[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2940":3,"related-2940":51},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":50},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",null,"Natural Resources for Human Health","2026-09-16T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,17,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","深度学习","精准农业","棉花病害识别",[32,33],"棉花叶病 CNN Transformer LSTM","EfficientNet 棉花病害检测","棉花叶病CNNTransformerLSTM-2940",0,"10.53365\u002Fnrfhh.1816",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":42,"direction":48,"ingested_from":49},"W7213544712",[40],{"name":41,"orcid":9},"Prajakta Sunil Gupta",{"tldr":43,"method":44,"finding":45,"direction":46,"opportunity":47},"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。","农业人工智能与决策模型","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","openalex","2026-09-19T23:30:19.558449Z",{"total":52,"page":21,"page_size":52,"items":53},6,[54,94,130,181,233,276],{"id":55,"title":56,"url":57,"summary":58,"summary_zh":59,"content":9,"source_name":60,"source_url":57,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":62,"score_detail":63,"sources":68,"tags":70,"search_phrases":72,"slug":75,"view_count":35,"doi":76,"paper":77,"created_at":93},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","2026-09-18T00:00:00Z",67,{"impact":17,"substance":64,"depth":65,"authority":66,"freshness":20,"relevant":21,"comment":67},18,16,13,"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。",[69],{"name":60,"url":57},[26,27,28,71,29],"杂草识别",[73,74],"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":76,"openalex_id":78,"authors":79,"venue":60,"cited_by_count":35,"oa_url":57,"card":88,"direction":46,"ingested_from":49},"W7213558223",[80,82,84,86],{"name":81,"orcid":9},"Njoku Camillus Ekene",{"name":83,"orcid":9},"Francis A. Okoye",{"name":85,"orcid":9},"Ebere Uzoka Chidi",{"name":87,"orcid":9},"OGBU MARY NNENNA",{"tldr":89,"method":90,"finding":91,"direction":46,"opportunity":92},"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。","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":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":107,"tags":109,"search_phrases":110,"slug":113,"view_count":35,"doi":114,"paper":115,"created_at":129},2335,"Deep Learning-Based Weed Classification, Detection, and Segmentation in Precision Agriculture: a Bibliometric and Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10343-026-01424-9","Deep Learning-Based Weed Classification, Detection, and Segmentation in Precision Agriculture: a Bibliometric and Comprehensive Review。Journal of Plant Diseases and Protection","基于深度学习的精准农业杂草分类、检测与分割：文献计量与综合综述。《植物病害与保护杂志》","Journal of Plant Diseases and Protection","2026-09-12T00:00:00Z",75,{"impact":104,"substance":18,"depth":64,"authority":105,"freshness":20,"relevant":21,"comment":106},15,14,"核心期刊发表的深度学习杂草识别综述，方法梳理与文献计量兼具，对精准农业植保智能化有参考价值，但属学术综述而非产业级突破。",[108],{"name":100,"url":97},[26,27,28,71,29],[111,112],"农业人工智能 智慧农业 杂草识别 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业杂草识别深度学习-2335","10.1007\u002Fs10343-026-01424-9",{"doi":114,"openalex_id":116,"authors":117,"venue":100,"cited_by_count":35,"oa_url":9,"card":124,"direction":46,"ingested_from":49},"W7212352052",[118,121],{"name":119,"orcid":120},"Furkan Ulaş","https:\u002F\u002Forcid.org\u002F0009-0002-3052-4457",{"name":122,"orcid":123},"Muhammad Aasım","https:\u002F\u002Forcid.org\u002F0000-0002-8524-9029",{"tldr":125,"method":126,"finding":127,"direction":46,"opportunity":128},"对精准农业中深度学习杂草分类、检测与分割研究进行文献计量与综述分析。","文献计量分析与综合综述，基于相关文献数据库。","梳理了深度学习在杂草识别三大任务中的方法、趋势与挑战。","可针对田间复杂光照与多物种场景，探索轻量化实时分割模型与跨数据集泛化。","2026-09-13T23:30:38.907624Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":140,"tags":142,"search_phrases":145,"slug":148,"view_count":35,"doi":149,"paper":150,"created_at":180},2961,"Advances in Bioacoustics Sensing and Signal Processing Technologies","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-7990\u002Faea9da","Abstract Driven by advances in flexible electronics, microfabrication, and artificial intelligence, bioacoustics is revolutionizing sensing and signal processing across health, ecology, and intelligent systems. This review systematically examines key progress in high-performance acoustic sensors (e.g., piezoelectric, triboelectric, MEMS) and acquisition schemes for multi-scale biological sounds, ranging from heart tones to plant stress emissions. Furthermore, we delve into advanced methodologies for acoustic signal preprocessing, feature extraction, and classification, with a particular emphasis on the powerful analytical capabilities of multimodal data fusion and deep learning algorithms in complex real-world scenarios. Crucially, although the sensing requirements and biological targets vary drastically across medical, ecological, and agricultural domains, this review establishes a unifying framework centered on the shared physical and algorithmic challenges of bioacoustic information flow. By juxtaposing these diverse fields, the potential strategies for the interdisciplinary integration of sensing methodologies are highlighted. We also critically assess the essential roles of acoustic physical modeling, standardized testing frameworks, and gold-standard databases in evaluating system performance. Finally, the review highlights cutting-edge applications of bioacoustics in human health monitoring, medical diagnostics, human-computer interaction, and precision agriculture. By synthesizing current technological convergences and outlining future trajectories, we provide a comprehensive perspective on the promising directions and pivotal challenges facing bioacoustics research.","摘要 在柔性电子、微加工和人工智能进步的推动下，生物声学正在革新健康、生态和智能系统中的传感与信号处理。本文系统梳理了高性能声学传感器（如压电、摩擦电、MEMS）以及从心音到植物胁迫发射等多尺度生物声音采集方案的关键进展。此外，我们深入探讨了声学信号预处理、特征提取和分类的先进方法，特别强调了多模态数据融合和深度学习算法在复杂现实场景中的强大分析能力。至关重要的是，尽管医学、生态和农业领域在传感需求和生物目标上差异巨大，本文建立了一个以生物声学信息流中共同的物理和算法挑战为中心的统一框架。通过将这些不同领域并置比较，凸显了传感方法学跨学科整合的潜在策略。我们还批判性地评估了声学物理建模、标准化测试框架和金标准数据库在评估系统性能中的重要作用。最后，本文重点介绍了生物声学在人体健康监测、医学诊断、人机交互和精准农业中的前沿应用。通过综合当前技术汇聚趋势并勾勒未来轨迹，我们为生物声学研究面临的有前景方向和关键挑战提供了全面视角。","International Journal of Extreme Manufacturing",78,{"impact":64,"substance":18,"depth":64,"authority":105,"freshness":20,"relevant":21,"comment":139},"该综述系统梳理生物声学传感与信号处理技术，并明确指向精准农业与植物胁迫声发射监测，对农业信息化具有跨领域参考价值，但属综述类论文，非产业级突破。",[141],{"name":136,"url":133},[26,27,29,143,144],"多模态融合","生物声学传感",[146,147],"生物声学 传感器 信号处理","植物胁迫 声发射 监测","生物声学传感器信号处理-2961","10.1088\u002F2631-7990\u002Faea9da",{"doi":149,"openalex_id":151,"authors":152,"venue":136,"cited_by_count":35,"oa_url":133,"card":175,"direction":46,"ingested_from":49},"W7213590695",[153,156,159,161,164,167,169,172],{"name":154,"orcid":155},"Chengyu Li","https:\u002F\u002Forcid.org\u002F0000-0002-2128-3420",{"name":157,"orcid":158},"Wenbo Li","https:\u002F\u002Forcid.org\u002F0000-0003-3599-3324",{"name":160,"orcid":9},"Jingyang Wu",{"name":162,"orcid":163},"Shuo Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5411-7269",{"name":165,"orcid":166},"Han Liao","https:\u002F\u002Forcid.org\u002F0009-0001-6256-7438",{"name":168,"orcid":9},"xiang Yu",{"name":170,"orcid":171},"Cheng Li","https:\u002F\u002Forcid.org\u002F0000-0003-3424-2414",{"name":173,"orcid":174},"Xiaoming Tao","https:\u002F\u002Forcid.org\u002F0000-0002-2406-0695",{"tldr":176,"method":177,"finding":178,"direction":48,"opportunity":179},"综述生物声学传感与信号处理进展，涵盖医疗、生态与农业应用。","综述压电\u002F摩擦电\u002FMEMS声传感器、深度学习与多模态融合方法。","建立跨领域统一框架，强调物理建模、标准测试与数据库的关键作用。","植物胁迫声发射的标准化采集与深度学习分类，可填补农业声学监测空白。","2026-09-19T23:30:51.998646Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":62,"score_detail":188,"sources":190,"tags":192,"search_phrases":195,"slug":198,"view_count":35,"doi":199,"paper":200,"created_at":232},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":17,"substance":64,"depth":65,"authority":66,"freshness":20,"relevant":21,"comment":189},"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。",[191],{"name":187,"url":184},[26,27,29,193,194],"遥感","土壤碳汇",[196,197],"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":199,"openalex_id":201,"authors":202,"venue":187,"cited_by_count":35,"oa_url":184,"card":226,"direction":230,"ingested_from":49},"W7213561504",[203,206,209,212,215,218,220,222,224],{"name":204,"orcid":205},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":207,"orcid":208},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":210,"orcid":211},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":213,"orcid":214},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":216,"orcid":217},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":219,"orcid":9},"Almaha Jamal",{"name":221,"orcid":9},"Afra Rashed",{"name":223,"orcid":9},"Hamda Yousif",{"name":225,"orcid":9},"Sarah Sadeq",{"tldr":227,"method":228,"finding":229,"direction":230,"opportunity":231},"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。","农业遥感与作物表型","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":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":246,"tags":248,"search_phrases":250,"slug":253,"view_count":35,"doi":254,"paper":255,"created_at":275},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。","Engineering Applications of Artificial Intelligence",82,{"impact":64,"substance":242,"depth":243,"authority":105,"freshness":244,"relevant":21,"comment":245},22,19,9,"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。",[247],{"name":239,"url":236},[27,28,29,193,249],"多源数据融合",[251,252],"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","10.1016\u002Fj.engappai.2026.116319",{"doi":254,"openalex_id":256,"authors":257,"venue":239,"cited_by_count":35,"oa_url":236,"card":270,"direction":230,"ingested_from":49},"W7213544281",[258,261,264,267],{"name":259,"orcid":260},"Shahid Karim","https:\u002F\u002Forcid.org\u002F0000-0001-9986-5052",{"name":262,"orcid":263},"Akeel Qadir","https:\u002F\u002Forcid.org\u002F0000-0003-0358-6505",{"name":265,"orcid":266},"Asif Ali Laghari","https:\u002F\u002Forcid.org\u002F0000-0001-5831-5943",{"name":268,"orcid":269},"Irfana Bibi","https:\u002F\u002Forcid.org\u002F0000-0003-2794-504X",{"tldr":271,"method":272,"finding":273,"direction":230,"opportunity":274},"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。","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。","2026-09-19T23:30:32.767384Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":61,"category":12,"cover_url":9,"hotness":283,"is_selected":14,"score":284,"score_detail":285,"sources":287,"tags":291,"search_phrases":293,"slug":296,"view_count":35,"doi":297,"paper":298,"created_at":308},2934,"A CNN-Based Approach for Leaf Disease Prediction in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825329","Plants play a crucial role in sustaining life by serving as a primary source of energy and mitigating global warming. However, they are increasingly vulnerable to diseases such as bacterial spot, late blight, and Septoria leaf spot, which significantly impact crop yield and agricultural productivity. Early and accurate detection of these diseases is essential for effective disease management and improved agricultural outcomes. This project aims to develop a deep learning-based approach for detecting plant leaf diseases using Convolutional Neural Networks (CNN). By leveraging benchmark datasets, the proposed CNN model demonstrates superior performance compared to traditional machine learning techniques, achieving an accuracy of 92%, precision of 89%, F1-score of 93%, and recall of 92.47%. The results highlight the effectiveness of CNN in automating disease identification, enabling timely intervention, and promoting sustainable agricultural practices.","植物在维持生命方面发挥着至关重要的作用，既是主要的能量来源，又能缓解全球变暖。然而，植物日益受到细菌性斑点病、晚疫病和壳针孢叶斑病等病害的威胁，严重影响作物产量和农业生产率。早期准确地检测这些病害对于有效防控病害和改善农业成果至关重要。本项目旨在开发一种基于深度学习的方法，利用卷积神经网络（CNN）检测植物叶片病害。通过利用基准数据集，所提出的CNN模型展现出优于传统机器学习技术的性能，达到了92%的准确率、89%的精确率、93%的F1分数和92.47%的召回率。结果表明，CNN在自动化病害识别方面具有显著效果，能够实现及时干预并促进可持续农业实践。","Zenodo (CERN European Organization for Nuclear Research)",25,65,{"impact":17,"substance":64,"depth":105,"authority":17,"freshness":244,"relevant":21,"comment":286},"基于CNN的叶片病害识别研究，方法常规、数据集为公开基准，准确率92%属中等水平，对智慧农业植保场景有一定参考价值但缺乏突破性。",[288,289],{"name":282,"url":279},{"name":282,"url":290},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825330",[26,27,28,292],"植物病害识别",[294,295],"CNN 植物叶片病害 识别","卷积神经网络 作物病害 检测","CNN植物叶片病害识别-2934","10.5281\u002Fzenodo.22825329",{"doi":297,"openalex_id":299,"authors":300,"venue":282,"cited_by_count":35,"oa_url":279,"card":303,"direction":48,"ingested_from":49},"W7213587327",[301],{"name":302,"orcid":9},"B.Yashmal Sai, K.Karthik, K.Neeraj, G. Mahabub Subhani",{"tldr":304,"method":305,"finding":306,"direction":46,"opportunity":307},"用CNN对植物叶片病害进行自动识别，在基准数据集上取得92%准确率。","基于卷积神经网络，使用植物叶片病害基准数据集训练与评估。","CNN优于传统机器学习方法，准确率92%、F1值93%，可支持及时干预。","可探索轻量化CNN在田间移动端实时检测，并结合多病害与早期症状识别。","2026-09-19T23:30:11.855376Z"]