[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2944":3,"related-2944":86},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":85},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。",null,"Scientific Reports","2026-09-18T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业遥感","多模态融合","功能分区",{"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":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,58],{"code":32,"score":13,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"deep learning",[],[27,29],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":57},"I3",7,[],[55,56],"遥感","remote sensing",[28],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[26],[64,65],"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",2,"10.1038\u002Fs41598-026-72129-2",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":77,"oa_url":6,"card":78,"direction":82,"ingested_from":84},"W7213550895",[72,74],{"name":73,"orcid":9},"Ruifen Wen",{"name":75,"orcid":76},"Juan Du","https:\u002F\u002Forcid.org\u002F0000-0002-7422-8767",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"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。","农业遥感与作物表型","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。","openalex","2026-09-19T23:30:32.908587Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,203,285,345,399,460],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":97,"sources":99,"tags":101,"primary_topic":103,"secondary_topics":104,"classification":115,"search_phrases":174,"slug":177,"view_count":21,"doi":178,"paper":179,"created_at":202},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science","2026-10-14T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":98},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[100],{"name":95,"url":92},[26,27,102,29,55],"产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[105,111],{"code":106,"slug":107,"name":108,"path":109,"parent_code":110},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":51,"slug":112,"name":113,"path":114,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":116},{"candidates":117,"agricultural_evidence":45},[118,126,134,141,147,154,161,168],{"code":32,"score":119,"supported":45,"title":120,"abstract":121,"tags":125},11,[],[122,123,124,47],"深度学习","机器学习","machine learning",[27,29],{"code":106,"score":20,"supported":45,"title":127,"abstract":128,"tags":133},[],[129,130,131,132],"苹果","芒果","orchard","apple",[],{"code":51,"score":135,"supported":45,"title":136,"abstract":137,"tags":140},8,[],[138,139],"无人机多光谱","multispectral",[102,55],{"code":59,"score":52,"supported":45,"title":142,"abstract":143,"tags":146},[],[144,145],"精准农业","precision agriculture",[26],{"code":148,"score":87,"supported":45,"title":149,"abstract":150,"tags":153},"H2",[],[151,152],"土壤","soil",[],{"code":155,"score":87,"supported":45,"title":156,"abstract":157,"tags":160},"I5",[],[158,159],"物联网","IoT",[],{"code":162,"score":163,"supported":45,"title":164,"abstract":165,"tags":167},"H6",3,[],[166],"农业生态",[],{"code":169,"score":163,"supported":45,"title":170,"abstract":171,"tags":173},"H7",[],[172],"气候适应",[],[175,176],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":178,"openalex_id":180,"authors":181,"venue":95,"cited_by_count":77,"oa_url":92,"card":195,"direction":201,"ingested_from":84},"W7221795850",[182,185,187,189,191,193],{"name":183,"orcid":184},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":186,"orcid":9},"Umarfarooque Momin",{"name":188,"orcid":9},"Sudeep Kumar E",{"name":190,"orcid":9},"Itfaq Ahmad Mir",{"name":192,"orcid":9},"Ab Rouf Khan",{"name":194,"orcid":9},"Ghulam Mohammad Mir",{"tldr":196,"method":197,"finding":198,"direction":199,"opportunity":200},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:08.750140Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":9,"source_name":209,"source_url":206,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":260,"slug":263,"view_count":77,"doi":264,"paper":265,"created_at":284},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":212,"substance":213,"depth":214,"authority":215,"freshness":20,"relevant":21,"comment":216},12,20,17,13,"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。",[218],{"name":209,"url":206},[26,27,28,220,221],"防灾减灾","作物病害预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":225},{"candidates":226,"agricultural_evidence":45},[227,233,238,243,250,256],{"code":59,"score":228,"supported":45,"title":229,"abstract":231,"tags":232},15,[230],"smart agriculture",[26,230],[26],{"code":32,"score":13,"supported":45,"title":234,"abstract":235,"tags":237},[],[122,47,236],"decision support",[27],{"code":169,"score":52,"supported":45,"title":239,"abstract":240,"tags":242},[],[241,172],"气候变化",[220],{"code":244,"score":87,"supported":45,"title":245,"abstract":246,"tags":249},"A8",[],[247,248],"粮食安全","food security",[],{"code":251,"score":163,"supported":45,"title":252,"abstract":253,"tags":255},"A7",[],[254],"间作",[],{"code":51,"score":21,"supported":14,"title":257,"abstract":258,"tags":259},[],[],[28],[261,262],"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":264,"openalex_id":266,"authors":267,"venue":209,"cited_by_count":77,"oa_url":206,"card":279,"direction":201,"ingested_from":84},"W7221778141",[268,271,274,276],{"name":269,"orcid":270},"Deepak Motwani","https:\u002F\u002Forcid.org\u002F0000-0002-0217-7155",{"name":272,"orcid":273},"Sellappan Palaniappan","https:\u002F\u002Forcid.org\u002F0009-0009-1168-2864",{"name":275,"orcid":9},"A. Daniel",{"name":277,"orcid":278},"Azween B. Abdullah","https:\u002F\u002Forcid.org\u002F0000-0003-4425-8604",{"tldr":280,"method":281,"finding":282,"direction":199,"opportunity":283},"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。","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":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":9,"source_name":291,"source_url":288,"published_at":292,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":293,"score_detail":294,"sources":296,"tags":298,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":325,"slug":328,"view_count":77,"doi":329,"paper":330,"created_at":344},4492,"A multimodal context-aware AI recommender for smart farming","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0359596","Agricultural productivity in developing regions is significantly affected by plant diseases and pest infestations, making early detection and timely intervention essential for improving crop yield and food security. This study proposes a multimodal context-aware AI recommendation framework that integrates maize leaf image analysis and environmental conditions to support intelligent agricultural decision-making. The framework combines a deep learning-based image classification model with weather variables, including temperature, humidity, rainfall, and solar radiation, through a multimodal fusion model for maize disease diagnosis. Based on the predicted crop condition, a context-aware recommendation engine integrates rule-based agronomic knowledge with the Qwen2.5:7B large language model to generate reliable and actionable natural-language recommendations for farmers. The generated recommendations are subsequently evaluated using Llama 3.1:8B in a blind LLM-based evaluation framework. Experiments were conducted using maize leaf images from the YEESI Lab dataset and a 61-day environmental dataset from the NASA POWER database, obtained for the geographic coordinates of Morogoro, Tanzania. Three maize conditions were considered: healthy plants, aphid infestation, and maize streak virus infection. The image-based model achieved an accuracy of 91%, while the weather-based model showed lower performance due to overlapping environmental characteristics among disease classes. The proposed multimodal fusion model achieved a classification accuracy of 94%, demonstrating the effectiveness of integrating visual and environmental information for disease diagnosis. The recommendation engine achieved an overall evaluation score of 4.80\u002F5.00 across five quality dimensions—Safety, Technical Accuracy, Relevance, Actionability, and Clarity—indicating that the generated recommendations were agronomically consistent, context-aware, and actionable. The proposed framework provides an end-to-end AI-driven decision-support solution that integrates multimodal disease diagnosis, and context-aware recommendation for precision agriculture.","发展中国家地区的农业生产力受植物病害和虫害侵染的显著影响，因此早期检测和及时干预对于提高作物产量和保障粮食安全至关重要。本研究提出了一种多模态情境感知AI推荐框架，该框架整合了玉米叶片图像分析与环境条件，以支持智能农业决策。该框架将基于深度学习的图像分类模型与天气变量（包括温度、湿度、降雨量和太阳辐射）相结合，通过多模态融合模型进行玉米病害诊断。基于预测的作物状况，情境感知推荐引擎将基于规则的农艺知识与Qwen2.5:7B大语言模型相结合，为农民生成可靠且可操作的自然语言建议。生成的建议随后通过Llama 3.1:8B在基于大语言模型的盲评框架中进行评估。实验使用来自YEESI Lab数据集的玉米叶片图像和来自NASA POWER数据库的61天环境数据集，该数据集获取自坦桑尼亚莫罗戈罗的地理坐标。研究考虑了三种玉米状况：健康植株、蚜虫侵染和玉米条纹病毒（maize streak virus）感染。基于图像的模型达到了91%的准确率，而基于天气的模型由于病害类别之间环境特征重叠而表现较低。所提出的多模态融合模型达到了94%的分类准确率，证明了整合视觉与环境信息进行病害诊断的有效性。推荐引擎在五个质量维度——安全性、技术准确性、相关性、可操作性和清晰度——上获得了4.80\u002F5.00的总体评估分数，表明生成的建议在农艺上一致、具有情境感知能力且可操作。所提出的框架提供了一种端到端的AI驱动决策支持解决方案，将多模态病害诊断与情境感知推荐相结合，服务于精准农业。","PLoS ONE","2026-10-08T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":215,"freshness":135,"relevant":21,"comment":295},"多模态融合诊断玉米病虫害并生成可执行农事建议，方法新颖、数据与评估扎实，对智慧农业决策支持有参考价值。",[297],{"name":291,"url":288},[26,27,299,144,29],"病虫害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":303},{"candidates":304,"agricultural_evidence":45},[305,310,314,321],{"code":32,"score":212,"supported":45,"title":306,"abstract":307,"tags":309},[],[122,47,308],"large language model",[27,29,299],{"code":59,"score":135,"supported":45,"title":311,"abstract":312,"tags":313},[],[144,145],[26,144],{"code":315,"score":87,"supported":45,"title":316,"abstract":317,"tags":320},"A3",[],[318,319],"玉米","maize",[],{"code":244,"score":87,"supported":45,"title":322,"abstract":323,"tags":324},[],[247,248],[],[326,327],"玉米叶病 多模态 诊断","Qwen2.5 农业推荐","玉米叶病多模态诊断-4492","10.1371\u002Fjournal.pone.0359596",{"doi":329,"openalex_id":331,"authors":332,"venue":291,"cited_by_count":77,"oa_url":288,"card":339,"direction":201,"ingested_from":84},"W7221033947",[333,336],{"name":334,"orcid":335},"John Telesphory Mhagama","https:\u002F\u002Forcid.org\u002F0009-0001-4103-5344",{"name":337,"orcid":338},"Kanwal Garg","https:\u002F\u002Forcid.org\u002F0000-0003-0048-9355",{"tldr":340,"method":341,"finding":342,"direction":199,"opportunity":343},"提出融合玉米叶片图像与气象数据的多模态AI推荐框架，实现病害诊断与农事建议生成。","深度学习图像分类+气象变量多模态融合，结合规则知识与Qwen2.5大模型生成建议","多模态融合诊断准确率达94%，推荐引擎在五个质量维度获4.80\u002F5.00分。","可探索多作物、多病害扩展及大模型推荐在真实田间场景的长期验证与本地化适配。","2026-10-09T23:30:08.136081Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":352,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":354,"score_detail":355,"sources":358,"tags":360,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":381,"slug":384,"view_count":67,"doi":385,"paper":386,"created_at":398},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":356,"depth":228,"authority":13,"freshness":135,"relevant":21,"comment":357},16,"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。",[359],{"name":351,"url":352},[26,361,27,28,144],"无人机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":365},{"candidates":366,"agricultural_evidence":45},[367,371,377],{"code":59,"score":135,"supported":45,"title":368,"abstract":369,"tags":370},[],[144,145],[26,144],{"code":32,"score":372,"supported":45,"title":373,"abstract":374,"tags":376},4,[],[375],"artificial intelligence",[27],{"code":51,"score":21,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[28],[382,383],"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":385,"openalex_id":387,"authors":388,"venue":351,"cited_by_count":77,"oa_url":352,"card":393,"direction":199,"ingested_from":84},"W7220406405",[389,391],{"name":390,"orcid":9},"Somitov Abdukadir Abdurayimovich",{"name":392,"orcid":9},"Worldly Knowledge Publishing Centre",{"tldr":394,"method":395,"finding":396,"direction":201,"opportunity":397},"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":400,"title":401,"url":402,"summary":403,"summary_zh":404,"content":9,"source_name":405,"source_url":402,"published_at":406,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":354,"score_detail":407,"sources":409,"tags":411,"primary_topic":414,"secondary_topics":415,"classification":421,"search_phrases":441,"slug":444,"view_count":163,"doi":445,"paper":446,"created_at":459},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","2026-09-29T00:00:00Z",{"impact":212,"substance":17,"depth":356,"authority":212,"freshness":20,"relevant":21,"comment":408},"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。",[410],{"name":405,"url":402},[26,27,28,412,413],"边缘计算","土地覆盖分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[416,417],{"code":51,"slug":112,"name":113,"path":114,"parent_code":36},{"code":155,"slug":418,"name":419,"path":420,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":422},{"candidates":423,"agricultural_evidence":45},[424,428,432,437],{"code":32,"score":17,"supported":45,"title":425,"abstract":426,"tags":427},[47],[122,47,375],[27],{"code":51,"score":228,"supported":45,"title":429,"abstract":430,"tags":431},[56],[55,56],[28],{"code":155,"score":228,"supported":45,"title":433,"abstract":435,"tags":436},[434],"edge computing",[412,434],[412],{"code":59,"score":21,"supported":14,"title":438,"abstract":439,"tags":440},[],[],[26],[442,443],"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":445,"openalex_id":447,"authors":448,"venue":405,"cited_by_count":77,"oa_url":402,"card":454,"direction":82,"ingested_from":84},"W7214769118",[449,451],{"name":450,"orcid":9},"Yuli Song",{"name":452,"orcid":453},"Hong Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3176-3994",{"tldr":455,"method":456,"finding":457,"direction":82,"opportunity":458},"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。","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":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":9,"source_name":466,"source_url":463,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":470,"tags":472,"primary_topic":474,"secondary_topics":475,"classification":476,"search_phrases":487,"slug":490,"view_count":163,"doi":491,"paper":492,"created_at":522},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":17,"substance":213,"depth":17,"authority":19,"freshness":135,"relevant":21,"comment":469},"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。",[471],{"name":466,"url":463},[26,27,144,29,473],"生物声学传感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":477},{"candidates":478,"agricultural_evidence":45},[479,483],{"code":32,"score":119,"supported":45,"title":480,"abstract":481,"tags":482},[],[122,47,375],[27,29],{"code":59,"score":135,"supported":45,"title":484,"abstract":485,"tags":486},[],[144,145],[26,144],[488,489],"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":491,"openalex_id":493,"authors":494,"venue":466,"cited_by_count":77,"oa_url":463,"card":517,"direction":199,"ingested_from":84},"W7213590695",[495,498,501,503,506,509,511,514],{"name":496,"orcid":497},"Chengyu Li","https:\u002F\u002Forcid.org\u002F0000-0002-2128-3420",{"name":499,"orcid":500},"Wenbo Li","https:\u002F\u002Forcid.org\u002F0000-0003-3599-3324",{"name":502,"orcid":9},"Jingyang Wu",{"name":504,"orcid":505},"Shuo Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5411-7269",{"name":507,"orcid":508},"Han Liao","https:\u002F\u002Forcid.org\u002F0009-0001-6256-7438",{"name":510,"orcid":9},"xiang Yu",{"name":512,"orcid":513},"Cheng Li","https:\u002F\u002Forcid.org\u002F0000-0003-3424-2414",{"name":515,"orcid":516},"Xiaoming Tao","https:\u002F\u002Forcid.org\u002F0000-0002-2406-0695",{"tldr":518,"method":519,"finding":520,"direction":201,"opportunity":521},"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"]