[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2999":3,"related-2999":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},2999,"基于改进DeepLabv3+的高标准农田田间道路提取与结构指标量化框架","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1986","沈阳农业大学刘永生等开发了基于MobileNetV2改进DeepLabv3+的高标准农田田间道路提取轻量化框架，集成Normalization-based Attention Module与Content-Aware ReAssembly of FEatures。三次随机种子训练平均mIoU 93.34%、mPA 96.75%、精度98.90%，模型参数6.14M、推理速度17.04 FPS；沥青、混凝土、砾石道路宽度预测R²分别为0.650、0.486、0.662，宽度MAE 0.130\u002F0.140\u002F0.100 m。第二验证区域连通性指数从0.4682提升至0.4795，支持高标准农田田间道路高效、可量化、可追溯的验收检查。",null,"MDPI Agriculture 16(18):1986","2026-09-16T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,13,8,1,"方法有创新、指标详实，对高标准农田道路验收有实用价值，但属细分技术论文，影响面有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","高标准农田","遥感","田间道路",[32,33],"沈阳农业大学 高标准农田 道路提取","DeepLabv3 田间道路 遥感","沈阳农业大学高标准农田道路提取-2999",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},"提出改进DeepLabv3+轻量框架，提取高标准农田田间道路并量化结构指标。","MobileNetV2+NAM+CARAFE改进DeepLabv3+，多区域遥感","mIoU 93.34%，道路宽度预测R²最高0.662，连通性指数提升至0.4795。","农业遥感与作物表型","可拓展至多作物、多地形道路提取，并结合时序遥感实现道路损毁动态监测。","agent","2026-09-20T00:03:08.023498Z",{"total":47,"page":21,"page_size":47,"items":48},6,[49,84,137,184,228,273],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":54,"content":8,"source_name":55,"source_url":52,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":57,"score_detail":58,"sources":62,"tags":64,"search_phrases":67,"slug":70,"view_count":35,"doi":71,"paper":72,"created_at":83},2948,"Machine learning and remote sensing for smallholder precision agriculture in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04538-2","While machine learning (ML) and remote sensing (RS) are frequently heralded as the definitive solutions for agricultural resilience in Sub-Saharan Africa, a profound ‘implementation gap’ persists between laboratory-validated computational maturity and field-level utility for smallholder farmers. This systematic review, conducted under preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, critically analyzes why sophisticated models optimized for large-scale monocultures fail within the fragmented, intercropped landscapes of Ethiopia. By synthesizing empirical evidence across four domains—in-season crop yield forecasting, digital soil mapping, real-time biotic stress detection, and agro-meteorological modeling—the review uncover a fundamental scale mismatch between coarse-resolution satellite observations and sub-hectare micro-plots. The critique identifies localized data scarcity, hardware constraints, and the ‘last-mile’ connectivity divide as the primary friction points obstructing the transition from macro-level pixels to actionable, site-specific agricultural intelligence. Moving beyond simple summary, the study propose a strategic roadmap centered on decentralized edge computing, tinyML optimizations, and a restructuring of extension services to integrate digital intelligence into daily smallholder decision-making. These structural shifts are essential to bridge the digital divide and secure Ethiopia’s national food security against escalating climate variability. This review foregrounds the significance of digital agriculture within the context of the sustainable development goals (SDGs), specifically addressing SDG 2 (zero hunger) and SDG 13 (climate action) by enhancing crop productivity and building resilience in smallholder systems.","尽管机器学习（ML）与遥感（RS）常被标榜为撒哈拉以南非洲农业韧性的终极解决方案，但实验室验证的计算成熟度与小农户田间实用性之间仍存在深刻的“实施鸿沟”。本系统综述依据系统综述和荟萃分析首选报告条目（PRISMA）2020指南开展，批判性地分析了为何针对大规模单一种植优化的复杂模型在埃塞俄比亚碎片化、间作化的景观中失效。通过综合四个领域的实证证据——季内作物产量预测、数字土壤制图、实时生物胁迫检测和农业气象建模——本综述揭示了粗分辨率卫星观测与亚公顷微地块之间的根本性尺度错配。该批判性分析将局部数据稀缺、硬件约束和“最后一公里”连接鸿沟确定为阻碍从宏观像元向可操作、因地制宜的农业智能转化的主要摩擦点。本研究超越简单的总结，提出了一条以去中心化边缘计算、tinyML优化和推广服务体系重构为核心的战略路线图，旨在将数字智能融入小农户的日常决策。这些结构性转变对于弥合数字鸿沟、保障埃塞俄比亚在日益加剧的气候变率下的国家粮食安全至关重要。本综述凸显了数字农业在可持续发展目标（SDGs）背景下的重要意义，特别是通过提升作物生产力和增强小农系统韧性来回应SDG 2（零饥饿）和SDG 13（气候行动）。","Discover Sustainability","2026-09-18T00:00:00Z",79,{"impact":18,"substance":59,"depth":18,"authority":19,"freshness":60,"relevant":21,"comment":61},21,9,"系统综述揭示机器学习与遥感在小农场景的落地鸿沟，并提出边缘计算与tinyML路线图，对数字农业与SDG研究有参考价值。",[63],{"name":55,"url":52},[26,27,65,66,29],"小农户","数字鸿沟",[68,69],"埃塞俄比亚 小农户 精准农业","机器学习 遥感 小农","埃塞俄比亚小农户精准农业-2948","10.1007\u002Fs43621-026-04538-2",{"doi":71,"openalex_id":73,"authors":74,"venue":55,"cited_by_count":35,"oa_url":52,"card":77,"direction":42,"ingested_from":82},"W7213562005",[75],{"name":76,"orcid":8},"Abrha Asefa",{"tldr":78,"method":79,"finding":80,"direction":42,"opportunity":81},"系统综述埃塞俄比亚小农精准农业中机器学习和遥感的应用鸿沟与出路。","PRISMA 2020 系统综述，综合四领域实证证据。","粗分辨率卫星与亚公顷微地块尺度不匹配，数据稀缺和连接鸿沟阻碍落地。","面向碎片化间作小农的 tinyML 边缘计算与本地化数据采集，是填补落地鸿沟的关键方向。","openalex","2026-09-19T23:30:33.334404Z",{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":92,"sources":95,"tags":97,"search_phrases":100,"slug":103,"view_count":35,"doi":104,"paper":105,"created_at":136},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",67,{"impact":93,"substance":18,"depth":16,"authority":19,"freshness":20,"relevant":21,"comment":94},12,"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。",[96],{"name":90,"url":87},[26,27,98,29,99],"精准农业","土壤碳汇",[101,102],"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":104,"openalex_id":106,"authors":107,"venue":90,"cited_by_count":35,"oa_url":87,"card":131,"direction":42,"ingested_from":82},"W7213561504",[108,111,114,117,120,123,125,127,129],{"name":109,"orcid":110},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":112,"orcid":113},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":115,"orcid":116},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":118,"orcid":119},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":121,"orcid":122},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":124,"orcid":8},"Almaha Jamal",{"name":126,"orcid":8},"Afra Rashed",{"name":128,"orcid":8},"Hamda Yousif",{"name":130,"orcid":8},"Sarah Sadeq",{"tldr":132,"method":133,"finding":134,"direction":42,"opportunity":135},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":8,"source_name":143,"source_url":140,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":57,"score_detail":144,"sources":147,"tags":149,"search_phrases":151,"slug":154,"view_count":35,"doi":155,"paper":156,"created_at":183},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",{"impact":18,"substance":59,"depth":18,"authority":145,"freshness":20,"relevant":21,"comment":146},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。",[148],{"name":143,"url":140},[26,27,150,98,29],"农业无人机",[152,153],"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":155,"openalex_id":157,"authors":158,"venue":143,"cited_by_count":35,"oa_url":8,"card":177,"direction":181,"ingested_from":82},"W7213547466",[159,161,163,165,168,170,172,175],{"name":160,"orcid":8},"Chenshuo Xie",{"name":162,"orcid":8},"Yejun Zhu",{"name":164,"orcid":8},"Dongfang Li",{"name":166,"orcid":167},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":169,"orcid":8},"Le Yang",{"name":171,"orcid":8},"Yuxuan Wan",{"name":173,"orcid":174},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":176,"orcid":8},"Mingfeng Wang",{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"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":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":8,"source_name":190,"source_url":191,"published_at":192,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":193,"score_detail":194,"sources":198,"tags":200,"search_phrases":203,"slug":206,"view_count":35,"doi":207,"paper":208,"created_at":227},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","2026-09-15T00:00:00Z",74,{"impact":16,"substance":195,"depth":196,"authority":19,"freshness":20,"relevant":21,"comment":197},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。",[199],{"name":190,"url":191},[26,27,29,201,202],"作物表型","三维重建",[204,205],"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":207,"openalex_id":209,"authors":210,"venue":190,"cited_by_count":35,"oa_url":221,"card":222,"direction":42,"ingested_from":82},"W7213397688",[211,214,216,219],{"name":212,"orcid":213},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":215,"orcid":8},"Theo Morales",{"name":217,"orcid":218},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":220,"orcid":8},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":223,"method":224,"finding":225,"direction":42,"opportunity":226},"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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":234,"source_url":231,"published_at":192,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":235,"score_detail":236,"sources":238,"tags":240,"search_phrases":243,"slug":246,"view_count":35,"doi":247,"paper":248,"created_at":272},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing",80,{"impact":18,"substance":17,"depth":18,"authority":145,"freshness":20,"relevant":21,"comment":237},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[239],{"name":234,"url":231},[26,27,241,29,242],"小麦","精准灌溉",[244,245],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":247,"openalex_id":249,"authors":250,"venue":234,"cited_by_count":35,"oa_url":231,"card":267,"direction":42,"ingested_from":82},"W7213246708",[251,254,256,258,261,264],{"name":252,"orcid":253},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":255,"orcid":8},"Qian Cheng",{"name":257,"orcid":8},"Fuyi Duan",{"name":259,"orcid":260},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":262,"orcid":263},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":265,"orcid":266},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":268,"method":269,"finding":270,"direction":42,"opportunity":271},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":8,"source_name":234,"source_url":276,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":279,"score_detail":280,"sources":282,"tags":284,"search_phrases":287,"slug":290,"view_count":35,"doi":291,"paper":292,"created_at":320},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。",82,{"impact":18,"substance":17,"depth":18,"authority":145,"freshness":12,"relevant":21,"comment":281},"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[283],{"name":234,"url":276},[26,27,29,285,286],"作物监测","植被指数",[288,289],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能 作物监测","农业人工智能作物监测智慧农业植被指数-2660","10.3390\u002Frs18183180",{"doi":291,"openalex_id":293,"authors":294,"venue":234,"cited_by_count":35,"oa_url":276,"card":315,"direction":42,"ingested_from":82},"W7213344870",[295,298,300,303,306,309,312],{"name":296,"orcid":297},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":299,"orcid":8},"Nikolas Hoskin",{"name":301,"orcid":302},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":304,"orcid":305},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":307,"orcid":308},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":310,"orcid":311},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":313,"orcid":314},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":316,"method":317,"finding":318,"direction":42,"opportunity":319},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z"]