[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3672":3,"related-3672":78},{"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,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":57,"slug":60,"view_count":42,"doi":61,"paper":62,"created_at":77},3672,"Hybrid Attention Transformers for Multi-Spectral Satellite Super-Resolution","https:\u002F\u002Fdoi.org\u002F10.31224\u002F8320","Spaceborne optical imaging missions, such as the European Space Agency's Copernicus Sentinel-2 constellation, provide vital multi-spectral observations worldwide, yet optical aperture diffraction limits native Ground Sampling Distance (GSD) to 10 m across visible and near-infrared (VNIR) bands. Traditional Single-Image Super-Resolution (SISR) algorithms designed for 8-bit photographic imagery introduce severe radiometric distortions that corrupt downstream biophysical canopy analyses. In this letter, we present HAT-Light, an edge-efficient continuous-scale Hybrid Attention Transformer engineered specifically for 4-channel (RGB+NIR), 16-bit Bottom-Of-Atmosphere (BOA) surface reflectance imagery. HAT-Light addresses the limitations of standard self-attention by integrating non-overlapping Window Multi-Head Self-Attention with Depthwise Convolutional Feed-Forward Networks (DW-FFN), effectively recovering translation-equivariant localized inductive biases essential for resolving fine agricultural field parcel boundaries and airport runway geometries. Arbitrary continuous magnification (s in [2.0, 4.0]) is enabled through harmonic sinusoidal Feature-wise Linear Modulation (FiLM) within a single checkpoint. Furthermore, we enforce physical radiance conservation through a composite multi-task loss suite that unites Smooth Charbonnier regression, 2D real Fourier transform (rFFT2) spectral alignment, directional Sobel edge penalties, and a numerically bounded Convex Cosine Spectral Angle Mapper (SAM) loss ensuring FP16 numerical stability. Evaluated across 600 curated Sentinel-2 test patches spanning five distinct biomes, HAT-Light establishes state-of-the-art accuracy (33.48 dB PSNR, 0.9048 SSIM, 1.37 deg SAM, and 1.89 ERGAS), outperforming Bicubic (+1.25 dB) and RCAN (+0.71 dB). Spatial edge transect profiling and agricultural NDVI correlation (R^2 = 0.898) confirm robust biophysical conservation. Under the Wald synthesis protocol on 100 authentic 2.5 m USGS NAIP aerial patches, HAT-Light achieves superior zero-shot transfer (0.8332 SSIM) at real-time edge throughput (78.7 FPS) on an edge GPU.","星载光学成像任务，如欧洲航天局哥白尼哨兵-2星座，在全球范围内提供了重要的多光谱观测数据，然而光学孔径衍射将可见光与近红外（VNIR）波段的原生地面采样距离（GSD）限制在10 m。专为8位摄影图像设计的传统单图像超分辨率（SISR）算法会引入严重的辐射畸变，从而破坏下游的植被生物物理分析。本文提出HAT-Light，一种面向边缘高效推理的连续尺度混合注意力Transformer，专为4通道（RGB+NIR）、16位大气底层（BOA）地表反射率影像设计。HAT-Light通过将非重叠窗口多头自注意力与深度可分离卷积前馈网络（DW-FFN）相结合，解决了标准自注意力的局限性，有效恢复了平移等变的局部归纳偏置，这对于分辨精细农田地块边界和机场跑道几何形态至关重要。通过谐波正弦特征级线性调制（FiLM），在单一检查点内实现了任意连续放大倍数（s∈[2.0, 4.0]）。此外，我们通过复合多任务损失套件强制物理辐射守恒，该套件融合了平滑Charbonnier回归、二维实傅里叶变换（rFFT2）频谱对齐、方向性Sobel边缘惩罚以及数值有界的凸余弦光谱角映射（SAM）损失，确保FP16数值稳定性。在涵盖五种不同生物群系的600个精选哨兵-2测试图像块上进行评估，HAT-Light确立了最先进的精度（33.48 dB PSNR、0.9048 SSIM、1.37° SAM和1.89 ERGAS），优于双三次插值（+1.25 dB）和RCAN（+0.71 dB）。空间边缘剖面分析和农业NDVI相关性（R²=0.898）证实了稳健的生物物理守恒性。在100个真实2.5 m USGS NAIP航空图像块上按照Wald合成协议进行测试，HAT-Light在边缘GPU上以实时边缘吞吐量（78.7 FPS）实现了优异的零样本迁移性能（0.8332 SSIM）。",null,"OpenAlex","2026-09-26T00:00:00Z","论文",10,true,81,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,13,9,1,"面向Sentinel-2多光谱16位反射率影像的超分辨率新方法，兼顾辐射保真与NDVI生物物理一致性，对农业遥感监测有实质技术增量。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业遥感","卫星影像","作物监测",[],{"status":34,"locked":35,"version":36,"method":37,"evidence":38},"unclassified",false,"2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":14,"reason":56},[40,46,51],{"code":41,"score":42,"supported":35,"title":43,"abstract":44,"tags":45},"I3",3,[],[],[31,29,30],{"code":47,"score":22,"supported":35,"title":48,"abstract":49,"tags":50},"I1",[],[],[27],{"code":52,"score":22,"supported":35,"title":53,"abstract":54,"tags":55},"I2",[],[],[28],"缺少足够的主题证据",[58,59],"Sentinel-2 超分辨率 农业遥感","HAT-Light 多光谱 卫星影像","Sentinel-2超分辨率农业遥感-3672","10.31224\u002F8320",{"doi":61,"openalex_id":63,"authors":64,"venue":9,"cited_by_count":68,"oa_url":69,"card":70,"direction":74,"ingested_from":76},"W7214467740",[65],{"name":66,"orcid":67},"Naman Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-8443-0985",0,"https:\u002F\u002Fengrxiv.org\u002Fpreprint\u002Fdownload\u002F8320\u002F13499\u002F11736",{"tldr":71,"method":72,"finding":73,"direction":74,"opportunity":75},"提出HAT-Light混合注意力Transformer，实现Sentinel-2多光谱16位影像连续","混合窗口自注意力与深度可分离卷积前馈网络，结合FiLM连续尺度调制和多任务物理约","在600个Sentinel-2测试块上达33.48dB PSNR，NDVI相关性R²=0.898，边","农业遥感与作物表型","可探索超分后多光谱影像对作物长势、病虫害等下游农学任务的定量增益与不确定性传播。","openalex","2026-09-28T23:30:28.027834Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,186,301,378,441,508],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":9,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":89,"score_detail":90,"sources":94,"tags":96,"primary_topic":99,"secondary_topics":105,"classification":117,"search_phrases":154,"slug":157,"view_count":68,"doi":158,"paper":159,"created_at":185},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":91,"substance":18,"depth":17,"authority":92,"freshness":13,"relevant":22,"comment":93},16,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[95],{"name":87,"url":84},[27,28,29,97,98],"氮素监测","紫花苜蓿",{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[106,111],{"code":41,"slug":107,"name":108,"path":109,"parent_code":110},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":118,"locked":35,"version":36,"method":37,"evidence":119},"classified",{"candidates":120,"agricultural_evidence":14},[121,128,135,141,150],{"code":41,"score":17,"supported":14,"title":122,"abstract":124,"tags":127},[123],"hyperspectral",[125,126,123],"遥感","remote sensing",[29],{"code":100,"score":129,"supported":14,"title":130,"abstract":132,"tags":134},15,[131],"alfalfa",[133,131],"苜蓿",[98],{"code":112,"score":13,"supported":14,"title":136,"abstract":137,"tags":140},[],[138,133,139],"饲草","forage",[98],{"code":52,"score":13,"supported":14,"title":142,"abstract":143,"tags":149},[],[144,145,146,147,148],"深度学习","机器学习","machine learning","deep learning","decision support",[28],{"code":47,"score":22,"supported":35,"title":151,"abstract":152,"tags":153},[],[],[27],[155,156],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":158,"openalex_id":160,"authors":161,"venue":87,"cited_by_count":68,"oa_url":84,"card":180,"direction":74,"ingested_from":76},"W7223154576",[162,164,166,168,170,173,175,178],{"name":163,"orcid":9},"Shuai Fu",{"name":165,"orcid":9},"Yunhao Li",{"name":167,"orcid":9},"Chunli Miao",{"name":169,"orcid":9},"Jie Liu",{"name":171,"orcid":172},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":174,"orcid":9},"Yongjia Hu",{"name":176,"orcid":177},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":179,"orcid":9},"吴彩霞",{"tldr":181,"method":182,"finding":183,"direction":74,"opportunity":184},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":89,"score_detail":194,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":208,"classification":209,"search_phrases":274,"slug":277,"view_count":68,"doi":278,"paper":279,"created_at":300},4987,"Smart Agriculture Research in Date Palm (Phoenix dactylifera L.) Production: A Comprehensive Bibliometric Study","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15203137","The date palm (Phoenix dactylifera L.) is fundamental to food security, livelihoods, and agro-ecosystem sustainability across the Arabian Peninsula, yet its production is increasingly constrained by water scarcity, salinity, climate change, and biotic stresses. Smart agriculture technologies offer opportunities to improve crop monitoring, resource-use efficiency, and decision-making; however, their application in date palm production remains fragmented and insufficiently validated. This study evaluates the development, geographic distribution, technological focus, research approaches, and thematic structure of smart agriculture research on date palms in the Arabian Peninsula and other major date-producing countries. A bibliometric analysis was conducted using peer-reviewed English-language articles and reviews indexed in Scopus and Web of Science from 2016 to 2026, with 2026 representing a partial year. Following screening and duplicate removal, 164 published studies were retained. Publication output increased gradually, although the trend was not statistically significant (β = 1.25 articles year−1, p = 0.068; Mann–Kendall τ = 0.489, p = 0.060). The literature was concentrated in the Gulf Cooperation Council (GCC) region, with Saudi Arabia contributing 54 publications (32.9%). General agronomy\u002Fother topics accounted for 46.3% of publications, while remote sensing\u002FUAVs (17.1%), GIS\u002Fgeospatial technologies (14.0%), IoT\u002Fsensors (13.4%), and AI\u002Fmachine learning (9.8%) represented the leading specific technology categories. Water and irrigation management dominated thematic applications (46.3%), whereas Smart Farming\u002FIoT accounted for only 11.0%. Observational and monitoring studies comprised 50.0% of publications, compared with only 6.1% involving field experiments, indicating limited field-scale validation. Internationally co-authored studies received significantly more citations than domestically authored studies (16.55 vs. 8.68; p = 0.0007). Thematic analysis identified four major clusters: geospatial monitoring, water management, pest control, and sustainability, with AI\u002Fmachine learning showing marked growth after 2022. Altogether, the field is expanding but remains fragmented, highlighting the need for integrated IoT and sensor networks, rigorous field trials, AI-driven decision-support systems, and stronger regional and international collaboration to enable scalable, context-specific smart agriculture for date palm production.","海枣（Phoenix dactylifera L.）对阿拉伯半岛的粮食安全、生计和农业生态系统可持续性至关重要，然而其生产日益受到水资源短缺、盐碱化、气候变化和生物胁迫的制约。智慧农业技术为改善作物监测、资源利用效率和决策提供了机遇；然而，其在海枣生产中的应用仍然零散且缺乏充分验证。本研究评估了阿拉伯半岛及其他主要海枣生产国海枣智慧农业研究的发展、地理分布、技术重点、研究方法和主题结构。采用文献计量学分析方法，基于Scopus和Web of Science数据库中2016年至2026年索引的同行评审英文论文和综述，其中2026年为部分年份。经过筛选和去重后，共保留164篇已发表研究。发文量逐渐增长，但该趋势未达到统计学显著性（β = 1.25篇\u002F年，p = 0.068；Mann–Kendall τ = 0.489，p = 0.060）。文献集中于海湾合作委员会（GCC）地区，其中沙特阿拉伯贡献了54篇论文（32.9%）。一般农学\u002F其他主题占论文总数的46.3%，而遥感\u002F无人机（17.1%）、GIS\u002F地理空间技术（14.0%）、物联网\u002F传感器（13.4%）和人工智能\u002F机器学习（9.8%）为主要的具体技术类别。水资源与灌溉管理在主题应用中占主导地位（46.3%），而智慧农业\u002F物联网仅占11.0%。观测与监测类研究占论文总数的50.0%，而涉及田间试验的仅占6.1%，表明田间尺度的验证有限。国际合著论文的引用次数显著高于国内独著论文（16.55 vs. 8.68；p = 0.0007）。主题分析识别出四大聚类：地理空间监测、水资源管理、病虫害防治和可持续性，其中人工智能\u002F机器学习在2022年后呈现显著增长。总体而言，该领域正在扩展但仍显零散，凸显了整合物联网与传感器网络、严格的田间试验、人工智能驱动的决策支持系统以及加强区域和国际合作的必要性，以推动可规模化、因地制宜的海枣生产智慧农业。","Plants","2026-10-14T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":92,"freshness":195,"relevant":22,"comment":196},8,"基于Scopus与WoS的164篇文献计量分析，揭示枣椰树智慧农业研究碎片化、田间验证不足，对干旱区智慧农业布局有参考价值。",[198],{"name":192,"url":189},[27,28,29,200,201],"农业物联网","枣椰树",{"code":203,"slug":204,"name":205,"path":206,"parent_code":207},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":118,"locked":35,"version":36,"method":37,"evidence":210},{"candidates":211,"agricultural_evidence":14},[212,221,226,232,237,241,248,255,262,268],{"code":213,"score":214,"supported":14,"title":215,"abstract":217,"tags":220},"I7",17,[216],"bibliometric",[218,219,216],"文献计量","研究方法",[],{"code":47,"score":129,"supported":14,"title":222,"abstract":224,"tags":225},[223],"smart agriculture",[27,223],[27],{"code":203,"score":21,"supported":14,"title":227,"abstract":228,"tags":231},[],[229,230,204],"灌溉","水资源",[],{"code":52,"score":233,"supported":14,"title":234,"abstract":235,"tags":236},7,[],[145,146],[28],{"code":41,"score":233,"supported":14,"title":238,"abstract":239,"tags":240},[],[125,126],[29],{"code":242,"score":233,"supported":14,"title":243,"abstract":244,"tags":247},"I5",[],[245,246],"物联网","IoT",[200],{"code":249,"score":79,"supported":14,"title":250,"abstract":251,"tags":254},"A8",[],[252,253],"粮食安全","food security",[],{"code":256,"score":79,"supported":14,"title":257,"abstract":258,"tags":261},"H7",[],[259,260],"气候变化","climate change",[],{"code":263,"score":42,"supported":14,"title":264,"abstract":265,"tags":267},"H6",[],[266],"农业生态",[],{"code":269,"score":42,"supported":14,"title":270,"abstract":271,"tags":273},"I4",[],[272],"GIS",[],[275,276],"枣椰树 智慧农业 文献计量","农业人工智能 农业物联网 农业遥感 智慧农业","枣椰树智慧农业文献计量-4987","10.3390\u002Fplants15203137",{"doi":278,"openalex_id":280,"authors":281,"venue":192,"cited_by_count":68,"oa_url":189,"card":294,"direction":298,"ingested_from":76},"W7221786893",[282,285,288,291],{"name":283,"orcid":284},"Sajid Safeer","https:\u002F\u002Forcid.org\u002F0000-0002-0120-5900",{"name":286,"orcid":287},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":289,"orcid":290},"Sajid Maqsood","https:\u002F\u002Forcid.org\u002F0000-0003-2099-8392",{"name":292,"orcid":293},"Cataldo Pulvento","https:\u002F\u002Forcid.org\u002F0000-0002-2650-1204",{"tldr":295,"method":296,"finding":297,"direction":298,"opportunity":299},"用文献计量分析2016-2026年枣椰树智慧农业研究，发现领域增长但碎片化、田间验证不足。","Scopus与Web of Science文献计量分析，164篇论文，含趋势检验","研究集中于海湾地区，遥感与灌溉为主，田间试验仅6.1%，国际合作论文引用更高。","智慧农业 \u002F 农业物联网","枣椰树智慧农业缺乏IoT传感器集成与田间验证，AI决策支持系统是待填补的研究空白。","2026-10-15T23:30:08.558630Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":322,"classification":323,"search_phrases":352,"slug":355,"view_count":68,"doi":356,"paper":357,"created_at":377},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",71,{"impact":310,"substance":311,"depth":214,"authority":20,"freshness":21,"relevant":22,"comment":312},12,20,"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。",[314],{"name":307,"url":304},[27,28,29,316,317],"防灾减灾","作物病害预测",{"code":52,"slug":319,"name":320,"path":321,"parent_code":110},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":118,"locked":35,"version":36,"method":37,"evidence":324},{"candidates":325,"agricultural_evidence":14},[326,330,334,339,343,348],{"code":47,"score":129,"supported":14,"title":327,"abstract":328,"tags":329},[223],[27,223],[27],{"code":52,"score":13,"supported":14,"title":331,"abstract":332,"tags":333},[],[144,147,148],[28],{"code":256,"score":233,"supported":14,"title":335,"abstract":336,"tags":338},[],[259,337],"气候适应",[316],{"code":249,"score":79,"supported":14,"title":340,"abstract":341,"tags":342},[],[252,253],[],{"code":100,"score":42,"supported":14,"title":344,"abstract":345,"tags":347},[],[346],"间作",[],{"code":41,"score":22,"supported":35,"title":349,"abstract":350,"tags":351},[],[],[29],[353,354],"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","10.1007\u002Fs10791-026-10579-y",{"doi":356,"openalex_id":358,"authors":359,"venue":307,"cited_by_count":68,"oa_url":304,"card":371,"direction":298,"ingested_from":76},"W7221778141",[360,363,366,368],{"name":361,"orcid":362},"Deepak Motwani","https:\u002F\u002Forcid.org\u002F0000-0002-0217-7155",{"name":364,"orcid":365},"Sellappan Palaniappan","https:\u002F\u002Forcid.org\u002F0009-0009-1168-2864",{"name":367,"orcid":9},"A. Daniel",{"name":369,"orcid":370},"Azween B. Abdullah","https:\u002F\u002Forcid.org\u002F0000-0003-4425-8604",{"tldr":372,"method":373,"finding":374,"direction":375,"opportunity":376},"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。","2026-10-15T23:30:08.408875Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":9,"source_name":384,"source_url":381,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":396,"search_phrases":423,"slug":426,"view_count":427,"doi":428,"paper":429,"created_at":440},4798,"Adaptability of Lightweight AI Models for Embedded Deployment: A Case Study of an Agricultural Monitoring Robot","https:\u002F\u002Fdoi.org\u002F10.54254\u002F2755-2721\u002F2026.ld37676","Many artificial intelligence programs require powerful computing hardware, whereas agricultural robots, drones, and smart cameras rely on small embedded devices with limited computing power, battery capacity, and heat dissipation. Lightweight models reduce parameters and computational costs while maintaining acceptable accuracy, enabling AI deployment on resource-constraint network devices. This paper reviews key lightweighting methods, including compact network architectures, model pruning, model quantization, and knowledge distillation. It compares common platforms such as Jetson, Raspberry Pi, and microcontrollers. Using an agricultural monitoring robot as an example, this study proposes a system combining a camera, a lightweight YOLO model, Jetson, and a microcontroller for real-time crop monitoring. Experiments are designed to evaluate accuracy, speed, power consumption, temperature, and battery life. Rather than pursuing maximum model complexity, this paper focuses on explaining how to select a model that is truly suitable for the actual device. For agricultural robots, effective deployment requires sufficient accuracy, real-time performance, and low energy consumption.","许多人工智能程序需要强大的计算硬件，而农业机器人、无人机和智能摄像头则依赖于计算能力、电池容量和散热能力均有限的小型嵌入式设备。轻量级模型在保持可接受精度的同时减少参数和计算成本，使人工智能能够部署在资源受限的网络设备上。本文综述了关键的轻量化方法，包括紧凑网络架构、模型剪枝、模型量化和知识蒸馏。本文比较了Jetson、树莓派和微控制器等常见平台。以农业监测机器人为例，本研究提出了一种结合摄像头、轻量级YOLO模型、Jetson和微控制器的系统，用于实时作物监测。实验旨在评估精度、速度、功耗、温度和电池寿命。本文并非追求最大化的模型复杂度，而是着重解释如何选择真正适合实际设备的模型。对于农业机器人而言，有效部署需要足够的精度、实时性能和低能耗。","Applied and Computational Engineering","2026-10-13T00:00:00Z",69,{"impact":310,"substance":17,"depth":91,"authority":20,"freshness":13,"relevant":22,"comment":388},"系统梳理轻量化模型在农业嵌入式设备上的部署路径，并以监测机器人案例给出可复现方案，对智慧农业工程落地有参考价值。",[390],{"name":384,"url":381},[27,28,392,393,31],"农业机器人","边缘计算",{"code":52,"slug":319,"name":320,"path":321,"parent_code":110},[],{"status":118,"locked":35,"version":36,"method":37,"evidence":397},{"candidates":398,"agricultural_evidence":14},[399,405,411,415,419],{"code":52,"score":233,"supported":14,"title":400,"abstract":401,"tags":404},[],[402,403],"artificial intelligence","YOLO",[28],{"code":406,"score":407,"supported":14,"title":408,"abstract":409,"tags":410},"F6",4,[],[392],[392],{"code":47,"score":22,"supported":35,"title":412,"abstract":413,"tags":414},[],[],[27],{"code":41,"score":22,"supported":35,"title":416,"abstract":417,"tags":418},[],[],[31],{"code":242,"score":22,"supported":35,"title":420,"abstract":421,"tags":422},[],[],[393],[424,425],"农业监测机器人 轻量YOLO Jetson","嵌入式AI 模型轻量化","农业监测机器人轻量YOLOJetson-4798",5,"10.54254\u002F2755-2721\u002F2026.ld37676",{"doi":428,"openalex_id":430,"authors":431,"venue":384,"cited_by_count":68,"oa_url":434,"card":435,"direction":298,"ingested_from":76},"W7221561538",[432],{"name":433,"orcid":9},"Jingen Zhou","https:\u002F\u002Face.ewapub.com\u002Farticle\u002Fview\u002F37676.pdf",{"tldr":436,"method":437,"finding":438,"direction":298,"opportunity":439},"综述轻量化AI方法，并以农业监测机器人为例，提出相机+轻量YOLO+Jetson+单片机的嵌入式部署","综述网络压缩、剪枝、量化、蒸馏，对比Jetson、树莓派、单片机，设计农业机器人","农业机器人部署需兼顾足够精度、实时性与低能耗，而非追求最大模型复杂度。","可研究面向田间多变环境的轻量化模型自适应选择与能效-精度联合优化部署策略。","2026-10-13T23:30:08.379081Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":447,"source_url":444,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":89,"score_detail":449,"sources":451,"tags":453,"primary_topic":456,"secondary_topics":457,"classification":458,"search_phrases":485,"slug":488,"view_count":22,"doi":489,"paper":490,"created_at":507},4563,"Seeing Crops in 3D: Remote Sensing for Smarter Precision Farming","https:\u002F\u002Fdoi.org\u002F10.12688\u002Fopenreseurope.25171.1","The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm\u002Fpixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.","数字技术在农业中的日益普及，正通过数据驱动的决策为提升生产力、可持续性和资源效率开辟新的机遇。本文介绍了RFSAT有限公司在H2020 AgriBIT项目中开发的综合精准农业框架，该框架目前正通过ENACT项目进一步完善。该框架将物联网（IoT）传感、厘米级精度的全球导航卫星系统（GNSS）定位、高分辨率多光谱遥感以及人工智能（AI）分析整合为一个智慧农业解决方案。一个关键的使能因素是由RFSAT开发的低成本、厘米级精度GNSS接收器，它能够对航空和地面观测进行精确的地理参照，进而生成空间分辨率超过1厘米\u002F像素的作物冠层高分辨率多光谱三维模型。这些数字表示支持在整个生长季对作物生长、冠层发育、生物量分布和植物健康进行详细监测。通过与由环境和小气候传感器组成的物联网监测基础设施相融合，地理参照的遥感产品形成了农业环境的综合数字孪生，机器学习技术在此基础上自动识别和定位作物异常，包括水分胁迫、养分缺乏、病害暴发和虫害侵扰。这些技术已在希腊、葡萄牙和意大利的运营试点基地进行了验证，涵盖果园、葡萄园和其他高价值作物，其中厘米级定位、多光谱三维建模、物联网监测和AI分析的结合大幅提高了监测精度、降低了投入品消耗并改善了管理决策。","Open Research Europe","2026-10-09T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":195,"relevant":22,"comment":450},"欧盟H2020项目论文，融合厘米级GNSS、多光谱3D建模与AI分析，方法新颖且多国验证，对智慧农业技术路线有参考价值。",[452],{"name":447,"url":444},[27,28,29,454,455],"数字孪生","精准农业",{"code":41,"slug":107,"name":108,"path":109,"parent_code":110},[],{"status":118,"locked":35,"version":36,"method":37,"evidence":459},{"candidates":460,"agricultural_evidence":14},[461,466,471,475,479],{"code":41,"score":17,"supported":14,"title":462,"abstract":463,"tags":465},[126],[125,126,464],"multispectral",[29],{"code":47,"score":310,"supported":14,"title":467,"abstract":468,"tags":470},[],[27,455,454,469],"digital twin",[454,27,455],{"code":52,"score":233,"supported":14,"title":472,"abstract":473,"tags":474},[],[145,402],[28],{"code":242,"score":79,"supported":14,"title":476,"abstract":477,"tags":478},[],[245,246],[],{"code":480,"score":42,"supported":14,"title":481,"abstract":482,"tags":484},"B2",[],[483],"葡萄",[],[486,487],"AgriBIT 精准农业 遥感","GNSS 多光谱 3D 作物","AgriBIT精准农业遥感-4563","10.12688\u002Fopenreseurope.25171.1",{"doi":489,"openalex_id":491,"authors":492,"venue":447,"cited_by_count":68,"oa_url":444,"card":502,"direction":298,"ingested_from":76},"W7221178461",[493,496,499],{"name":494,"orcid":495},"Artur Krukowski","https:\u002F\u002Forcid.org\u002F0000-0002-6635-7585",{"name":497,"orcid":498},"Emmanouela Vogiatzaki","https:\u002F\u002Forcid.org\u002F0000-0001-9239-2045",{"name":500,"orcid":501},"İzzet Kale","https:\u002F\u002Forcid.org\u002F0000-0001-5562-6885",{"tldr":503,"method":504,"finding":505,"direction":74,"opportunity":506},"提出融合物联网、厘米级GNSS、多光谱遥感和AI的精准农业框架，构建作物数字孪生。","低成本厘米级GNSS、多光谱3D建模、IoT传感器与机器学习，在希腊、葡萄牙、意","厘米定位与多光谱3D建模结合AI可提升监测精度、减少投入并改善管理决策。","可探索低成本厘米级GNSS与多光谱3D数字孪生在更多作物和区域的可扩展性及实时决策优化。","2026-10-10T23:30:07.913557Z",{"id":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":9,"source_name":514,"source_url":515,"published_at":516,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":517,"score_detail":518,"sources":520,"tags":522,"primary_topic":524,"secondary_topics":525,"classification":526,"search_phrases":542,"slug":545,"view_count":546,"doi":547,"paper":548,"created_at":560},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":17,"substance":91,"depth":129,"authority":13,"freshness":195,"relevant":22,"comment":519},"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。",[521],{"name":514,"url":515},[27,523,28,29,455],"无人机",{"code":52,"slug":319,"name":320,"path":321,"parent_code":110},[],{"status":118,"locked":35,"version":36,"method":37,"evidence":527},{"candidates":528,"agricultural_evidence":14},[529,534,538],{"code":47,"score":195,"supported":14,"title":530,"abstract":531,"tags":533},[],[455,532],"precision agriculture",[27,455],{"code":52,"score":407,"supported":14,"title":535,"abstract":536,"tags":537},[],[402],[28],{"code":41,"score":22,"supported":35,"title":539,"abstract":540,"tags":541},[],[],[29],[543,544],"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",2,"10.5281\u002Fzenodo.23168044",{"doi":547,"openalex_id":549,"authors":550,"venue":514,"cited_by_count":68,"oa_url":515,"card":555,"direction":375,"ingested_from":76},"W7220406405",[551,553],{"name":552,"orcid":9},"Somitov Abdukadir Abdurayimovich",{"name":554,"orcid":9},"Worldly Knowledge Publishing Centre",{"tldr":556,"method":557,"finding":558,"direction":298,"opportunity":559},"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"]