[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3001":3,"related-3001":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。",null,"MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z","论文",10,false,74,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,22,18,13,6,1,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","精准农业","遥感","作物表型","春小麦",[32,33],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","农业遥感与作物表型","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","agent","2026-09-20T00:03:08.168753Z",{"total":20,"page":21,"page_size":20,"items":47},[48,105,154,195,233,287],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":53,"content":8,"source_name":54,"source_url":51,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":62,"tags":64,"search_phrases":67,"slug":70,"view_count":35,"doi":71,"paper":72,"created_at":104},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","2026-09-18T00:00:00Z",67,{"impact":58,"substance":18,"depth":59,"authority":19,"freshness":60,"relevant":21,"comment":61},12,16,8,"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。",[63],{"name":54,"url":51},[26,65,27,28,66],"农业人工智能","土壤碳汇",[68,69],"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":71,"openalex_id":73,"authors":74,"venue":54,"cited_by_count":35,"oa_url":51,"card":98,"direction":42,"ingested_from":103},"W7213561504",[75,78,81,84,87,90,92,94,96],{"name":76,"orcid":77},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":79,"orcid":80},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":82,"orcid":83},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":85,"orcid":86},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":88,"orcid":89},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":91,"orcid":8},"Almaha Jamal",{"name":93,"orcid":8},"Afra Rashed",{"name":95,"orcid":8},"Hamda Yousif",{"name":97,"orcid":8},"Sarah Sadeq",{"tldr":99,"method":100,"finding":101,"direction":42,"opportunity":102},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","openalex","2026-09-19T23:30:33.273156Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":8,"source_name":111,"source_url":108,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":112,"score_detail":113,"sources":117,"tags":119,"search_phrases":121,"slug":124,"view_count":35,"doi":125,"paper":126,"created_at":153},2908,"An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112447","An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization。Computers and Electronics in Agriculture","一种信息驱动的空地协同框架，用于基于无人机耕作缺陷识别与再耕作路径优化。《农业计算机与电子》","Computers and Electronics in Agriculture",79,{"impact":18,"substance":114,"depth":18,"authority":115,"freshness":60,"relevant":21,"comment":116},21,14,"发表于农业信息领域核心期刊，提出空地协同的无人机耕地缺陷识别与再耕路径优化框架，方法新颖且面向精准农业实际需求，具备较高参考价值。",[118],{"name":111,"url":108},[26,65,120,27,28],"农业无人机",[122,123],"无人机 耕地缺陷 识别","再耕路径 优化","无人机耕地缺陷识别-2908","10.1016\u002Fj.compag.2026.112447",{"doi":125,"openalex_id":127,"authors":128,"venue":111,"cited_by_count":35,"oa_url":8,"card":147,"direction":151,"ingested_from":103},"W7213547466",[129,131,133,135,138,140,142,145],{"name":130,"orcid":8},"Chenshuo Xie",{"name":132,"orcid":8},"Yejun Zhu",{"name":134,"orcid":8},"Dongfang Li",{"name":136,"orcid":137},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":139,"orcid":8},"Le Yang",{"name":141,"orcid":8},"Yuxuan Wan",{"name":143,"orcid":144},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":146,"orcid":8},"Mingfeng Wang",{"tldr":148,"method":149,"finding":150,"direction":151,"opportunity":152},"提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。","无人机遥感影像与信息驱动算法，识别缺陷并规划重耕路径。","框架能有效识别耕整缺陷并生成优化补耕路径，提升作业质量。","智慧农业 \u002F 农业物联网","可探索多机协同与实时动态重规划，结合土壤传感器提升缺陷识别精度。","2026-09-19T23:30:02.081036Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":8,"source_name":160,"source_url":161,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":162,"sources":166,"tags":168,"search_phrases":170,"slug":173,"view_count":35,"doi":174,"paper":175,"created_at":194},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。","arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926",{"impact":59,"substance":163,"depth":164,"authority":19,"freshness":60,"relevant":21,"comment":165},20,17,"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[167],{"name":160,"url":161},[26,65,28,29,169],"三维重建",[171,172],"农业人工智能 三维重建 作物表型 智慧农业","农业人工智能 三维重建","农业人工智能三维重建作物表型智慧农业-2781","10.48550\u002Farxiv.2609.16926",{"doi":174,"openalex_id":176,"authors":177,"venue":160,"cited_by_count":35,"oa_url":188,"card":189,"direction":42,"ingested_from":103},"W7213397688",[178,181,183,186],{"name":179,"orcid":180},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":182,"orcid":8},"Theo Morales",{"name":184,"orcid":185},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":187,"orcid":8},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":190,"method":191,"finding":192,"direction":42,"opportunity":193},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","2026-09-17T23:30:27.096274Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":8,"source_name":201,"source_url":198,"published_at":202,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":203,"score_detail":204,"sources":206,"tags":208,"search_phrases":211,"slug":214,"view_count":35,"doi":215,"paper":216,"created_at":232},2530,"Seeing the Green from Above: A Review of Remote Sensing Techniques for Vegetation Cover Discrimination","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189410","This review synthesizes recent regional applications of satellite, unmanned aerial vehicle (UAV), and hyperspectral\u002Fspectroradiometric remote sensing for vegetation cover discrimination, including crop and natural vegetation discrimination, plant disease and pest detection, and yield assessment. The reviewed evidence demonstrates complementary rather than universally superior capabilities among sensing platforms. Satellite observations provide repeated large-area monitoring but remain constrained by spatial resolution, cloud interference, and spectral mixing, whereas UAVs offer very-high-resolution and flexible field-scale observations at the expense of spatial coverage and greater acquisition and processing requirements. Hyperspectral and spectroradiometric approaches provide detailed spectral information for distinguishing subtle vegetation differences, but are limited by data complexity and operational scalability. The quantitative results reported in the reviewed studies illustrate this variability: satellite-based crop discrimination achieved approximately 90% overall accuracy with QuickBird and 81% overall accuracy (κ = 0.74) with Sentinel-2 at a 10 m resolution, while a UAV hyperspectral vegetation classification study achieved 94.5% accuracy. However, these values are not directly comparable because the vegetation targets, sensors, acquisition conditions, and analytical methods differed among studies. Recent evidence also indicates that the phenological timing, spectral band selection, spatial resolution, and representative training data strongly influence the discrimination performance, while the transfer of disease detection models from controlled experiments to operational field conditions remains a major challenge. By integrating evidence from satellite, UAV, and ground-based spectroradiometric approaches, this review provides a comprehensive framework for understanding the complementary capabilities of these technologies for vegetation cover discrimination and highlights their importance for improving vegetation monitoring, precision agriculture, and sustainable ecosystem management.","本文综述了近年来卫星、无人机（UAV）及高光谱\u002F光谱辐射遥感在植被覆盖判别中的区域应用，包括作物与自然植被判别、植物病虫害检测及产量评估。所综述的证据表明，各传感平台之间的能力是互补的，而非普遍存在优劣之分。卫星观测可提供重复的大面积监测，但仍受空间分辨率、云层干扰及光谱混合的限制；无人机则能够提供超高分辨率且灵活的田块尺度观测，但代价是空间覆盖范围有限，且采集与处理要求更高。高光谱与光谱辐射方法可提供精细的光谱信息，用于区分细微的植被差异，但受数据复杂性及业务化可扩展性的制约。所综述研究报告的定量结果体现了这种差异性：基于卫星的作物判别在QuickBird下总体精度约为90%，在10 m分辨率的Sentinel-2下总体精度为81%（κ = 0.74），而一项无人机高光谱植被分类研究达到了94.5%的精度。然而，这些数值之间并不能直接比较，因为各研究中的植被目标、传感器、采集条件及分析方法均不相同。近期证据还表明，物候时序、光谱波段选择、空间分辨率及代表性训练数据对判别性能有显著影响，而将病害检测模型从受控实验推广至实际田间条件仍是一项重大挑战。通过整合来自卫星、无人机及地面光谱辐射方法的证据，本综述为理解这些技术在植被覆盖判别中的互补能力提供了一个综合框架，并强调了其对改进植被监测、精准农业及可持续生态系统管理的重要意义。","Sustainability","2026-09-14T00:00:00Z",76,{"impact":18,"substance":163,"depth":164,"authority":19,"freshness":60,"relevant":21,"comment":205},"系统综述卫星、无人机与高光谱遥感在植被识别、病虫害检测与估产中的互补能力，数据翔实、结论审慎，对农业遥感应用有参考价值。",[207],{"name":201,"url":198},[26,27,28,209,210],"病虫害监测","作物估产",[212,213],"病虫害监测 作物估产 智慧农业 精准农业","病虫害监测 作物估产","病虫害监测作物估产智慧农业精准农业-2530","10.3390\u002Fsu18189410",{"doi":215,"openalex_id":217,"authors":218,"venue":201,"cited_by_count":35,"oa_url":198,"card":227,"direction":42,"ingested_from":103},"W7212851379",[219,222,225],{"name":220,"orcid":221},"Ghada A. Khdery","https:\u002F\u002Forcid.org\u002F0000-0002-1492-6029",{"name":223,"orcid":224},"Mohamed S. Shokr","https:\u002F\u002Forcid.org\u002F0000-0003-0328-7679",{"name":226,"orcid":8},"Aleksandra O. Utkina",{"tldr":228,"method":229,"finding":230,"direction":42,"opportunity":231},"综述卫星、无人机与高光谱遥感在植被覆盖判别中的区域应用与互补能力。","综述卫星、无人机、高光谱\u002F光谱辐射遥感在作物与植被判别中的案例。","各平台能力互补而非普遍优越，物候、波段、分辨率与训练数据显著影响精度。","可探索跨平台遥感数据融合与病害检测模型从受控实验到田间业务的迁移。","2026-09-15T23:30:20.628746Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":8,"source_name":239,"source_url":236,"published_at":202,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":203,"score_detail":240,"sources":243,"tags":245,"search_phrases":247,"slug":250,"view_count":35,"doi":251,"paper":252,"created_at":286},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing",{"impact":59,"substance":114,"depth":164,"authority":19,"freshness":241,"relevant":21,"comment":242},9,"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[244],{"name":239,"url":236},[26,65,246,27,28],"水稻",[248,249],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":251,"openalex_id":253,"authors":254,"venue":239,"cited_by_count":35,"oa_url":280,"card":281,"direction":151,"ingested_from":103},"W7212834743",[255,257,259,261,263,266,268,270,272,275,277],{"name":256,"orcid":8},"Amrutha Lakshmi Gubbala",{"name":258,"orcid":8},"Santosha Rathod",{"name":260,"orcid":8},"Ramesh Dasyam",{"name":262,"orcid":8},"Mahender Kumar Rapolu",{"name":264,"orcid":265},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":267,"orcid":8},"Pundarikakshudu Kurra",{"name":269,"orcid":8},"Hanuma Raviteja Madireddy",{"name":271,"orcid":8},"Prajwal R. Shashishekhar",{"name":273,"orcid":274},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":276,"orcid":8},"Anil Kumar",{"name":278,"orcid":279},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":282,"method":283,"finding":284,"direction":42,"opportunity":285},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","2026-09-15T23:30:08.600535Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":8,"content":8,"source_name":292,"source_url":290,"published_at":293,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":294,"score_detail":295,"sources":297,"tags":299,"search_phrases":302,"slug":305,"view_count":35,"doi":306,"paper":307,"created_at":319},2300,"Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10720368\u002Fv1","Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework。Research Square","Research Square","2026-09-11T00:00:00Z",57,{"impact":58,"substance":16,"depth":115,"authority":60,"freshness":60,"relevant":21,"comment":296},"预印本论文提出卫星耦合土壤感知框架，面向离网地形智慧农业优化，方法有创新但尚缺同行评议，属细分领域进展。",[298],{"name":292,"url":290},[26,27,28,300,301],"土壤监测","卫星遥感",[303,304],"卫星遥感 土壤监测 智慧农业 精准农业","卫星遥感 土壤监测","卫星遥感土壤监测智慧农业精准农业-2300","10.21203\u002Frs.3.rs-10720368\u002Fv1",{"doi":306,"openalex_id":308,"authors":309,"venue":292,"cited_by_count":35,"oa_url":290,"card":8,"direction":151,"ingested_from":103},"W7212267038",[310,312,314,317],{"name":311,"orcid":8},"Masood Ahmad",{"name":313,"orcid":8},"Shahid Kamal",{"name":315,"orcid":316},"Fasee Ullah","https:\u002F\u002Forcid.org\u002F0000-0001-6167-5253",{"name":318,"orcid":8},"Ishtiaq Wahid","2026-09-13T23:30:09.626390Z"]