[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2942":3,"related-2942":61},{"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":60},2942,"Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116319","Multisource remote sensing image fusion has become an important solution to a long-standing limitation in Earth observation: individual sensors rarely provide high spatial detail, rich spectral information, reliable structural sensitivity, and frequent temporal coverage at the same time. This review examines how deep learning and artificial intelligence are being used to integrate multispectral, hyperspectral, panchromatic, optical, and synthetic aperture radar imagery for more reliable interpretation of complex ground scenes. It provides a technical synthesis of convolutional neural networks, autoencoders, generative adversarial networks, transformer architectures, diffusion models, and hybrid model driven approaches, with attention to their fusion mechanisms, reconstruction behavior, computational demand, and suitability for operational use. Applications include land cover mapping, precision agriculture, environmental monitoring, urban analysis, disaster assessment, and defense related interpretation. Rather than treating each fusion task separately, this review connects sensor heterogeneity, spatial and spectral resolution trade offs, radiometric correction, geometric correction, registration, noise reduction, and fusion level design within a single framework. The analysis indicates that convolutional models remain effective for stable local detail recovery, adversarial learning can improve visual sharpness but may introduce spectral distortion, transformer models better capture long range spatial and spectral relationships, and diffusion models offer refined reconstruction at greater computational cost. The review further identifies open challenges involving misregistration, spectral bias, limited labeled data, weak generalization across sensors, high memory requirements, and limited interpretability. Future progress should prioritize sensor aware learning, self supervised training, uncertainty aware evaluation, lightweight deployment, and application oriented benchmarks to improve reliability in operational Earth observation.","多源遥感图像融合已成为解决地球观测领域一个长期局限的重要方案：单一传感器很少能够同时提供高空间细节、丰富光谱信息、可靠的结构敏感性以及频繁的时间覆盖。本文综述了如何利用深度学习和人工智能整合多光谱、高光谱、全色、光学和合成孔径雷达（synthetic aperture radar, SAR）影像，以更可靠地解译复杂地表场景。文章对卷积神经网络、自编码器、生成对抗网络、Transformer架构、扩散模型以及混合模型驱动方法进行了技术综合，重点关注其融合机制、重建行为、计算需求以及业务化适用性。应用领域包括土地覆盖制图、精准农业、环境监测、城市分析、灾害评估和国防相关解译。本文并非将每种融合任务分开处理，而是在一个统一框架内将传感器异质性、空间与光谱分辨率权衡、辐射校正、几何校正、配准、降噪和融合层级设计联系起来。分析表明，卷积模型在稳定的局部细节恢复方面仍然有效，对抗学习可以提升视觉锐度但可能引入光谱失真，Transformer模型能更好地捕捉长程空间与光谱关系，而扩散模型以更高的计算成本提供精细重建。本文进一步指出了涉及配准误差、光谱偏差、标注数据有限、跨传感器泛化能力弱、高内存需求以及可解释性有限等开放挑战。未来的进展应优先关注传感器感知学习、自监督训练、不确定性感知评估、轻量化部署以及面向应用的基准测试，以提高业务化地球观测的可靠性。",null,"Engineering Applications of Artificial Intelligence","2026-09-18T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,14,9,1,"发表于核心期刊的综述，系统梳理深度学习多源遥感融合的方法、应用与挑战，对农业遥感与精准农业有直接参考价值，时效性强，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","深度学习","精准农业","遥感","多源数据融合",[33,34],"多源遥感 图像融合 深度学习","农业人工智能 多源数据融合 深度学习 精准农业","多源遥感图像融合深度学习-2942",0,"10.1016\u002Fj.engappai.2026.116319",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":53,"direction":57,"ingested_from":59},"W7213544281",[41,44,47,50],{"name":42,"orcid":43},"Shahid Karim","https:\u002F\u002Forcid.org\u002F0000-0001-9986-5052",{"name":45,"orcid":46},"Akeel Qadir","https:\u002F\u002Forcid.org\u002F0000-0003-0358-6505",{"name":48,"orcid":49},"Asif Ali Laghari","https:\u002F\u002Forcid.org\u002F0000-0001-5831-5943",{"name":51,"orcid":52},"Irfana Bibi","https:\u002F\u002Forcid.org\u002F0000-0003-2794-504X",{"tldr":54,"method":55,"finding":56,"direction":57,"opportunity":58},"综述深度学习多源遥感图像融合方法、挑战与未来方向。","综述CNN、GAN、Transformer、扩散模型等融合机制与重建行为。","CNN擅局部细节，GAN易谱失真，Transformer长程关系强，扩散模型精度高但算力大。","农业遥感与作物表型","面向农业的轻量、自监督、不确定性感知融合与基准数据集构建。","openalex","2026-09-19T23:30:32.767384Z",{"total":62,"page":22,"page_size":62,"items":63},6,[64,106,156,190,237,284],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":69,"content":9,"source_name":70,"source_url":67,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":78,"tags":80,"search_phrases":83,"slug":86,"view_count":36,"doi":87,"paper":88,"created_at":105},2963,"A comprehensive review of deep learning methods for weed classification in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-01916-7","Weeds today are among the factors contributing to low agricultural productivity. As the world’s population continues to grow, there is an urgent need to meet global food demand. Nigeria currently lacks sufficient crop production to feed its growing population, and weeds are among the core contributors to poor agricultural yield. This study conducts a comprehensive review of Deep Learning (DL) approaches to weed classification in precision agriculture, covering literature from 2018 to 2025, was carried out. We employed a mix of quantitative and qualitative methods in the course of this review paper. Our data source is centred on Scopus-indexed papers, published with Sensors, Electronics, and Agriculture in MDPI as well as IEEE, Thomson Reuters, and Springer. The study systematically reviewed and analysed machine learning (ML), DL, and instance segmentation techniques to identify the key technological and environmental barriers, such as data limitations, class imbalance, environmental variability, and model scalability issues that affect the effectiveness and efficiency of these models when deployed in real time. These findings show that while weed management models like the YOLO variants, ResNet, and Vision Transformers achieved high accuracy in training and testing, they are associated with several challenges in their real world-deployment, such as occlusion, small object detection, and environmental adaptability. Overall, this research provides recommended solutions to enhance model robustness, scalability, and efficiency. It further provides a summary of the current state and future directions for AI-driven weed management.","杂草是当前导致农业生产力低下的因素之一。随着世界人口持续增长，满足全球粮食需求已成为迫切任务。尼日利亚目前的作物产量不足以养活其不断增长的人口，而杂草是导致农业产量低下的核心因素之一。本研究对精准农业中基于深度学习（Deep Learning，DL）的杂草分类方法进行了全面综述，涵盖2018年至2025年的文献。在综述过程中，我们采用了定量与定性相结合的方法。数据来源集中于Scopus索引论文，这些论文发表于MDPI旗下的Sensors、Electronics和Agriculture，以及IEEE、Thomson Reuters和Springer。本研究系统综述并分析了机器学习（Machine Learning，ML）、深度学习及实例分割技术，以识别影响这些模型实时部署效果与效率的关键技术和环境障碍，如数据局限性、类别不平衡、环境变异性及模型可扩展性问题。研究结果表明，尽管YOLO系列、ResNet和视觉Transformer（Vision Transformer）等杂草管理模型在训练和测试中达到了较高精度，但在实际部署中仍面临诸多挑战，如遮挡、小目标检测和环境适应性等问题。总体而言，本研究提出了增强模型鲁棒性、可扩展性和效率的推荐解决方案，并进一步总结了人工智能驱动杂草管理的现状与未来方向。","Discover Artificial Intelligence",67,{"impact":73,"substance":17,"depth":74,"authority":75,"freshness":76,"relevant":22,"comment":77},12,16,13,8,"系统综述2018—2025年深度学习杂草分类方法，指出遮挡、小目标与环境适应性等落地瓶颈，对农业AI研究有参考价值，但属综述类论文、非突破性成果。",[79],{"name":70,"url":67},[81,27,28,82,29],"智慧农业","杂草识别",[84,85],"深度学习 杂草分类 精准农业","YOLO 杂草识别 模型部署","深度学习杂草分类精准农业-2963","10.1007\u002Fs44163-026-01916-7",{"doi":87,"openalex_id":89,"authors":90,"venue":70,"cited_by_count":36,"oa_url":67,"card":99,"direction":103,"ingested_from":59},"W7213558223",[91,93,95,97],{"name":92,"orcid":9},"Njoku Camillus Ekene",{"name":94,"orcid":9},"Francis A. Okoye",{"name":96,"orcid":9},"Ebere Uzoka Chidi",{"name":98,"orcid":9},"OGBU MARY NNENNA",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。","混合定量定性法，基于Scopus及MDPI、IEEE等文献，分析ML、DL与实例","YOLO、ResNet、ViT等精度高，但实际部署受遮挡、小目标与环境适应性限制。","农业人工智能与决策模型","可研究轻量化、跨域自适应模型，解决小目标与遮挡下的实时杂草识别难题。","2026-09-19T23:30:56.875661Z",{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":113,"sources":115,"tags":117,"search_phrases":119,"slug":122,"view_count":36,"doi":123,"paper":124,"created_at":155},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal",{"impact":73,"substance":17,"depth":74,"authority":75,"freshness":76,"relevant":22,"comment":114},"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[116],{"name":112,"url":109},[81,27,29,30,118],"土壤碳汇",[120,121],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":123,"openalex_id":125,"authors":126,"venue":112,"cited_by_count":36,"oa_url":109,"card":150,"direction":57,"ingested_from":59},"W7213561504",[127,130,133,136,139,142,144,146,148],{"name":128,"orcid":129},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":131,"orcid":132},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":134,"orcid":135},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":137,"orcid":138},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":140,"orcid":141},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":143,"orcid":9},"Almaha Jamal",{"name":145,"orcid":9},"Afra Rashed",{"name":147,"orcid":9},"Hamda Yousif",{"name":149,"orcid":9},"Sarah Sadeq",{"tldr":151,"method":152,"finding":153,"direction":57,"opportunity":154},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":161,"content":9,"source_name":162,"source_url":159,"published_at":163,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":164,"score_detail":165,"sources":169,"tags":171,"search_phrases":173,"slug":176,"view_count":36,"doi":177,"paper":178,"created_at":189},2940,"A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection","https:\u002F\u002Fdoi.org\u002F10.53365\u002Fnrfhh.1816","Cotton production is frequently affected by leaf diseases that can reduce plant productivity, deteriorate crop quality, and cause considerable financial losses for farmers. Consequently, rapid and reliable disease identification is an important requirement for precision agriculture and effective crop protection. Conventional image-based approaches predominantly employ CNN architectures for visual classification; however, such methods may have limited capability in learning long-range spatial dependencies and modeling changes associated with disease development over time. To overcome these limitations, this research introduces an integrated hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Transformer-based self-attention, and Long Short-Term Memory (LSTM) networks for intelligent cotton leaf disease recognition. The proposed framework utilizes a pre-trained EfficientNet network to extract discriminative spatial characteristics from cotton leaf images. The extracted representations are subsequently processed through a multi-head self-attention Transformer module, which enables the network to identify relationships between distant and relevant regions of the leaf. An LSTM component is then incorporated to learn sequential dependencies and provide a foundation for analyzing disease evolution and progression. To improve model reliability and generalization, the network is trained using an appropriately organized cotton leaf image dataset together with image augmentation, transfer learning, and fine-tuning techniques. The experimental evaluation indicates that the proposed CNN–Transformer–LSTM architecture provides improved disease classification performance and more effective feature learning compared with conventional CNN-based models. Performance assessment using classification metrics, confusion matrices, and ROC curves demonstrates strong discrimination among the considered cotton disease categories. The combined learning of local visual characteristics, global contextual information, and sequential dependencies provides a comprehensive framework for automated cotton disease assessment. The proposed approach can serve as a scalable artificial intelligence solution for precision agriculture applications. Its architecture also provides opportunities for future integration with IoT-based crop monitoring systems, environmental data acquisition, and disease progression prediction, supporting early warning systems and intelligent crop health management in smart farming environments.","棉花生产常受叶片病害影响，这些病害会降低植株生产力、恶化作物品质，并给农民造成相当大的经济损失。因此，快速可靠的病害识别是精准农业和有效作物保护的重要需求。传统的基于图像的方法主要采用CNN架构进行视觉分类；然而，此类方法在学习长程空间依赖关系以及建模与病害随时间发展相关的变化方面能力有限。为克服这些局限，本研究提出了一种集成混合深度学习框架，将卷积神经网络（CNN）、基于Transformer的自注意力机制和长短期记忆（LSTM）网络相结合，用于智能棉花叶片病害识别。所提出的框架利用预训练的EfficientNet网络从棉花叶片图像中提取具有判别力的空间特征。提取到的表示随后通过多头自注意力Transformer模块进行处理，使网络能够识别叶片中相距较远且相关区域之间的关系。随后引入LSTM组件以学习序列依赖关系，并为分析病害演变和进展提供基础。为提高模型可靠性和泛化能力，网络使用组织良好的棉花叶片图像数据集进行训练，并结合图像增强、迁移学习和微调技术。实验评估表明，与传统的基于CNN的模型相比，所提出的CNN–Transformer–LSTM架构提供了更好的病害分类性能和更有效的特征学习。使用分类指标、混淆矩阵和ROC曲线进行的性能评估表明，该方法在所考虑的棉花病害类别之间具有较强的判别能力。局部视觉特征、全局上下文信息和序列依赖关系的联合学习为自动化棉花病害评估提供了一个全面的框架。所提出的方法可作为精准农业应用中可扩展的人工智能解决方案。其架构也为未来与基于物联网的作物监测系统、环境数据采集和病害进展预测的集成提供了机会，从而支持预警系统和智能","Natural Resources for Human Health","2026-09-16T00:00:00Z",69,{"impact":73,"substance":166,"depth":167,"authority":73,"freshness":76,"relevant":22,"comment":168},20,17,"提出CNN-Transformer-LSTM混合框架用于棉花叶病识别，方法有创新但属实验室验证阶段，产业影响有限。",[170],{"name":162,"url":159},[81,27,28,29,172],"棉花病害识别",[174,175],"棉花叶病 CNN Transformer LSTM","EfficientNet 棉花病害检测","棉花叶病CNNTransformerLSTM-2940","10.53365\u002Fnrfhh.1816",{"doi":177,"openalex_id":179,"authors":180,"venue":162,"cited_by_count":36,"oa_url":9,"card":183,"direction":188,"ingested_from":59},"W7213544712",[181],{"name":182,"orcid":9},"Prajakta Sunil Gupta",{"tldr":184,"method":185,"finding":186,"direction":103,"opportunity":187},"提出CNN-Transformer-LSTM混合框架，实现棉花叶片病害自动识别。","EfficientNet提取特征，Transformer自注意力与LSTM建模，","混合模型分类性能优于传统CNN，能同时学习局部特征、全局上下文和序列依赖。","可融合IoT环境数据与时间序列，开展病害进展预测和早期预警系统研究。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:19.558449Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":201,"tags":203,"search_phrases":205,"slug":208,"view_count":36,"doi":209,"paper":210,"created_at":236},2908,"An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112447","An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization。Computers and Electronics in Agriculture","一种信息驱动的空地协同框架，用于基于无人机耕作缺陷识别与再耕作路径优化。《农业计算机与电子》","Computers and Electronics in Agriculture",79,{"impact":17,"substance":199,"depth":17,"authority":20,"freshness":76,"relevant":22,"comment":200},21,"发表于农业信息领域核心期刊，提出空地协同的无人机耕地缺陷识别与再耕路径优化框架，方法新颖且面向精准农业实际需求，具备较高参考价值。",[202],{"name":196,"url":193},[81,27,204,29,30],"农业无人机",[206,207],"无人机 耕地缺陷 识别","再耕路径 优化","无人机耕地缺陷识别-2908","10.1016\u002Fj.compag.2026.112447",{"doi":209,"openalex_id":211,"authors":212,"venue":196,"cited_by_count":36,"oa_url":9,"card":231,"direction":188,"ingested_from":59},"W7213547466",[213,215,217,219,222,224,226,229],{"name":214,"orcid":9},"Chenshuo Xie",{"name":216,"orcid":9},"Yejun Zhu",{"name":218,"orcid":9},"Dongfang Li",{"name":220,"orcid":221},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":223,"orcid":9},"Le Yang",{"name":225,"orcid":9},"Yuxuan Wan",{"name":227,"orcid":228},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":230,"orcid":9},"Mingfeng Wang",{"tldr":232,"method":233,"finding":234,"direction":188,"opportunity":235},"提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。","无人机遥感影像与信息驱动算法，识别缺陷并规划重耕路径。","框架能有效识别耕整缺陷并生成优化补耕路径，提升作业质量。","可探索多机协同与实时动态重规划，结合土壤传感器提升缺陷识别精度。","2026-09-19T23:30:02.081036Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":163,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":247,"tags":249,"search_phrases":252,"slug":255,"view_count":36,"doi":256,"paper":257,"created_at":283},2790,"High-resolution land cover mapping from coarse labels via a noisy label learning-guided cross-scale framework","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2726002","High-resolution remote sensing images (HRSIs) provide essential data support for land cover mapping, where deep learning has shown great promise. However, deep learning-based methods rely on abundant high-quality annotations, while low-resolution coarse labels are difficult to use directly in HRSIs training. In this paper, a novel noisy label learning-guided cross-scale framework (NL-CSF) is proposed, which is designed to achieve high-resolution land cover mapping from coarse labels. First, a spectral-based label mask filtering strategy is developed to preliminarily optimize coarse labels. Then, an adaptive noise evaluation scheme is introduced that assigns loss weights based on the noise differences between image and label patches in the training set. Finally, we design a cross-scale transfer Transformer (CSTT) model based on the vision Transformer (ViT) architecture, and the training process is guided by a noise-weighted loss function. Two cross-scale datasets are utilized to evaluate the performance of NL-CSF in multiple spatial scale differences (10 m to 3 m, 3 m to 0.5 m, and 10 m to 0.5 m). Experimental results demonstrate that NL-CSF improves overall accuracy (OA) by at least 7%, 6%, and 4% across the three cross-scale tasks in the first dataset, and by at least 2%, 9%, and 4% in the second dataset, respectively, compared with existing methods. Furthermore, the proposed framework is applied to cross-scale mapping across Jianye District of Nanjing (urban), Sheyang County of Yancheng (agricultural), and the Yellow River Delta of Dongying (wetland), leveraging a low-resolution land cover product and high-resolution PlanetScope images to generate more precise land cover maps. These results demonstrate the effectiveness of the proposed framework in mitigating the impact of noisy coarse labels and generating reliable high-resolution land cover maps.","高分辨率遥感影像(high-resolution remote sensing images, HRSIs)为土地覆盖制图提供了重要的数据支撑，深度学习在此领域展现出巨大潜力。然而，基于深度学习的方法依赖于大量高质量标注，而低分辨率粗标签难以直接用于高分辨率遥感影像训练。本文提出了一种新的噪声标签学习引导的跨尺度框架(noisy label learning-guided cross-scale framework, NL-CSF)，旨在从粗标签实现高分辨率土地覆盖制图。首先，提出了一种基于光谱的标签掩膜过滤策略，对粗标签进行初步优化。然后，引入了一种自适应噪声评估方案，根据训练集中影像块与标签块之间的噪声差异分配损失权重。最后，基于视觉Transformer(vision Transformer, ViT)架构设计了跨尺度迁移Transformer(cross-scale transfer Transformer, CSTT)模型，并以噪声加权损失函数引导训练过程。利用两个跨尺度数据集评估NL-CSF在多种空间尺度差异(10 m至3 m、3 m至0.5 m、10 m至0.5 m)下的性能。实验结果表明，与现有方法相比，NL-CSF在第一个数据集的三个跨尺度任务中总体精度(overall accuracy, OA)分别至少提升7%、6%和4%，在第二个数据集中分别至少提升2%、9%和4%。此外，将所提框架应用于南京建邺区(城市)、盐城射阳县(农业)和东营黄河三角洲(湿地)的跨尺度制图，利用低分辨率土地覆盖产品和高分PlanetScope影像生成更精确的土地覆盖图。这些结果证明了所提框架在减轻噪声粗标签影响和生成可靠高分辨率土地覆盖图方面的有效性。","GIScience & Remote Sensing",81,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":246},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图，精度提升显著，对农业遥感监测有实用价值。",[248],{"name":243,"url":240},[27,28,30,250,251],"土地覆盖","高分辨率制图",[253,254],"农业人工智能 高分辨率制图 土地覆盖 深度学习","农业人工智能 高分辨率制图","农业人工智能高分辨率制图土地覆盖深度学习-2790","10.1080\u002F15481603.2026.2726002",{"doi":256,"openalex_id":258,"authors":259,"venue":243,"cited_by_count":36,"oa_url":240,"card":278,"direction":57,"ingested_from":59},"W7213283296",[260,263,266,269,272,274,276],{"name":261,"orcid":262},"Xiangyu Nie","https:\u002F\u002Forcid.org\u002F0009-0001-5095-6401",{"name":264,"orcid":265},"Cong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-5386-7343",{"name":267,"orcid":268},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8162-9422",{"name":270,"orcid":271},"Hong Fang","https:\u002F\u002Forcid.org\u002F0000-0003-3707-0910",{"name":273,"orcid":9},"Zhen Dong",{"name":275,"orcid":9},"Sicong Liu",{"name":277,"orcid":9},"Zhaohui Xue",{"tldr":279,"method":280,"finding":281,"direction":57,"opportunity":282},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图。","谱掩膜过滤粗标签、自适应噪声评估加权损失、基于ViT的跨尺度迁移Transfor","在多个跨尺度任务上总体精度提升2%-9%，并在城市、农业、湿地场景生成更精确土地覆盖图。","可探索将粗标签跨尺度学习用于作物精细分类与长时序农情监测，降低高精度标注依赖。","2026-09-17T23:30:34.952106Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":9,"source_name":290,"source_url":287,"published_at":291,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":295,"tags":297,"search_phrases":299,"slug":302,"view_count":36,"doi":303,"paper":304,"created_at":338},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing","2026-09-14T00:00:00Z",76,{"impact":74,"substance":199,"depth":167,"authority":75,"freshness":21,"relevant":22,"comment":294},"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[296],{"name":290,"url":287},[81,27,298,29,30],"水稻",[300,301],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":303,"openalex_id":305,"authors":306,"venue":290,"cited_by_count":36,"oa_url":332,"card":333,"direction":188,"ingested_from":59},"W7212834743",[307,309,311,313,315,318,320,322,324,327,329],{"name":308,"orcid":9},"Amrutha Lakshmi Gubbala",{"name":310,"orcid":9},"Santosha Rathod",{"name":312,"orcid":9},"Ramesh Dasyam",{"name":314,"orcid":9},"Mahender Kumar Rapolu",{"name":316,"orcid":317},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":319,"orcid":9},"Pundarikakshudu Kurra",{"name":321,"orcid":9},"Hanuma Raviteja Madireddy",{"name":323,"orcid":9},"Prajwal R. Shashishekhar",{"name":325,"orcid":326},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":328,"orcid":9},"Anil Kumar",{"name":330,"orcid":331},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":334,"method":335,"finding":336,"direction":57,"opportunity":337},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","2026-09-15T23:30:08.600535Z"]