并引入注意力机制，实现作物病害预测与气候韧性灾害管理。","CNN提取图像特征、LSTM预测气候，注意力多模态融合，贝叶斯不确定性估计与深度","框架在分类精度与气候预测误差上均优于CNN、LSTM及CNN-LSTM基线，且性能提升具统计显著性。","农业人工智能与决策模型","可探索轻量化边缘部署与多作物跨区域迁移，结合实时物联网数据提升不确定性感知的灾害预警时效。","智慧农业 \u002F 农业物联网","openalex","2026-10-15T23:30:08.408875Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":8,"source_name":173,"source_url":174,"published_at":175,"category":84,"cover_url":8,"hotness":12,"is_selected":13,"score":176,"score_detail":177,"sources":181,"tags":183,"primary_topic":186,"secondary_topics":187,"classification":188,"search_phrases":206,"slug":209,"view_count":71,"doi":210,"paper":211,"created_at":223},4373,"APPLICATION POTENTIAL OF UNMANNED AERIAL VEHICLES AND TRENDS IN THEIR DEVELOPMENT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23168044","This article examines the application potential and principal development trends of unmanned aerial vehicles across civilian, public-service, industrial, scientific, and defense-related domains. Particular attention is given to platform diversification, sensor and payload integration, artificial intelligence, autonomy, communications, energy efficiency, airspace integration, safety, cybersecurity, regulation, and human capital. The study applies a structured qualitative review of open official documents and peer-reviewed or professionally reviewed publications, combining international evidence with official sources from the Republic of Uzbekistan. The findings show that unmanned aerial vehicles are evolving from remotely piloted, task-specific platforms toward increasingly autonomous, networked, modular, data-driven aerial systems. Their strongest growth areas include precision agriculture, geospatial mapping, infrastructure inspection, environmental monitoring, emergency response, logistics, communications support, and selected security and defense functions. The article remains at the strategic, technological, regulatory, and systems-engineering level and does not address tactical employment procedures or operational vulnerabilities.","本文考察了无人机在民用、公共服务、工业、科学及国防相关领域的应用潜力与主要发展趋势，特别关注平台多样化、传感器与载荷集成、人工智能、自主性、通信、能效、空域整合、安全、网络安全、监管及人力资本等方面。研究采用结构化定性综述方法，对公开官方文件及经同行评审或专业评审的出版物进行分析，并将国际证据与乌兹别克斯坦共和国的官方来源相结合。研究结果表明，无人机正从远程驾驶的专用任务平台，向日益自主化、网络化、模块化、数据驱动的空中系统演进。其增长最为强劲的领域包括精准农业、地理空间测绘、基础设施巡检、环境监测、应急响应、物流、通信支持以及特定的安全与国防功能。本文保持在战略、技术、监管和系统工程层面，不涉及战术运用程序或作战脆弱性。","HSR (London)","https:\u002F\u002Fhoughtonstreetreview.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F2110","2026-10-05T00:00:00Z",67,{"impact":178,"substance":18,"depth":101,"authority":12,"freshness":179,"relevant":21,"comment":180},18,8,"综述性论文，将无人机与精准农业、农业遥感等智慧农业场景关联，有战略参考价值但非突破性成果。",[182],{"name":173,"url":174},[26,184,27,28,185],"无人机","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":189},{"candidates":190,"agricultural_evidence":46},[191,196,202],{"code":45,"score":179,"supported":46,"title":192,"abstract":193,"tags":195},[],[185,194],"precision agriculture",[26,185],{"code":32,"score":197,"supported":46,"title":198,"abstract":199,"tags":201},4,[],[200],"artificial intelligence",[27],{"code":63,"score":21,"supported":13,"title":203,"abstract":204,"tags":205},[],[],[28],[207,208],"无人机 精准农业 应用","农业人工智能 农业遥感 智慧农业 精准农业","无人机精准农业应用-4373","10.5281\u002Fzenodo.23168044",{"doi":210,"openalex_id":212,"authors":213,"venue":173,"cited_by_count":142,"oa_url":174,"card":218,"direction":162,"ingested_from":165},"W7220406405",[214,216],{"name":215,"orcid":8},"Somitov Abdukadir Abdurayimovich",{"name":217,"orcid":8},"Worldly Knowledge Publishing Centre",{"tldr":219,"method":220,"finding":221,"direction":164,"opportunity":222},"综述无人机应用潜力与发展趋势，强调其向自主化、网络化、数据驱动系统演进。","结构化定性综述官方文件与同行评议文献，结合国际与乌兹别克斯坦证据。","无人机最强增长领域包括精准农业、地理空间测绘、基础设施巡检与环境监测等。","可聚焦精准农业中无人机多源数据融合与自主决策的落地瓶颈及监管适配研究。","2026-10-07T23:30:46.084251Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":8,"source_name":230,"source_url":227,"published_at":231,"category":84,"cover_url":8,"hotness":12,"is_selected":46,"score":232,"score_detail":233,"sources":237,"tags":239,"primary_topic":242,"secondary_topics":243,"classification":244,"search_phrases":257,"slug":260,"view_count":197,"doi":261,"paper":262,"created_at":295},3864,"A Knowledge‐Augmented Wearable System for Interpretable Plant Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Ffle2.70020","ABSTRACT In precision agriculture, modern sensing technologies can autonomously collect and process vast plant data using statistical or machine‐learning models. However, an “interpretability gap” persists: these outputs often remain black‐box correlations rather than interpretable physiological insights, which means users cannot validate their credibility, trace causal links to plant physiology, or translate them into actionable decisions. To bridge this gap, we propose a knowledge‐augmented wearable system for interpretable plant health monitoring (K‐Wear). By coupling a soft wearable sensing platform with a knowledge‐guided reasoning framework, K‐Wear leverages a large language model to transform raw sensing signals into mechanistic diagnoses and recommendations for proactive management. In simulated evaluations, the system achieved 99% diagnostic accuracy and first correctly detected plant stress on day 4.95 on average. In a real 14‐day potassium‐deficiency monitoring experiment, K‐Wear successfully identified early stress signals on day 4 and generated targeted intervention recommendations, enabling timely treatment and recovery of plant health.","在精准农业中，现代传感技术能够利用统计或机器学习模型自主采集并处理海量植物数据。然而，“可解释性鸿沟”依然存在：这些输出往往仍是黑箱式的相关性结果，而非可解释的生理学洞见，这意味着用户无法验证其可信度、追溯其与植物生理的因果联系，或将其转化为可操作的决策。为弥合这一鸿沟，我们提出了一种用于可解释植物健康监测的知识增强可穿戴系统（K‐Wear）。通过将柔性可穿戴传感平台与知识引导的推理框架相结合，K‐Wear利用大语言模型将原始传感信号转化为机理诊断和主动管理建议。在模拟评估中，该系统达到了99%的诊断准确率，并最早在平均第4.95天正确检测到植物胁迫。在一项为期14天的真实缺钾监测实验中，K‐Wear成功在第4天识别出早期胁迫信号，并生成有针对性的干预建议，从而使植物健康得到及时治疗和恢复。","FlexTech","2026-09-29T00:00:00Z",84,{"impact":234,"substance":235,"depth":178,"authority":19,"freshness":179,"relevant":21,"comment":236},22,23,"将可穿戴传感与大模型知识推理结合，实现可解释的植物健康诊断，方法新颖、实验数据扎实，对智慧农业感知决策有参考价值。",[238],{"name":230,"url":227},[26,27,29,240,241],"可穿戴传感","植物健康监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":245},{"candidates":246,"agricultural_evidence":46},[247,253],{"code":32,"score":179,"supported":46,"title":248,"abstract":249,"tags":252},[],[250,251],"机器学习","large language model",[27,29],{"code":45,"score":115,"supported":46,"title":254,"abstract":255,"tags":256},[],[185,194],[26],[258,259],"K-Wear 可穿戴 植物健康监测","大语言模型 植物胁迫 诊断","K-Wear可穿戴植物健康监测-3864","10.1002\u002Ffle2.70020",{"doi":261,"openalex_id":263,"authors":264,"venue":230,"cited_by_count":142,"oa_url":227,"card":290,"direction":162,"ingested_from":165},"W7214803187",[265,267,269,271,274,276,279,281,284,287],{"name":266,"orcid":8},"Xingxing Dong",{"name":268,"orcid":8},"Yihan Luo",{"name":270,"orcid":8},"Jiang Qin",{"name":272,"orcid":273},"Jie Ye","https:\u002F\u002Forcid.org\u002F0000-0001-7728-3588",{"name":275,"orcid":8},"Yihui Fan",{"name":277,"orcid":278},"Menghao Pu","https:\u002F\u002Forcid.org\u002F0009-0003-1421-6785",{"name":280,"orcid":8},"Zisheng Zong",{"name":282,"orcid":283},"Guoqiang Xu","https:\u002F\u002Forcid.org\u002F0009-0007-5481-1422",{"name":285,"orcid":286},"Han Ding","https:\u002F\u002Forcid.org\u002F0000-0003-2905-6369",{"name":288,"orcid":289},"Zhigang Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3719-406X",{"tldr":291,"method":292,"finding":293,"direction":164,"opportunity":294},"提出知识增强可穿戴系统K-Wear，将植物传感信号转化为可解释诊断与建议。","软可穿戴传感平台+知识引导推理框架+大语言模型。","模拟诊断准确率99%，真实钾缺乏实验第4天检出早期胁迫并给出干预建议。","可探索知识增强LLM推理在更多作物胁迫类型与田间长期监测中的泛化能力。","2026-09-30T23:30:55.022226Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":8,"source_name":302,"source_url":299,"published_at":231,"category":84,"cover_url":8,"hotness":12,"is_selected":13,"score":176,"score_detail":303,"sources":305,"tags":307,"primary_topic":310,"secondary_topics":311,"classification":321,"search_phrases":343,"slug":346,"view_count":57,"doi":347,"paper":348,"created_at":362},3839,"A remote sensing image processing method based on edge computing and deep learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00508-2","The rapid advancement of Artificial Intelligence (AI) and edge computing has increased the demand for accurate and low-latency remote sensing image classification; however, conventional cloud-centric approaches face challenges related to processing latency, communication overhead, and efficient analysis of diverse land-cover scenes. This research proposes a remote sensing image processing method based on edge computing and deep learning (DL), integrating an Adaptive Chameleon Swarm-tuned Efficient Three-Dimensional Convolutional Neural Network (ACS-E3DCNN). The Remote Sensing Image Dataset obtained from Kaggle contains 5,631 RGB images categorized into five land-cover classes, namely agriculture, barren land, urban, vegetation, and water. During preprocessing, image normalization, adaptive histogram equalization, and non-local means filtering are applied to reduce noise, enhance contrast, and improve image quality. Subsequently, Principal Component Analysis (PCA) is employed for dimensionality refinement. The Efficient Three-Dimensional Convolutional Neural Network captures discriminative image representations with reduced parameter complexity, while the Adaptive Chameleon Swarm algorithm optimizes critical hyperparameters, including learning rate, kernel depth, and filter dimensions, to improve convergence and classification performance. Experimental results implemented using Python 3.10.1 demonstrate that the proposed model outperforms existing approaches, achieving an accuracy of 98.10%, F1-score of 96.85%, PSNR of 38.05, SSIM of 0.97, and latency of 85 ms under the 80:20 data split, with a statistically significant paired t-test result ( p \u003C 0.05). The proposed model provides an intelligent, reliable, and efficient approach for remote sensing image classification, supporting accurate land-cover mapping, environmental monitoring, and low-latency edge-enabled image processing.","人工智能（AI）与边缘计算的快速发展，提升了对高精度、低延迟遥感图像分类的需求；然而，传统的以云为中心的方法在处理延迟、通信开销以及高效分析多样化土地覆盖场景方面面临挑战。本研究提出了一种基于边缘计算与深度学习（DL）的遥感图像处理方法，集成了自适应变色龙群优化高效三维卷积神经网络（ACS-E3DCNN）。从Kaggle获取的遥感图像数据集包含5，631幅RGB图像，分为农业、裸地、城市、植被和水体五个土地覆盖类别。在预处理阶段，采用图像归一化、自适应直方图均衡化和非局部均值滤波来降低噪声、增强对比度并提升图像质量。随后，采用主成分分析（PCA）进行降维优化。高效三维卷积神经网络以较低的参数复杂度捕获具有判别性的图像表征，而自适应变色龙群算法则优化关键超参数，包括学习率、卷积核深度和滤波器尺寸，以改善收敛性和分类性能。使用Python 3.10.1实现的实验结果表明，所提模型优于现有方法，在80：20数据划分下达到了98.10%的准确率、96.85%的F1分数、38.05的PSNR、0.97的SSIM以及85 ms的延迟，配对t检验结果具有统计学显著性（p \u003C 0.05）。所提模型为遥感图像分类提供了一种智能、可靠且高效的方法，支持精确的土地覆盖制图、环境监测以及低延迟的边缘图像处理。","Discover Internet of Things",{"impact":87,"substance":178,"depth":18,"authority":87,"freshness":20,"relevant":21,"comment":304},"该论文提出边缘计算与深度学习结合的遥感图像分类方法，在农业土地覆盖识别上精度高、延迟低，对智慧农业遥感应用有参考价值，但属方法类研究，产业影响有限。",[306],{"name":302,"url":299},[26,27,28,308,309],"边缘计算","土地覆盖分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[312,316],{"code":63,"slug":313,"name":314,"path":315,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":317,"slug":318,"name":319,"path":320,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":13,"version":40,"method":41,"evidence":322},{"candidates":323,"agricultural_evidence":46},[324,328,334,339],{"code":32,"score":178,"supported":46,"title":325,"abstract":326,"tags":327},[110],[109,110,200],[27],{"code":63,"score":101,"supported":46,"title":329,"abstract":331,"tags":333},[330],"remote sensing",[332,330],"遥感",[28],{"code":317,"score":101,"supported":46,"title":335,"abstract":337,"tags":338},[336],"edge computing",[308,336],[308],{"code":45,"score":21,"supported":13,"title":340,"abstract":341,"tags":342},[],[],[26],[344,345],"遥感图像 土地覆盖 分类","边缘计算 深度学习 遥感","遥感图像土地覆盖分类-3839","10.1007\u002Fs43926-026-00508-2",{"doi":347,"openalex_id":349,"authors":350,"venue":302,"cited_by_count":142,"oa_url":299,"card":356,"direction":360,"ingested_from":165},"W7214769118",[351,353],{"name":352,"orcid":8},"Yuli Song",{"name":354,"orcid":355},"Hong Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3176-3994",{"tldr":357,"method":358,"finding":359,"direction":360,"opportunity":361},"提出边缘计算与深度学习结合的遥感图像分类方法，用改进3D CNN实现低延迟高精度土地覆盖识别。","ACS-E3DCNN模型，结合自适应变色龙群优化超参数，用Kaggle 5631","准确率98.10%、F1为96.85%、延迟85ms，优于现有方法，支持低延迟边缘处理。","农业遥感与作物表型","可探索边缘端轻量化模型在农田实时监测中的部署，结合多光谱与时序数据提升作物分类泛化性。","2026-09-30T23:30:27.868009Z",{"id":364,"title":365,"url":366,"summary":367,"summary_zh":368,"content":8,"source_name":369,"source_url":366,"published_at":370,"category":84,"cover_url":8,"hotness":12,"is_selected":13,"score":371,"score_detail":372,"sources":375,"tags":377,"primary_topic":380,"secondary_topics":381,"classification":382,"search_phrases":397,"slug":400,"view_count":71,"doi":401,"paper":402,"created_at":415},2944,"Rule-driven functional zoning index prediction for sustainable agricultural landscapes design based on multimodal fusion deep learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72129-2","Abstract This study develops a rule-based functional zoning index system for agricultural landscapes that integrates multi-source variables, including remote sensing spectral information, landscape structure, topographic conditions, crop configuration, and land cover. To enable automated prediction of the proposed index system, a Multi-Modal Landscape Fusion Network (MMLF-Net) model is developed. By integrating multi-source data, including remote sensing images, topographic features, crop structures, and land cover, the model constructs an end-to-end AL functional zoning system. Experimental verification is carried out in the Fresno region of California, the United States of America. The results show that MMLF-Net effectively improves the accuracy of functional identification and the stability of zoning, achieving an overall accuracy of 87.34% and a Kappa coefficient of 0.84. Among all functional types, the F1 scores for intensive production and natural fallow zones exceed 0.90, demonstrating the advantages of multimodal feature fusion in functional identification. Further landscape pattern analysis reveals several key structural characteristics, including overextended production-ecology interfaces, fragmented ecological patches, and complex morphologies within composite management zones. These findings provide a quantitative basis for ecological restoration layout and agricultural pollution prevention and control. This study aims to offer a practical spatial decision-making tool for the sustainable development of regional agriculture.","摘要 本研究构建了一套基于规则的农业景观功能分区指标体系，该体系整合了多源变量，包括遥感光谱信息、景观结构、地形条件、作物配置和土地覆盖。为实现对所提指标体系的自动化预测，本研究开发了多模态景观融合网络（MMLF-Net）模型。通过整合遥感影像、地形特征、作物结构和土地覆盖等多源数据，该模型构建了端到端的农业景观功能分区系统。实验验证在美国加利福尼亚州弗雷斯诺地区开展。结果表明，MMLF-Net有效提升了功能识别的精度和分区的稳定性，总体精度达到87.34%，Kappa系数为0.84。在所有功能类型中，集约生产区和自然休耕区的F1分数均超过0.90，证明了多模态特征融合在功能识别中的优势。进一步的景观格局分析揭示了若干关键结构特征，包括生产-生态界面过度延伸、生态斑块破碎化以及复合管理区内形态复杂等。这些发现为生态修复布局和农业污染防治提供了定量依据。本研究旨在为区域农业可持续发展提供实用的空间决策工具。","Scientific Reports","2026-09-18T00:00:00Z",81,{"impact":178,"substance":234,"depth":178,"authority":373,"freshness":20,"relevant":21,"comment":374},14,"多模态深度学习用于农业景观功能分区，方法新颖、指标可靠，对农业空间决策有参考价值。",[376],{"name":369,"url":366},[26,27,28,378,379],"多模态融合","功能分区",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":383},{"candidates":384,"agricultural_evidence":46},[385,389,393],{"code":32,"score":12,"supported":46,"title":386,"abstract":387,"tags":388},[110],[],[27,378],{"code":63,"score":115,"supported":46,"title":390,"abstract":391,"tags":392},[],[332,330],[28],{"code":45,"score":21,"supported":13,"title":394,"abstract":395,"tags":396},[],[],[26],[398,399],"MMLF-Net 农业景观 功能分区","Fresno 农业景观 多模态","MMLF-Net农业景观功能分区-2944","10.1038\u002Fs41598-026-72129-2",{"doi":401,"openalex_id":403,"authors":404,"venue":369,"cited_by_count":142,"oa_url":366,"card":410,"direction":360,"ingested_from":165},"W7213550895",[405,407],{"name":406,"orcid":8},"Ruifen Wen",{"name":408,"orcid":409},"Juan Du","https:\u002F\u002Forcid.org\u002F0000-0002-7422-8767",{"tldr":411,"method":412,"finding":413,"direction":360,"opportunity":414},"构建规则驱动农业景观功能分区指数，并用多模态融合深度学习实现自动预测。","多模态融合网络MMLF-Net，融合遥感、地形、作物结构与土地覆盖数据。","MMLF-Net总体精度87.34%、Kappa 0.84，集约生产与自然休耕区F1超0.90。","可探索规则驱动指数与可解释深度学习的耦合，并迁移到不同农业景观区验证泛化性。","2026-09-19T23:30:32.908587Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":8,"source_name":422,"source_url":419,"published_at":370,"category":84,"cover_url":8,"hotness":12,"is_selected":13,"score":371,"score_detail":423,"sources":425,"tags":427,"primary_topic":429,"secondary_topics":430,"classification":432,"search_phrases":461,"slug":464,"view_count":197,"doi":465,"paper":466,"created_at":482},2906,"UAV remote sensing for crop lodging monitoring: A comprehensive review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112446","Global agriculture is facing increasing pressure to improve productivity while minimizing losses caused by extreme climate events. Crop lodging poses a major threat to food security by reducing crop yield, deteriorating grain quality, and increasing harvesting costs. As a flexible, high-resolution remote sensing platform, unmanned aerial vehicles (UAVs) have emerged as an effective solution for eliminating the gap between labor-intensive ground surveys and low-resolution satellite observations. This review systematically synthesizes the current state of research on UAV-based crop lodging monitoring through a comprehensive analysis of peer-reviewed literature. It covers the complete technical workflow, from lodging mechanisms to lodging quantification. The capabilities of diverse sensing modalities, including RGB, multispectral imagery (MSI), hyperspectral imagery (HSI), thermal infrared (TIR), light detection and ranging (LiDAR), and synthetic aperture radar (SAR), are systematically evaluated for detecting lodging-induced variations in color, texture, spectral characteristics, canopy temperature, structural attributes, and radar backscatter. In addition, recent advances in analytical approaches are reviewed, encompassing traditional statistical analyses, machine learning techniques, and state-of-the-art deep learning models. Furthermore, the review summarizes UAV-based lodging assessments across a wide range of crop species and examines methods for quantifying key lodging parameters, including lodging severity, lodging type, and affected area. By identifying current research gaps and emerging opportunities, this review outlines future research directions and provides valuable guidance for advancing accurate, efficient, and real-time crop lodging monitoring in smart agriculture.","全球农业正面临日益增大的压力，既要提高生产力，又要尽量减少极端气候事件造成的损失。作物倒伏通过降低作物产量、恶化籽粒品质并增加收获成本，对粮食安全构成重大威胁。作为一种灵活的高分辨率遥感平台，无人机（UAV）已成为弥合劳动密集型地面调查与低分辨率卫星观测之间差距的有效解决方案。本文通过系统分析同行评议文献，全面综述了基于无人机的作物倒伏监测研究现状，涵盖从倒伏机理到倒伏量化的完整技术流程。系统评估了多种传感模态的能力，包括RGB影像、多光谱影像（MSI）、高光谱影像（HSI）、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR），用于检测倒伏引起的颜色、纹理、光谱特征、冠层温度、结构属性和雷达后向散射变化。此外，综述了分析方法的最新进展，涵盖传统统计分析、机器学习技术和前沿深度学习模型。同时，本文总结了针对多种作物类型的基于无人机的倒伏评估研究，并探讨了关键倒伏参数的量化方法，包括倒伏严重程度、倒伏类型和受影响面积。通过识别当前研究空白和新兴机遇，本文展望了未来研究方向，为推进智慧农业中准确、高效、实时的作物倒伏监测提供了有价值的指导。","Computers and Electronics in Agriculture",{"impact":178,"substance":234,"depth":178,"authority":101,"freshness":179,"relevant":21,"comment":424},"核心期刊发表的无人机遥感作物倒伏监测综述，系统梳理多模态传感与深度学习分析路径，对智慧农业精准监测有实质参考价值。",[426],{"name":422,"url":419},[26,184,27,28,428],"作物倒伏",{"code":63,"slug":313,"name":314,"path":315,"parent_code":36},[431],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":433},{"candidates":434,"agricultural_evidence":46},[435,441,446,450,454],{"code":63,"score":178,"supported":46,"title":436,"abstract":437,"tags":440},[330],[332,330,438,439],"multispectral","hyperspectral",[28],{"code":32,"score":12,"supported":46,"title":442,"abstract":443,"tags":445},[],[109,250,444,110],"machine learning",[27],{"code":45,"score":115,"supported":46,"title":447,"abstract":448,"tags":449},[],[26,103],[26],{"code":122,"score":74,"supported":46,"title":451,"abstract":452,"tags":453},[],[125,126],[],{"code":455,"score":74,"supported":46,"title":456,"abstract":457,"tags":460},"J3",[],[458,459],"全球农业","global agriculture",[],[462,463],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906","10.1016\u002Fj.compag.2026.112446",{"doi":465,"openalex_id":467,"authors":468,"venue":422,"cited_by_count":142,"oa_url":419,"card":477,"direction":164,"ingested_from":165},"W7213542473",[469,472,474],{"name":470,"orcid":471},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":473,"orcid":8},"Changxing Geng",{"name":475,"orcid":476},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":478,"method":479,"finding":480,"direction":360,"opportunity":481},"综述无人机遥感在作物倒伏监测中的技术流程、传感器与分析模型。","系统综述RGB、多光谱、高光谱、热红外、LiDAR、SAR及机器学习深度学习方法","多模态遥感结合深度学习可实现倒伏程度、类型与面积的精准量化。","多源数据融合与实时轻量化模型是倒伏监测的潜在突破方向。","2026-09-19T23:30:01.740139Z"]