[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2906":3,"related-2906":115},{"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":43,"search_phrases":88,"slug":91,"view_count":92,"doi":93,"paper":94,"created_at":114},2906,"UAV remote sensing for crop lodging monitoring: A comprehensive review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112446","Global agriculture is facing increasing pressure to improve productivity while minimizing losses caused by extreme climate events. Crop lodging poses a major threat to food security by reducing crop yield, deteriorating grain quality, and increasing harvesting costs. As a flexible, high-resolution remote sensing platform, unmanned aerial vehicles (UAVs) have emerged as an effective solution for eliminating the gap between labor-intensive ground surveys and low-resolution satellite observations. This review systematically synthesizes the current state of research on UAV-based crop lodging monitoring through a comprehensive analysis of peer-reviewed literature. It covers the complete technical workflow, from lodging mechanisms to lodging quantification. The capabilities of diverse sensing modalities, including RGB, multispectral imagery (MSI), hyperspectral imagery (HSI), thermal infrared (TIR), light detection and ranging (LiDAR), and synthetic aperture radar (SAR), are systematically evaluated for detecting lodging-induced variations in color, texture, spectral characteristics, canopy temperature, structural attributes, and radar backscatter. In addition, recent advances in analytical approaches are reviewed, encompassing traditional statistical analyses, machine learning techniques, and state-of-the-art deep learning models. Furthermore, the review summarizes UAV-based lodging assessments across a wide range of crop species and examines methods for quantifying key lodging parameters, including lodging severity, lodging type, and affected area. By identifying current research gaps and emerging opportunities, this review outlines future research directions and provides valuable guidance for advancing accurate, efficient, and real-time crop lodging monitoring in smart agriculture.","全球农业正面临日益增大的压力，既要提高生产力，又要尽量减少极端气候事件造成的损失。作物倒伏通过降低作物产量、恶化籽粒品质并增加收获成本，对粮食安全构成重大威胁。作为一种灵活的高分辨率遥感平台，无人机（UAV）已成为弥合劳动密集型地面调查与低分辨率卫星观测之间差距的有效解决方案。本文通过系统分析同行评议文献，全面综述了基于无人机的作物倒伏监测研究现状，涵盖从倒伏机理到倒伏量化的完整技术流程。系统评估了多种传感模态的能力，包括RGB影像、多光谱影像（MSI）、高光谱影像（HSI）、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR），用于检测倒伏引起的颜色、纹理、光谱特征、冠层温度、结构属性和雷达后向散射变化。此外，综述了分析方法的最新进展，涵盖传统统计分析、机器学习技术和前沿深度学习模型。同时，本文总结了针对多种作物类型的基于无人机的倒伏评估研究，并探讨了关键倒伏参数的量化方法，包括倒伏严重程度、倒伏类型和受影响面积。通过识别当前研究空白和新兴机遇，本文展望了未来研究方向，为推进智慧农业中准确、高效、实时的作物倒伏监测提供了有价值的指导。",null,"Computers and Electronics in Agriculture","2026-09-18T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,15,8,1,"核心期刊发表的无人机遥感作物倒伏监测综述，系统梳理多模态传感与深度学习分析路径，对智慧农业精准监测有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","农业人工智能","农业遥感","作物倒伏",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,58,66,73,81],{"code":32,"score":17,"supported":50,"title":51,"abstract":53,"tags":57},true,[52],"remote sensing",[54,52,55,56],"遥感","multispectral","hyperspectral",[29],{"code":39,"score":13,"supported":50,"title":59,"abstract":60,"tags":65},[],[61,62,63,64],"深度学习","机器学习","machine learning","deep learning",[28],{"code":67,"score":68,"supported":50,"title":69,"abstract":70,"tags":72},"I1",7,[],[26,71],"smart agriculture",[26],{"code":74,"score":75,"supported":50,"title":76,"abstract":77,"tags":80},"A8",6,[],[78,79],"粮食安全","food security",[],{"code":82,"score":75,"supported":50,"title":83,"abstract":84,"tags":87},"J3",[],[85,86],"全球农业","global agriculture",[],[89,90],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906",4,"10.1016\u002Fj.compag.2026.112446",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":105,"oa_url":6,"card":106,"direction":112,"ingested_from":113},"W7213542473",[97,100,102],{"name":98,"orcid":99},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":101,"orcid":9},"Changxing Geng",{"name":103,"orcid":104},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"综述无人机遥感在作物倒伏监测中的技术流程、传感器与分析模型。","系统综述RGB、多光谱、高光谱、热红外、LiDAR、SAR及机器学习深度学习方法","多模态遥感结合深度学习可实现倒伏程度、类型与面积的精准量化。","农业遥感与作物表型","多源数据融合与实时轻量化模型是倒伏监测的潜在突破方向。","智慧农业 \u002F 农业物联网","openalex","2026-09-19T23:30:01.740139Z",{"total":75,"page":21,"page_size":75,"items":116},[117,198,270,335,419,515],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":132,"tags":134,"primary_topic":137,"secondary_topics":143,"classification":146,"search_phrases":172,"slug":175,"view_count":21,"doi":176,"paper":177,"created_at":197},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":127,"substance":128,"depth":17,"authority":129,"freshness":130,"relevant":21,"comment":131},16,21,13,9,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[133],{"name":123,"url":120},[26,27,28,135,136],"遥感监测","病害预警",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[144,145],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":147},{"candidates":148,"agricultural_evidence":50},[149,157,161,166],{"code":138,"score":150,"supported":50,"title":151,"abstract":153,"tags":156},17,[152],"apple",[154,155,152],"苹果","葡萄",[],{"code":39,"score":19,"supported":50,"title":158,"abstract":159,"tags":160},[64],[61,64],[28],{"code":32,"score":162,"supported":50,"title":163,"abstract":164,"tags":165},12,[55],[55],[135],{"code":67,"score":68,"supported":50,"title":167,"abstract":168,"tags":171},[],[169,170],"精准农业","precision agriculture",[26],[173,174],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":176,"openalex_id":178,"authors":179,"venue":123,"cited_by_count":105,"oa_url":191,"card":192,"direction":112,"ingested_from":113},"W7221558726",[180,183,186,188],{"name":181,"orcid":182},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":184,"orcid":185},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":187,"orcid":9},"Fatih Sinan Bir",{"name":189,"orcid":190},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":193,"method":194,"finding":195,"direction":110,"opportunity":196},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":206,"score_detail":207,"sources":209,"tags":211,"primary_topic":213,"secondary_topics":214,"classification":216,"search_phrases":243,"slug":246,"view_count":105,"doi":247,"paper":248,"created_at":269},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",68,{"impact":162,"substance":17,"depth":127,"authority":129,"freshness":130,"relevant":21,"comment":208},"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[210],{"name":204,"url":201},[26,27,28,54,212],"葡萄园管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[215],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":217},{"candidates":218,"agricultural_evidence":50},[219,223,227,231,236],{"code":32,"score":17,"supported":50,"title":220,"abstract":221,"tags":222},[55],[54,52,55],[54],{"code":39,"score":19,"supported":50,"title":224,"abstract":225,"tags":226},[64],[61,64],[28],{"code":67,"score":68,"supported":50,"title":228,"abstract":229,"tags":230},[],[169,170],[26],{"code":138,"score":75,"supported":50,"title":232,"abstract":233,"tags":235},[],[155,234],"grape",[],{"code":237,"score":75,"supported":50,"title":238,"abstract":239,"tags":242},"I4",[],[240,241],"空间分析","GIS",[],[244,245],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":247,"openalex_id":249,"authors":250,"venue":204,"cited_by_count":105,"oa_url":201,"card":264,"direction":110,"ingested_from":113},"W7220993218",[251,253,256,258,261],{"name":252,"orcid":9},"Dimitra Passa",{"name":254,"orcid":255},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":257,"orcid":9},"Georgios Nektarios Tselos",{"name":259,"orcid":260},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":262,"orcid":263},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":265,"method":266,"finding":267,"direction":110,"opportunity":268},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":287,"classification":294,"search_phrases":315,"slug":318,"view_count":319,"doi":320,"paper":321,"created_at":334},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",67,{"impact":162,"substance":17,"depth":127,"authority":162,"freshness":130,"relevant":21,"comment":280},"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。",[282],{"name":276,"url":273},[26,28,29,284,285],"边缘计算","土地覆盖分类",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[288,289],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":290,"slug":291,"name":292,"path":293,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":44,"locked":14,"version":45,"method":46,"evidence":295},{"candidates":296,"agricultural_evidence":50},[297,302,306,311],{"code":39,"score":17,"supported":50,"title":298,"abstract":299,"tags":301},[64],[61,64,300],"artificial intelligence",[28],{"code":32,"score":19,"supported":50,"title":303,"abstract":304,"tags":305},[52],[54,52],[29],{"code":290,"score":19,"supported":50,"title":307,"abstract":309,"tags":310},[308],"edge computing",[284,308],[284],{"code":67,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[26],[316,317],"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",3,"10.1007\u002Fs43926-026-00508-2",{"doi":320,"openalex_id":322,"authors":323,"venue":276,"cited_by_count":105,"oa_url":273,"card":329,"direction":110,"ingested_from":113},"W7214769118",[324,326],{"name":325,"orcid":9},"Yuli Song",{"name":327,"orcid":328},"Hong Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3176-3994",{"tldr":330,"method":331,"finding":332,"direction":110,"opportunity":333},"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":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":347,"tags":349,"primary_topic":351,"secondary_topics":357,"classification":360,"search_phrases":381,"slug":384,"view_count":92,"doi":385,"paper":386,"created_at":418},2320,"Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183121","Geospatial artificial intelligence (GeoAI) integrates multimodal remote sensing with deep learning to model complex agricultural systems at scale. Within this framework, accurate and non-destructive prediction of seed composition from in-season standing crops is essential for breeding and precision agriculture. This study developed an end-to-end convolutional neural network (CNN) framework to estimate eight seed traits (protein, oil, sucrose, fiber, starch, ash, complex and simple carbohydrates) from UAV-based multisensor imagery and associated genotype and phenological metadata. A total of 372 soybean samples were collected over two growing seasons (2020–2021) from two fields in Missouri, with UAV flights capturing multispectral (MSI), thermal (THR), and LiDAR (LDR) data at four time points spanning vegetative to reproductive growth stages. CNN models were trained in single- and multi-date configurations, incorporating genotype (GEN) and days after sowing (DAS) as additional features. The highest accuracy was achieved for sucrose (R2 = 0.80), followed by simple carbohydrate (R2 = 0.70) and starch (R2 = 0.55), with notable gains from GEN and DAS. Multi-date models incorporating earlier acquisitions often matched or outperformed later or all-date combinations. Among modalities, MSI provided the most robust estimates, with limited added value from LDR or THR. Unlike feature-based pipelines prone to multicollinearity, this image-to-trait approach enables automated, scalable prediction of soybean seed composition for in-season, field-level assessment.","地理空间人工智能（GeoAI）将多模态遥感与深度学习相结合，以在大尺度上对复杂农业系统进行建模。在该框架下，对当季未收获作物进行准确且无损的种子成分预测，对于育种和精准农业至关重要。本研究开发了一种端到端的卷积神经网络（CNN）框架，利用基于无人机（UAV）的多传感器影像以及相关的基因型和物候元数据，估算八种种子性状（蛋白质、油分、蔗糖、纤维、淀粉、灰分、复杂碳水化合物和简单碳水化合物）。研究在2020—2021两个生长季内，从密苏里州两块田地共采集372份大豆样本，无人机飞行在从营养生长期到生殖生长期的四个时间点获取了多光谱（MSI）、热红外（THR）和激光雷达（LDR）数据。CNN模型以单日期和多日期配置进行训练，并将基因型（GEN）和播种后天数（DAS）作为附加特征纳入。蔗糖的估算精度最高（R² = 0.80），其次为简单碳水化合物（R² = 0.70）和淀粉（R² = 0.55），且GEN和DAS的加入带来了显著提升。纳入早期采集数据的多日期模型往往与后期或全日期组合的表现相当，甚至更优。在各模态中，MSI提供了最稳健的估算结果，而LDR或THR的附加价值有限。与易受多重共线性影响的特征工程流程不同，这种从图像到性状的方法能够实现大豆种子成分的自动化、可扩展预测，用于当季田块尺度的评估。","Remote Sensing","2026-09-11T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":345,"freshness":20,"relevant":21,"comment":346},14,"基于多模态无人机影像与基因型信息的大豆籽粒成分无损预测研究，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[348],{"name":341,"url":338},[26,27,28,54,350],"大豆育种",{"code":352,"slug":353,"name":354,"path":355,"parent_code":356},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[358,359],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":361},{"candidates":362,"agricultural_evidence":50},[363,369,373,377],{"code":352,"score":19,"supported":50,"title":364,"abstract":366,"tags":368},[365],"soybean",[367,365],"大豆",[350],{"code":39,"score":13,"supported":50,"title":370,"abstract":371,"tags":372},[],[61,64,300],[28],{"code":32,"score":13,"supported":50,"title":374,"abstract":375,"tags":376},[],[54,52,55],[54],{"code":67,"score":68,"supported":50,"title":378,"abstract":379,"tags":380},[],[169,170],[26],[382,383],"农业人工智能 大豆育种 智慧农业 无人机","农业人工智能 大豆育种","农业人工智能大豆育种智慧农业无人机-2320","10.3390\u002Frs18183121",{"doi":385,"openalex_id":387,"authors":388,"venue":341,"cited_by_count":105,"oa_url":338,"card":413,"direction":110,"ingested_from":113},"W7212223520",[389,392,395,398,401,403,405,408,411],{"name":390,"orcid":391},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":393,"orcid":394},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":396,"orcid":397},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":399,"orcid":400},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":402,"orcid":9},"Aviskar Giri",{"name":404,"orcid":9},"Ashutosh Pawar",{"name":406,"orcid":407},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":409,"orcid":410},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":412,"orcid":9},"Felix Fritschi",{"tldr":414,"method":415,"finding":416,"direction":110,"opportunity":417},"用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。","372份大豆样本，多光谱\u002F热红外\u002FLiDAR影像加基因型与播后天数，端到端CNN","蔗糖预测最优(R²=0.80)，基因型与播期提升精度，多光谱最稳健。","可探索基因型-表型-环境交互建模，并迁移至其他作物与多时相早期预测。","2026-09-13T23:30:22.776177Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":9,"source_name":425,"source_url":422,"published_at":426,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":436,"secondary_topics":437,"classification":439,"search_phrases":487,"slug":490,"view_count":105,"doi":491,"paper":492,"created_at":514},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":162,"substance":17,"depth":127,"authority":130,"freshness":13,"relevant":21,"comment":429},"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[431],{"name":425,"url":422},[26,28,433,434,435],"无损检测","多光谱成像","食品质量检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[438],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":440},{"candidates":441,"agricultural_evidence":50},[442,446,452,459,466,473,477,483],{"code":32,"score":162,"supported":50,"title":443,"abstract":444,"tags":445},[55],[55],[434],{"code":39,"score":13,"supported":50,"title":447,"abstract":448,"tags":451},[],[61,62,449,63,64,300,450],"计算机视觉","computer vision",[28],{"code":453,"score":68,"supported":50,"title":454,"abstract":455,"tags":458},"E4",[],[456,457],"食品安全","food safety",[433],{"code":460,"score":75,"supported":50,"title":461,"abstract":462,"tags":465},"A1",[],[463,464],"稻米","rice",[],{"code":467,"score":75,"supported":50,"title":468,"abstract":469,"tags":472},"B1",[],[470,471],"番茄","tomato",[],{"code":138,"score":75,"supported":50,"title":474,"abstract":475,"tags":476},[],[154,152],[],{"code":478,"score":319,"supported":50,"title":479,"abstract":480,"tags":482},"E1",[],[481],"农产品加工",[],{"code":67,"score":21,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[26],[488,489],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":491,"openalex_id":493,"authors":494,"venue":425,"cited_by_count":105,"oa_url":422,"card":507,"direction":513,"ingested_from":113},"W7223139905",[495,497,499,501,503,505],{"name":496,"orcid":9},"Eshwari Take",{"name":498,"orcid":9},"Avanish Raut",{"name":500,"orcid":9},"Vedant Joshi",{"name":502,"orcid":9},"Shravani Akolkar",{"name":504,"orcid":9},"R. M. Gharat",{"name":506,"orcid":9},"Shruti Dive",{"tldr":508,"method":509,"finding":510,"direction":511,"opportunity":512},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":516,"title":517,"url":518,"summary":519,"summary_zh":520,"content":9,"source_name":204,"source_url":518,"published_at":426,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":521,"sources":524,"tags":526,"primary_topic":529,"secondary_topics":530,"classification":532,"search_phrases":547,"slug":550,"view_count":105,"doi":551,"paper":552,"created_at":562},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",{"impact":20,"substance":522,"depth":150,"authority":162,"freshness":13,"relevant":21,"comment":523},20,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[525],{"name":204,"url":518},[26,28,527,54,528],"产量预测","可解释性",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[531],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":533},{"candidates":534,"agricultural_evidence":50},[535,539,543],{"code":39,"score":162,"supported":50,"title":536,"abstract":537,"tags":538},[450],[449],[28],{"code":32,"score":20,"supported":50,"title":540,"abstract":541,"tags":542},[],[54,52],[527,54],{"code":67,"score":21,"supported":14,"title":544,"abstract":545,"tags":546},[],[],[26],[548,549],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":551,"openalex_id":553,"authors":554,"venue":204,"cited_by_count":105,"oa_url":518,"card":557,"direction":110,"ingested_from":113},"W7223114002",[555],{"name":556,"orcid":9},"Kadiatou Barry",{"tldr":558,"method":559,"finding":560,"direction":110,"opportunity":561},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z"]